From f2f662e6d00d8e1cd0c7320e5a5086ad7066c120 Mon Sep 17 00:00:00 2001 From: renierts Date: Thu, 18 Apr 2024 08:59:46 +0200 Subject: [PATCH 001/103] - Prepared a very basic package structure. - Added .gitignore - Added a directory for documentation. - Added a directory for unit tests. --- .gitignore | 160 +++++++++++++++++++++++++++ docs/Makefile | 20 ++++ docs/make.bat | 35 ++++++ docs/source/conf.py | 28 +++++ docs/source/index.rst | 20 ++++ src/fusionaihub/__init__.py | 0 src/fusionaihub/base/__init__.py | 0 src/fusionaihub/core/__init__.py | 0 src/fusionaihub/datasets/__init__.py | 0 src/fusionaihub/display/__init__.py | 0 src/fusionaihub/feature/__init__.py | 0 src/fusionaihub/util/__init__.py | 0 12 files changed, 263 insertions(+) create mode 100644 .gitignore create mode 100644 docs/Makefile create mode 100644 docs/make.bat create mode 100644 docs/source/conf.py create mode 100644 docs/source/index.rst create mode 100644 src/fusionaihub/__init__.py create mode 100644 src/fusionaihub/base/__init__.py create mode 100644 src/fusionaihub/core/__init__.py create mode 100644 src/fusionaihub/datasets/__init__.py create mode 100644 src/fusionaihub/display/__init__.py create mode 100644 src/fusionaihub/feature/__init__.py create mode 100644 src/fusionaihub/util/__init__.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..6769e21 --- /dev/null +++ b/.gitignore @@ -0,0 +1,160 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ \ No newline at end of file diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d0c3cbf --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..dc1312a --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 0000000..05c079d --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,28 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +project = 'FusionAIHub' +copyright = '2024, Peter steiner, Max Curie, Nathaniel Chen, Azarakhsh Jalalvand' +author = 'Peter steiner, Max Curie, Nathaniel Chen, Azarakhsh Jalalvand' +release = '0.0' + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [] + +templates_path = ['_templates'] +exclude_patterns = [] + + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = 'alabaster' +html_static_path = ['_static'] diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 0000000..845386f --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,20 @@ +.. FusionAIHub documentation master file, created by + sphinx-quickstart on Thu Apr 18 08:53:16 2024. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to FusionAIHub's documentation! +======================================= + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/src/fusionaihub/__init__.py b/src/fusionaihub/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fusionaihub/base/__init__.py b/src/fusionaihub/base/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fusionaihub/core/__init__.py b/src/fusionaihub/core/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fusionaihub/datasets/__init__.py b/src/fusionaihub/datasets/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fusionaihub/display/__init__.py b/src/fusionaihub/display/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fusionaihub/feature/__init__.py b/src/fusionaihub/feature/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fusionaihub/util/__init__.py b/src/fusionaihub/util/__init__.py new file mode 100644 index 0000000..e69de29 From d04ed07469bfad422d97774614e10f447d727305 Mon Sep 17 00:00:00 2001 From: maxcurie <47543965+maxtcurie@users.noreply.github.com> Date: Thu, 18 Apr 2024 10:43:12 -0400 Subject: [PATCH 002/103] Initial publication With a script of fetching data, transferring data, and object (work-in-progress) that read and unify the dataset. --- Data_fetching/check_copy_and_rm.py | 166 ++++++++ Data_fetching/fetch_GAdata.py | 180 +++++++++ Data_fetching/fetch_toksearch.py | 593 +++++++++++++++++++++++++++++ Data_fetching/mygadata.py | 86 +++++ Dataset_prep/0read_data_run.ipynb | 258 +++++++++++++ Dataset_prep/data_prep_obj.py | 545 ++++++++++++++++++++++++++ 6 files changed, 1828 insertions(+) create mode 100644 Data_fetching/check_copy_and_rm.py create mode 100644 Data_fetching/fetch_GAdata.py create mode 100644 Data_fetching/fetch_toksearch.py create mode 100644 Data_fetching/mygadata.py create mode 100644 Dataset_prep/0read_data_run.ipynb create mode 100644 Dataset_prep/data_prep_obj.py diff --git a/Data_fetching/check_copy_and_rm.py b/Data_fetching/check_copy_and_rm.py new file mode 100644 index 0000000..e30ebd4 --- /dev/null +++ b/Data_fetching/check_copy_and_rm.py @@ -0,0 +1,166 @@ +import paramiko +import socket +import getpass +import numpy as np +import time +import os +import re + +#this script is intented to copy the file from iris and + +num_min=140000 +num_max=200000-1 + +subtask=2 #total paraelle one wants +residue=1 #transfer num%subtask==residue + +fetching_name_list=['actu','basic','profiles'] +diag_name=fetching_name_list[0] + +remote_directory = '/cscratch/curiem/Data_fetch_Basic' +local_directory = '/scratch/gpfs/EKOLEMEN/big_d3d_data/Basic_fetch' + +# Set up logging +paramiko.util.log_to_file("paramiko.log") + +# SSH settings for the proxy server +proxy_host = 'cybele.gat.com' +proxy_port = 2039 # Example port, modify as necessary +proxy_user = 'curiem' +proxy_password = getpass.getpass(f"Enter SSH password for {proxy_host}: ") + +# SSH settings for the destination server +destination_host = 'iris.gat.com' +destination_user = 'curiem' +destination_password = proxy_password + + +def create_ssh_connection(): + try: + # Tunneling through the proxy + proxy_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + proxy_sock.connect((proxy_host, proxy_port)) + + proxy_transport = paramiko.Transport(proxy_sock) + proxy_transport.connect(username=proxy_user, password=proxy_password) + proxy_channel = proxy_transport.open_channel('direct-tcpip', (destination_host, 22), (proxy_host, proxy_port)) + + # Create an SSH client and connect through the proxy channel + ssh_client = paramiko.SSHClient() + ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) # Use with caution in production + ssh_client.connect(destination_host, username=destination_user, password=destination_password, sock=proxy_channel) + + return ssh_client, proxy_transport + except paramiko.AuthenticationException: + print("Authentication failed, please verify your credentials.") + return None, None + except paramiko.SSHException as sshException: + print(f"Could not establish SSH connection: {sshException}") + return None, None + except Exception as e: + print(f"Exception in connecting to the SSH Server: {e}") + return None, None + + +def extract_shot_numbers_remote(sftp, remote_path, suffix): + """ + Extract shot numbers from file names in a remote directory via SFTP, based on a given suffix. + + Args: + - sftp (paramiko.SFTPClient): An active SFTP client session. + - remote_path (str): The remote directory path to search for files. + - suffix (str): The suffix pattern to match in the file names. + + + + + Returns: + - set: A set of unique shot numbers extracted from the file names. + """ + try: + # List all files in the remote directory with their attributes + files_attr = sftp.listdir_attr(remote_path) + + # Regular expression pattern to match the shot numbers, incorporating the suffix variable + pattern = re.compile(rf'(\d+)_({suffix})\.h5') + + # Extract shot numbers that match the pattern from the file names + shot_numbers = { + int(match.group(1)) + for attr in files_attr + for match in [pattern.search(attr.filename)] if match + } + + return shot_numbers + except Exception as e: + print(f"Failed to extract shot numbers: {e}") + return set() + + + + +def copy_file(sftp, remote_path, local_path): + # Check the action and perform the corresponding task + sftp.get(remote_path, local_path) + message = f"File {remote_path} copied to {local_path} successfully." + return message + +def remove_file(sftp, remote_path): + sftp.remove(remote_path) + message = f"File {remote_path} removed successfully." + return message + +def copy_n_rm_file(sftp, remote_path, local_path): + message=copy_file(sftp, remote_path, local_path) + print(message) + message=remove_file(sftp, remote_path) + print(message) + + +def search_copy_and_delete(diag_name, remote_directory, local_directory, retries=3): + for attempt in range(retries): + try: + ssh_client, proxy_transport = create_ssh_connection() + + sftp = ssh_client.open_sftp() + while 1==1: + shot_numbers=extract_shot_numbers_remote(sftp, remote_directory, diag_name) + shot_numbers=list(shot_numbers) + + shot_numbers.sort() + shot_numbers=np.array(shot_numbers) + shot_numbers=shot_numbers[(num_min<=shot_numbers) & (shot_numbers<=num_max)] + + #print(shot_numbers) + if len(shot_numbers)<=2*subtask: + print('No files to copy, waiting for 10 min') + #wait for 10min + time.sleep(600) + continue + cp_shot_num=shot_numbers[:-2*subtask] + print(cp_shot_num) + for shot_num in cp_shot_num: + if int(shot_num)%subtask==residue: + for name_tmp in fetching_name_list: + remote_path=f'{remote_directory}/{shot_num}_{name_tmp}.h5' + local_path=f'{local_directory}/{shot_num}_{name_tmp}.h5' + copy_n_rm_file(sftp, remote_path, local_path) + + + ssh_client.close() + proxy_transport.close() + + break + except Exception as e: + print(f"Error processing on attempt {attempt + 1}: {e}") + if attempt < retries - 1: + time.sleep(5) + else: + print(f"Failed processing after {retries} attempts.") + ssh_client.close() + proxy_transport.close() + finally: + ssh_client.close() + proxy_transport.close() + +search_copy_and_delete(diag_name, remote_directory, local_directory, retries=3) \ No newline at end of file diff --git a/Data_fetching/fetch_GAdata.py b/Data_fetching/fetch_GAdata.py new file mode 100644 index 0000000..9007fcb --- /dev/null +++ b/Data_fetching/fetch_GAdata.py @@ -0,0 +1,180 @@ +import MDSplus +from mygadata import gadata +#import matplotlib.pyplot as plt +import h5py +import pickle +import numpy as np +import time +from tqdm import tqdm +import sys +import subprocess + + +#To run the code +#module purge & module load defaults +#python2.7 fetch_data.py + +#*******start of user block************ +output_path='/cscratch/curiem/Data_fetch_Basic' +ece_pcece=False +size_GB=400 +directory_path="/cscratch/curiem" #to check the total file sizes +shot_list=np.arange(170000,200000) + +interval=1000 +#shot_list=shot_list[:10] +#*******end of user block************ + +def size_limiter_sleep(directory_path="/cscratch/curiem", size_GB=450): + try: + size = subprocess.check_output(['du', '-sh', directory_path]).split()[0].decode('utf-8') + except subprocess.CalledProcessError as e: + print("Error fetching directory size: "+str(e)) + sys.exit(1) + + + print("Size of" +directory_path+": "+str(size)) + + if size[-1] == "G" and float(size[:-1]) > size_GB: + print("Size exceeds "+str(size_GB)+"GB. Sleeping for 1hr...") + + # Sleep for 1 hour + time.sleep(3600) # 3600 seconds = 1 hour + print("1 hour has passed. Checking size again...") + + try: + size = subprocess.check_output(['du', '-sh', directory_path]).split()[0].decode('utf-8') + except subprocess.CalledProcessError as e: + print("Error fetching directory size: "+str(e)) + sys.exit(1) + + if size[-1] == "G" and float(size[:-1]) > size_GB: + print("Size still exceeds "+str(size_GB)+"GB. Stoping") + sys.exit(1) + + + +def data2dict(shotn, signame, hf, atlconn) : + dict_group = hf.create_group(str(signame)) + try: + data = gadata(signame, shotn, connection=atlconn) + dict_group['xdata'] = data.xdata + dict_group['ydata'] = data.ydata + dict_group['zdata'] = data.zdata + dict_group['xunits'] = data.xunits + dict_group['yunits'] = data.yunits + dict_group['zunits'] = data.zunits + except: + print('%s not available, filled with NULL!' % (signame)) + dict_group['xdata'] = [] + dict_group['ydata'] = [] + dict_group['zdata'] = [] + dict_group['xunits'] = [] + dict_group['yunits'] = [] + dict_group['zunits'] = [] + del atlconn + #global atlconn + atlconn = MDSplus.Connection('atlas.gat.com') + pass + return atlconn + +atlconn = MDSplus.Connection('atlas.gat.com') +ech_gytname = ['lei','luk','r2d'] + +#shot_list = np.loadtxt('DIIID_BES_Shot_List_Fatima.txt',delimiter='\n',dtype=np.int32) +# shot_list = np.load('tm-control-shots.npy');shot_list=np.unique(shot_list).astype(np.int) +# shot_list = [np.int32(sys.argv[1])] +# shot_list=[193266,193273,193280] +cannot_find=['triangularity_u','triangularity_l','pech','neutronsrate']\ + +['fplastic', 'fzns',\ + 'fncrate01', 'fncrate02', 'fncrate03', 'fncrate04',\ + 'plasticfx1', 'plasticfx2', 'plasticfx3', 'plasticfx4',\ + 'neutronsrate1','neutronsrate2','neutronsrate3', 'neutronsrate4'] + +#basic is fundimental measured quantities (in contrast of fitted quantities) + +signal_list= { +'profiles':['betap','betan','pres', \ + 'wmhd','li',\ + 'q0','q95','qmin','qpsi','rhoqmin',\ + 'r0','aminor',\ + 'kappa','tritop','tribot',\ + 'alpha','psirz',\ + 'ssibry', 'ssimag',\ + 'rmaxis','zmaxis',\ + 'volume',\ + 'drsep','gapbot','gapin','gapout','gaptop',\ + 'zxpt1','zxpt2',\ + 'edensfit', 'etempfit',\ + 'trotfit','itempfit','idensfit',\ + 'n1rms','n2rms','n3rms'], + \ +'basic':['ip', 'ipsip', 'iptipp','pcbcoil', 'bcoil','bt','vloop']\ + +[ 'plasticfix', 'fzns']\ + +['fs00','fs01','fs02','fs03','fs04','fs05'],\ +'actu': ['pinjf_%dl' % k for k in [15,21,30,33]]+['pinjf_%dr' % k for k in [15,21,30,33]]\ + +['tinj_%dl' % k for k in [15,21,30,33]]+['tinj_%dr' % k for k in [15,21,30,33]]\ + +['echpwrc','echpwr']\ + +['ec%sfpwrc' % (x) for x in ech_gytname]\ + +['ec%sxmfrac' % (x) for x in ech_gytname]\ + +['ec%spolang' % (x) for x in ech_gytname]\ + +['gasa', 'gasb', 'gasc', 'gasd', 'gase']\ + +['c19', 'c79', 'c139', 'c199', 'c259', 'c319', \ + 'iu30', 'iu90', 'iu150', 'iu210', 'iu270', 'iu330', \ + 'il30', 'il90', 'il150', 'il210', 'il270', 'il330']\ + +['ecoila', 'ecoilb', 'e567up', 'e567dn', 'e89dn', 'e89up']\ + +['f1a','f2a','f3a','f4a','f5a','f6a','f7a','f8a','f9a',\ + 'f1b','f2b','f3b','f4b','f5b','f6b','f7b','f8b','f9b'] +} + + +for i in tqdm(range(len(shot_list))): + shotn=shot_list[i] + t1=time.time() + + for grpname,signals in signal_list.items(): + hf = h5py.File(output_path+'/'+ str(shotn)+'_'+grpname+'.h5','w') + for signame in signals: + atlconn=data2dict(shotn,signame,hf,atlconn) + hf.close() + + + if ece_pcece: + hf = h5py.File(output_path+'/'+ str(shotn)+'_ece.h5','w') + pece_group = hf.create_group('pcece') + ece_group = hf.create_group('ece') + rtece_group = hf.create_group('rtece') + + for k in range(40): + print('chn %i' % (k+1)) + pece_data = gadata('pcece%d' % (k+1), shotn, connection=atlconn) + pece_group['pcece%02d' % (k+1)] = pece_data.zdata + ece_data = gadata('tecef%02d' % (k+1), shotn, connection=atlconn) + ece_group['tecef%02d' % (k+1)] = ece_data.zdata + + rtece_data = gadata('ecsdata%d' % (k+97), shotn, connection=atlconn) + rtece_group['ecsdata%d' % (k+97)] = rtece_data.zdata + + pece_group['xdata'] = pece_data.xdata + pece_group['ydata'] = pece_data.ydata + pece_group['xunits'] = pece_data.xunits + pece_group['yunits'] = pece_data.yunits + pece_group['pceceunits'] = pece_data.zunits + + ece_group['xdata'] = ece_data.xdata + ece_group['ydata'] = ece_data.ydata + ece_group['xunits'] = ece_data.xunits + ece_group['yunits'] = ece_data.yunits + ece_group['eceunits'] = ece_data.zunits + + rtece_group['xdata'] = rtece_data.xdata + rtece_group['ydata'] = rtece_data.ydata + rtece_group['xunits'] = rtece_data.xunits + rtece_group['yunits'] = rtece_data.yunits + rtece_group['rteceunits'] = rtece_data.zunits + hf.close() + if i % interval == 0: + size_limiter_sleep(size_GB=size_GB) + print('Shot #%d'%(shotn,)) + print(i) +# print('time per shot:%ds' % (time.time()-t1)) diff --git a/Data_fetching/fetch_toksearch.py b/Data_fetching/fetch_toksearch.py new file mode 100644 index 0000000..3654647 --- /dev/null +++ b/Data_fetching/fetch_toksearch.py @@ -0,0 +1,593 @@ +from toksearch import MdsSignal,PtDataSignal +from toksearch import Pipeline +import numpy as np +import os +import time +import h5py +import subprocess +import sys + +#this one runs on iris, run the following +#module purge +#module load toksearch + +#for copy: +#scp -r -o 'ProxyCommand ssh -p 2039 curiem@cybele.gat.com -W %h:%p' curiem@iris.gat.com:/cscratch/curiem/Data_fetch_TS/15* ./ + +#***********start of user block****************** +#limit of the size +size_GB=400 + +#After fetching (interval) discharges, check the total directory size +interval=100 + +#Root directory of the user for total size check +directory_path="/cscratch/curiem" + +#list of discharges to fetch +shots = np.arange(150000,170000,dtype=int) + +# one can set start_shot the where to start. (usually used for restarting the fetching due to unexpected termination) +start_shot=min(shots) + +#path to save the files +path = '/cscratch/curiem/Data_fetch_CER/' + +#diag_names=[mag,mag_hi,bes,ece_cali,ece_s, co2_den, co2_pl, co2_s, ts,ts_rz,ts_error,cer, mse,custom] +diag_name='cer' + +#custom sig_names_custom, the suffix is fixed to be custom for now. +if diag_name=='custom': + sig_names_custom=[''] + names_custom=[''] + tree='' #ptdata fo PTDATA, other trees names for MDS+ +#***********end of user block****************** + +shots.sort() + +def size_limiter(directory_path="/cscratch/curiem", size_GB=450): + try: + size = subprocess.check_output(['du', '-sh', directory_path]).split()[0].decode('utf-8') + except subprocess.CalledProcessError as e: + print(f"Error fetching directory size: {e}") + return + + print(f"Size of {directory_path}: {size}") + + if size[-1] == "G" and float(size[:-1]) > size_GB: + print(f"Size exceeds {size_GB}GB. Stopping...") + sys.exit(1) + +def size_limiter_sleep(directory_path="/cscratch/curiem", size_GB=450): + try: + size = subprocess.check_output(['du', '-sh', directory_path]).split()[0].decode('utf-8') + except subprocess.CalledProcessError as e: + print(f"Error fetching directory size: {e}") + sys.exit(1) + + + print(f"Size of {directory_path}: {size}") + + if size[-1] == "G" and float(size[:-1]) > size_GB: + print(f"Size exceeds {size_GB}GB. Sleeping for 1hr...") + + # Sleep for 1 hour + time.sleep(3600) # 3600 seconds = 1 hour + print("1 hour has passed. Checking size again...") + + try: + size = subprocess.check_output(['du', '-sh', directory_path]).split()[0].decode('utf-8') + except subprocess.CalledProcessError as e: + print(f"Error fetching directory size: {e}") + sys.exit(1) + + if size[-1] == "G" and float(size[:-1]) > size_GB: + print(f"Size still exceeds {size_GB}GB. Stoping") + sys.exit(1) + +def save_dict_to_hdf5(dictionary, h5file): + for key, value in dictionary.items(): + if isinstance(value, dict): + group = h5file.create_group(key) + save_dict_to_hdf5(value, group) + else: + h5file.create_dataset(key, data=value) + +#generate the name and signal to fetch i ntoksearch +def signal_gen(diag_name='zipfit',sig_names_custom=[''],names_custom=[''],tree_custom=''): + signals=[] + names=[] + + + #Counter: fncrate** + #Adjustable scintillator: fplastic, fzns + #Fixed scintillator: plasticfx* + #Approximate calibrated signal: neutronsrate* + if diag_name=='neutron': + sig_names=['fplastic', 'fzns',\ + 'fncrate01', 'fncrate02', 'fncrate03', 'fncrate04',\ + 'plasticfx1', 'plasticfx2', 'plasticfx3', 'plasticfx4',\ + 'neutronsrate1','neutronsrate2','neutronsrate3', 'neutronsrate4'] + names= ['fplastic', 'fzns',\ + 'fncrate01', 'fncrate02', 'fncrate03', 'fncrate04',\ + 'plasticfx1', 'plasticfx2', 'plasticfx3', 'plasticfx4',\ + 'cali.neutronsrate1','cali.neutronsrate2','cali.neutronsrate3', 'cali.neutronsrate4'] + + elif diag_name=='mag_full': + sig_name_without_d=['mpi11m322', 'mpi1a322', 'mpi2a322', 'mpi3a322', 'mpi4a322', 'mpi5a322', 'mpi8a322', 'mpi89a322', 'mpi9a322', 'mpi79fa322', 'mpi79na322', 'mpi7fa322', 'mpi7na322', 'mpi67a322', 'mpi6fa322', 'mpi6na322', 'mpi66m322', 'mpi1b322', 'mpi2b322', 'mpi3b322', 'mpi4b322', 'mpi5b322', 'mpi8b322', 'mpi89b322', 'mpi9b322', 'mpi79b322', 'mpi7fb322', 'mpi7nb322', 'mpi67b322', 'mpi6fb322', 'mpi6nb322', 'mpi2a067', 'mpi11m067', 'mpi2b067', 'mpi67a097', 'mpi67a067', 'mpi66m067', 'mpi67b097', 'mpi67b067', 'mpi1a139', 'mpi2a139', 'mpi3a139', 'mpi4a139', 'mpi5a139', 'mpi79a147', 'mpi67a142', 'mpi67a157', 'mpi6na132', 'mpi6na157', 'mpi66m157', 'mpi6nb157', 'mpi6fb142', 'mpi67b157', 'mpi7nb142', 'mpi79b142', 'mpi5b139', 'mpi4b139', 'mpi3b139', 'mpi2b139', 'mpi1b139', 'mpi1b157', 'mpi1u157', 'mpi2u157', 'mpi3u157', 'mpi4u157', 'mpi5u157', 'mpi6u157', 'mpi7u157', 'dsl1u180', 'dsl2u180', 'dsl3u180', 'dsl4u157', 'dsl5u157', 'dsl6u157', 'mpi66m127', 'mpi66m132', 'mpi66m137', 'mpi66b137', 'mpi6nb137', 'mpi66m307', 'mpi66m312', 'mpi6na312', 'mpi66b312', 'mpi6nb312', 'mpi66m322', 'mpi1l020', 'mpi2l020', 'mpi1l050', 'mpi1l110', 'mpi1l180', 'mpi2l180', 'mpi3l180', 'mpi1l230', 'mpi1l320', 'mpi66m020', 'mpi66m067', 'mpi66m097', 'mpi66m127', 'mpi66m132', 'mpi66m137', 'mpi66m157', 'mpi66m200', 'mpi66m247', 'mpi66m277', 'mpi66m307', 'mpi66m312', 'mpi66m322', 'mpi66m340', 'mpi67a022', 'mpi67a037', 'mpi67a1', 'mpi67a052', 'mpi67a067', 'mpi67a082', 'mpi67a097', 'mpi67a2', 'mpi67a142', 'mpi67a157', 'mpi67a3', 'mpi67a217', 'mpi67a4', 'mpi67a262', 'mpi67a277', 'mpi67a5', 'mpi67a307', 'mpi67a337', 'mpi67a6', 'mpi67b022', 'mpi67b037', 'mpi67b1', 'mpi67b052', 'mpi67b097', 'mpi67b2', 'mpi67b157', 'mpi67b3', 'mpi67b217', 'mpi67b4', 'mpi67b277', 'mpi67b5', 'mpi67b337', 'mpi67b6', 'mpi79a072', 'mpi79a147', 'mpi79a222', 'mpi79a272', 'mpi79b067', 'mpi79b142', 'mpi79b217', 'mpi79b277', 'mpi5a139', 'mpi4a139', 'mpi3a139', 'mpi2a139', 'mpi1a139', 'mpi1b139', 'mpi2b139', 'mpi3b139', 'mpi4b139', 'mpi5b139', 'mpi5a199', 'mpi4a199', 'mpi3a199', 'mpi2a199', 'mpi1a199', 'mpi1b199', 'mpi2b199', 'mpi3b199', 'mpi4b199', 'mpi5b199', 'mpi1a011', 'mpi1a049', 'mpi1a109', 'mpi1a139', 'mpi1a199', 'mpi1a244', 'mpi1a274', 'mpi1a341', 'mpi1b011', 'mpi1b049', 'mpi1b109', 'mpi1b139', 'mpi1b199', 'mpi1b244', 'mpi1b274', 'mpi1b341', 'isl66m017', 'isl66m042', 'isl66m072', 'isl66m102', 'isl66m132', 'isl66m197', 'isl66m252', 'isl66m312', 'isl67a017', 'isl67a052', 'isl67a072', 'isl67a112', 'isl67a132', 'isl67a197', 'isl67a252', 'isl67a312', 'isl67b017', 'isl67b052', 'isl67b072', 'isl67b112', 'isl67b132', 'isl67b197', 'isl67b252', 'isl67b312', 'isl79a072', 'isl79a147', 'isl79a222', 'isl79a272', 'isl79b067', 'isl79b142', 'isl79b217', 'isl79b277', 'isl5a139', 'isl4a139', 'isl3a139', 'isl2a139', 'isl1a139', 'isl1b139', 'isl2b139', 'isl3b139', 'isl4b139', 'isl5b139', 'isl5a199', 'isl4a199', 'isl3a199', 'isl2a199', 'isl1a199', 'isl1b199', 'isl2b199', 'isl3b199', 'isl4b199', 'isl5b199', 'isl1a011', 'isl1a049', 'isl1a109', 'isl1a139', 'isl1a199', 'isl1a244', 'isl1a274', 'isl1a341', 'isl1b011', 'isl1b049', 'isl1b109', 'isl1b139', 'isl1b199', 'isl1b244', 'isl1b274', 'isl1b341', 'dsl12a067', 'dsl34a067', 'dsl59a067', 'dsl79a067', 'dsl67a067', 'dsl66m052', 'dsl67b067', 'dsl79b067', 'dsl59b067', 'dsl34b067', 'dsl12b067', 'dsl12a157', 'dsl34a157', 'dsl59a157', 'dsl79a157', 'dsl67a157', 'dsl66m152', 'dsl67b157', 'dsl79b157', 'dsl59b157', 'dsl34b157', 'dsl12b157', 'dsl67a067', 'dsl67a157', 'sl67fa345', 'sl67na345', 'dsl66m052', 'sl66a132', 'sl66b132', 'dsl66m152', 'sl66a312', 'sl66b312', 'sl67nb015', 'sl67fb015', 'dsl67b067', 'dsl67b157', 'esl66m019', 'esl019', 'esl66m079', 'esl079', 'esl66m139', 'esl139', 'esl66m199', 'esl199', 'esl66m259', 'esl259', 'esl66m319', 'esl319', 'esl67a004', 'esl67a034', 'esl67a064', 'esl67a094', 'esl67a124', 'esl67a154', 'esl67a184', 'esl67a214', 'esl67a244', 'esl67a274', 'esl67a304', 'esl67a334', 'esl67b004', 'esl67b034', 'esl67b064', 'esl67b094', 'esl67b124', 'esl67b154', 'esl67b184', 'esl67b214', 'esl67b244', 'esl67b274', 'esl67b304', 'esl67b334', 'bti66m053', 'bti66m132', 'bti66m233', 'bti66m312', 'psf1a', 'psf1a', 'psf1a', 'psf1a', 'psf6natotl', 'psf6na', 'psi11mtotl', 'psi11m', 'psi6atotl', 'psi6a', 'psf1a', 'psf6natotl', 'psi11mtotl', 'psi6atotl', 'psf2a', 'psf3a', 'psf4a', 'psf5a', 'psf8a', 'psf9a', 'psf7fa', 'psf7na', 'psf6fa', 'psf6na', 'psf6nb', 'psf6fb', 'psf7nb', 'psf7fb', 'psf9b', 'psf8b', 'psf5b', 'psf4b', 'psf3b', 'psf2b', 'psf1b', 'psi11m', 'psi12a', 'psi23a', 'psi34a', 'psi45a', 'psi58a', 'psi9a', 'psi7a', 'psi6a', 'psi6b', 'psi7b', 'psi9b', 'psi89nb', 'psi89fb', 'psi58b', 'psi45b', 'psi34b', 'psi23b', 'psi12b', 'psi1l', 'psi2l', 'psi3l', 'mpi1b', 'mpi66m020', 'mpi66m097', 'mpi66m020', 'mpi66m097', 'mpi66m067', 'mpi66m247', 'mpi66m097', 'mpi66m277', 'mpi66m127', 'mpi66m307', 'mpi66m157', 'mpi66m340', 'mpi66m200', 'mpi66m020', 'mpi66m247', 'mpi66m127', 'mpi66m277', 'mpi66m157', 'mpi66m307', 'mpi66m200', 'mpi66m340', 'mpi66m067', 'mpi67a022', 'mpi67a217', 'mpi67a037', 'mpi67a067', 'mpi67a052', 'mpi67a022', 'mpi67a067', 'mpi67a262', 'mpi67a082', 'mpi67a052', 'mpi67a097', 'mpi67a082', 'mpi67a142', 'mpi67a037', 'mpi67a217', 'mpi67a097', 'mpi67a262', 'mpi67a277', 'mpi67a277', 'mpi67a307', 'mpi67a307', 'mpi67a337', 'mpi67a337', 'mpi67a142', 'mpi67b022', 'mpi67b052', 'mpi67b037', 'mpi67b217', 'mpi67b052', 'mpi67b037', 'mpi67b097', 'mpi67b277', 'mpi67b157', 'mpi67b337', 'mpi67b217', 'mpi67b097', 'mpi67b277', 'mpi67b157', 'mpi67b337', 'mpi67b022', 'mpi79a072', 'mpi79a222', 'mpi79a147', 'mpi79a072', 'mpi79a222', 'mpi79a272', 'mpi79a272', 'mpi79a147', 'mpi79b067', 'mpi79b217', 'mpi79b142', 'mpi79b067', 'mpi79b217', 'mpi79b277', 'mpi79b277', 'mpi79b142', 'mpi1a011', 'mpi1a199', 'mpi1a049', 'mpi1a244', 'mpi1a109', 'mpi1a011', 'mpi1a139', 'mpi1a341', 'mpi1a199', 'mpi1a139', 'mpi1a244', 'mpi1a274', 'mpi1a274', 'mpi1a109', 'mpi1a341', 'mpi1a049', 'mpi1b011', 'mpi1b199', 'mpi1b049', 'mpi1b244', 'mpi1b109', 'mpi1b011', 'mpi1b139', 'mpi1b341', 'mpi1b199', 'mpi1b139', 'mpi1b244', 'mpi1b274', 'mpi1b274', 'mpi1b109', 'mpi1b341', 'mpi1b049', 'mpi5a199', 'mpi5a139', 'mpi4a199', 'mpi4a139', 'mpi3a199', 'mpi3a139', 'mpi2a199', 'mpi2a139', 'mpi1a199', 'mpi1a139', 'mpi1b199', 'mpi1b139', 'mpi2b199', 'mpi2b139', 'mpi3b199', 'mpi3b139', 'mpi4b199', 'mpi4b139', 'mpi5b199', 'mpi5b139', 'isl66m017', 'isl66m042', 'isl66m042', 'isl66m072', 'isl66m072', 'isl66m252', 'isl66m102', 'isl66m132', 'isl66m132', 'isl66m312', 'isl66m197', 'isl66m017', 'isl66m252', 'isl66m102', 'isl66m312', 'isl66m197', 'isl67a017', 'isl67a052', 'isl67a052', 'isl67a072', 'isl67a072', 'isl67a252', 'isl67a112', 'isl67a132', 'isl67a132', 'isl67a312', 'isl67a197', 'isl67a017', 'isl67a252', 'isl67a112', 'isl67a312', 'isl67a197', 'isl67b017', 'isl67b052', 'isl67b052', 'isl67b072', 'isl67b072', 'isl67b252', 'isl67b112', 'isl67b132', 'isl67b132', 'isl67b312', 'isl67b197', 'isl67b017', 'isl67b252', 'isl67b112', 'isl67b312', 'isl67b197', 'isl79a072', 'isl79a222', 'isl79a147', 'isl79a072', 'isl79a222', 'isl79a272', 'isl79a272', 'isl79a147', 'isl79b067', 'isl79b217', 'isl79b142', 'isl79b067', 'isl79b217', 'isl79b277', 'isl79b277', 'isl79b142', 'isl1a011', 'isl1a199', 'isl1a049', 'isl1a244', 'isl1a109', 'isl1a011', 'isl1a139', 'isl1a341', 'isl1a199', 'isl1a139', 'isl1a244', 'isl1a274', 'isl1a274', 'isl1a109', 'isl1a341', 'isl1a049', 'isl1b011', 'isl1b199', 'isl1b049', 'isl1b244', 'isl1b109', 'isl1b011', 'isl1b139', 'isl1b341', 'isl1b199', 'isl1b139', 'isl1b244', 'isl1b274', 'isl1b274', 'isl1b109', 'isl1b341', 'isl1b049', 'isl5a199', 'isl5a139', 'isl4a199', 'isl4a139', 'isl3a199', 'isl3a139', 'isl2a199', 'isl2a139', 'isl1a199', 'isl1a139', 'isl1b199', 'isl1b139', 'isl2b199', 'isl2b139', 'isl3b199', 'isl3b139', 'isl4b199', 'isl4b139', 'isl5b199', 'isl5b139', 'esl66m019', 'esl66m079', 'esl66m079', 'esl66m259', 'esl66m139', 'esl66m319', 'esl66m199', 'esl66m019', 'esl66m259', 'esl66m139', 'esl66m319', 'esl66m199', 'esl66m079', 'esl66m259', 'esl66m139', 'esl66m319', 'esl66m199', 'esl66m019', 'esl67a004', 'esl67a244', 'esl67a034', 'esl67a154', 'esl67a064', 'esl67a184', 'esl67a094', 'esl67a274', 'esl67a124', 'esl67a304', 'esl67a154', 'esl67a334', 'esl67a184', 'esl67a004', 'esl67a214', 'esl67a094', 'esl67a244', 'esl67a124', 'esl67a274', 'esl67a034', 'esl67a304', 'esl67a064', 'esl67a334', 'esl67a214', 'esl67b004', 'esl67b244', 'esl67b034', 'esl67b154', 'esl67b064', 'esl67b184', 'esl67b094', 'esl67b274', 'esl67b124', 'esl67b304', 'esl67b154', 'esl67b334', 'esl67b184', 'esl67b004', 'esl67b214', 'esl67b094', 'esl67b244', 'esl67b124', 'esl67b274', 'esl67b034', 'esl67b304', 'esl67b064', 'esl67b334', 'esl67b214', 'bti66m053', 'bti66m233', 'bti66m132', 'bti66m053', 'bti66m233', 'bti66m312', 'bti66m312', 'bti66m132', 'mpi2a067', 'mpi1u157', 'isl79a'] + + name_without_d=['mpi.11.m.322', 'mpi.1.a.322', 'mpi.2.a.322', 'mpi.3.a.322', 'mpi.4.a.322', 'mpi.5.a.322', 'mpi.8.a.322', 'mpi.89.a.322', 'mpi.9.a.322', 'mpi.79.fa.322', 'mpi.79.na.322', 'mpi.7.fa.322', 'mpi.7.na.322', 'mpi.67.a.322', 'mpi.6.fa.322', 'mpi.6.na.322', 'mpi.66.m.322', 'mpi.1.b.322', 'mpi.2.b.322', 'mpi.3.b.322', 'mpi.4.b.322', 'mpi.5.b.322', 'mpi.8.b.322', 'mpi.89.b.322', 'mpi.9.b.322', 'mpi.79.b.322', 'mpi.7.fb.322', 'mpi.7.nb.322', 'mpi.67.b.322', 'mpi.6.fb.322', 'mpi.6.nb.322', 'mpi.2.a.067', 'mpi.11.m.067', 'mpi.2.b.067', 'mpi.67.a.097', 'mpi.67.a.067', 'mpi.66.m.067', 'mpi.67.b.097', 'mpi.67.b.067', 'mpi.1.a.139', 'mpi.2.a.139', 'mpi.3.a.139', 'mpi.4.a.139', 'mpi.5.a.139', 'mpi.79.a.147', 'mpi.67.a.142', 'mpi.67.a.157', 'mpi.6.na.132', 'mpi.6.na.157', 'mpi.66.m.157', 'mpi.6.nb.157', 'mpi.6.fb.142', 'mpi.67.b.157', 'mpi.7.nb.142', 'mpi.79.b.142', 'mpi.5.b.139', 'mpi.4.b.139', 'mpi.3.b.139', 'mpi.2.b.139', 'mpi.1.b.139', 'mpi.1.b.157', 'mpi.1.u.157', 'mpi.2.u.157', 'mpi.3.u.157', 'mpi.4.u.157', 'mpi.5.u.157', 'mpi.6.u.157', 'mpi.7.u.157', 'dsl.1.u.180', 'dsl.2.u.180', 'dsl.3.u.180', 'dsl.4.u.157', 'dsl.5.u.157', 'dsl.6.u.157', 'mpi.66.m.127', 'mpi.66.m.132', 'mpi.66.m.137', 'mpi.66.b.137', 'mpi.6.nb.137', 'mpi.66.m.307', 'mpi.66.m.312', 'mpi.6.na.312', 'mpi.66.b.312', 'mpi.6.nb.312', 'mpi.66.m.322', 'mpi.1.l.020', 'mpi.2.l.020', 'mpi.1.l.050', 'mpi.1.l.110', 'mpi.1.l.180', 'mpi.2.l.180', 'mpi.3.l.180', 'mpi.1.l.230', 'mpi.1.l.320', 'mpi.66.m.020', 'mpi.66.m.067', 'mpi.66.m.097', 'mpi.66.m.127', 'mpi.66.m.132', 'mpi.66.m.137', 'mpi.66.m.157', 'mpi.66.m.200', 'mpi.66.m.247', 'mpi.66.m.277', 'mpi.66.m.307', 'mpi.66.m.312', 'mpi.66.m.322', 'mpi.66.m.340', 'mpi.67.a.022', 'mpi.67.a.037', 'mpi.67.a.1', 'mpi.67.a.052', 'mpi.67.a.067', 'mpi.67.a.082', 'mpi.67.a.097', 'mpi.67.a.2', 'mpi.67.a.142', 'mpi.67.a.157', 'mpi.67.a.3', 'mpi.67.a.217', 'mpi.67.a.4', 'mpi.67.a.262', 'mpi.67.a.277', 'mpi.67.a.5', 'mpi.67.a.307', 'mpi.67.a.337', 'mpi.67.a.6', 'mpi.67.b.022', 'mpi.67.b.037', 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'mpid.79.a.272', 'mpid.79.b.067', 'mpid.79.b.142', 'mpid.79.b.217', 'mpid.79.b.277', 'mpid.1.a.011', 'mpid.1.a.049', 'mpid.1.a.109', 'mpid.1.a.139', 'mpid.1.a.199', 'mpid.1.a.244', 'mpid.1.a.274', 'mpid.1.a.341', 'mpid.1.b.011', 'mpid.1.b.049', 'mpid.1.b.109', 'mpid.1.b.139', 'mpid.1.b.199', 'mpid.1.b.244', 'mpid.1.b.274', 'mpid.1.b.341', 'mpid.5.a.199', 'mpid.4.a.199', 'mpid.3.a.199', 'mpid.2.a.199', 'mpid.1.a.199', 'mpid.1.b.199', 'mpid.2.b.199', 'mpid.3.b.199', 'mpid.4.b.199', 'mpid.5.b.199', 'isld.66.m.017', 'isld.66.m.042', 'isld.66.m.072', 'isld.079.u.', 'isld.66.m.102', 'isld.66.m.132', 'isld.139.u.', 'isld.66.m.197', 'isld.199.u.', 'isld.66.m.252', 'isld.66.m.312', 'isld.67.a.017', 'isld.67.a.052', 'isld.67.a.072', 'isld.67.a.112', 'isld.67.a.132', 'isld.67.a.197', 'isld.67.a.252', 'isld.67.a.312', 'isld.67.b.017', 'isld.67.b.052', 'isld.67.b.072', 'isld.67.b.112', 'isld.67.b.132', 'isld.67.b.197', 'isld.67.b.252', 'isld.67.b.312', 'isld.79.a.072', 'isld.79.a.147', 'isld.79.a.222', 'isld.79.a.272', 'isld.79.b.067', 'isld.79.b.142', 'isld.79.b.217', 'isld.79.b.277', 'isld.1.a.011', 'isld.1.a.049', 'isld.1.a.109', 'isld.1.a.139', 'isld.1.a.199', 'isld.1.a.244', 'isld.1.a.274', 'isld.1.a.341', 'isld.1.b.011', 'isld.1.b.049', 'isld.1.b.109', 'isld.1.b.139', 'isld.1.b.199', 'isld.1.b.244', 'isld.1.b.274', 'isld.1.b.341', 'isld.5.a.199', 'isld.4.a.199', 'isld.3.a.199', 'isld.2.a.199', 'isld.1.a.199', 'isld.1.b.199', 'isld.2.b.199', 'isld.3.b.199', 'isld.4.b.199', 'isld.5.b.199', 'esld.66.m.019', 'esld.66.m.079', 'esld.079.u.', 'esld.66.m.139', 'esld.139.u.', 'esld.66.m.199', 'esld.199.u.', 'esld.66.m.259', 'esld.66.m.319', 'esld.079..', 'esld.139..', 'esld.199..', 'esld.67.a.004', 'esld.67.a.034', 'esld.67.a.064', 'esld.67.a.094', 'esld.67.a.124', 'esld.67.a.154', 'esld.67.a.184', 'esld.67.a.214', 'esld.67.a.244', 'esld.67.a.274', 'esld.67.a.304', 'esld.67.a.334', 'esld.67.b.004', 'esld.67.b.034', 'esld.67.b.064', 'esld.67.b.094', 'esld.67.b.124', 'esld.67.b.154', 'esld.67.b.184', 'esld.67.b.214', 'esld.67.b.244', 'esld.67.b.274', 'esld.67.b.304', 'esld.67.b.334', 'btid.66.m.053', 'btid.66.m.132', 'btid.66.m.233', 'btid.66.m.312'] + sig_names=sig_name_with_d+sig_name_without_d + names=name_with_d+name_without_d + + for name in sig_names: + signals.append(PtDataSignal(name)) + + elif diag_name=='custom': + sig_names=sig_names_custom + names=names_custom + if tree_custom=='ptdata': + signals.append(PtDataSignal(name)) + else: + signals.append(MdsSignal(name, tree_custom, location='remote://atlas.gat.com')) + + elif diag_name=='mag_hi': + sig_names=[f"b{i}" for i in range(1, 9)] + names=[f"b{i}" for i in range(1, 9)] + for name in sig_names: + signals.append(PtDataSignal(name)) + + elif diag_name=='bes': + sig_names = [f"besfu{i:02}" for i in range(1, 65)] + + sig_names.append('bes_r') + sig_names.append('bes_z') + names=[f"{i:02}" for i in range(1, 65)] + + names.append('r') + names.append('z') + + for name in sig_names: + signals.append(PtDataSignal(name)) + + elif diag_name=='ece_cali': + channels = range(1,49) # 48 + + for chan in channels: + name = r'\TECEF%02d'%chan + signals.append(MdsSignal(name, 'ECE', location='remote://atlas.gat.com')) + names.append(r'%02d'%chan) + + elif diag_name=='ece_s': + channels = range(1,49) # 48 + + for chan in channels: + name = r'\TECE%02d'%chan + signals.append(MdsSignal(name, 'ECE', location='remote://atlas.gat.com')) + names.append(r'%02d'%chan) + + elif diag_name=='co2_den': + nums = range(1,15) + chords = ['r0', 'v1', 'v2', 'v3'] + phases = ['den'] + + for phase in phases: + for chord in chords: + for num in nums: + name = r'\den{}_uf_{}'.format(chord, num) + signals.append(MdsSignal(name, 'BCI', location='remote://atlas.gat.com')) + names.append(f'{chord}_{num}') + + elif diag_name=='co2_pl': + nums = range(1,15) + chords = ['r0', 'v1', 'v2', 'v3'] + phases = ['pl'] + + for phase in phases: + for chord in chords: + for num in nums: + name = r'\pl1{}_uf_{}'.format(chord, num) + signals.append(MdsSignal(name, 'BCI', location='remote://atlas.gat.com')) + names.append(f'{chord}_{num}') + + + elif diag_name=='ts': + #thomson_mds_scale={'density': 1e19, 'temp': 1e3} + thomson_mds_areas=['core','divertor','tangential'] + thomson_sig_names= ['density', 'temp'] + thomson_names=['dens','temp'] + treename='electrons' + + + + for thomson_mds_area in thomson_mds_areas: + for thomson_sig_name, thomson_name in zip(thomson_sig_names,thomson_names): + + name=r'TS.BLESSED.{}.{}'.format(thomson_mds_area,thomson_sig_name) + + signals.append(MdsSignal(name, treename, location='remote://atlas.gat.com')) + + names.append(r'{}.{}'.format(thomson_mds_area,thomson_name)) + + elif diag_name=='ts_rz': + #thomson_mds_scale={'density': 1e19, 'temp': 1e3} + thomson_mds_areas=['core','divertor','tangential'] + thomson_sig_names= ['r', 'z'] + treename='electrons' + + for thomson_mds_area in thomson_mds_areas: + for thomson_sig_name in thomson_sig_names: + name=r'TS.BLESSED.{}.{}'.format(thomson_mds_area,thomson_sig_name) + signals.append(MdsSignal(name, treename, location='remote://atlas.gat.com')) + + names.append(r'{}.{}'.format(thomson_mds_area,thomson_sig_name)) + + elif diag_name=='ts_error': + #thomson_mds_scale={'density': 1e19, 'temp': 1e3} + thomson_mds_areas=['core','divertor','tangential'] + thomson_sig_names= ['DENSITY_E', 'TEMP_E'] + thomson_names=['dens','temp'] + treename='electrons' + + for thomson_mds_area in thomson_mds_areas: + for thomson_sig_name, thomson_name in zip(thomson_sig_names,thomson_names): + signals.append(MdsSignal(name, treename, location='remote://atlas.gat.com')) + + names.append(r'{}.{}'.format(thomson_mds_area,thomson_name)) + + + elif diag_name=='mag': + sig_names=['DSL1U180', 'DSL2U180', 'DSL3U180', 'DSL4U157', 'DSL5U157', 'DSL6U157', 'MPI11M067', 'MPI11M322', 'MPI1A139', 'MPI1A322', 'MPI1B139', 'MPI1B157', 'MPI1B322', 'MPI1L180', 'MPI1U157', 'MPI2A067', 'MPI2A139', 'MPI2A322', 'MPI2B067', 'MPI2B139', 'MPI2B322', 'MPI2L180', 'MPI2U157', 'MPI3A139', 'MPI3A322', 'MPI3B139', 'MPI3B322', 'MPI3L180', 'MPI3U157', 'MPI4A139', 'MPI4A322', 'MPI4B139', 'MPI4B322', 'MPI4U157', 'MPI5A139', 'MPI5A322', 'MPI5B139', 'MPI5B322', 'MPI5U157', 'MPI66M067', 'MPI66M157', 'MPI66M247', 'MPI66M322', 'MPI67A097', 'MPI67A142', 'MPI67A157', 'MPI67A322', 'MPI67B097', 'MPI67B157', 'MPI67B322', 'MPI6FA322', 'MPI6FB142', 'MPI6FB322', 'MPI6NA132', 'MPI6NA157', 'MPI6NA322', 'MPI6NB157', 'MPI6NB322', 'MPI6U157', 'MPI79A147', 'MPI79B142', 'MPI79B322', 'MPI79FA322', 'MPI79NA322', 'MPI7FA322', 'MPI7FB322', 'MPI7NA322', 'MPI7NB142', 'MPI7NB322', 'MPI7U157', 'MPI89A322', 'MPI89B322', 'MPI8A322', 'MPI8B322', 'MPI9A322', 'MPI9B322', 'PSF1A', 'PSF1B', 'PSF2A', 'PSF2B', 'PSF3A', 'PSF3B', 'PSF4A', 'PSF4B', 'PSF5A', 'PSF5B', 'PSF6FA', 'PSF6FB', 'PSF6NA', 'PSF6NB', 'PSF7FA', 'PSF7FB', 'PSF7NA', 'PSF7NB', 'PSF8A', 'PSF8B', 'PSF9A', 'PSF9B', 'PSI11M', 'PSI12A', 'PSI12B', 'PSI1L', 'PSI23A', 'PSI23B', 'PSI2L', 'PSI34A', 'PSI34B', 'PSI3L', 'PSI45A', 'PSI45B', 'PSI58A', 'PSI58B', 'PSI6A', 'PSI6B', 'PSI7A', 'PSI7B', 'PSI89FB', 'PSI89NB', 'PSI9A', 'PSI9B'] + #Poloidal Flux Loops (Wb/rad): psf, psi. + #Poloidal Field Probes: mpi, dsl + + #E-coil Currents (A): can be found in actu + #F-coil Currents (A): can be found in actu + #I-coil Currents (A): can be found in actu + #C-coil Currents (A): can be found in actu + + #Miscellaneous data: can be found in basic + + #https://diii-d.gat.com/d3d-wiki/images/6/68/Mag_eq_2013_LABEL.pdf + names=['dsl.1u180', 'dsl.2u180', 'dsl.3u180', 'dsl.4u157', 'dsl.5u157', 'dsl.6u157', \ + 'mpi.11m067', 'mpi.11m322', 'mpi.1a139', 'mpi.1a322', 'mpi.1b139', 'mpi.1b157', 'mpi.1b322', \ + 'mpi.1l180', 'mpi.1u157', 'mpi.2a067', 'mpi.2a139', 'mpi.2a322', 'mpi.2b067', 'mpi.2b139', \ + 'mpi.2b322', 'mpi.2l180', 'mpi.2u157', 'mpi.3a139', 'mpi.3a322', 'mpi.3b139', 'mpi.3b322', \ + 'mpi.3l180', 'mpi.3u157', 'mpi.4a139', 'mpi.4a322', 'mpi.4b139', 'mpi.4b322', 'mpi.4u157', \ + 'mpi.5a139', 'mpi.5a322', 'mpi.5b139', 'mpi.5b322', 'mpi.5u157', 'mpi.66m067', 'mpi.66m157', \ + 'mpi.66m247', 'mpi.66m322', 'mpi.67a097', 'mpi.67a142', 'mpi.67a157', 'mpi.67a322', \ + 'mpi.67b097', 'mpi.67b157', 'mpi.67b322', 'mpi.6fa322', 'mpi.6fb142', 'mpi.6fb322', \ + 'mpi.6na132', 'mpi.6na157', 'mpi.6na322', 'mpi.6nb157', 'mpi.6nb322', 'mpi.6u157', \ + 'mpi.79a147', 'mpi.79b142', 'mpi.79b322', 'mpi.79fa322', 'mpi.79na322', 'mpi.7fa322', \ + 'mpi.7fb322', 'mpi.7na322', 'mpi.7nb142', 'mpi.7nb322', 'mpi.7u157', 'mpi.89a322', \ + 'mpi.89b322', 'mpi.8a322', 'mpi.8b322', 'mpi.9a322', 'mpi.9b322', \ + 'psf.1a', 'psf.1b', 'psf.2a', 'psf.2b', 'psf.3a', 'psf.3b', 'psf.4a', 'psf.4b', \ + 'psf.5a', 'psf.5b', 'psf.6fa', 'psf.6fb', 'psf.6na', 'psf.6nb', 'psf.7fa', \ + 'psf.7fb', 'psf.7na', 'psf.7nb', 'psf.8a', 'psf.8b', 'psf.9a', 'psf.9b', \ + 'psi.11m', 'psi.12a', 'psi.12b', 'psi.1l', 'psi.23a', 'psi.23b', 'psi.2l', 'psi.34a', \ + 'psi.34b', 'psi.3l', 'psi.45a', 'psi.45b', 'psi.58a', 'psi.58b', 'psi.6a', 'psi.6b', \ + 'psi.7a', 'psi.7b', 'psi.89fb', 'psi.89nb', 'psi.9a', 'psi.9b',\ + ] + + for name in sig_names: + signals.append(PtDataSignal(name)) + + elif diag_name=='cer': + channels = ["v%i"%i for i in range(1, 33)] + \ + ["t%i"%i for i in range(1, 49)] + + name_channels = ["v%02d"%i for i in range(1, 33)] + \ + ["t%02d"%i for i in range(1, 49)] + outputs=['amp','samp','ti','sti','rot','srot','r','phi','nz','fz','zeff','vb','svb'] + + sig_names=[r'\cerq{}{}'.format(output, channel) for channel in channels + for output in outputs] + names=[r'q.{}.{}'.format(output, channel) for channel in name_channels + for output in outputs] + + treename='ions' + signals=[] + for name in sig_names: + signals.append(MdsSignal(name, treename, location='remote://atlas.gat.com')) + + elif diag_name=='mse': + treename='mse' + sig_names=[r'\msep%02d'%i for i in range(1, 70)] + names=[r'%02d'%i for i in range(1, 70)] + + signals=[] + for name in sig_names: + signals.append(MdsSignal(name, treename, location='remote://atlas.gat.com')) + + return names, signals + +def fetch_ece_2d_array_data(path, shots, diag_name): + names, signals = signal_gen(diag_name) + + for n,shot in enumerate(shots): + if shot>=start_shot: + start = time.time() + pipeline = Pipeline([shot]) + + for i,name in enumerate(names): + pipeline.fetch(name, signals[i]) + + records = pipeline.compute_serial() + shot_data = dict(records[0]) + data_h5={} + + try: + data_h5['xdata']=shot_data[names[0]]['times'] + data_h5['xunits']=shot_data[names[0]]['units']['times'] + data_h5['yunits']='' + data_h5['zunits']=shot_data[names[0]]['units']['data'] + data_tmp=[] + for name in names: + if len(shot_data[name]['data'])<=10: + break + print(len(shot_data[name]['data'])) + data_tmp.append(shot_data[name]['data'][:len(data_h5['xdata'])]) + + data_tmp=np.array(data_tmp,dtype='float') + + data_h5['zdata']=data_tmp + data_h5['ydata']=[] + + if len(data_h5['xdata'])>3500000: + data_h5['xdata']=data_h5['xdata'][:3500000] + data_h5['zdata']=data_h5['zdata'][:,:3500000] + except: + data_h5['xdata']=[] + data_h5['ydata']=[] + data_h5['zdata']=[] + + data_h5['xunits']='' + data_h5['yunits']='' + data_h5['zunits']='' + + + with h5py.File(f'{path}{shot}_{diag_name}.h5', 'w') as h5file: + save_dict_to_hdf5(data_h5, h5file) + + if 1==0: + pass + if n % interval == 0: + size_limiter_sleep(size_GB=size_GB) + print(f'shot={shot}') + print(n, flush=True) + +#fetching the data +def fetch_single_data(path, shots, diag_name): + names, signals = signal_gen(diag_name) + + if not os.path.isdir(path): + os.makedirs(path) + + for n,shot in enumerate(shots): + if shot>=start_shot: + try: + start = time.time() + pipeline = Pipeline([shot]) + + for i,name in enumerate(names): + pipeline.fetch(name,signals[i]) + + records = pipeline.compute_serial() + shot_data = dict(records[0]) + #print(shot_data.keys()) + ##print(shot_data) + data_h5={} + #print(names) + for name in names: + data_h5[name]={} + #print(data_h5) + data_tmp=shot_data[name] + #print(name) + + try: + + total_len=len(data_tmp['times']) + dt=data_tmp['times'][1]-data_tmp['times'][0] + cut_index=int(np.min([total_len+1,7500/dt])) + + if diag_name in diag_3d: + #print(data_tmp.keys()) + data_h5[name]['xdata']=data_tmp['times'][:cut_index] + data_h5[name]['ydata']=data_tmp['rhon'][:] + data_h5[name]['zdata']=data_tmp['data'][:cut_index] + + data_h5[name]['xunits']=data_tmp['units']['times'] + data_h5[name]['yunits']='rhon' + data_h5[name]['zunits']=data_tmp['units']['data'] + else: + #print(data_tmp.keys()) + data_h5[name]['xdata']=data_tmp['times'][:cut_index] + data_h5[name]['ydata']=[] + data_h5[name]['zdata']=data_tmp['data'][:cut_index] + + data_h5[name]['xunits']=data_tmp['units']['times'] + data_h5[name]['yunits']='' + data_h5[name]['zunits']=data_tmp['units']['data'] + except: + data_h5[name]['xdata']=[] + data_h5[name]['ydata']=[] + data_h5[name]['zdata']=[] + + data_h5[name]['xunits']='' + data_h5[name]['yunits']='' + data_h5[name]['zunits']='' + + #print(data_h5.keys()) + # Save to h5 + with h5py.File(f'{path}{shot}_{diag_name}.h5', 'w') as hf: + for key in data_h5.keys(): + group = hf.create_group(key) + for subkey, value in data_h5[key].items(): + # Check if the value is a string type + if isinstance(value, np.ndarray) and np.issubdtype(value.dtype, np.str_): + # Create a special dtype for storing string data + str_dtype = h5py.string_dtype(encoding='utf-8') + group.create_dataset(subkey, data=value.astype(object), dtype=str_dtype) + else: + group.create_dataset(subkey, data=value) + + except: + pass + if n % interval == 0: + size_limiter_sleep(size_GB=size_GB) + print(f'shot={shot}') + print(n, flush=True) + +def fetch_co2_chunked_data(path, shots, diag_name): + chords=['r0', 'v1', 'v2', 'v3'] + nums = range(1,15) + names, signals = signal_gen(diag_name) + for n,shot in enumerate(shots): + if shot>=start_shot: + try: + start = time.time() + pipeline = Pipeline([shot]) + + for i,name in enumerate(names): + pipeline.fetch(name, signals[i]) + + records = pipeline.compute_serial() + shot_data = dict(records[0]) + data_h5={} + for chord in chords: + data_h5[chord]={} + for num in nums: + for i_chord,chord in enumerate(chords): + #print(f'num={num}') + #print(f'chord={chord}') + + + data_tmp=shot_data[f'{chord}_{num}'] + + try: + len(data_tmp['data']) + #print(len(data_tmp['data'])) + except: + break + + if num==1: + data_h5[chord]['xdata']=data_tmp['times'] + data_h5[chord]['ydata']=[] + data_h5[chord]['zdata']=data_tmp['data'] + + data_h5[chord]['xunits']=data_tmp['units']['times'] + data_h5[chord]['yunits']='' + data_h5[chord]['zunits']=data_tmp['units']['data'] + else: + data_h5[chord]['xdata']=np.concatenate([data_h5[chord]['xdata'],data_tmp['times']]) + data_h5[chord]['zdata']=np.concatenate([data_h5[chord]['zdata'],data_tmp['data']]) + shot_data=None + + # Save to h5 + with h5py.File(f'{path}{shot}_{diag_name}.h5', 'w') as hf: + for key in data_h5.keys(): + group = hf.create_group(key) + for subkey, value in data_h5[key].items(): + # Check if the value is a string type + if isinstance(value, np.ndarray) and np.issubdtype(value.dtype, np.str_): + # Create a special dtype for storing string data + str_dtype = h5py.string_dtype(encoding='utf-8') + group.create_dataset(subkey, data=value.astype(object), dtype=str_dtype) + else: + group.create_dataset(subkey, data=value) + except: + pass + + if n % interval == 0: + size_limiter_sleep(size_GB=size_GB) + print(f'shot={shot}') + print(n, flush=True) + +def fetch_co2_chunked_data_2d(path, shots, diag_name): + chords=['r0', 'v1', 'v2', 'v3'] + nums = range(1,15) + names, signals = signal_gen(diag_name) + for n,shot in enumerate(shots): + if shot>=start_shot: + try: + start = time.time() + pipeline = Pipeline([shot]) + + for i,name in enumerate(names): + pipeline.fetch(name, signals[i]) + + records = pipeline.compute_serial() + shot_data = dict(records[0]) + data_h5={} + for chord in chords: + data_h5[chord]={} + for num in nums: + for i_chord,chord in enumerate(chords): + #print(f'num={num}') + #print(f'chord={chord}') + + + data_tmp=shot_data[f'{chord}_{num}'] + + try: + len(data_tmp['data']) + #print(len(data_tmp['data'])) + except: + break + + if num==1: + data_h5[chord]['xdata']=data_tmp['times'] + data_h5[chord]['ydata']=[] + data_h5[chord]['zdata']=data_tmp['data'] + + data_h5[chord]['xunits']=data_tmp['units']['times'] + data_h5[chord]['yunits']='' + data_h5[chord]['zunits']=data_tmp['units']['data'] + else: + data_h5[chord]['xdata']=np.concatenate([data_h5[chord]['xdata'],data_tmp['times']]) + data_h5[chord]['zdata']=np.concatenate([data_h5[chord]['zdata'],data_tmp['data']]) + shot_data=None + data_h5_new={} + data_h5_new['xdata']=np.array(data_h5[chords[0]]['xdata']) + data_h5_new['zdata']=np.array([data_h5[chord]['zdata'] for chord in chords]) + data_h5_new['keys']=np.array(chords) + + data_h5=None + # Save to h5 + with h5py.File(f'{path}{shot}_{diag_name}.h5', 'w') as hf: + for key in data_h5_new.keys(): + group = hf.create_group(key) + for subkey, value in data_h5_new[key].items(): + # Check if the value is a string type + if isinstance(value, np.ndarray) and np.issubdtype(value.dtype, np.str_): + # Create a special dtype for storing string data + str_dtype = h5py.string_dtype(encoding='utf-8') + group.create_dataset(subkey, data=value.astype(object), dtype=str_dtype) + else: + group.create_dataset(subkey, data=value) + except: + pass + + if n % interval == 0: + size_limiter_sleep(size_GB=size_GB) + print(f'shot={shot}') + print(n, flush=True) + + +def fetch_data(path, shots, diag_name): + if diag_name in ['co2_den','co2_pl']: + fetch_co2_chunked_data(path, shots, diag_name) + elif diag_name in ['ece_s','ece_cali']: + fetch_ece_2d_array_data(path, shots, diag_name) + else: + fetch_single_data(path, shots, diag_name) + + + +if __name__ == "__main__": + fetch_data(path, shots, diag_name) + \ No newline at end of file diff --git a/Data_fetching/mygadata.py b/Data_fetching/mygadata.py new file mode 100644 index 0000000..073dfde --- /dev/null +++ b/Data_fetching/mygadata.py @@ -0,0 +1,86 @@ +import MDSplus +import numpy +import time +import sys + +class gadata: + """GA Data Obj""" + def __init__(self,signal,shot,tree=None,connection=None,nomds=False): + # Save object values + self.signal = signal + self.shot = shot + self.zdata = -1 + self.xdata = -1 + self.ydata = -1 + self.zunits = '' + self.xunits = '' + self.yunits = '' + self.rank = -1 + self.connection = connection + + ## Retrieve Data + t0 = time.time() + found = 0 + + # Create the MDSplus connection (thin) if not passed in + if self.connection is None: + print 'No connection!!!' + self.connection = MDSplus.Connection('atlas.gat.com') + + # Retrieve data from MDSplus (thin) + if nomds == False: + try: + if tree != None: + tag = self.signal + fstree = tree + found = 1 + else: + tag = self.connection.get('findsig("'+self.signal+'",_fstree)').value + fstree = self.connection.get('_fstree').value + self.connection.openTree(fstree,shot) + self.zdata = self.connection.get('_s = '+tag).data() + self.zunits = self.connection.get('units_of(_s)').data() + self.rank = numpy.rank(self.zdata) + self.xdata = self.connection.get('dim_of(_s)').data() + self.xunits = self.connection.get('units_of(dim_of(_s))').data() + if self.xunits == '' or self.xunits == ' ': + self.xunits = self.connection.get('units(dim_of(_s))').data() + if self.rank > 1: + self.ydata = self.connection.get('dim_of(_s,1)').data() + self.yunits = self.connection.get('units_of(dim_of(_s,1))').data() + if self.yunits == '' or self.yunits == ' ': + self.yunits = self.connection.get('units(dim_of(_s,1))').data() + + found = 1 + + # MDSplus seems to return 2-D arrays transposed. Change them back. + if numpy.rank(self.zdata) == 2: self.zdata = numpy.transpose(self.zdata) + if numpy.rank(self.ydata) == 2: self.ydata = numpy.transpose(self.ydata) + if numpy.rank(self.xdata) == 2: self.xdata = numpy.transpose(self.xdata) + + except Exception as e: + #print ' Signal not in MDSplus: %s' % (signal,) + pass + + + # Retrieve data from PTDATA + if found == 0: + self.zdata = self.connection.get('_s = ptdata2("'+signal+'",'+str(shot)+')') + if len(self.zdata) != 1: + self.xdata = self.connection.get('dim_of(_s)') + self.rank = 1 + found = 1 + + # Retrieve data from Pseudo-pointname + if found == 0: + self.zdata = self.connection.get('_s = pseudo("'+signal+'",'+str(shot)+')') + if len(self.zdata) != 1: + self.xdata = self.connection.get('dim_of(_s)') + self.rank = 1 + found = 1 + + if found == 0: + #print " No such signal: %s" % (signal,) + return + + print ' GADATA Retrieval Time : ',time.time() - t0 diff --git a/Dataset_prep/0read_data_run.ipynb b/Dataset_prep/0read_data_run.ipynb new file mode 100644 index 0000000..10d2fe5 --- /dev/null +++ b/Dataset_prep/0read_data_run.ipynb @@ -0,0 +1,258 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "df7f60cd-0236-4ab9-9082-ca9b7a29f660", + "metadata": {}, + "outputs": [], + "source": [ + "import data_prep_obj as data_prep\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "443e7c7b-b0ed-477f-bc94-0c0af0e53a48", + "metadata": {}, + "source": [ + "## filter the discharge with file size" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5db78b8f-06a8-4c64-a3dc-b3949df7d85e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "range(170000, 170011)\n", + "['ece_s', 'co2_s', 'ts', 'co2_pl']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████| 11/11 [00:00<00:00, 12798.15it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'ece_s': [170000, 170001, 170002, 170003, 170004, 170005, 170006, 170007, 170008, 170009, 170010], 'co2_s': [170000, 170001, 170002, 170003, 170004, 170005, 170006, 170007, 170008, 170009, 170010], 'ts': [170000, 170001, 170002, 170003, 170004, 170005, 170006, 170007, 170008, 170009, 170010], 'co2_pl': [170000, 170001, 170002, 170003, 170004, 170005, 170006, 170007, 170008, 170009, 170010]}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "spectro_analysis=False\n", + "\n", + "discharge_min=170000\n", + "discharge_max=170010\n", + "discharge_search_list=range(discharge_min,discharge_max+1)\n", + "suffix_list=['ece_s','co2_s','ts','co2_pl']\n", + "prep_obj_1=data_prep.DatasetPrep(discharge_search_list, suffix_list)\n", + "print(prep_obj_1.discharge_search_list)\n", + "print(prep_obj_1.suffix_list)\n", + "\n", + "discharge_list=prep_obj_1.filter_discharges()\n", + "print(discharge_list)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "26306ab3-df3c-4504-900f-1bfdb4ddcb05", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mag\n", + "dict_keys(['dsl', 'mpi', 'psf', 'psi'])\n" + ] + }, + { + "ename": "KeyError", + "evalue": "\"Unable to open object (object 'core.z' doesn't exist)\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[22], line 14\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28mprint\u001b[39m(key)\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28mprint\u001b[39m(file_dict[key]\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28mdict\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlist\u001b[39m(\u001b[43minput_file\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcore.z\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzdata\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mdict\u001b[39m)\n", + "File \u001b[0;32mh5py/_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mh5py/_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/.conda/envs/tfp2-gpu/lib/python3.10/site-packages/h5py/_hl/group.py:305\u001b[0m, in \u001b[0;36mGroup.__getitem__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 303\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid HDF5 object reference\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 304\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(name, (\u001b[38;5;28mbytes\u001b[39m, \u001b[38;5;28mstr\u001b[39m)):\n\u001b[0;32m--> 305\u001b[0m oid \u001b[38;5;241m=\u001b[39m \u001b[43mh5o\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_e\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlapl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_lapl\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 306\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 307\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAccessing a group is done with bytes or str, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m not \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mtype\u001b[39m(name)))\n", + "File \u001b[0;32mh5py/_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mh5py/_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mh5py/h5o.pyx:190\u001b[0m, in \u001b[0;36mh5py.h5o.open\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: \"Unable to open object (object 'core.z' doesn't exist)\"" + ] + } + ], + "source": [ + "discharge=174823\n", + "suffix='mag'\n", + "\n", + "discharge_obj=data_prep.DichargePerp(discharge, [suffix])\n", + "input_file=discharge_obj.get_data(discharge,suffix)\n", + "input_multi_level=discharge_obj.data_division(input_file,suffix)\n", + "\n", + "file_dict=discharge_obj.get_full_data()\n", + "\n", + "for key in file_dict.keys():\n", + " print(key)\n", + " print(file_dict[key].keys())\n", + " for key2 in file_dict[key].keys():\n", + " \n", + " print(len(file_dict[key].keys()))\n", + "\n", + "dict=list(input_file['core.z']['zdata'])\n", + "print(dict)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1ce0daa1-3d5c-4512-bf5e-bb48d3aa2886", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTime (ms)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlim(\u001b[38;5;241m2000\u001b[39m,\u001b[38;5;241m2700\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mylim\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", + "File \u001b[0;32m~/.conda/envs/tfp2-gpu/lib/python3.10/site-packages/matplotlib/pyplot.py:1831\u001b[0m, in \u001b[0;36mylim\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1829\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kwargs:\n\u001b[1;32m 1830\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mget_ylim()\n\u001b[0;32m-> 1831\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_ylim\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1832\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n", + "File \u001b[0;32m~/.conda/envs/tfp2-gpu/lib/python3.10/site-packages/matplotlib/_api/deprecation.py:454\u001b[0m, in \u001b[0;36mmake_keyword_only..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m name_idx:\n\u001b[1;32m 449\u001b[0m warn_deprecated(\n\u001b[1;32m 450\u001b[0m since, message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPassing the \u001b[39m\u001b[38;5;132;01m%(name)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%(obj_type)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 451\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpositionally is deprecated since Matplotlib \u001b[39m\u001b[38;5;132;01m%(since)s\u001b[39;00m\u001b[38;5;124m; the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 452\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter will become keyword-only \u001b[39m\u001b[38;5;132;01m%(removal)s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 453\u001b[0m name\u001b[38;5;241m=\u001b[39mname, obj_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m()\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 454\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.conda/envs/tfp2-gpu/lib/python3.10/site-packages/matplotlib/axes/_base.py:3882\u001b[0m, in \u001b[0;36m_AxesBase.set_ylim\u001b[0;34m(self, bottom, top, emit, auto, ymin, ymax)\u001b[0m\n\u001b[1;32m 3880\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot pass both \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtop\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mymax\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 3881\u001b[0m top \u001b[38;5;241m=\u001b[39m ymax\n\u001b[0;32m-> 3882\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43myaxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_set_lim\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbottom\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43memit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43memit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mauto\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauto\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.conda/envs/tfp2-gpu/lib/python3.10/site-packages/matplotlib/axis.py:1185\u001b[0m, in \u001b[0;36mAxis._set_lim\u001b[0;34m(self, v0, v1, emit, auto)\u001b[0m\n\u001b[1;32m 1183\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes\u001b[38;5;241m.\u001b[39m_process_unit_info([(name, (v0, v1))], convert\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 1184\u001b[0m v0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes\u001b[38;5;241m.\u001b[39m_validate_converted_limits(v0, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_units)\n\u001b[0;32m-> 1185\u001b[0m v1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_converted_limits\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_units\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1187\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m v0 \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m v1 \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1188\u001b[0m \u001b[38;5;66;03m# Axes init calls set_xlim(0, 1) before get_xlim() can be called,\u001b[39;00m\n\u001b[1;32m 1189\u001b[0m \u001b[38;5;66;03m# so only grab the limits if we really need them.\u001b[39;00m\n\u001b[1;32m 1190\u001b[0m old0, old1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_view_interval()\n", + "File \u001b[0;32m~/.conda/envs/tfp2-gpu/lib/python3.10/site-packages/matplotlib/axes/_base.py:3569\u001b[0m, in \u001b[0;36m_AxesBase._validate_converted_limits\u001b[0;34m(self, limit, convert)\u001b[0m\n\u001b[1;32m 3566\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m limit \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 3567\u001b[0m converted_limit \u001b[38;5;241m=\u001b[39m convert(limit)\n\u001b[1;32m 3568\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(converted_limit, Real)\n\u001b[0;32m-> 3569\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misfinite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconverted_limit\u001b[49m\u001b[43m)\u001b[49m):\n\u001b[1;32m 3570\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAxis limits cannot be NaN or Inf\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 3571\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m converted_limit\n", + "\u001b[0;31mTypeError\u001b[0m: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "plt.plot(dict['xdata'][:],dict['zdata'][:3,:].T)\n", + "plt.xlabel('Time (ms)')\n", + "plt.xlim(2000,2700)\n", + "plt.ylim(0,10**20)\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ea02d260-c68f-4e5a-beb1-061833d86c9d", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "#discharge_obj.plot_time_series(file_dict['co2_pl']['only']['r0'])\n", + "\n", + "if spectro_analysis:\n", + " freq, time, amp_f_t=discharge_obj.spectro_calc(file_dict['co2_pl']['only']['r0']['xdata'][:],\\\n", + " file_dict['co2_pl']['only']['r0']['zdata'][:],\\\n", + " plot=True)\n", + " \n", + " freq_enhanced, time_enhanced, amp_f_t_enhanced=discharge_obj.spec_filters(freq, time, amp_f_t)\n", + " \n", + " discharge_obj.spectro_plot(freq_enhanced, time_enhanced, amp_f_t_enhanced)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c298ce0a-0d9c-4d3d-b89b-616eb757da17", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'co2_s'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m time_std \u001b[38;5;241m=\u001b[39mfile_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mts\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcore.temp\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcore.temp\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxdata\u001b[39m\u001b[38;5;124m'\u001b[39m][:]\n\u001b[0;32m----> 2\u001b[0m time \u001b[38;5;241m=\u001b[39m\u001b[43mfile_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mco2_s\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124monly\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr0\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mxdata\u001b[39m\u001b[38;5;124m'\u001b[39m][:]\n\u001b[1;32m 3\u001b[0m data\u001b[38;5;241m=\u001b[39m file_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mco2_s\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124monly\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr0\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzdata\u001b[39m\u001b[38;5;124m'\u001b[39m][:]\n\u001b[1;32m 4\u001b[0m matched_time, matched_data\u001b[38;5;241m=\u001b[39mdischarge_obj\u001b[38;5;241m.\u001b[39mtime_matching(time, data, time_std, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmerge_asof\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "\u001b[0;31mKeyError\u001b[0m: 'co2_s'" + ] + } + ], + "source": [ + " \n", + "time_std =file_dict['ts']['core.temp']['core.temp']['xdata'][:]\n", + "time =file_dict['co2_s']['only']['r0']['xdata'][:]\n", + "data= file_dict['co2_s']['only']['r0']['zdata'][:]\n", + "matched_time, matched_data=discharge_obj.time_matching(time, data, time_std, mode='merge_asof')\n", + "\n", + "plt.plot(matched_time,time_std)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f70167a3-4566-44df-83ce-463f3d065e20", + "metadata": {}, + "outputs": [], + "source": [ + "time =file_dict['ts']['core.temp']['core.temp']['xdata'][:]\n", + "data =file_dict['ts']['core.temp']['core.temp']['zdata'][20,:]\n", + "time_std =file_dict['co2_pl']['only']['r0']['xdata'][:]\n", + "\n", + "data_interp=discharge_obj.time_interp(time, data, time_std)\n", + "\n", + "plt.plot(time_std,data_interp)\n", + "plt.plot(time,data)\n", + "plt.xlim(2100,2110)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Dataset_prep/data_prep_obj.py b/Dataset_prep/data_prep_obj.py new file mode 100644 index 0000000..99f2d12 --- /dev/null +++ b/Dataset_prep/data_prep_obj.py @@ -0,0 +1,545 @@ +import h5py +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import scipy +from scipy import signal +import os +from tqdm import tqdm + +import glob +import cv2 + + + +# In[2]: + +file_normal_size = { + 'ece_cali': 600 * 1024**2, #600MB + 'ece_s': 25 * 1024**2, + 'co2_pl': 600 * 1024**2, + 'co2_den': 600 * 1024**2, + 'co2_s': 0.2 * 1024**2, + 'ts': 0.5 * 1024**2, + 'cer': 8 * 1024**2, + 'mse': 4 * 1024**2, + 'mag': 50 * 1024**2, + 'mag_hi': 1000 * 1024**2, + 'actu': 100 * 1024**2, + 'basic': 40 * 1024**2, + 'profiles': 5* 1024**2, + } + +#names of the cyrotrons +ech_gytname = ['lei','luk','r2d'] + +multi_level=['cer','mag','profiles','basic','actu','ts','ts_error','ts_rz'] +no_level=['ece_cali','ece_s'] + +file_keys={ 'co2_s':['r0', 'v1', 'v2', 'v3'],\ + 'co2_den':['r0', 'v1', 'v2', 'v3'],\ + 'co2_pl':['r0', 'v1', 'v2', 'v3'],\ + + 'ece_cali':[],\ + 'ece_s':[],\ + + 'ts':{r'{}.{}'.format(area,sig):[r'{}.{}'.format(area,sig)] for area in ['core','divertor','tangential'] + for sig in ['dens','temp']}, + 'ts_error':{r'{}.{}'.format(area,sig):[r'{}.{}'.format(area,sig)] for area in ['core','divertor','tangential'] + for sig in ['dens','temp']}, + + 'ts_rz':{r'{}.{}'.format(area,sig):[r'{}.{}'.format(area,sig)] for area in ['core','divertor','tangential'] + for sig in ['r', 'z']}, + + 'cer': { + output: [f'q.{output}.v{channel:02d}' for channel in range(1, 33)] + + [f'q.{output}.t{channel:02d}' for channel in range(1, 49)] + for output in ['amp', 'samp', 'ti', 'sti', 'rot', 'srot', 'r', 'phi', 'nz', 'fz', 'zeff', 'vb', 'svb'] + },\ + + 'mse':[r'%02d'%i for i in range(1, 70)],\ + + 'mag':{'dsl':['dsl.1u180', 'dsl.2u180', 'dsl.3u180', 'dsl.4u157', 'dsl.5u157', 'dsl.6u157'],\ + 'mpi':['mpi.11m067', 'mpi.11m322', 'mpi.1a139', 'mpi.1a322', 'mpi.1b139', 'mpi.1b157', 'mpi.1b322', \ + 'mpi.1l180', 'mpi.1u157', 'mpi.2a067', 'mpi.2a139', 'mpi.2a322', 'mpi.2b067', 'mpi.2b139', \ + 'mpi.2b322', 'mpi.2l180', 'mpi.2u157', 'mpi.3a139', 'mpi.3a322', 'mpi.3b139', 'mpi.3b322', \ + 'mpi.3l180', 'mpi.3u157', 'mpi.4a139', 'mpi.4a322', 'mpi.4b139', 'mpi.4b322', 'mpi.4u157', \ + 'mpi.5a139', 'mpi.5a322', 'mpi.5b139', 'mpi.5b322', 'mpi.5u157', 'mpi.66m067', 'mpi.66m157', \ + 'mpi.66m247', 'mpi.66m322', 'mpi.67a097', 'mpi.67a142', 'mpi.67a157', 'mpi.67a322', \ + 'mpi.67b097', 'mpi.67b157', 'mpi.67b322', 'mpi.6fa322', 'mpi.6fb142', 'mpi.6fb322', \ + 'mpi.6na132', 'mpi.6na157', 'mpi.6na322', 'mpi.6nb157', 'mpi.6nb322', 'mpi.6u157', \ + 'mpi.79a147', 'mpi.79b142', 'mpi.79b322', 'mpi.79fa322', 'mpi.79na322', 'mpi.7fa322', \ + 'mpi.7fb322', 'mpi.7na322', 'mpi.7nb142', 'mpi.7nb322', 'mpi.7u157', 'mpi.89a322', \ + 'mpi.89b322', 'mpi.8a322', 'mpi.8b322', 'mpi.9a322', 'mpi.9b322'],\ + 'psf':['psf.1a', 'psf.1b', 'psf.2a', 'psf.2b', 'psf.3a', 'psf.3b', 'psf.4a', 'psf.4b', \ + 'psf.5a', 'psf.5b', 'psf.6fa', 'psf.6fb', 'psf.6na', 'psf.6nb', 'psf.7fa', \ + 'psf.7fb', 'psf.7na', 'psf.7nb', 'psf.8a', 'psf.8b', 'psf.9a', 'psf.9b'],\ + 'psi':['psi.11m', 'psi.12a', 'psi.12b', 'psi.1l', 'psi.23a', 'psi.23b', 'psi.2l', 'psi.34a', \ + 'psi.34b', 'psi.3l', 'psi.45a', 'psi.45b', 'psi.58a', 'psi.58b', 'psi.6a', 'psi.6b', \ + 'psi.7a', 'psi.7b', 'psi.89fb', 'psi.89nb', 'psi.9a', 'psi.9b']\ + }, \ + 'mag_hi':[f'b{i}' for i in range(1,9)],\ + 'profiles':{'pressure': ['betap','betan','pres'], \ + 'other': ['wmhd','li'],\ + 'q_info':['q0','q95','qmin','qpsi'],\ + 'q_rho_info':['rhoqmin'],\ + 'mag_geo_para':['alpha','r0','aminor',\ + 'kappa','tritop','tribot',\ + 'rmaxis','zmaxis',\ + 'volume'],\ + 'mag_map':['psirz','ssibry', 'ssimag'],\ + 'divertor_geo':['drsep',\ + 'gapbot','gapin','gapout','gaptop',\ + 'zxpt1','zxpt2'],\ + 'profile':['edensfit', 'etempfit',\ + 'itempfit','idensfit',\ + 'trotfit'],\ + 'mag_mode_number':['n1rms','n2rms','n3rms']},\ + + 'basic':{'mag':['ip', 'ipsip', 'iptipp','pcbcoil', 'bcoil','bt','vloop'],\ + 'neutron':[ 'plasticfix', 'fzns'],\ + 'd_alpha':['fs00','fs01','fs02','fs03','fs04','fs05']},\ + 'actu': {'pinj': ['pinjf_%dl' % k for k in [15,21,30,33]]+['pinjf_%dr' % k for k in [15,21,30,33]],\ + 'tinj':['tinj_%dl' % k for k in [15,21,30,33]]+['tinj_%dr' % k for k in [15,21,30,33]],\ + 'ech':['echpwrc','echpwr']\ + +['ec%sfpwrc' % (x) for x in ech_gytname]\ + +['ec%sxmfrac' % (x) for x in ech_gytname]\ + +['ec%spolang' % (x) for x in ech_gytname], + 'gas':['gasa', 'gasb', 'gasc', 'gasd', 'gase'], + 'rmp_current':['c19', 'c79', 'c139', 'c199', 'c259', 'c319', \ + 'iu30', 'iu90', 'iu150', 'iu210', 'iu270', 'iu330', \ + 'il30', 'il90', 'il150', 'il210', 'il270', 'il330'], + 'coil_field_strength':['ecoila', 'ecoilb', 'e567up', 'e567dn', 'e89dn', 'e89up']\ + +['f1a','f2a','f3a','f4a','f5a','f6a','f7a','f8a','f9a',\ + 'f1b','f2b','f3b','f4b','f5b','f6b','f7b','f8b','f9b'] } + + } + +data_keys=['xdata','ydata','zdata'] +unit_keys=['xunits','yunits','zunits'] + + +spec_params_default={ + 'window': 'hamm', + 'scaling': 'density', # {'density', 'spectrum'} + 'detrend': 'linear', # {'linear', 'constant', False} + 'eps': 1e-11} + +#object that contains the functions to maniupulate one discharge +class DichargePerp(): + def __init__(self,discharge=174823,suffix_list=['co2_s']): + self.discharge=discharge + self.suffix_list=suffix_list + + def file_path_gen(self,discharge,suffix): + return f'/scratch/gpfs/EKOLEMEN/big_d3d_data/{str(discharge)[:2]}0000/{discharge}_{suffix}.h5' + + def get_data(self,discharge,suffix): + discharge_path=self.file_path_gen(discharge,suffix) + input_file = h5py.File(discharge_path, 'r') + return input_file + + #divide the data into sub catagory + def data_division(self,input_file,input_suffix): + if input_suffix in multi_level: + input_multi_level={} + for key in file_keys[input_suffix].keys(): + keys_of_this_catagory=file_keys[input_suffix][key] + input_multi_level[key]={key_i:input_file[key_i] for key_i in keys_of_this_catagory} + else: + input_multi_level={'only': input_file} + return input_multi_level + + def get_full_data(self): + file_dict={} + for suffix in self.suffix_list: + input_file=self.get_data(self.discharge,suffix) + file_dict[suffix]=self.data_division(input_file,suffix) + self.file_dict=file_dict + return file_dict + + @staticmethod + def spec_filters(freq, time, amp_f_t,spec_params=spec_params_default,thr=0.9, gaussblr_win=(31,3)): + def norm(amp_f_t): + mn = amp_f_t.mean() + std = amp_f_t.std() + return((amp_f_t-mn)/std) + + def rescale(amp_f_t): + return (amp_f_t-amp_f_t.min())/(amp_f_t.max()-amp_f_t.min()) + + def quantfilt(amp_f_t,thr=0.9): + filt = np.quantile(amp_f_t,thr,axis=0) + out = np.where(amp_f_t target: + high = mid - 1 + else: + return mid + # Update the best index if the current mid is closer to the target + if abs(time1[mid] - target) < abs(time1[best_idx] - target): + best_idx = mid + return best_idx + + # Align data1 to time_std + matched_data = [] + matched_time = [] + for t in time_std: + closest_idx = find_closest(t) + if mode=='2d': + matched_data.append(data1[:,closest_idx]) + else: + matched_data.append(data1[closest_idx]) + matched_time.append(time1[closest_idx]) + + return matched_time, matched_data + + @classmethod + def time_matching(cls,time, data, time_std, mode='merge_asof'): + if len(data.shape)==1: + if mode=='merge_asof': + return cls.time_matching_merge_asof_1d(time, data, time_std) + elif mode=='binary': + return cls.time_matching_binary_search(time, data, time_std, mode='1d') + elif len(data.shape)==2: + if mode=='merge_asof': + return cls.time_matching_merge_asof_2d(time, data, time_std) + elif mode=='binary': + return cls.time_matching_binary_search(time, data, time_std, mode='2d') + else: + print('The data has to be 1d arry or 2d array') + + @staticmethod + def time_interp_past_looking(time, data, time_std, mode='extrapolate'): + if mode=='extrapolate': + pass + elif mode=='fill': + pass + + @staticmethod + def time_interp(time, data, time_std): + return np.interp(time_std,time, data) + + + +class DatasetPrep(DichargePerp): + def __init__(self,discharge_search_list,suffix_list): + self.discharge_search_list=discharge_search_list + self.suffix_list=suffix_list + + def filter_discharges(self): + suffix_list=self.suffix_list + discharge_search_list=self.discharge_search_list + # Define the criteria for the files you're interested in + + criteria = {key:file_normal_size[key]*0.5 for key in suffix_list} + + discharge_list = {key: [] for key in suffix_list} + + for discharge in tqdm(discharge_search_list): + for suffix, size_limit in criteria.items(): + discharge_path=self.file_path_gen(discharge,suffix) + # Check if the file exists + if os.path.isfile(discharge_path): + # Get the size of the file + file_size = os.path.getsize(discharge_path) + if file_size > size_limit: + discharge_list[suffix].append(discharge) + + else: + pass + + return discharge_list + + +class data_obj_rest(): + + def save_dict_to_hdf5(dictionary, h5file): + for key, value in dictionary.items(): + if isinstance(value, dict): + group = h5file.create_group(key) + save_dict_to_hdf5(value, group) + else: + h5file.create_dataset(key, data=value) + + def TS_interp_(discharge,write_h5=True,plot=False): + TS_Z_min_set=[0.0, 0.03, 0.09, 0.1, 0.15, 0.16, 0.21, 0.22, 0.26, 0.27, 0.28,\ + 0.3, 0.31, 0.32, 0.36, 0.37, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, \ + 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55,\ + 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66,\ + 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, \ + 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, \ + 0.89, 0.9, 0.91, 0.92, 0.93] + str_shot=str(discharge)[:2] + path = f'/scratch/gpfs/EKOLEMEN/big_d3d_data/{str_shot}0000/' + + TS_file = h5py.File(path + str(discharge) + '_TS.h5', 'r') + TS_RZ_file = h5py.File(path + str(discharge) + '_TS_RZ.h5', 'r') + + TS_Z=TS_RZ_file['S.BLESSED.CORE.Z']['zdata'][:] + order_index=np.argsort(TS_Z) + TS_Z_sort=TS_Z[order_index] + + TS_interp={} + TS_keys=['TS.BLESSED.CORE.density','TS.BLESSED.CORE.temp'] + for key in TS_keys: + TS_interp_list=[] + TS_time=TS_file[key]['xdata'][:] + for i in range(len(TS_time)): + TS_data=TS_file[key]['zdata'][:,i]*0.1**19 + TS_data_sort=TS_data[order_index] + TS_interp_tmp=np.interp(TS_Z_min_set,TS_Z_sort,TS_data_sort) + TS_interp_list.append(TS_interp_tmp) + #########*********************start herere + TS_interp[key]={'xdata':np.array(TS_time),'ydata':np.array(TS_Z_min_set),'zdata':np.array(TS_interp_list).T*10.**19} + + if write_h5: + + with h5py.File(f'{path}{discharge}_TS_core_interp.h5', 'w') as h5file: + save_dict_to_hdf5(TS_interp, h5file) + + if plot: + plt.clf() + plt.scatter(TS_Z_min_set,TS_interp[key]['zdata'][:,600],label='interp') + plt.scatter(TS_Z_sort,(TS_file[key]['zdata'][:,600])[order_index],label='origin') + plt.legend() + plt.show() + + return 0 + + + def read_file(discharge,file_suffix,df_time): + + path=find_path(discharge) + file=h5py.File(f'{path}{discharge}_{file_suffix}.h5', 'r') + keys=file.keys() + + + for i,key in enumerate(keys): + dict_tmp={'xdata':file[key]['xdata']} + if len(file[key]['zdata'].shape)==2: + for j in range(file[key]['zdata'].shape[0]): + dict_tmp[key+str(j)]=file[key]['zdata'][j,:] + elif len(file[key]['zdata'].shape)==1: + dict_tmp[key]=file[key]['zdata'] + + df_tmp=pd.DataFrame(dict_tmp).astype('float32') + + if i ==0: + df= pd.merge_asof(df_time,df_tmp,on='xdata',direction='nearest') + else: + df= pd.merge_asof(df,df_tmp,on='xdata',direction='nearest') + + file.close() + return df + + def hdf5_generator(discharge_list,h5_profiles,data_filename='diag2diag.pkl'): + all_X=[] + all_y=[] + all_time=[] + discharg_read_list=[] + len_list=[] + for discharge in tqdm(discharge_list): + print(discharge) + try: + dfs={} + #creating the standard time + path=find_path(discharge) + + file = h5py.File(f'{path}{discharge}_shape.h5', 'r') + t_min=0 + t_max=file['R0']['xdata'][-1] + file.close() + + file=h5py.File(f'{path}{discharge}_TS.h5', 'r') + df_time=pd.DataFrame({'xdata':file[list(file.keys())[0]]['xdata']}) + time=file[list(file.keys())[0]]['xdata'][:] + + time_index=(time >= t_min) & (time <= t_max) + time_tmp=time[time_index] + df_time=pd.DataFrame({'xdata':time_tmp}) + file.close() + + #Read all the files + for file_suffix in h5_profiles: + df=read_file(discharge,file_suffix,df_time) + dfs[file_suffix]=df + + #summarize all the data in this dicharge + df_tmp=np.concatenate([dfs[key].to_numpy()[1:] for key in dfs.keys()], axis=1) + + key_list_dict={} + key_list=[] + for key in dfs.keys(): + key_list_dict[key]=list(dfs[key].keys()) + for key_ in key_list_dict[key]: + key_list.append(key_) + + #add this discharge to the total file + all_X.append(df_tmp) + all_time.append(df_time['xdata']) + all_time_tmp= np.concatenate(all_time, axis=0) + all_X_tmp = np.concatenate(all_X, axis=0) + len_list.append(df_time['xdata'].shape[0]) + discharg_read_list.append(discharge) + # Serialize the data and save to a file + with open(data_filename, 'wb') as file: + pickle.dump([all_X_tmp,all_time_tmp,discharg_read_list,len_list,key_list,key_list_dict], file) + + except Exception as e: + #if 2==1: + print(f"Error: {e}") + continue + finally: + #if 2==1: + try: + file.close() + except: + continue + + return [all_X_tmp] From 2124c02d532c92745910a374b4f1e207300ddff9 Mon Sep 17 00:00:00 2001 From: renierts Date: Fri, 19 Apr 2024 13:19:15 +0200 Subject: [PATCH 003/103] Cosmetic changes of Max's code. --- Dataset_prep/data_prep_obj.py | 545 ---------------- .../Data_fetching}/check_copy_and_rm.py | 0 .../Data_fetching}/fetch_GAdata.py | 0 .../Data_fetching}/fetch_toksearch.py | 0 .../Data_fetching}/mygadata.py | 0 .../Dataset_prep}/0read_data_run.ipynb | 0 examples/Dataset_prep/data_prep_obj.py | 583 ++++++++++++++++++ pyproject.toml | 28 + .../__init__.py | 0 .../base/__init__.py | 0 .../core/__init__.py | 0 .../datasets/__init__.py | 0 .../display/__init__.py | 0 .../feature/__init__.py | 0 .../util/__init__.py | 0 15 files changed, 611 insertions(+), 545 deletions(-) delete mode 100644 Dataset_prep/data_prep_obj.py rename {Data_fetching => examples/Data_fetching}/check_copy_and_rm.py (100%) rename {Data_fetching => examples/Data_fetching}/fetch_GAdata.py (100%) rename {Data_fetching => examples/Data_fetching}/fetch_toksearch.py (100%) rename {Data_fetching => examples/Data_fetching}/mygadata.py (100%) rename {Dataset_prep => examples/Dataset_prep}/0read_data_run.ipynb (100%) create mode 100644 examples/Dataset_prep/data_prep_obj.py create mode 100644 pyproject.toml rename src/{fusionaihub => fusion_ai_hub}/__init__.py (100%) rename src/{fusionaihub => fusion_ai_hub}/base/__init__.py (100%) rename src/{fusionaihub => fusion_ai_hub}/core/__init__.py (100%) rename src/{fusionaihub => fusion_ai_hub}/datasets/__init__.py (100%) rename src/{fusionaihub => fusion_ai_hub}/display/__init__.py (100%) rename src/{fusionaihub => fusion_ai_hub}/feature/__init__.py (100%) rename src/{fusionaihub => fusion_ai_hub}/util/__init__.py (100%) diff --git a/Dataset_prep/data_prep_obj.py b/Dataset_prep/data_prep_obj.py deleted file mode 100644 index 99f2d12..0000000 --- a/Dataset_prep/data_prep_obj.py +++ /dev/null @@ -1,545 +0,0 @@ -import h5py -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -import scipy -from scipy import signal -import os -from tqdm import tqdm - -import glob -import cv2 - - - -# In[2]: - -file_normal_size = { - 'ece_cali': 600 * 1024**2, #600MB - 'ece_s': 25 * 1024**2, - 'co2_pl': 600 * 1024**2, - 'co2_den': 600 * 1024**2, - 'co2_s': 0.2 * 1024**2, - 'ts': 0.5 * 1024**2, - 'cer': 8 * 1024**2, - 'mse': 4 * 1024**2, - 'mag': 50 * 1024**2, - 'mag_hi': 1000 * 1024**2, - 'actu': 100 * 1024**2, - 'basic': 40 * 1024**2, - 'profiles': 5* 1024**2, - } - -#names of the cyrotrons -ech_gytname = ['lei','luk','r2d'] - -multi_level=['cer','mag','profiles','basic','actu','ts','ts_error','ts_rz'] -no_level=['ece_cali','ece_s'] - -file_keys={ 'co2_s':['r0', 'v1', 'v2', 'v3'],\ - 'co2_den':['r0', 'v1', 'v2', 'v3'],\ - 'co2_pl':['r0', 'v1', 'v2', 'v3'],\ - - 'ece_cali':[],\ - 'ece_s':[],\ - - 'ts':{r'{}.{}'.format(area,sig):[r'{}.{}'.format(area,sig)] for area in ['core','divertor','tangential'] - for sig in ['dens','temp']}, - 'ts_error':{r'{}.{}'.format(area,sig):[r'{}.{}'.format(area,sig)] for area in ['core','divertor','tangential'] - for sig in ['dens','temp']}, - - 'ts_rz':{r'{}.{}'.format(area,sig):[r'{}.{}'.format(area,sig)] for area in ['core','divertor','tangential'] - for sig in ['r', 'z']}, - - 'cer': { - output: [f'q.{output}.v{channel:02d}' for channel in range(1, 33)] + - [f'q.{output}.t{channel:02d}' for channel in range(1, 49)] - for output in ['amp', 'samp', 'ti', 'sti', 'rot', 'srot', 'r', 'phi', 'nz', 'fz', 'zeff', 'vb', 'svb'] - },\ - - 'mse':[r'%02d'%i for i in range(1, 70)],\ - - 'mag':{'dsl':['dsl.1u180', 'dsl.2u180', 'dsl.3u180', 'dsl.4u157', 'dsl.5u157', 'dsl.6u157'],\ - 'mpi':['mpi.11m067', 'mpi.11m322', 'mpi.1a139', 'mpi.1a322', 'mpi.1b139', 'mpi.1b157', 'mpi.1b322', \ - 'mpi.1l180', 'mpi.1u157', 'mpi.2a067', 'mpi.2a139', 'mpi.2a322', 'mpi.2b067', 'mpi.2b139', \ - 'mpi.2b322', 'mpi.2l180', 'mpi.2u157', 'mpi.3a139', 'mpi.3a322', 'mpi.3b139', 'mpi.3b322', \ - 'mpi.3l180', 'mpi.3u157', 'mpi.4a139', 'mpi.4a322', 'mpi.4b139', 'mpi.4b322', 'mpi.4u157', \ - 'mpi.5a139', 'mpi.5a322', 'mpi.5b139', 'mpi.5b322', 'mpi.5u157', 'mpi.66m067', 'mpi.66m157', \ - 'mpi.66m247', 'mpi.66m322', 'mpi.67a097', 'mpi.67a142', 'mpi.67a157', 'mpi.67a322', \ - 'mpi.67b097', 'mpi.67b157', 'mpi.67b322', 'mpi.6fa322', 'mpi.6fb142', 'mpi.6fb322', \ - 'mpi.6na132', 'mpi.6na157', 'mpi.6na322', 'mpi.6nb157', 'mpi.6nb322', 'mpi.6u157', \ - 'mpi.79a147', 'mpi.79b142', 'mpi.79b322', 'mpi.79fa322', 'mpi.79na322', 'mpi.7fa322', \ - 'mpi.7fb322', 'mpi.7na322', 'mpi.7nb142', 'mpi.7nb322', 'mpi.7u157', 'mpi.89a322', \ - 'mpi.89b322', 'mpi.8a322', 'mpi.8b322', 'mpi.9a322', 'mpi.9b322'],\ - 'psf':['psf.1a', 'psf.1b', 'psf.2a', 'psf.2b', 'psf.3a', 'psf.3b', 'psf.4a', 'psf.4b', \ - 'psf.5a', 'psf.5b', 'psf.6fa', 'psf.6fb', 'psf.6na', 'psf.6nb', 'psf.7fa', \ - 'psf.7fb', 'psf.7na', 'psf.7nb', 'psf.8a', 'psf.8b', 'psf.9a', 'psf.9b'],\ - 'psi':['psi.11m', 'psi.12a', 'psi.12b', 'psi.1l', 'psi.23a', 'psi.23b', 'psi.2l', 'psi.34a', \ - 'psi.34b', 'psi.3l', 'psi.45a', 'psi.45b', 'psi.58a', 'psi.58b', 'psi.6a', 'psi.6b', \ - 'psi.7a', 'psi.7b', 'psi.89fb', 'psi.89nb', 'psi.9a', 'psi.9b']\ - }, \ - 'mag_hi':[f'b{i}' for i in range(1,9)],\ - 'profiles':{'pressure': ['betap','betan','pres'], \ - 'other': ['wmhd','li'],\ - 'q_info':['q0','q95','qmin','qpsi'],\ - 'q_rho_info':['rhoqmin'],\ - 'mag_geo_para':['alpha','r0','aminor',\ - 'kappa','tritop','tribot',\ - 'rmaxis','zmaxis',\ - 'volume'],\ - 'mag_map':['psirz','ssibry', 'ssimag'],\ - 'divertor_geo':['drsep',\ - 'gapbot','gapin','gapout','gaptop',\ - 'zxpt1','zxpt2'],\ - 'profile':['edensfit', 'etempfit',\ - 'itempfit','idensfit',\ - 'trotfit'],\ - 'mag_mode_number':['n1rms','n2rms','n3rms']},\ - - 'basic':{'mag':['ip', 'ipsip', 'iptipp','pcbcoil', 'bcoil','bt','vloop'],\ - 'neutron':[ 'plasticfix', 'fzns'],\ - 'd_alpha':['fs00','fs01','fs02','fs03','fs04','fs05']},\ - 'actu': {'pinj': ['pinjf_%dl' % k for k in [15,21,30,33]]+['pinjf_%dr' % k for k in [15,21,30,33]],\ - 'tinj':['tinj_%dl' % k for k in [15,21,30,33]]+['tinj_%dr' % k for k in [15,21,30,33]],\ - 'ech':['echpwrc','echpwr']\ - +['ec%sfpwrc' % (x) for x in ech_gytname]\ - +['ec%sxmfrac' % (x) for x in ech_gytname]\ - +['ec%spolang' % (x) for x in ech_gytname], - 'gas':['gasa', 'gasb', 'gasc', 'gasd', 'gase'], - 'rmp_current':['c19', 'c79', 'c139', 'c199', 'c259', 'c319', \ - 'iu30', 'iu90', 'iu150', 'iu210', 'iu270', 'iu330', \ - 'il30', 'il90', 'il150', 'il210', 'il270', 'il330'], - 'coil_field_strength':['ecoila', 'ecoilb', 'e567up', 'e567dn', 'e89dn', 'e89up']\ - +['f1a','f2a','f3a','f4a','f5a','f6a','f7a','f8a','f9a',\ - 'f1b','f2b','f3b','f4b','f5b','f6b','f7b','f8b','f9b'] } - - } - -data_keys=['xdata','ydata','zdata'] -unit_keys=['xunits','yunits','zunits'] - - -spec_params_default={ - 'window': 'hamm', - 'scaling': 'density', # {'density', 'spectrum'} - 'detrend': 'linear', # {'linear', 'constant', False} - 'eps': 1e-11} - -#object that contains the functions to maniupulate one discharge -class DichargePerp(): - def __init__(self,discharge=174823,suffix_list=['co2_s']): - self.discharge=discharge - self.suffix_list=suffix_list - - def file_path_gen(self,discharge,suffix): - return f'/scratch/gpfs/EKOLEMEN/big_d3d_data/{str(discharge)[:2]}0000/{discharge}_{suffix}.h5' - - def get_data(self,discharge,suffix): - discharge_path=self.file_path_gen(discharge,suffix) - input_file = h5py.File(discharge_path, 'r') - return input_file - - #divide the data into sub catagory - def data_division(self,input_file,input_suffix): - if input_suffix in multi_level: - input_multi_level={} - for key in file_keys[input_suffix].keys(): - keys_of_this_catagory=file_keys[input_suffix][key] - input_multi_level[key]={key_i:input_file[key_i] for key_i in keys_of_this_catagory} - else: - input_multi_level={'only': input_file} - return input_multi_level - - def get_full_data(self): - file_dict={} - for suffix in self.suffix_list: - input_file=self.get_data(self.discharge,suffix) - file_dict[suffix]=self.data_division(input_file,suffix) - self.file_dict=file_dict - return file_dict - - @staticmethod - def spec_filters(freq, time, amp_f_t,spec_params=spec_params_default,thr=0.9, gaussblr_win=(31,3)): - def norm(amp_f_t): - mn = amp_f_t.mean() - std = amp_f_t.std() - return((amp_f_t-mn)/std) - - def rescale(amp_f_t): - return (amp_f_t-amp_f_t.min())/(amp_f_t.max()-amp_f_t.min()) - - def quantfilt(amp_f_t,thr=0.9): - filt = np.quantile(amp_f_t,thr,axis=0) - out = np.where(amp_f_t target: - high = mid - 1 - else: - return mid - # Update the best index if the current mid is closer to the target - if abs(time1[mid] - target) < abs(time1[best_idx] - target): - best_idx = mid - return best_idx - - # Align data1 to time_std - matched_data = [] - matched_time = [] - for t in time_std: - closest_idx = find_closest(t) - if mode=='2d': - matched_data.append(data1[:,closest_idx]) - else: - matched_data.append(data1[closest_idx]) - matched_time.append(time1[closest_idx]) - - return matched_time, matched_data - - @classmethod - def time_matching(cls,time, data, time_std, mode='merge_asof'): - if len(data.shape)==1: - if mode=='merge_asof': - return cls.time_matching_merge_asof_1d(time, data, time_std) - elif mode=='binary': - return cls.time_matching_binary_search(time, data, time_std, mode='1d') - elif len(data.shape)==2: - if mode=='merge_asof': - return cls.time_matching_merge_asof_2d(time, data, time_std) - elif mode=='binary': - return cls.time_matching_binary_search(time, data, time_std, mode='2d') - else: - print('The data has to be 1d arry or 2d array') - - @staticmethod - def time_interp_past_looking(time, data, time_std, mode='extrapolate'): - if mode=='extrapolate': - pass - elif mode=='fill': - pass - - @staticmethod - def time_interp(time, data, time_std): - return np.interp(time_std,time, data) - - - -class DatasetPrep(DichargePerp): - def __init__(self,discharge_search_list,suffix_list): - self.discharge_search_list=discharge_search_list - self.suffix_list=suffix_list - - def filter_discharges(self): - suffix_list=self.suffix_list - discharge_search_list=self.discharge_search_list - # Define the criteria for the files you're interested in - - criteria = {key:file_normal_size[key]*0.5 for key in suffix_list} - - discharge_list = {key: [] for key in suffix_list} - - for discharge in tqdm(discharge_search_list): - for suffix, size_limit in criteria.items(): - discharge_path=self.file_path_gen(discharge,suffix) - # Check if the file exists - if os.path.isfile(discharge_path): - # Get the size of the file - file_size = os.path.getsize(discharge_path) - if file_size > size_limit: - discharge_list[suffix].append(discharge) - - else: - pass - - return discharge_list - - -class data_obj_rest(): - - def save_dict_to_hdf5(dictionary, h5file): - for key, value in dictionary.items(): - if isinstance(value, dict): - group = h5file.create_group(key) - save_dict_to_hdf5(value, group) - else: - h5file.create_dataset(key, data=value) - - def TS_interp_(discharge,write_h5=True,plot=False): - TS_Z_min_set=[0.0, 0.03, 0.09, 0.1, 0.15, 0.16, 0.21, 0.22, 0.26, 0.27, 0.28,\ - 0.3, 0.31, 0.32, 0.36, 0.37, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, \ - 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55,\ - 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66,\ - 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, \ - 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, \ - 0.89, 0.9, 0.91, 0.92, 0.93] - str_shot=str(discharge)[:2] - path = f'/scratch/gpfs/EKOLEMEN/big_d3d_data/{str_shot}0000/' - - TS_file = h5py.File(path + str(discharge) + '_TS.h5', 'r') - TS_RZ_file = h5py.File(path + str(discharge) + '_TS_RZ.h5', 'r') - - TS_Z=TS_RZ_file['S.BLESSED.CORE.Z']['zdata'][:] - order_index=np.argsort(TS_Z) - TS_Z_sort=TS_Z[order_index] - - TS_interp={} - TS_keys=['TS.BLESSED.CORE.density','TS.BLESSED.CORE.temp'] - for key in TS_keys: - TS_interp_list=[] - TS_time=TS_file[key]['xdata'][:] - for i in range(len(TS_time)): - TS_data=TS_file[key]['zdata'][:,i]*0.1**19 - TS_data_sort=TS_data[order_index] - TS_interp_tmp=np.interp(TS_Z_min_set,TS_Z_sort,TS_data_sort) - TS_interp_list.append(TS_interp_tmp) - #########*********************start herere - TS_interp[key]={'xdata':np.array(TS_time),'ydata':np.array(TS_Z_min_set),'zdata':np.array(TS_interp_list).T*10.**19} - - if write_h5: - - with h5py.File(f'{path}{discharge}_TS_core_interp.h5', 'w') as h5file: - save_dict_to_hdf5(TS_interp, h5file) - - if plot: - plt.clf() - plt.scatter(TS_Z_min_set,TS_interp[key]['zdata'][:,600],label='interp') - plt.scatter(TS_Z_sort,(TS_file[key]['zdata'][:,600])[order_index],label='origin') - plt.legend() - plt.show() - - return 0 - - - def read_file(discharge,file_suffix,df_time): - - path=find_path(discharge) - file=h5py.File(f'{path}{discharge}_{file_suffix}.h5', 'r') - keys=file.keys() - - - for i,key in enumerate(keys): - dict_tmp={'xdata':file[key]['xdata']} - if len(file[key]['zdata'].shape)==2: - for j in range(file[key]['zdata'].shape[0]): - dict_tmp[key+str(j)]=file[key]['zdata'][j,:] - elif len(file[key]['zdata'].shape)==1: - dict_tmp[key]=file[key]['zdata'] - - df_tmp=pd.DataFrame(dict_tmp).astype('float32') - - if i ==0: - df= pd.merge_asof(df_time,df_tmp,on='xdata',direction='nearest') - else: - df= pd.merge_asof(df,df_tmp,on='xdata',direction='nearest') - - file.close() - return df - - def hdf5_generator(discharge_list,h5_profiles,data_filename='diag2diag.pkl'): - all_X=[] - all_y=[] - all_time=[] - discharg_read_list=[] - len_list=[] - for discharge in tqdm(discharge_list): - print(discharge) - try: - dfs={} - #creating the standard time - path=find_path(discharge) - - file = h5py.File(f'{path}{discharge}_shape.h5', 'r') - t_min=0 - t_max=file['R0']['xdata'][-1] - file.close() - - file=h5py.File(f'{path}{discharge}_TS.h5', 'r') - df_time=pd.DataFrame({'xdata':file[list(file.keys())[0]]['xdata']}) - time=file[list(file.keys())[0]]['xdata'][:] - - time_index=(time >= t_min) & (time <= t_max) - time_tmp=time[time_index] - df_time=pd.DataFrame({'xdata':time_tmp}) - file.close() - - #Read all the files - for file_suffix in h5_profiles: - df=read_file(discharge,file_suffix,df_time) - dfs[file_suffix]=df - - #summarize all the data in this dicharge - df_tmp=np.concatenate([dfs[key].to_numpy()[1:] for key in dfs.keys()], axis=1) - - key_list_dict={} - key_list=[] - for key in dfs.keys(): - key_list_dict[key]=list(dfs[key].keys()) - for key_ in key_list_dict[key]: - key_list.append(key_) - - #add this discharge to the total file - all_X.append(df_tmp) - all_time.append(df_time['xdata']) - all_time_tmp= np.concatenate(all_time, axis=0) - all_X_tmp = np.concatenate(all_X, axis=0) - len_list.append(df_time['xdata'].shape[0]) - discharg_read_list.append(discharge) - # Serialize the data and save to a file - with open(data_filename, 'wb') as file: - pickle.dump([all_X_tmp,all_time_tmp,discharg_read_list,len_list,key_list,key_list_dict], file) - - except Exception as e: - #if 2==1: - print(f"Error: {e}") - continue - finally: - #if 2==1: - try: - file.close() - except: - continue - - return [all_X_tmp] diff --git a/Data_fetching/check_copy_and_rm.py b/examples/Data_fetching/check_copy_and_rm.py similarity index 100% rename from Data_fetching/check_copy_and_rm.py rename to examples/Data_fetching/check_copy_and_rm.py diff --git a/Data_fetching/fetch_GAdata.py b/examples/Data_fetching/fetch_GAdata.py similarity index 100% rename from Data_fetching/fetch_GAdata.py rename to examples/Data_fetching/fetch_GAdata.py diff --git a/Data_fetching/fetch_toksearch.py b/examples/Data_fetching/fetch_toksearch.py similarity index 100% rename from Data_fetching/fetch_toksearch.py rename to examples/Data_fetching/fetch_toksearch.py diff --git a/Data_fetching/mygadata.py b/examples/Data_fetching/mygadata.py similarity index 100% rename from Data_fetching/mygadata.py rename to examples/Data_fetching/mygadata.py diff --git a/Dataset_prep/0read_data_run.ipynb b/examples/Dataset_prep/0read_data_run.ipynb similarity index 100% rename from Dataset_prep/0read_data_run.ipynb rename to examples/Dataset_prep/0read_data_run.ipynb diff --git a/examples/Dataset_prep/data_prep_obj.py b/examples/Dataset_prep/data_prep_obj.py new file mode 100644 index 0000000..dd3b7e1 --- /dev/null +++ b/examples/Dataset_prep/data_prep_obj.py @@ -0,0 +1,583 @@ +""" +Data Preparation object from Max Curie with small adaptions by Peter Steiner. +""" +import h5py +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from scipy import signal +import os +from tqdm import tqdm +import pickle +import cv2 + + +file_normal_size = { + 'ece_cali': 600 * 1024**2, # 600MB + 'ece_s': 25 * 1024**2, + 'co2_pl': 600 * 1024**2, + 'co2_den': 600 * 1024**2, + 'co2_s': 0.2 * 1024**2, + 'ts': 0.5 * 1024**2, + 'cer': 8 * 1024**2, + 'mse': 4 * 1024**2, + 'mag': 50 * 1024**2, + 'mag_hi': 1000 * 1024**2, + 'actu': 100 * 1024**2, + 'basic': 40 * 1024**2, + 'profiles': 5 * 1024**2, + } + +# names of the cyrotrons +ech_gytname = ['lei', 'luk', 'r2d'] + +multi_level = ['cer', 'mag', 'profiles', 'basic', 'actu', + 'ts', 'ts_error', 'ts_rz'] +no_level = ['ece_cali', 'ece_s'] + +file_keys = {'co2_s': ['r0', 'v1', 'v2', 'v3'], + 'co2_den': ['r0', 'v1', 'v2', 'v3'], + 'co2_pl': ['r0', 'v1', 'v2', 'v3'], + 'ece_cali': [], + 'ece_s': [], + 'ts': {r'{}.{}'.format(area,sig): + [r'{}.{}'.format(area,sig)] + for area in ['core', 'divertor', 'tangential'] + for sig in ['dens', 'temp']}, + 'ts_error': {r'{}.{}'.format(area, sig): + [r'{}.{}'.format(area, sig)] + for area in ['core', 'divertor', 'tangential'] + for sig in ['dens', 'temp']}, + 'ts_rz': {r'{}.{}'.format(area,sig): + [r'{}.{}'.format(area, sig)] + for area in ['core','divertor','tangential'] + for sig in ['r', 'z']}, + 'cer': {output: [f'q.{output}.v{channel:02d}' + for channel in range(1, 33)] + + [f'q.{output}.t{channel:02d}' + for channel in range(1, 49)] + for output in ['amp', 'samp', 'ti', 'sti', 'rot', 'srot', + 'r', 'phi', 'nz', 'fz', 'zeff', 'vb', + 'svb']}, + 'mse': [r'%02d' % i for i in range(1, 70)], + 'mag': {'dsl': ['dsl.1u180', 'dsl.2u180', 'dsl.3u180', + 'dsl.4u157', 'dsl.5u157', 'dsl.6u157'], + 'mpi': [ + 'mpi.11m067', 'mpi.11m322', 'mpi.1a139', 'mpi.1a322', + 'mpi.1b139', 'mpi.1b157', 'mpi.1b322', 'mpi.1l180', + 'mpi.1u157', 'mpi.2a067', 'mpi.2a139', 'mpi.2a322', + 'mpi.2b067', 'mpi.2b139', 'mpi.2b322', 'mpi.2l180', + 'mpi.2u157', 'mpi.3a139', 'mpi.3a322', 'mpi.3b139', + 'mpi.3b322', 'mpi.3l180', 'mpi.3u157', 'mpi.4a139', + 'mpi.4a322', 'mpi.4b139', 'mpi.4b322', 'mpi.4u157', + 'mpi.5a139', 'mpi.5a322', 'mpi.5b139', 'mpi.5b322', + 'mpi.5u157', 'mpi.66m067', 'mpi.66m157', 'mpi.66m247', + 'mpi.66m322', 'mpi.67a097', 'mpi.67a142', + 'mpi.67a157', 'mpi.67a322', 'mpi.67b097', + 'mpi.67b157', 'mpi.67b322', 'mpi.6fa322', + 'mpi.6fb142', 'mpi.6fb322', 'mpi.6na132', + 'mpi.6na157', 'mpi.6na322', 'mpi.6nb157', + 'mpi.6nb322', 'mpi.6u157', 'mpi.79a147', 'mpi.79b142', + 'mpi.79b322', 'mpi.79fa322', 'mpi.79na322', + 'mpi.7fa322', 'mpi.7fb322', 'mpi.7na322', + 'mpi.7nb142', 'mpi.7nb322', 'mpi.7u157', 'mpi.89a322', + 'mpi.89b322', 'mpi.8a322', 'mpi.8b322', 'mpi.9a322', + 'mpi.9b322'], + 'psf': ['psf.1a', 'psf.1b', 'psf.2a', 'psf.2b', 'psf.3a', + 'psf.3b', 'psf.4a', 'psf.4b', 'psf.5a', 'psf.5b', + 'psf.6fa', 'psf.6fb', 'psf.6na', 'psf.6nb', + 'psf.7fa', 'psf.7fb', 'psf.7na', 'psf.7nb', + 'psf.8a', 'psf.8b', 'psf.9a', 'psf.9b'], + 'psi': ['psi.11m', 'psi.12a', 'psi.12b', 'psi.1l', + 'psi.23a', 'psi.23b', 'psi.2l', 'psi.34a', + 'psi.34b', 'psi.3l', 'psi.45a', 'psi.45b', + 'psi.58a', 'psi.58b', 'psi.6a', 'psi.6b', + 'psi.7a', 'psi.7b', 'psi.89fb', 'psi.89nb', + 'psi.9a', 'psi.9b']}, + 'mag_hi': [f'b{i}' for i in range(1, 9)], + 'profiles': {'pressure': ['betap', 'betan', 'pres'], + 'other': ['wmhd', 'li'], + 'q_info': ['q0', 'q95', 'qmin', 'qpsi'], + 'q_rho_info': ['rhoqmin'], + 'mag_geo_para': ['alpha', 'r0', 'aminor', 'kappa', + 'tritop', 'tribot', 'rmaxis', + 'zmaxis', 'volume'], + 'mag_map': ['psirz', 'ssibry', 'ssimag'], + 'divertor_geo': ['drsep', 'gapbot', 'gapin', + 'gapout', 'gaptop', 'zxpt1', + 'zxpt2'], + 'profile': ['edensfit', 'etempfit', 'itempfit', + 'idensfit', 'trotfit'], + 'mag_mode_number': ['n1rms', 'n2rms', 'n3rms']}, + 'basic': {'mag': ['ip', 'ipsip', 'iptipp', 'pcbcoil', 'bcoil', + 'bt', 'vloop'], + 'neutron': ['plasticfix', 'fzns'], + 'd_alpha': ['fs00', 'fs01', 'fs02', 'fs03', 'fs04', + 'fs05']}, + 'actu': {'pinj': ['pinjf_%dl' % k for k in [15, 21, 30, 33]] + + ['pinjf_%dr' % k for k in [15, 21, 30, 33]], + 'tinj': ['tinj_%dl' % k for k in [15, 21, 30, 33]] + + ['tinj_%dr' % k for k in [15, 21, 30, 33]], + 'ech': ['echpwrc', 'echpwr'] + + ['ec%sfpwrc' % (x) for x in ech_gytname] + + ['ec%sxmfrac' % (x) for x in ech_gytname] + + ['ec%spolang' % (x) for x in ech_gytname], + 'gas': ['gasa', 'gasb', 'gasc', 'gasd', 'gase'], + 'rmp_current': ['c19', 'c79', 'c139', 'c199', 'c259', + 'c319', 'iu30', 'iu90', 'iu150', 'iu210', + 'iu270', 'iu330', 'il30', 'il90', + 'il150', 'il210', 'il270', 'il330'], + 'coil_field_strength': ['ecoila', 'ecoilb', 'e567up', + 'e567dn', 'e89dn', 'e89up'] + + ['f1a', 'f2a', 'f3a', 'f4a', + 'f5a', 'f6a', 'f7a', 'f8a', + 'f9a', 'f1b', 'f2b', 'f3b', + 'f4b', 'f5b', 'f6b', 'f7b', + 'f8b', 'f9b']}, } + +data_keys = ['xdata', 'ydata', 'zdata'] +unit_keys = ['xunits', 'yunits', 'zunits'] + +spec_params_default = {'window': 'hamm', + 'scaling': 'density', # {'density', 'spectrum'} + 'detrend': 'linear', # {'linear', 'constant', False} + 'eps': 1e-11} + +class DichargePerp(): + """object that contains the functions to manipulate one discharge.""" + def __init__(self, discharge=174823, suffix_list=('co2_s', )): + self.discharge = discharge + self.suffix_list = suffix_list + + def file_path_gen(self, discharge, suffix): + return (f'/scratch/gpfs/EKOLEMEN/big_d3d_data/' + f'{str(discharge)[:2]}0000/{discharge}_{suffix}.h5') + + def get_data(self,discharge, suffix): + discharge_path=self.file_path_gen(discharge, suffix) + input_file = h5py.File(discharge_path, 'r') + return input_file + + # divide the data into subcategory + def data_division(self, input_file, input_suffix): + if input_suffix in multi_level: + input_multi_level = {} + for key in file_keys[input_suffix].keys(): + keys_of_this_category = file_keys[input_suffix][key] + input_multi_level[key] = {key_i: input_file[key_i] + for key_i in keys_of_this_category} + else: + input_multi_level = {'only': input_file} + return input_multi_level + + def get_full_data(self): + file_dict = {} + for suffix in self.suffix_list: + input_file = self.get_data(self.discharge, suffix) + file_dict[suffix] = self.data_division(input_file, suffix) + self.file_dict = file_dict + return file_dict + + @staticmethod + def spec_filters(freq, time, amp_f_t, spec_params=spec_params_default, + thr=0.9, gaussblr_win=(31, 3)): + def norm(amp_f_t): + mn = amp_f_t.mean() + std = amp_f_t.std() + return (amp_f_t-mn) / std + + def rescale(amp_f_t): + return (amp_f_t-amp_f_t.min())/(amp_f_t.max()-amp_f_t.min()) + + def quantfilt(amp_f_t, thr=0.9): + filt = np.quantile(amp_f_t, thr, axis=0) + out = np.where(amp_f_t < filt, 0, amp_f_t) + return out + + # gaussian filtering + def gaussblr(amp_f_t, filt=(31, 3)): + amp_f_t = (rescale(amp_f_t)*255).astype('uint8') + out = cv2.GaussianBlur(amp_f_t,filt,0) + return rescale(out) + + # mean filtering + def meansub(amp_f_t): + mn = np.mean(amp_f_t, axis=1)[:, np.newaxis] + out = np.absolute(amp_f_t - mn) + return rescale(out) + + # morphological filtering + def morph(amp_f_t): + amp_f_t = (rescale(amp_f_t)*255).astype('uint8') + se1 = cv2.getStructuringElement(cv2.MORPH_RECT, (4, 4)) + se2 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 1)) + mask = cv2.morphologyEx(amp_f_t, cv2.MORPH_CLOSE, se1) + mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, se2) + return rescale(mask) + + def apply_all(freq, time, amp_f_t, spec_params, thr=thr, + gaussblr_win=gaussblr_win): + + Sxx = np.log(amp_f_t + spec_params['eps']) + # rescale the pixels to (0,1) + Sxx = (Sxx-np.min(Sxx))/(np.max(Sxx)-np.min(Sxx)) + + Sxx_enhanced = quantfilt(Sxx, thr) + Sxx_enhanced = gaussblr(Sxx_enhanced, gaussblr_win) + Sxx_enhanced = meansub(Sxx_enhanced) + Sxx_enhanced = morph(Sxx_enhanced) + Sxx_enhanced = meansub(Sxx_enhanced) + + return freq, time, Sxx_enhanced + + return apply_all(freq, time, amp_f_t, spec_params, thr=thr, + gaussblr_win=gaussblr_win) + + @staticmethod + def spectro_calc(sig_time, data, spec_params=spec_params_default, + plot=False): + spec_params['fs'] = 1./np.mean(sig_time[1:]-sig_time[:-1]) + # default 1024 + spec_params['nperseg'] = max(int(0.6*spec_params['fs']), 1) + # default: nperseg / 4 + spec_params['noverlap'] = max(int(spec_params['nperseg']/4), 1) + print(spec_params) + + freq, time, amp_f_t = signal.spectrogram( + data, nperseg=spec_params['nperseg'], + noverlap=spec_params['noverlap'], fs=spec_params['fs'], + window=spec_params['window'], scaling=spec_params['scaling'], + detrend=spec_params['detrend']) + if plot: + plt.clf() + plt.imshow(amp_f_t.T,aspect='auto',cmap='hot', + extent=[time[0], time[-1], freq[-1], freq[0]]) + plt.colorbar() + plt.ylabel('kHz') + plt.xlabel('ms') + plt.gca().invert_yaxis() + plt.show() + return freq, time, amp_f_t + + @staticmethod + def spectro_plot(freq, time, amp_f_t): + plt.clf() + plt.imshow(amp_f_t,aspect='auto',cmap='hot', + extent=[time[0], time[-1], freq[-1], freq[0]]) + plt.colorbar() + plt.ylabel('kHz') + plt.xlabel('ms') + plt.gca().invert_yaxis() + plt.show() + + @staticmethod + def time_serie_plot(dict): + plt.clf() + if dict['zdata'][:].shape == 1: + plt.plot(dict['xdata'][:],dict['zdata'][:]) + else: + plt.plot(dict['xdata'][:],dict['zdata'][:].T) + plt.xlabel('Time (ms)') + plt.show() + + @staticmethod + def time_matching_merge_asof_1d(time1, data1, time_std): + # Convert input arrays to DataFrames + df1 = pd.DataFrame({'time1': time1, 'data1': data1}) + df2 = pd.DataFrame({'time_std': time_std}) + + # Sort the DataFrames by the time columns + df1.sort_values('time1', inplace=True) + df2.sort_values('time_std', inplace=True) + + # Perform the asof merge + merged_df = pd.merge_asof(df2, df1, left_on='time_std', + right_on='time1', direction='nearest') + + # Drop unnecessary columns and handle NaN values + merged_df.drop(columns='time_std', inplace=True) + merged_df.dropna(inplace=True) + + # Extract the matched time and data + matched_time = merged_df['time1'].values + matched_data = merged_df['data1'].values + + return matched_time, matched_data + + @staticmethod + def time_matching_merge_asof_2d(time1, data1, time_std): + # Create DataFrames + # Note: We assume time1 and time_std are already float arrays + # representing time in seconds + + # Transpose data1 to align time as rows + df1 = pd.DataFrame(data1.T, index=time1) + df_std = pd.DataFrame(index=time_std) + + # Reset index to include time in the DataFrame directly for merging + df1 = df1.reset_index().rename(columns={'index': 'time1'}) + df_std = df_std.reset_index().rename(columns={'index': 'time_std'}) + + # Perform merge_asof to find the closest matches + merged = pd.merge_asof(df_std.sort_values('time_std'), + df1.sort_values('time1'), left_on='time_std', + right_on='time1', direction='nearest') + + # Extract the aligned times (as float) + matched_time = merged['time1'].values + + # Extract indices from the merged DataFrame + indices = merged['time1'].apply( + lambda x: np.where(df1['time1'] == x)[0][0] + if x in df1['time1'].values else -1).values + + # Use indices to fetch data from the original 2D array, + # handle missing indices + matched_data = np.array([data1[:, int(idx)] if idx != -1 + else np.full(data1.shape[0], np.nan) + for idx in indices]).T + + return matched_time, matched_data + + @staticmethod + def time_matching_binary_search(time1, data1, time_std, mode='2d'): + # Function to find the closest time in time1 to each time in time_std + def find_closest(target): + # Binary search for the closest timestamp + low, high = 0, len(time1) - 1 + best_idx = low + while low <= high: + mid = (low + high) // 2 + if time1[mid] < target: + low = mid + 1 + elif time1[mid] > target: + high = mid - 1 + else: + return mid + # Update the best index if the current mid is closer to the + # target + if abs(time1[mid] - target) < abs(time1[best_idx] - target): + best_idx = mid + return best_idx + + # Align data1 to time_std + matched_data = [] + matched_time = [] + for t in time_std: + closest_idx = find_closest(t) + if mode == '2d': + matched_data.append(data1[:, closest_idx]) + else: + matched_data.append(data1[closest_idx]) + matched_time.append(time1[closest_idx]) + + return matched_time, matched_data + + @classmethod + def time_matching(cls,time, data, time_std, mode='merge_asof'): + if len(data.shape) == 1: + if mode == 'merge_asof': + return cls.time_matching_merge_asof_1d(time, data, time_std) + elif mode == 'binary': + return cls.time_matching_binary_search(time, data, time_std, + mode='1d') + elif len(data.shape) == 2: + if mode == 'merge_asof': + return cls.time_matching_merge_asof_2d(time, data, time_std) + elif mode == 'binary': + return cls.time_matching_binary_search(time, data, time_std, + mode='2d') + else: + print('The data has to be 1d array or 2d array') + + @staticmethod + def time_interp_past_looking(time, data, time_std, mode='extrapolate'): + if mode == 'extrapolate': + pass + elif mode == 'fill': + pass + + @staticmethod + def time_interp(time, data, time_std): + return np.interp(time_std, time, data) + + +class DatasetPrep(DichargePerp): + def __init__(self, discharge_search_list, suffix_list): + self.discharge_search_list = discharge_search_list + self.suffix_list = suffix_list + + def filter_discharges(self): + suffix_list = self.suffix_list + discharge_search_list = self.discharge_search_list + # Define the criteria for the files you're interested in + criteria = {key: file_normal_size[key]*0.5 for key in suffix_list} + + discharge_list = {key: [] for key in suffix_list} + + for discharge in tqdm(discharge_search_list): + for suffix, size_limit in criteria.items(): + discharge_path = self.file_path_gen(discharge,suffix) + # Check if the file exists + if os.path.isfile(discharge_path): + # Get the size of the file + file_size = os.path.getsize(discharge_path) + if file_size > size_limit: + discharge_list[suffix].append(discharge) + else: + pass + return discharge_list + + +class data_obj_rest(): + + def save_dict_to_hdf5(dictionary, h5file): + for key, value in dictionary.items(): + if isinstance(value, dict): + group = h5file.create_group(key) + save_dict_to_hdf5(value, group) + else: + h5file.create_dataset(key, data=value) + + def TS_interp_(discharge,write_h5=True,plot=False): + TS_Z_min_set = [0.0, 0.03, 0.09, 0.1, 0.15, 0.16, + 0.21, 0.22, 0.26, 0.27, 0.28, 0.3, 0.31, 0.32, 0.36, + 0.37, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, + 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, + 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, + 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, + 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, + 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, + 0.92, 0.93] + str_shot = str(discharge)[:2] + path = f'/scratch/gpfs/EKOLEMEN/big_d3d_data/{str_shot}0000/' + + TS_file = h5py.File(path + str(discharge) + '_TS.h5', 'r') + TS_RZ_file = h5py.File(path + str(discharge) + '_TS_RZ.h5', 'r') + + TS_Z = TS_RZ_file['S.BLESSED.CORE.Z']['zdata'][:] + order_index = np.argsort(TS_Z) + TS_Z_sort = TS_Z[order_index] + + TS_interp = {} + TS_keys = ['TS.BLESSED.CORE.density', 'TS.BLESSED.CORE.temp'] + for key in TS_keys: + TS_interp_list = [] + TS_time = TS_file[key]['xdata'][:] + for i in range(len(TS_time)): + TS_data = TS_file[key]['zdata'][:, i]*0.1**19 + TS_data_sort = TS_data[order_index] + TS_interp_tmp = np.interp(TS_Z_min_set, TS_Z_sort, + TS_data_sort) + TS_interp_list.append(TS_interp_tmp) + # start here + TS_interp[key] = {'xdata': np.array(TS_time), + 'ydata': np.array(TS_Z_min_set), + 'zdata': np.array(TS_interp_list).T*10.**19} + + if write_h5: + with h5py.File(f'{path}{discharge}_TS_core_interp.h5', 'w') as h5file: + save_dict_to_hdf5(TS_interp, h5file) + + if plot: + plt.clf() + plt.scatter(TS_Z_min_set, TS_interp[key]['zdata'][:, 600], + label='interp') + plt.scatter(TS_Z_sort, (TS_file[key]['zdata'][:,600])[order_index], + label='origin') + plt.legend() + plt.show() + + return 0 + + + def read_file(discharge, file_suffix, df_time): + + path = find_path(discharge) + file = h5py.File(f'{path}{discharge}_{file_suffix}.h5', 'r') + keys = file.keys() + + for i, key in enumerate(keys): + dict_tmp = {'xdata': file[key]['xdata']} + if len(file[key]['zdata'].shape) == 2: + for j in range(file[key]['zdata'].shape[0]): + dict_tmp[key+str(j)] = file[key]['zdata'][j, :] + elif len(file[key]['zdata'].shape) == 1: + dict_tmp[key] = file[key]['zdata'] + + df_tmp = pd.DataFrame(dict_tmp).astype('float32') + if i == 0: + df = pd.merge_asof(df_time, df_tmp, on='xdata', + direction='nearest') + else: + df = pd.merge_asof(df, df_tmp, on='xdata', + direction='nearest') + file.close() + return df + + def hdf5_generator(discharge_list, h5_profiles, + data_filename='diag2diag.pkl'): + all_X =[] + all_y = [] + all_time = [] + discharg_read_list = [] + len_list = [] + for discharge in tqdm(discharge_list): + print(discharge) + try: + dfs = {} + # creating the standard time + path=find_path(discharge) + + file = h5py.File(f'{path}{discharge}_shape.h5', 'r') + t_min = 0 + t_max = file['R0']['xdata'][-1] + file.close() + + file = h5py.File(f'{path}{discharge}_TS.h5', 'r') + df_time = pd.DataFrame({'xdata': file[list(file.keys())[0]]['xdata']}) + time = file[list(file.keys())[0]]['xdata'][:] + + time_index = (time >= t_min) & (time <= t_max) + time_tmp = time[time_index] + df_time = pd.DataFrame({'xdata': time_tmp}) + file.close() + + # Read all the files + for file_suffix in h5_profiles: + df = read_file(discharge, file_suffix, df_time) + dfs[file_suffix] = df + + # summarize all the data in this dicharge + df_tmp = np.concatenate( + [dfs[key].to_numpy()[1:] for key in dfs.keys()], axis=1) + + key_list_dict = {} + key_list = [] + for key in dfs.keys(): + key_list_dict[key]=list(dfs[key].keys()) + for key_ in key_list_dict[key]: + key_list.append(key_) + + # add this discharge to the total file + all_X.append(df_tmp) + all_time.append(df_time['xdata']) + all_time_tmp= np.concatenate(all_time, axis=0) + all_X_tmp = np.concatenate(all_X, axis=0) + len_list.append(df_time['xdata'].shape[0]) + discharg_read_list.append(discharge) + # Serialize the data and save to a file + with open(data_filename, 'wb') as file: + pickle.dump([all_X_tmp, all_time_tmp, discharg_read_list, + len_list, key_list, key_list_dict], file) + + except Exception as e: # if 2==1: + print(f"Error: {e}") + continue + finally: # if 2==1: + try: + file.close() + except: + continue + + return [all_X_tmp] diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..304962f --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,28 @@ +[project] +name = "fusion_ai_hub" +version = "0.0.1" +authors = [ + {name="Peter Steiner", email="peter.steiner@princeton.edu"}, + {name="Max Tian Curie", email="max.curie@princeton.edu"}, + {name="Azarakhsh Jalalvand", email="azarakhsh.jalalvand@princeton.edu"} +] +description = "FusionAIHub - Fetch nuclear fusion data, preprocess it, and use it for training machine learning models." +readme = "README.md" +requires-python = ">=3.9" +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", +] +license = {file = "LICENSE"} +dependencies = [ + "h5py", "numpy", "pandas", "matplotlib", "scipy", "tqdm", "opencv-python" +] + +[project.urls] +Homepage = "https://github.com" +Documentation = "https://readthedocs.org" +Repository = "https://github.com/PlasmaControl/FusionAIHub" +Issues = "https://github.com/PlasmaControl/FusionAIHub/issues" +Changelog = "https://github.com" + diff --git a/src/fusionaihub/__init__.py b/src/fusion_ai_hub/__init__.py similarity index 100% rename from src/fusionaihub/__init__.py rename to src/fusion_ai_hub/__init__.py diff --git a/src/fusionaihub/base/__init__.py b/src/fusion_ai_hub/base/__init__.py similarity index 100% rename from src/fusionaihub/base/__init__.py rename to src/fusion_ai_hub/base/__init__.py diff --git a/src/fusionaihub/core/__init__.py b/src/fusion_ai_hub/core/__init__.py similarity index 100% rename from src/fusionaihub/core/__init__.py rename to src/fusion_ai_hub/core/__init__.py diff --git a/src/fusionaihub/datasets/__init__.py b/src/fusion_ai_hub/datasets/__init__.py similarity index 100% rename from src/fusionaihub/datasets/__init__.py rename to src/fusion_ai_hub/datasets/__init__.py diff --git a/src/fusionaihub/display/__init__.py b/src/fusion_ai_hub/display/__init__.py similarity index 100% rename from src/fusionaihub/display/__init__.py rename to src/fusion_ai_hub/display/__init__.py diff --git a/src/fusionaihub/feature/__init__.py b/src/fusion_ai_hub/feature/__init__.py similarity index 100% rename from src/fusionaihub/feature/__init__.py rename to src/fusion_ai_hub/feature/__init__.py diff --git a/src/fusionaihub/util/__init__.py b/src/fusion_ai_hub/util/__init__.py similarity index 100% rename from src/fusionaihub/util/__init__.py rename to src/fusion_ai_hub/util/__init__.py From 12f6c8b20c7bb31180d9db47ab2aa658bcb43193 Mon Sep 17 00:00:00 2001 From: maxcurie <47543965+maxtcurie@users.noreply.github.com> Date: Wed, 24 Apr 2024 17:56:11 -0400 Subject: [PATCH 004/103] Added normalization and flat top finder --- examples/Data_fetching/.gitignore | 162 +++++ examples/Data_fetching/fetch_GAdata.py | 51 +- examples/Data_fetching/fetch_toksearch.py | 34 +- examples/Dataset_prep/data_prep_obj.py | 694 +++++++++++++++++++++- 4 files changed, 898 insertions(+), 43 deletions(-) create mode 100644 examples/Data_fetching/.gitignore diff --git a/examples/Data_fetching/.gitignore b/examples/Data_fetching/.gitignore new file mode 100644 index 0000000..0c19d74 --- /dev/null +++ b/examples/Data_fetching/.gitignore @@ -0,0 +1,162 @@ + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class +*tmp.py + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ \ No newline at end of file diff --git a/examples/Data_fetching/fetch_GAdata.py b/examples/Data_fetching/fetch_GAdata.py index 9007fcb..82e82f5 100644 --- a/examples/Data_fetching/fetch_GAdata.py +++ b/examples/Data_fetching/fetch_GAdata.py @@ -15,13 +15,14 @@ #python2.7 fetch_data.py #*******start of user block************ -output_path='/cscratch/curiem/Data_fetch_Basic' +output_path='/cscratch/curiem/Data_fetch_CO2_s' ece_pcece=False size_GB=400 directory_path="/cscratch/curiem" #to check the total file sizes -shot_list=np.arange(170000,200000) - +shots = list(np.arange(150000,170000,dtype=int)) +shot_list=shots interval=1000 +suffix_list=['co2_s'] #shot_list=shot_list[:10] #*******end of user block************ @@ -80,6 +81,7 @@ def data2dict(shotn, signame, hf, atlconn) : atlconn = MDSplus.Connection('atlas.gat.com') ech_gytname = ['lei','luk','r2d'] +co2_chords=['r0', 'v1', 'v2', 'v3'] #shot_list = np.loadtxt('DIIID_BES_Shot_List_Fatima.txt',delimiter='\n',dtype=np.int32) # shot_list = np.load('tm-control-shots.npy');shot_list=np.unique(shot_list).astype(np.int) @@ -93,7 +95,9 @@ def data2dict(shotn, signame, hf, atlconn) : #basic is fundimental measured quantities (in contrast of fitted quantities) -signal_list= { +signal_list_all= { +'co2_s':[r'\den{}'.format(chord) for chord in co2_chords],\ +'mag_hi':["b"+str(i) for i in range(1, 9)],\ 'profiles':['betap','betan','pres', \ 'wmhd','li',\ 'q0','q95','qmin','qpsi','rhoqmin',\ @@ -127,6 +131,45 @@ def data2dict(shotn, signame, hf, atlconn) : 'f1b','f2b','f3b','f4b','f5b','f6b','f7b','f8b','f9b'] } +name_list_all= { +'co2_s':[r'{}'.format(chord) for chord in co2_chords],\ +'mag_hi':["b"+str(i) for i in range(1, 9)],\ +'profiles':['betap','betan','pres', \ + 'wmhd','li',\ + 'q0','q95','qmin','qpsi','rhoqmin',\ + 'r0','aminor',\ + 'kappa','tritop','tribot',\ + 'alpha','psirz',\ + 'ssibry', 'ssimag',\ + 'rmaxis','zmaxis',\ + 'volume',\ + 'drsep','gapbot','gapin','gapout','gaptop',\ + 'zxpt1','zxpt2',\ + 'edensfit', 'etempfit',\ + 'trotfit','itempfit','idensfit',\ + 'n1rms','n2rms','n3rms'], + \ +'basic':['ip', 'ipsip', 'iptipp','pcbcoil', 'bcoil','bt','vloop']\ + +[ 'plasticfix', 'fzns']\ + +['fs00','fs01','fs02','fs03','fs04','fs05'],\ +'actu': ['pinjf_%dl' % k for k in [15,21,30,33]]+['pinjf_%dr' % k for k in [15,21,30,33]]\ + +['tinj_%dl' % k for k in [15,21,30,33]]+['tinj_%dr' % k for k in [15,21,30,33]]\ + +['echpwrc','echpwr']\ + +['ec%sfpwrc' % (x) for x in ech_gytname]\ + +['ec%sxmfrac' % (x) for x in ech_gytname]\ + +['ec%spolang' % (x) for x in ech_gytname]\ + +['gasa', 'gasb', 'gasc', 'gasd', 'gase']\ + +['c19', 'c79', 'c139', 'c199', 'c259', 'c319', \ + 'iu30', 'iu90', 'iu150', 'iu210', 'iu270', 'iu330', \ + 'il30', 'il90', 'il150', 'il210', 'il270', 'il330']\ + +['ecoila', 'ecoilb', 'e567up', 'e567dn', 'e89dn', 'e89up']\ + +['f1a','f2a','f3a','f4a','f5a','f6a','f7a','f8a','f9a',\ + 'f1b','f2b','f3b','f4b','f5b','f6b','f7b','f8b','f9b'] +} + +signal_list={suffix:signal_list_all[suffix] for suffix in suffix_list} + +name_list= {suffix:name_list_all[suffix] for suffix in suffix_list} for i in tqdm(range(len(shot_list))): shotn=shot_list[i] diff --git a/examples/Data_fetching/fetch_toksearch.py b/examples/Data_fetching/fetch_toksearch.py index 3654647..1aade03 100644 --- a/examples/Data_fetching/fetch_toksearch.py +++ b/examples/Data_fetching/fetch_toksearch.py @@ -6,6 +6,7 @@ import h5py import subprocess import sys +from tqdm import tqdm #this one runs on iris, run the following #module purge @@ -25,16 +26,17 @@ directory_path="/cscratch/curiem" #list of discharges to fetch -shots = np.arange(150000,170000,dtype=int) +shots = list(np.arange(150000,170000,dtype=int)) +#shots = list(np.arange(175910,190000,dtype=int)) # one can set start_shot the where to start. (usually used for restarting the fetching due to unexpected termination) start_shot=min(shots) #path to save the files -path = '/cscratch/curiem/Data_fetch_CER/' +path = f'/cscratch/curiem/Data_fetch_CO2_s/' -#diag_names=[mag,mag_hi,bes,ece_cali,ece_s, co2_den, co2_pl, co2_s, ts,ts_rz,ts_error,cer, mse,custom] -diag_name='cer' +#diag_names=[mag,mag_hi,bes,ece_cali,ece_s, co2_den, co2_pl, co2_s, ts,ts_rz,ts_error,custom] +diag_name='co2_s' #custom sig_names_custom, the suffix is fixed to be custom for now. if diag_name=='custom': @@ -170,6 +172,16 @@ def signal_gen(diag_name='zipfit',sig_names_custom=[''],names_custom=[''],tree_c signals.append(MdsSignal(name, 'ECE', location='remote://atlas.gat.com')) names.append(r'%02d'%chan) + elif diag_name=='co2_s': + chords = ['r0', 'v1', 'v2', 'v3'] + + + for chord in chords: + name = r'\den{}'.format(chord) + signals.append(MdsSignal(name, 'BCI', location='remote://atlas.gat.com')) + names.append(f'{chord}') + + elif diag_name=='co2_den': nums = range(1,15) chords = ['r0', 'v1', 'v2', 'v3'] @@ -226,7 +238,7 @@ def signal_gen(diag_name='zipfit',sig_names_custom=[''],names_custom=[''],tree_c names.append(r'{}.{}'.format(thomson_mds_area,thomson_sig_name)) - elif diag_name=='ts_error': + elif diag_name=='TS_ERROR': #thomson_mds_scale={'density': 1e19, 'temp': 1e3} thomson_mds_areas=['core','divertor','tangential'] thomson_sig_names= ['DENSITY_E', 'TEMP_E'] @@ -286,7 +298,7 @@ def signal_gen(diag_name='zipfit',sig_names_custom=[''],names_custom=[''],tree_c sig_names=[r'\cerq{}{}'.format(output, channel) for channel in channels for output in outputs] - names=[r'q.{}.{}'.format(output, channel) for channel in name_channels + names=[r'cer.{}.{}'.format(output, channel) for channel in name_channels for output in outputs] treename='ions' @@ -308,7 +320,7 @@ def signal_gen(diag_name='zipfit',sig_names_custom=[''],names_custom=[''],tree_c def fetch_ece_2d_array_data(path, shots, diag_name): names, signals = signal_gen(diag_name) - for n,shot in enumerate(shots): + for n,shot in tqdm(enumerate(shots)): if shot>=start_shot: start = time.time() pipeline = Pipeline([shot]) @@ -367,9 +379,9 @@ def fetch_single_data(path, shots, diag_name): if not os.path.isdir(path): os.makedirs(path) - for n,shot in enumerate(shots): + for n,shot in tqdm(enumerate(shots)): if shot>=start_shot: - try: + if 1==1: start = time.time() pipeline = Pipeline([shot]) @@ -435,7 +447,7 @@ def fetch_single_data(path, shots, diag_name): else: group.create_dataset(subkey, data=value) - except: + if 1==2: pass if n % interval == 0: size_limiter_sleep(size_GB=size_GB) @@ -446,7 +458,7 @@ def fetch_co2_chunked_data(path, shots, diag_name): chords=['r0', 'v1', 'v2', 'v3'] nums = range(1,15) names, signals = signal_gen(diag_name) - for n,shot in enumerate(shots): + for n,shot in tqdm(enumerate(shots)): if shot>=start_shot: try: start = time.time() diff --git a/examples/Dataset_prep/data_prep_obj.py b/examples/Dataset_prep/data_prep_obj.py index dd3b7e1..2420e4d 100644 --- a/examples/Dataset_prep/data_prep_obj.py +++ b/examples/Dataset_prep/data_prep_obj.py @@ -28,6 +28,437 @@ 'profiles': 5 * 1024**2, } + +avg_list={'co2_s': {'r0': 5.961484014154121, 'v1': -8.22111284268937, 'v2': 6.344910008129705, 'v3': 4.328119030528726}, \ + 'co2_den': {'r0': 6.9296784646054235, 'v1': -9.284441581907231, 'v2': 7.373509474284704, 'v3': 5.027566163746425}, 'co2_pl': {'r0': 1.7374204, 'v1': -2.6094196, 'v2': -1.1489629, 'v3': -1.1252769}, 'ece_cali': {'ece_cali': [0.20893963, 0.12774856, 0.13745612, 0.20247421, 0.34440163, + 0.60262373, 0.66603392, 0.76447305, 0.88263744, 1.02150685, + 1.16817018, 1.30320441, 1.4518409 , 1.56385316, 1.66534622, + 1.72105876, 1.74185541, 1.83246238, 1.93203416, 2.01143143, + 2.08425715, 2.11510808, 2.22542016, 2.29812408, 2.35032174, + 2.29788245, 2.22418809, 2.26958848, 2.27961308, 2.13412786, + 2.08813635, 2.12741291, 2.60351518, 2.49858828, 2.40936239, + 2.23847532, 2.03189087, 1.92861781, 1.79510848, 1.77163682, + 0.30883403, 0.44442737, 0.56559379, 0.54799893, 0.62026258, + 0.65768334, 0.68811054, 0.68055001] }, 'ece_s': {'ece_s': [0.08584737, 0.05780916, 0.061087 , 0.08869078, 0.14763243, + 0.2584688 , 0.28624459, 0.32994842, 0.38132434, 0.44180371, + 0.5039844 , 0.56298781, 0.62323814, 0.67640968, 0.71432546, + 0.74403155, 0.7484862 , 0.7900538 , 0.82948537, 0.86571757, + 0.89989974, 0.90831094, 0.95536707, 0.99480042, 1.01156296, + 0.99122688, 0.9654906 , 0.97362054, 0.96864245, 0.9185536 , + 0.90745512, 0.92003376, 1.11705779, 1.07533205, 1.03214442, + 0.96556191, 0.87798738, 0.82471379, 0.78254752, 0.7623152 , + 0.10797436, 0.17498507, 0.21833791, 0.23441809, 0.26207619, + 0.26715684, 0.30475438, 0.30516581] }, 'ts': {'core.dens': [0.0206573 , 0.02273723, 0.03008627, 0.02697151, 0.04085645, + 0.06387276, 0.0558053 , 0.06451483, 0.08294583, 0.09555718, + 0.17719237, 0.22596197, 0.34866633, 0.48804962, 0.70802082, + 0.96147464, 1.33869104, 1.54778518, 1.66705032, 1.68639355, + 1.76600186, 1.77834179, 1.81300757, 1.86991192, 1.82749834, + 1.83304439, 1.86443489, 1.91185937, 1.91697839, 1.92917273, + 1.94256332, 1.96919655, 2.04108931, 2.1734384 , 2.17873709, + 2.21568359, 2.2562559 , 2.2724558 , 2.37268523, 2.33087342] , 'core.temp': [0.04313143, 0.02095982, 0.03874498, 0.05936599, 0.06048159, + 0.0724493 , 0.12284724, 0.08397523, 0.1665927 , 0.15987559, + 0.20999825, 0.28544226, 0.53844154, 0.95033735, 1.1925813 , + 1.5936897 , 1.8543715 , 2.2159667 , 2.6548545 , 2.6115215 , + 2.8906064 , 2.9641142 , 3.0341778 , 3.1378953 , 3.2817786 , + 3.2998369 , 3.3953443 , 3.621902 , 3.7870445 , 4.0227976 , + 4.197787 , 4.3938704 , 5.1899633 , 6.3529873 , 6.4848704 , + 7.2024207 , 7.9320636 , 8.748535 , 9.514734 , 9.936282 ], + 'divertor.dens': [13.07862871, 4.09257651, 3.42569876, 4.34937471, 3.47240192, + 3.55858774, 4.13357949, 6.91416951] , 'divertor.temp': [ 4.912342 , 0.82680666, 1.638078 , 2.5478451 , 2.5880477 , + 3.275134 , 4.093607 , 10.133815 ] , 'tangential.dens': [2.31724222, 2.31432445, 2.28171407, 2.3820687 , 2.55593377, + 2.41514733] , 'tangential.temp': [0.94976425, 1.0228319 , 1.0419364 , 1.0851678 , 1.2703744 , + 1.2370783 ] }, 'ts_error': {'core.dens': [0.58320681, 0.55906454, 0.74842744, 0.55708311, 0.85056662, + 1.32191982, 1.4088409 , 2.07654662, 2.10008292, 2.98143154, + 5.02302231, 6.25136888, 6.88582818, 6.14681397, 7.05265831, + 6.68781645, 7.81220338, 8.52329903, 8.36451255, 9.11189696, + 8.55097338, 7.45192188, 7.37140851, 8.74282399, 8.54578735, + 7.21970752, 5.02604401, 4.80131268, 5.95745281, 6.88717577, + 6.95248688, 6.13058621, 8.39298857, 8.10257601, 7.24310376, + 6.49387289, 6.11134489, 5.51627359, 5.48644365, 6.25646235] , 'core.temp': [0.26047334, 0.09455868, 0.22123039, 0.32492593, 0.32333547, + 0.3222272 , 0.7120584 , 0.4540971 , 0.9215022 , 0.89692396, + 1.1397355 , 1.5596155 , 2.003468 , 2.306727 , 2.3110852 , + 2.8224263 , 2.3556364 , 2.5785322 , 2.7340143 , 2.7409067 , + 2.7862887 , 2.8193336 , 2.803931 , 2.7903335 , 3.0551565 , + 2.7775638 , 1.8546137 , 1.8820401 , 2.6592715 , 3.5817459 , + 3.1250322 , 2.8227775 , 4.4379373 , 5.4327197 , 5.3897243 , + 5.357903 , 5.418874 , 5.3132143 , 5.2673607 , 5.8839746 ], + 'divertor.dens': [23.36652036, 4.81574073, 4.69183499, 4.67569821, 4.45556532, + 5.47507145, 4.86280284, 6.50936229] , 'divertor.temp': [3.1028507 , 0.38552743, 0.7590671 , 0.7857776 , 0.91813165, + 1.195608 , 1.1747509 , 2.233286 ] , 'tangential.dens': [4.36654655, 3.76463633, 3.03009985, 3.18819823, 3.33737849, + 4.09021571] , 'tangential.temp': [4.1905775, 3.4313176, 3.1727965, 3.04693 , 3.6034732, 4.51128 ], + }, 'ts_rz': {'core.r': 1.9399999, 'core.z': 0.61496246, 'divertor.r': 1.49, 'divertor.z': -1.1383374, 'tangential.r': 1.8376166, 'tangential.z': -0.58}, 'cer': {'q.amp.t01': 7.365575667140224, 'q.amp.t02': 8.157370662714786, 'q.amp.t03': 1.022917013813834, 'q.amp.t04': 1.1358540348821744, 'q.amp.t05': 1.4359301184932869, 'q.amp.t06': 1.798909659944321, 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'e567dn': 1000, 'e567up': 1000, 'e89dn': 100, 'e89up': 100, 'echpwr': 1.0, 'echpwrc': 1.0, 'ecleifpwrc': 1.0, 'ecleipolang': 10, 'ecleixmfrac': 1.0, 'eclukfpwrc': 1.0, 'eclukpolang': 10, 'eclukxmfrac': 1.0, 'ecoila': 1000, 'ecoilb': 100, 'ecr2dfpwrc': 1.0, 'ecr2dpolang': 1.0, 'ecr2dxmfrac': 1.0, 'f1a': 100, 'f1b': 100, 'f2a': 100, 'f2b': 100, 'f3a': 100, 'f3b': 100, 'f4a': 100, 'f4b': 100, 'f5a': 100, 'f5b': 100, 'f6a': 100, 'f6b': 100, 'f7a': 100, 'f7b': 1000, 'f8a': 100, 'f8b': 100, 'f9a': 100, 'f9b': 100, 'gasa': 1, 'gasb': 0.001, 'gasc': 0.001, 'gasd': 0.001, 'gase': 0.001, 'il150': 100, 'il210': 100, 'il270': 10, 'il30': 100, 'il330': 100, 'il90': 1, 'iu150': 100, 'iu210': 1, 'iu270': 100, 'iu30': 10, 'iu330': 100, 'iu90': 100, 'pinjf_15l': 100000, 'pinjf_15r': 1.0, 'pinjf_21l': 100000, 'pinjf_21r': 1.0, 'pinjf_30l': 100000, 'pinjf_30r': 10000, 'pinjf_33l': 10000, 'pinjf_33r': 100000, 'tinj_15l': 1, 'tinj_15r': 1.0, 'tinj_21l': 1, 'tinj_21r': 1.0, 'tinj_30l': 1, 'tinj_30r': 0.01, 'tinj_33l': 0.1, 'tinj_33r': 1} + } + # names of the cyrotrons ech_gytname = ['lei', 'luk', 'r2d'] @@ -35,11 +466,11 @@ 'ts', 'ts_error', 'ts_rz'] no_level = ['ece_cali', 'ece_s'] -file_keys = {'co2_s': ['r0', 'v1', 'v2', 'v3'], - 'co2_den': ['r0', 'v1', 'v2', 'v3'], - 'co2_pl': ['r0', 'v1', 'v2', 'v3'], - 'ece_cali': [], - 'ece_s': [], +file_keys = {'co2_s': {'co2_s': ['r0', 'v1', 'v2', 'v3']}, + 'co2_den': {'co2_den': ['r0', 'v1', 'v2', 'v3']}, + 'co2_pl': {'co2_pl': ['r0', 'v1', 'v2', 'v3']}, + 'ece_cali': {'ece_cali':[]}, + 'ece_s': {'ece_s':[]}, 'ts': {r'{}.{}'.format(area,sig): [r'{}.{}'.format(area,sig)] for area in ['core', 'divertor', 'tangential'] @@ -143,20 +574,113 @@ 'detrend': 'linear', # {'linear', 'constant', False} 'eps': 1e-11} + + class DichargePerp(): """object that contains the functions to manipulate one discharge.""" - def __init__(self, discharge=174823, suffix_list=('co2_s', )): - self.discharge = discharge - self.suffix_list = suffix_list + def __init__(self): + self.file_keys=file_keys + self.avg_list=avg_list + self.std_list=std_list + self.norm_factor_list=norm_factor_list + self.file_normal_size=file_normal_size + + @staticmethod + def file_path_gen(discharge, suffix): + return (f'/scratch/gpfs/EKOLEMEN/big_d3d_data/{str(discharge)[:2]}0000/{discharge}_{suffix}.h5') + + def hdf5_to_dict(self, group): + result = {} + for key in group.keys(): + if isinstance(group[key], h5py.Dataset): + result[key] = group[key][()] + elif isinstance(group[key], h5py.Group): + result[key] = self.hdf5_to_dict(group[key]) + return result + + def order_of_magnitude_normal_factor_calc(self,discharge): + norm_factor_list_tmp={} + for suffix in self.file_keys.keys(): + file_dict=self.get_data(discharge, suffix, norm=False) + norm_factor_list_tmp[suffix]={} + for key in file_dict.keys(): + mean_tmp=abs(np.mean(file_dict[key]['zdata'][:])) + try: + exponent=self.get_order_of_magnitude(mean_tmp) + norm_factor_list_tmp[suffix][key]=10**exponent + except: + norm_factor_list_tmp[suffix][key]=1. + return norm_factor_list_tmp + + def avg_factor_calc(self,discharge): + avg_factor={} + for suffix in self.file_keys.keys(): + file_dict=self.get_data(discharge, suffix, norm=True) + avg_factor[suffix]={} + for key in file_dict.keys(): + data=file_dict[key]['zdata'][:] + avg_tmp=np.mean(data,axis=len(data.shape)-1) + if np.isnan(avg_tmp).any(): + avg_tmp=0. + + avg_factor[suffix][key]=avg_tmp - def file_path_gen(self, discharge, suffix): - return (f'/scratch/gpfs/EKOLEMEN/big_d3d_data/' - f'{str(discharge)[:2]}0000/{discharge}_{suffix}.h5') + return avg_factor - def get_data(self,discharge, suffix): + def std_factor_calc(self,discharge): + std_factor={} + for suffix in self.file_keys.keys(): + file_dict=self.get_data(discharge, suffix, norm=True) + std_factor[suffix]={} + for key in file_dict.keys(): + data=file_dict[key]['zdata'][:] + std_tmp=np.std(data,axis=len(data.shape)-1) + if np.isnan(std_tmp).any(): + std_tmp=1. + std_factor[suffix][key]=std_tmp + + return std_factor + #all: apply tha same avg and mean to all the data + #individual:apply tha individual avg and individual mean to the individual data row + #std_all_avg_individual: apply tha individual avg to the individual data row, but apply the same std to the group + #mode=[all,individual,std_all_avg_individual] + @staticmethod + def norm_data(data,avg_,std_,mode='all'): + avg_=np.array(avg_) + std_=np.array(std_) + if mode == 'all': + std_all=(np.mean(std_**2))**0.5 + avg_all=np.mean(avg_) + elif mode=='std_all_avg_individual': + std_all=(np.mean(std_**2))**0.5 + avg_all=np.expand_dims(avg_,axis=1) + + elif mode=='individual': + std_all=np.expand_dims(avg_,axis=1) + avg_all=np.expand_dims(avg_,axis=1) + + + data_norm=(data-avg_all)/std_all + + return data_norm + + def get_data(self,discharge, suffix, norm=True): discharge_path=self.file_path_gen(discharge, suffix) input_file = h5py.File(discharge_path, 'r') - return input_file + input_dict_tmp = self.hdf5_to_dict(input_file) + if suffix in no_level: + input_dict={suffix:input_dict_tmp} + else: + input_dict=input_dict_tmp + + if norm and (suffix in self.norm_factor_list): + for key in input_dict.keys(): + if self.norm_factor_list[suffix][key]=='log': + input_dict[key]['zdata']=np.log(np.array(input_dict[key]['zdata'][:])) + else: + input_dict[key]['zdata']=np.array(input_dict[key]['zdata'][:])/self.norm_factor_list[suffix][key] + + return input_dict # divide the data into subcategory def data_division(self, input_file, input_suffix): @@ -167,20 +691,37 @@ def data_division(self, input_file, input_suffix): input_multi_level[key] = {key_i: input_file[key_i] for key_i in keys_of_this_category} else: - input_multi_level = {'only': input_file} + input_multi_level = {input_suffix: input_file} return input_multi_level - def get_full_data(self): - file_dict = {} - for suffix in self.suffix_list: - input_file = self.get_data(self.discharge, suffix) - file_dict[suffix] = self.data_division(input_file, suffix) - self.file_dict = file_dict - return file_dict - + + #norm_mode=[no, all,individual,std_all_avg_individual] + #no, means no normalizations + def get_full_data(self, discharge, suffix_list, norm_mode='all'): + all_file_dict = {} + for suffix in suffix_list: + file_dict=self.get_data(discharge, suffix, norm=True) + all_file_dict[suffix]={} + for key in file_dict.keys(): + if norm_mode=='no': + all_file_dict[suffix][key]={'xdata':file_dict[key]['xdata'][:],\ + 'zdata':file_dict[key]['zdata'][:]} + else: + all_file_dict[suffix][key]={'xdata':file_dict[key]['xdata'][:],\ + 'zdata':self.norm_data(file_dict[key]['zdata'][:],\ + self.std_list[suffix][key], + self.avg_list[suffix][key],\ + mode=norm_mode)} + return all_file_dict + + @staticmethod + def get_order_of_magnitude(num): + exponent = int(np.log10(abs(num))) + return exponent + + @staticmethod - def spec_filters(freq, time, amp_f_t, spec_params=spec_params_default, - thr=0.9, gaussblr_win=(31, 3)): + def spec_filters(freq, time, amp_f_t, spec_params=spec_params_default,thr=0.9, gaussblr_win=(31, 3)): def norm(amp_f_t): mn = amp_f_t.mean() std = amp_f_t.std() @@ -234,8 +775,7 @@ def apply_all(freq, time, amp_f_t, spec_params, thr=thr, gaussblr_win=gaussblr_win) @staticmethod - def spectro_calc(sig_time, data, spec_params=spec_params_default, - plot=False): + def spectro_calc(sig_time, data, spec_params=spec_params_default,plot=False): spec_params['fs'] = 1./np.mean(sig_time[1:]-sig_time[:-1]) # default 1024 spec_params['nperseg'] = max(int(0.6*spec_params['fs']), 1) @@ -401,9 +941,80 @@ def time_interp_past_looking(time, data, time_std, mode='extrapolate'): def time_interp(time, data, time_std): return np.interp(time_std, time, data) + + def time_matching(self, discharge=None, suffix_list=None): + if discharge is None: + discharge = self.discharge + if suffix_list is None: + suffix_list = self.suffix_list + for suffix in suffix_list: + file_dict=discharge_obj.get_data(discharge, suffix, norm=True) + + + def time_matching_windows(data): + file_dict=discharge_obj.get_data(self.discharge, suffix, norm=True) + + @staticmethod + def find_plateau(series, window_size=40, threshold=0.1, plot=False): + """ + Finds the start and end times of the plateau region in the given time series. + + Args: + series (pd.Series): Time series data with time as index and values as data. + window_size (int, optional): Window size for rolling average and standard deviation. Default is 40. + threshold (float, optional): Threshold for determining the plateau region. Default is 0.1. + + Returns: + tuple: (t_min, t_max) representing the start and end times of the plateau region. + """ + rolling_avg = series.rolling(window=window_size, center=True).mean() + data_rolling_std = series.rolling(window=window_size, center=True).std() + + # Normalize the rolling standard deviation + normalized_std = data_rolling_std / rolling_avg + + # Find the start and end indices of the plateau region + plateau_start = normalized_std[normalized_std < threshold].index[0] + plateau_end = normalized_std[normalized_std < threshold].index[-1] + + if plot: + plt.clf() + plt.plot(normalized_std.index,normalized_std.values) + plt.axvline(plateau_start,color='red') + plt.axvline(plateau_end,color='red') + plt.show() + + return plateau_start, plateau_end + + def flat_top_finder(self,discharge, window_size=500, plot=False): + file_dict = self.get_data(discharge, 'basic', norm=True) + time = file_dict['ip']['xdata'][:] + data = file_dict['ip']['zdata'][:] + time=time[data>3] + data=data[data>3] + dip = np.gradient(data, time) + + # Convert data and time to a pandas Series + series = pd.Series(data, index=time) + t_max, t_min=self.find_plateau(series,window_size=window_size, threshold=0.01, plot=plot) + + if plot: + plt.clf() + plt.plot(series.index, series.values) + plt.axvline(t_min,color='red') + plt.axvline(t_max,color='red') + + plt.ylim(0,20) + plt.show() + + return t_max, t_min + + def deal_with_missing_data(self): + pass + class DatasetPrep(DichargePerp): - def __init__(self, discharge_search_list, suffix_list): + def __init__(self, discharge_search_list=[174823], suffix_list=['ts']): self.discharge_search_list = discharge_search_list self.suffix_list = suffix_list @@ -429,6 +1040,32 @@ def filter_discharges(self): return discharge_list + def normalization(data): + pass + + def merge_multi_discharge(self,discharge_list_tmp,): + pass + + +class post_processing(): + def smooth_rolling_avg(self, time, data, smooth_point, center_window=10000, edge_window=500, time_spread=5): + indx_min=np.argmin(abs(time-(smooth_point-time_spread))) + indx_max=np.argmin(abs(time-(smooth_point+time_spread))) + smoothed_section=np.zeros(indx_max-indx_min+1) + for i in range(indx_min,indx_max+1): + current_time=time[i] + window_size=center_window-abs(smooth_point-current_time)/time_spread*(center_window-edge_window) + smoothed_section[i-indx_min]=np.mean(data[i-int(window_size/2):i+int(window_size/2)+1]) + return smoothed_section,indx_min,indx_max + + + def smooth_rolling_avg_and_put_back(self, time, data, smooth_point_list, center_window=10000, edge_window=500, time_spread=5): + data_smooth=data.copy() + for smooth_point in smooth_point_list: + smoothed_section,indx_min,indx_max=self.smooth_rolling_avg(time, data, smooth_point, center_window=center_window, edge_window=edge_window, time_spread=time_spread) + data_smooth[indx_min:indx_max+1]=smoothed_section + return data_smooth + class data_obj_rest(): def save_dict_to_hdf5(dictionary, h5file): @@ -439,7 +1076,7 @@ def save_dict_to_hdf5(dictionary, h5file): else: h5file.create_dataset(key, data=value) - def TS_interp_(discharge,write_h5=True,plot=False): + def TS_interp_Z(discharge,write_h5=True,plot=False): TS_Z_min_set = [0.0, 0.03, 0.09, 0.1, 0.15, 0.16, 0.21, 0.22, 0.26, 0.27, 0.28, 0.3, 0.31, 0.32, 0.36, 0.37, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, @@ -581,3 +1218,4 @@ def hdf5_generator(discharge_list, h5_profiles, continue return [all_X_tmp] + From 1ba5ff6a0936cde5c3e617def12e20b7590e251a Mon Sep 17 00:00:00 2001 From: maxcurie <47543965+maxtcurie@users.noreply.github.com> Date: Tue, 30 Apr 2024 10:29:13 -0400 Subject: [PATCH 005/103] Time series full pipeline Work Time series full pipeline take suffix, output aligned dict with window. --- examples/Data_fetching/fetch_toksearch.py | 10 +- examples/Dataset_prep/data_prep_obj.py | 348 +++++++++++++++--- .../Dataset_prep/tamplet_dataset_survey.py | 25 ++ .../Dataset_prep/tamplet_discharge_study.py | 52 +++ examples/Dataset_prep/tamplet_spectro.py | 15 + 5 files changed, 400 insertions(+), 50 deletions(-) create mode 100644 examples/Dataset_prep/tamplet_dataset_survey.py create mode 100644 examples/Dataset_prep/tamplet_discharge_study.py create mode 100644 examples/Dataset_prep/tamplet_spectro.py diff --git a/examples/Data_fetching/fetch_toksearch.py b/examples/Data_fetching/fetch_toksearch.py index 1aade03..4626cb8 100644 --- a/examples/Data_fetching/fetch_toksearch.py +++ b/examples/Data_fetching/fetch_toksearch.py @@ -339,9 +339,13 @@ def fetch_ece_2d_array_data(path, shots, diag_name): data_h5['zunits']=shot_data[names[0]]['units']['data'] data_tmp=[] for name in names: - if len(shot_data[name]['data'])<=10: + try : + len_tmp=len(shot_data[name]['data']) + if len_tmp<10: + break + except: break - print(len(shot_data[name]['data'])) + #print(len(shot_data[name]['data'])) data_tmp.append(shot_data[name]['data'][:len(data_h5['xdata'])]) data_tmp=np.array(data_tmp,dtype='float') @@ -365,8 +369,6 @@ def fetch_ece_2d_array_data(path, shots, diag_name): with h5py.File(f'{path}{shot}_{diag_name}.h5', 'w') as h5file: save_dict_to_hdf5(data_h5, h5file) - if 1==0: - pass if n % interval == 0: size_limiter_sleep(size_GB=size_GB) print(f'shot={shot}') diff --git a/examples/Dataset_prep/data_prep_obj.py b/examples/Dataset_prep/data_prep_obj.py index 2420e4d..6b4d670 100644 --- a/examples/Dataset_prep/data_prep_obj.py +++ b/examples/Dataset_prep/data_prep_obj.py @@ -714,12 +714,12 @@ def get_full_data(self, discharge, suffix_list, norm_mode='all'): mode=norm_mode)} return all_file_dict + @staticmethod def get_order_of_magnitude(num): exponent = int(np.log10(abs(num))) return exponent - @staticmethod def spec_filters(freq, time, amp_f_t, spec_params=spec_params_default,thr=0.9, gaussblr_win=(31, 3)): def norm(amp_f_t): @@ -821,7 +821,22 @@ def time_serie_plot(dict): plt.show() @staticmethod - def time_matching_merge_asof_1d(time1, data1, time_std): + def cut_time(time, data, t_min, t_max): + t_indx_min=np.argmin(abs(np.array(time)-t_min)) + t_indx_max=np.argmin(abs(np.array(time)-t_max)) + + return time[t_indx_min:t_indx_max], data[...,t_indx_min:t_indx_max] + + + # Function to get windowed data + @staticmethod + def get_windowed_data(df, center_index, window_size=5): + start = max(center_index - window_size, 0) + end = min(center_index + window_size + 1, len(df)) + return df.iloc[start:end].drop(columns=['xdata']) + + @staticmethod + def time_matching_merge_asof_1d(time1, data1, time_std, left_window=0, right_window=0): # Convert input arrays to DataFrames df1 = pd.DataFrame({'time1': time1, 'data1': data1}) df2 = pd.DataFrame({'time_std': time_std}) @@ -845,7 +860,7 @@ def time_matching_merge_asof_1d(time1, data1, time_std): return matched_time, matched_data @staticmethod - def time_matching_merge_asof_2d(time1, data1, time_std): + def time_matching_merge_asof_2d(time1, data1, time_std,left_window=0, right_window=0): # Create DataFrames # Note: We assume time1 and time_std are already float arrays # representing time in seconds @@ -880,7 +895,7 @@ def time_matching_merge_asof_2d(time1, data1, time_std): return matched_time, matched_data @staticmethod - def time_matching_binary_search(time1, data1, time_std, mode='2d'): + def time_matching_binary_search(time1, data1, time_std, left_window=0, right_window=0): # Function to find the closest time in time1 to each time in time_std def find_closest(target): # Binary search for the closest timestamp @@ -905,30 +920,188 @@ def find_closest(target): matched_time = [] for t in time_std: closest_idx = find_closest(t) - if mode == '2d': - matched_data.append(data1[:, closest_idx]) + matched_data.append(data1[..., closest_idx-left_window:closest_idx+right_window+1]) + matched_time.append(time1[closest_idx-left_window:closest_idx+right_window+1]) + matched_time=np.array(matched_time) + matched_data=np.array(matched_data) + + return matched_time, matched_data + + @staticmethod + def estimate_closest_indx_constant_dt(left_indx,time,target,dt): + time_distant=target-time[left_indx] + n_time=int(np.floor(time_distant/dt)) + indx_start=left_indx+n_time + return indx_start + + @staticmethod + def estimate_closest_indx_varing_dt(left_indx,time,target,dt,dt_std): + time_distant=target-time[left_indx] + n_time_min=int(np.floor(time_distant/(dt+dt_std))) + n_time_max=int(np.floor(time_distant/(dt-dt_std))) + indx_start=left_indx+n_time_min + indx_end=left_indx+n_time_max + return indx_start,indx_end + + @staticmethod + def find_closest_indx_constant_dt(indx_start,time,target): + if indx_start==0: + return 0 + #on target + if time[indx_start]==target: + return indx_start + #indx_start on the left + elif time[indx_start]target: + i=indx_start + while time[i]>target: + i-=1 + return i + + @staticmethod + def find_closest_indx_binary(time,target,indx_start,indx_end): + # Binary search for the closest timestamp + low, high = indx_start, indx_end + best_idx = low + while low <= high: + mid = (low + high) // 2 + if time[mid] < target: + low = mid + 1 + elif time[mid] > target: + high = mid - 1 else: - matched_data.append(data1[closest_idx]) - matched_time.append(time1[closest_idx]) + return mid + # Update the best index if the current mid is closer to the + # target + if abs(time[mid] - target) < abs(time[best_idx] - target): + best_idx = mid + return best_idx + + @staticmethod + def custom_padding(time, data, left_slicing_indx, right_slicing_indx, padding='last'): + #padding to the left + if left_slicing_indx<0: + padding_config = [(0, 0)] * (data.ndim - 1) + + padd_len=-left_slicing_indx + if padding=='nan': + data_tmp=numpy.pad(data[..., 0:right_slicing_indx+1],padding_config+(padd_len, 0),mode='empty') + time_tmp=numpy.pad(time[0:right_slicing_indx+1],padding_config+(padd_len, 0),mode='empty') + elif padding=='last': + data_tmp=numpy.pad(data[..., 0:right_slicing_indx+1],padding_config+(padd_len, 0),mode='constant',constant_values=data[...,0]) + time_tmp=numpy.pad(time[0:right_slicing_indx+1],padding_config+(padd_len, 0),mode='constant',constant_values=time[0]) + elif padding=='zeros': + data_tmp=numpy.pad(data[..., 0:right_slicing_indx+1],padding_config+(padd_len, 0),mode='constant',constant_values=0) + time_tmp=numpy.pad(time[0:right_slicing_indx+1],padding_config+(padd_len, 0),mode='constant',constant_values=0) + elif (right_slicing_indx+1)>len(time): + padd_len=right_slicing_indx-len(time) + padding_config = [(0, 0)] * (data.ndim - 1) + + if padding=='nan': + data_tmp=numpy.pad(data[..., left_slicing_indx:],padding_config+(0,padd_len),mode='empty') + time_tmp=numpy.pad(time[left_slicing_indx:],padding_config+(0,padd_len),mode='empty') + elif padding=='last': + data_tmp=numpy.pad(data[..., left_slicing_indx:],padding_config+(0,padd_len),mode='constant',constant_values=data[...,-1]) + time_tmp=numpy.pad(time[left_slicing_indx:],padding_config+(0,padd_len),mode='constant',constant_values=time[-1]) + elif padding=='zeros': + data_tmp=numpy.pad(data[..., left_slicing_indx:],padding_config+(0,padd_len),mode='constant',constant_values=0) + time_tmp=numpy.pad(time[left_slicing_indx:],padding_config+(0,padd_len),mode='constant',constant_values=0) + else: + data_tmp=data[..., left_slicing_indx:right_slicing_indx+1] + time_tmp=time[left_slicing_indx:right_slicing_indx+1] + + return time_tmp,data_tmp + + + #assume the time is sorted + def time_matching_dynamic_search(self,time, data, time_std, left_window=0, right_window=0, padding='last'): + + dt,dt_std,if_dt_even=self.check_even_time_spacing(time,time_start=0.01) + + # Align data to time_std + matched_data = [] + matched_time = [] + + left_indx=0 + #dt is constant + #print(f'std_norm={std_norm}') + if if_dt_even: + for target in time_std: + indx_start=self.estimate_closest_indx_constant_dt(left_indx,time,target,dt) + closest_idx=self.find_closest_indx_constant_dt(indx_start,time,target) + + left_slicing_indx=closest_idx-left_window + right_slicing_indx=closest_idx+right_window + time_tmp,data_tmp=self.custom_padding(time, data, left_slicing_indx,right_slicing_indx, padding=padding) + + matched_data.append(data_tmp) + matched_time.append(time_tmp) + + left_indx=closest_idx + + else: + for target in time_std: + indx_start,indx_end=self.estimate_closest_indx_varing_dt(left_indx,time,target,dt,dt_std) + closest_idx=self.find_closest_indx_binary(time,target,indx_start,indx_end) + + left_slicing_indx=closest_idx-left_window + right_slicing_indx=closest_idx+right_window + time_tmp,data_tmp=self.custom_padding(time, data, left_slicing_indx,right_slicing_indx, padding=padding) + + matched_time.append(time_tmp) + matched_data.append(data_tmp) + + + left_indx=closest_idx + + matched_time=np.array(matched_time) + matched_data=np.array(matched_data) + return matched_time, matched_data - @classmethod - def time_matching(cls,time, data, time_std, mode='merge_asof'): - if len(data.shape) == 1: - if mode == 'merge_asof': - return cls.time_matching_merge_asof_1d(time, data, time_std) - elif mode == 'binary': - return cls.time_matching_binary_search(time, data, time_std, - mode='1d') - elif len(data.shape) == 2: - if mode == 'merge_asof': - return cls.time_matching_merge_asof_2d(time, data, time_std) - elif mode == 'binary': - return cls.time_matching_binary_search(time, data, time_std, - mode='2d') + + def time_matching(self,time, data, time_std, left_window=0, right_window=0, mode='merge_asof', padding='last'): + if mode == 'merge_asof': + if len(data.shape) == 1: + return self.time_matching_merge_asof_1d(time, data, time_std,\ + left_window=left_window, right_window=right_window) + elif len(data.shape) == 2: + return self.time_matching_merge_asof_2d(time, data, time_std,\ + left_window=left_window, right_window=right_window) + else: + print('The data has to be 1d array or 2d array') + elif mode == 'binary': + return self.time_matching_binary_search(time, data, time_std,\ + left_window=left_window, right_window=right_window) + elif mode == 'dynamic': + return self.time_matching_dynamic_search(time, data, time_std,\ + left_window=left_window, right_window=right_window,padding=padding) + + @staticmethod + def check_even_time_spacing(time,time_start=0.01): + time_start_indx=np.argmin(abs(time-time_start)) + + time=np.array(time) + dt_tmp=time[time_start_indx+1:time_start_indx+101]-time[time_start_indx:time_start_indx+100] + + + dt=np.mean(dt_tmp) + dt_std=np.std(dt_tmp) + std_norm=dt_std/dt + + if std_norm>0.1**5: + if_dt_even=False else: - print('The data has to be 1d array or 2d array') + if_dt_even=True + + return dt,dt_std,if_dt_even + @staticmethod def time_interp_past_looking(time, data, time_std, mode='extrapolate'): @@ -937,22 +1110,24 @@ def time_interp_past_looking(time, data, time_std, mode='extrapolate'): elif mode == 'fill': pass - @staticmethod - def time_interp(time, data, time_std): - return np.interp(time_std, time, data) - - def time_matching(self, discharge=None, suffix_list=None): - if discharge is None: - discharge = self.discharge - if suffix_list is None: - suffix_list = self.suffix_list - for suffix in suffix_list: - file_dict=discharge_obj.get_data(discharge, suffix, norm=True) + def time_interp_1d(self, time, data, time_std, mode='normal'): + if mode=='normal': + return np.interp(time_std, time, data) + else: + return self.time_interp_past_looking(time, data, time_std, mode) + + def time_interp(self, time, data, time_std, mode='normal'): + if len(data.shape) == 1: + return self.time_interp_1d(time, data, time_std, mode=mode) + elif len(data.shape) == 2: + data_interp=[] + for i in range(data.shape[0]): + data_interp.append(self.time_interp_1d(time, data[i,:], time_std, mode=mode)) - def time_matching_windows(data): - file_dict=discharge_obj.get_data(self.discharge, suffix, norm=True) + return np.array(data_interp) + @staticmethod def find_plateau(series, window_size=40, threshold=0.1, plot=False): @@ -971,7 +1146,9 @@ def find_plateau(series, window_size=40, threshold=0.1, plot=False): data_rolling_std = series.rolling(window=window_size, center=True).std() # Normalize the rolling standard deviation - normalized_std = data_rolling_std / rolling_avg + normalized_std = data_rolling_std / abs(rolling_avg) + if min(normalized_std)>=threshold: + return 0,0 # Find the start and end indices of the plateau region plateau_start = normalized_std[normalized_std < threshold].index[0] @@ -980,31 +1157,38 @@ def find_plateau(series, window_size=40, threshold=0.1, plot=False): if plot: plt.clf() plt.plot(normalized_std.index,normalized_std.values) + plt.xlabel('time (ms)') + plt.ylabel(f'std/avg(window={window_size})') + plt.axvline(plateau_start,color='red') plt.axvline(plateau_end,color='red') plt.show() return plateau_start, plateau_end - def flat_top_finder(self,discharge, window_size=500, plot=False): + def flat_top_finder(self,discharge, window_size=500, threshold=0.01, plot=False): file_dict = self.get_data(discharge, 'basic', norm=True) time = file_dict['ip']['xdata'][:] data = file_dict['ip']['zdata'][:] - time=time[data>3] - data=data[data>3] - dip = np.gradient(data, time) + if max(data)<=3: + t_max, t_min = 0,0 + else: + time=time[data>3] + data=data[data>3] + + dip = np.gradient(data, time) - # Convert data and time to a pandas Series - series = pd.Series(data, index=time) - t_max, t_min=self.find_plateau(series,window_size=window_size, threshold=0.01, plot=plot) + # Convert data and time to a pandas Series + series = pd.Series(data, index=time) + t_max, t_min=self.find_plateau(series,window_size=window_size, threshold=threshold, plot=plot) if plot: plt.clf() plt.plot(series.index, series.values) plt.axvline(t_min,color='red') plt.axvline(t_max,color='red') - - plt.ylim(0,20) + plt.xlabel('time (ms)') + plt.ylabel(r'Plasma current $I_p$') plt.show() return t_max, t_min @@ -1012,6 +1196,75 @@ def flat_top_finder(self,discharge, window_size=500, plot=False): def deal_with_missing_data(self): pass + + def time_series_full_pipeline(self,discharge,suffix_list,time_std_key, time_std=[],custom_time_std=False,Ip_window_size=500, Ip_std_threshold=0.01, plot_Ip=False, norm_mode='all', interp_suffix=[], interp_mode='normal', time_matching_mode='dynamic', left_window={'ece_s':50}, right_window={'ece_s':50}, time_matching_padding='zeros', plot_matched_data=True): + ''' + + time_std: the standard time + + interp_suffix: the list suffix and key to + e.g. interp_suffix=[['ts','core.dens'],['ts','core.dens']] #e.g. [['ts','core.dens'],['ts','core.dens']] + interp_mode='normal' + + time_matching_mode = ['merge_asof','binary','dynamic'] only 'dynamic' works for now (04/29/2024) + + time_matching_padding=['zeros', 'last', 'nan'] + + plot_matched_data: plot the matched data + ''' + + #get all the data and normalize the data + all_file_dict=self.get_full_data(discharge, suffix_list, norm_mode=norm_mode) + if custom_time_std: + pass + else: + time_std=all_file_dict[time_std_key[0]][time_std_key[1]]['xdata'][:] + + t_min, t_max=self.flat_top_finder(discharge, window_size=Ip_window_size, threshold=Ip_std_threshold, plot=plot_Ip) + + #time_interp + for item in interp_suffix: + data_interp=self.time_interp(all_file_dict[item[0]][item[1]]['xdata'][:], \ + all_file_dict[item[0]][item[1]]['zdata'][:], \ + time_std, mode=interp_mode) + all_file_dict[item[0]][item[1]]['xdata']=time_std + all_file_dict[item[0]][item[1]]['zdata']=data_interp + + #cut_time for standard time + [key1,_]=time_std_key + for key2 in all_file_dict[key1].keys(): + time_cut,data_cut=self.cut_time(all_file_dict[key1][key2]['xdata'][:], \ + all_file_dict[key1][key2]['zdata'][:], \ + t_min, t_max) + all_file_dict[key1][key2]={'xdata':time_cut,'zdata':data_cut} + + time_std=time_cut + + #time matching + for key1 in all_file_dict.keys(): + if key1==time_std_key[0] and (not custom_time_std): + continue + for key2 in all_file_dict[key1].keys(): + matched_time, matched_data=self.time_matching(\ + all_file_dict[key1][key2]['xdata'][:], \ + all_file_dict[key1][key2]['zdata'][:], \ + time_std, \ + left_window=left_window[key1], \ + right_window=right_window[key1], \ + mode=time_matching_mode,\ + padding=time_matching_padding) + + all_file_dict[item[0]][item[1]]['xdata']=matched_time + all_file_dict[item[0]][item[1]]['zdata']=matched_data + + if plot_matched_data: + plt.clf() + for i in range(len(matched_time)): + plt.plot(matched_time[i,:],matched_data[i,:,:].T) + plt.xlabel('Time (ms)') + plt.ylabel(f'{key1}-{key2}') + plt.show() + return all_file_dict class DatasetPrep(DichargePerp): def __init__(self, discharge_search_list=[174823], suffix_list=['ts']): @@ -1066,6 +1319,9 @@ def smooth_rolling_avg_and_put_back(self, time, data, smooth_point_list, center_ data_smooth[indx_min:indx_max+1]=smoothed_section return data_smooth + def denorm_data(): + pass + class data_obj_rest(): def save_dict_to_hdf5(dictionary, h5file): diff --git a/examples/Dataset_prep/tamplet_dataset_survey.py b/examples/Dataset_prep/tamplet_dataset_survey.py new file mode 100644 index 0000000..ec88c55 --- /dev/null +++ b/examples/Dataset_prep/tamplet_dataset_survey.py @@ -0,0 +1,25 @@ +import data_prep_obj as data_prep +import pickle + +discharge_min=170000 +discharge_max=200000-1 + +discharge_search_list=range(discharge_min,discharge_max+1) +suffix_list=['ece_cali','co2_pl'] +prep_obj_1=data_prep.DatasetPrep(discharge_search_list, suffix_list) +print(prep_obj_1.discharge_search_list) +print(prep_obj_1.suffix_list) + +discharge_list=prep_obj_1.filter_discharges() +print(discharge_list) + + +with open('co2_ece_discharge.pkl', 'wb') as file: + pickle.dump(discharge_list, file) + +for key in discharge_list.keys(): + print(f'{key}:{len(discharge_list[key])}') + +over_lap=set(discharge_list['co2_pl']) & set(discharge_list['ece_cali']) + +print(f'overlap:{len(over_lap)}') \ No newline at end of file diff --git a/examples/Dataset_prep/tamplet_discharge_study.py b/examples/Dataset_prep/tamplet_discharge_study.py new file mode 100644 index 0000000..6f6998f --- /dev/null +++ b/examples/Dataset_prep/tamplet_discharge_study.py @@ -0,0 +1,52 @@ +import data_prep_obj as data_prep +import matplotlib.pyplot as plt +import numpy as np + +#************start of user block************ +discharge=174823 +suffix_list=['ts','ece_s'] + +#setting for finding flat top +Ip_window_size=500 +Ip_std_threshold=0.01 +plot_Ip=False + +#mode of normalization +norm_mode='all' + +#the key for the standard time [suffix, key] +custom_time_std=False +time_std_key=['ts','core.dens'] +time_std=[] + +#the list suffix and key to e +interp_suffix=[['ts','core.dens'],['ts','core.dens']] #e.g. [['ts','core.dens'],['ts','core.dens']] +interp_mode='normal' + +#time_matching_mode = ['merge_asof','binary','dynamic'] only 'dynamic' works for now (04/29/2024) +time_matching_mode='dynamic' + + +left_window={'ece_s':50} +right_window={'ece_s':50} + +time_matching_padding='zeros' #['zeros', 'last', 'nan'] + +plot_matched_data=True #plot the matched data + +#************end of user block************ +#initalize the object +discharge_obj=data_prep.DichargePerp() + +dict_=discharge_obj.time_series_full_pipeline(discharge,suffix_list,time_std_key,time_std=time_std,\ + custom_time_std=custom_time_std,\ + Ip_window_size=Ip_window_size, \ + Ip_std_threshold=Ip_std_threshold, \ + plot_Ip=plot_Ip, norm_mode=norm_mode, \ + interp_suffix=interp_suffix, \ + interp_mode=interp_mode, \ + time_matching_mode=time_matching_mode, \ + left_window=left_window, \ + right_window=right_window, \ + time_matching_padding=time_matching_padding, \ + plot_matched_data=plot_matched_data) \ No newline at end of file diff --git a/examples/Dataset_prep/tamplet_spectro.py b/examples/Dataset_prep/tamplet_spectro.py new file mode 100644 index 0000000..f51bd85 --- /dev/null +++ b/examples/Dataset_prep/tamplet_spectro.py @@ -0,0 +1,15 @@ +import data_prep_obj as data_prep + +discharge_obj=data_prep.DichargePerp() + +suffix_list=['co2_pl'] + +all_file_dict=discharge_obj.get_full_data(discharge, suffix_list, norm_mode='no') + +freq, time, amp_f_t=discharge_obj.spectro_calc(file_dict['co2_pl']['r0']['xdata'][:],\ + file_dict['co2_pl']['r0']['zdata'][:],\ + plot=True) + +freq_enhanced, time_enhanced, amp_f_t_enhanced=discharge_obj.spec_filters(freq, time, amp_f_t) + +discharge_obj.spectro_plot(freq_enhanced, time_enhanced, amp_f_t_enhanced) \ No newline at end of file From 9e67a55aed77da2e726231233ed3f26aea7f05a7 Mon Sep 17 00:00:00 2001 From: maxcurie <47543965+maxtcurie@users.noreply.github.com> Date: Tue, 30 Apr 2024 14:12:55 -0400 Subject: [PATCH 006/103] Closet index improvement Minor improvement on the finding the closest index. --- examples/Dataset_prep/data_prep_obj.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/examples/Dataset_prep/data_prep_obj.py b/examples/Dataset_prep/data_prep_obj.py index 6b4d670..a1678e6 100644 --- a/examples/Dataset_prep/data_prep_obj.py +++ b/examples/Dataset_prep/data_prep_obj.py @@ -955,12 +955,18 @@ def find_closest_indx_constant_dt(indx_start,time,target): i=indx_start while time[i] abs(time[i-1] - target): + return i-1 return i + #indx_start on the left elif time[indx_start]>target: i=indx_start while time[i]>target: i-=1 + if abs(time[i] - target) > abs(time[i+1] - target): + return i+1 return i @staticmethod From 60109eedfa09693133c80435f95f58be032c4240 Mon Sep 17 00:00:00 2001 From: maxcurie <47543965+maxtcurie@users.noreply.github.com> Date: Wed, 1 May 2024 10:45:35 -0400 Subject: [PATCH 007/103] Fixed padding and binary search bug --- examples/Dataset_prep/data_prep_obj.py | 60 +++++++++++++++----------- 1 file changed, 36 insertions(+), 24 deletions(-) diff --git a/examples/Dataset_prep/data_prep_obj.py b/examples/Dataset_prep/data_prep_obj.py index a1678e6..a48eb86 100644 --- a/examples/Dataset_prep/data_prep_obj.py +++ b/examples/Dataset_prep/data_prep_obj.py @@ -972,7 +972,7 @@ def find_closest_indx_constant_dt(indx_start,time,target): @staticmethod def find_closest_indx_binary(time,target,indx_start,indx_end): # Binary search for the closest timestamp - low, high = indx_start, indx_end + low, high = max(0,indx_start), min(indx_end,len(time)-1) best_idx = low while low <= high: mid = (low + high) // 2 @@ -992,32 +992,34 @@ def find_closest_indx_binary(time,target,indx_start,indx_end): @staticmethod def custom_padding(time, data, left_slicing_indx, right_slicing_indx, padding='last'): #padding to the left + if left_slicing_indx<0: padding_config = [(0, 0)] * (data.ndim - 1) padd_len=-left_slicing_indx + if padding=='nan': - data_tmp=numpy.pad(data[..., 0:right_slicing_indx+1],padding_config+(padd_len, 0),mode='empty') - time_tmp=numpy.pad(time[0:right_slicing_indx+1],padding_config+(padd_len, 0),mode='empty') + data_tmp=np.pad(data[..., 0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='empty') + time_tmp=np.pad(time[0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='empty') elif padding=='last': - data_tmp=numpy.pad(data[..., 0:right_slicing_indx+1],padding_config+(padd_len, 0),mode='constant',constant_values=data[...,0]) - time_tmp=numpy.pad(time[0:right_slicing_indx+1],padding_config+(padd_len, 0),mode='constant',constant_values=time[0]) + data_tmp=np.pad(data[..., 0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='constant',constant_values=data[...,0]) + time_tmp=np.pad(time[0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='constant',constant_values=time[0]) elif padding=='zeros': - data_tmp=numpy.pad(data[..., 0:right_slicing_indx+1],padding_config+(padd_len, 0),mode='constant',constant_values=0) - time_tmp=numpy.pad(time[0:right_slicing_indx+1],padding_config+(padd_len, 0),mode='constant',constant_values=0) + data_tmp=np.pad(data[..., 0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='constant',constant_values=0) + time_tmp=np.pad(time[0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='constant',constant_values=0) elif (right_slicing_indx+1)>len(time): padd_len=right_slicing_indx-len(time) padding_config = [(0, 0)] * (data.ndim - 1) if padding=='nan': - data_tmp=numpy.pad(data[..., left_slicing_indx:],padding_config+(0,padd_len),mode='empty') - time_tmp=numpy.pad(time[left_slicing_indx:],padding_config+(0,padd_len),mode='empty') + data_tmp=np.pad(data[..., left_slicing_indx:],padding_config+[(0,padd_len)],mode='empty') + time_tmp=np.pad(time[left_slicing_indx:],padding_config+[(0,padd_len)],mode='empty') elif padding=='last': - data_tmp=numpy.pad(data[..., left_slicing_indx:],padding_config+(0,padd_len),mode='constant',constant_values=data[...,-1]) - time_tmp=numpy.pad(time[left_slicing_indx:],padding_config+(0,padd_len),mode='constant',constant_values=time[-1]) + data_tmp=np.pad(data[..., left_slicing_indx:],padding_config+[(0,padd_len)],mode='constant',constant_values=data[...,-1]) + time_tmp=np.pad(time[left_slicing_indx:],padding_config+[(0,padd_len)],mode='constant',constant_values=time[-1]) elif padding=='zeros': - data_tmp=numpy.pad(data[..., left_slicing_indx:],padding_config+(0,padd_len),mode='constant',constant_values=0) - time_tmp=numpy.pad(time[left_slicing_indx:],padding_config+(0,padd_len),mode='constant',constant_values=0) + data_tmp=np.pad(data[..., left_slicing_indx:],padding_config+[(0,padd_len)],mode='constant',constant_values=0) + time_tmp=np.pad(time[left_slicing_indx:],padding_config+[(0,padd_len)],mode='constant',constant_values=0) else: data_tmp=data[..., left_slicing_indx:right_slicing_indx+1] time_tmp=time[left_slicing_indx:right_slicing_indx+1] @@ -1238,30 +1240,40 @@ def time_series_full_pipeline(self,discharge,suffix_list,time_std_key, time_std= #cut_time for standard time [key1,_]=time_std_key - for key2 in all_file_dict[key1].keys(): - time_cut,data_cut=self.cut_time(all_file_dict[key1][key2]['xdata'][:], \ - all_file_dict[key1][key2]['zdata'][:], \ - t_min, t_max) - all_file_dict[key1][key2]={'xdata':time_cut,'zdata':data_cut} + + if custom_time_std: + time_cut,data_cut=self.cut_time(time_std, time_std,t_min, t_max) + else: + for key2 in all_file_dict[key1].keys(): + time_cut,data_cut=self.cut_time(all_file_dict[key1][key2]['xdata'][:], \ + all_file_dict[key1][key2]['zdata'][:], \ + t_min, t_max) + all_file_dict[key1][key2]={'xdata':time_cut,'zdata':data_cut} time_std=time_cut #time matching for key1 in all_file_dict.keys(): - if key1==time_std_key[0] and (not custom_time_std): - continue for key2 in all_file_dict[key1].keys(): + print([key1,key2]) + try: + left_window_tmp=left_window[key1][key2] + right_window_tmp=right_window[key1][key2] + except: + left_window_tmp=0 + right_window_tmp=0 + matched_time, matched_data=self.time_matching(\ all_file_dict[key1][key2]['xdata'][:], \ all_file_dict[key1][key2]['zdata'][:], \ time_std, \ - left_window=left_window[key1], \ - right_window=right_window[key1], \ + left_window=left_window_tmp, \ + right_window=right_window_tmp, \ mode=time_matching_mode,\ padding=time_matching_padding) - all_file_dict[item[0]][item[1]]['xdata']=matched_time - all_file_dict[item[0]][item[1]]['zdata']=matched_data + all_file_dict[key1][key2]['xdata']=matched_time + all_file_dict[key1][key2]['zdata']=matched_data if plot_matched_data: plt.clf() From da9597a9d3e0e9d0076f83836af68e54eb2cf9ae Mon Sep 17 00:00:00 2001 From: maxcurie <47543965+maxtcurie@users.noreply.github.com> Date: Wed, 1 May 2024 10:57:51 -0400 Subject: [PATCH 008/103] Change default setting, indent error, and plotting error --- examples/Dataset_prep/data_prep_obj.py | 28 +++++++++++++++++--------- 1 file changed, 19 insertions(+), 9 deletions(-) diff --git a/examples/Dataset_prep/data_prep_obj.py b/examples/Dataset_prep/data_prep_obj.py index a48eb86..a13a39c 100644 --- a/examples/Dataset_prep/data_prep_obj.py +++ b/examples/Dataset_prep/data_prep_obj.py @@ -1205,7 +1205,7 @@ def deal_with_missing_data(self): pass - def time_series_full_pipeline(self,discharge,suffix_list,time_std_key, time_std=[],custom_time_std=False,Ip_window_size=500, Ip_std_threshold=0.01, plot_Ip=False, norm_mode='all', interp_suffix=[], interp_mode='normal', time_matching_mode='dynamic', left_window={'ece_s':50}, right_window={'ece_s':50}, time_matching_padding='zeros', plot_matched_data=True): + def time_series_full_pipeline(self,discharge,suffix_list,time_std_key, time_std=[],custom_time_std=False,Ip_window_size=500, Ip_std_threshold=0.01, plot_Ip=False, norm_mode='all', interp_suffix=[], interp_mode='normal', time_matching_mode='dynamic', left_window={'ece_s':50}, right_window={'ece_s':50}, time_matching_padding='zeros', plot_matched_data=False): ''' time_std: the standard time @@ -1255,7 +1255,7 @@ def time_series_full_pipeline(self,discharge,suffix_list,time_std_key, time_std= #time matching for key1 in all_file_dict.keys(): for key2 in all_file_dict[key1].keys(): - print([key1,key2]) + try: left_window_tmp=left_window[key1][key2] right_window_tmp=right_window[key1][key2] @@ -1275,13 +1275,23 @@ def time_series_full_pipeline(self,discharge,suffix_list,time_std_key, time_std= all_file_dict[key1][key2]['xdata']=matched_time all_file_dict[key1][key2]['zdata']=matched_data - if plot_matched_data: - plt.clf() - for i in range(len(matched_time)): - plt.plot(matched_time[i,:],matched_data[i,:,:].T) - plt.xlabel('Time (ms)') - plt.ylabel(f'{key1}-{key2}') - plt.show() + if plot_matched_data: + for key1 in all_file_dict.keys(): + for key2 in all_file_dict[key1].keys(): + print([key1,key2]) + plt.clf() + if all_file_dict[key1][key2]['zdata'].shape[2]==1: + + plt.plot(all_file_dict[key1][key2]['xdata'][:,0],\ + all_file_dict[key1][key2]['zdata'][:,:,0]) + else: + for i in range(len(all_file_dict[key1][key2]['xdata'][:])): + plt.plot(all_file_dict[key1][key2]['xdata'][i,:].T,\ + all_file_dict[key1][key2]['zdata'][i,:,:].T) + plt.xlabel('Time (ms)') + plt.ylabel(f'{key1}-{key2}') + plt.show() + return all_file_dict class DatasetPrep(DichargePerp): From a22af0cbf8a49be572e53a234bd89a961561a4e8 Mon Sep 17 00:00:00 2001 From: renierts Date: Wed, 15 May 2024 09:20:07 -0400 Subject: [PATCH 009/103] - Added time domain filters - Added spectrogram utilities. - Further cosmetic changes in Max's code --- README.md | 3 + examples/Data_fetching/check_copy_and_rm.py | 99 +++--- examples/Data_fetching/fetch_GAdata.py | 299 +++++++++--------- examples/Data_fetching/fetch_toksearch.py | 171 +++++++--- pyproject.toml | 5 +- src/fusion_ai_hub/__init__.py | 1 + src/fusion_ai_hub/core/__init__.py | 4 + .../core/spectral_representation/__init__.py | 4 + .../core/spectral_representation/sft.py | 53 ++++ .../core/time_domain_filtering/__init__.py | 5 + .../core/time_domain_filtering/filtering.py | 131 ++++++++ .../core/time_domain_filtering/preemphasis.py | 137 ++++++++ src/fusion_ai_hub/sampling/__init__.py | 0 src/fusion_ai_hub/util/utils.py | 5 + 14 files changed, 665 insertions(+), 252 deletions(-) create mode 100644 src/fusion_ai_hub/core/spectral_representation/__init__.py create mode 100644 src/fusion_ai_hub/core/spectral_representation/sft.py create mode 100644 src/fusion_ai_hub/core/time_domain_filtering/__init__.py create mode 100644 src/fusion_ai_hub/core/time_domain_filtering/filtering.py create mode 100644 src/fusion_ai_hub/core/time_domain_filtering/preemphasis.py create mode 100644 src/fusion_ai_hub/sampling/__init__.py create mode 100644 src/fusion_ai_hub/util/utils.py diff --git a/README.md b/README.md index d793a80..7a531b0 100644 --- a/README.md +++ b/README.md @@ -1 +1,4 @@ # FusionAIHub + + +This will be the readme of the FusionAIHub. \ No newline at end of file diff --git a/examples/Data_fetching/check_copy_and_rm.py b/examples/Data_fetching/check_copy_and_rm.py index e30ebd4..1e7f12b 100644 --- a/examples/Data_fetching/check_copy_and_rm.py +++ b/examples/Data_fetching/check_copy_and_rm.py @@ -1,21 +1,21 @@ +"""This script is intended to copy the file from iris and...""" import paramiko +import paramiko.util import socket import getpass import numpy as np import time -import os import re -#this script is intented to copy the file from iris and -num_min=140000 -num_max=200000-1 +num_min = 140000 +num_max = 200000-1 -subtask=2 #total paraelle one wants -residue=1 #transfer num%subtask==residue +subtask = 2 # total paraelle one wants +residue = 1 # transfer num%subtask==residue -fetching_name_list=['actu','basic','profiles'] -diag_name=fetching_name_list[0] +fetching_name_list = ['actu', 'basic', 'profiles'] +diag_name = fetching_name_list[0] remote_directory = '/cscratch/curiem/Data_fetch_Basic' local_directory = '/scratch/gpfs/EKOLEMEN/big_d3d_data/Basic_fetch' @@ -43,12 +43,16 @@ def create_ssh_connection(): proxy_transport = paramiko.Transport(proxy_sock) proxy_transport.connect(username=proxy_user, password=proxy_password) - proxy_channel = proxy_transport.open_channel('direct-tcpip', (destination_host, 22), (proxy_host, proxy_port)) + proxy_channel = proxy_transport.open_channel( + kind='direct-tcpip', dest_addr=(destination_host, 22), + src_addr=(proxy_host, proxy_port)) # Create an SSH client and connect through the proxy channel ssh_client = paramiko.SSHClient() - ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) # Use with caution in production - ssh_client.connect(destination_host, username=destination_user, password=destination_password, sock=proxy_channel) + # Use with caution in production + ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) + ssh_client.connect(destination_host, username=destination_user, + password=destination_password, sock=proxy_channel) return ssh_client, proxy_transport except paramiko.AuthenticationException: @@ -64,24 +68,30 @@ def create_ssh_connection(): def extract_shot_numbers_remote(sftp, remote_path, suffix): """ - Extract shot numbers from file names in a remote directory via SFTP, based on a given suffix. - - Args: - - sftp (paramiko.SFTPClient): An active SFTP client session. - - remote_path (str): The remote directory path to search for files. - - suffix (str): The suffix pattern to match in the file names. - - - - - Returns: - - set: A set of unique shot numbers extracted from the file names. + Extract shot numbers from file names in a remote directory via SFTP, based + on a given suffix. + + Parameters + ---------- + sftp : SFTP object + An active SFTP client session. + remote_path : str + The remote directory path to search for files. + suffix : str + The suffix pattern to match in the file names. + + Returns + ------- + + set + A set of unique shot numbers extracted from the file names. """ try: # List all files in the remote directory with their attributes files_attr = sftp.listdir_attr(remote_path) - # Regular expression pattern to match the shot numbers, incorporating the suffix variable + # Regular expression pattern to match the shot numbers, + # incorporating the suffix variable pattern = re.compile(rf'(\d+)_({suffix})\.h5') # Extract shot numbers that match the pattern from the file names @@ -96,9 +106,6 @@ def extract_shot_numbers_remote(sftp, remote_path, suffix): print(f"Failed to extract shot numbers: {e}") return set() - - - def copy_file(sftp, remote_path, local_path): # Check the action and perform the corresponding task sftp.get(remote_path, local_path) @@ -111,45 +118,46 @@ def remove_file(sftp, remote_path): return message def copy_n_rm_file(sftp, remote_path, local_path): - message=copy_file(sftp, remote_path, local_path) + message = copy_file(sftp, remote_path, local_path) print(message) - message=remove_file(sftp, remote_path) + message = remove_file(sftp, remote_path) print(message) -def search_copy_and_delete(diag_name, remote_directory, local_directory, retries=3): +def search_copy_and_delete(diag_name, remote_directory, local_directory, + retries=3): for attempt in range(retries): try: ssh_client, proxy_transport = create_ssh_connection() sftp = ssh_client.open_sftp() - while 1==1: - shot_numbers=extract_shot_numbers_remote(sftp, remote_directory, diag_name) - shot_numbers=list(shot_numbers) + while 1 == 1: + shot_numbers = extract_shot_numbers_remote( + sftp, remote_directory, diag_name) + shot_numbers = list(shot_numbers) shot_numbers.sort() - shot_numbers=np.array(shot_numbers) - shot_numbers=shot_numbers[(num_min<=shot_numbers) & (shot_numbers<=num_max)] + shot_numbers = np.array(shot_numbers) + shot_numbers = shot_numbers[ + (num_min <= shot_numbers) & (shot_numbers <= num_max)] - #print(shot_numbers) - if len(shot_numbers)<=2*subtask: + # print(shot_numbers) + if len(shot_numbers) <= 2*subtask: print('No files to copy, waiting for 10 min') - #wait for 10min + # wait for 10min time.sleep(600) continue - cp_shot_num=shot_numbers[:-2*subtask] + cp_shot_num = shot_numbers[:-2*subtask] print(cp_shot_num) for shot_num in cp_shot_num: - if int(shot_num)%subtask==residue: + if int(shot_num) % subtask == residue: for name_tmp in fetching_name_list: - remote_path=f'{remote_directory}/{shot_num}_{name_tmp}.h5' - local_path=f'{local_directory}/{shot_num}_{name_tmp}.h5' + remote_path = f'{remote_directory}/{shot_num}_{name_tmp}.h5' + local_path = f'{local_directory}/{shot_num}_{name_tmp}.h5' copy_n_rm_file(sftp, remote_path, local_path) - ssh_client.close() proxy_transport.close() - break except Exception as e: print(f"Error processing on attempt {attempt + 1}: {e}") @@ -163,4 +171,5 @@ def search_copy_and_delete(diag_name, remote_directory, local_directory, retries ssh_client.close() proxy_transport.close() -search_copy_and_delete(diag_name, remote_directory, local_directory, retries=3) \ No newline at end of file + +search_copy_and_delete(diag_name, remote_directory, local_directory, retries=3) diff --git a/examples/Data_fetching/fetch_GAdata.py b/examples/Data_fetching/fetch_GAdata.py index 9007fcb..40f3dac 100644 --- a/examples/Data_fetching/fetch_GAdata.py +++ b/examples/Data_fetching/fetch_GAdata.py @@ -1,6 +1,6 @@ import MDSplus from mygadata import gadata -#import matplotlib.pyplot as plt +# import matplotlib.pyplot as plt import h5py import pickle import numpy as np @@ -9,172 +9,165 @@ import sys import subprocess +# To run the code +# module purge & module load defaults +# python2.7 fetch_data.py -#To run the code -#module purge & module load defaults -#python2.7 fetch_data.py +# *******start of user block************ +output_path = '/cscratch/curiem/Data_fetch_Basic' +ece_pcece = False +size_GB = 400 +directory_path = "/cscratch/curiem" # to check the total file sizes +shot_list = np.arange(170000, 200000) -#*******start of user block************ -output_path='/cscratch/curiem/Data_fetch_Basic' -ece_pcece=False -size_GB=400 -directory_path="/cscratch/curiem" #to check the total file sizes -shot_list=np.arange(170000,200000) +interval = 1000 +# shot_list = shot_list[:10] +# *******end of user block************ -interval=1000 -#shot_list=shot_list[:10] -#*******end of user block************ def size_limiter_sleep(directory_path="/cscratch/curiem", size_GB=450): - try: - size = subprocess.check_output(['du', '-sh', directory_path]).split()[0].decode('utf-8') - except subprocess.CalledProcessError as e: - print("Error fetching directory size: "+str(e)) - sys.exit(1) - - - print("Size of" +directory_path+": "+str(size)) - - if size[-1] == "G" and float(size[:-1]) > size_GB: - print("Size exceeds "+str(size_GB)+"GB. Sleeping for 1hr...") - - # Sleep for 1 hour - time.sleep(3600) # 3600 seconds = 1 hour - print("1 hour has passed. Checking size again...") - - try: - size = subprocess.check_output(['du', '-sh', directory_path]).split()[0].decode('utf-8') - except subprocess.CalledProcessError as e: - print("Error fetching directory size: "+str(e)) - sys.exit(1) - - if size[-1] == "G" and float(size[:-1]) > size_GB: - print("Size still exceeds "+str(size_GB)+"GB. Stoping") - sys.exit(1) - - - -def data2dict(shotn, signame, hf, atlconn) : - dict_group = hf.create_group(str(signame)) - try: - data = gadata(signame, shotn, connection=atlconn) - dict_group['xdata'] = data.xdata - dict_group['ydata'] = data.ydata - dict_group['zdata'] = data.zdata - dict_group['xunits'] = data.xunits - dict_group['yunits'] = data.yunits - dict_group['zunits'] = data.zunits - except: - print('%s not available, filled with NULL!' % (signame)) - dict_group['xdata'] = [] - dict_group['ydata'] = [] - dict_group['zdata'] = [] - dict_group['xunits'] = [] - dict_group['yunits'] = [] - dict_group['zunits'] = [] - del atlconn - #global atlconn - atlconn = MDSplus.Connection('atlas.gat.com') - pass - return atlconn + try: + size = subprocess.check_output( + ['du', '-sh', directory_path]).split()[0].decode('utf-8') + except subprocess.CalledProcessError as e: + print("Error fetching directory size: "+str(e)) + sys.exit(1) + + print("Size of" + directory_path + ": "+str(size)) + + if size[-1] == "G" and float(size[:-1]) > size_GB: + print("Size exceeds "+str(size_GB)+"GB. Sleeping for 1hr...") + # Sleep for 1 hour + time.sleep(3600) # 3600 seconds = 1 hour + print("1 hour has passed. Checking size again...") + try: + size = subprocess.check_output( + ['du', '-sh', directory_path]).split()[0].decode('utf-8') + except subprocess.CalledProcessError as e: + print("Error fetching directory size: "+str(e)) + sys.exit(1) + + if size[-1] == "G" and float(size[:-1]) > size_GB: + print("Size still exceeds "+str(size_GB)+"GB. Stopping") + sys.exit(1) + + +def data2dict(shotn, signame, hf, atlconn): + dict_group = hf.create_group(str(signame)) + try: + data = gadata(signame, shotn, connection=atlconn) + dict_group['xdata'] = data.xdata + dict_group['ydata'] = data.ydata + dict_group['zdata'] = data.zdata + dict_group['xunits'] = data.xunits + dict_group['yunits'] = data.yunits + dict_group['zunits'] = data.zunits + except: + print('%s not available, filled with NULL!' % (signame)) + dict_group['xdata'] = [] + dict_group['ydata'] = [] + dict_group['zdata'] = [] + dict_group['xunits'] = [] + dict_group['yunits'] = [] + dict_group['zunits'] = [] + del atlconn + # global atlconn + atlconn = MDSplus.Connection('atlas.gat.com') + pass + return atlconn + atlconn = MDSplus.Connection('atlas.gat.com') -ech_gytname = ['lei','luk','r2d'] +ech_gytname = ['lei', 'luk', 'r2d'] -#shot_list = np.loadtxt('DIIID_BES_Shot_List_Fatima.txt',delimiter='\n',dtype=np.int32) -# shot_list = np.load('tm-control-shots.npy');shot_list=np.unique(shot_list).astype(np.int) +# shot_list = np.loadtxt('DIIID_BES_Shot_List_Fatima.txt', +# delimiter='\n', dtype=np.int32) +# shot_list = np.load('tm-control-shots.npy') +# shot_list = np.unique(shot_list).astype(np.int) # shot_list = [np.int32(sys.argv[1])] # shot_list=[193266,193273,193280] -cannot_find=['triangularity_u','triangularity_l','pech','neutronsrate']\ - +['fplastic', 'fzns',\ - 'fncrate01', 'fncrate02', 'fncrate03', 'fncrate04',\ - 'plasticfx1', 'plasticfx2', 'plasticfx3', 'plasticfx4',\ - 'neutronsrate1','neutronsrate2','neutronsrate3', 'neutronsrate4'] +cannot_find = (['triangularity_u', 'triangularity_l', 'pech', 'neutronsrate'] + + ['fplastic', 'fzns', 'fncrate01', 'fncrate02', 'fncrate03', + 'fncrate04', 'plasticfx1', 'plasticfx2', 'plasticfx3', + 'plasticfx4', 'neutronsrate1', 'neutronsrate2', + 'neutronsrate3', 'neutronsrate4']) -#basic is fundimental measured quantities (in contrast of fitted quantities) - -signal_list= { -'profiles':['betap','betan','pres', \ - 'wmhd','li',\ - 'q0','q95','qmin','qpsi','rhoqmin',\ - 'r0','aminor',\ - 'kappa','tritop','tribot',\ - 'alpha','psirz',\ - 'ssibry', 'ssimag',\ - 'rmaxis','zmaxis',\ - 'volume',\ - 'drsep','gapbot','gapin','gapout','gaptop',\ - 'zxpt1','zxpt2',\ - 'edensfit', 'etempfit',\ - 'trotfit','itempfit','idensfit',\ - 'n1rms','n2rms','n3rms'], - \ -'basic':['ip', 'ipsip', 'iptipp','pcbcoil', 'bcoil','bt','vloop']\ - +[ 'plasticfix', 'fzns']\ - +['fs00','fs01','fs02','fs03','fs04','fs05'],\ -'actu': ['pinjf_%dl' % k for k in [15,21,30,33]]+['pinjf_%dr' % k for k in [15,21,30,33]]\ - +['tinj_%dl' % k for k in [15,21,30,33]]+['tinj_%dr' % k for k in [15,21,30,33]]\ - +['echpwrc','echpwr']\ - +['ec%sfpwrc' % (x) for x in ech_gytname]\ - +['ec%sxmfrac' % (x) for x in ech_gytname]\ - +['ec%spolang' % (x) for x in ech_gytname]\ - +['gasa', 'gasb', 'gasc', 'gasd', 'gase']\ - +['c19', 'c79', 'c139', 'c199', 'c259', 'c319', \ - 'iu30', 'iu90', 'iu150', 'iu210', 'iu270', 'iu330', \ - 'il30', 'il90', 'il150', 'il210', 'il270', 'il330']\ - +['ecoila', 'ecoilb', 'e567up', 'e567dn', 'e89dn', 'e89up']\ - +['f1a','f2a','f3a','f4a','f5a','f6a','f7a','f8a','f9a',\ - 'f1b','f2b','f3b','f4b','f5b','f6b','f7b','f8b','f9b'] -} - +# basic is fundamental measured quantities (in contrast of fitted quantities) + +signal_list = {'profiles': ['betap', 'betan', 'pres', 'wmhd', 'li', + 'q0', 'q95', 'qmin', 'qpsi', 'rhoqmin', 'r0', + 'aminor', 'kappa', 'tritop', 'tribot', 'alpha', + 'psirz', 'ssibry', 'ssimag', 'rmaxis', 'zmaxis', + 'volume', 'drsep', 'gapbot', 'gapin', 'gapout', + 'gaptop', 'zxpt1', 'zxpt2', 'edensfit', 'etempfit', + 'trotfit', 'itempfit', 'idensfit', 'n1rms','n2rms', + 'n3rms'], + 'basic': ['ip', 'ipsip', 'iptipp', 'pcbcoil', 'bcoil', 'bt', + 'vloop'] + ['plasticfix', 'fzns'] + + ['fs00', 'fs01', 'fs02', 'fs03', 'fs04', 'fs05'], + 'actu': ['pinjf_%dl' % k for k in [15, 21, 30, 33]] + + ['pinjf_%dr' % k for k in [15,21,30,33]] + + ['tinj_%dl' % k for k in [15,21,30,33]] + + ['tinj_%dr' % k for k in [15,21,30,33]] + + ['echpwrc','echpwr'] + + ['ec%sfpwrc' % (x) for x in ech_gytname] + + ['ec%sxmfrac' % (x) for x in ech_gytname] + + ['ec%spolang' % (x) for x in ech_gytname] + + ['gasa', 'gasb', 'gasc', 'gasd', 'gase'] + + ['c19', 'c79', 'c139', 'c199', 'c259', 'c319', 'iu30', + 'iu90', 'iu150', 'iu210', 'iu270', 'iu330', 'il30', + 'il90', 'il150', 'il210', 'il270', 'il330'] + + ['ecoila', 'ecoilb', 'e567up', 'e567dn', 'e89dn', + 'e89up'] + + ['f1a','f2a','f3a','f4a','f5a','f6a','f7a','f8a','f9a', + 'f1b','f2b','f3b','f4b','f5b','f6b','f7b','f8b','f9b']} for i in tqdm(range(len(shot_list))): - shotn=shot_list[i] - t1=time.time() - - for grpname,signals in signal_list.items(): - hf = h5py.File(output_path+'/'+ str(shotn)+'_'+grpname+'.h5','w') - for signame in signals: - atlconn=data2dict(shotn,signame,hf,atlconn) - hf.close() - - - if ece_pcece: - hf = h5py.File(output_path+'/'+ str(shotn)+'_ece.h5','w') - pece_group = hf.create_group('pcece') - ece_group = hf.create_group('ece') - rtece_group = hf.create_group('rtece') + shotn = shot_list[i] + t1 = time.time() + + for grpname, signals in signal_list.items(): + hf = h5py.File(output_path + '/' + str(shotn) + '_'+grpname+'.h5', 'w') + for signame in signals: + atlconn = data2dict(shotn, signame, hf, atlconn) + hf.close() + + if ece_pcece: + hf = h5py.File(output_path + '/' + str(shotn)+'_ece.h5', 'w') + pece_group = hf.create_group('pcece') + ece_group = hf.create_group('ece') + rtece_group = hf.create_group('rtece') - for k in range(40): - print('chn %i' % (k+1)) - pece_data = gadata('pcece%d' % (k+1), shotn, connection=atlconn) - pece_group['pcece%02d' % (k+1)] = pece_data.zdata - ece_data = gadata('tecef%02d' % (k+1), shotn, connection=atlconn) - ece_group['tecef%02d' % (k+1)] = ece_data.zdata + for k in range(40): + print('chn %i' % (k+1)) + pece_data = gadata('pcece%d' % (k+1), shotn, connection=atlconn) + pece_group['pcece%02d' % (k+1)] = pece_data.zdata + ece_data = gadata('tecef%02d' % (k+1), shotn, connection=atlconn) + ece_group['tecef%02d' % (k+1)] = ece_data.zdata - rtece_data = gadata('ecsdata%d' % (k+97), shotn, connection=atlconn) - rtece_group['ecsdata%d' % (k+97)] = rtece_data.zdata + rtece_data = gadata('ecsdata%d' % (k+97), shotn, connection=atlconn) + rtece_group['ecsdata%d' % (k+97)] = rtece_data.zdata - pece_group['xdata'] = pece_data.xdata - pece_group['ydata'] = pece_data.ydata - pece_group['xunits'] = pece_data.xunits - pece_group['yunits'] = pece_data.yunits - pece_group['pceceunits'] = pece_data.zunits + pece_group['xdata'] = pece_data.xdata + pece_group['ydata'] = pece_data.ydata + pece_group['xunits'] = pece_data.xunits + pece_group['yunits'] = pece_data.yunits + pece_group['pceceunits'] = pece_data.zunits - ece_group['xdata'] = ece_data.xdata - ece_group['ydata'] = ece_data.ydata - ece_group['xunits'] = ece_data.xunits - ece_group['yunits'] = ece_data.yunits - ece_group['eceunits'] = ece_data.zunits - - rtece_group['xdata'] = rtece_data.xdata - rtece_group['ydata'] = rtece_data.ydata - rtece_group['xunits'] = rtece_data.xunits - rtece_group['yunits'] = rtece_data.yunits - rtece_group['rteceunits'] = rtece_data.zunits - hf.close() - if i % interval == 0: - size_limiter_sleep(size_GB=size_GB) - print('Shot #%d'%(shotn,)) - print(i) -# print('time per shot:%ds' % (time.time()-t1)) + ece_group['xdata'] = ece_data.xdata + ece_group['ydata'] = ece_data.ydata + ece_group['xunits'] = ece_data.xunits + ece_group['yunits'] = ece_data.yunits + ece_group['eceunits'] = ece_data.zunits + + rtece_group['xdata'] = rtece_data.xdata + rtece_group['ydata'] = rtece_data.ydata + rtece_group['xunits'] = rtece_data.xunits + rtece_group['yunits'] = rtece_data.yunits + rtece_group['rteceunits'] = rtece_data.zunits + hf.close() + if i % interval == 0: + size_limiter_sleep(size_GB=size_GB) + print('Shot #%d'%(shotn,)) + print(i) +# print('time per shot:%ds' % (time.time()-t1)) diff --git a/examples/Data_fetching/fetch_toksearch.py b/examples/Data_fetching/fetch_toksearch.py index 3654647..ea25ed2 100644 --- a/examples/Data_fetching/fetch_toksearch.py +++ b/examples/Data_fetching/fetch_toksearch.py @@ -1,4 +1,4 @@ -from toksearch import MdsSignal,PtDataSignal +from toksearch import MdsSignal, PtDataSignal from toksearch import Pipeline import numpy as np import os @@ -7,47 +7,50 @@ import subprocess import sys -#this one runs on iris, run the following -#module purge -#module load toksearch +# this one runs on iris, run the following +# module purge +# module load toksearch -#for copy: -#scp -r -o 'ProxyCommand ssh -p 2039 curiem@cybele.gat.com -W %h:%p' curiem@iris.gat.com:/cscratch/curiem/Data_fetch_TS/15* ./ +# for copy: +# scp -r -o 'ProxyCommand ssh -p 2039 curiem@cybele.gat.com -W %h:%p' curiem@iris.gat.com:/cscratch/curiem/Data_fetch_TS/15* ./ -#***********start of user block****************** -#limit of the size -size_GB=400 +# ***********start of user block****************** +# limit of the size +size_GB = 400 -#After fetching (interval) discharges, check the total directory size -interval=100 +# After fetching (interval) discharges, check the total directory size +interval = 100 -#Root directory of the user for total size check -directory_path="/cscratch/curiem" +# Root directory of the user for total size check +directory_path = "/cscratch/curiem" -#list of discharges to fetch -shots = np.arange(150000,170000,dtype=int) +# list of discharges to fetch +shots = np.arange(150000, 170000, dtype=int) -# one can set start_shot the where to start. (usually used for restarting the fetching due to unexpected termination) -start_shot=min(shots) +# one can set start_shot the where to start. +# (usually used for restarting the fetching due to unexpected termination) +start_shot = min(shots) -#path to save the files +# path to save the files path = '/cscratch/curiem/Data_fetch_CER/' -#diag_names=[mag,mag_hi,bes,ece_cali,ece_s, co2_den, co2_pl, co2_s, ts,ts_rz,ts_error,cer, mse,custom] -diag_name='cer' +# diag_names=[mag,mag_hi,bes,ece_cali,ece_s, co2_den, co2_pl, co2_s, ts,ts_rz,ts_error,cer, mse,custom] +diag_name = 'cer' -#custom sig_names_custom, the suffix is fixed to be custom for now. -if diag_name=='custom': - sig_names_custom=[''] - names_custom=[''] - tree='' #ptdata fo PTDATA, other trees names for MDS+ -#***********end of user block****************** +# custom sig_names_custom, the suffix is fixed to be custom for now. +if diag_name == 'custom': + sig_names_custom = [''] + names_custom = [''] + tree = '' # ptdata fo PTDATA, other trees names for MDS+ +# ***********end of user block****************** shots.sort() + def size_limiter(directory_path="/cscratch/curiem", size_GB=450): try: - size = subprocess.check_output(['du', '-sh', directory_path]).split()[0].decode('utf-8') + size = subprocess.check_output( + ['du', '-sh', directory_path]).split()[0].decode('utf-8') except subprocess.CalledProcessError as e: print(f"Error fetching directory size: {e}") return @@ -60,12 +63,12 @@ def size_limiter(directory_path="/cscratch/curiem", size_GB=450): def size_limiter_sleep(directory_path="/cscratch/curiem", size_GB=450): try: - size = subprocess.check_output(['du', '-sh', directory_path]).split()[0].decode('utf-8') + size = subprocess.check_output( + ['du', '-sh', directory_path]).split()[0].decode('utf-8') except subprocess.CalledProcessError as e: print(f"Error fetching directory size: {e}") sys.exit(1) - print(f"Size of {directory_path}: {size}") if size[-1] == "G" and float(size[:-1]) > size_GB: @@ -76,15 +79,17 @@ def size_limiter_sleep(directory_path="/cscratch/curiem", size_GB=450): print("1 hour has passed. Checking size again...") try: - size = subprocess.check_output(['du', '-sh', directory_path]).split()[0].decode('utf-8') + size = subprocess.check_output( + ['du', '-sh', directory_path]).split()[0].decode('utf-8') except subprocess.CalledProcessError as e: print(f"Error fetching directory size: {e}") sys.exit(1) if size[-1] == "G" and float(size[:-1]) > size_GB: - print(f"Size still exceeds {size_GB}GB. Stoping") + print(f"Size still exceeds {size_GB}GB. Stopping") sys.exit(1) + def save_dict_to_hdf5(dictionary, h5file): for key, value in dictionary.items(): if isinstance(value, dict): @@ -93,28 +98,90 @@ def save_dict_to_hdf5(dictionary, h5file): else: h5file.create_dataset(key, data=value) -#generate the name and signal to fetch i ntoksearch -def signal_gen(diag_name='zipfit',sig_names_custom=[''],names_custom=[''],tree_custom=''): - signals=[] - names=[] - - - #Counter: fncrate** - #Adjustable scintillator: fplastic, fzns - #Fixed scintillator: plasticfx* - #Approximate calibrated signal: neutronsrate* - if diag_name=='neutron': - sig_names=['fplastic', 'fzns',\ - 'fncrate01', 'fncrate02', 'fncrate03', 'fncrate04',\ - 'plasticfx1', 'plasticfx2', 'plasticfx3', 'plasticfx4',\ - 'neutronsrate1','neutronsrate2','neutronsrate3', 'neutronsrate4'] - names= ['fplastic', 'fzns',\ - 'fncrate01', 'fncrate02', 'fncrate03', 'fncrate04',\ - 'plasticfx1', 'plasticfx2', 'plasticfx3', 'plasticfx4',\ - 'cali.neutronsrate1','cali.neutronsrate2','cali.neutronsrate3', 'cali.neutronsrate4'] + +# generate the name and signal to fetch i ntoksearch +def signal_gen(diag_name='zipfit', sig_names_custom=[''], names_custom=[''], + tree_custom=''): + signals = [] + names = [] + # Counter: fncrate** + # Adjustable scintillator: fplastic, fzns + # Fixed scintillator: plasticfx* + # Approximate calibrated signal: neutronsrate* + if diag_name == 'neutron': + sig_names = ['fplastic', 'fzns', 'fncrate01', 'fncrate02', 'fncrate03', + 'fncrate04', 'plasticfx1', 'plasticfx2', 'plasticfx3', + 'plasticfx4', 'neutronsrate1', 'neutronsrate2', + 'neutronsrate3', 'neutronsrate4'] + names = ['fplastic', 'fzns', 'fncrate01', 'fncrate02', 'fncrate03', + 'fncrate04', 'plasticfx1', 'plasticfx2', 'plasticfx3', + 'plasticfx4', 'cali.neutronsrate1', 'cali.neutronsrate2', + 'cali.neutronsrate3', 'cali.neutronsrate4'] - elif diag_name=='mag_full': - sig_name_without_d=['mpi11m322', 'mpi1a322', 'mpi2a322', 'mpi3a322', 'mpi4a322', 'mpi5a322', 'mpi8a322', 'mpi89a322', 'mpi9a322', 'mpi79fa322', 'mpi79na322', 'mpi7fa322', 'mpi7na322', 'mpi67a322', 'mpi6fa322', 'mpi6na322', 'mpi66m322', 'mpi1b322', 'mpi2b322', 'mpi3b322', 'mpi4b322', 'mpi5b322', 'mpi8b322', 'mpi89b322', 'mpi9b322', 'mpi79b322', 'mpi7fb322', 'mpi7nb322', 'mpi67b322', 'mpi6fb322', 'mpi6nb322', 'mpi2a067', 'mpi11m067', 'mpi2b067', 'mpi67a097', 'mpi67a067', 'mpi66m067', 'mpi67b097', 'mpi67b067', 'mpi1a139', 'mpi2a139', 'mpi3a139', 'mpi4a139', 'mpi5a139', 'mpi79a147', 'mpi67a142', 'mpi67a157', 'mpi6na132', 'mpi6na157', 'mpi66m157', 'mpi6nb157', 'mpi6fb142', 'mpi67b157', 'mpi7nb142', 'mpi79b142', 'mpi5b139', 'mpi4b139', 'mpi3b139', 'mpi2b139', 'mpi1b139', 'mpi1b157', 'mpi1u157', 'mpi2u157', 'mpi3u157', 'mpi4u157', 'mpi5u157', 'mpi6u157', 'mpi7u157', 'dsl1u180', 'dsl2u180', 'dsl3u180', 'dsl4u157', 'dsl5u157', 'dsl6u157', 'mpi66m127', 'mpi66m132', 'mpi66m137', 'mpi66b137', 'mpi6nb137', 'mpi66m307', 'mpi66m312', 'mpi6na312', 'mpi66b312', 'mpi6nb312', 'mpi66m322', 'mpi1l020', 'mpi2l020', 'mpi1l050', 'mpi1l110', 'mpi1l180', 'mpi2l180', 'mpi3l180', 'mpi1l230', 'mpi1l320', 'mpi66m020', 'mpi66m067', 'mpi66m097', 'mpi66m127', 'mpi66m132', 'mpi66m137', 'mpi66m157', 'mpi66m200', 'mpi66m247', 'mpi66m277', 'mpi66m307', 'mpi66m312', 'mpi66m322', 'mpi66m340', 'mpi67a022', 'mpi67a037', 'mpi67a1', 'mpi67a052', 'mpi67a067', 'mpi67a082', 'mpi67a097', 'mpi67a2', 'mpi67a142', 'mpi67a157', 'mpi67a3', 'mpi67a217', 'mpi67a4', 'mpi67a262', 'mpi67a277', 'mpi67a5', 'mpi67a307', 'mpi67a337', 'mpi67a6', 'mpi67b022', 'mpi67b037', 'mpi67b1', 'mpi67b052', 'mpi67b097', 'mpi67b2', 'mpi67b157', 'mpi67b3', 'mpi67b217', 'mpi67b4', 'mpi67b277', 'mpi67b5', 'mpi67b337', 'mpi67b6', 'mpi79a072', 'mpi79a147', 'mpi79a222', 'mpi79a272', 'mpi79b067', 'mpi79b142', 'mpi79b217', 'mpi79b277', 'mpi5a139', 'mpi4a139', 'mpi3a139', 'mpi2a139', 'mpi1a139', 'mpi1b139', 'mpi2b139', 'mpi3b139', 'mpi4b139', 'mpi5b139', 'mpi5a199', 'mpi4a199', 'mpi3a199', 'mpi2a199', 'mpi1a199', 'mpi1b199', 'mpi2b199', 'mpi3b199', 'mpi4b199', 'mpi5b199', 'mpi1a011', 'mpi1a049', 'mpi1a109', 'mpi1a139', 'mpi1a199', 'mpi1a244', 'mpi1a274', 'mpi1a341', 'mpi1b011', 'mpi1b049', 'mpi1b109', 'mpi1b139', 'mpi1b199', 'mpi1b244', 'mpi1b274', 'mpi1b341', 'isl66m017', 'isl66m042', 'isl66m072', 'isl66m102', 'isl66m132', 'isl66m197', 'isl66m252', 'isl66m312', 'isl67a017', 'isl67a052', 'isl67a072', 'isl67a112', 'isl67a132', 'isl67a197', 'isl67a252', 'isl67a312', 'isl67b017', 'isl67b052', 'isl67b072', 'isl67b112', 'isl67b132', 'isl67b197', 'isl67b252', 'isl67b312', 'isl79a072', 'isl79a147', 'isl79a222', 'isl79a272', 'isl79b067', 'isl79b142', 'isl79b217', 'isl79b277', 'isl5a139', 'isl4a139', 'isl3a139', 'isl2a139', 'isl1a139', 'isl1b139', 'isl2b139', 'isl3b139', 'isl4b139', 'isl5b139', 'isl5a199', 'isl4a199', 'isl3a199', 'isl2a199', 'isl1a199', 'isl1b199', 'isl2b199', 'isl3b199', 'isl4b199', 'isl5b199', 'isl1a011', 'isl1a049', 'isl1a109', 'isl1a139', 'isl1a199', 'isl1a244', 'isl1a274', 'isl1a341', 'isl1b011', 'isl1b049', 'isl1b109', 'isl1b139', 'isl1b199', 'isl1b244', 'isl1b274', 'isl1b341', 'dsl12a067', 'dsl34a067', 'dsl59a067', 'dsl79a067', 'dsl67a067', 'dsl66m052', 'dsl67b067', 'dsl79b067', 'dsl59b067', 'dsl34b067', 'dsl12b067', 'dsl12a157', 'dsl34a157', 'dsl59a157', 'dsl79a157', 'dsl67a157', 'dsl66m152', 'dsl67b157', 'dsl79b157', 'dsl59b157', 'dsl34b157', 'dsl12b157', 'dsl67a067', 'dsl67a157', 'sl67fa345', 'sl67na345', 'dsl66m052', 'sl66a132', 'sl66b132', 'dsl66m152', 'sl66a312', 'sl66b312', 'sl67nb015', 'sl67fb015', 'dsl67b067', 'dsl67b157', 'esl66m019', 'esl019', 'esl66m079', 'esl079', 'esl66m139', 'esl139', 'esl66m199', 'esl199', 'esl66m259', 'esl259', 'esl66m319', 'esl319', 'esl67a004', 'esl67a034', 'esl67a064', 'esl67a094', 'esl67a124', 'esl67a154', 'esl67a184', 'esl67a214', 'esl67a244', 'esl67a274', 'esl67a304', 'esl67a334', 'esl67b004', 'esl67b034', 'esl67b064', 'esl67b094', 'esl67b124', 'esl67b154', 'esl67b184', 'esl67b214', 'esl67b244', 'esl67b274', 'esl67b304', 'esl67b334', 'bti66m053', 'bti66m132', 'bti66m233', 'bti66m312', 'psf1a', 'psf1a', 'psf1a', 'psf1a', 'psf6natotl', 'psf6na', 'psi11mtotl', 'psi11m', 'psi6atotl', 'psi6a', 'psf1a', 'psf6natotl', 'psi11mtotl', 'psi6atotl', 'psf2a', 'psf3a', 'psf4a', 'psf5a', 'psf8a', 'psf9a', 'psf7fa', 'psf7na', 'psf6fa', 'psf6na', 'psf6nb', 'psf6fb', 'psf7nb', 'psf7fb', 'psf9b', 'psf8b', 'psf5b', 'psf4b', 'psf3b', 'psf2b', 'psf1b', 'psi11m', 'psi12a', 'psi23a', 'psi34a', 'psi45a', 'psi58a', 'psi9a', 'psi7a', 'psi6a', 'psi6b', 'psi7b', 'psi9b', 'psi89nb', 'psi89fb', 'psi58b', 'psi45b', 'psi34b', 'psi23b', 'psi12b', 'psi1l', 'psi2l', 'psi3l', 'mpi1b', 'mpi66m020', 'mpi66m097', 'mpi66m020', 'mpi66m097', 'mpi66m067', 'mpi66m247', 'mpi66m097', 'mpi66m277', 'mpi66m127', 'mpi66m307', 'mpi66m157', 'mpi66m340', 'mpi66m200', 'mpi66m020', 'mpi66m247', 'mpi66m127', 'mpi66m277', 'mpi66m157', 'mpi66m307', 'mpi66m200', 'mpi66m340', 'mpi66m067', 'mpi67a022', 'mpi67a217', 'mpi67a037', 'mpi67a067', 'mpi67a052', 'mpi67a022', 'mpi67a067', 'mpi67a262', 'mpi67a082', 'mpi67a052', 'mpi67a097', 'mpi67a082', 'mpi67a142', 'mpi67a037', 'mpi67a217', 'mpi67a097', 'mpi67a262', 'mpi67a277', 'mpi67a277', 'mpi67a307', 'mpi67a307', 'mpi67a337', 'mpi67a337', 'mpi67a142', 'mpi67b022', 'mpi67b052', 'mpi67b037', 'mpi67b217', 'mpi67b052', 'mpi67b037', 'mpi67b097', 'mpi67b277', 'mpi67b157', 'mpi67b337', 'mpi67b217', 'mpi67b097', 'mpi67b277', 'mpi67b157', 'mpi67b337', 'mpi67b022', 'mpi79a072', 'mpi79a222', 'mpi79a147', 'mpi79a072', 'mpi79a222', 'mpi79a272', 'mpi79a272', 'mpi79a147', 'mpi79b067', 'mpi79b217', 'mpi79b142', 'mpi79b067', 'mpi79b217', 'mpi79b277', 'mpi79b277', 'mpi79b142', 'mpi1a011', 'mpi1a199', 'mpi1a049', 'mpi1a244', 'mpi1a109', 'mpi1a011', 'mpi1a139', 'mpi1a341', 'mpi1a199', 'mpi1a139', 'mpi1a244', 'mpi1a274', 'mpi1a274', 'mpi1a109', 'mpi1a341', 'mpi1a049', 'mpi1b011', 'mpi1b199', 'mpi1b049', 'mpi1b244', 'mpi1b109', 'mpi1b011', 'mpi1b139', 'mpi1b341', 'mpi1b199', 'mpi1b139', 'mpi1b244', 'mpi1b274', 'mpi1b274', 'mpi1b109', 'mpi1b341', 'mpi1b049', 'mpi5a199', 'mpi5a139', 'mpi4a199', 'mpi4a139', 'mpi3a199', 'mpi3a139', 'mpi2a199', 'mpi2a139', 'mpi1a199', 'mpi1a139', 'mpi1b199', 'mpi1b139', 'mpi2b199', 'mpi2b139', 'mpi3b199', 'mpi3b139', 'mpi4b199', 'mpi4b139', 'mpi5b199', 'mpi5b139', 'isl66m017', 'isl66m042', 'isl66m042', 'isl66m072', 'isl66m072', 'isl66m252', 'isl66m102', 'isl66m132', 'isl66m132', 'isl66m312', 'isl66m197', 'isl66m017', 'isl66m252', 'isl66m102', 'isl66m312', 'isl66m197', 'isl67a017', 'isl67a052', 'isl67a052', 'isl67a072', 'isl67a072', 'isl67a252', 'isl67a112', 'isl67a132', 'isl67a132', 'isl67a312', 'isl67a197', 'isl67a017', 'isl67a252', 'isl67a112', 'isl67a312', 'isl67a197', 'isl67b017', 'isl67b052', 'isl67b052', 'isl67b072', 'isl67b072', 'isl67b252', 'isl67b112', 'isl67b132', 'isl67b132', 'isl67b312', 'isl67b197', 'isl67b017', 'isl67b252', 'isl67b112', 'isl67b312', 'isl67b197', 'isl79a072', 'isl79a222', 'isl79a147', 'isl79a072', 'isl79a222', 'isl79a272', 'isl79a272', 'isl79a147', 'isl79b067', 'isl79b217', 'isl79b142', 'isl79b067', 'isl79b217', 'isl79b277', 'isl79b277', 'isl79b142', 'isl1a011', 'isl1a199', 'isl1a049', 'isl1a244', 'isl1a109', 'isl1a011', 'isl1a139', 'isl1a341', 'isl1a199', 'isl1a139', 'isl1a244', 'isl1a274', 'isl1a274', 'isl1a109', 'isl1a341', 'isl1a049', 'isl1b011', 'isl1b199', 'isl1b049', 'isl1b244', 'isl1b109', 'isl1b011', 'isl1b139', 'isl1b341', 'isl1b199', 'isl1b139', 'isl1b244', 'isl1b274', 'isl1b274', 'isl1b109', 'isl1b341', 'isl1b049', 'isl5a199', 'isl5a139', 'isl4a199', 'isl4a139', 'isl3a199', 'isl3a139', 'isl2a199', 'isl2a139', 'isl1a199', 'isl1a139', 'isl1b199', 'isl1b139', 'isl2b199', 'isl2b139', 'isl3b199', 'isl3b139', 'isl4b199', 'isl4b139', 'isl5b199', 'isl5b139', 'esl66m019', 'esl66m079', 'esl66m079', 'esl66m259', 'esl66m139', 'esl66m319', 'esl66m199', 'esl66m019', 'esl66m259', 'esl66m139', 'esl66m319', 'esl66m199', 'esl66m079', 'esl66m259', 'esl66m139', 'esl66m319', 'esl66m199', 'esl66m019', 'esl67a004', 'esl67a244', 'esl67a034', 'esl67a154', 'esl67a064', 'esl67a184', 'esl67a094', 'esl67a274', 'esl67a124', 'esl67a304', 'esl67a154', 'esl67a334', 'esl67a184', 'esl67a004', 'esl67a214', 'esl67a094', 'esl67a244', 'esl67a124', 'esl67a274', 'esl67a034', 'esl67a304', 'esl67a064', 'esl67a334', 'esl67a214', 'esl67b004', 'esl67b244', 'esl67b034', 'esl67b154', 'esl67b064', 'esl67b184', 'esl67b094', 'esl67b274', 'esl67b124', 'esl67b304', 'esl67b154', 'esl67b334', 'esl67b184', 'esl67b004', 'esl67b214', 'esl67b094', 'esl67b244', 'esl67b124', 'esl67b274', 'esl67b034', 'esl67b304', 'esl67b064', 'esl67b334', 'esl67b214', 'bti66m053', 'bti66m233', 'bti66m132', 'bti66m053', 'bti66m233', 'bti66m312', 'bti66m312', 'bti66m132', 'mpi2a067', 'mpi1u157', 'isl79a'] + elif diag_name == 'mag_full': + sig_name_without_d = [ + 'mpi11m322', 'mpi1a322', 'mpi2a322', 'mpi3a322', 'mpi4a322', + 'mpi5a322', 'mpi8a322', 'mpi89a322', 'mpi9a322', 'mpi79fa322', + 'mpi79na322', 'mpi7fa322', 'mpi7na322', 'mpi67a322', 'mpi6fa322', + 'mpi6na322', 'mpi66m322', 'mpi1b322', 'mpi2b322', 'mpi3b322', + 'mpi4b322', 'mpi5b322', 'mpi8b322', 'mpi89b322', 'mpi9b322', + 'mpi79b322', 'mpi7fb322', 'mpi7nb322', 'mpi67b322', 'mpi6fb322', + 'mpi6nb322', 'mpi2a067', 'mpi11m067', 'mpi2b067', 'mpi67a097', + 'mpi67a067', 'mpi66m067', 'mpi67b097', 'mpi67b067', 'mpi1a139', + 'mpi2a139', 'mpi3a139', 'mpi4a139', 'mpi5a139', 'mpi79a147', + 'mpi67a142', 'mpi67a157', 'mpi6na132', 'mpi6na157', 'mpi66m157', + 'mpi6nb157', 'mpi6fb142', 'mpi67b157', 'mpi7nb142', 'mpi79b142', + 'mpi5b139', 'mpi4b139', 'mpi3b139', 'mpi2b139', 'mpi1b139', + 'mpi1b157', 'mpi1u157', 'mpi2u157', 'mpi3u157', 'mpi4u157', + 'mpi5u157', 'mpi6u157', 'mpi7u157', 'dsl1u180', 'dsl2u180', + 'dsl3u180', 'dsl4u157', 'dsl5u157', 'dsl6u157', 'mpi66m127', + 'mpi66m132', 'mpi66m137', 'mpi66b137', 'mpi6nb137', 'mpi66m307', + 'mpi66m312', 'mpi6na312', 'mpi66b312', 'mpi6nb312', 'mpi66m322', + 'mpi1l020', 'mpi2l020', 'mpi1l050', 'mpi1l110', 'mpi1l180', + 'mpi2l180', 'mpi3l180', 'mpi1l230', 'mpi1l320', 'mpi66m020', + 'mpi66m067', 'mpi66m097', 'mpi66m127', 'mpi66m132', 'mpi66m137', + 'mpi66m157', 'mpi66m200', 'mpi66m247', 'mpi66m277', 'mpi66m307', + 'mpi66m312', 'mpi66m322', 'mpi66m340', 'mpi67a022', 'mpi67a037', + 'mpi67a1', 'mpi67a052', 'mpi67a067', 'mpi67a082', 'mpi67a097', + 'mpi67a2', 'mpi67a142', 'mpi67a157', 'mpi67a3', 'mpi67a217', + 'mpi67a4', 'mpi67a262', 'mpi67a277', 'mpi67a5', 'mpi67a307', + 'mpi67a337', 'mpi67a6', 'mpi67b022', 'mpi67b037', 'mpi67b1', + 'mpi67b052', 'mpi67b097', 'mpi67b2', 'mpi67b157', 'mpi67b3', + 'mpi67b217', 'mpi67b4', 'mpi67b277', 'mpi67b5', 'mpi67b337', + 'mpi67b6', 'mpi79a072', 'mpi79a147', 'mpi79a222', 'mpi79a272', + 'mpi79b067', 'mpi79b142', 'mpi79b217', 'mpi79b277', 'mpi5a139', + 'mpi4a139', 'mpi3a139', 'mpi2a139', 'mpi1a139', 'mpi1b139', + 'mpi2b139', 'mpi3b139', 'mpi4b139', 'mpi5b139', 'mpi5a199', + 'mpi4a199', 'mpi3a199', 'mpi2a199', 'mpi1a199', 'mpi1b199', + 'mpi2b199', 'mpi3b199', 'mpi4b199', 'mpi5b199', 'mpi1a011', + 'mpi1a049', 'mpi1a109', 'mpi1a139', 'mpi1a199', 'mpi1a244', + 'mpi1a274', 'mpi1a341', 'mpi1b011', 'mpi1b049', 'mpi1b109', + 'mpi1b139', 'mpi1b199', 'mpi1b244', 'mpi1b274', 'mpi1b341', + 'isl66m017', 'isl66m042', 'isl66m072', 'isl66m102', 'isl66m132', + 'isl66m197', 'isl66m252', 'isl66m312', 'isl67a017', 'isl67a052', + 'isl67a072', 'isl67a112', 'isl67a132', 'isl67a197', 'isl67a252', + 'isl67a312', 'isl67b017', 'isl67b052', 'isl67b072', 'isl67b112', + 'isl67b132', 'isl67b197', 'isl67b252', 'isl67b312', 'isl79a072', + 'isl79a147', 'isl79a222', 'isl79a272', 'isl79b067', 'isl79b142', + 'isl79b217', 'isl79b277', 'isl5a139', 'isl4a139', 'isl3a139', + 'isl2a139', 'isl1a139', 'isl1b139', 'isl2b139', 'isl3b139', + 'isl4b139', 'isl5b139', 'isl5a199', 'isl4a199', 'isl3a199', + 'isl2a199', 'isl1a199', 'isl1b199', 'isl2b199', 'isl3b199', + 'isl4b199', 'isl5b199', 'isl1a011', 'isl1a049', 'isl1a109', + 'isl1a139', 'isl1a199', 'isl1a244', 'isl1a274', 'isl1a341', + 'isl1b011', 'isl1b049', 'isl1b109', 'isl1b139', 'isl1b199', + 'isl1b244', 'isl1b274', 'isl1b341', 'dsl12a067', 'dsl34a067', + 'dsl59a067', 'dsl79a067', 'dsl67a067', 'dsl66m052', 'dsl67b067', + 'dsl79b067', 'dsl59b067', 'dsl34b067', 'dsl12b067', 'dsl12a157', + 'dsl34a157', 'dsl59a157', 'dsl79a157', 'dsl67a157', 'dsl66m152', + 'dsl67b157', 'dsl79b157', 'dsl59b157', 'dsl34b157', 'dsl12b157', + 'dsl67a067', 'dsl67a157', 'sl67fa345', 'sl67na345', 'dsl66m052', + 'sl66a132', 'sl66b132', 'dsl66m152', 'sl66a312', 'sl66b312', + 'sl67nb015', 'sl67fb015', 'dsl67b067', 'dsl67b157', 'esl66m019', + 'esl019', 'esl66m079', 'esl079', 'esl66m139', 'esl139', + 'esl66m199', 'esl199', 'esl66m259', 'esl259', 'esl66m319', + 'esl319', 'esl67a004', 'esl67a034', 'esl67a064', 'esl67a094', + 'esl67a124', 'esl67a154', 'esl67a184', 'esl67a214', 'esl67a244', 'esl67a274', 'esl67a304', 'esl67a334', 'esl67b004', 'esl67b034', 'esl67b064', 'esl67b094', 'esl67b124', 'esl67b154', 'esl67b184', 'esl67b214', 'esl67b244', 'esl67b274', 'esl67b304', 'esl67b334', 'bti66m053', 'bti66m132', 'bti66m233', 'bti66m312', 'psf1a', 'psf1a', 'psf1a', 'psf1a', 'psf6natotl', 'psf6na', 'psi11mtotl', 'psi11m', 'psi6atotl', 'psi6a', 'psf1a', 'psf6natotl', 'psi11mtotl', 'psi6atotl', 'psf2a', 'psf3a', 'psf4a', 'psf5a', 'psf8a', 'psf9a', 'psf7fa', 'psf7na', 'psf6fa', 'psf6na', 'psf6nb', 'psf6fb', 'psf7nb', 'psf7fb', 'psf9b', 'psf8b', 'psf5b', 'psf4b', 'psf3b', 'psf2b', 'psf1b', 'psi11m', 'psi12a', 'psi23a', 'psi34a', 'psi45a', 'psi58a', 'psi9a', 'psi7a', 'psi6a', 'psi6b', 'psi7b', 'psi9b', 'psi89nb', 'psi89fb', 'psi58b', 'psi45b', 'psi34b', 'psi23b', 'psi12b', 'psi1l', 'psi2l', 'psi3l', 'mpi1b', 'mpi66m020', 'mpi66m097', 'mpi66m020', 'mpi66m097', 'mpi66m067', 'mpi66m247', 'mpi66m097', 'mpi66m277', 'mpi66m127', 'mpi66m307', 'mpi66m157', 'mpi66m340', 'mpi66m200', 'mpi66m020', 'mpi66m247', 'mpi66m127', 'mpi66m277', 'mpi66m157', 'mpi66m307', 'mpi66m200', 'mpi66m340', 'mpi66m067', 'mpi67a022', 'mpi67a217', 'mpi67a037', 'mpi67a067', 'mpi67a052', 'mpi67a022', 'mpi67a067', 'mpi67a262', 'mpi67a082', 'mpi67a052', 'mpi67a097', 'mpi67a082', 'mpi67a142', 'mpi67a037', 'mpi67a217', 'mpi67a097', 'mpi67a262', 'mpi67a277', 'mpi67a277', 'mpi67a307', 'mpi67a307', 'mpi67a337', 'mpi67a337', 'mpi67a142', 'mpi67b022', 'mpi67b052', 'mpi67b037', 'mpi67b217', 'mpi67b052', 'mpi67b037', 'mpi67b097', 'mpi67b277', 'mpi67b157', 'mpi67b337', 'mpi67b217', 'mpi67b097', 'mpi67b277', 'mpi67b157', 'mpi67b337', 'mpi67b022', 'mpi79a072', 'mpi79a222', 'mpi79a147', 'mpi79a072', 'mpi79a222', 'mpi79a272', 'mpi79a272', 'mpi79a147', 'mpi79b067', 'mpi79b217', 'mpi79b142', 'mpi79b067', 'mpi79b217', 'mpi79b277', 'mpi79b277', 'mpi79b142', 'mpi1a011', 'mpi1a199', 'mpi1a049', 'mpi1a244', 'mpi1a109', 'mpi1a011', 'mpi1a139', 'mpi1a341', 'mpi1a199', 'mpi1a139', 'mpi1a244', 'mpi1a274', 'mpi1a274', 'mpi1a109', 'mpi1a341', 'mpi1a049', 'mpi1b011', 'mpi1b199', 'mpi1b049', 'mpi1b244', 'mpi1b109', 'mpi1b011', 'mpi1b139', 'mpi1b341', 'mpi1b199', 'mpi1b139', 'mpi1b244', 'mpi1b274', 'mpi1b274', 'mpi1b109', 'mpi1b341', 'mpi1b049', 'mpi5a199', 'mpi5a139', 'mpi4a199', 'mpi4a139', 'mpi3a199', 'mpi3a139', 'mpi2a199', 'mpi2a139', 'mpi1a199', 'mpi1a139', 'mpi1b199', 'mpi1b139', 'mpi2b199', 'mpi2b139', 'mpi3b199', 'mpi3b139', 'mpi4b199', 'mpi4b139', 'mpi5b199', 'mpi5b139', 'isl66m017', 'isl66m042', 'isl66m042', 'isl66m072', 'isl66m072', 'isl66m252', 'isl66m102', 'isl66m132', 'isl66m132', 'isl66m312', 'isl66m197', 'isl66m017', 'isl66m252', 'isl66m102', 'isl66m312', 'isl66m197', 'isl67a017', 'isl67a052', 'isl67a052', 'isl67a072', 'isl67a072', 'isl67a252', 'isl67a112', 'isl67a132', 'isl67a132', 'isl67a312', 'isl67a197', 'isl67a017', 'isl67a252', 'isl67a112', 'isl67a312', 'isl67a197', 'isl67b017', 'isl67b052', 'isl67b052', 'isl67b072', 'isl67b072', 'isl67b252', 'isl67b112', 'isl67b132', 'isl67b132', 'isl67b312', 'isl67b197', 'isl67b017', 'isl67b252', 'isl67b112', 'isl67b312', 'isl67b197', 'isl79a072', 'isl79a222', 'isl79a147', 'isl79a072', 'isl79a222', 'isl79a272', 'isl79a272', 'isl79a147', 'isl79b067', 'isl79b217', 'isl79b142', 'isl79b067', 'isl79b217', 'isl79b277', 'isl79b277', 'isl79b142', 'isl1a011', 'isl1a199', 'isl1a049', 'isl1a244', 'isl1a109', 'isl1a011', 'isl1a139', 'isl1a341', 'isl1a199', 'isl1a139', 'isl1a244', 'isl1a274', 'isl1a274', 'isl1a109', 'isl1a341', 'isl1a049', 'isl1b011', 'isl1b199', 'isl1b049', 'isl1b244', 'isl1b109', 'isl1b011', 'isl1b139', 'isl1b341', 'isl1b199', 'isl1b139', 'isl1b244', 'isl1b274', 'isl1b274', 'isl1b109', 'isl1b341', 'isl1b049', 'isl5a199', 'isl5a139', 'isl4a199', 'isl4a139', 'isl3a199', 'isl3a139', 'isl2a199', 'isl2a139', 'isl1a199', 'isl1a139', 'isl1b199', 'isl1b139', 'isl2b199', 'isl2b139', 'isl3b199', 'isl3b139', 'isl4b199', 'isl4b139', 'isl5b199', 'isl5b139', 'esl66m019', 'esl66m079', 'esl66m079', 'esl66m259', 'esl66m139', 'esl66m319', 'esl66m199', 'esl66m019', 'esl66m259', 'esl66m139', 'esl66m319', 'esl66m199', 'esl66m079', 'esl66m259', 'esl66m139', 'esl66m319', 'esl66m199', 'esl66m019', 'esl67a004', 'esl67a244', 'esl67a034', 'esl67a154', 'esl67a064', 'esl67a184', 'esl67a094', 'esl67a274', 'esl67a124', 'esl67a304', 'esl67a154', 'esl67a334', 'esl67a184', 'esl67a004', 'esl67a214', 'esl67a094', 'esl67a244', 'esl67a124', 'esl67a274', 'esl67a034', 'esl67a304', 'esl67a064', 'esl67a334', 'esl67a214', 'esl67b004', 'esl67b244', 'esl67b034', 'esl67b154', 'esl67b064', 'esl67b184', 'esl67b094', 'esl67b274', 'esl67b124', 'esl67b304', 'esl67b154', 'esl67b334', 'esl67b184', 'esl67b004', 'esl67b214', 'esl67b094', 'esl67b244', 'esl67b124', 'esl67b274', 'esl67b034', 'esl67b304', 'esl67b064', 'esl67b334', 'esl67b214', 'bti66m053', 'bti66m233', 'bti66m132', 'bti66m053', 'bti66m233', 'bti66m312', 'bti66m312', 'bti66m132', 'mpi2a067', 'mpi1u157', 'isl79a'] name_without_d=['mpi.11.m.322', 'mpi.1.a.322', 'mpi.2.a.322', 'mpi.3.a.322', 'mpi.4.a.322', 'mpi.5.a.322', 'mpi.8.a.322', 'mpi.89.a.322', 'mpi.9.a.322', 'mpi.79.fa.322', 'mpi.79.na.322', 'mpi.7.fa.322', 'mpi.7.na.322', 'mpi.67.a.322', 'mpi.6.fa.322', 'mpi.6.na.322', 'mpi.66.m.322', 'mpi.1.b.322', 'mpi.2.b.322', 'mpi.3.b.322', 'mpi.4.b.322', 'mpi.5.b.322', 'mpi.8.b.322', 'mpi.89.b.322', 'mpi.9.b.322', 'mpi.79.b.322', 'mpi.7.fb.322', 'mpi.7.nb.322', 'mpi.67.b.322', 'mpi.6.fb.322', 'mpi.6.nb.322', 'mpi.2.a.067', 'mpi.11.m.067', 'mpi.2.b.067', 'mpi.67.a.097', 'mpi.67.a.067', 'mpi.66.m.067', 'mpi.67.b.097', 'mpi.67.b.067', 'mpi.1.a.139', 'mpi.2.a.139', 'mpi.3.a.139', 'mpi.4.a.139', 'mpi.5.a.139', 'mpi.79.a.147', 'mpi.67.a.142', 'mpi.67.a.157', 'mpi.6.na.132', 'mpi.6.na.157', 'mpi.66.m.157', 'mpi.6.nb.157', 'mpi.6.fb.142', 'mpi.67.b.157', 'mpi.7.nb.142', 'mpi.79.b.142', 'mpi.5.b.139', 'mpi.4.b.139', 'mpi.3.b.139', 'mpi.2.b.139', 'mpi.1.b.139', 'mpi.1.b.157', 'mpi.1.u.157', 'mpi.2.u.157', 'mpi.3.u.157', 'mpi.4.u.157', 'mpi.5.u.157', 'mpi.6.u.157', 'mpi.7.u.157', 'dsl.1.u.180', 'dsl.2.u.180', 'dsl.3.u.180', 'dsl.4.u.157', 'dsl.5.u.157', 'dsl.6.u.157', 'mpi.66.m.127', 'mpi.66.m.132', 'mpi.66.m.137', 'mpi.66.b.137', 'mpi.6.nb.137', 'mpi.66.m.307', 'mpi.66.m.312', 'mpi.6.na.312', 'mpi.66.b.312', 'mpi.6.nb.312', 'mpi.66.m.322', 'mpi.1.l.020', 'mpi.2.l.020', 'mpi.1.l.050', 'mpi.1.l.110', 'mpi.1.l.180', 'mpi.2.l.180', 'mpi.3.l.180', 'mpi.1.l.230', 'mpi.1.l.320', 'mpi.66.m.020', 'mpi.66.m.067', 'mpi.66.m.097', 'mpi.66.m.127', 'mpi.66.m.132', 'mpi.66.m.137', 'mpi.66.m.157', 'mpi.66.m.200', 'mpi.66.m.247', 'mpi.66.m.277', 'mpi.66.m.307', 'mpi.66.m.312', 'mpi.66.m.322', 'mpi.66.m.340', 'mpi.67.a.022', 'mpi.67.a.037', 'mpi.67.a.1', 'mpi.67.a.052', 'mpi.67.a.067', 'mpi.67.a.082', 'mpi.67.a.097', 'mpi.67.a.2', 'mpi.67.a.142', 'mpi.67.a.157', 'mpi.67.a.3', 'mpi.67.a.217', 'mpi.67.a.4', 'mpi.67.a.262', 'mpi.67.a.277', 'mpi.67.a.5', 'mpi.67.a.307', 'mpi.67.a.337', 'mpi.67.a.6', 'mpi.67.b.022', 'mpi.67.b.037', 'mpi.67.b.1', 'mpi.67.b.052', 'mpi.67.b.097', 'mpi.67.b.2', 'mpi.67.b.157', 'mpi.67.b.3', 'mpi.67.b.217', 'mpi.67.b.4', 'mpi.67.b.277', 'mpi.67.b.5', 'mpi.67.b.337', 'mpi.67.b.6', 'mpi.79.a.072', 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email="peter.steiner@princeton.edu"}, {name="Max Tian Curie", email="max.curie@princeton.edu"}, + {name="Nathaniel Chen", email="nathaniel@princeton.edu"}, {name="Azarakhsh Jalalvand", email="azarakhsh.jalalvand@princeton.edu"} ] description = "FusionAIHub - Fetch nuclear fusion data, preprocess it, and use it for training machine learning models." @@ -16,7 +17,8 @@ classifiers = [ ] license = {file = "LICENSE"} dependencies = [ - "h5py", "numpy", "pandas", "matplotlib", "scipy", "tqdm", "opencv-python" + "h5py", "numpy", "pandas", "matplotlib", "scipy", "tqdm", "opencv-python", + "paramiko" ] [project.urls] @@ -25,4 +27,3 @@ Documentation = "https://readthedocs.org" Repository = "https://github.com/PlasmaControl/FusionAIHub" Issues = "https://github.com/PlasmaControl/FusionAIHub/issues" Changelog = "https://github.com" - diff --git a/src/fusion_ai_hub/__init__.py b/src/fusion_ai_hub/__init__.py index e69de29..83986cb 100644 --- a/src/fusion_ai_hub/__init__.py +++ b/src/fusion_ai_hub/__init__.py @@ -0,0 +1 @@ +from . import core \ No newline at end of file diff --git a/src/fusion_ai_hub/core/__init__.py b/src/fusion_ai_hub/core/__init__.py index e69de29..a310d75 100644 --- a/src/fusion_ai_hub/core/__init__.py +++ b/src/fusion_ai_hub/core/__init__.py @@ -0,0 +1,4 @@ +from . import time_domain_filtering +from . import spectral_representation + +__all__ = ["time_domain_filtering", "spectral_representation", ] diff --git a/src/fusion_ai_hub/core/spectral_representation/__init__.py b/src/fusion_ai_hub/core/spectral_representation/__init__.py new file mode 100644 index 0000000..84a33b0 --- /dev/null +++ b/src/fusion_ai_hub/core/spectral_representation/__init__.py @@ -0,0 +1,4 @@ +from .sft import spectrogram, stft + + +__all__ = ["spectrogram", "stft"] diff --git a/src/fusion_ai_hub/core/spectral_representation/sft.py b/src/fusion_ai_hub/core/spectral_representation/sft.py new file mode 100644 index 0000000..34bae0a --- /dev/null +++ b/src/fusion_ai_hub/core/spectral_representation/sft.py @@ -0,0 +1,53 @@ +import numpy as np +from scipy import signal +from typing import Any, Literal, Optional, Tuple, Union + + +__all__ = ['spectrogram', 'stft'] + + +def spectrogram(y: np.ndarray, *, fs: float = 48000, n_fft: int = 2048, + hop_length: int = 256, win_length: int = 2048, + window: Union[str, float, Tuple[str, Any, ...]] = "hamming", + scaling: Literal["magnitude", "psd"] = "magnitude", + detrend: Literal["linear", "constant"] = "constant", + pad_mode: Literal["zeros", "edge", "even", "odd"] = 'zeros', + return_t_f: bool = False) \ + -> Union[np.ndarray, Tuple[np.ndarray, ...]]: + fft_window = signal.get_window(window, win_length, fftbins=True) + + sft = signal.ShortTimeFFT(fft_window, hop_length, fs, fft_mode='onesided', + mfft=n_fft, dual_win=None, scale_to=scaling, + phase_shift=None) + + t = sft.t(y.shape[0]) + f = sft.f + + spectrogram_matrix = sft.spectrogram(y, detr=detrend, padding=pad_mode) + + if return_t_f: + return spectrogram_matrix, t, f + return spectrogram_matrix + + +def stft(y: np.ndarray, *, fs: float = 48000, n_fft: int = 2048, + hop_length: Optional[int] = None, win_length: Optional[int] = None, + window: Union[str, float, Tuple[str, Any, ...]] = "hamming", + scaling: Literal["magnitude", "psd"] = "magnitude", + pad_mode: Literal["zeros", "edge", "even", "odd"] = 'zeros', + return_t_f: bool = False) \ + -> Union[np.ndarray, Tuple[np.ndarray, ...]]: + fft_window = signal.get_window(window, win_length, fftbins=True) + + sft = signal.ShortTimeFFT(fft_window, hop_length, fs, fft_mode='onesided', + mfft=n_fft, dual_win=None, scale_to=scaling, + phase_shift=None) + + t = sft.t(y.shape[0]) + f = sft.f + + stft_matrix = sft.stft(y, padding=pad_mode) + + if return_t_f: + return stft_matrix, t, f + return stft_matrix diff --git a/src/fusion_ai_hub/core/time_domain_filtering/__init__.py b/src/fusion_ai_hub/core/time_domain_filtering/__init__.py new file mode 100644 index 0000000..1212ec2 --- /dev/null +++ b/src/fusion_ai_hub/core/time_domain_filtering/__init__.py @@ -0,0 +1,5 @@ +from .filtering import lfilter, filtfilt +from .preemphasis import preemphasis, deemphasis + + +__all__ = ["lfilter", "filtfilt", "preemphasis", "deemphasis"] diff --git a/src/fusion_ai_hub/core/time_domain_filtering/filtering.py b/src/fusion_ai_hub/core/time_domain_filtering/filtering.py new file mode 100644 index 0000000..349e882 --- /dev/null +++ b/src/fusion_ai_hub/core/time_domain_filtering/filtering.py @@ -0,0 +1,131 @@ +""" +Filtering +========= + +Time-domain filters +------------------- +.. autosummary:: + :toctree: generated/ + + mel + chroma + wavelet + semitone_filterbank + +Window functions +---------------- +.. autosummary:: + :toctree: generated/ + + window_bandwidth + get_window +""" +import numpy as np +from scipy import signal +from typing import Optional, Tuple, Union +from numpy.typing import ArrayLike +from scipy.signal import get_window + + +__all__ = ["lfilter", "filtfilt"] + + +def lfilter(y: np.ndarray, *, b: ArrayLike, a: Optional[ArrayLike] = None, + zi: Optional[ArrayLike] = None, return_zf: bool = False) \ + -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]: + """ + Filter a signal with a FIR or an IIR digital filter: + + y[n] = b[0]*x[n] + b[1]*x[n-1] + ... + b[M]*x[n-M] + - a[1]*y[n-1] - ... - a[N]*y[n-N] + + + Parameters + ---------- + y : np.ndarray [shape=(..., n_samples,)] + The signal on which to apply the digital filter. + Can be a multichannel signal. + + b : ArrayLike + The numerator coefficient vector in a 1-D sequence. + + a : ArrayLike + The denominator coefficient vector in a 1-D sequence. Note that only + the coefficients beginning from ``a[1]`` need to be supplied. If ``a`` + is ``None``, then the resulting filter is a FIR filter. + + zi : ArrayLike + Initial filter state. When making successive calls to non-overlapping + frames, this can be set to the ``zf`` returned from the previous call. + + return_zf : + If ``True``, return the final filter state. + If ``False``, only return the pre-emphasized signal. + + Returns + ------- + y_out : np.ndarray [shape=(..., n_samples,)] + Filtered signal. + zf : np.ndarray + If ``return_zf=True``, the final filter state is also returned. + """ + b = np.asarray(b, dtype=y.dtype) + + if a is None: + a = [1.0] + else: + a = np.insert(a, 0, 1.0) + + a = np.asarray(a, dtype=y.dtype) + + if zi is None: + zi = signal.lfilter_zi(b, a) + + zi = np.atleast_1d(zi) + + y_out, zf = signal.lfilter(b, a, y, zi=np.asarray(zi, dtype=y.dtype)) + + if return_zf: + return y_out, zf + + return y_out + + +def filtfilt(y: np.ndarray, *, b: ArrayLike, a: Optional[ArrayLike] = None) \ + -> np.ndarray: + """ + Filter a signal with a FIR or IIR digital filter forward and backward. + + This function applies a linear digital filter twice, once forward and once + backwards. The combined filter has zero phase and a filter order twice that + of the original. + + Parameters + ---------- + y : np.ndarray [shape=(..., n_samples,)] + The signal on which to apply the digital filter. + Can be a multichannel signal. + + b : ArrayLike + The numerator coefficient vector in a 1-D sequence. + + a : ArrayLike + The denominator coefficient vector in a 1-D sequence. Note that only + the coefficients beginning from ``a[1]`` need to be supplied. If ``a`` + is ``None``, then the resulting filter is a FIR filter. + + Returns + ------- + y_out : np.ndarray [shape=(..., n_samples)] + Filtered signal. + """ + b = np.asarray(b, dtype=y.dtype) + + if a is None: + a = [1.0] + else: + a = np.insert(a, 0, 1.0) + + a = np.asarray(a, dtype=y.dtype) + y_out = signal.filtfilt(b, a, y) + return y_out diff --git a/src/fusion_ai_hub/core/time_domain_filtering/preemphasis.py b/src/fusion_ai_hub/core/time_domain_filtering/preemphasis.py new file mode 100644 index 0000000..a09e3b4 --- /dev/null +++ b/src/fusion_ai_hub/core/time_domain_filtering/preemphasis.py @@ -0,0 +1,137 @@ +""" +Preemphasis +=========== + +.. autosummary:: + :toctree: generated/ + + preemphasis + deemphasis +""" +import numpy as np +from typing import Optional, Tuple, Union +from numpy.typing import ArrayLike +from .filtering import lfilter + + +__all__ = ['preemphasis', 'deemphasis'] + + +def preemphasis(y: np.ndarray, *, coef: float = 0.97, + zi: Optional[ArrayLike] = None, return_zf: bool = False) \ + -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]: + """ + Pre-emphasis a signal with a first-order differencing filter: + + y[n] -> y[n] - coef * y[n-1] + + This function is taken from librosa and modified for own purposes. + + Parameters + ---------- + y : np.ndarray [shape=(..., n_samples,)] + The signal on which to apply the pre-emphasis filter. + Can be a multichannel signal. + + coef : float, default=0.97 + The pre-emphasis coefficient. It should be between 0 and 1. + + At the limit ``coef=0``, the signal is unchanged. + + At the limit ``coef=1``, the result is the first-order difference of + the signal. + + zi : ArrayLike + Initial filter state. When making successive calls to non-overlapping + frames, this can be set to the ``zf`` returned from the previous call. + + By default, ``zi`` is initialized as ``2*y[0] - y[1]``. + + return_zf : bool, default=False + If ``True``, return the final filter state. + If ``False``, only return the pre-emphasized signal. + + Returns + ------- + y_out : np.ndarray [shape=(..., n_samples,)] + pre-emphasized signal + zf : np.ndarray + If ``return_zf=True``, the final filter state is also returned. + """ + b = np.asarray([1.0, -coef], dtype=y.dtype) + + if zi is None: + zi = 2 * y[..., 0:1] - y[..., 1:2] + + y_out, zf = lfilter(y, b=b, zi=zi, return_zf=True) + + if return_zf: + return y_out, zf + + return y_out + + +def deemphasis(y: np.ndarray, *, coef: float = 0.97, + zi: Optional[ArrayLike] = None, return_zf: bool = False) \ + -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]: + """ + De-emphasize an audio signal with the inverse operation of preemphasis. + + If y = preemphasis(x, coef=coef, zi=zi), the deemphasis is: + + >>> x[i] = y[i] + coef * x[i-1] + >>> x = deemphasis(y, coef=coef, zi=zi) + + Parameters + ---------- + y : np.ndarray [shape=(..., n_samples,)] + The signal on which to apply the pre-emphasis filter. + Can be a multichannel signal. + + coef : float, default=0.97 + The pre-emphasis coefficient. It should be between 0 and 1. + + At the limit ``coef=0``, the signal is unchanged. + + At the limit ``coef=1``, the result is the first-order difference of + the signal. + + zi : number + Initial filter state. If inverting a previous preemphasis(), the same + value should be used. + + By default, ``zi`` is initialized as + ``((2 - coef) * y[0] - y[1]) / (3 - coef)``. This value corresponds to + the transformation of the default initialization of ``zi`` in + ``preemphasis()``, ``2*x[0] - x[1]``. + + return_zf : boolean + If ``True``, return the final filter state. + If ``False``, only return the pre-emphasized signal. + + Returns + ------- + y_out : np.ndarray [shape=(..., n_samples)] + pre-emphasized signal + zf : np.ndarray + If ``return_zf=True``, the final filter state is also returned. + """ + b = np.array([1.0, -coef], dtype=y.dtype) + + if zi is None: + # initialize with all zeros + zi = np.zeros(list(y.shape[:-1]) + [1], dtype=y.dtype) + y_out, zf = lfilter(y, b=b, zi=zi, return_zf=True) + + # factor in the linear extrapolation + y_out -= (((2 - coef) * y[..., 0:1] - y[..., 1:2]) / (3 - coef) + * (coef ** np.arange(y.shape[-1])) + ) + + else: + y_out, zf = lfilter(y, b=b, zi=zi, return_zf=True) + + if return_zf: + return y_out, zf + else: + return y_out diff --git a/src/fusion_ai_hub/sampling/__init__.py b/src/fusion_ai_hub/sampling/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fusion_ai_hub/util/utils.py b/src/fusion_ai_hub/util/utils.py new file mode 100644 index 0000000..6cd4b51 --- /dev/null +++ b/src/fusion_ai_hub/util/utils.py @@ -0,0 +1,5 @@ +import numpy as np +from typing import Any + + + From 21f6df985c4b6c82edcfe9502a8bcb14031e1972 Mon Sep 17 00:00:00 2001 From: renierts Date: Thu, 16 May 2024 19:08:14 -0400 Subject: [PATCH 010/103] Added docstrings for STFT and spectrogram. Added functionalities to resample a time-series and an empty module for time-series interpolation. --- .../core/fusion_signal/__init__.py | 0 .../core/fusion_signal/interpolation.py | 0 .../core/fusion_signal/resampling.py | 93 ++++++++++ .../core/spectral_representation/sft.py | 163 ++++++++++++++++++ 4 files changed, 256 insertions(+) create mode 100644 src/fusion_ai_hub/core/fusion_signal/__init__.py create mode 100644 src/fusion_ai_hub/core/fusion_signal/interpolation.py create mode 100644 src/fusion_ai_hub/core/fusion_signal/resampling.py diff --git a/src/fusion_ai_hub/core/fusion_signal/__init__.py b/src/fusion_ai_hub/core/fusion_signal/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fusion_ai_hub/core/fusion_signal/interpolation.py b/src/fusion_ai_hub/core/fusion_signal/interpolation.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fusion_ai_hub/core/fusion_signal/resampling.py b/src/fusion_ai_hub/core/fusion_signal/resampling.py new file mode 100644 index 0000000..a88eec5 --- /dev/null +++ b/src/fusion_ai_hub/core/fusion_signal/resampling.py @@ -0,0 +1,93 @@ +import numpy as np +from scipy import signal + + +def resample(y: np.ndarray, *, orig_fs: float, target_fs: float, + resample_type: str = "scipy", scale: bool = False, + axis: int = -1) -> np.ndarray: + """ + Resample a time series from orig_fs to target_fs. + + Parameters + ---------- + y : np.ndarray [shape=(..., n, ...)] + time series, with `n` samples along the specified axis. + + orig_fs : number > 0 [scalar] + original sampling frequency of ``y`` + + target_fs : number > 0 [scalar] + target sampling frequency + + resample_type : str (default: `scipy`) + resample type + + 'fft' or 'scipy' + `scipy.signal.resample` Fourier method. + 'polyphase' + `scipy.signal.resample_poly` polyphase filtering. (fast) + 'linear' + `samplerate` linear interpolation. (very fast, but not bandlimited) + + .. note:: + Not all options yield a bandlimited interpolator. If you use + `polyphase`, `linear`, or `zero_order_hold`, you need to be aware + of possible aliasing effects. + + .. note:: + When using ``res_type='polyphase'``, only integer sampling rates + are supported. + + scale : bool + Scale the resampled signal so that ``y`` and ``y_hat`` have + approximately equal total energy. + + axis : int + The target axis along which to resample. Defaults to the trailing axis. + + Returns + ------- + y_hat : np.ndarray [shape=(..., n * target_sr / orig_sr, ...)] + ``y`` resampled from ``orig_sr`` to ``target_sr`` along the target axis + + Raises + ------ + ValueError + If ``res_type='polyphase'`` and ``orig_sr`` or ``target_sr`` are not + both integer-valued. + + See Also + -------- + librosa.util.fix_length + scipy.signal.resample + """ + if orig_fs == target_fs: + return y + + ratio = float(target_fs) / orig_fs + + n_samples = int(np.ceil(y.shape[axis] * ratio)) + + if resample_type in ("scipy", "fft"): + y_hat = signal.resample(y, n_samples, axis=axis) + elif resample_type == "polyphase": + if int(orig_fs) != orig_fs or int(target_fs) != target_fs: + raise ValueError("polyphase resampling is only supported for " + "integer-valued sampling rates.") + + # For polyphase resampling, we need up- and down-sampling ratios + # We can get those from the greatest common divisor of the rates + # as long as the rates are integrable + orig_fs = int(orig_fs) + target_fs = int(target_fs) + gcd = np.gcd(orig_fs, target_fs) + y_hat = signal.resample_poly(y, target_fs // gcd, orig_fs // gcd, + axis=axis) + else: + raise NameError("Unknown resampling type.") + + if scale: + y_hat /= np.sqrt(ratio) + + # Match dtypes + return np.asarray(y_hat, dtype=y.dtype) diff --git a/src/fusion_ai_hub/core/spectral_representation/sft.py b/src/fusion_ai_hub/core/spectral_representation/sft.py index 34bae0a..bd9abc9 100644 --- a/src/fusion_ai_hub/core/spectral_representation/sft.py +++ b/src/fusion_ai_hub/core/spectral_representation/sft.py @@ -14,6 +14,90 @@ def spectrogram(y: np.ndarray, *, fs: float = 48000, n_fft: int = 2048, pad_mode: Literal["zeros", "edge", "even", "odd"] = 'zeros', return_t_f: bool = False) \ -> Union[np.ndarray, Tuple[np.ndarray, ...]]: + """ + Spectrogram. + + The spectrogram is the absolute square of the STFT, and thus is always + non-negative. This is a convenience function for calling ``stft`` / + ``stft_detrend``. It represents a signal in the time-frequency domain by + computing discrete Fourier transforms (DFT) over short overlapping windows. + + This function returns a real-valued matrix D such that it is the squared + absolute value of the complex STFT coefficients. + + Parameters + ---------- + y : np.ndarray [shape=(…, n)], real-valued + The input signal. Multi-channel supported. + fs : float + The sampling frequency in Hertz + n_fft : int + Length of the windowed signal after padding with zeros. The number of + rows in the matrix ``D`` is ``(1 + n_fft/2)``. It is always recommended + setting n_fft to a power of two for optimizing the speed of the fast + Fourier transform (FFT) algorithm. + hop_length : int + Number of audio samples between adjacent STFT columns. + + Smaller values increase the number of columns in ``D`` without + affecting the frequency resolution of the STFT. + + If unspecified, defaults to ``win_length // 4`` (see below). + win_length : int + Each frame of the input signal is windowed by window of length + ``win_length`` and then padded with zeros to match ``n_fft``. + + Smaller values improve the temporal resolution of the STFT (i.e. the + ability to discriminate impulses that are closely spaced in time) at + the expense of frequency resolution (i.e. the ability to discriminate + pure tones that are closely spaced in frequency). This effect is known + as the time-frequency localization trade-off and needs to be adjusted + according to the properties of the input signal y. + + If unspecified, defaults to ``win_length = n_fft``. + window : str, float, tuple + Either: + - a window specification (string, tuple, or number); see + ``scipy.signal.get_window`` for details + - a window function, such as ``scipy.signal.windows.hann`` + + Defaults to a raised cosine window (‘hamming’), which is adequate for + most applications in signal processing. + scaling : str + Normalization applied to the window function (‘magnitude’, ‘psd’ or + None). + + If not None, the FFTs can be either interpreted as a magnitude or a + power spectral density spectrum. + + The window function can be scaled by calling the ``scale_to`` method, + or it is set by the initializer parameter ``scale_to``. + detrend : str + If detr is set to ‘constant’, the mean is subtracted, if set to + “linear”, the linear trend is removed. This is achieved by calling + ``scipy.signal.detrend``. If detr is a function, detr is applied to + each segment. All other parameters have the same meaning as in stft. + + Note that due to the detrending, the original signal cannot be + reconstructed by the istft. + pad_mode : str + Kind of values which are added, when the sliding window sticks out on + either the lower or upper end of the input x. Zeros are added if the + default ‘zeros’ is set. For ‘edge’ either the first or the last value + of x is used. ‘even’ pads by reflecting the signal on the first or last + sample and ‘odd’ additionally multiplies it with -1. + return_t_f : + If ``return_t_f=True``, the time and frequency bins are also returned. + + Returns + ------- + spectrogram_matrix : np.ndarray [shape=(..., n_samples,)] + Real-valued matrix of magnitude spectrogram. + t : np.ndarray + If ``return_t_f=True``, the time steps are returned. + f : np.ndarray + If ``return_t_f=True``, the frequency bins are returned. + """ fft_window = signal.get_window(window, win_length, fftbins=True) sft = signal.ShortTimeFFT(fft_window, hop_length, fs, fft_mode='onesided', @@ -37,6 +121,85 @@ def stft(y: np.ndarray, *, fs: float = 48000, n_fft: int = 2048, pad_mode: Literal["zeros", "edge", "even", "odd"] = 'zeros', return_t_f: bool = False) \ -> Union[np.ndarray, Tuple[np.ndarray, ...]]: + """ + STFT. + + The STFT represents a signal in the time-frequency domain by computing + discrete Fourier transforms (DFT) over short overlapping windows. + + This function returns a complex-valued matrix D such that + + - ``np.abs(D[..., f, t])`` is the magnitude of frequency bin ``f`` + at frame ``t``, and + + - ``np.angle(D[..., f, t])`` is the phase of frequency bin ``f`` + at frame ``t``. + + Parameters + ---------- + y : np.ndarray [shape=(…, n)], real-valued + The input signal. Multi-channel supported. + fs : float + The sampling frequency in Hertz + n_fft : int + Length of the windowed signal after padding with zeros. The number of + rows in the matrix ``D`` is ``(1 + n_fft/2)``. It is always recommended + setting n_fft to a power of two for optimizing the speed of the fast + Fourier transform (FFT) algorithm. + hop_length : int + Number of audio samples between adjacent STFT columns. + + Smaller values increase the number of columns in ``D`` without + affecting the frequency resolution of the STFT. + + If unspecified, defaults to ``win_length // 4`` (see below). + win_length : int + Each frame of the input signal is windowed by window of length + ``win_length`` and then padded with zeros to match ``n_fft``. + + Smaller values improve the temporal resolution of the STFT (i.e. the + ability to discriminate impulses that are closely spaced in time) at + the expense of frequency resolution (i.e. the ability to discriminate + pure tones that are closely spaced in frequency). This effect is known + as the time-frequency localization trade-off and needs to be adjusted + according to the properties of the input signal y. + + If unspecified, defaults to ``win_length = n_fft``. + window : str, float, tuple + Either: + - a window specification (string, tuple, or number); see + ``scipy.signal.get_window`` for details + - a window function, such as ``scipy.signal.windows.hann`` + + Defaults to a raised cosine window (‘hamming’), which is adequate for + most applications in signal processing. + scaling : str + Normalization applied to the window function (‘magnitude’, ‘psd’ or + None). + + If not None, the FFTs can be either interpreted as a magnitude or a + power spectral density spectrum. + + The window function can be scaled by calling the ``scale_to`` method, + or it is set by the initializer parameter ``scale_to``. + pad_mode : str + Kind of values which are added, when the sliding window sticks out on + either the lower or upper end of the input x. Zeros are added if the + default ‘zeros’ is set. For ‘edge’ either the first or the last value + of x is used. ‘even’ pads by reflecting the signal on the first or last + sample and ‘odd’ additionally multiplies it with -1. + return_t_f : + If ``return_t_f=True``, the time and frequency bins are also returned. + + Returns + ------- + spectrogram_matrix : np.ndarray [shape=(..., n_samples,)] + Real-valued matrix of magnitude spectrogram. + t : np.ndarray + If ``return_t_f=True``, the time steps are returned. + f : np.ndarray + If ``return_t_f=True``, the frequency bins are returned. + """ fft_window = signal.get_window(window, win_length, fftbins=True) sft = signal.ShortTimeFFT(fft_window, hop_length, fs, fft_mode='onesided', From 1107a661f8f931ea2836b153ebb2a320b1b45528 Mon Sep 17 00:00:00 2001 From: renierts Date: Fri, 17 May 2024 09:50:19 -0400 Subject: [PATCH 011/103] Added linear interpolation to fill missing values in a time-series. --- .../core/fusion_signal/interpolation.py | 53 +++++++++++++++++++ .../core/fusion_signal/resampling.py | 11 ---- 2 files changed, 53 insertions(+), 11 deletions(-) diff --git a/src/fusion_ai_hub/core/fusion_signal/interpolation.py b/src/fusion_ai_hub/core/fusion_signal/interpolation.py index e69de29..df313a2 100644 --- a/src/fusion_ai_hub/core/fusion_signal/interpolation.py +++ b/src/fusion_ai_hub/core/fusion_signal/interpolation.py @@ -0,0 +1,53 @@ +import numpy as np +from scipy import interpolate +from typing import Optional + + +def interpolate_signal(y: np.ndarray, t_ori: np.ndarray, t_new: np.ndarray, + axis: Optional[int] = 0) -> np.ndarray: + """ + Resample and interpolate missing values in a multivariate time-series. + + Parameters + ---------- + y : np.ndarray, shape = (..., n) + The input signal to be interpolated. + t_ori : np.ndarray, shape = (n): + Array of original times. If None, assumes evenly spaced intervals. + t_new : np.ndarray: + Array of target times for resampling. + axis : int + Axis along which to interpolate. 0 for rows, 1 for columns. + + Returns + ------- + np.ndarray: Resampled and interpolated time-series. + """ + if axis not in [0, 1]: + raise ValueError("Axis must be 0 or 1.") + + # Transpose data if we are interpolating along columns + if axis == 1: + y = y.T + + num_samples, num_features = y.shape + if t_ori is None: + t_ori = np.arange(num_samples) + + # Prepare the resampled array + resampled_data = np.empty((len(t_new), num_features)) + + # Resample and interpolate each feature (row in transposed data) + for i in range(num_features): + feature_data = y[:, i] + nans = np.isnan(feature_data) + known_times = t_ori[~nans] + known_values = feature_data[~nans] + resampled_data[:, i] = np.interp(t_new, known_times, known_values) + + # Transpose back if needed + if axis == 1: + resampled_data = resampled_data.T + + return resampled_data + diff --git a/src/fusion_ai_hub/core/fusion_signal/resampling.py b/src/fusion_ai_hub/core/fusion_signal/resampling.py index a88eec5..1be5ad3 100644 --- a/src/fusion_ai_hub/core/fusion_signal/resampling.py +++ b/src/fusion_ai_hub/core/fusion_signal/resampling.py @@ -49,17 +49,6 @@ def resample(y: np.ndarray, *, orig_fs: float, target_fs: float, ------- y_hat : np.ndarray [shape=(..., n * target_sr / orig_sr, ...)] ``y`` resampled from ``orig_sr`` to ``target_sr`` along the target axis - - Raises - ------ - ValueError - If ``res_type='polyphase'`` and ``orig_sr`` or ``target_sr`` are not - both integer-valued. - - See Also - -------- - librosa.util.fix_length - scipy.signal.resample """ if orig_fs == target_fs: return y From 8ccf8c32a5993658cf96cc6a98223bec90f0322f Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Tue, 28 May 2024 08:34:17 -0400 Subject: [PATCH 012/103] Update data_prep_obj.py --- examples/Dataset_prep/data_prep_obj.py | 1642 ++++++++++++------------ 1 file changed, 821 insertions(+), 821 deletions(-) diff --git a/examples/Dataset_prep/data_prep_obj.py b/examples/Dataset_prep/data_prep_obj.py index a13a39c..7ebaaee 100644 --- a/examples/Dataset_prep/data_prep_obj.py +++ b/examples/Dataset_prep/data_prep_obj.py @@ -585,921 +585,921 @@ def __init__(self): self.norm_factor_list=norm_factor_list self.file_normal_size=file_normal_size - @staticmethod - def file_path_gen(discharge, suffix): - return (f'/scratch/gpfs/EKOLEMEN/big_d3d_data/{str(discharge)[:2]}0000/{discharge}_{suffix}.h5') - - def hdf5_to_dict(self, group): - result = {} - for key in group.keys(): - if isinstance(group[key], h5py.Dataset): - result[key] = group[key][()] - elif isinstance(group[key], h5py.Group): - result[key] = self.hdf5_to_dict(group[key]) - return result - - def order_of_magnitude_normal_factor_calc(self,discharge): - norm_factor_list_tmp={} - for suffix in self.file_keys.keys(): - file_dict=self.get_data(discharge, suffix, norm=False) - norm_factor_list_tmp[suffix]={} - for key in file_dict.keys(): - mean_tmp=abs(np.mean(file_dict[key]['zdata'][:])) - try: - exponent=self.get_order_of_magnitude(mean_tmp) - norm_factor_list_tmp[suffix][key]=10**exponent - except: - norm_factor_list_tmp[suffix][key]=1. - return norm_factor_list_tmp - - def avg_factor_calc(self,discharge): - avg_factor={} - for suffix in self.file_keys.keys(): - file_dict=self.get_data(discharge, suffix, norm=True) - avg_factor[suffix]={} - for key in file_dict.keys(): - data=file_dict[key]['zdata'][:] - avg_tmp=np.mean(data,axis=len(data.shape)-1) - if np.isnan(avg_tmp).any(): - avg_tmp=0. - - avg_factor[suffix][key]=avg_tmp - - return avg_factor - - def std_factor_calc(self,discharge): - std_factor={} - for suffix in self.file_keys.keys(): - file_dict=self.get_data(discharge, suffix, norm=True) - std_factor[suffix]={} - for key in file_dict.keys(): - data=file_dict[key]['zdata'][:] - std_tmp=np.std(data,axis=len(data.shape)-1) - if np.isnan(std_tmp).any(): - std_tmp=1. - std_factor[suffix][key]=std_tmp - - return std_factor + # @staticmethod + # def file_path_gen(discharge, suffix): + # return (f'/scratch/gpfs/EKOLEMEN/big_d3d_data/{str(discharge)[:2]}0000/{discharge}_{suffix}.h5') + + # def hdf5_to_dict(self, group): + # result = {} + # for key in group.keys(): + # if isinstance(group[key], h5py.Dataset): + # result[key] = group[key][()] + # elif isinstance(group[key], h5py.Group): + # result[key] = self.hdf5_to_dict(group[key]) + # return result + + # def order_of_magnitude_normal_factor_calc(self,discharge): + # norm_factor_list_tmp={} + # for suffix in self.file_keys.keys(): + # file_dict=self.get_data(discharge, suffix, norm=False) + # norm_factor_list_tmp[suffix]={} + # for key in file_dict.keys(): + # mean_tmp=abs(np.mean(file_dict[key]['zdata'][:])) + # try: + # exponent=self.get_order_of_magnitude(mean_tmp) + # norm_factor_list_tmp[suffix][key]=10**exponent + # except: + # norm_factor_list_tmp[suffix][key]=1. + # return norm_factor_list_tmp + + # def avg_factor_calc(self,discharge): + # avg_factor={} + # for suffix in self.file_keys.keys(): + # file_dict=self.get_data(discharge, suffix, norm=True) + # avg_factor[suffix]={} + # for key in file_dict.keys(): + # data=file_dict[key]['zdata'][:] + # avg_tmp=np.mean(data,axis=len(data.shape)-1) + # if np.isnan(avg_tmp).any(): + # avg_tmp=0. + + # avg_factor[suffix][key]=avg_tmp + + # return avg_factor + + # def std_factor_calc(self,discharge): + # std_factor={} + # for suffix in self.file_keys.keys(): + # file_dict=self.get_data(discharge, suffix, norm=True) + # std_factor[suffix]={} + # for key in file_dict.keys(): + # data=file_dict[key]['zdata'][:] + # std_tmp=np.std(data,axis=len(data.shape)-1) + # if np.isnan(std_tmp).any(): + # std_tmp=1. + # std_factor[suffix][key]=std_tmp + + # return std_factor #all: apply tha same avg and mean to all the data #individual:apply tha individual avg and individual mean to the individual data row #std_all_avg_individual: apply tha individual avg to the individual data row, but apply the same std to the group #mode=[all,individual,std_all_avg_individual] - @staticmethod - def norm_data(data,avg_,std_,mode='all'): - avg_=np.array(avg_) - std_=np.array(std_) - if mode == 'all': - std_all=(np.mean(std_**2))**0.5 - avg_all=np.mean(avg_) - elif mode=='std_all_avg_individual': - std_all=(np.mean(std_**2))**0.5 - avg_all=np.expand_dims(avg_,axis=1) - - elif mode=='individual': - std_all=np.expand_dims(avg_,axis=1) - avg_all=np.expand_dims(avg_,axis=1) - - - data_norm=(data-avg_all)/std_all - - return data_norm - - def get_data(self,discharge, suffix, norm=True): - discharge_path=self.file_path_gen(discharge, suffix) - input_file = h5py.File(discharge_path, 'r') - input_dict_tmp = self.hdf5_to_dict(input_file) - if suffix in no_level: - input_dict={suffix:input_dict_tmp} - else: - input_dict=input_dict_tmp - - if norm and (suffix in self.norm_factor_list): - for key in input_dict.keys(): - if self.norm_factor_list[suffix][key]=='log': - input_dict[key]['zdata']=np.log(np.array(input_dict[key]['zdata'][:])) - else: - input_dict[key]['zdata']=np.array(input_dict[key]['zdata'][:])/self.norm_factor_list[suffix][key] + # @staticmethod + # def norm_data(data,avg_,std_,mode='all'): + # avg_=np.array(avg_) + # std_=np.array(std_) + # if mode == 'all': + # std_all=(np.mean(std_**2))**0.5 + # avg_all=np.mean(avg_) + # elif mode=='std_all_avg_individual': + # std_all=(np.mean(std_**2))**0.5 + # avg_all=np.expand_dims(avg_,axis=1) + + # elif mode=='individual': + # std_all=np.expand_dims(avg_,axis=1) + # avg_all=np.expand_dims(avg_,axis=1) + + + # data_norm=(data-avg_all)/std_all + + # return data_norm + + # def get_data(self,discharge, suffix, norm=True): + # discharge_path=self.file_path_gen(discharge, suffix) + # input_file = h5py.File(discharge_path, 'r') + # input_dict_tmp = self.hdf5_to_dict(input_file) + # if suffix in no_level: + # input_dict={suffix:input_dict_tmp} + # else: + # input_dict=input_dict_tmp + + # if norm and (suffix in self.norm_factor_list): + # for key in input_dict.keys(): + # if self.norm_factor_list[suffix][key]=='log': + # input_dict[key]['zdata']=np.log(np.array(input_dict[key]['zdata'][:])) + # else: + # input_dict[key]['zdata']=np.array(input_dict[key]['zdata'][:])/self.norm_factor_list[suffix][key] - return input_dict + # return input_dict # divide the data into subcategory - def data_division(self, input_file, input_suffix): - if input_suffix in multi_level: - input_multi_level = {} - for key in file_keys[input_suffix].keys(): - keys_of_this_category = file_keys[input_suffix][key] - input_multi_level[key] = {key_i: input_file[key_i] - for key_i in keys_of_this_category} - else: - input_multi_level = {input_suffix: input_file} - return input_multi_level + # def data_division(self, input_file, input_suffix): + # if input_suffix in multi_level: + # input_multi_level = {} + # for key in file_keys[input_suffix].keys(): + # keys_of_this_category = file_keys[input_suffix][key] + # input_multi_level[key] = {key_i: input_file[key_i] + # for key_i in keys_of_this_category} + # else: + # input_multi_level = {input_suffix: input_file} + # return input_multi_level #norm_mode=[no, all,individual,std_all_avg_individual] #no, means no normalizations - def get_full_data(self, discharge, suffix_list, norm_mode='all'): - all_file_dict = {} - for suffix in suffix_list: - file_dict=self.get_data(discharge, suffix, norm=True) - all_file_dict[suffix]={} - for key in file_dict.keys(): - if norm_mode=='no': - all_file_dict[suffix][key]={'xdata':file_dict[key]['xdata'][:],\ - 'zdata':file_dict[key]['zdata'][:]} - else: - all_file_dict[suffix][key]={'xdata':file_dict[key]['xdata'][:],\ - 'zdata':self.norm_data(file_dict[key]['zdata'][:],\ - self.std_list[suffix][key], - self.avg_list[suffix][key],\ - mode=norm_mode)} - return all_file_dict - - - @staticmethod - def get_order_of_magnitude(num): - exponent = int(np.log10(abs(num))) - return exponent - - @staticmethod - def spec_filters(freq, time, amp_f_t, spec_params=spec_params_default,thr=0.9, gaussblr_win=(31, 3)): - def norm(amp_f_t): - mn = amp_f_t.mean() - std = amp_f_t.std() - return (amp_f_t-mn) / std + # def get_full_data(self, discharge, suffix_list, norm_mode='all'): + # all_file_dict = {} + # for suffix in suffix_list: + # file_dict=self.get_data(discharge, suffix, norm=True) + # all_file_dict[suffix]={} + # for key in file_dict.keys(): + # if norm_mode=='no': + # all_file_dict[suffix][key]={'xdata':file_dict[key]['xdata'][:],\ + # 'zdata':file_dict[key]['zdata'][:]} + # else: + # all_file_dict[suffix][key]={'xdata':file_dict[key]['xdata'][:],\ + # 'zdata':self.norm_data(file_dict[key]['zdata'][:],\ + # self.std_list[suffix][key], + # self.avg_list[suffix][key],\ + # mode=norm_mode)} + # return all_file_dict + + + # @staticmethod + # def get_order_of_magnitude(num): + # exponent = int(np.log10(abs(num))) + # return exponent + + # @staticmethod + # def spec_filters(freq, time, amp_f_t, spec_params=spec_params_default,thr=0.9, gaussblr_win=(31, 3)): + # def norm(amp_f_t): + # mn = amp_f_t.mean() + # std = amp_f_t.std() + # return (amp_f_t-mn) / std - def rescale(amp_f_t): - return (amp_f_t-amp_f_t.min())/(amp_f_t.max()-amp_f_t.min()) + # def rescale(amp_f_t): + # return (amp_f_t-amp_f_t.min())/(amp_f_t.max()-amp_f_t.min()) - def quantfilt(amp_f_t, thr=0.9): - filt = np.quantile(amp_f_t, thr, axis=0) - out = np.where(amp_f_t < filt, 0, amp_f_t) - return out + # def quantfilt(amp_f_t, thr=0.9): + # filt = np.quantile(amp_f_t, thr, axis=0) + # out = np.where(amp_f_t < filt, 0, amp_f_t) + # return out - # gaussian filtering - def gaussblr(amp_f_t, filt=(31, 3)): - amp_f_t = (rescale(amp_f_t)*255).astype('uint8') - out = cv2.GaussianBlur(amp_f_t,filt,0) - return rescale(out) + # # gaussian filtering + # def gaussblr(amp_f_t, filt=(31, 3)): + # amp_f_t = (rescale(amp_f_t)*255).astype('uint8') + # out = cv2.GaussianBlur(amp_f_t,filt,0) + # return rescale(out) - # mean filtering - def meansub(amp_f_t): - mn = np.mean(amp_f_t, axis=1)[:, np.newaxis] - out = np.absolute(amp_f_t - mn) - return rescale(out) + # # mean filtering + # def meansub(amp_f_t): + # mn = np.mean(amp_f_t, axis=1)[:, np.newaxis] + # out = np.absolute(amp_f_t - mn) + # return rescale(out) - # morphological filtering - def morph(amp_f_t): - amp_f_t = (rescale(amp_f_t)*255).astype('uint8') - se1 = cv2.getStructuringElement(cv2.MORPH_RECT, (4, 4)) - se2 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 1)) - mask = cv2.morphologyEx(amp_f_t, cv2.MORPH_CLOSE, se1) - mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, se2) - return rescale(mask) + # # morphological filtering + # def morph(amp_f_t): + # amp_f_t = (rescale(amp_f_t)*255).astype('uint8') + # se1 = cv2.getStructuringElement(cv2.MORPH_RECT, (4, 4)) + # se2 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 1)) + # mask = cv2.morphologyEx(amp_f_t, cv2.MORPH_CLOSE, se1) + # mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, se2) + # return rescale(mask) - def apply_all(freq, time, amp_f_t, spec_params, thr=thr, - gaussblr_win=gaussblr_win): + # def apply_all(freq, time, amp_f_t, spec_params, thr=thr, + # gaussblr_win=gaussblr_win): - Sxx = np.log(amp_f_t + spec_params['eps']) - # rescale the pixels to (0,1) - Sxx = (Sxx-np.min(Sxx))/(np.max(Sxx)-np.min(Sxx)) + # Sxx = np.log(amp_f_t + spec_params['eps']) + # # rescale the pixels to (0,1) + # Sxx = (Sxx-np.min(Sxx))/(np.max(Sxx)-np.min(Sxx)) - Sxx_enhanced = quantfilt(Sxx, thr) - Sxx_enhanced = gaussblr(Sxx_enhanced, gaussblr_win) - Sxx_enhanced = meansub(Sxx_enhanced) - Sxx_enhanced = morph(Sxx_enhanced) - Sxx_enhanced = meansub(Sxx_enhanced) + # Sxx_enhanced = quantfilt(Sxx, thr) + # Sxx_enhanced = gaussblr(Sxx_enhanced, gaussblr_win) + # Sxx_enhanced = meansub(Sxx_enhanced) + # Sxx_enhanced = morph(Sxx_enhanced) + # Sxx_enhanced = meansub(Sxx_enhanced) - return freq, time, Sxx_enhanced - - return apply_all(freq, time, amp_f_t, spec_params, thr=thr, - gaussblr_win=gaussblr_win) - - @staticmethod - def spectro_calc(sig_time, data, spec_params=spec_params_default,plot=False): - spec_params['fs'] = 1./np.mean(sig_time[1:]-sig_time[:-1]) - # default 1024 - spec_params['nperseg'] = max(int(0.6*spec_params['fs']), 1) - # default: nperseg / 4 - spec_params['noverlap'] = max(int(spec_params['nperseg']/4), 1) - print(spec_params) + # return freq, time, Sxx_enhanced + + # return apply_all(freq, time, amp_f_t, spec_params, thr=thr, + # gaussblr_win=gaussblr_win) + + # @staticmethod + # def spectro_calc(sig_time, data, spec_params=spec_params_default,plot=False): + # spec_params['fs'] = 1./np.mean(sig_time[1:]-sig_time[:-1]) + # # default 1024 + # spec_params['nperseg'] = max(int(0.6*spec_params['fs']), 1) + # # default: nperseg / 4 + # spec_params['noverlap'] = max(int(spec_params['nperseg']/4), 1) + # print(spec_params) - freq, time, amp_f_t = signal.spectrogram( - data, nperseg=spec_params['nperseg'], - noverlap=spec_params['noverlap'], fs=spec_params['fs'], - window=spec_params['window'], scaling=spec_params['scaling'], - detrend=spec_params['detrend']) - if plot: - plt.clf() - plt.imshow(amp_f_t.T,aspect='auto',cmap='hot', - extent=[time[0], time[-1], freq[-1], freq[0]]) - plt.colorbar() - plt.ylabel('kHz') - plt.xlabel('ms') - plt.gca().invert_yaxis() - plt.show() - return freq, time, amp_f_t + # freq, time, amp_f_t = signal.spectrogram( + # data, nperseg=spec_params['nperseg'], + # noverlap=spec_params['noverlap'], fs=spec_params['fs'], + # window=spec_params['window'], scaling=spec_params['scaling'], + # detrend=spec_params['detrend']) + # if plot: + # plt.clf() + # plt.imshow(amp_f_t.T,aspect='auto',cmap='hot', + # extent=[time[0], time[-1], freq[-1], freq[0]]) + # plt.colorbar() + # plt.ylabel('kHz') + # plt.xlabel('ms') + # plt.gca().invert_yaxis() + # plt.show() + # return freq, time, amp_f_t - @staticmethod - def spectro_plot(freq, time, amp_f_t): - plt.clf() - plt.imshow(amp_f_t,aspect='auto',cmap='hot', - extent=[time[0], time[-1], freq[-1], freq[0]]) - plt.colorbar() - plt.ylabel('kHz') - plt.xlabel('ms') - plt.gca().invert_yaxis() - plt.show() + # @staticmethod + # def spectro_plot(freq, time, amp_f_t): + # plt.clf() + # plt.imshow(amp_f_t,aspect='auto',cmap='hot', + # extent=[time[0], time[-1], freq[-1], freq[0]]) + # plt.colorbar() + # plt.ylabel('kHz') + # plt.xlabel('ms') + # plt.gca().invert_yaxis() + # plt.show() - @staticmethod - def time_serie_plot(dict): - plt.clf() - if dict['zdata'][:].shape == 1: - plt.plot(dict['xdata'][:],dict['zdata'][:]) - else: - plt.plot(dict['xdata'][:],dict['zdata'][:].T) - plt.xlabel('Time (ms)') - plt.show() + # @staticmethod + # def time_serie_plot(dict): + # plt.clf() + # if dict['zdata'][:].shape == 1: + # plt.plot(dict['xdata'][:],dict['zdata'][:]) + # else: + # plt.plot(dict['xdata'][:],dict['zdata'][:].T) + # plt.xlabel('Time (ms)') + # plt.show() - @staticmethod - def cut_time(time, data, t_min, t_max): - t_indx_min=np.argmin(abs(np.array(time)-t_min)) - t_indx_max=np.argmin(abs(np.array(time)-t_max)) + # @staticmethod + # def cut_time(time, data, t_min, t_max): + # t_indx_min=np.argmin(abs(np.array(time)-t_min)) + # t_indx_max=np.argmin(abs(np.array(time)-t_max)) - return time[t_indx_min:t_indx_max], data[...,t_indx_min:t_indx_max] + # return time[t_indx_min:t_indx_max], data[...,t_indx_min:t_indx_max] # Function to get windowed data - @staticmethod - def get_windowed_data(df, center_index, window_size=5): - start = max(center_index - window_size, 0) - end = min(center_index + window_size + 1, len(df)) - return df.iloc[start:end].drop(columns=['xdata']) - - @staticmethod - def time_matching_merge_asof_1d(time1, data1, time_std, left_window=0, right_window=0): - # Convert input arrays to DataFrames - df1 = pd.DataFrame({'time1': time1, 'data1': data1}) - df2 = pd.DataFrame({'time_std': time_std}) + # @staticmethod + # def get_windowed_data(df, center_index, window_size=5): + # start = max(center_index - window_size, 0) + # end = min(center_index + window_size + 1, len(df)) + # return df.iloc[start:end].drop(columns=['xdata']) + + # @staticmethod + # def time_matching_merge_asof_1d(time1, data1, time_std, left_window=0, right_window=0): + # # Convert input arrays to DataFrames + # df1 = pd.DataFrame({'time1': time1, 'data1': data1}) + # df2 = pd.DataFrame({'time_std': time_std}) - # Sort the DataFrames by the time columns - df1.sort_values('time1', inplace=True) - df2.sort_values('time_std', inplace=True) + # # Sort the DataFrames by the time columns + # df1.sort_values('time1', inplace=True) + # df2.sort_values('time_std', inplace=True) - # Perform the asof merge - merged_df = pd.merge_asof(df2, df1, left_on='time_std', - right_on='time1', direction='nearest') + # # Perform the asof merge + # merged_df = pd.merge_asof(df2, df1, left_on='time_std', + # right_on='time1', direction='nearest') - # Drop unnecessary columns and handle NaN values - merged_df.drop(columns='time_std', inplace=True) - merged_df.dropna(inplace=True) + # # Drop unnecessary columns and handle NaN values + # merged_df.drop(columns='time_std', inplace=True) + # merged_df.dropna(inplace=True) - # Extract the matched time and data - matched_time = merged_df['time1'].values - matched_data = merged_df['data1'].values + # # Extract the matched time and data + # matched_time = merged_df['time1'].values + # matched_data = merged_df['data1'].values - return matched_time, matched_data + # return matched_time, matched_data - @staticmethod - def time_matching_merge_asof_2d(time1, data1, time_std,left_window=0, right_window=0): - # Create DataFrames - # Note: We assume time1 and time_std are already float arrays - # representing time in seconds - - # Transpose data1 to align time as rows - df1 = pd.DataFrame(data1.T, index=time1) - df_std = pd.DataFrame(index=time_std) - - # Reset index to include time in the DataFrame directly for merging - df1 = df1.reset_index().rename(columns={'index': 'time1'}) - df_std = df_std.reset_index().rename(columns={'index': 'time_std'}) - - # Perform merge_asof to find the closest matches - merged = pd.merge_asof(df_std.sort_values('time_std'), - df1.sort_values('time1'), left_on='time_std', - right_on='time1', direction='nearest') - - # Extract the aligned times (as float) - matched_time = merged['time1'].values - - # Extract indices from the merged DataFrame - indices = merged['time1'].apply( - lambda x: np.where(df1['time1'] == x)[0][0] - if x in df1['time1'].values else -1).values + # @staticmethod + # def time_matching_merge_asof_2d(time1, data1, time_std,left_window=0, right_window=0): + # # Create DataFrames + # # Note: We assume time1 and time_std are already float arrays + # # representing time in seconds + + # # Transpose data1 to align time as rows + # df1 = pd.DataFrame(data1.T, index=time1) + # df_std = pd.DataFrame(index=time_std) + + # # Reset index to include time in the DataFrame directly for merging + # df1 = df1.reset_index().rename(columns={'index': 'time1'}) + # df_std = df_std.reset_index().rename(columns={'index': 'time_std'}) + + # # Perform merge_asof to find the closest matches + # merged = pd.merge_asof(df_std.sort_values('time_std'), + # df1.sort_values('time1'), left_on='time_std', + # right_on='time1', direction='nearest') + + # # Extract the aligned times (as float) + # matched_time = merged['time1'].values + + # # Extract indices from the merged DataFrame + # indices = merged['time1'].apply( + # lambda x: np.where(df1['time1'] == x)[0][0] + # if x in df1['time1'].values else -1).values - # Use indices to fetch data from the original 2D array, - # handle missing indices - matched_data = np.array([data1[:, int(idx)] if idx != -1 - else np.full(data1.shape[0], np.nan) - for idx in indices]).T + # # Use indices to fetch data from the original 2D array, + # # handle missing indices + # matched_data = np.array([data1[:, int(idx)] if idx != -1 + # else np.full(data1.shape[0], np.nan) + # for idx in indices]).T - return matched_time, matched_data + # return matched_time, matched_data - @staticmethod - def time_matching_binary_search(time1, data1, time_std, left_window=0, right_window=0): - # Function to find the closest time in time1 to each time in time_std - def find_closest(target): - # Binary search for the closest timestamp - low, high = 0, len(time1) - 1 - best_idx = low - while low <= high: - mid = (low + high) // 2 - if time1[mid] < target: - low = mid + 1 - elif time1[mid] > target: - high = mid - 1 - else: - return mid - # Update the best index if the current mid is closer to the - # target - if abs(time1[mid] - target) < abs(time1[best_idx] - target): - best_idx = mid - return best_idx - - # Align data1 to time_std - matched_data = [] - matched_time = [] - for t in time_std: - closest_idx = find_closest(t) - matched_data.append(data1[..., closest_idx-left_window:closest_idx+right_window+1]) - matched_time.append(time1[closest_idx-left_window:closest_idx+right_window+1]) - matched_time=np.array(matched_time) - matched_data=np.array(matched_data) - - return matched_time, matched_data - - @staticmethod - def estimate_closest_indx_constant_dt(left_indx,time,target,dt): - time_distant=target-time[left_indx] - n_time=int(np.floor(time_distant/dt)) - indx_start=left_indx+n_time - return indx_start - - @staticmethod - def estimate_closest_indx_varing_dt(left_indx,time,target,dt,dt_std): - time_distant=target-time[left_indx] - n_time_min=int(np.floor(time_distant/(dt+dt_std))) - n_time_max=int(np.floor(time_distant/(dt-dt_std))) - indx_start=left_indx+n_time_min - indx_end=left_indx+n_time_max - return indx_start,indx_end - - @staticmethod - def find_closest_indx_constant_dt(indx_start,time,target): - if indx_start==0: - return 0 - #on target - if time[indx_start]==target: - return indx_start - #indx_start on the left - elif time[indx_start] abs(time[i-1] - target): - return i-1 - return i - - #indx_start on the left - elif time[indx_start]>target: - i=indx_start - while time[i]>target: - i-=1 - if abs(time[i] - target) > abs(time[i+1] - target): - return i+1 - return i - - @staticmethod - def find_closest_indx_binary(time,target,indx_start,indx_end): - # Binary search for the closest timestamp - low, high = max(0,indx_start), min(indx_end,len(time)-1) - best_idx = low - while low <= high: - mid = (low + high) // 2 - if time[mid] < target: - low = mid + 1 - elif time[mid] > target: - high = mid - 1 - else: - return mid - # Update the best index if the current mid is closer to the - # target - if abs(time[mid] - target) < abs(time[best_idx] - target): - best_idx = mid - return best_idx + # @staticmethod + # def time_matching_binary_search(time1, data1, time_std, left_window=0, right_window=0): + # # Function to find the closest time in time1 to each time in time_std + # def find_closest(target): + # # Binary search for the closest timestamp + # low, high = 0, len(time1) - 1 + # best_idx = low + # while low <= high: + # mid = (low + high) // 2 + # if time1[mid] < target: + # low = mid + 1 + # elif time1[mid] > target: + # high = mid - 1 + # else: + # return mid + # # Update the best index if the current mid is closer to the + # # target + # if abs(time1[mid] - target) < abs(time1[best_idx] - target): + # best_idx = mid + # return best_idx + + # # Align data1 to time_std + # matched_data = [] + # matched_time = [] + # for t in time_std: + # closest_idx = find_closest(t) + # matched_data.append(data1[..., closest_idx-left_window:closest_idx+right_window+1]) + # matched_time.append(time1[closest_idx-left_window:closest_idx+right_window+1]) + # matched_time=np.array(matched_time) + # matched_data=np.array(matched_data) + + # return matched_time, matched_data + + # @staticmethod + # def estimate_closest_indx_constant_dt(left_indx,time,target,dt): + # time_distant=target-time[left_indx] + # n_time=int(np.floor(time_distant/dt)) + # indx_start=left_indx+n_time + # return indx_start + + # @staticmethod + # def estimate_closest_indx_varing_dt(left_indx,time,target,dt,dt_std): + # time_distant=target-time[left_indx] + # n_time_min=int(np.floor(time_distant/(dt+dt_std))) + # n_time_max=int(np.floor(time_distant/(dt-dt_std))) + # indx_start=left_indx+n_time_min + # indx_end=left_indx+n_time_max + # return indx_start,indx_end + + # @staticmethod + # def find_closest_indx_constant_dt(indx_start,time,target): + # if indx_start==0: + # return 0 + # #on target + # if time[indx_start]==target: + # return indx_start + # #indx_start on the left + # elif time[indx_start] abs(time[i-1] - target): + # return i-1 + # return i + + # #indx_start on the left + # elif time[indx_start]>target: + # i=indx_start + # while time[i]>target: + # i-=1 + # if abs(time[i] - target) > abs(time[i+1] - target): + # return i+1 + # return i + + # @staticmethod + # def find_closest_indx_binary(time,target,indx_start,indx_end): + # # Binary search for the closest timestamp + # low, high = max(0,indx_start), min(indx_end,len(time)-1) + # best_idx = low + # while low <= high: + # mid = (low + high) // 2 + # if time[mid] < target: + # low = mid + 1 + # elif time[mid] > target: + # high = mid - 1 + # else: + # return mid + # # Update the best index if the current mid is closer to the + # # target + # if abs(time[mid] - target) < abs(time[best_idx] - target): + # best_idx = mid + # return best_idx - @staticmethod - def custom_padding(time, data, left_slicing_indx, right_slicing_indx, padding='last'): - #padding to the left - - if left_slicing_indx<0: - padding_config = [(0, 0)] * (data.ndim - 1) - - padd_len=-left_slicing_indx - - if padding=='nan': - data_tmp=np.pad(data[..., 0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='empty') - time_tmp=np.pad(time[0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='empty') - elif padding=='last': - data_tmp=np.pad(data[..., 0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='constant',constant_values=data[...,0]) - time_tmp=np.pad(time[0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='constant',constant_values=time[0]) - elif padding=='zeros': - data_tmp=np.pad(data[..., 0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='constant',constant_values=0) - time_tmp=np.pad(time[0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='constant',constant_values=0) - elif (right_slicing_indx+1)>len(time): - padd_len=right_slicing_indx-len(time) - padding_config = [(0, 0)] * (data.ndim - 1) - - if padding=='nan': - data_tmp=np.pad(data[..., left_slicing_indx:],padding_config+[(0,padd_len)],mode='empty') - time_tmp=np.pad(time[left_slicing_indx:],padding_config+[(0,padd_len)],mode='empty') - elif padding=='last': - data_tmp=np.pad(data[..., left_slicing_indx:],padding_config+[(0,padd_len)],mode='constant',constant_values=data[...,-1]) - time_tmp=np.pad(time[left_slicing_indx:],padding_config+[(0,padd_len)],mode='constant',constant_values=time[-1]) - elif padding=='zeros': - data_tmp=np.pad(data[..., left_slicing_indx:],padding_config+[(0,padd_len)],mode='constant',constant_values=0) - time_tmp=np.pad(time[left_slicing_indx:],padding_config+[(0,padd_len)],mode='constant',constant_values=0) - else: - data_tmp=data[..., left_slicing_indx:right_slicing_indx+1] - time_tmp=time[left_slicing_indx:right_slicing_indx+1] - - return time_tmp,data_tmp + # @staticmethod + # def custom_padding(time, data, left_slicing_indx, right_slicing_indx, padding='last'): + # #padding to the left + + # if left_slicing_indx<0: + # padding_config = [(0, 0)] * (data.ndim - 1) + + # padd_len=-left_slicing_indx + + # if padding=='nan': + # data_tmp=np.pad(data[..., 0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='empty') + # time_tmp=np.pad(time[0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='empty') + # elif padding=='last': + # data_tmp=np.pad(data[..., 0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='constant',constant_values=data[...,0]) + # time_tmp=np.pad(time[0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='constant',constant_values=time[0]) + # elif padding=='zeros': + # data_tmp=np.pad(data[..., 0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='constant',constant_values=0) + # time_tmp=np.pad(time[0:right_slicing_indx+1],padding_config+[(padd_len, 0)],mode='constant',constant_values=0) + # elif (right_slicing_indx+1)>len(time): + # padd_len=right_slicing_indx-len(time) + # padding_config = [(0, 0)] * (data.ndim - 1) + + # if padding=='nan': + # data_tmp=np.pad(data[..., left_slicing_indx:],padding_config+[(0,padd_len)],mode='empty') + # time_tmp=np.pad(time[left_slicing_indx:],padding_config+[(0,padd_len)],mode='empty') + # elif padding=='last': + # data_tmp=np.pad(data[..., left_slicing_indx:],padding_config+[(0,padd_len)],mode='constant',constant_values=data[...,-1]) + # time_tmp=np.pad(time[left_slicing_indx:],padding_config+[(0,padd_len)],mode='constant',constant_values=time[-1]) + # elif padding=='zeros': + # data_tmp=np.pad(data[..., left_slicing_indx:],padding_config+[(0,padd_len)],mode='constant',constant_values=0) + # time_tmp=np.pad(time[left_slicing_indx:],padding_config+[(0,padd_len)],mode='constant',constant_values=0) + # else: + # data_tmp=data[..., left_slicing_indx:right_slicing_indx+1] + # time_tmp=time[left_slicing_indx:right_slicing_indx+1] + + # return time_tmp,data_tmp #assume the time is sorted - def time_matching_dynamic_search(self,time, data, time_std, left_window=0, right_window=0, padding='last'): + # def time_matching_dynamic_search(self,time, data, time_std, left_window=0, right_window=0, padding='last'): - dt,dt_std,if_dt_even=self.check_even_time_spacing(time,time_start=0.01) - - # Align data to time_std - matched_data = [] - matched_time = [] - - left_indx=0 - #dt is constant - #print(f'std_norm={std_norm}') - if if_dt_even: - for target in time_std: - indx_start=self.estimate_closest_indx_constant_dt(left_indx,time,target,dt) - closest_idx=self.find_closest_indx_constant_dt(indx_start,time,target) + # dt,dt_std,if_dt_even=self.check_even_time_spacing(time,time_start=0.01) + + # # Align data to time_std + # matched_data = [] + # matched_time = [] + + # left_indx=0 + # #dt is constant + # #print(f'std_norm={std_norm}') + # if if_dt_even: + # for target in time_std: + # indx_start=self.estimate_closest_indx_constant_dt(left_indx,time,target,dt) + # closest_idx=self.find_closest_indx_constant_dt(indx_start,time,target) - left_slicing_indx=closest_idx-left_window - right_slicing_indx=closest_idx+right_window - time_tmp,data_tmp=self.custom_padding(time, data, left_slicing_indx,right_slicing_indx, padding=padding) + # left_slicing_indx=closest_idx-left_window + # right_slicing_indx=closest_idx+right_window + # time_tmp,data_tmp=self.custom_padding(time, data, left_slicing_indx,right_slicing_indx, padding=padding) - matched_data.append(data_tmp) - matched_time.append(time_tmp) + # matched_data.append(data_tmp) + # matched_time.append(time_tmp) - left_indx=closest_idx + # left_indx=closest_idx - else: - for target in time_std: - indx_start,indx_end=self.estimate_closest_indx_varing_dt(left_indx,time,target,dt,dt_std) - closest_idx=self.find_closest_indx_binary(time,target,indx_start,indx_end) + # else: + # for target in time_std: + # indx_start,indx_end=self.estimate_closest_indx_varing_dt(left_indx,time,target,dt,dt_std) + # closest_idx=self.find_closest_indx_binary(time,target,indx_start,indx_end) - left_slicing_indx=closest_idx-left_window - right_slicing_indx=closest_idx+right_window - time_tmp,data_tmp=self.custom_padding(time, data, left_slicing_indx,right_slicing_indx, padding=padding) + # left_slicing_indx=closest_idx-left_window + # right_slicing_indx=closest_idx+right_window + # time_tmp,data_tmp=self.custom_padding(time, data, left_slicing_indx,right_slicing_indx, padding=padding) - matched_time.append(time_tmp) - matched_data.append(data_tmp) + # matched_time.append(time_tmp) + # matched_data.append(data_tmp) - left_indx=closest_idx + # left_indx=closest_idx - matched_time=np.array(matched_time) - matched_data=np.array(matched_data) + # matched_time=np.array(matched_time) + # matched_data=np.array(matched_data) - return matched_time, matched_data + # return matched_time, matched_data - def time_matching(self,time, data, time_std, left_window=0, right_window=0, mode='merge_asof', padding='last'): - if mode == 'merge_asof': - if len(data.shape) == 1: - return self.time_matching_merge_asof_1d(time, data, time_std,\ - left_window=left_window, right_window=right_window) - elif len(data.shape) == 2: - return self.time_matching_merge_asof_2d(time, data, time_std,\ - left_window=left_window, right_window=right_window) - else: - print('The data has to be 1d array or 2d array') - elif mode == 'binary': - return self.time_matching_binary_search(time, data, time_std,\ - left_window=left_window, right_window=right_window) - elif mode == 'dynamic': - return self.time_matching_dynamic_search(time, data, time_std,\ - left_window=left_window, right_window=right_window,padding=padding) - - @staticmethod - def check_even_time_spacing(time,time_start=0.01): - time_start_indx=np.argmin(abs(time-time_start)) - - time=np.array(time) - dt_tmp=time[time_start_indx+1:time_start_indx+101]-time[time_start_indx:time_start_indx+100] + # def time_matching(self,time, data, time_std, left_window=0, right_window=0, mode='merge_asof', padding='last'): + # if mode == 'merge_asof': + # if len(data.shape) == 1: + # return self.time_matching_merge_asof_1d(time, data, time_std,\ + # left_window=left_window, right_window=right_window) + # elif len(data.shape) == 2: + # return self.time_matching_merge_asof_2d(time, data, time_std,\ + # left_window=left_window, right_window=right_window) + # else: + # print('The data has to be 1d array or 2d array') + # elif mode == 'binary': + # return self.time_matching_binary_search(time, data, time_std,\ + # left_window=left_window, right_window=right_window) + # elif mode == 'dynamic': + # return self.time_matching_dynamic_search(time, data, time_std,\ + # left_window=left_window, right_window=right_window,padding=padding) + + # @staticmethod + # def check_even_time_spacing(time,time_start=0.01): + # time_start_indx=np.argmin(abs(time-time_start)) + + # time=np.array(time) + # dt_tmp=time[time_start_indx+1:time_start_indx+101]-time[time_start_indx:time_start_indx+100] - dt=np.mean(dt_tmp) - dt_std=np.std(dt_tmp) - std_norm=dt_std/dt + # dt=np.mean(dt_tmp) + # dt_std=np.std(dt_tmp) + # std_norm=dt_std/dt - if std_norm>0.1**5: - if_dt_even=False - else: - if_dt_even=True + # if std_norm>0.1**5: + # if_dt_even=False + # else: + # if_dt_even=True - return dt,dt_std,if_dt_even + # return dt,dt_std,if_dt_even - @staticmethod - def time_interp_past_looking(time, data, time_std, mode='extrapolate'): - if mode == 'extrapolate': - pass - elif mode == 'fill': - pass + # @staticmethod + # def time_interp_past_looking(time, data, time_std, mode='extrapolate'): + # if mode == 'extrapolate': + # pass + # elif mode == 'fill': + # pass - def time_interp_1d(self, time, data, time_std, mode='normal'): - if mode=='normal': - return np.interp(time_std, time, data) - else: - return self.time_interp_past_looking(time, data, time_std, mode) + # def time_interp_1d(self, time, data, time_std, mode='normal'): + # if mode=='normal': + # return np.interp(time_std, time, data) + # else: + # return self.time_interp_past_looking(time, data, time_std, mode) - def time_interp(self, time, data, time_std, mode='normal'): - if len(data.shape) == 1: - return self.time_interp_1d(time, data, time_std, mode=mode) - elif len(data.shape) == 2: - data_interp=[] - for i in range(data.shape[0]): - data_interp.append(self.time_interp_1d(time, data[i,:], time_std, mode=mode)) - - return np.array(data_interp) + # def time_interp(self, time, data, time_std, mode='normal'): + # if len(data.shape) == 1: + # return self.time_interp_1d(time, data, time_std, mode=mode) + # elif len(data.shape) == 2: + # data_interp=[] + # for i in range(data.shape[0]): + # data_interp.append(self.time_interp_1d(time, data[i,:], time_std, mode=mode)) + + # return np.array(data_interp) - @staticmethod - def find_plateau(series, window_size=40, threshold=0.1, plot=False): - """ - Finds the start and end times of the plateau region in the given time series. - - Args: - series (pd.Series): Time series data with time as index and values as data. - window_size (int, optional): Window size for rolling average and standard deviation. Default is 40. - threshold (float, optional): Threshold for determining the plateau region. Default is 0.1. - - Returns: - tuple: (t_min, t_max) representing the start and end times of the plateau region. - """ - rolling_avg = series.rolling(window=window_size, center=True).mean() - data_rolling_std = series.rolling(window=window_size, center=True).std() - - # Normalize the rolling standard deviation - normalized_std = data_rolling_std / abs(rolling_avg) - if min(normalized_std)>=threshold: - return 0,0 - - # Find the start and end indices of the plateau region - plateau_start = normalized_std[normalized_std < threshold].index[0] - plateau_end = normalized_std[normalized_std < threshold].index[-1] - - if plot: - plt.clf() - plt.plot(normalized_std.index,normalized_std.values) - plt.xlabel('time (ms)') - plt.ylabel(f'std/avg(window={window_size})') - - plt.axvline(plateau_start,color='red') - plt.axvline(plateau_end,color='red') - plt.show() - - return plateau_start, plateau_end + # @staticmethod + # def find_plateau(series, window_size=40, threshold=0.1, plot=False): + # """ + # Finds the start and end times of the plateau region in the given time series. + + # Args: + # series (pd.Series): Time series data with time as index and values as data. + # window_size (int, optional): Window size for rolling average and standard deviation. Default is 40. + # threshold (float, optional): Threshold for determining the plateau region. Default is 0.1. + + # Returns: + # tuple: (t_min, t_max) representing the start and end times of the plateau region. + # """ + # rolling_avg = series.rolling(window=window_size, center=True).mean() + # data_rolling_std = series.rolling(window=window_size, center=True).std() + + # # Normalize the rolling standard deviation + # normalized_std = data_rolling_std / abs(rolling_avg) + # if min(normalized_std)>=threshold: + # return 0,0 + + # # Find the start and end indices of the plateau region + # plateau_start = normalized_std[normalized_std < threshold].index[0] + # plateau_end = normalized_std[normalized_std < threshold].index[-1] + + # if plot: + # plt.clf() + # plt.plot(normalized_std.index,normalized_std.values) + # plt.xlabel('time (ms)') + # plt.ylabel(f'std/avg(window={window_size})') + + # plt.axvline(plateau_start,color='red') + # plt.axvline(plateau_end,color='red') + # plt.show() + + # return plateau_start, plateau_end - def flat_top_finder(self,discharge, window_size=500, threshold=0.01, plot=False): - file_dict = self.get_data(discharge, 'basic', norm=True) - time = file_dict['ip']['xdata'][:] - data = file_dict['ip']['zdata'][:] - if max(data)<=3: - t_max, t_min = 0,0 - else: - time=time[data>3] - data=data[data>3] - - dip = np.gradient(data, time) - - # Convert data and time to a pandas Series - series = pd.Series(data, index=time) - t_max, t_min=self.find_plateau(series,window_size=window_size, threshold=threshold, plot=plot) - - if plot: - plt.clf() - plt.plot(series.index, series.values) - plt.axvline(t_min,color='red') - plt.axvline(t_max,color='red') - plt.xlabel('time (ms)') - plt.ylabel(r'Plasma current $I_p$') - plt.show() - - return t_max, t_min + # def flat_top_finder(self,discharge, window_size=500, threshold=0.01, plot=False): + # file_dict = self.get_data(discharge, 'basic', norm=True) + # time = file_dict['ip']['xdata'][:] + # data = file_dict['ip']['zdata'][:] + # if max(data)<=3: + # t_max, t_min = 0,0 + # else: + # time=time[data>3] + # data=data[data>3] + + # dip = np.gradient(data, time) + + # # Convert data and time to a pandas Series + # series = pd.Series(data, index=time) + # t_max, t_min=self.find_plateau(series,window_size=window_size, threshold=threshold, plot=plot) + + # if plot: + # plt.clf() + # plt.plot(series.index, series.values) + # plt.axvline(t_min,color='red') + # plt.axvline(t_max,color='red') + # plt.xlabel('time (ms)') + # plt.ylabel(r'Plasma current $I_p$') + # plt.show() + + # return t_max, t_min def deal_with_missing_data(self): pass - def time_series_full_pipeline(self,discharge,suffix_list,time_std_key, time_std=[],custom_time_std=False,Ip_window_size=500, Ip_std_threshold=0.01, plot_Ip=False, norm_mode='all', interp_suffix=[], interp_mode='normal', time_matching_mode='dynamic', left_window={'ece_s':50}, right_window={'ece_s':50}, time_matching_padding='zeros', plot_matched_data=False): - ''' - - time_std: the standard time - - interp_suffix: the list suffix and key to - e.g. interp_suffix=[['ts','core.dens'],['ts','core.dens']] #e.g. [['ts','core.dens'],['ts','core.dens']] - interp_mode='normal' - - time_matching_mode = ['merge_asof','binary','dynamic'] only 'dynamic' works for now (04/29/2024) - - time_matching_padding=['zeros', 'last', 'nan'] - - plot_matched_data: plot the matched data - ''' - - #get all the data and normalize the data - all_file_dict=self.get_full_data(discharge, suffix_list, norm_mode=norm_mode) - if custom_time_std: - pass - else: - time_std=all_file_dict[time_std_key[0]][time_std_key[1]]['xdata'][:] - - t_min, t_max=self.flat_top_finder(discharge, window_size=Ip_window_size, threshold=Ip_std_threshold, plot=plot_Ip) - - #time_interp - for item in interp_suffix: - data_interp=self.time_interp(all_file_dict[item[0]][item[1]]['xdata'][:], \ - all_file_dict[item[0]][item[1]]['zdata'][:], \ - time_std, mode=interp_mode) - all_file_dict[item[0]][item[1]]['xdata']=time_std - all_file_dict[item[0]][item[1]]['zdata']=data_interp - - #cut_time for standard time - [key1,_]=time_std_key - - if custom_time_std: - time_cut,data_cut=self.cut_time(time_std, time_std,t_min, t_max) - else: - for key2 in all_file_dict[key1].keys(): - time_cut,data_cut=self.cut_time(all_file_dict[key1][key2]['xdata'][:], \ - all_file_dict[key1][key2]['zdata'][:], \ - t_min, t_max) - all_file_dict[key1][key2]={'xdata':time_cut,'zdata':data_cut} + # def time_series_full_pipeline(self,discharge,suffix_list,time_std_key, time_std=[],custom_time_std=False,Ip_window_size=500, Ip_std_threshold=0.01, plot_Ip=False, norm_mode='all', interp_suffix=[], interp_mode='normal', time_matching_mode='dynamic', left_window={'ece_s':50}, right_window={'ece_s':50}, time_matching_padding='zeros', plot_matched_data=False): + # ''' + + # time_std: the standard time + + # interp_suffix: the list suffix and key to + # e.g. interp_suffix=[['ts','core.dens'],['ts','core.dens']] #e.g. [['ts','core.dens'],['ts','core.dens']] + # interp_mode='normal' + + # time_matching_mode = ['merge_asof','binary','dynamic'] only 'dynamic' works for now (04/29/2024) + + # time_matching_padding=['zeros', 'last', 'nan'] + + # plot_matched_data: plot the matched data + # ''' + + # #get all the data and normalize the data + # all_file_dict=self.get_full_data(discharge, suffix_list, norm_mode=norm_mode) + # if custom_time_std: + # pass + # else: + # time_std=all_file_dict[time_std_key[0]][time_std_key[1]]['xdata'][:] + + # t_min, t_max=self.flat_top_finder(discharge, window_size=Ip_window_size, threshold=Ip_std_threshold, plot=plot_Ip) + + # #time_interp + # for item in interp_suffix: + # data_interp=self.time_interp(all_file_dict[item[0]][item[1]]['xdata'][:], \ + # all_file_dict[item[0]][item[1]]['zdata'][:], \ + # time_std, mode=interp_mode) + # all_file_dict[item[0]][item[1]]['xdata']=time_std + # all_file_dict[item[0]][item[1]]['zdata']=data_interp + + # #cut_time for standard time + # [key1,_]=time_std_key + + # if custom_time_std: + # time_cut,data_cut=self.cut_time(time_std, time_std,t_min, t_max) + # else: + # for key2 in all_file_dict[key1].keys(): + # time_cut,data_cut=self.cut_time(all_file_dict[key1][key2]['xdata'][:], \ + # all_file_dict[key1][key2]['zdata'][:], \ + # t_min, t_max) + # all_file_dict[key1][key2]={'xdata':time_cut,'zdata':data_cut} - time_std=time_cut + # time_std=time_cut - #time matching - for key1 in all_file_dict.keys(): - for key2 in all_file_dict[key1].keys(): + # #time matching + # for key1 in all_file_dict.keys(): + # for key2 in all_file_dict[key1].keys(): - try: - left_window_tmp=left_window[key1][key2] - right_window_tmp=right_window[key1][key2] - except: - left_window_tmp=0 - right_window_tmp=0 + # try: + # left_window_tmp=left_window[key1][key2] + # right_window_tmp=right_window[key1][key2] + # except: + # left_window_tmp=0 + # right_window_tmp=0 - matched_time, matched_data=self.time_matching(\ - all_file_dict[key1][key2]['xdata'][:], \ - all_file_dict[key1][key2]['zdata'][:], \ - time_std, \ - left_window=left_window_tmp, \ - right_window=right_window_tmp, \ - mode=time_matching_mode,\ - padding=time_matching_padding) + # matched_time, matched_data=self.time_matching(\ + # all_file_dict[key1][key2]['xdata'][:], \ + # all_file_dict[key1][key2]['zdata'][:], \ + # time_std, \ + # left_window=left_window_tmp, \ + # right_window=right_window_tmp, \ + # mode=time_matching_mode,\ + # padding=time_matching_padding) - all_file_dict[key1][key2]['xdata']=matched_time - all_file_dict[key1][key2]['zdata']=matched_data + # all_file_dict[key1][key2]['xdata']=matched_time + # all_file_dict[key1][key2]['zdata']=matched_data - if plot_matched_data: - for key1 in all_file_dict.keys(): - for key2 in all_file_dict[key1].keys(): - print([key1,key2]) - plt.clf() - if all_file_dict[key1][key2]['zdata'].shape[2]==1: + # if plot_matched_data: + # for key1 in all_file_dict.keys(): + # for key2 in all_file_dict[key1].keys(): + # print([key1,key2]) + # plt.clf() + # if all_file_dict[key1][key2]['zdata'].shape[2]==1: - plt.plot(all_file_dict[key1][key2]['xdata'][:,0],\ - all_file_dict[key1][key2]['zdata'][:,:,0]) - else: - for i in range(len(all_file_dict[key1][key2]['xdata'][:])): - plt.plot(all_file_dict[key1][key2]['xdata'][i,:].T,\ - all_file_dict[key1][key2]['zdata'][i,:,:].T) - plt.xlabel('Time (ms)') - plt.ylabel(f'{key1}-{key2}') - plt.show() - - return all_file_dict - -class DatasetPrep(DichargePerp): - def __init__(self, discharge_search_list=[174823], suffix_list=['ts']): - self.discharge_search_list = discharge_search_list - self.suffix_list = suffix_list + # plt.plot(all_file_dict[key1][key2]['xdata'][:,0],\ + # all_file_dict[key1][key2]['zdata'][:,:,0]) + # else: + # for i in range(len(all_file_dict[key1][key2]['xdata'][:])): + # plt.plot(all_file_dict[key1][key2]['xdata'][i,:].T,\ + # all_file_dict[key1][key2]['zdata'][i,:,:].T) + # plt.xlabel('Time (ms)') + # plt.ylabel(f'{key1}-{key2}') + # plt.show() + + # return all_file_dict + +# class DatasetPrep(DichargePerp): +# def __init__(self, discharge_search_list=[174823], suffix_list=['ts']): +# self.discharge_search_list = discharge_search_list +# self.suffix_list = suffix_list - def filter_discharges(self): - suffix_list = self.suffix_list - discharge_search_list = self.discharge_search_list - # Define the criteria for the files you're interested in - criteria = {key: file_normal_size[key]*0.5 for key in suffix_list} +# def filter_discharges(self): +# suffix_list = self.suffix_list +# discharge_search_list = self.discharge_search_list +# # Define the criteria for the files you're interested in +# criteria = {key: file_normal_size[key]*0.5 for key in suffix_list} - discharge_list = {key: [] for key in suffix_list} +# discharge_list = {key: [] for key in suffix_list} - for discharge in tqdm(discharge_search_list): - for suffix, size_limit in criteria.items(): - discharge_path = self.file_path_gen(discharge,suffix) - # Check if the file exists - if os.path.isfile(discharge_path): - # Get the size of the file - file_size = os.path.getsize(discharge_path) - if file_size > size_limit: - discharge_list[suffix].append(discharge) - else: - pass - return discharge_list - - - def normalization(data): - pass - - def merge_multi_discharge(self,discharge_list_tmp,): - pass - - -class post_processing(): - def smooth_rolling_avg(self, time, data, smooth_point, center_window=10000, edge_window=500, time_spread=5): - indx_min=np.argmin(abs(time-(smooth_point-time_spread))) - indx_max=np.argmin(abs(time-(smooth_point+time_spread))) - smoothed_section=np.zeros(indx_max-indx_min+1) - for i in range(indx_min,indx_max+1): - current_time=time[i] - window_size=center_window-abs(smooth_point-current_time)/time_spread*(center_window-edge_window) - smoothed_section[i-indx_min]=np.mean(data[i-int(window_size/2):i+int(window_size/2)+1]) - return smoothed_section,indx_min,indx_max - - - def smooth_rolling_avg_and_put_back(self, time, data, smooth_point_list, center_window=10000, edge_window=500, time_spread=5): - data_smooth=data.copy() - for smooth_point in smooth_point_list: - smoothed_section,indx_min,indx_max=self.smooth_rolling_avg(time, data, smooth_point, center_window=center_window, edge_window=edge_window, time_spread=time_spread) - data_smooth[indx_min:indx_max+1]=smoothed_section - return data_smooth - - def denorm_data(): - pass - -class data_obj_rest(): +# for discharge in tqdm(discharge_search_list): +# for suffix, size_limit in criteria.items(): +# discharge_path = self.file_path_gen(discharge,suffix) +# # Check if the file exists +# if os.path.isfile(discharge_path): +# # Get the size of the file +# file_size = os.path.getsize(discharge_path) +# if file_size > size_limit: +# discharge_list[suffix].append(discharge) +# else: +# pass +# return discharge_list + + +# def normalization(data): +# pass + +# def merge_multi_discharge(self,discharge_list_tmp,): +# pass + + +# class post_processing(): +# def smooth_rolling_avg(self, time, data, smooth_point, center_window=10000, edge_window=500, time_spread=5): +# indx_min=np.argmin(abs(time-(smooth_point-time_spread))) +# indx_max=np.argmin(abs(time-(smooth_point+time_spread))) +# smoothed_section=np.zeros(indx_max-indx_min+1) +# for i in range(indx_min,indx_max+1): +# current_time=time[i] +# window_size=center_window-abs(smooth_point-current_time)/time_spread*(center_window-edge_window) +# smoothed_section[i-indx_min]=np.mean(data[i-int(window_size/2):i+int(window_size/2)+1]) +# return smoothed_section,indx_min,indx_max + + +# def smooth_rolling_avg_and_put_back(self, time, data, smooth_point_list, center_window=10000, edge_window=500, time_spread=5): +# data_smooth=data.copy() +# for smooth_point in smooth_point_list: +# smoothed_section,indx_min,indx_max=self.smooth_rolling_avg(time, data, smooth_point, center_window=center_window, edge_window=edge_window, time_spread=time_spread) +# data_smooth[indx_min:indx_max+1]=smoothed_section +# return data_smooth + +# def denorm_data(): +# pass + +# class data_obj_rest(): - def save_dict_to_hdf5(dictionary, h5file): - for key, value in dictionary.items(): - if isinstance(value, dict): - group = h5file.create_group(key) - save_dict_to_hdf5(value, group) - else: - h5file.create_dataset(key, data=value) - - def TS_interp_Z(discharge,write_h5=True,plot=False): - TS_Z_min_set = [0.0, 0.03, 0.09, 0.1, 0.15, 0.16, - 0.21, 0.22, 0.26, 0.27, 0.28, 0.3, 0.31, 0.32, 0.36, - 0.37, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, - 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, - 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, - 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, - 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, - 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, - 0.92, 0.93] - str_shot = str(discharge)[:2] - path = f'/scratch/gpfs/EKOLEMEN/big_d3d_data/{str_shot}0000/' +# # def save_dict_to_hdf5(dictionary, h5file): +# # for key, value in dictionary.items(): +# # if isinstance(value, dict): +# # group = h5file.create_group(key) +# # save_dict_to_hdf5(value, group) +# # else: +# # h5file.create_dataset(key, data=value) + +# def TS_interp_Z(discharge,write_h5=True,plot=False): +# TS_Z_min_set = [0.0, 0.03, 0.09, 0.1, 0.15, 0.16, +# 0.21, 0.22, 0.26, 0.27, 0.28, 0.3, 0.31, 0.32, 0.36, +# 0.37, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, +# 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, +# 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, +# 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, +# 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, +# 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, +# 0.92, 0.93] +# str_shot = str(discharge)[:2] +# path = f'/scratch/gpfs/EKOLEMEN/big_d3d_data/{str_shot}0000/' - TS_file = h5py.File(path + str(discharge) + '_TS.h5', 'r') - TS_RZ_file = h5py.File(path + str(discharge) + '_TS_RZ.h5', 'r') +# TS_file = h5py.File(path + str(discharge) + '_TS.h5', 'r') +# TS_RZ_file = h5py.File(path + str(discharge) + '_TS_RZ.h5', 'r') - TS_Z = TS_RZ_file['S.BLESSED.CORE.Z']['zdata'][:] - order_index = np.argsort(TS_Z) - TS_Z_sort = TS_Z[order_index] +# TS_Z = TS_RZ_file['S.BLESSED.CORE.Z']['zdata'][:] +# order_index = np.argsort(TS_Z) +# TS_Z_sort = TS_Z[order_index] - TS_interp = {} - TS_keys = ['TS.BLESSED.CORE.density', 'TS.BLESSED.CORE.temp'] - for key in TS_keys: - TS_interp_list = [] - TS_time = TS_file[key]['xdata'][:] - for i in range(len(TS_time)): - TS_data = TS_file[key]['zdata'][:, i]*0.1**19 - TS_data_sort = TS_data[order_index] - TS_interp_tmp = np.interp(TS_Z_min_set, TS_Z_sort, - TS_data_sort) - TS_interp_list.append(TS_interp_tmp) - # start here - TS_interp[key] = {'xdata': np.array(TS_time), - 'ydata': np.array(TS_Z_min_set), - 'zdata': np.array(TS_interp_list).T*10.**19} - - if write_h5: - with h5py.File(f'{path}{discharge}_TS_core_interp.h5', 'w') as h5file: - save_dict_to_hdf5(TS_interp, h5file) - - if plot: - plt.clf() - plt.scatter(TS_Z_min_set, TS_interp[key]['zdata'][:, 600], - label='interp') - plt.scatter(TS_Z_sort, (TS_file[key]['zdata'][:,600])[order_index], - label='origin') - plt.legend() - plt.show() +# TS_interp = {} +# TS_keys = ['TS.BLESSED.CORE.density', 'TS.BLESSED.CORE.temp'] +# for key in TS_keys: +# TS_interp_list = [] +# TS_time = TS_file[key]['xdata'][:] +# for i in range(len(TS_time)): +# TS_data = TS_file[key]['zdata'][:, i]*0.1**19 +# TS_data_sort = TS_data[order_index] +# TS_interp_tmp = np.interp(TS_Z_min_set, TS_Z_sort, +# TS_data_sort) +# TS_interp_list.append(TS_interp_tmp) +# # start here +# TS_interp[key] = {'xdata': np.array(TS_time), +# 'ydata': np.array(TS_Z_min_set), +# 'zdata': np.array(TS_interp_list).T*10.**19} + +# if write_h5: +# with h5py.File(f'{path}{discharge}_TS_core_interp.h5', 'w') as h5file: +# save_dict_to_hdf5(TS_interp, h5file) + +# if plot: +# plt.clf() +# plt.scatter(TS_Z_min_set, TS_interp[key]['zdata'][:, 600], +# label='interp') +# plt.scatter(TS_Z_sort, (TS_file[key]['zdata'][:,600])[order_index], +# label='origin') +# plt.legend() +# plt.show() - return 0 +# return 0 - def read_file(discharge, file_suffix, df_time): - - path = find_path(discharge) - file = h5py.File(f'{path}{discharge}_{file_suffix}.h5', 'r') - keys = file.keys() - - for i, key in enumerate(keys): - dict_tmp = {'xdata': file[key]['xdata']} - if len(file[key]['zdata'].shape) == 2: - for j in range(file[key]['zdata'].shape[0]): - dict_tmp[key+str(j)] = file[key]['zdata'][j, :] - elif len(file[key]['zdata'].shape) == 1: - dict_tmp[key] = file[key]['zdata'] + # def read_file(discharge, file_suffix, df_time): + + # path = find_path(discharge) + # file = h5py.File(f'{path}{discharge}_{file_suffix}.h5', 'r') + # keys = file.keys() + + # for i, key in enumerate(keys): + # dict_tmp = {'xdata': file[key]['xdata']} + # if len(file[key]['zdata'].shape) == 2: + # for j in range(file[key]['zdata'].shape[0]): + # dict_tmp[key+str(j)] = file[key]['zdata'][j, :] + # elif len(file[key]['zdata'].shape) == 1: + # dict_tmp[key] = file[key]['zdata'] - df_tmp = pd.DataFrame(dict_tmp).astype('float32') - if i == 0: - df = pd.merge_asof(df_time, df_tmp, on='xdata', - direction='nearest') - else: - df = pd.merge_asof(df, df_tmp, on='xdata', - direction='nearest') - file.close() - return df - - def hdf5_generator(discharge_list, h5_profiles, - data_filename='diag2diag.pkl'): - all_X =[] - all_y = [] - all_time = [] - discharg_read_list = [] - len_list = [] - for discharge in tqdm(discharge_list): - print(discharge) - try: - dfs = {} - # creating the standard time - path=find_path(discharge) + # df_tmp = pd.DataFrame(dict_tmp).astype('float32') + # if i == 0: + # df = pd.merge_asof(df_time, df_tmp, on='xdata', + # direction='nearest') + # else: + # df = pd.merge_asof(df, df_tmp, on='xdata', + # direction='nearest') + # file.close() + # return df + + # def hdf5_generator(discharge_list, h5_profiles, + # data_filename='diag2diag.pkl'): + # all_X =[] + # all_y = [] + # all_time = [] + # discharg_read_list = [] + # len_list = [] + # for discharge in tqdm(discharge_list): + # print(discharge) + # try: + # dfs = {} + # # creating the standard time + # path=find_path(discharge) - file = h5py.File(f'{path}{discharge}_shape.h5', 'r') - t_min = 0 - t_max = file['R0']['xdata'][-1] - file.close() + # file = h5py.File(f'{path}{discharge}_shape.h5', 'r') + # t_min = 0 + # t_max = file['R0']['xdata'][-1] + # file.close() - file = h5py.File(f'{path}{discharge}_TS.h5', 'r') - df_time = pd.DataFrame({'xdata': file[list(file.keys())[0]]['xdata']}) - time = file[list(file.keys())[0]]['xdata'][:] + # file = h5py.File(f'{path}{discharge}_TS.h5', 'r') + # df_time = pd.DataFrame({'xdata': file[list(file.keys())[0]]['xdata']}) + # time = file[list(file.keys())[0]]['xdata'][:] - time_index = (time >= t_min) & (time <= t_max) - time_tmp = time[time_index] - df_time = pd.DataFrame({'xdata': time_tmp}) - file.close() + # time_index = (time >= t_min) & (time <= t_max) + # time_tmp = time[time_index] + # df_time = pd.DataFrame({'xdata': time_tmp}) + # file.close() - # Read all the files - for file_suffix in h5_profiles: - df = read_file(discharge, file_suffix, df_time) - dfs[file_suffix] = df - - # summarize all the data in this dicharge - df_tmp = np.concatenate( - [dfs[key].to_numpy()[1:] for key in dfs.keys()], axis=1) - - key_list_dict = {} - key_list = [] - for key in dfs.keys(): - key_list_dict[key]=list(dfs[key].keys()) - for key_ in key_list_dict[key]: - key_list.append(key_) + # # Read all the files + # for file_suffix in h5_profiles: + # df = read_file(discharge, file_suffix, df_time) + # dfs[file_suffix] = df + + # # summarize all the data in this dicharge + # df_tmp = np.concatenate( + # [dfs[key].to_numpy()[1:] for key in dfs.keys()], axis=1) + + # key_list_dict = {} + # key_list = [] + # for key in dfs.keys(): + # key_list_dict[key]=list(dfs[key].keys()) + # for key_ in key_list_dict[key]: + # key_list.append(key_) - # add this discharge to the total file - all_X.append(df_tmp) - all_time.append(df_time['xdata']) - all_time_tmp= np.concatenate(all_time, axis=0) - all_X_tmp = np.concatenate(all_X, axis=0) - len_list.append(df_time['xdata'].shape[0]) - discharg_read_list.append(discharge) - # Serialize the data and save to a file - with open(data_filename, 'wb') as file: - pickle.dump([all_X_tmp, all_time_tmp, discharg_read_list, - len_list, key_list, key_list_dict], file) - - except Exception as e: # if 2==1: - print(f"Error: {e}") - continue - finally: # if 2==1: - try: - file.close() - except: - continue - - return [all_X_tmp] + # # add this discharge to the total file + # all_X.append(df_tmp) + # all_time.append(df_time['xdata']) + # all_time_tmp= np.concatenate(all_time, axis=0) + # all_X_tmp = np.concatenate(all_X, axis=0) + # len_list.append(df_time['xdata'].shape[0]) + # discharg_read_list.append(discharge) + # # Serialize the data and save to a file + # with open(data_filename, 'wb') as file: + # pickle.dump([all_X_tmp, all_time_tmp, discharg_read_list, + # len_list, key_list, key_list_dict], file) + + # except Exception as e: # if 2==1: + # print(f"Error: {e}") + # continue + # finally: # if 2==1: + # try: + # file.close() + # except: + # continue + + # return [all_X_tmp] From e0b3eca6cafb9d9d021936d0dcfd5ee3eee45115 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Tue, 11 Jun 2024 13:57:56 -0400 Subject: [PATCH 013/103] Added functions --- src/fusion_ai_hub/base/util/dict_to_hdf5.py | 7 ++ src/fusion_ai_hub/base/util/divide_data.py | 11 ++++ src/fusion_ai_hub/base/util/generate_hdf5.py | 66 +++++++++++++++++++ src/fusion_ai_hub/base/util/hdf5_to_dict.py | 8 +++ src/fusion_ai_hub/base/util/read_file.py | 23 +++++++ .../core/magnitude_scaling/compute_norms.py | 42 ++++++++++++ .../core/magnitude_scaling/norm.py | 19 ++++++ .../core/magnitude_scaling/rescale.py | 0 .../core/time_domain_processing/cut_time.py | 6 ++ .../get_windowed_data.py | 6 ++ src/fusion_ai_hub/datasets/fetch/fetch.py | 0 src/fusion_ai_hub/datasets/toy_loader/load.py | 17 +++++ src/fusion_ai_hub/display/specshow.py | 10 +++ src/fusion_ai_hub/display/waveshow.py | 9 +++ src/fusion_ai_hub/sampling/match_times.py | 23 +++++++ 15 files changed, 247 insertions(+) create mode 100644 src/fusion_ai_hub/base/util/dict_to_hdf5.py create mode 100644 src/fusion_ai_hub/base/util/divide_data.py create mode 100644 src/fusion_ai_hub/base/util/generate_hdf5.py create mode 100644 src/fusion_ai_hub/base/util/hdf5_to_dict.py create mode 100644 src/fusion_ai_hub/base/util/read_file.py create mode 100644 src/fusion_ai_hub/core/magnitude_scaling/compute_norms.py create mode 100644 src/fusion_ai_hub/core/magnitude_scaling/norm.py create mode 100644 src/fusion_ai_hub/core/magnitude_scaling/rescale.py create mode 100644 src/fusion_ai_hub/core/time_domain_processing/cut_time.py create mode 100644 src/fusion_ai_hub/core/time_domain_processing/get_windowed_data.py create mode 100644 src/fusion_ai_hub/datasets/fetch/fetch.py create mode 100644 src/fusion_ai_hub/datasets/toy_loader/load.py create mode 100644 src/fusion_ai_hub/display/specshow.py create mode 100644 src/fusion_ai_hub/display/waveshow.py create mode 100644 src/fusion_ai_hub/sampling/match_times.py diff --git a/src/fusion_ai_hub/base/util/dict_to_hdf5.py b/src/fusion_ai_hub/base/util/dict_to_hdf5.py new file mode 100644 index 0000000..d1992b1 --- /dev/null +++ b/src/fusion_ai_hub/base/util/dict_to_hdf5.py @@ -0,0 +1,7 @@ +def save_dict_to_hdf5(dictionary, h5file): + for key, value in dictionary.items(): + if isinstance(value, dict): + group = h5file.create_group(key) + save_dict_to_hdf5(value, group) + else: + h5file.create_dataset(key, data=value) \ No newline at end of file diff --git a/src/fusion_ai_hub/base/util/divide_data.py b/src/fusion_ai_hub/base/util/divide_data.py new file mode 100644 index 0000000..2748d95 --- /dev/null +++ b/src/fusion_ai_hub/base/util/divide_data.py @@ -0,0 +1,11 @@ + # divide the data into subcategory +def data_division(self, input_file, input_suffix): + if input_suffix in multi_level: + input_multi_level = {} + for key in file_keys[input_suffix].keys(): + keys_of_this_category = file_keys[input_suffix][key] + input_multi_level[key] = {key_i: input_file[key_i] + for key_i in keys_of_this_category} + else: + input_multi_level = {input_suffix: input_file} + return input_multi_level \ No newline at end of file diff --git a/src/fusion_ai_hub/base/util/generate_hdf5.py b/src/fusion_ai_hub/base/util/generate_hdf5.py new file mode 100644 index 0000000..df52710 --- /dev/null +++ b/src/fusion_ai_hub/base/util/generate_hdf5.py @@ -0,0 +1,66 @@ +def hdf5_generator(discharge_list, h5_profiles, + data_filename='diag2diag.pkl'): + all_X =[] + all_y = [] + all_time = [] + discharg_read_list = [] + len_list = [] + for discharge in tqdm(discharge_list): + print(discharge) + try: + dfs = {} + # creating the standard time + path=find_path(discharge) + + file = h5py.File(f'{path}{discharge}_shape.h5', 'r') + t_min = 0 + t_max = file['R0']['xdata'][-1] + file.close() + + file = h5py.File(f'{path}{discharge}_TS.h5', 'r') + df_time = pd.DataFrame({'xdata': file[list(file.keys())[0]]['xdata']}) + time = file[list(file.keys())[0]]['xdata'][:] + + time_index = (time >= t_min) & (time <= t_max) + time_tmp = time[time_index] + df_time = pd.DataFrame({'xdata': time_tmp}) + file.close() + + # Read all the files + for file_suffix in h5_profiles: + df = read_file(discharge, file_suffix, df_time) + dfs[file_suffix] = df + + # summarize all the data in this dicharge + df_tmp = np.concatenate( + [dfs[key].to_numpy()[1:] for key in dfs.keys()], axis=1) + + key_list_dict = {} + key_list = [] + for key in dfs.keys(): + key_list_dict[key]=list(dfs[key].keys()) + for key_ in key_list_dict[key]: + key_list.append(key_) + + # add this discharge to the total file + all_X.append(df_tmp) + all_time.append(df_time['xdata']) + all_time_tmp= np.concatenate(all_time, axis=0) + all_X_tmp = np.concatenate(all_X, axis=0) + len_list.append(df_time['xdata'].shape[0]) + discharg_read_list.append(discharge) + # Serialize the data and save to a file + with open(data_filename, 'wb') as file: + pickle.dump([all_X_tmp, all_time_tmp, discharg_read_list, + len_list, key_list, key_list_dict], file) + + except Exception as e: # if 2==1: + print(f"Error: {e}") + continue + finally: # if 2==1: + try: + file.close() + except: + continue + + return [all_X_tmp] \ No newline at end of file diff --git a/src/fusion_ai_hub/base/util/hdf5_to_dict.py b/src/fusion_ai_hub/base/util/hdf5_to_dict.py new file mode 100644 index 0000000..8a7ec9f --- /dev/null +++ b/src/fusion_ai_hub/base/util/hdf5_to_dict.py @@ -0,0 +1,8 @@ +def hdf5_to_dict(self, group): + result = {} + for key in group.keys(): + if isinstance(group[key], h5py.Dataset): + result[key] = group[key][()] + elif isinstance(group[key], h5py.Group): + result[key] = self.hdf5_to_dict(group[key]) + return result \ No newline at end of file diff --git a/src/fusion_ai_hub/base/util/read_file.py b/src/fusion_ai_hub/base/util/read_file.py new file mode 100644 index 0000000..13515ab --- /dev/null +++ b/src/fusion_ai_hub/base/util/read_file.py @@ -0,0 +1,23 @@ +def read_file(discharge, file_suffix, df_time): + + path = find_path(discharge) + file = h5py.File(f'{path}{discharge}_{file_suffix}.h5', 'r') + keys = file.keys() + + for i, key in enumerate(keys): + dict_tmp = {'xdata': file[key]['xdata']} + if len(file[key]['zdata'].shape) == 2: + for j in range(file[key]['zdata'].shape[0]): + dict_tmp[key+str(j)] = file[key]['zdata'][j, :] + elif len(file[key]['zdata'].shape) == 1: + dict_tmp[key] = file[key]['zdata'] + + df_tmp = pd.DataFrame(dict_tmp).astype('float32') + if i == 0: + df = pd.merge_asof(df_time, df_tmp, on='xdata', + direction='nearest') + else: + df = pd.merge_asof(df, df_tmp, on='xdata', + direction='nearest') + file.close() + return df \ No newline at end of file diff --git a/src/fusion_ai_hub/core/magnitude_scaling/compute_norms.py b/src/fusion_ai_hub/core/magnitude_scaling/compute_norms.py new file mode 100644 index 0000000..f77a70c --- /dev/null +++ b/src/fusion_ai_hub/core/magnitude_scaling/compute_norms.py @@ -0,0 +1,42 @@ +def order_of_magnitude_normal_factor_calc(self,discharge): + norm_factor_list_tmp={} + for suffix in self.file_keys.keys(): + file_dict=self.get_data(discharge, suffix, norm=False) + norm_factor_list_tmp[suffix]={} + for key in file_dict.keys(): + mean_tmp=abs(np.mean(file_dict[key]['zdata'][:])) + try: + exponent=self.get_order_of_magnitude(mean_tmp) + norm_factor_list_tmp[suffix][key]=10**exponent + except: + norm_factor_list_tmp[suffix][key]=1. + return norm_factor_list_tmp + +def avg_factor_calc(self,discharge): + avg_factor={} + for suffix in self.file_keys.keys(): + file_dict=self.get_data(discharge, suffix, norm=True) + avg_factor[suffix]={} + for key in file_dict.keys(): + data=file_dict[key]['zdata'][:] + avg_tmp=np.mean(data,axis=len(data.shape)-1) + if np.isnan(avg_tmp).any(): + avg_tmp=0. + + avg_factor[suffix][key]=avg_tmp + + return avg_factor + +def std_factor_calc(self,discharge): + std_factor={} + for suffix in self.file_keys.keys(): + file_dict=self.get_data(discharge, suffix, norm=True) + std_factor[suffix]={} + for key in file_dict.keys(): + data=file_dict[key]['zdata'][:] + std_tmp=np.std(data,axis=len(data.shape)-1) + if np.isnan(std_tmp).any(): + std_tmp=1. + std_factor[suffix][key]=std_tmp + + return std_factor \ No newline at end of file diff --git a/src/fusion_ai_hub/core/magnitude_scaling/norm.py b/src/fusion_ai_hub/core/magnitude_scaling/norm.py new file mode 100644 index 0000000..61524d8 --- /dev/null +++ b/src/fusion_ai_hub/core/magnitude_scaling/norm.py @@ -0,0 +1,19 @@ +@staticmethod +def norm_data(data,avg_,std_,mode='all'): + avg_=np.array(avg_) + std_=np.array(std_) + if mode == 'all': + std_all=(np.mean(std_**2))**0.5 + avg_all=np.mean(avg_) + elif mode=='std_all_avg_individual': + std_all=(np.mean(std_**2))**0.5 + avg_all=np.expand_dims(avg_,axis=1) + + elif mode=='individual': + std_all=np.expand_dims(avg_,axis=1) + avg_all=np.expand_dims(avg_,axis=1) + + + data_norm=(data-avg_all)/std_all + + return data_norm \ No newline at end of file diff --git a/src/fusion_ai_hub/core/magnitude_scaling/rescale.py b/src/fusion_ai_hub/core/magnitude_scaling/rescale.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fusion_ai_hub/core/time_domain_processing/cut_time.py b/src/fusion_ai_hub/core/time_domain_processing/cut_time.py new file mode 100644 index 0000000..f88553c --- /dev/null +++ b/src/fusion_ai_hub/core/time_domain_processing/cut_time.py @@ -0,0 +1,6 @@ +@staticmethod +def cut_time(time, data, t_min, t_max): + t_indx_min=np.argmin(abs(np.array(time)-t_min)) + t_indx_max=np.argmin(abs(np.array(time)-t_max)) + + return time[t_indx_min:t_indx_max], data[...,t_indx_min:t_indx_max] diff --git a/src/fusion_ai_hub/core/time_domain_processing/get_windowed_data.py b/src/fusion_ai_hub/core/time_domain_processing/get_windowed_data.py new file mode 100644 index 0000000..e73ab23 --- /dev/null +++ b/src/fusion_ai_hub/core/time_domain_processing/get_windowed_data.py @@ -0,0 +1,6 @@ +# Function to get windowed data +@staticmethod +def get_windowed_data(df, center_index, window_size=5): + start = max(center_index - window_size, 0) + end = min(center_index + window_size + 1, len(df)) + return df.iloc[start:end].drop(columns=['xdata']) \ No newline at end of file diff --git a/src/fusion_ai_hub/datasets/fetch/fetch.py b/src/fusion_ai_hub/datasets/fetch/fetch.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fusion_ai_hub/datasets/toy_loader/load.py b/src/fusion_ai_hub/datasets/toy_loader/load.py new file mode 100644 index 0000000..448c2f4 --- /dev/null +++ b/src/fusion_ai_hub/datasets/toy_loader/load.py @@ -0,0 +1,17 @@ +def get_data(self,discharge, suffix, norm=True): + discharge_path=self.file_path_gen(discharge, suffix) + input_file = h5py.File(discharge_path, 'r') + input_dict_tmp = self.hdf5_to_dict(input_file) + if suffix in no_level: + input_dict={suffix:input_dict_tmp} + else: + input_dict=input_dict_tmp + + if norm and (suffix in self.norm_factor_list): + for key in input_dict.keys(): + if self.norm_factor_list[suffix][key]=='log': + input_dict[key]['zdata']=np.log(np.array(input_dict[key]['zdata'][:])) + else: + input_dict[key]['zdata']=np.array(input_dict[key]['zdata'][:])/self.norm_factor_list[suffix][key] + + return input_dict \ No newline at end of file diff --git a/src/fusion_ai_hub/display/specshow.py b/src/fusion_ai_hub/display/specshow.py new file mode 100644 index 0000000..c62f276 --- /dev/null +++ b/src/fusion_ai_hub/display/specshow.py @@ -0,0 +1,10 @@ +@staticmethod +def spectro_plot(freq, time, amp_f_t): + plt.clf() + plt.imshow(amp_f_t,aspect='auto',cmap='hot', + extent=[time[0], time[-1], freq[-1], freq[0]]) + plt.colorbar() + plt.ylabel('kHz') + plt.xlabel('ms') + plt.gca().invert_yaxis() + plt.show() \ No newline at end of file diff --git a/src/fusion_ai_hub/display/waveshow.py b/src/fusion_ai_hub/display/waveshow.py new file mode 100644 index 0000000..1054a7d --- /dev/null +++ b/src/fusion_ai_hub/display/waveshow.py @@ -0,0 +1,9 @@ +@staticmethod +def time_serie_plot(dict): + plt.clf() + if dict['zdata'][:].shape == 1: + plt.plot(dict['xdata'][:],dict['zdata'][:]) + else: + plt.plot(dict['xdata'][:],dict['zdata'][:].T) + plt.xlabel('Time (ms)') + plt.show() \ No newline at end of file diff --git a/src/fusion_ai_hub/sampling/match_times.py b/src/fusion_ai_hub/sampling/match_times.py new file mode 100644 index 0000000..2c5d178 --- /dev/null +++ b/src/fusion_ai_hub/sampling/match_times.py @@ -0,0 +1,23 @@ +@staticmethod +def time_matching_merge_asof_1d(time1, data1, time_std, left_window=0, right_window=0): + # Convert input arrays to DataFrames + df1 = pd.DataFrame({'time1': time1, 'data1': data1}) + df2 = pd.DataFrame({'time_std': time_std}) + + # Sort the DataFrames by the time columns + df1.sort_values('time1', inplace=True) + df2.sort_values('time_std', inplace=True) + + # Perform the asof merge + merged_df = pd.merge_asof(df2, df1, left_on='time_std', + right_on='time1', direction='nearest') + + # Drop unnecessary columns and handle NaN values + merged_df.drop(columns='time_std', inplace=True) + merged_df.dropna(inplace=True) + + # Extract the matched time and data + matched_time = merged_df['time1'].values + matched_data = merged_df['data1'].values + + return matched_time, matched_data \ No newline at end of file From 1a784e7d62752220248af31f3058868ddfa36fe0 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Wed, 12 Jun 2024 05:30:57 -0700 Subject: [PATCH 014/103] Updates to syntax --- docs/functions.md | 106 +++++++++++++++ examples/Dataset_prep/data_prep_obj.py | 2 +- src/fusion_ai_hub/base/util/divide_data.py | 23 ++-- src/fusion_ai_hub/base/util/generate_hdf5.py | 2 + src/fusion_ai_hub/base/util/hdf5_to_dict.py | 43 ++++-- src/fusion_ai_hub/base/util/read_file.py | 122 ++++++++++++++---- .../core/magnitude_scaling/compute_norms.py | 106 +++++++++------ .../core/magnitude_scaling/norm.py | 3 +- .../core/time_domain_processing/cut_time.py | 38 +++++- .../get_windowed_data.py | 34 ++++- src/fusion_ai_hub/datasets/fetch/fetch.py | 11 ++ src/fusion_ai_hub/datasets/toy_loader/load.py | 22 ++++ src/fusion_ai_hub/display/specshow.py | 93 ++++++++++++- src/fusion_ai_hub/display/waveshow.py | 90 +++++++++++-- src/fusion_ai_hub/sampling/match_times.py | 4 + 15 files changed, 591 insertions(+), 108 deletions(-) create mode 100644 docs/functions.md diff --git a/docs/functions.md b/docs/functions.md new file mode 100644 index 0000000..9416d38 --- /dev/null +++ b/docs/functions.md @@ -0,0 +1,106 @@ +## Mermaid Diagram + +```mermaid +graph LR + + A[hub] + + subgraph base + direction LR + + A1[file.py] + A1 --> B1[load] + A1 --> B2[save] + A1 --> B3[merge] + end + + subgraph core + direction LR + + A2[scaling.py] + A2 --> B4[compute_norms] + A2 --> B5[norm] + A2 --> B6[rescale] + + A3[spectral.py] + A3 --> B7[spectrogram] + + A4[time_domain] + B8[filtering.py] + B9[preemphasis.py] + B10[windowing.py] + A4 --> B8 + A4 --> B9 + A4 --> B10 + B8 --> C1[lfilt] + B8 --> C2[filtfilt] + B9 --> C3[preemphasis] + B9 --> C4[deemphasis] + B10 --> C5[cut_time] + B10 --> C6[get_window] + B10 --> C7[splice_time] + end + + subgraph datasets + direction LR + + A5[query] + B11[retrieve.py] + B12[modify.py - permission] + A5 --> B11 + A5 --> B12 + end + + subgraph display + direction LR + + A6[display.py] + A6 --> B13[specshow] + A6 --> B14[waveshow] + end + + subgraph feature_extract + direction LR + + A7[filterbanks.py] + + A8[morphological_filters.py] + + A9[frame_operations.py] + + A10[delta_features.py] + A10 --> B30[closest_index] + A10 --> B31[time_matching_binary] + end + + subgraph resampling + direction LR + + A2000[interpolation.py] + A2000 --> B2000[interpolate_signal] + + A3000[resampling.py] + A3000 --> B3000[resample] + end + + subgraph util + direction LR + + A4000[util.py] + end + + subgraph physics + direction LR + + A41[flattop_finder.py] + end + + A --> base + A --> core + A --> datasets + A --> display + A --> feature_extract + A --> resampling + A --> util + A --> physics +``` diff --git a/examples/Dataset_prep/data_prep_obj.py b/examples/Dataset_prep/data_prep_obj.py index 7ebaaee..8c011d4 100644 --- a/examples/Dataset_prep/data_prep_obj.py +++ b/examples/Dataset_prep/data_prep_obj.py @@ -800,7 +800,7 @@ def __init__(self): # return freq, time, amp_f_t # @staticmethod - # def spectro_plot(freq, time, amp_f_t): + # def spectro_plot(freq, time, amp_f_tz): # plt.clf() # plt.imshow(amp_f_t,aspect='auto',cmap='hot', # extent=[time[0], time[-1], freq[-1], freq[0]]) diff --git a/src/fusion_ai_hub/base/util/divide_data.py b/src/fusion_ai_hub/base/util/divide_data.py index 2748d95..5e461a2 100644 --- a/src/fusion_ai_hub/base/util/divide_data.py +++ b/src/fusion_ai_hub/base/util/divide_data.py @@ -1,11 +1,12 @@ - # divide the data into subcategory -def data_division(self, input_file, input_suffix): - if input_suffix in multi_level: - input_multi_level = {} - for key in file_keys[input_suffix].keys(): - keys_of_this_category = file_keys[input_suffix][key] - input_multi_level[key] = {key_i: input_file[key_i] - for key_i in keys_of_this_category} - else: - input_multi_level = {input_suffix: input_file} - return input_multi_level \ No newline at end of file +# don't need this +# reference divide the data into subcategory +# def data_division(self, input_file, input_suffix): +# if input_suffix in multi_level: +# input_multi_level = {} +# for key in file_keys[input_suffix].keys(): +# keys_of_this_category = file_keys[input_suffix][key] +# input_multi_level[key] = {key_i: input_file[key_i] +# for key_i in keys_of_this_category} +# else: +# input_multi_level = {input_suffix: input_file} +# return input_multi_level \ No newline at end of file diff --git a/src/fusion_ai_hub/base/util/generate_hdf5.py b/src/fusion_ai_hub/base/util/generate_hdf5.py index df52710..20371ce 100644 --- a/src/fusion_ai_hub/base/util/generate_hdf5.py +++ b/src/fusion_ai_hub/base/util/generate_hdf5.py @@ -1,3 +1,5 @@ + +# reference def hdf5_generator(discharge_list, h5_profiles, data_filename='diag2diag.pkl'): all_X =[] diff --git a/src/fusion_ai_hub/base/util/hdf5_to_dict.py b/src/fusion_ai_hub/base/util/hdf5_to_dict.py index 8a7ec9f..a0fdb15 100644 --- a/src/fusion_ai_hub/base/util/hdf5_to_dict.py +++ b/src/fusion_ai_hub/base/util/hdf5_to_dict.py @@ -1,8 +1,35 @@ -def hdf5_to_dict(self, group): - result = {} - for key in group.keys(): - if isinstance(group[key], h5py.Dataset): - result[key] = group[key][()] - elif isinstance(group[key], h5py.Group): - result[key] = self.hdf5_to_dict(group[key]) - return result \ No newline at end of file +import h5py +from pathlib import Path + +def hdf5_to_dict(file_path: str) -> dict: + """ + Convert an HDF5 file to a dictionary. + + Parameters + ---------- + file_path : str + Path to the HDF5 file. + + Returns + ------- + dict + A dictionary containing the contents of the HDF5 file. + """ + with h5py.File(file_path, 'r') as f: + result = {} + for key in f.keys(): + if isinstance(f[key], h5py.Dataset): + result[key] = f[key][()] + elif isinstance(f[key], h5py.Group): + result[key] = hdf5_to_dict(f[key]) + return result + +# reference +# def hdf5_to_dict(self, group): +# result = {} +# for key in group.keys(): +# if isinstance(group[key], h5py.Dataset): +# result[key] = group[key][()] +# elif isinstance(group[key], h5py.Group): +# result[key] = self.hdf5_to_dict(group[key]) +# return result \ No newline at end of file diff --git a/src/fusion_ai_hub/base/util/read_file.py b/src/fusion_ai_hub/base/util/read_file.py index 13515ab..29f676d 100644 --- a/src/fusion_ai_hub/base/util/read_file.py +++ b/src/fusion_ai_hub/base/util/read_file.py @@ -1,23 +1,101 @@ -def read_file(discharge, file_suffix, df_time): - - path = find_path(discharge) - file = h5py.File(f'{path}{discharge}_{file_suffix}.h5', 'r') - keys = file.keys() - - for i, key in enumerate(keys): - dict_tmp = {'xdata': file[key]['xdata']} - if len(file[key]['zdata'].shape) == 2: - for j in range(file[key]['zdata'].shape[0]): - dict_tmp[key+str(j)] = file[key]['zdata'][j, :] - elif len(file[key]['zdata'].shape) == 1: - dict_tmp[key] = file[key]['zdata'] +import numpy as np +import h5py + +def read_data(file_path: str) -> np.ndarray: + """ + Read data from an HDF5 file. + + Parameters + ---------- + file_path : str + Path to the HDF5 file. + + Returns + ------- + np.ndarray + The data stored in the HDF5 file. + """ + with h5py.File(file_path, 'r') as f: + data = f['data'][()] + return data + +def read_time(file_path: str) -> np.ndarray: + """ + Read time from an HDF5 file. + + Parameters + ---------- + file_path : str + Path to the HDF5 file. + + Returns + ------- + np.ndarray + The time stored in the HDF5 file. + """ + with h5py.File(file_path, 'r') as f: + time = f['time'][()] + return time + +def read_attributes(file_path: str) -> list: + """ + Read attributes from an HDF5 file. + + Parameters + ---------- + file_path : str + Path to the HDF5 file. + + Returns + ------- + list + A list of attributes stored in the HDF5 file. + """ + with h5py.File(file_path, 'r') as f: + attributes = list(f.attrs.keys()) + return attributes + +def read_file(file_path: str) -> dict: + """ + Read data, time, and attributes from an HDF5 file. + + Parameters + ---------- + file_path : str + Path to the HDF5 file. + + Returns + ------- + dict + A dictionary containing the data, time, and attributes stored in the HDF5 file. + """ + with h5py.File(file_path, 'r') as f: + data = f['data'][()] + time = f['time'][()] + attributes = list(f.attrs.keys()) + return {'data': data, 'time': time, 'attributes': attributes} + +# reference +# def read_file(discharge, file_suffix, df_time): + +# path = find_path(discharge) +# file = h5py.File(f'{path}{discharge}_{file_suffix}.h5', 'r') +# keys = file.keys() + +# for i, key in enumerate(keys): +# dict_tmp = {'xdata': file[key]['xdata']} +# if len(file[key]['zdata'].shape) == 2: +# for j in range(file[key]['zdata'].shape[0]): +# dict_tmp[key+str(j)] = file[key]['zdata'][j, :] +# elif len(file[key]['zdata'].shape) == 1: +# dict_tmp[key] = file[key]['zdata'] - df_tmp = pd.DataFrame(dict_tmp).astype('float32') - if i == 0: - df = pd.merge_asof(df_time, df_tmp, on='xdata', - direction='nearest') - else: - df = pd.merge_asof(df, df_tmp, on='xdata', - direction='nearest') - file.close() - return df \ No newline at end of file +# df_tmp = pd.DataFrame(dict_tmp).astype('float32') +# if i == 0: +# df = pd.merge_asof(df_time, df_tmp, on='xdata', +# direction='nearest') +# else: +# df = pd.merge_asof(df, df_tmp, on='xdata', +# direction='nearest') +# file.close() +# return df \ No newline at end of file diff --git a/src/fusion_ai_hub/core/magnitude_scaling/compute_norms.py b/src/fusion_ai_hub/core/magnitude_scaling/compute_norms.py index f77a70c..e0116ed 100644 --- a/src/fusion_ai_hub/core/magnitude_scaling/compute_norms.py +++ b/src/fusion_ai_hub/core/magnitude_scaling/compute_norms.py @@ -1,42 +1,64 @@ -def order_of_magnitude_normal_factor_calc(self,discharge): - norm_factor_list_tmp={} - for suffix in self.file_keys.keys(): - file_dict=self.get_data(discharge, suffix, norm=False) - norm_factor_list_tmp[suffix]={} - for key in file_dict.keys(): - mean_tmp=abs(np.mean(file_dict[key]['zdata'][:])) - try: - exponent=self.get_order_of_magnitude(mean_tmp) - norm_factor_list_tmp[suffix][key]=10**exponent - except: - norm_factor_list_tmp[suffix][key]=1. - return norm_factor_list_tmp - -def avg_factor_calc(self,discharge): - avg_factor={} - for suffix in self.file_keys.keys(): - file_dict=self.get_data(discharge, suffix, norm=True) - avg_factor[suffix]={} - for key in file_dict.keys(): - data=file_dict[key]['zdata'][:] - avg_tmp=np.mean(data,axis=len(data.shape)-1) - if np.isnan(avg_tmp).any(): - avg_tmp=0. - - avg_factor[suffix][key]=avg_tmp - - return avg_factor - -def std_factor_calc(self,discharge): - std_factor={} - for suffix in self.file_keys.keys(): - file_dict=self.get_data(discharge, suffix, norm=True) - std_factor[suffix]={} - for key in file_dict.keys(): - data=file_dict[key]['zdata'][:] - std_tmp=np.std(data,axis=len(data.shape)-1) - if np.isnan(std_tmp).any(): - std_tmp=1. - std_factor[suffix][key]=std_tmp - - return std_factor \ No newline at end of file +import numpy as np + +# dalpha signal needs log, but no other signals +# density needs to multiply by 10^-19 + +def oom(x: float) -> int: + """ + Calculate the order of magnitude of a number. + + Parameters + ---------- + x : float + The input number. + + Returns + ------- + int: Order of magnitude of the input number. + """ + return int(np.floor(np.log10(abs(x)))) + + +# references (legacy) +# def order_of_magnitude_normal_factor_calc(self,discharge): +# norm_factor_list_tmp={} +# for suffix in self.file_keys.keys(): +# file_dict=self.get_data(discharge, suffix, norm=False) +# norm_factor_list_tmp[suffix]={} +# for key in file_dict.keys(): +# mean_tmp=abs(np.mean(file_dict[key]['zdata'][:])) +# try: +# exponent=self.get_order_of_magnitude(mean_tmp) +# norm_factor_list_tmp[suffix][key]=10**exponent +# except: +# norm_factor_list_tmp[suffix][key]=1. +# return norm_factor_list_tmp + +# def avg_factor_calc(self,discharge): +# avg_factor={} +# for suffix in self.file_keys.keys(): +# file_dict=self.get_data(discharge, suffix, norm=True) +# avg_factor[suffix]={} +# for key in file_dict.keys(): +# data=file_dict[key]['zdata'][:] +# avg_tmp=np.mean(data,axis=len(data.shape)-1) +# if np.isnan(avg_tmp).any(): +# avg_tmp=0. + +# avg_factor[suffix][key]=avg_tmp + +# return avg_factor + +# def std_factor_calc(self,discharge): +# std_factor={} +# for suffix in self.file_keys.keys(): +# file_dict=self.get_data(discharge, suffix, norm=True) +# std_factor[suffix]={} +# for key in file_dict.keys(): +# data=file_dict[key]['zdata'][:] +# std_tmp=np.std(data,axis=len(data.shape)-1) +# if np.isnan(std_tmp).any(): +# std_tmp=1. +# std_factor[suffix][key]=std_tmp + +# return std_factor \ No newline at end of file diff --git a/src/fusion_ai_hub/core/magnitude_scaling/norm.py b/src/fusion_ai_hub/core/magnitude_scaling/norm.py index 61524d8..317278b 100644 --- a/src/fusion_ai_hub/core/magnitude_scaling/norm.py +++ b/src/fusion_ai_hub/core/magnitude_scaling/norm.py @@ -1,4 +1,5 @@ -@staticmethod +import numpy as np + def norm_data(data,avg_,std_,mode='all'): avg_=np.array(avg_) std_=np.array(std_) diff --git a/src/fusion_ai_hub/core/time_domain_processing/cut_time.py b/src/fusion_ai_hub/core/time_domain_processing/cut_time.py index f88553c..d299e83 100644 --- a/src/fusion_ai_hub/core/time_domain_processing/cut_time.py +++ b/src/fusion_ai_hub/core/time_domain_processing/cut_time.py @@ -1,6 +1,34 @@ -@staticmethod -def cut_time(time, data, t_min, t_max): - t_indx_min=np.argmin(abs(np.array(time)-t_min)) - t_indx_max=np.argmin(abs(np.array(time)-t_max)) +import numpy as np - return time[t_indx_min:t_indx_max], data[...,t_indx_min:t_indx_max] +def cut_time(t: np.ndarray, data: np.ndarray, t_min: float, t_max: float) -> Tuple[np.ndarray, np.ndarray]: + """ + Cut the time-series data between two specified times. + + Parameters + ---------- + t : np.ndarray, shape = (n,) + Array of times. + data : np.ndarray, shape = (..., n) + The input data to be cut. + t_min : float + Minimum time to cut data. + t_max : float + Maximum time to cut data. + + Returns + ------- + np.ndarray: Cut time array. + np.ndarray: Cut data array. + """ + t_indx_min = np.argmin(abs(np.array(t) - t_min)) + t_indx_max = np.argmin(abs(np.array(t) - t_max)) + + return t[t_indx_min:t_indx_max], data[..., t_indx_min:t_indx_max] + +# reference +# @staticmethod +# def cut_time(time, data, t_min, t_max): +# t_indx_min=np.argmin(abs(np.array(time)-t_min)) +# t_indx_max=np.argmin(abs(np.array(time)-t_max)) + +# return time[t_indx_min:t_indx_max], data[...,t_indx_min:t_indx_max] diff --git a/src/fusion_ai_hub/core/time_domain_processing/get_windowed_data.py b/src/fusion_ai_hub/core/time_domain_processing/get_windowed_data.py index e73ab23..5fb6989 100644 --- a/src/fusion_ai_hub/core/time_domain_processing/get_windowed_data.py +++ b/src/fusion_ai_hub/core/time_domain_processing/get_windowed_data.py @@ -1,6 +1,32 @@ -# Function to get windowed data -@staticmethod -def get_windowed_data(df, center_index, window_size=5): +import pandas as pd + +def get_window(df: pd.DataFrame, center_index: int, window_size: int = 5) -> pd.DataFrame: + """ + Get windowed data. + + Parameters + ---------- + df : pd.DataFrame + The input DataFrame. + center_index : int + The center index of the window. + window_size : int + The size of the window. + + Returns + ------- + pd.DataFrame + The windowed data. + """ start = max(center_index - window_size, 0) end = min(center_index + window_size + 1, len(df)) - return df.iloc[start:end].drop(columns=['xdata']) \ No newline at end of file + windowed = df.iloc[start:end].drop(columns=['xdata']) + + return windowed + +# Function to get windowed data +# @staticmethod +# def get_windowed_data(df, center_index, window_size=5): +# start = max(center_index - window_size, 0) +# end = min(center_index + window_size + 1, len(df)) +# return df.iloc[start:end].drop(columns=['xdata']) \ No newline at end of file diff --git a/src/fusion_ai_hub/datasets/fetch/fetch.py b/src/fusion_ai_hub/datasets/fetch/fetch.py index e69de29..7e91ea6 100644 --- a/src/fusion_ai_hub/datasets/fetch/fetch.py +++ b/src/fusion_ai_hub/datasets/fetch/fetch.py @@ -0,0 +1,11 @@ + import tqdm + + """ + Get datasets from tigerdata + + 1. Send command to copy specific zip or tarball from tigerdata to scratch + + 2. Unzip or untar the file into scratch + + 3. Say what is copied, and also give a timer + """ \ No newline at end of file diff --git a/src/fusion_ai_hub/datasets/toy_loader/load.py b/src/fusion_ai_hub/datasets/toy_loader/load.py index 448c2f4..acba5df 100644 --- a/src/fusion_ai_hub/datasets/toy_loader/load.py +++ b/src/fusion_ai_hub/datasets/toy_loader/load.py @@ -1,3 +1,25 @@ +import h5py + +def load(file_path: str) -> dict: + """ + Read data, time, and attributes from an HDF5 file. + + Parameters + ---------- + file_path : str + Path to the HDF5 file. + + Returns + ------- + dict + A dictionary containing the data, time, and attributes stored in the HDF5 file. + """ + with h5py.File(file_path, 'r') as f: + data = f['data'][()] + time = f['time'][()] + attributes = list(f.attrs.keys()) + return {'data': data, 'time': time, 'attributes': attributes} + def get_data(self,discharge, suffix, norm=True): discharge_path=self.file_path_gen(discharge, suffix) input_file = h5py.File(discharge_path, 'r') diff --git a/src/fusion_ai_hub/display/specshow.py b/src/fusion_ai_hub/display/specshow.py index c62f276..23e5fb1 100644 --- a/src/fusion_ai_hub/display/specshow.py +++ b/src/fusion_ai_hub/display/specshow.py @@ -1,10 +1,93 @@ -@staticmethod -def spectro_plot(freq, time, amp_f_t): +import numpy as np +import seaborn as sns +import matplotlib.pyplot as plt +from typing import TYPE_CHECKING, Any, Optional + +from matplotlib.collections import QuadMesh + +def specshow(data: np.ndarray, + *, + x_coords: Optional[np.ndarray] = None, + y_coords: Optional[np.ndarray] = None, + x_axis: Optional[str] = None, + y_axis: Optional[str] = None, + sr: float = 22050, + hop_length: int = 512, + n_fft: Optional[int] = None, + win_length: Optional[int] = None, + fmin: Optional[float] = None, + fmax: Optional[float] = None, + auto_aspect=True, + **kwargs: Any, + ) -> QuadMesh: + """ + Display a spectrogram/chromagram/cqt/etc. + + Parameters + ---------- + data : np.ndarray [shape=(d, n)] + The audio samples. Multichannel audio will be downmixed. + x_coords : np.ndarray [shape=(n,)] + The x-coordinates for the waveform. By default, these are + assumed to be sample indices. + y_coords : np.ndarray [shape=(n,)] + The y-coordinates for the waveform. By default, these are + assumed to be sample values. + x_axis : str + Label for the x-axis. By default, this is 'Time'. + y_axis : str + Label for the y-axis. By default, this is 'Amplitude'. + sr : number > 0 [scalar] + The sample rate of the data. + hop_length : int > 0 + The number of samples between successive frames. + n_fft : int > 0 + The number of samples per frame. + win_length : int <= n_fft + The number of samples in each STFT window. + fmin : float > 0 + The frequency of the lowest spectrogram bin. + fmax : float > 0 + The frequency of the highest spectrogram bin. + auto_aspect : bool + Automatically set the aspect ratio of the plot to match the + spectrogram's aspect ratio. + kwargs : additional keyword arguments + + Returns + ------- + fig : matplotlib.figure.Figure + The figure containing the waveform and spectrogram plots. + ax : matplotlib.axes.Axes + The axis containing the waveform and spectrogram plots. + """ + plt.clf() - plt.imshow(amp_f_t,aspect='auto',cmap='hot', - extent=[time[0], time[-1], freq[-1], freq[0]]) + plt.imshow(data,aspect='auto',cmap='hot', + extent=[x_coords[0], x_coords[-1], y_coords[-1], y_coords[0]]) plt.colorbar() plt.ylabel('kHz') plt.xlabel('ms') plt.gca().invert_yaxis() - plt.show() \ No newline at end of file + plt.show() + +# @staticmethod +# def time_serie_plot(dict): +# plt.clf() +# if dict['zdata'][:].shape == 1: +# plt.plot(dict['xdata'][:],dict['zdata'][:]) +# else: +# plt.plot(dict['xdata'][:],dict['zdata'][:].T) +# plt.xlabel('Time (ms)') +# plt.show() + +# @staticmethod +# def spectro_plot(freq, time, amp_f_t): +# plt.clf() +# plt.imshow(amp_f_t,aspect='auto',cmap='hot', +# extent=[time[0], time[-1], freq[-1], freq[0]]) +# plt.colorbar() +# plt.ylabel('kHz') +# plt.xlabel('ms') +# plt.gca().invert_yaxis() +# plt.show() \ No newline at end of file diff --git a/src/fusion_ai_hub/display/waveshow.py b/src/fusion_ai_hub/display/waveshow.py index 1054a7d..d4a241f 100644 --- a/src/fusion_ai_hub/display/waveshow.py +++ b/src/fusion_ai_hub/display/waveshow.py @@ -1,9 +1,81 @@ -@staticmethod -def time_serie_plot(dict): - plt.clf() - if dict['zdata'][:].shape == 1: - plt.plot(dict['xdata'][:],dict['zdata'][:]) - else: - plt.plot(dict['xdata'][:],dict['zdata'][:].T) - plt.xlabel('Time (ms)') - plt.show() \ No newline at end of file +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +from typing import TYPE_CHECKING, Any, Optional + +from matplotlib.collections import QuadMesh + +def waveshow( + y: np.ndarray, + *, + sr: float = 22050, + max_points: int = 11025, + axis: Optional[str] = "time", + offset: float = 0.0, + marker: Union[str, MplPath, MarkerStyle] = "", + where: str = "post", + label: Optional[str] = None, + transpose: bool = False, + ax: Optional[mplaxes.Axes] = None, + x_axis: Optional[Union[str, Deprecated]] = Deprecated(), + **kwargs: Any, + ) -> QuadMesh: + """ + Display a spectrogram/chromagram/cqt/etc. + + Parameters + ---------- + data : np.ndarray [shape=(d, n)] + The audio samples. Multichannel audio will be downmixed. + x_coords : np.ndarray [shape=(n,)] + The x-coordinates for the waveform. By default, these are + assumed to be sample indices. + y_coords : np.ndarray [shape=(n,)] + The y-coordinates for the waveform. By default, these are + assumed to be sample values. + x_axis : str + Label for the x-axis. By default, this is 'Time'. + y_axis : str + Label for the y-axis. By default, this is 'Amplitude'. + sr : number > 0 [scalar] + The sample rate of the data. + hop_length : int > 0 + The number of samples between successive frames. + n_fft : int > 0 + The number of samples per frame. + win_length : int <= n_fft + The number of samples in each STFT window. + fmin : float > 0 + The frequency of the lowest spectrogram bin. + fmax : float > 0 + The frequency of the highest spectrogram bin. + auto_aspect : bool + Automatically set the aspect ratio of the plot to match the + spectrogram's aspect ratio. + kwargs : additional keyword arguments + + Returns + ------- + fig : matplotlib.figure.Figure + The figure containing the waveform and spectrogram plots. + ax : matplotlib.axes.Axes + The axis containing the waveform and spectrogram plots. + See Also + -------- + librosa.display.waveshow + librosa.display.specshow + """ + + plt.clf + plt.plot(y) + plt.show() + +# @staticmethod +# def time_serie_plot(dict): +# plt.clf() +# if dict['zdata'][:].shape == 1: +# plt.plot(dict['xdata'][:],dict['zdata'][:]) +# else: +# plt.plot(dict['xdata'][:],dict['zdata'][:].T) +# plt.xlabel('Time (ms)') +# plt.show() \ No newline at end of file diff --git a/src/fusion_ai_hub/sampling/match_times.py b/src/fusion_ai_hub/sampling/match_times.py index 2c5d178..212b03b 100644 --- a/src/fusion_ai_hub/sampling/match_times.py +++ b/src/fusion_ai_hub/sampling/match_times.py @@ -1,3 +1,7 @@ +import pandas as pd + +def merge_times_1d(t) +# reference @staticmethod def time_matching_merge_asof_1d(time1, data1, time_std, left_window=0, right_window=0): # Convert input arrays to DataFrames From 1f87285d2dbaaad4e8db3d93ecea8fc7785f6815 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Wed, 12 Jun 2024 09:45:49 -0700 Subject: [PATCH 015/103] file.py combines some base class functions --- src/fusion_ai_hub/base/file.py | 180 +++++++++++++++++++++++++++++++++ 1 file changed, 180 insertions(+) create mode 100644 src/fusion_ai_hub/base/file.py diff --git a/src/fusion_ai_hub/base/file.py b/src/fusion_ai_hub/base/file.py new file mode 100644 index 0000000..a81215d --- /dev/null +++ b/src/fusion_ai_hub/base/file.py @@ -0,0 +1,180 @@ +import numpy as np + +import h5py +import os +from pathlib import Path + +from typing import Any, Union + + +def load_data( + path: Union[str, int, Any[os.Pathlike]], + ) -> np.ndarray: + """_summary_ + + Parameters + ---------- + path : Union[str, int, Any[os.Pathlike]] + _description_ + + Returns + ------- + np.ndarray + _description_ + """ + + with h5py.File(path, 'r') as f: + data = f['data'][()] + return data + + +def load_time( + path: Union[str, int, Any[os.Pathlike]], + ) -> np.ndarray: + """_summary_ + + Parameters + ---------- + path : Union[str, int, Any[os.Pathlike]] + _description_ + + Returns + ------- + np.ndarray + _description_ + """ + + with h5py.File(path, 'r') as f: + time = f['time'][()] + return time + + +def load_attributes( + path: Union[str, int, Any[os.Pathlike]], + ) -> list: + """_summary_ + + Parameters + ---------- + path : Union[str, int, Any[os.Pathlike]] + _description_ + + Returns + ------- + list + _description_ + """ + + with h5py.File(path, 'r') as f: + attributes = list(f.attrs.keys()) + return attributes + + +def load( + path: Union[str, int, Any[os.Pathlike]], + ) -> np.ndarray: + """_summary_ + + Parameters + ---------- + path : Union[str, int, Any[os.Pathlike]] + _description_ + + Returns + ------- + np.ndarray + _description_ + """ + + with h5py.File(path, 'r') as f: + data = f['data'][()] + time = f['time'][()] + attributes = list(f.attrs.keys()) + + return {'data': data, + 'time': time, + 'attributes': attributes, + } + + +def dict_to_hdf5( + dictionary: dict, + h5file: h5py.File, + compression: str = None, + ) -> None: + """_summary_ + + Parameters + ---------- + dictionary : dict + _description_ + h5file : h5py.File + _description_ + compression : str, optional + _description_, by default None + """ + + for key, value in dictionary.items(): + if isinstance(value, dict): + group = h5file.create_group(key) + dict_to_hdf5(value, group, compression) + else: + if isinstance(value, (list, tuple)): + value = np.array(value) + h5file.create_dataset(key, + data=value, + compression=compression, + chunks=True, + ) + + +def save( + dictionary: dict, + path: Union[str, int, Any[os.Pathlike]], + compression: str = None, + ) -> None: + """_summary_ + + Parameters + ---------- + dictionary : dict + _description_ + path : Union[str, int, Any[os.Pathlike]] + _description_ + compression : str, optional + _description_, by default None + """ + + if not path.endswith('.h5'): + path += '.h5' + + path.mkdir(parents=True, exist_ok=True) + with h5py.File(path, 'w') as f: + dict_to_hdf5(dictionary, f, compression) + + print(f'Saved to {path}') + + +def merge( + path_1: Union[str, int, Any[os.Pathlike]], + path_2: Union[str, int, Any[os.Pathlike]], + path_out: Union[str, int, Any[os.Pathlike]], + ) -> None: + """_summary_ + + Parameters + ---------- + path_1 : Union[str, int, Any[os.Pathlike]] + _description_ + path_2 : Union[str, int, Any[os.Pathlike]] + _description_ + path_out : Union[str, int, Any[os.Pathlike]] + _description_ + + Returns + ------- + _type_ + _description_ + """ + + return None \ No newline at end of file From 6574656ec4087f90e12fa77323671b4eb7269e04 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Wed, 12 Jun 2024 11:46:52 -0700 Subject: [PATCH 016/103] load and scaling updates --- docs/functions.md | 130 ++++++-------- src/fusion_ai_hub/base/file.py | 180 ------------------- src/fusion_ai_hub/base/load.py | 75 ++++++++ src/fusion_ai_hub/base/merge.py | 99 ++++++++++ src/fusion_ai_hub/base/save.py | 64 +++++++ src/fusion_ai_hub/base/util/dict_to_hdf5.py | 7 - src/fusion_ai_hub/base/util/divide_data.py | 12 -- src/fusion_ai_hub/base/util/generate_hdf5.py | 68 ------- src/fusion_ai_hub/base/util/hdf5_to_dict.py | 35 ---- src/fusion_ai_hub/base/util/read_file.py | 101 ----------- src/fusion_ai_hub/core/scaling.py | 105 +++++++++++ 11 files changed, 402 insertions(+), 474 deletions(-) delete mode 100644 src/fusion_ai_hub/base/file.py create mode 100644 src/fusion_ai_hub/base/load.py create mode 100644 src/fusion_ai_hub/base/merge.py create mode 100644 src/fusion_ai_hub/base/save.py delete mode 100644 src/fusion_ai_hub/base/util/dict_to_hdf5.py delete mode 100644 src/fusion_ai_hub/base/util/divide_data.py delete mode 100644 src/fusion_ai_hub/base/util/generate_hdf5.py delete mode 100644 src/fusion_ai_hub/base/util/hdf5_to_dict.py delete mode 100644 src/fusion_ai_hub/base/util/read_file.py create mode 100644 src/fusion_ai_hub/core/scaling.py diff --git a/docs/functions.md b/docs/functions.md index 9416d38..3b1acde 100644 --- a/docs/functions.md +++ b/docs/functions.md @@ -1,106 +1,94 @@ -## Mermaid Diagram +## Function Organization ```mermaid graph LR - - A[hub] - - subgraph base - direction LR - - A1[file.py] - A1 --> B1[load] - A1 --> B2[save] - A1 --> B3[merge] - end - - subgraph core + subgraph physics direction LR - - A2[scaling.py] - A2 --> B4[compute_norms] - A2 --> B5[norm] - A2 --> B6[rescale] - - A3[spectral.py] - A3 --> B7[spectrogram] - - A4[time_domain] - B8[filtering.py] - B9[preemphasis.py] - B10[windowing.py] - A4 --> B8 - A4 --> B9 - A4 --> B10 - B8 --> C1[lfilt] - B8 --> C2[filtfilt] - B9 --> C3[preemphasis] - B9 --> C4[deemphasis] - B10 --> C5[cut_time] - B10 --> C6[get_window] - B10 --> C7[splice_time] + A41[flattop_finder.py] end - subgraph datasets + subgraph util direction LR - - A5[query] - B11[retrieve.py] - B12[modify.py - permission] - A5 --> B11 - A5 --> B12 + A4000[util.py] end - subgraph display + subgraph resampling direction LR + A2000[interpolation.py] + A2000 --> B2000[interpolate_signal] - A6[display.py] - A6 --> B13[specshow] - A6 --> B14[waveshow] + A3000[resampling.py] + A3000 --> B3000[resample] end subgraph feature_extract direction LR - A7[filterbanks.py] - A8[morphological_filters.py] - A9[frame_operations.py] - A10[delta_features.py] A10 --> B30[closest_index] A10 --> B31[time_matching_binary] end - subgraph resampling + subgraph display direction LR + A6[display.py] + A6 --> B13[specshow] + A6 --> B14[waveshow] + end - A2000[interpolation.py] - A2000 --> B2000[interpolate_signal] - - A3000[resampling.py] - A3000 --> B3000[resample] + subgraph datasets + direction LR + A5[query] + B11[retrieve.py] + B12[modify.py - permission] + A5 --> B11 + A5 --> B12 end - subgraph util + subgraph core direction LR + A2[scaling.py] + A2 --> B4[signal_optimize] + A2 --> B5[get_scaling_factor] + A2 --> B6[normalize] + A2 --> Ba[standardize] - A4000[util.py] + A3[spectral.py] + A3 --> B7[spectrogram] + + A4[time_domain] + B8[filtering.py] + B9[preemphasis.py] + B10[windowing.py] + A4 --> B8 + A4 --> B9 + A4 --> B10 + B8 --> C1[lfilt] + B8 --> C2[filtfilt] + B9 --> C3[preemphasis] + B9 --> C4[deemphasis] + B10 --> C5[cut_time] + B10 --> C6[get_window] + B10 --> C7[splice_time] end - subgraph physics + subgraph base direction LR - - A41[flattop_finder.py] + base1[load.py] + base2[save.py] + base3[merge.py] + base1 --> base1a[list_signals] + base1 --> base1b[load_sample] + base1 --> base1c[load_time] + base1 --> base1d[load_attributes] + base1 --> base1e[load_channels] + base1 --> base1f[load] + base2 --> base2a[dict_to_hdf5] + base2 --> base2b[save] + base3 --> base3a[merge] end - A --> base - A --> core - A --> datasets - A --> display - A --> feature_extract - A --> resampling - A --> util - A --> physics + A[hub] ``` diff --git a/src/fusion_ai_hub/base/file.py b/src/fusion_ai_hub/base/file.py deleted file mode 100644 index a81215d..0000000 --- a/src/fusion_ai_hub/base/file.py +++ /dev/null @@ -1,180 +0,0 @@ -import numpy as np - -import h5py -import os -from pathlib import Path - -from typing import Any, Union - - -def load_data( - path: Union[str, int, Any[os.Pathlike]], - ) -> np.ndarray: - """_summary_ - - Parameters - ---------- - path : Union[str, int, Any[os.Pathlike]] - _description_ - - Returns - ------- - np.ndarray - _description_ - """ - - with h5py.File(path, 'r') as f: - data = f['data'][()] - return data - - -def load_time( - path: Union[str, int, Any[os.Pathlike]], - ) -> np.ndarray: - """_summary_ - - Parameters - ---------- - path : Union[str, int, Any[os.Pathlike]] - _description_ - - Returns - ------- - np.ndarray - _description_ - """ - - with h5py.File(path, 'r') as f: - time = f['time'][()] - return time - - -def load_attributes( - path: Union[str, int, Any[os.Pathlike]], - ) -> list: - """_summary_ - - Parameters - ---------- - path : Union[str, int, Any[os.Pathlike]] - _description_ - - Returns - ------- - list - _description_ - """ - - with h5py.File(path, 'r') as f: - attributes = list(f.attrs.keys()) - return attributes - - -def load( - path: Union[str, int, Any[os.Pathlike]], - ) -> np.ndarray: - """_summary_ - - Parameters - ---------- - path : Union[str, int, Any[os.Pathlike]] - _description_ - - Returns - ------- - np.ndarray - _description_ - """ - - with h5py.File(path, 'r') as f: - data = f['data'][()] - time = f['time'][()] - attributes = list(f.attrs.keys()) - - return {'data': data, - 'time': time, - 'attributes': attributes, - } - - -def dict_to_hdf5( - dictionary: dict, - h5file: h5py.File, - compression: str = None, - ) -> None: - """_summary_ - - Parameters - ---------- - dictionary : dict - _description_ - h5file : h5py.File - _description_ - compression : str, optional - _description_, by default None - """ - - for key, value in dictionary.items(): - if isinstance(value, dict): - group = h5file.create_group(key) - dict_to_hdf5(value, group, compression) - else: - if isinstance(value, (list, tuple)): - value = np.array(value) - h5file.create_dataset(key, - data=value, - compression=compression, - chunks=True, - ) - - -def save( - dictionary: dict, - path: Union[str, int, Any[os.Pathlike]], - compression: str = None, - ) -> None: - """_summary_ - - Parameters - ---------- - dictionary : dict - _description_ - path : Union[str, int, Any[os.Pathlike]] - _description_ - compression : str, optional - _description_, by default None - """ - - if not path.endswith('.h5'): - path += '.h5' - - path.mkdir(parents=True, exist_ok=True) - with h5py.File(path, 'w') as f: - dict_to_hdf5(dictionary, f, compression) - - print(f'Saved to {path}') - - -def merge( - path_1: Union[str, int, Any[os.Pathlike]], - path_2: Union[str, int, Any[os.Pathlike]], - path_out: Union[str, int, Any[os.Pathlike]], - ) -> None: - """_summary_ - - Parameters - ---------- - path_1 : Union[str, int, Any[os.Pathlike]] - _description_ - path_2 : Union[str, int, Any[os.Pathlike]] - _description_ - path_out : Union[str, int, Any[os.Pathlike]] - _description_ - - Returns - ------- - _type_ - _description_ - """ - - return None \ No newline at end of file diff --git a/src/fusion_ai_hub/base/load.py b/src/fusion_ai_hub/base/load.py new file mode 100644 index 0000000..a280c28 --- /dev/null +++ b/src/fusion_ai_hub/base/load.py @@ -0,0 +1,75 @@ +import numpy as np + +import h5py +import os +from pathlib import Path + +from typing import Any, Union + +def list_signals( + path: Union[str, int, Any[os.Pathlike]], + ) -> list: + + with h5py.File(path, 'r') as f: + signals = list(f.keys()) + + return signals + + +def load_sample( + path: Union[str, int, Any[os.Pathlike]], + signal: Union[str, list[str]], + ) -> np.ndarray: + + with h5py.File(path, 'r') as f: + data = f[signal] + sample = data["data"][()] + + return sample + + +def load_time( + path: Union[str, int, Any[os.Pathlike]], + signal: Union[str, list[str]], + ) -> np.ndarray: + + with h5py.File(path, 'r') as f: + data = f[signal] + time = data["time"][()] + + return time + + +def load_attributes( + path: Union[str, int, Any[os.Pathlike]], + signal: Union[str, list[str]], + ) -> list: + + with h5py.File(path, 'r') as f: + data = f[signal] + attributes = list(data.attrs.keys()) + + return attributes + + +def load_channels( + path: Union[str, int, Any[os.Pathlike]], + signal: Union[str, list[str]], + ) -> list: + + with h5py.File(path, 'r') as f: + data = f[signal] + channels = data.attrs["channel_ids"] + + return channels + + +def load( + path: Union[str, int, Any[os.Pathlike]], + signal: Union[str, list[str]], + ) -> np.ndarray: + + with h5py.File(path, 'r') as f: + data = f[signal] + + return data \ No newline at end of file diff --git a/src/fusion_ai_hub/base/merge.py b/src/fusion_ai_hub/base/merge.py new file mode 100644 index 0000000..b3a9308 --- /dev/null +++ b/src/fusion_ai_hub/base/merge.py @@ -0,0 +1,99 @@ +import numpy as np + +import h5py +import os +from pathlib import Path + +from typing import Any, Union + +def merge( + path_1: Union[str, int, Any[os.Pathlike]], + path_2: Union[str, int, Any[os.Pathlike]], + path_out: Union[str, int, Any[os.Pathlike]], + ) -> None: + """_summary_ + + Parameters + ---------- + path_1 : Union[str, int, Any[os.Pathlike]] + _description_ + path_2 : Union[str, int, Any[os.Pathlike]] + _description_ + path_out : Union[str, int, Any[os.Pathlike]] + _description_ + + Returns + ------- + _type_ + _description_ + """ + + return None + +# reference +# def hdf5_generator(discharge_list, h5_profiles, +# data_filename='diag2diag.pkl'): +# all_X =[] +# all_y = [] +# all_time = [] +# discharg_read_list = [] +# len_list = [] +# for discharge in tqdm(discharge_list): +# print(discharge) +# try: +# dfs = {} +# # creating the standard time +# path=find_path(discharge) + +# file = h5py.File(f'{path}{discharge}_shape.h5', 'r') +# t_min = 0 +# t_max = file['R0']['xdata'][-1] +# file.close() + +# file = h5py.File(f'{path}{discharge}_TS.h5', 'r') +# df_time = pd.DataFrame({'xdata': file[list(file.keys())[0]]['xdata']}) +# time = file[list(file.keys())[0]]['xdata'][:] + +# time_index = (time >= t_min) & (time <= t_max) +# time_tmp = time[time_index] +# df_time = pd.DataFrame({'xdata': time_tmp}) +# file.close() + +# # Read all the files +# for file_suffix in h5_profiles: +# df = read_file(discharge, file_suffix, df_time) +# dfs[file_suffix] = df + +# # summarize all the data in this dicharge +# df_tmp = np.concatenate( +# [dfs[key].to_numpy()[1:] for key in dfs.keys()], axis=1) + +# key_list_dict = {} +# key_list = [] +# for key in dfs.keys(): +# key_list_dict[key]=list(dfs[key].keys()) +# for key_ in key_list_dict[key]: +# key_list.append(key_) + +# # add this discharge to the total file +# all_X.append(df_tmp) +# all_time.append(df_time['xdata']) +# all_time_tmp= np.concatenate(all_time, axis=0) +# all_X_tmp = np.concatenate(all_X, axis=0) +# len_list.append(df_time['xdata'].shape[0]) +# discharg_read_list.append(discharge) +# # Serialize the data and save to a file +# with open(data_filename, 'wb') as file: +# pickle.dump([all_X_tmp, all_time_tmp, discharg_read_list, +# len_list, key_list, key_list_dict], file) + +# except Exception as e: # if 2==1: +# print(f"Error: {e}") +# continue +# finally: # if 2==1: +# try: +# file.close() +# except: +# continue + +# return [all_X_tmp] \ No newline at end of file diff --git a/src/fusion_ai_hub/base/save.py b/src/fusion_ai_hub/base/save.py new file mode 100644 index 0000000..06f61de --- /dev/null +++ b/src/fusion_ai_hub/base/save.py @@ -0,0 +1,64 @@ +import numpy as np + +import h5py +import os +from pathlib import Path + +from typing import Any, Union + +def dict_to_hdf5( + dictionary: dict, + h5file: h5py.File, + compression: str = None, + ) -> None: + """_summary_ + + Parameters + ---------- + dictionary : dict + _description_ + h5file : h5py.File + _description_ + compression : str, optional + _description_, by default None + """ + + for key, value in dictionary.items(): + if isinstance(value, dict): + group = h5file.create_group(key) + dict_to_hdf5(value, group, compression) + else: + if isinstance(value, (list, tuple)): + value = np.array(value) + h5file.create_dataset(key, + data=value, + compression=compression, + chunks=True, + ) + + +def save( + dictionary: dict, + path: Union[str, int, Any[os.Pathlike]], + compression: str = None, + ) -> None: + """_summary_ + + Parameters + ---------- + dictionary : dict + _description_ + path : Union[str, int, Any[os.Pathlike]] + _description_ + compression : str, optional + _description_, by default None + """ + + if not path.endswith('.h5'): + path += '.h5' + + path.mkdir(parents=True, exist_ok=True) + with h5py.File(path, 'w') as f: + dict_to_hdf5(dictionary, f, compression) + + print(f'Saved to {path}') \ No newline at end of file diff --git a/src/fusion_ai_hub/base/util/dict_to_hdf5.py b/src/fusion_ai_hub/base/util/dict_to_hdf5.py deleted file mode 100644 index d1992b1..0000000 --- a/src/fusion_ai_hub/base/util/dict_to_hdf5.py +++ /dev/null @@ -1,7 +0,0 @@ -def save_dict_to_hdf5(dictionary, h5file): - for key, value in dictionary.items(): - if isinstance(value, dict): - group = h5file.create_group(key) - save_dict_to_hdf5(value, group) - else: - h5file.create_dataset(key, data=value) \ No newline at end of file diff --git a/src/fusion_ai_hub/base/util/divide_data.py b/src/fusion_ai_hub/base/util/divide_data.py deleted file mode 100644 index 5e461a2..0000000 --- a/src/fusion_ai_hub/base/util/divide_data.py +++ /dev/null @@ -1,12 +0,0 @@ -# don't need this -# reference divide the data into subcategory -# def data_division(self, input_file, input_suffix): -# if input_suffix in multi_level: -# input_multi_level = {} -# for key in file_keys[input_suffix].keys(): -# keys_of_this_category = file_keys[input_suffix][key] -# input_multi_level[key] = {key_i: input_file[key_i] -# for key_i in keys_of_this_category} -# else: -# input_multi_level = {input_suffix: input_file} -# return input_multi_level \ No newline at end of file diff --git a/src/fusion_ai_hub/base/util/generate_hdf5.py b/src/fusion_ai_hub/base/util/generate_hdf5.py deleted file mode 100644 index 20371ce..0000000 --- a/src/fusion_ai_hub/base/util/generate_hdf5.py +++ /dev/null @@ -1,68 +0,0 @@ - -# reference -def hdf5_generator(discharge_list, h5_profiles, - data_filename='diag2diag.pkl'): - all_X =[] - all_y = [] - all_time = [] - discharg_read_list = [] - len_list = [] - for discharge in tqdm(discharge_list): - print(discharge) - try: - dfs = {} - # creating the standard time - path=find_path(discharge) - - file = h5py.File(f'{path}{discharge}_shape.h5', 'r') - t_min = 0 - t_max = file['R0']['xdata'][-1] - file.close() - - file = h5py.File(f'{path}{discharge}_TS.h5', 'r') - df_time = pd.DataFrame({'xdata': file[list(file.keys())[0]]['xdata']}) - time = file[list(file.keys())[0]]['xdata'][:] - - time_index = (time >= t_min) & (time <= t_max) - time_tmp = time[time_index] - df_time = pd.DataFrame({'xdata': time_tmp}) - file.close() - - # Read all the files - for file_suffix in h5_profiles: - df = read_file(discharge, file_suffix, df_time) - dfs[file_suffix] = df - - # summarize all the data in this dicharge - df_tmp = np.concatenate( - [dfs[key].to_numpy()[1:] for key in dfs.keys()], axis=1) - - key_list_dict = {} - key_list = [] - for key in dfs.keys(): - key_list_dict[key]=list(dfs[key].keys()) - for key_ in key_list_dict[key]: - key_list.append(key_) - - # add this discharge to the total file - all_X.append(df_tmp) - all_time.append(df_time['xdata']) - all_time_tmp= np.concatenate(all_time, axis=0) - all_X_tmp = np.concatenate(all_X, axis=0) - len_list.append(df_time['xdata'].shape[0]) - discharg_read_list.append(discharge) - # Serialize the data and save to a file - with open(data_filename, 'wb') as file: - pickle.dump([all_X_tmp, all_time_tmp, discharg_read_list, - len_list, key_list, key_list_dict], file) - - except Exception as e: # if 2==1: - print(f"Error: {e}") - continue - finally: # if 2==1: - try: - file.close() - except: - continue - - return [all_X_tmp] \ No newline at end of file diff --git a/src/fusion_ai_hub/base/util/hdf5_to_dict.py b/src/fusion_ai_hub/base/util/hdf5_to_dict.py deleted file mode 100644 index a0fdb15..0000000 --- a/src/fusion_ai_hub/base/util/hdf5_to_dict.py +++ /dev/null @@ -1,35 +0,0 @@ -import h5py -from pathlib import Path - -def hdf5_to_dict(file_path: str) -> dict: - """ - Convert an HDF5 file to a dictionary. - - Parameters - ---------- - file_path : str - Path to the HDF5 file. - - Returns - ------- - dict - A dictionary containing the contents of the HDF5 file. - """ - with h5py.File(file_path, 'r') as f: - result = {} - for key in f.keys(): - if isinstance(f[key], h5py.Dataset): - result[key] = f[key][()] - elif isinstance(f[key], h5py.Group): - result[key] = hdf5_to_dict(f[key]) - return result - -# reference -# def hdf5_to_dict(self, group): -# result = {} -# for key in group.keys(): -# if isinstance(group[key], h5py.Dataset): -# result[key] = group[key][()] -# elif isinstance(group[key], h5py.Group): -# result[key] = self.hdf5_to_dict(group[key]) -# return result \ No newline at end of file diff --git a/src/fusion_ai_hub/base/util/read_file.py b/src/fusion_ai_hub/base/util/read_file.py deleted file mode 100644 index 29f676d..0000000 --- a/src/fusion_ai_hub/base/util/read_file.py +++ /dev/null @@ -1,101 +0,0 @@ -import numpy as np -import h5py - -def read_data(file_path: str) -> np.ndarray: - """ - Read data from an HDF5 file. - - Parameters - ---------- - file_path : str - Path to the HDF5 file. - - Returns - ------- - np.ndarray - The data stored in the HDF5 file. - """ - with h5py.File(file_path, 'r') as f: - data = f['data'][()] - return data - -def read_time(file_path: str) -> np.ndarray: - """ - Read time from an HDF5 file. - - Parameters - ---------- - file_path : str - Path to the HDF5 file. - - Returns - ------- - np.ndarray - The time stored in the HDF5 file. - """ - with h5py.File(file_path, 'r') as f: - time = f['time'][()] - return time - -def read_attributes(file_path: str) -> list: - """ - Read attributes from an HDF5 file. - - Parameters - ---------- - file_path : str - Path to the HDF5 file. - - Returns - ------- - list - A list of attributes stored in the HDF5 file. - """ - with h5py.File(file_path, 'r') as f: - attributes = list(f.attrs.keys()) - return attributes - -def read_file(file_path: str) -> dict: - """ - Read data, time, and attributes from an HDF5 file. - - Parameters - ---------- - file_path : str - Path to the HDF5 file. - - Returns - ------- - dict - A dictionary containing the data, time, and attributes stored in the HDF5 file. - """ - with h5py.File(file_path, 'r') as f: - data = f['data'][()] - time = f['time'][()] - attributes = list(f.attrs.keys()) - return {'data': data, 'time': time, 'attributes': attributes} - -# reference -# def read_file(discharge, file_suffix, df_time): - -# path = find_path(discharge) -# file = h5py.File(f'{path}{discharge}_{file_suffix}.h5', 'r') -# keys = file.keys() - -# for i, key in enumerate(keys): -# dict_tmp = {'xdata': file[key]['xdata']} -# if len(file[key]['zdata'].shape) == 2: -# for j in range(file[key]['zdata'].shape[0]): -# dict_tmp[key+str(j)] = file[key]['zdata'][j, :] -# elif len(file[key]['zdata'].shape) == 1: -# dict_tmp[key] = file[key]['zdata'] - -# df_tmp = pd.DataFrame(dict_tmp).astype('float32') -# if i == 0: -# df = pd.merge_asof(df_time, df_tmp, on='xdata', -# direction='nearest') -# else: -# df = pd.merge_asof(df, df_tmp, on='xdata', -# direction='nearest') -# file.close() -# return df \ No newline at end of file diff --git a/src/fusion_ai_hub/core/scaling.py b/src/fusion_ai_hub/core/scaling.py new file mode 100644 index 0000000..49b0ab0 --- /dev/null +++ b/src/fusion_ai_hub/core/scaling.py @@ -0,0 +1,105 @@ +import numpy as np + +from typing import Union, Callable, Literal + + +def signal_optimize( + signal: Union[str, list], + apply: bool = True, +) -> dict: + """ + The idea is to have a user input a list of files and then the function will read through a library to see if the channel has any special scaling rules. If it will automatically apply the scaling. There's also an option to only output the scaling rule considerations without applying them. I'm thinking there could be some dictionary file with this. + + For example, the signal 'dalpha' needs to be log transformed, but no other signals need to be log transformed. The signal 'density' needs to be multiplied by 10^-19. + + Parameters + ---------- + signal : Union[str, list] + _description_ + + Returns + ------- + dict + _description_ + """ + + return None + + +def get_scaling_factor( + data: dict, + scaling: Union[Literal["mean", "std", "norm", "oom", "min", "max"], Callable] = "mean", +) -> dict: + """ + Apply a scaling operation specified by the `scaling` parameter to each array in the provided `data` dictionary. + Supports standard operations like 'mean', 'std', 'min', and 'max', or any function that operates over a numpy array. + Also supports getting order of magnitude (oom). + + Parameters + ---------- + data : dict + Dictionary where keys are channel identifiers and values are dicts with key 'zdata' pointing to numpy arrays. + scaling : Union[Literal["mean", "std", "min", "max"], Callable], optional + The scaling operation to apply. Can be one of 'mean', 'std', 'min', 'max', or a function that accepts a numpy array. + Defaults to 'mean'. + + Returns + ------- + dict + Dictionary with the same keys as `data`, where each value is the result of the scaling operation applied to `data[key]['zdata']`. + + Examples + -------- + >>> data = {'channel1': {'zdata': np.array([1, 2, 3])}} + >>> print(get_scaling_factor(data, scaling='max')) + {'channel1': 3} + """ + + # Map string identifiers to numpy functions + scaling_functions = { + "mean": np.mean, + "std": np.std, + "norm": np.linalg.norm, + "oom": lambda x: np.floor(np.log10(abs(x))), + "min": np.min, + "max": np.max, + } + + # If the scaling argument is a string, use the corresponding numpy function + if isinstance(scaling, str): + scaling_function = scaling_functions.get(scaling) + if scaling_function is None: + raise ValueError(f"Unsupported scaling operation: {scaling}") + elif callable(scaling): + scaling_function = scaling + else: + raise TypeError("Scaling must be either a string key for predefined functions or a callable.") + + scaled_values = {} + for key, value in data.items(): + try: + data_array = value['zdata'] + scaled_value = scaling_function(data_array) + if np.isnan(scaled_value).any(): + scaled_value = 0 # Handle NaN values, if any + scaled_values[key] = scaled_value + except Exception as e: + raise RuntimeError(f"Error processing {key}: {str(e)}") + + return scaled_values + +def normalize( + data: dict, + norm: dict, + std: dict, +) -> dict: + + return None + +def standardize( + data: dict, + mean: dict, + std: dict, +) -> dict: + + return None \ No newline at end of file From 4d82e93adbf7592485b0f0b8bbf4f4040e18c1ac Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Wed, 12 Jun 2024 13:57:32 -0700 Subject: [PATCH 017/103] init updates --- src/fusion_ai_hub/base/__init__.py | 13 ++++++++ src/fusion_ai_hub/core/scaling.py | 1 + src/fusion_ai_hub/display/display.py | 44 ++++++++++++++++++++++++++++ 3 files changed, 58 insertions(+) create mode 100644 src/fusion_ai_hub/display/display.py diff --git a/src/fusion_ai_hub/base/__init__.py b/src/fusion_ai_hub/base/__init__.py index e69de29..2507e73 100644 --- a/src/fusion_ai_hub/base/__init__.py +++ b/src/fusion_ai_hub/base/__init__.py @@ -0,0 +1,13 @@ +from .load import * +from .save import * +from .merge import * + +__all__ = ["list_signals", + "load_sample", + "load_time", + "load_attributes", + "load_channels", + "load", + "dict_to_hdf5", + "save", + "merge",] \ No newline at end of file diff --git a/src/fusion_ai_hub/core/scaling.py b/src/fusion_ai_hub/core/scaling.py index 49b0ab0..1867b18 100644 --- a/src/fusion_ai_hub/core/scaling.py +++ b/src/fusion_ai_hub/core/scaling.py @@ -2,6 +2,7 @@ from typing import Union, Callable, Literal +# actually just use sklearn.preprocessing def signal_optimize( signal: Union[str, list], diff --git a/src/fusion_ai_hub/display/display.py b/src/fusion_ai_hub/display/display.py new file mode 100644 index 0000000..fb02e88 --- /dev/null +++ b/src/fusion_ai_hub/display/display.py @@ -0,0 +1,44 @@ +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns + +def visualize(y: np.ndarray, + t: np.ndarray, + labels: list, + xlabel: str = None, + ylabel: str = None, + title: str = None, + ) -> plt.Figure: + + fig, axs = plt.subplots() + + for k, label in enumerate(labels): + sns.lineplot(x=t[:, k], y=y[:, k], label=label) + + axs.set_xlabel(xlabel) + axs.set_ylabel(ylabel) + axs.set_title(title) + plt.show() + + return fig + +def spectrogram(y: np.ndarray, + t: np.ndarray, + f: np.ndarray, + labels: list, + xlabel: str = None, + ylabel: str = None, + title: str = None, + ) -> plt.Figure: + + fig, axs = plt.subplots() + + for k, label in enumerate(labels): + sns.heatmap(y[:, :, k], xticklabels=t[:, k], yticklabels=f, ax=axs, label=label) + + axs.set_xlabel(xlabel) + axs.set_ylabel(ylabel) + axs.set_title(title) + plt.show() + + return fig \ No newline at end of file From d8afde8781adaeec22570ec767caf937fa45fc0a Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Thu, 13 Jun 2024 11:29:03 -0700 Subject: [PATCH 018/103] small edits --- src/fusion_ai_hub/base/load.py | 115 +++++++++++++++++++-------------- src/fusion_ai_hub/base/save.py | 30 ++++----- 2 files changed, 79 insertions(+), 66 deletions(-) diff --git a/src/fusion_ai_hub/base/load.py b/src/fusion_ai_hub/base/load.py index a280c28..363b18f 100644 --- a/src/fusion_ai_hub/base/load.py +++ b/src/fusion_ai_hub/base/load.py @@ -1,75 +1,92 @@ import numpy as np - import h5py -import os +from typing import Any, Union, List, Optional from pathlib import Path -from typing import Any, Union - -def list_signals( - path: Union[str, int, Any[os.Pathlike]], - ) -> list: +def list_signals(path: Union[str, Path], + ) -> List[str]: with h5py.File(path, 'r') as f: signals = list(f.keys()) return signals - -def load_sample( - path: Union[str, int, Any[os.Pathlike]], - signal: Union[str, list[str]], - ) -> np.ndarray: +def load_sample(path: Union[str, Path], + signal_name: Optional[Union[str, List[str]]] = None, + ) -> dict: + samples = {} with h5py.File(path, 'r') as f: - data = f[signal] - sample = data["data"][()] - - return sample - + if signal_name is None: + signal_name = list(f.keys()) + if isinstance(signal_name, str): + signal_name = [signal_name] + for signal in signal_name: + data = f[signal] + samples[signal] = data["data"][()] + + return samples -def load_time( - path: Union[str, int, Any[os.Pathlike]], - signal: Union[str, list[str]], - ) -> np.ndarray: +def load_time(path: Union[str, Path], + signal_name: Optional[Union[str, List[str]]] = None, + ) -> dict: + times = {} with h5py.File(path, 'r') as f: - data = f[signal] - time = data["time"][()] - - return time + if signal_name is None: + signal_name = list(f.keys()) + if isinstance(signal_name, str): + signal_name = [signal_name] + for signal in signal_name: + data = f[signal] + times[signal] = data["time"][()] + + return times - -def load_attributes( - path: Union[str, int, Any[os.Pathlike]], - signal: Union[str, list[str]], - ) -> list: +def load_attributes(path: Union[str, Path], + signal_name: Optional[Union[str, List[str]]] = None, + ) -> dict: + attributes = {} with h5py.File(path, 'r') as f: - data = f[signal] - attributes = list(data.attrs.keys()) - + if signal_name is None: + signal_name = list(f.keys()) + if isinstance(signal_name, str): + signal_name = [signal_name] + for signal in signal_name: + data = f[signal] + attributes[signal] = list(data.attrs.keys()) + return attributes - -def load_channels( - path: Union[str, int, Any[os.Pathlike]], - signal: Union[str, list[str]], - ) -> list: +def load_channels(path: Union[str, Path], + signal_name: Optional[Union[str, List[str]]] = None, + ) -> dict: + channels = {} with h5py.File(path, 'r') as f: - data = f[signal] - channels = data.attrs["channel_ids"] - + if signal_name is None: + signal_name = list(f.keys()) + if isinstance(signal_name, str): + signal_name = [signal_name] + for signal in signal_name: + data = f[signal] + channels[signal] = data.attrs["channel_ids"] + return channels - -def load( - path: Union[str, int, Any[os.Pathlike]], - signal: Union[str, list[str]], - ) -> np.ndarray: +def load(path: Union[str, Path], + signal_name: Optional[Union[str, List[str]]] = None, + ) -> dict: + loaded_data = {} with h5py.File(path, 'r') as f: - data = f[signal] - - return data \ No newline at end of file + if signal_name is None: + signal_name = list(f.keys()) + if isinstance(signal_name, str): + signal_name = [signal_name] + for signal in signal_name: + data = f[signal] + loaded_data[signal] = data + + return loaded_data \ No newline at end of file diff --git a/src/fusion_ai_hub/base/save.py b/src/fusion_ai_hub/base/save.py index 06f61de..d9e38b3 100644 --- a/src/fusion_ai_hub/base/save.py +++ b/src/fusion_ai_hub/base/save.py @@ -6,6 +6,8 @@ from typing import Any, Union +# also do other file formats + def dict_to_hdf5( dictionary: dict, h5file: h5py.File, @@ -40,25 +42,19 @@ def dict_to_hdf5( def save( dictionary: dict, path: Union[str, int, Any[os.Pathlike]], + file_format: str = 'h5', compression: str = None, ) -> None: - """_summary_ - - Parameters - ---------- - dictionary : dict - _description_ - path : Union[str, int, Any[os.Pathlike]] - _description_ - compression : str, optional - _description_, by default None - """ - - if not path.endswith('.h5'): - path += '.h5' - path.mkdir(parents=True, exist_ok=True) - with h5py.File(path, 'w') as f: - dict_to_hdf5(dictionary, f, compression) + if file_format == 'h5': + if not path.endswith('.h5'): + path += '.h5' + + path.mkdir(parents=True, exist_ok=True) + with h5py.File(path, 'w') as f: + dict_to_hdf5(dictionary, f, compression) + + else: + raise ValueError(f"Unsupported file format: {file_format}") print(f'Saved to {path}') \ No newline at end of file From 552254b3f8e3da189ebd6e5b59b83881af2b8307 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Thu, 13 Jun 2024 20:48:08 -0400 Subject: [PATCH 019/103] Co-authored-by: Josh Josephy-Zack Co-authored-by: Alvin Garcia --- hackathon/Hackathon.ipynb | 363 +++++++++++++++++++++++++++++++++++ hackathon/co2_to_mse.ipynb | 384 +++++++++++++++++++++++++++++++++++++ requirements.txt | 10 + 3 files changed, 757 insertions(+) create mode 100644 hackathon/Hackathon.ipynb create mode 100644 hackathon/co2_to_mse.ipynb create mode 100644 requirements.txt diff --git a/hackathon/Hackathon.ipynb b/hackathon/Hackathon.ipynb new file mode 100644 index 0000000..3ce6293 --- /dev/null +++ b/hackathon/Hackathon.ipynb @@ -0,0 +1,363 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import h5py\n", + "from joblib import dump, load\n", + "import numpy as np\n", + "import pandas as pdx\n", + "from sklearn.neighbors import NearestNeighbors\n", + "from sklearn import preprocessing\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "from torch.utils.data import DataLoader, Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "affa2a7c", + "metadata": {}, + "outputs": [], + "source": [ + "sns.set_theme()\n", + "%matplotlib inline\n", + "\n", + "visualize=True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22223acf", + "metadata": {}, + "outputs": [], + "source": [ + "filename = \"/scratch/gpfs/EKOLEMEN/hackathon/170000.h5\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b59aaa81", + "metadata": {}, + "outputs": [], + "source": [ + "with h5py.File(filename, \"r\") as f:\n", + " co2_data = f[\"co2_phase\"]\n", + " print(co2_data.keys())\n", + " print(co2_data.attrs.keys())\n", + " co2_channels = co2_data.attrs[\"channel_ids\"]\n", + " co2_samples = co2_data[\"data\"][()]\n", + " co2_time_ms = co2_data[\"time\"][()]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f66df09d", + "metadata": {}, + "outputs": [], + "source": [ + "with h5py.File(filename, \"r\") as f:\n", + " mse_data = f[\"mse\"]\n", + " print(mse_data.attrs.keys())\n", + " print(mse_data.attrs[\"channel_ids\"])\n", + " mse_channels = mse_data.attrs[\"channel_ids\"]\n", + " mse_samples = mse_data[\"data\"][()]\n", + " mse_time_ms = mse_data[\"time\"][()]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9b6bd80", + "metadata": {}, + "outputs": [], + "source": [ + "if visualize:\n", + " fig, axs = plt.subplots()\n", + "\n", + " for k, label in enumerate([\"01\", \"02\", \"03\", \"04\"]):\n", + " sns.lineplot(x=mse_time_ms[:, k], y=mse_samples[:, k], label=label)\n", + " axs.set_xlabel(\"Time (ms)\")\n", + " axs.set_ylabel(\"MSE\")\n", + " axs.set_xlim((1800, 1850))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "afa472f4", + "metadata": {}, + "outputs": [], + "source": [ + "if visualize:\n", + " fig, axs = plt.subplots()\n", + "\n", + " for k, label in enumerate([\"01\", \"02\", \"03\", \"04\"]):\n", + " sns.lineplot(x=mse_time_ms[:, k], y=mse_samples[:, k], label=label)\n", + " axs.set_xlabel(\"Time (ms)\")\n", + " axs.set_ylabel(\"MSE\")\n", + " axs.set_xlim((1800, 1850))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2871019c", + "metadata": {}, + "outputs": [], + "source": [ + "if visualize:\n", + " fig, axs = plt.subplots()\n", + "\n", + " axs.vlines(x=mse_time_ms[:, 0], ymin=-1600, ymax=800, linewidths=(0.1,))\n", + " for k, label in enumerate([\"r0\", \"v1\", \"v2\", \"v3\"]):\n", + " sns.lineplot(x=co2_time_ms[:, k], y=co2_samples[:, k], label=label)\n", + " axs.set_xlabel(\"Time (ms)\")\n", + " axs.set_ylabel(\"CO2\")\n", + " axs.set_xlim((1800, 1850))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19b26fc5", + "metadata": {}, + "outputs": [], + "source": [ + "nbrs = NearestNeighbors(n_neighbors=1).fit(co2_time_ms[:, 0].reshape(-1, 1))\n", + "distances, indices = nbrs.kneighbors(mse_time_ms[:, 0].reshape(-1, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1659f7f3", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axs = plt.subplots()\n", + "\n", + "sns.histplot(data=distances, bins=\"sqrt\", ax=axs)\n", + "axs.set_xlabel(\"CO2 MSE Time difference (ms)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0691dac0", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axs = plt.subplots()\n", + "\n", + "sns.histplot(data=distances, bins=\"sqrt\", ax=axs)\n", + "axs.set_xlabel(\"ECE MSE Time difference (ms)\")" + ] + }, + { + "cell_type": "markdown", + "id": "fad233c8", + "metadata": {}, + "source": [ + "# How to deal with missing channels?\n", + "\n", + "## Ideas\n", + "\n", + "- Only use channels that are always available across different discharges?\n", + "- Channel interpolation?\n", + "- ..." + ] + }, + { + "cell_type": "markdown", + "id": "8a27106d", + "metadata": {}, + "source": [ + "# Preprocessing\n", + "\n", + "## Ideas\n", + "\n", + "- All data have different physical units, and we need to get rid of them\n", + "- We should normalize the inputs before the ML task:\n", + " - Standardization?\n", + " - Min-Max normalization?\n", + " - Robust methods like median- and percentile-based normalization?\n", + " - Advanced methods for making skew distribution more similar to normal distributions?\n", + "- Inside models, we can use batch normalization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae43b538", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axs = plt.subplots()\n", + "\n", + "sns.histplot(data=co2_samples[:, 0], bins=\"sqrt\", ax=axs)\n", + "sns.histplot(data=co2_samples[:, 1], bins=\"sqrt\", ax=axs)\n", + "sns.histplot(data=co2_samples[:, 2], bins=\"sqrt\", ax=axs)\n", + "sns.histplot(data=co2_samples[:, 3], bins=\"sqrt\", ax=axs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "955fb151", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axs = plt.subplots()\n", + "\n", + "sns.histplot(data=X_trf[:, 0], bins=\"sqrt\", ax=axs)\n", + "sns.histplot(data=X_trf[:, 1], bins=\"sqrt\", ax=axs)\n", + "sns.histplot(data=X_trf[:, 2], bins=\"sqrt\", ax=axs)\n", + "sns.histplot(data=X_trf[:, 3], bins=\"sqrt\", ax=axs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e05feaf", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "import torch.nn as nn\n", + "\n", + "class MLP(nn.Module):\n", + " def __init__(self, input_size, output_size, hidden_sizes):\n", + " super(MLP, self).__init__()\n", + " \n", + " self.input_size = input_size\n", + " self.output_size = output_size\n", + " self.hidden_sizes = hidden_sizes\n", + " \n", + " self.layers = nn.ModuleList()\n", + " \n", + " # Add input layer\n", + " self.layers.append(nn.Linear(input_size, hidden_sizes[0]))\n", + " \n", + " # Add hidden layers\n", + " for i in range(len(hidden_sizes) - 1):\n", + " self.layers.append(nn.Linear(hidden_sizes[i], hidden_sizes[i+1]))\n", + " \n", + " # Add output layer\n", + " self.layers.append(nn.Linear(hidden_sizes[-1], output_size))\n", + " \n", + " def forward(self, x):\n", + " for layer in self.layers:\n", + " x = torch.relu(layer(x))\n", + " return x\n", + "\n", + "# Example usage\n", + "input_size = 5 # co2\n", + "output_size = 1 # mse\n", + "hidden_sizes = [20, 30, 40]\n", + "\n", + "model = MLP(input_size, output_size, hidden_sizes)\n", + "print(model)" + ] + }, + { + "cell_type": "markdown", + "id": "7dac0ed1", + "metadata": {}, + "source": [ + "Will be using valid padding (so start from before to after window)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14188e30", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from torch.utils.data import Dataset\n", + "\n", + "class VariableWindowDataset(Dataset):\n", + " def __init__(self, data, labels, window_size):\n", + " self.data = data\n", + " self.labels = labels\n", + " self.window_size = window_size\n", + " \n", + " def __len__(self):\n", + " return len(self.data)\n", + " \n", + " def __getitem__(self, index):\n", + " x = self.data[index]\n", + " y = self.labels[index]\n", + " \n", + " start_index = index\n", + " end_index = start_index + self.window_size\n", + " x = x[start_index:end_index]\n", + " \n", + " return x, y\n", + "\n", + "# Example usage\n", + "data = [...] # Your data\n", + "labels = [...] # Your labels\n", + "window_size = 10\n", + "\n", + "dataset = VariableWindowDataset(data, labels, window_size)\n", + "\n", + "# Create a dataloader\n", + "batch_size = 32\n", + "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)" + ] + }, + { + "cell_type": "markdown", + "id": "5304b167", + "metadata": {}, + "source": [ + "# Evaluation" + ] + }, + { + "cell_type": "markdown", + "id": "7a3263f3", + "metadata": {}, + "source": [ + "# Potential applications?!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/hackathon/co2_to_mse.ipynb b/hackathon/co2_to_mse.ipynb new file mode 100644 index 0000000..17d7228 --- /dev/null +++ b/hackathon/co2_to_mse.ipynb @@ -0,0 +1,384 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hackathon: Get CO2 from MSE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setup Notes\n", + "I set up .venv in FUSIONAIHUB to work with all the packages above. You should be able to see it if you're in the FUSIONAIHUB directory and click on kernel.\n", + "\n", + "Don't use the HDF5 file. Use the preprocessed data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import h5py\n", + "from joblib import dump, load\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.neighbors import NearestNeighbors\n", + "from sklearn import preprocessing\n", + "import random\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "import os\n", + "from pathlib import Path\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "from torch.utils.data import DataLoader, Dataset, random_split" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class FusionDataset(Dataset):\n", + " \n", + " def __init__(self, file_list, window_size=1):\n", + " self.file_list = file_list\n", + " self.window_size = window_size\n", + " self.inputs = None\n", + " self.targets = None\n", + " self.setup()\n", + " \n", + " def setup(self):\n", + " inputs = [None] * len(self.file_list)\n", + " targets = [None] * len(self.file_list)\n", + " missing_columns = self._identify_missing_columns()\n", + " for k, f in enumerate(self.file_list):\n", + " ece, mse = self._load_joblib_file(f, missing_columns)\n", + " neighbors = NearestNeighbors(n_neighbors=1).fit(\n", + " np.array(ece.index).reshape(-1, 1))\n", + " distances, indices = neighbors.kneighbors(\n", + " np.asarray(mse.index).reshape(-1, 1))\n", + " indices = indices.flatten()\n", + " ece = [ece.iloc[idx-self.window_size+1:idx+1] for idx in indices]\n", + " inputs[k] = np.asarray(ece)\n", + " targets[k] = np.asarray(mse)\n", + " self.inputs = np.vstack(inputs)\n", + " self.targets = np.vstack(targets)\n", + " print(self.inputs.shape)\n", + " print(self.targets.shape)\n", + " \n", + " def _identify_missing_columns(self):\n", + " targets = []\n", + " for file in self.file_list:\n", + " targets.append(load(file)[\"mse\"])\n", + " df = pd.concat(targets)\n", + " missing_columns = df.columns[df.isna().any()]\n", + " return missing_columns\n", + " \n", + " def _load_joblib_file(self, file_name, missing_columns):\n", + " print(file_name)\n", + " data = load(file_name)\n", + " ece_dataset = data[\"ece_cali\"]\n", + " mse_dataset = data[\"mse\"].drop(missing_columns, axis=1, errors='ignore')\n", + " return ece_dataset, mse_dataset\n", + " \n", + " def __getitem__(self, index):\n", + " return (torch.tensor(self.inputs[index], dtype=torch.float32),\n", + " torch.tensor(self.targets[index], dtype=torch.float32).unsqueeze(0))\n", + " \n", + " def __len__(self):\n", + " return len(self.inputs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dir = Path(\"/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/\")\n", + "file_list = list(dir.glob(\"*.joblib\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = FusionDataset(file_list, window_size=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "split_ratio = 0.8\n", + "train_dataset, test_dataset = random_split(dataset, [split_ratio, 1 - split_ratio])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('train_data length:', len(train_dataset))\n", + "print('test_data length:', len(test_dataset))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 32\n", + "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n", + "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Preprocessing\n", + "\n", + "Do any preprocessing here if needed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "data1 is smaller set input\n", + "data2 is bigger set output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset Module(s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Goal: Trying to correlate 1 point for lower resolution with a window of data for higher resolution. So we want to be able to correlate the same time and find a fast consistent way to match the times" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neural Network Modules" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "class Config:\n", + " def __init__(self, input_dim, embed_dim, output_dim, hidden_dim, device = \"cpu\"):\n", + " self.IN_DIM = input_dim\n", + " self.EMBED_DIM = embed_dim\n", + " self.OUT_DIM = output_dim\n", + " self.HIDDEN = hidden_dim\n", + " self.DEVICE = device\n", + "\n", + "def build_model(in_dim, out_dim, layers, hidden, activation, normalize=lambda x: x):\n", + " model = [normalize(nn.Linear(in_dim, hidden))]\n", + " model += [activation()]\n", + " for i in range(layers - 1):\n", + " model += [normalize(nn.Linear(hidden, hidden))]\n", + " model += [activation()]\n", + " model += [normalize(nn.Linear(hidden, out_dim))]\n", + " return nn.Sequential(*model)\n", + "\n", + "class Decoder(nn.Module):\n", + "\n", + " def __init__(self, embed, hidden, out_dim, layers=2):\n", + " super().__init__()\n", + " self.fc1 = build_model(embed, hidden, layers, hidden, nn.ReLU)\n", + " self.fc2 = nn.Linear(hidden, out_dim)\n", + "\n", + " def forward(self, z):\n", + " x = F.relu(self.fc1(z))\n", + " return self.fc2(x), x\n", + "\n", + "class Encoder(nn.Module):\n", + "\n", + " def __init__(self, in_dim, hidden, embed, layers=2):\n", + " super().__init__()\n", + "\n", + " self.fc1 = nn.Linear(in_dim, hidden)\n", + " self.encoder = build_model(hidden, embed, layers, hidden, nn.ReLU)\n", + "\n", + " def forward(self, x):\n", + " embed = F.relu(self.fc1(x))\n", + " return self.encoder(F.relu(embed))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "class HackNet(nn.Module):\n", + " def __init__(self, config):\n", + " super().__init__()\n", + "\n", + " self.encoder = Encoder(in_dim=config.IN_DIM, hidden=config.HIDDEN, embed=config.EMBED_DIM)\n", + " self.decoder = Decoder(embed=config.EMBED_DIM, hidden=config.HIDDEN, out_dim=config.OUT_DIM)\n", + " self._memory_unit = nn.GRU(config.EMBED_DIM, config.HIDDEN,\n", + " num_layers= 2,\n", + " batch_first=True)\n", + "\n", + " self.encoder = self.encoder.to(config.DEVICE)\n", + " self.decoder = self.decoder.to(config.DEVICE)\n", + " self._memory_unit = self._memory_unit.to(config.DEVICE)\n", + "\n", + " def forward(self, x): # B X T X IN_DIM\n", + "\n", + " embed = self.encoder(x) #B X T X EMBED_DIM\n", + " mem_out = self._memory_unit(embed)[0]\n", + "\n", + " pred, _ = self.decoder(torch.cat([embed, mem_out])) #B X T X OUT_DIM\n", + "\n", + " return pred" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_174944/2646173492.py:14: UserWarning: Using a target size (torch.Size([32, 1, 43])) that is different to the input size (torch.Size([64, 1, 43])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " loss = F.smooth_l1_loss(pred, y) #decide loss function, start with L1 distance\n" + ] + }, + { + "ename": "RuntimeError", + "evalue": "The size of tensor a (64) must match the size of tensor b (32) at non-singleton dimension 0", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[45], line 52\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_epochs):\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data_x, data_y \u001b[38;5;129;01min\u001b[39;00m train_loader:\n\u001b[0;32m---> 52\u001b[0m train_loss \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_epoch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_y\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data_x, data_y \u001b[38;5;129;01min\u001b[39;00m test_loader:\n\u001b[1;32m 55\u001b[0m val_loss \u001b[38;5;241m=\u001b[39m val_epoch(net, opt, data_x, data_y)\n", + "Cell \u001b[0;32mIn[45], line 14\u001b[0m, in \u001b[0;36mtrain_epoch\u001b[0;34m(network, optim, data_x, data_y)\u001b[0m\n\u001b[1;32m 12\u001b[0m pred \u001b[38;5;241m=\u001b[39m network(x)\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m#compute loss func\u001b[39;00m\n\u001b[0;32m---> 14\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msmooth_l1_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpred\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m#decide loss function, start with L1 distance\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m#peform optimization\u001b[39;00m\n\u001b[1;32m 16\u001b[0m optim\u001b[38;5;241m.\u001b[39mzero_grad()\n", + "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/nn/functional.py:3265\u001b[0m, in \u001b[0;36msmooth_l1_loss\u001b[0;34m(input, target, size_average, reduce, reduction, beta)\u001b[0m\n\u001b[1;32m 3262\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m size_average \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m reduce \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 3263\u001b[0m reduction \u001b[38;5;241m=\u001b[39m _Reduction\u001b[38;5;241m.\u001b[39mlegacy_get_string(size_average, reduce)\n\u001b[0;32m-> 3265\u001b[0m expanded_input, expanded_target \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbroadcast_tensors\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3267\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m beta \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0.0\u001b[39m:\n\u001b[1;32m 3268\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_nn\u001b[38;5;241m.\u001b[39ml1_loss(expanded_input, expanded_target, _Reduction\u001b[38;5;241m.\u001b[39mget_enum(reduction))\n", + "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/functional.py:76\u001b[0m, in \u001b[0;36mbroadcast_tensors\u001b[0;34m(*tensors)\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function(tensors):\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(broadcast_tensors, tensors, \u001b[38;5;241m*\u001b[39mtensors)\n\u001b[0;32m---> 76\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_VF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbroadcast_tensors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mRuntimeError\u001b[0m: The size of tensor a (64) must match the size of tensor b (32) at non-singleton dimension 0" + ] + } + ], + "source": [ + "def train_epoch(network, optim, data_x, data_y):\n", + " # data_x: (bs, window_size, x_dim); data_y: (bs, y_dim)\n", + "\n", + " dataset_size = data_x.shape[0]\n", + " network.train()\n", + " idxes = np.arange(0, dataset_size)\n", + " random.shuffle(idxes)\n", + "\n", + " x = data_x[idxes]\n", + " y = data_y[idxes]\n", + "\n", + " pred = network(x)\n", + " #compute loss func\n", + " loss = F.smooth_l1_loss(pred, y) #decide loss function, start with L1 distance\n", + " #peform optimization\n", + " optim.zero_grad()\n", + " loss.backward()\n", + " opt.step()\n", + "\n", + " return loss.item() #for logging\n", + "\n", + "\n", + "def val_epoch(network, optim, data_x, data_y):\n", + " network.eval()\n", + " dataset_size = data_x.shape[0]\n", + " val_idxes = np.arange(0, dataset_size)\n", + " random.shuffle(val_idxes)\n", + "\n", + " x = data_x[val_idxes]\n", + " y = data_y[val_idxes]\n", + "\n", + " with torch.no_grad():\n", + "\n", + " #compute model output\n", + " pred = network(x)\n", + " #compute loss func\n", + " loss = F.smooth_l1_loss(pred, y)\n", + "\n", + " return loss.item() #for logging\n", + "\n", + "\n", + "#training epoch code\n", + "config = Config(48, 64, 43, 64)\n", + "net = HackNet(config)\n", + "opt = torch.optim.Adam(net.parameters(), lr=3e-4)\n", + "\n", + "num_epochs = 100\n", + "\n", + "for epoch in range(num_epochs):\n", + "\n", + " for data_x, data_y in train_loader:\n", + " train_loss = train_epoch(net, opt, data_x, data_y)\n", + " \n", + " for data_x, data_y in test_loader:\n", + " val_loss = val_epoch(net, opt, data_x, data_y)\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..a2c1898 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,10 @@ +torch==1.12.1+cu116 +torchvision==0.13.1+cu116 +torchaudio==0.12.1+cu116 +numpy +pandas +scikit-learn +matplotlib +seaborn +jupyter +h5py \ No newline at end of file From 4c3d872b079cba4c18efaf7cd30540c621994576 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Thu, 13 Jun 2024 21:25:17 -0400 Subject: [PATCH 020/103] stuff --- hackathon/co2_to_mse.ipynb | 147 ++++++++++++++++++++++++------------- 1 file changed, 98 insertions(+), 49 deletions(-) diff --git a/hackathon/co2_to_mse.ipynb b/hackathon/co2_to_mse.ipynb index 17d7228..d8e149c 100644 --- a/hackathon/co2_to_mse.ipynb +++ b/hackathon/co2_to_mse.ipynb @@ -19,11 +19,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "import h5py\n", + "# import h5py\n", "from joblib import dump, load\n", "import numpy as np\n", "import pandas as pd\n", @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -112,16 +112,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170037.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170014.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170006.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170020.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170013.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170012.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170008.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170015.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170007.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170003.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170000.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170002.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170009.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170005.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170021.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170004.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170019.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170011.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170018.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170017.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170016.joblib\n", + "/scratch/gpfs/EKOLEMEN/hackathon/preprocessed/170038.joblib\n", + "(82552, 1, 48)\n", + "(82552, 43)\n" + ] + } + ], "source": [ "dataset = FusionDataset(file_list, window_size=1)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -131,9 +162,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train_data length: 66042\n", + "test_data length: 16510\n" + ] + } + ], "source": [ "print('train_data length:', len(train_dataset))\n", "print('test_data length:', len(test_dataset))" @@ -141,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -190,12 +230,13 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "class Config:\n", - " def __init__(self, input_dim, embed_dim, output_dim, hidden_dim, device = \"cpu\"):\n", + " def __init__(self, input_dim, embed_dim, hidden_dim, output_dim, device):\n", + " print('device:',device)\n", " self.IN_DIM = input_dim\n", " self.EMBED_DIM = embed_dim\n", " self.OUT_DIM = output_dim\n", @@ -237,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -247,9 +288,7 @@ "\n", " self.encoder = Encoder(in_dim=config.IN_DIM, hidden=config.HIDDEN, embed=config.EMBED_DIM)\n", " self.decoder = Decoder(embed=config.EMBED_DIM, hidden=config.HIDDEN, out_dim=config.OUT_DIM)\n", - " self._memory_unit = nn.GRU(config.EMBED_DIM, config.HIDDEN,\n", - " num_layers= 2,\n", - " batch_first=True)\n", + " self._memory_unit = nn.GRU(config.EMBED_DIM, config.HIDDEN, num_layers= 2, batch_first=True)\n", "\n", " self.encoder = self.encoder.to(config.DEVICE)\n", " self.decoder = self.decoder.to(config.DEVICE)\n", @@ -260,39 +299,16 @@ " embed = self.encoder(x) #B X T X EMBED_DIM\n", " mem_out = self._memory_unit(embed)[0]\n", "\n", - " pred, _ = self.decoder(torch.cat([embed, mem_out])) #B X T X OUT_DIM\n", + " pred, _ = self.decoder(mem_out) #B X T X OUT_DIM\n", "\n", " return pred" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 22, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_174944/2646173492.py:14: UserWarning: Using a target size (torch.Size([32, 1, 43])) that is different to the input size (torch.Size([64, 1, 43])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", - " loss = F.smooth_l1_loss(pred, y) #decide loss function, start with L1 distance\n" - ] - }, - { - "ename": "RuntimeError", - "evalue": "The size of tensor a (64) must match the size of tensor b (32) at non-singleton dimension 0", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[45], line 52\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_epochs):\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data_x, data_y \u001b[38;5;129;01min\u001b[39;00m train_loader:\n\u001b[0;32m---> 52\u001b[0m train_loss \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_epoch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_y\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data_x, data_y \u001b[38;5;129;01min\u001b[39;00m test_loader:\n\u001b[1;32m 55\u001b[0m val_loss \u001b[38;5;241m=\u001b[39m val_epoch(net, opt, data_x, data_y)\n", - "Cell \u001b[0;32mIn[45], line 14\u001b[0m, in \u001b[0;36mtrain_epoch\u001b[0;34m(network, optim, data_x, data_y)\u001b[0m\n\u001b[1;32m 12\u001b[0m pred \u001b[38;5;241m=\u001b[39m network(x)\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m#compute loss func\u001b[39;00m\n\u001b[0;32m---> 14\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msmooth_l1_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpred\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m#decide loss function, start with L1 distance\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m#peform optimization\u001b[39;00m\n\u001b[1;32m 16\u001b[0m optim\u001b[38;5;241m.\u001b[39mzero_grad()\n", - "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/nn/functional.py:3265\u001b[0m, in \u001b[0;36msmooth_l1_loss\u001b[0;34m(input, target, size_average, reduce, reduction, beta)\u001b[0m\n\u001b[1;32m 3262\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m size_average \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m reduce \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 3263\u001b[0m reduction \u001b[38;5;241m=\u001b[39m _Reduction\u001b[38;5;241m.\u001b[39mlegacy_get_string(size_average, reduce)\n\u001b[0;32m-> 3265\u001b[0m expanded_input, expanded_target \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbroadcast_tensors\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3267\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m beta \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0.0\u001b[39m:\n\u001b[1;32m 3268\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_nn\u001b[38;5;241m.\u001b[39ml1_loss(expanded_input, expanded_target, _Reduction\u001b[38;5;241m.\u001b[39mget_enum(reduction))\n", - "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/functional.py:76\u001b[0m, in \u001b[0;36mbroadcast_tensors\u001b[0;34m(*tensors)\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function(tensors):\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(broadcast_tensors, tensors, \u001b[38;5;241m*\u001b[39mtensors)\n\u001b[0;32m---> 76\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_VF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbroadcast_tensors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mRuntimeError\u001b[0m: The size of tensor a (64) must match the size of tensor b (32) at non-singleton dimension 0" - ] - } - ], + "outputs": [], "source": [ "def train_epoch(network, optim, data_x, data_y):\n", " # data_x: (bs, window_size, x_dim); data_y: (bs, y_dim)\n", @@ -332,11 +348,43 @@ " #compute loss func\n", " loss = F.smooth_l1_loss(pred, y)\n", "\n", - " return loss.item() #for logging\n", - "\n", - "\n", + " return loss.item() #for logging#training epoch code" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "device: cpu\n", + "Epoch 0 Train Loss: 0.2336178719997406 Val Loss: 0.25213396549224854\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[23], line 13\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_epochs):\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data_x, data_y \u001b[38;5;129;01min\u001b[39;00m train_loader:\n\u001b[0;32m---> 13\u001b[0m train_loss \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_epoch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_y\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data_x, data_y \u001b[38;5;129;01min\u001b[39;00m test_loader:\n\u001b[1;32m 16\u001b[0m val_loss \u001b[38;5;241m=\u001b[39m val_epoch(net, opt, data_x, data_y)\n", + "Cell \u001b[0;32mIn[22], line 17\u001b[0m, in \u001b[0;36mtrain_epoch\u001b[0;34m(network, optim, data_x, data_y)\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m#peform optimization\u001b[39;00m\n\u001b[1;32m 16\u001b[0m optim\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m---> 17\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 18\u001b[0m opt\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\u001b[38;5;241m.\u001b[39mitem()\n", + "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/_tensor.py:525\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 515\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 517\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 518\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 523\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m 524\u001b[0m )\n\u001b[0;32m--> 525\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 526\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m 527\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/autograd/__init__.py:267\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 262\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m 265\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 266\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 267\u001b[0m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 268\u001b[0m \u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 269\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 270\u001b[0m \u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 271\u001b[0m \u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 273\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 275\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/autograd/graph.py:744\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[0;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 742\u001b[0m unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[1;32m 743\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 744\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 745\u001b[0m \u001b[43m \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 746\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 748\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ "#training epoch code\n", - "config = Config(48, 64, 43, 64)\n", + "# input_dim, embed_dim, hidden_dim, output_dim\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "config = Config(48, 64, 64, 43, device=device)\n", "net = HackNet(config)\n", "opt = torch.optim.Adam(net.parameters(), lr=3e-4)\n", "\n", @@ -349,7 +397,8 @@ " \n", " for data_x, data_y in test_loader:\n", " val_loss = val_epoch(net, opt, data_x, data_y)\n", - " break" + " \n", + " print(f\"Epoch {epoch} Train Loss: {train_loss} Val Loss: {val_loss}\")" ] }, { @@ -362,7 +411,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -380,5 +429,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From f1d8aa02add68e8f85b38d20aa84cae27c6881fe Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Thu, 13 Jun 2024 21:54:12 -0400 Subject: [PATCH 021/103] gpu compatability --- hackathon/co2_to_mse.ipynb | 82 +++++++++++++++++++++++++++++++------- 1 file changed, 68 insertions(+), 14 deletions(-) diff --git a/hackathon/co2_to_mse.ipynb b/hackathon/co2_to_mse.ipynb index d8e149c..b7f90ef 100644 --- a/hackathon/co2_to_mse.ipynb +++ b/hackathon/co2_to_mse.ipynb @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -230,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -278,10 +278,39 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# updated HackNet (can work with GPU)\n", + "class HackNet(nn.Module):\n", + " def __init__(self, config):\n", + " super().__init__()\n", + " self.encoder = Encoder(in_dim=config.IN_DIM, hidden=config.HIDDEN, embed=config.EMBED_DIM)\n", + " self.decoder = Decoder(embed=config.EMBED_DIM, hidden=config.HIDDEN, out_dim=config.OUT_DIM)\n", + " self._memory_unit = nn.GRU(config.EMBED_DIM, config.HIDDEN, num_layers=2, batch_first=True)\n", + "\n", + " def forward(self, x):\n", + " embed = self.encoder(x)\n", + " mem_out = self._memory_unit(embed)[0]\n", + " pred, _ = self.decoder(mem_out)\n", + " return pred\n", + "\n", + " def to(self, device):\n", + " super().to(device)\n", + " self.encoder = self.encoder.to(device)\n", + " self.decoder = self.decoder.to(device)\n", + " self._memory_unit = self._memory_unit.to(device)\n", + " return self" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ + "# original HackNet\n", "class HackNet(nn.Module):\n", " def __init__(self, config):\n", " super().__init__()\n", @@ -299,14 +328,14 @@ " embed = self.encoder(x) #B X T X EMBED_DIM\n", " mem_out = self._memory_unit(embed)[0]\n", "\n", - " pred, _ = self.decoder(mem_out) #B X T X OUT_DIM\n", + " pred, _ = self.decoder(torch.cat([embed, mem_out])) #B X T X OUT_DIM #B X T X OUT_DIM\n", "\n", " return pred" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -327,7 +356,7 @@ " #peform optimization\n", " optim.zero_grad()\n", " loss.backward()\n", - " opt.step()\n", + " optim.step() # (nathan) was opt.step() before\n", "\n", " return loss.item() #for logging\n", "\n", @@ -353,15 +382,40 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "device: cpu\n", - "Epoch 0 Train Loss: 0.2336178719997406 Val Loss: 0.25213396549224854\n" + "device: cuda\n", + "Epoch 0 Train Loss: 0.255791574716568 Val Loss: 0.2838430106639862\n", + "Epoch 1 Train Loss: 0.23289497196674347 Val Loss: 0.2714169919490814\n", + "Epoch 2 Train Loss: 0.2525785565376282 Val Loss: 0.31792107224464417\n", + "Epoch 3 Train Loss: 0.24364440143108368 Val Loss: 0.2633882164955139\n", + "Epoch 4 Train Loss: 0.22275318205356598 Val Loss: 0.2613389492034912\n", + "Epoch 5 Train Loss: 0.2087143063545227 Val Loss: 0.24688249826431274\n", + "Epoch 6 Train Loss: 0.24294227361679077 Val Loss: 0.2491239607334137\n", + "Epoch 7 Train Loss: 0.23406414687633514 Val Loss: 0.20855343341827393\n", + "Epoch 8 Train Loss: 0.26939481496810913 Val Loss: 0.21229057013988495\n", + "Epoch 9 Train Loss: 0.2777935564517975 Val Loss: 0.24400869011878967\n", + "Epoch 10 Train Loss: 0.2552683353424072 Val Loss: 0.23029792308807373\n", + "Epoch 11 Train Loss: 0.3095128834247589 Val Loss: 0.24108797311782837\n", + "Epoch 12 Train Loss: 0.25221654772758484 Val Loss: 0.24853092432022095\n", + "Epoch 13 Train Loss: 0.2726132869720459 Val Loss: 0.2304145246744156\n", + "Epoch 14 Train Loss: 0.24462772905826569 Val Loss: 0.23836518824100494\n", + "Epoch 15 Train Loss: 0.2791774868965149 Val Loss: 0.26178693771362305\n", + "Epoch 16 Train Loss: 0.2374017834663391 Val Loss: 0.2145753651857376\n", + "Epoch 17 Train Loss: 0.26172196865081787 Val Loss: 0.22734738886356354\n", + "Epoch 18 Train Loss: 0.24134452641010284 Val Loss: 0.23207946121692657\n", + "Epoch 19 Train Loss: 0.2221219688653946 Val Loss: 0.26842987537384033\n", + "Epoch 20 Train Loss: 0.25536271929740906 Val Loss: 0.22756478190422058\n", + "Epoch 21 Train Loss: 0.25661006569862366 Val Loss: 0.23108401894569397\n", + "Epoch 22 Train Loss: 0.23620621860027313 Val Loss: 0.27084627747535706\n", + "Epoch 23 Train Loss: 0.21768540143966675 Val Loss: 0.1903669238090515\n", + "Epoch 24 Train Loss: 0.26772356033325195 Val Loss: 0.21332523226737976\n", + "Epoch 25 Train Loss: 0.25931689143180847 Val Loss: 0.24254707992076874\n" ] }, { @@ -371,11 +425,11 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[23], line 13\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_epochs):\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data_x, data_y \u001b[38;5;129;01min\u001b[39;00m train_loader:\n\u001b[0;32m---> 13\u001b[0m train_loss \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_epoch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_y\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data_x, data_y \u001b[38;5;129;01min\u001b[39;00m test_loader:\n\u001b[1;32m 16\u001b[0m val_loss \u001b[38;5;241m=\u001b[39m val_epoch(net, opt, data_x, data_y)\n", - "Cell \u001b[0;32mIn[22], line 17\u001b[0m, in \u001b[0;36mtrain_epoch\u001b[0;34m(network, optim, data_x, data_y)\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m#peform optimization\u001b[39;00m\n\u001b[1;32m 16\u001b[0m optim\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m---> 17\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 18\u001b[0m opt\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\u001b[38;5;241m.\u001b[39mitem()\n", - "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/_tensor.py:525\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 515\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 517\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 518\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 523\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m 524\u001b[0m )\n\u001b[0;32m--> 525\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 526\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m 527\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/autograd/__init__.py:267\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 262\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m 265\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 266\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 267\u001b[0m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 268\u001b[0m \u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 269\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 270\u001b[0m \u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 271\u001b[0m \u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 273\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 275\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/autograd/graph.py:744\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[0;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 742\u001b[0m unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[1;32m 743\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 744\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 745\u001b[0m \u001b[43m \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 746\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 748\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n", + "Cell \u001b[0;32mIn[18], line 13\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_epochs):\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data_x, data_y \u001b[38;5;129;01min\u001b[39;00m train_loader:\n\u001b[0;32m---> 13\u001b[0m train_loss \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_epoch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_y\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data_x, data_y \u001b[38;5;129;01min\u001b[39;00m test_loader:\n\u001b[1;32m 16\u001b[0m val_loss \u001b[38;5;241m=\u001b[39m val_epoch(net, opt, data_x, data_y)\n", + "Cell \u001b[0;32mIn[17], line 18\u001b[0m, in \u001b[0;36mtrain_epoch\u001b[0;34m(network, optim, data_x, data_y)\u001b[0m\n\u001b[1;32m 16\u001b[0m optim\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m 17\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[0;32m---> 18\u001b[0m \u001b[43moptim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\u001b[38;5;241m.\u001b[39mitem()\n", + "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/optim/optimizer.py:391\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 388\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 389\u001b[0m )\n\u001b[0;32m--> 391\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 392\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[1;32m 394\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n", + "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/optim/optimizer.py:79\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.._use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 78\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[0;32m---> 79\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_grad_enabled\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprev_grad\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 80\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n", + "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/autograd/grad_mode.py:186\u001b[0m, in \u001b[0;36mset_grad_enabled.__init__\u001b[0;34m(self, mode)\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprev \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mis_grad_enabled()\n\u001b[1;32m 185\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode \u001b[38;5;241m=\u001b[39m mode\n\u001b[0;32m--> 186\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_set_grad_enabled\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } From 30f014c8a652374f45afb3de3b04df3b090ea3c8 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Sat, 15 Jun 2024 16:15:43 -0400 Subject: [PATCH 022/103] most recent with debugging --- hackathon/co2_to_mse.ipynb | 65127 ++++++++++++++++++++++++++++++++++- 1 file changed, 65058 insertions(+), 69 deletions(-) diff --git a/hackathon/co2_to_mse.ipynb b/hackathon/co2_to_mse.ipynb index b7f90ef..db1c682 100644 --- a/hackathon/co2_to_mse.ipynb +++ b/hackathon/co2_to_mse.ipynb @@ -230,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 93, "metadata": {}, "outputs": [], "source": [ @@ -256,7 +256,7 @@ "\n", " def __init__(self, embed, hidden, out_dim, layers=2):\n", " super().__init__()\n", - " self.fc1 = build_model(embed, hidden, layers, hidden, nn.ReLU)\n", + " self.fc1 = build_model(embed+hidden, hidden, layers, hidden, nn.ReLU)\n", " self.fc2 = nn.Linear(hidden, out_dim)\n", "\n", " def forward(self, z):\n", @@ -278,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ @@ -293,7 +293,7 @@ " def forward(self, x):\n", " embed = self.encoder(x)\n", " mem_out = self._memory_unit(embed)[0]\n", - " pred, _ = self.decoder(mem_out)\n", + " pred, _ = self.decoder(torch.cat([embed, mem_out],axis=2))\n", " return pred\n", "\n", " def to(self, device):\n", @@ -306,36 +306,7 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# original HackNet\n", - "class HackNet(nn.Module):\n", - " def __init__(self, config):\n", - " super().__init__()\n", - "\n", - " self.encoder = Encoder(in_dim=config.IN_DIM, hidden=config.HIDDEN, embed=config.EMBED_DIM)\n", - " self.decoder = Decoder(embed=config.EMBED_DIM, hidden=config.HIDDEN, out_dim=config.OUT_DIM)\n", - " self._memory_unit = nn.GRU(config.EMBED_DIM, config.HIDDEN, num_layers= 2, batch_first=True)\n", - "\n", - " self.encoder = self.encoder.to(config.DEVICE)\n", - " self.decoder = self.decoder.to(config.DEVICE)\n", - " self._memory_unit = self._memory_unit.to(config.DEVICE)\n", - "\n", - " def forward(self, x): # B X T X IN_DIM\n", - "\n", - " embed = self.encoder(x) #B X T X EMBED_DIM\n", - " mem_out = self._memory_unit(embed)[0]\n", - "\n", - " pred, _ = self.decoder(torch.cat([embed, mem_out])) #B X T X OUT_DIM #B X T X OUT_DIM\n", - "\n", - " return pred" - ] - }, - { - "cell_type": "code", - "execution_count": 17, + "execution_count": 97, "metadata": {}, "outputs": [], "source": [ @@ -349,7 +320,6 @@ "\n", " x = data_x[idxes]\n", " y = data_y[idxes]\n", - "\n", " pred = network(x)\n", " #compute loss func\n", " loss = F.smooth_l1_loss(pred, y) #decide loss function, start with L1 distance\n", @@ -382,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 100, "metadata": {}, "outputs": [ { @@ -390,32 +360,23 @@ "output_type": "stream", "text": [ "device: cuda\n", - "Epoch 0 Train Loss: 0.255791574716568 Val Loss: 0.2838430106639862\n", - "Epoch 1 Train Loss: 0.23289497196674347 Val Loss: 0.2714169919490814\n", - "Epoch 2 Train Loss: 0.2525785565376282 Val Loss: 0.31792107224464417\n", - "Epoch 3 Train Loss: 0.24364440143108368 Val Loss: 0.2633882164955139\n", - "Epoch 4 Train Loss: 0.22275318205356598 Val Loss: 0.2613389492034912\n", - "Epoch 5 Train Loss: 0.2087143063545227 Val Loss: 0.24688249826431274\n", - "Epoch 6 Train Loss: 0.24294227361679077 Val Loss: 0.2491239607334137\n", - "Epoch 7 Train Loss: 0.23406414687633514 Val Loss: 0.20855343341827393\n", - "Epoch 8 Train Loss: 0.26939481496810913 Val Loss: 0.21229057013988495\n", - "Epoch 9 Train Loss: 0.2777935564517975 Val Loss: 0.24400869011878967\n", - "Epoch 10 Train Loss: 0.2552683353424072 Val Loss: 0.23029792308807373\n", - "Epoch 11 Train Loss: 0.3095128834247589 Val Loss: 0.24108797311782837\n", - "Epoch 12 Train Loss: 0.25221654772758484 Val Loss: 0.24853092432022095\n", - "Epoch 13 Train Loss: 0.2726132869720459 Val Loss: 0.2304145246744156\n", - "Epoch 14 Train Loss: 0.24462772905826569 Val Loss: 0.23836518824100494\n", - "Epoch 15 Train Loss: 0.2791774868965149 Val Loss: 0.26178693771362305\n", - "Epoch 16 Train Loss: 0.2374017834663391 Val Loss: 0.2145753651857376\n", - "Epoch 17 Train Loss: 0.26172196865081787 Val Loss: 0.22734738886356354\n", - "Epoch 18 Train Loss: 0.24134452641010284 Val Loss: 0.23207946121692657\n", - "Epoch 19 Train Loss: 0.2221219688653946 Val Loss: 0.26842987537384033\n", - "Epoch 20 Train Loss: 0.25536271929740906 Val Loss: 0.22756478190422058\n", - "Epoch 21 Train Loss: 0.25661006569862366 Val Loss: 0.23108401894569397\n", - "Epoch 22 Train Loss: 0.23620621860027313 Val Loss: 0.27084627747535706\n", - "Epoch 23 Train Loss: 0.21768540143966675 Val Loss: 0.1903669238090515\n", - "Epoch 24 Train Loss: 0.26772356033325195 Val Loss: 0.21332523226737976\n", - "Epoch 25 Train Loss: 0.25931689143180847 Val Loss: 0.24254707992076874\n" + "Epoch 0 Train Loss: 0.25513583421707153 Val Loss: 0.24469204246997833\n", + "Epoch 1 Train Loss: 0.2659304738044739 Val Loss: 0.23944751918315887\n", + "Epoch 2 Train Loss: 0.2457357496023178 Val Loss: 0.24234223365783691\n", + "Epoch 3 Train Loss: 0.25822871923446655 Val Loss: 0.23999018967151642\n", + "Epoch 4 Train Loss: 0.21395975351333618 Val Loss: 0.22520765662193298\n", + "Epoch 5 Train Loss: 0.2915404140949249 Val Loss: 0.25070616602897644\n", + "Epoch 6 Train Loss: 0.23592528700828552 Val Loss: 0.2476009577512741\n", + "Epoch 7 Train Loss: 0.24763169884681702 Val Loss: 0.26266419887542725\n", + "Epoch 8 Train Loss: 0.2499246895313263 Val Loss: 0.252627432346344\n", + "Epoch 9 Train Loss: 0.2591034173965454 Val Loss: 0.2148347795009613\n", + "Epoch 10 Train Loss: 0.24732984602451324 Val Loss: 0.25751248002052307\n", + "Epoch 11 Train Loss: 0.21846677362918854 Val Loss: 0.3136787712574005\n", + "Epoch 12 Train Loss: 0.20617620646953583 Val Loss: 0.27623942494392395\n", + "Epoch 13 Train Loss: 0.2601115107536316 Val Loss: 0.22286640107631683\n", + "Epoch 14 Train Loss: 0.2129838913679123 Val Loss: 0.2422345131635666\n", + "Epoch 15 Train Loss: 0.21845406293869019 Val Loss: 0.2498026043176651\n", + "Epoch 16 Train Loss: 0.2497348189353943 Val Loss: 0.27690985798835754\n" ] }, { @@ -425,11 +386,13 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[18], line 13\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_epochs):\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data_x, data_y \u001b[38;5;129;01min\u001b[39;00m train_loader:\n\u001b[0;32m---> 13\u001b[0m train_loss \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_epoch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_y\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data_x, data_y \u001b[38;5;129;01min\u001b[39;00m test_loader:\n\u001b[1;32m 16\u001b[0m val_loss \u001b[38;5;241m=\u001b[39m val_epoch(net, opt, data_x, data_y)\n", - "Cell \u001b[0;32mIn[17], line 18\u001b[0m, in \u001b[0;36mtrain_epoch\u001b[0;34m(network, optim, data_x, data_y)\u001b[0m\n\u001b[1;32m 16\u001b[0m optim\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m 17\u001b[0m loss\u001b[38;5;241m.\u001b[39mbackward()\n\u001b[0;32m---> 18\u001b[0m \u001b[43moptim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\u001b[38;5;241m.\u001b[39mitem()\n", - "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/optim/optimizer.py:391\u001b[0m, in \u001b[0;36mOptimizer.profile_hook_step..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 388\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must return None or a tuple of (new_args, new_kwargs), but got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 389\u001b[0m )\n\u001b[0;32m--> 391\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 392\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_optimizer_step_code()\n\u001b[1;32m 394\u001b[0m \u001b[38;5;66;03m# call optimizer step post hooks\u001b[39;00m\n", - "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/optim/optimizer.py:79\u001b[0m, in \u001b[0;36m_use_grad_for_differentiable.._use_grad\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 78\u001b[0m torch\u001b[38;5;241m.\u001b[39m_dynamo\u001b[38;5;241m.\u001b[39mgraph_break()\n\u001b[0;32m---> 79\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_grad_enabled\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprev_grad\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 80\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n", - "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/autograd/grad_mode.py:186\u001b[0m, in \u001b[0;36mset_grad_enabled.__init__\u001b[0;34m(self, mode)\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprev \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mis_grad_enabled()\n\u001b[1;32m 185\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode \u001b[38;5;241m=\u001b[39m mode\n\u001b[0;32m--> 186\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_set_grad_enabled\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[100], line 13\u001b[0m\n\u001b[1;32m 9\u001b[0m num_epochs \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_epochs):\n\u001b[0;32m---> 13\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_y\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 14\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_loss\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtrain_epoch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_y\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data_x, data_y \u001b[38;5;129;01min\u001b[39;00m test_loader:\n", + "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/utils/data/dataloader.py:631\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 628\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 629\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m 630\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 631\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 632\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 633\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 635\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n", + "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/utils/data/dataloader.py:675\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 673\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 674\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index() \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m--> 675\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m 676\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[1;32m 677\u001b[0m data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n", + "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/utils/data/_utils/fetch.py:49\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_collation:\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__getitems__\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__:\n\u001b[0;32m---> 49\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__getitems__\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpossibly_batched_index\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 51\u001b[0m data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n", + "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/utils/data/dataset.py:419\u001b[0m, in \u001b[0;36mSubset.__getitems__\u001b[0;34m(self, indices)\u001b[0m\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__([\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindices[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m indices]) \u001b[38;5;66;03m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[1;32m 418\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 419\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m]\u001b[49m\n", + "File \u001b[0;32m/scratch/gpfs/nc1514/FusionAIHub/.venv/lib64/python3.11/site-packages/torch/utils/data/dataset.py:419\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__([\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindices[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m indices]) \u001b[38;5;66;03m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[1;32m 418\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 419\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m indices]\n", + "Cell \u001b[0;32mIn[2], line 46\u001b[0m, in \u001b[0;36mFusionDataset.__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, index):\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (torch\u001b[38;5;241m.\u001b[39mtensor(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minputs[index], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat32),\n\u001b[0;32m---> 46\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtensor\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtargets\u001b[49m\u001b[43m[\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat32\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m))\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } @@ -438,9 +401,10 @@ "#training epoch code\n", "# input_dim, embed_dim, hidden_dim, output_dim\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "config = Config(48, 64, 64, 43, device=device)\n", + "# input_dim, embed_dim, hidden_dim, output_dim\n", + "config = Config(48, 24, 64, 43, device=device)\n", "net = HackNet(config)\n", - "opt = torch.optim.Adam(net.parameters(), lr=3e-4)\n", + "opt = torch.optim.Adam(net.parameters(), lr=3e-3)\n", "\n", "num_epochs = 100\n", "\n", @@ -455,6 +419,65031 @@ " print(f\"Epoch {epoch} Train Loss: {train_loss} Val Loss: {val_loss}\")" ] }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "device: cuda\n", + "Epoch 0 Train Loss: 0.2882300447383823 Val Loss: 0.2893289668962013\n", + "Epoch 1 Train Loss: 0.289524489407276 Val Loss: 0.2895522813579833\n", + "Epoch 2 Train Loss: 0.28950670627198477 Val Loss: 0.28919077809004823\n", + "Epoch 3 Train Loss: 0.28949106930820057 Val Loss: 0.2895732760256113\n", + "Epoch 4 Train Loss: 0.2895325559169747 Val Loss: 0.28947223578543624\n", + "Epoch 5 Train Loss: 0.28953749303121207 Val Loss: 0.28922719964685367\n", + "Epoch 6 Train Loss: 0.2895047937036138 Val Loss: 0.2893783446950044\n", + "Epoch 7 Train Loss: 0.28951890379652495 Val Loss: 0.28954099661620086\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "KeyboardInterrupt\n", + "\n" + ] + } + ], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import numpy as np\n", + "import random\n", + "\n", + "class Config:\n", + " def __init__(self, input_dim, embed_dim, hidden_dim, output_dim, device):\n", + " print('device:', device)\n", + " self.IN_DIM = input_dim\n", + " self.EMBED_DIM = embed_dim\n", + " self.OUT_DIM = output_dim\n", + " self.HIDDEN = hidden_dim\n", + " self.DEVICE = device\n", + "\n", + "def build_model(in_dim, out_dim, layers, hidden, activation, normalize=lambda x: x):\n", + " model = [normalize(nn.Linear(in_dim, hidden))]\n", + " model += [activation()]\n", + " for i in range(layers - 1):\n", + " model += [normalize(nn.Linear(hidden, hidden))]\n", + " model += [activation()]\n", + " model += [normalize(nn.Linear(hidden, out_dim))]\n", + " return nn.Sequential(*model)\n", + "\n", + "class Decoder(nn.Module):\n", + " def __init__(self, embed, hidden, out_dim, layers=2):\n", + " super().__init__()\n", + " self.fc1 = build_model(embed + hidden, hidden, layers, hidden, nn.ReLU)\n", + " self.fc2 = nn.Linear(hidden, out_dim)\n", + "\n", + " def forward(self, z):\n", + " x = F.relu(self.fc1(z))\n", + " return self.fc2(x), x\n", + "\n", + "class Encoder(nn.Module):\n", + " def __init__(self, in_dim, hidden, embed, layers=2):\n", + " super().__init__()\n", + " self.fc1 = nn.Linear(in_dim, hidden)\n", + " self.encoder = build_model(hidden, embed, layers, hidden, nn.ReLU)\n", + "\n", + " def forward(self, x):\n", + " embed = F.relu(self.fc1(x))\n", + " return self.encoder(F.relu(embed))\n", + "\n", + "class HackNet(nn.Module):\n", + " def __init__(self, config):\n", + " super().__init__()\n", + " self.encoder = Encoder(in_dim=config.IN_DIM, hidden=config.HIDDEN, embed=config.EMBED_DIM)\n", + " self.decoder = Decoder(embed=config.EMBED_DIM, hidden=config.HIDDEN, out_dim=config.OUT_DIM)\n", + " self._memory_unit = nn.GRU(config.EMBED_DIM, config.HIDDEN, num_layers=2, batch_first=True)\n", + "\n", + " def forward(self, x):\n", + " embed = self.encoder(x)\n", + " mem_out = self._memory_unit(embed)[0]\n", + " pred, _ = self.decoder(torch.cat([embed, mem_out], axis=2))\n", + " return pred\n", + "\n", + " def to(self, device):\n", + " super().to(device)\n", + " self.encoder = self.encoder.to(device)\n", + " self.decoder = self.decoder.to(device)\n", + " self._memory_unit = self._memory_unit.to(device)\n", + " return self\n", + "\n", + "def init_weights(m):\n", + " if isinstance(m, nn.Linear):\n", + " torch.nn.init.xavier_uniform_(m.weight)\n", + " if m.bias is not None:\n", + " torch.nn.init.zeros_(m.bias)\n", + "\n", + "def train_epoch(network, optim, data_x, data_y, device):\n", + " network.train()\n", + " data_x, data_y = data_x.to(device), data_y.to(device)\n", + " pred = network(data_x)\n", + " loss = F.smooth_l1_loss(pred, data_y)\n", + " optim.zero_grad()\n", + " loss.backward()\n", + " optim.step()\n", + " return loss.item()\n", + "\n", + "def val_epoch(network, data_x, data_y, device):\n", + " network.eval()\n", + " data_x, data_y = data_x.to(device), data_y.to(device)\n", + " with torch.no_grad():\n", + " pred = network(data_x)\n", + " loss = F.smooth_l1_loss(pred, data_y)\n", + " return loss.item()\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "config = Config(48, 32, 32, 43, device=device)\n", + "net = HackNet(config).to(device)\n", + "net.apply(init_weights)\n", + "opt = torch.optim.Adam(net.parameters(), lr=3e-2)\n", + "scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=30, gamma=0.1)\n", + "\n", + "num_epochs = 100\n", + "\n", + "for epoch in range(num_epochs):\n", + " train_losses = []\n", + " for data_x, data_y in train_loader:\n", + " train_loss = train_epoch(net, opt, data_x, data_y, device)\n", + " train_losses.append(train_loss)\n", + "\n", + " val_losses = []\n", + " for data_x, data_y in test_loader:\n", + " val_loss = val_epoch(net, data_x, data_y, device)\n", + " val_losses.append(val_loss)\n", + " \n", + " scheduler.step()\n", + " print(f\"Epoch {epoch} Train Loss: {np.mean(train_losses)} Val Loss: {np.mean(val_losses)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "device: cuda\n", + "Data X Sample: tensor([[2.5248, 2.8795, 3.3178, 3.4744, 3.7137, 3.6817, 3.6853, 3.8954, 3.8080,\n", + " 3.7376, 3.8988, 3.7470, 3.7128, 3.4790, 3.5080, 3.1878, 3.2737, 3.3619,\n", + " 3.5064, 3.3047, 3.4620, 3.4384, 3.1159, 2.9795, 2.9353, 2.8261, 2.8238,\n", + " 2.8387, 2.5047, 2.5972, 2.6701, 2.8683, 3.0031, 2.1581, 3.3385, 3.7164,\n", + " 3.7874, 4.3385, 4.8045, 4.7465, 1.9950, 1.1809, 1.1580, 2.0782, 3.0643,\n", + " 3.2765, 2.2301, 3.1355]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.5874, -0.7096, -0.5364, -0.9002, -1.0180, -0.5296, 0.1658, 0.4428,\n", + " 0.4455, 0.4555, 0.6139, -1.2103, -0.1196, -1.0701, -1.0084, -0.3170,\n", + " -0.4965, -0.0202, 0.8535, -0.4856, 3.2938, -0.7402, 0.9853, -0.3912,\n", + " 2.1636, -0.3847, 0.6208, -0.0277, -0.0251, 0.1360, -0.5399, 0.7173,\n", + " 0.7008, -0.1736, 0.8664, 0.7216, 0.5774, -0.5460, 0.4623, 0.4803,\n", + " 0.2581, 0.5875, 0.7075]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1526, 0.1948, -0.8397, -0.1771, 0.1925, 0.4051, -0.0299, 0.2512,\n", + " -0.2499, -0.9009, 0.1035, 0.0727, -0.3053, -0.1178, -0.2528, 0.0234,\n", + " 0.1070, -0.0471, 0.2589, 0.5721, 0.0606, -0.1789, 0.1521, 0.1133,\n", + " -0.2962, 0.0208, -0.4979, 0.0808, 0.0643, 0.5250, -0.4063, -0.0677,\n", + " 1.0211, -0.0668, -0.2354, 0.3427, -0.2702, -0.5066, 0.2909, 0.3038,\n", + " -0.2086, -0.0199, -0.0342]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0019626065623015165\n", + "Grad encoder.fc1.bias: 0.0006645735120400786\n", + "Grad encoder.encoder.0.weight: 0.0010383427143096924\n", + "Grad encoder.encoder.0.bias: 0.0009610212873667479\n", + "Grad encoder.encoder.2.weight: 0.0013235664227977395\n", + "Grad encoder.encoder.2.bias: 0.0015217209002003074\n", + "Grad encoder.encoder.4.weight: 0.0023265699855983257\n", + "Grad encoder.encoder.4.bias: 0.0032013687305152416\n", + "Grad decoder.fc1.0.weight: 0.0006504359771497548\n", + "Grad decoder.fc1.0.bias: 0.0023963681887835264\n", + "Grad decoder.fc1.2.weight: 0.0008163329912349582\n", + "Grad decoder.fc1.2.bias: 0.003047160804271698\n", + "Grad decoder.fc1.4.weight: 0.000589795527048409\n", + "Grad decoder.fc1.4.bias: 0.003081107046455145\n", + "Grad decoder.fc2.weight: 0.0005353273008950055\n", + "Grad decoder.fc2.bias: 0.004170812200754881\n", + "Grad _memory_unit.weight_ih_l0: 0.0001168445814982988\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 0.00012427015462890267\n", + "Grad _memory_unit.bias_hh_l0: 7.101728988345712e-05\n", + "Grad _memory_unit.weight_ih_l1: 5.631285603158176e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00045630845124833286\n", + "Grad _memory_unit.bias_hh_l1: 0.00023757017333991826\n", + "Data X Sample: tensor([[1.5934, 1.7318, 1.8918, 2.0226, 2.1002, 2.1038, 2.1461, 2.2799, 2.3629,\n", + " 2.3111, 2.3507, 2.3273, 2.3968, 2.2357, 2.1537, 2.1854, 2.1840, 2.2226,\n", + " 2.3025, 2.3153, 2.4340, 2.4326, 2.4749, 2.3859, 2.4718, 2.4813, 2.5308,\n", + " 2.5940, 2.3105, 2.4170, 2.4007, 2.4373, 2.2176, 1.4463, 1.9266, 1.7828,\n", + " 1.6951, 1.5027, 1.6036, 1.5746, 1.2361, 0.7528, 0.7417, 1.2228, 2.0019,\n", + " 1.9957, 1.3734, 1.9392]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 5.5152e-01, 1.2389e-01, 7.6703e-02, 1.2072e+00, 1.1326e+00,\n", + " 2.3322e-01, -4.2521e-01, -6.4957e-01, -3.9461e-01, -4.8878e-01,\n", + " -5.3414e-01, -6.1981e-03, -8.7905e-01, 2.4246e-01, 1.9656e+00,\n", + " 9.9103e-01, -2.5015e-01, 1.1732e+00, 3.8035e-02, 6.0857e-01,\n", + " -5.6636e-01, 1.1638e+00, -2.7437e-01, 5.7454e-01, -5.1581e-01,\n", + " 8.2909e+00, 1.2895e+00, 7.8885e-01, 6.3427e-01, 1.1981e+00,\n", + " 9.0160e-01, -9.5900e-01, 1.0718e+00, 3.9178e-01, 8.9680e-01,\n", + " 3.4802e-01, 7.0630e-01, 9.7600e-01, -4.3414e-01, -6.5642e-01,\n", + " -6.6117e-01, -5.7699e-01, -5.8067e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.0650, -0.0628, -0.1273, -0.0221, 0.1008, 0.1072, -0.0018, -0.0772,\n", + " 0.0776, -0.0784, 0.0644, -0.0018, -0.0049, -0.0519, -0.0474, -0.0605,\n", + " 0.0805, -0.0465, 0.0456, 0.1295, -0.0196, 0.0564, -0.0250, 0.0487,\n", + " 0.0877, 0.0552, -0.0101, -0.0377, -0.0490, 0.0916, -0.0358, -0.0176,\n", + " 0.1528, -0.0518, 0.0113, 0.0152, -0.0094, 0.0157, -0.0032, 0.0450,\n", + " -0.0261, 0.0555, 0.0282]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002842979156412184\n", + "Grad encoder.fc1.bias: 0.0001127902214648202\n", + "Grad encoder.encoder.0.weight: 0.00014062674017623067\n", + "Grad encoder.encoder.0.bias: 0.00015602758503518999\n", + "Grad encoder.encoder.2.weight: 0.00014727655798196793\n", + "Grad encoder.encoder.2.bias: 0.00021784740965813398\n", + "Grad encoder.encoder.4.weight: 0.0003416147083044052\n", + "Grad encoder.encoder.4.bias: 0.0005747794639319181\n", + "Grad decoder.fc1.0.weight: 0.00012863497249782085\n", + "Grad decoder.fc1.0.bias: 0.0006387124303728342\n", + "Grad decoder.fc1.2.weight: 0.00012282823445275426\n", + "Grad decoder.fc1.2.bias: 0.0009198386105708778\n", + "Grad decoder.fc1.4.weight: 0.00011133018415421247\n", + "Grad decoder.fc1.4.bias: 0.0012767617590725422\n", + "Grad decoder.fc2.weight: 0.00010900987399509177\n", + "Grad decoder.fc2.bias: 0.002697062911465764\n", + "Grad _memory_unit.weight_ih_l0: 2.7649217372527346e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 4.116521085961722e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.3722923288005404e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.4107776223681867e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00012296237400732934\n", + "Grad _memory_unit.bias_hh_l1: 6.519615999422967e-05\n", + "Data X Sample: tensor([[1.4618, 1.5992, 1.7070, 1.8323, 1.9704, 1.9963, 2.1816, 1.9364, 2.3897,\n", + " 3.8024, 4.0796, 4.0021, 4.0280, 3.8061, 3.6820, 3.6021, 3.4687, 3.4548,\n", + " 3.5188, 3.3438, 3.3025, 3.2394, 3.1207, 2.9795, 2.8321, 2.9619, 2.9144,\n", + " 2.9733, 2.8517, 2.9509, 3.1461, 3.1916, 3.4387, 2.4784, 4.2577, 4.7380,\n", + " 4.8887, 5.4277, 5.8187, 5.6657, 1.1359, 0.6552, 0.6427, 1.0965, 1.6213,\n", + " 1.8070, 1.2034, 1.7828]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.3289, -0.2833, -0.4220, -0.0382, 0.8130, 0.0208, 0.3546, 0.7691,\n", + " 1.1328, 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+ "Grad encoder.encoder.4.bias: 0.0007844069041311741\n", + "Grad decoder.fc1.0.weight: 0.00011368098057573661\n", + "Grad decoder.fc1.0.bias: 0.0005642512114718556\n", + "Grad decoder.fc1.2.weight: 9.12231917027384e-05\n", + "Grad decoder.fc1.2.bias: 0.0006932855467312038\n", + "Grad decoder.fc1.4.weight: 8.874213381204754e-05\n", + "Grad decoder.fc1.4.bias: 0.001072871033102274\n", + "Grad decoder.fc2.weight: 8.341463399119675e-05\n", + "Grad decoder.fc2.bias: 0.002246562158688903\n", + "Grad _memory_unit.weight_ih_l0: 2.3364800654235296e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.9016566006466746e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.1676263713743538e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.4029441445018165e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00011929281754419208\n", + "Grad _memory_unit.bias_hh_l1: 6.484198092948645e-05\n", + "Data X Sample: tensor([[1.6379, 1.8250, 2.0060, 2.1822, 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1.3253e-01,\n", + " -1.9349e-01, -2.5989e-01, -5.4902e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-6.3044e-03, -2.0778e-02, -4.6462e-03, 6.4170e-02, 7.3142e-02,\n", + " -3.0919e-02, -1.8602e-02, -7.7755e-02, 3.0564e-02, 5.0866e-02,\n", + " 1.6898e-02, -3.6340e-03, 2.5550e-02, -6.8196e-02, 1.0881e-02,\n", + " -6.3080e-02, 2.1183e-02, -5.4159e-04, -1.4310e-03, 3.3319e-02,\n", + " -4.2839e-02, 6.9560e-02, 1.1123e-02, -4.0283e-03, 6.5857e-02,\n", + " -7.8433e-02, 3.8620e-02, 3.3025e-02, -5.7788e-05, 3.6411e-02,\n", + " 2.3656e-02, 2.8189e-02, -5.0644e-02, -6.0019e-04, 1.1192e-02,\n", + " -1.9848e-02, 3.1338e-02, 7.3853e-02, -7.4001e-03, 2.6292e-02,\n", + " -1.5739e-02, -4.3986e-02, -4.1256e-02]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00021673760784324259\n", + "Grad encoder.fc1.bias: 7.858202297938988e-05\n", + "Grad encoder.encoder.0.weight: 9.591842535883188e-05\n", + "Grad encoder.encoder.0.bias: 9.391657658852637e-05\n", + "Grad 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"Grad _memory_unit.bias_ih_l1: 7.754896068945527e-05\n", + "Grad _memory_unit.bias_hh_l1: 4.183818236924708e-05\n", + "Data X Sample: tensor([[2.5343, 2.9319, 3.1886, 3.4547, 3.4405, 3.6625, 3.6529, 3.8372, 3.8665,\n", + " 3.8908, 3.8639, 3.8638, 3.7150, 3.6589, 3.5231, 3.5488, 3.5143, 3.4316,\n", + " 3.4548, 3.2601, 3.1921, 3.2226, 2.9857, 2.8516, 2.7833, 2.7529, 2.6986,\n", + " 2.7897, 2.6678, 2.7839, 2.7856, 2.8462, 2.9635, 2.1352, 3.6130, 4.0083,\n", + " 4.1965, 4.6008, 5.0771, 5.0813, 1.8422, 1.0575, 1.1257, 2.0179, 2.8423,\n", + " 3.2251, 2.1213, 3.0808]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.2487, -0.0573, 1.2744, -0.3673, -0.5211, -0.8845, 0.1490, 0.1213,\n", + " -0.2628, -0.1268, 0.4119, -0.0454, 0.3170, 0.3783, 0.7529, 0.2551,\n", + " 0.1962, -0.8568, -0.3075, -0.0485, -0.2044, -0.7789, -0.1216, 1.5788,\n", + " -0.2723, 1.1304, 0.6258, -0.7079, -0.7764, -0.7090, -0.3677, -0.5129,\n", + " -0.2227, -0.1185, -0.0589, 0.0360, 0.0849, -0.7231, 1.1238, 0.3249,\n", + " 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2.3043,\n", + " 2.2636, 2.0052, 2.0666, 2.1487, 2.1222, 2.1406, 1.4578, 2.1055, 2.0406,\n", + " 1.8583, 1.6405, 1.5529, 1.4101, 1.1263, 0.6313, 0.6629, 1.0907, 1.6332,\n", + " 1.9213, 1.3054, 1.9470]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.8845, 0.3122, 0.1366, 0.0111, -0.4372, -1.1291, -1.9877, -0.9260,\n", + " -0.6579, -1.0596, -0.9297, 0.0061, -4.2844, 0.1387, 1.2293, 0.8714,\n", + " 0.3687, 0.0546, 0.3908, 0.1433, 0.3825, 0.8502, 0.0296, 0.3740,\n", + " -1.0741, -0.1258, -1.7049, 1.1212, 0.6777, 0.6426, 0.9459, 0.4749,\n", + " 0.0310, -0.2673, 0.4113, -0.2057, -1.0293, -0.8483, -0.4704, -0.6442,\n", + " -0.3850, -0.2322, -0.3288]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.0268, -0.0626, -0.0049, 0.0037, -0.0025, -0.0279, -0.0454, -0.0459,\n", + " 0.0064, 0.0362, 0.0291, 0.0050, 0.0504, -0.0041, 0.0145, -0.0367,\n", + " -0.0352, -0.0028, -0.0556, 0.0309, -0.0430, -0.0004, -0.0311, -0.0274,\n", + " 0.0595, 0.0244, 0.0472, 0.0091, -0.0074, 0.0075, 0.0362, 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"Data Y Sample: tensor([[-0.3229, -0.3779, 0.2249, 0.8131, 0.1135, 0.7413, 0.3628, 0.7357,\n", + " 0.5985, 1.0843, 0.6835, -0.5477, -1.2893, 0.0977, -1.1084, -0.8576,\n", + " -0.2310, 1.4506, -0.5843, 2.5455, -0.3468, -0.3170, -1.5679, -1.0120,\n", + " 0.7508, -0.7128, -1.1682, -1.2020, -0.3960, -0.7549, -1.3854, -0.6272,\n", + " -0.5635, -1.4729, -0.4833, -0.2115, -0.2469, -0.1625, -0.5988, 0.5289,\n", + " 0.2582, 0.5276, -0.1922]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1001, -0.2119, -0.1168, -0.0171, 0.0846, -0.1276, 0.0266, 0.0971,\n", + " 0.0623, -0.1081, 0.2360, 0.0710, -0.0709, -0.1681, -0.1748, -0.2150,\n", + " 0.0847, -0.1298, -0.0514, 0.0755, -0.0969, 0.1530, -0.1259, -0.0323,\n", + " 0.2024, 0.1700, -0.0275, -0.0821, -0.2288, -0.0481, -0.0646, -0.0420,\n", + " 0.0197, -0.1247, -0.1489, -0.1587, -0.0406, -0.0206, 0.0413, 0.1445,\n", + " 0.1433, 0.1667, 0.1292]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0001354703854303807\n", + 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device='cuda:0')\n", + "Prediction Sample: tensor([[ 1.1991e-01, -4.1932e-02, 8.9268e-03, 3.0695e-02, -1.3182e-02,\n", + " 6.8257e-03, -1.4050e-01, -1.2237e-01, -1.7497e-02, 2.8485e-02,\n", + " -1.1911e-01, -3.2351e-03, 1.7491e-01, 1.2054e-01, 1.5256e-01,\n", + " 8.6370e-02, -5.0705e-02, 6.9114e-02, 1.5388e-04, 4.3440e-02,\n", + " -6.5369e-02, -4.6055e-04, 2.9869e-02, 9.8780e-03, 2.5263e-02,\n", + " -1.5718e-02, 1.5379e-01, 5.0877e-02, 1.1290e-01, 1.1882e-01,\n", + " 1.5836e-01, 1.2293e-01, 7.0043e-02, 1.0997e-01, -5.1400e-02,\n", + " 1.1038e-02, -7.3408e-03, 8.7073e-02, 1.5042e-02, -6.1722e-03,\n", + " -4.1530e-02, -1.3329e-02, -1.6195e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00023678955039940774\n", + "Grad encoder.fc1.bias: 0.00012534830602817237\n", + "Grad encoder.encoder.0.weight: 8.675674325786531e-05\n", + "Grad encoder.encoder.0.bias: 0.00014302569616120309\n", + "Grad encoder.encoder.2.weight: 7.677286339458078e-05\n", + "Grad 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"Grad _memory_unit.bias_hh_l1: 4.753027678816579e-05\n", + "Data X Sample: tensor([[1.5117, 1.6881, 1.8272, 1.9482, 1.9892, 2.1097, 2.1107, 2.2047, 2.2408,\n", + " 2.2242, 2.2492, 2.3974, 2.1727, 2.1812, 2.2041, 2.2305, 2.2108, 2.1800,\n", + " 2.2405, 2.1740, 2.3677, 2.4158, 2.3158, 2.3534, 2.3967, 2.4578, 2.3549,\n", + " 2.3819, 2.2376, 2.3581, 2.3272, 2.3958, 2.2484, 1.4715, 1.9928, 1.8680,\n", + " 1.6892, 1.5027, 1.5973, 1.5916, 1.2266, 0.7428, 0.6912, 1.2802, 1.7839,\n", + " 1.8813, 1.2510, 1.7359]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.6273, 0.3528, 0.6578, 0.8395, -1.3051, -1.4234, -1.4497, -1.3194,\n", + " -0.9798, -1.4264, -1.4986, -0.3728, 1.0594, 0.4797, -2.5041, 1.4892,\n", + " 1.0573, 0.5325, 0.8582, -0.5704, -0.0776, -0.0828, -0.2943, -0.1546,\n", + " 0.1042, 0.0531, 1.3632, 0.5654, 1.5488, 1.6911, 1.2898, 0.5418,\n", + " 0.5940, 1.2149, 1.3561, -0.5428, 0.9953, 0.7178, -0.5252, -1.7797,\n", + " -1.8169, -1.1132, -0.9801]], device='cuda:0')\n", + "Prediction 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0.9418, 1.6878, 2.5926,\n", + " 2.7848, 1.8289, 2.4787]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.2716, 0.2747, -0.4364, 0.7720, 0.8169, -0.3762, -0.5940, 0.6995,\n", + " -0.4072, -0.2919, -0.3235, 0.2213, -0.1138, 0.3489, -0.4367, 0.1867,\n", + " -0.4925, 1.7341, 0.6113, -0.8633, -1.1649, 1.1979, 0.6011, 0.7562,\n", + " 0.3553, 0.4388, -0.4261, 0.5546, 0.4018, -0.1895, 0.6746, -1.1702,\n", + " -0.2780, 1.2047, 0.2702, -0.5472, 0.4246, 0.5417, -0.7130, 0.0791,\n", + " -1.5565, 0.0931, 0.6469]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.0121, -0.0181, -0.0434, -0.0180, -0.0100, -0.0869, -0.0789, -0.0361,\n", + " 0.0518, 0.0840, -0.0033, -0.0385, -0.0165, -0.0090, 0.0611, -0.0199,\n", + " -0.0359, -0.0057, -0.0168, 0.0282, -0.0138, -0.0529, -0.0376, -0.0740,\n", + " -0.0202, 0.0851, 0.0850, 0.0507, 0.0408, 0.0719, 0.0882, -0.0146,\n", + " -0.0336, 0.0024, -0.0477, -0.0486, -0.0322, 0.0356, 0.0440, 0.0376,\n", + " 0.0318, 0.0592, -0.0302]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003196452744305134\n", + "Grad encoder.fc1.bias: 0.0001792948751244694\n", + "Grad encoder.encoder.0.weight: 0.00013020067126490176\n", + "Grad encoder.encoder.0.bias: 0.00021884392481297255\n", + "Grad encoder.encoder.2.weight: 0.00010715040843933821\n", + "Grad encoder.encoder.2.bias: 0.0003342135460115969\n", + "Grad encoder.encoder.4.weight: 0.0002407551946816966\n", + "Grad encoder.encoder.4.bias: 0.0007852376438677311\n", + "Grad decoder.fc1.0.weight: 7.662992720725015e-05\n", + "Grad decoder.fc1.0.bias: 0.0004871446581091732\n", + "Grad decoder.fc1.2.weight: 9.454997780267149e-05\n", + "Grad decoder.fc1.2.bias: 0.0007112072198651731\n", + "Grad decoder.fc1.4.weight: 0.00011396967602195218\n", + "Grad decoder.fc1.4.bias: 0.0010608076117932796\n", + "Grad decoder.fc2.weight: 0.00018533447291702032\n", + "Grad decoder.fc2.bias: 0.0017126285238191485\n", + "Grad _memory_unit.weight_ih_l0: 2.0066529032192193e-05\n", + "Grad 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-1.7995e-01, -3.8964e-01, -9.7415e-01,\n", + " -3.1817e-02, -7.4931e-01, 4.7220e-01, -1.9703e-02, 4.7798e-01,\n", + " 8.3565e-01, 3.8637e-01, 6.4728e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1557, -0.3073, -0.1012, -0.2369, 0.0299, -0.0093, 0.0777, 0.1922,\n", + " 0.1786, 0.0584, 0.2403, -0.1696, -0.2159, -0.2776, -0.1877, -0.2435,\n", + " -0.2699, -0.1088, -0.0359, -0.0528, -0.0375, -0.1311, -0.1007, 0.0362,\n", + " -0.0477, 0.0961, -0.0016, -0.0658, -0.1318, -0.1616, -0.0811, -0.1762,\n", + " -0.1855, -0.3203, -0.1640, -0.1327, 0.0461, 0.0350, -0.0019, 0.2948,\n", + " 0.3027, 0.3399, 0.2267]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00022328225895762444\n", + "Grad encoder.fc1.bias: 0.0002169461513403803\n", + "Grad encoder.encoder.0.weight: 8.788480772636831e-05\n", + "Grad encoder.encoder.0.bias: 0.0002110385539708659\n", + "Grad encoder.encoder.2.weight: 9.145504736807197e-05\n", + "Grad encoder.encoder.2.bias: 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-0.3572, -0.2758,\n", + " -0.1877, -0.1930, -0.2882, 0.1529, 0.3091, 0.3119, 0.2519, 0.2817,\n", + " 0.0869, 0.0456, 0.0930, 0.0895, -0.1638, -0.0390, 0.1001, 0.0450,\n", + " 0.0878, 0.0481, 0.2013, 0.0313, 0.2357, 0.2846, 0.4525, 0.2742,\n", + " 0.2886, 0.3333, 0.0005, 0.1771, -0.0667, -0.0936, 0.1601, -0.1975,\n", + " -0.2458, -0.1149, -0.1923]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0007737851119600236\n", + "Grad encoder.fc1.bias: 0.0004199981631245464\n", + "Grad encoder.encoder.0.weight: 0.00023564687580801547\n", + "Grad encoder.encoder.0.bias: 0.0003607137477956712\n", + "Grad encoder.encoder.2.weight: 0.0001838477182900533\n", + "Grad encoder.encoder.2.bias: 0.00048316415632143617\n", + "Grad encoder.encoder.4.weight: 0.0003775092773139477\n", + "Grad encoder.encoder.4.bias: 0.0010332572273910046\n", + "Grad decoder.fc1.0.weight: 0.00012645959213841707\n", + "Grad decoder.fc1.0.bias: 0.0007113819010555744\n", + "Grad decoder.fc1.2.weight: 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-0.4071, -0.3807,\n", + " -0.3311, -0.3223, -0.3727, 0.2242, 0.2697, 0.3327, 0.1486, 0.2759,\n", + " 0.2269, 0.1593, 0.0638, 0.1354, -0.0438, -0.0015, 0.0829, -0.0254,\n", + " 0.0514, 0.0199, 0.1369, 0.0095, 0.2994, 0.3071, 0.4292, 0.2056,\n", + " 0.2604, 0.3935, 0.1333, 0.2905, -0.0719, -0.0656, 0.1236, -0.4166,\n", + " -0.2439, -0.3103, -0.1362]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00039055803790688515\n", + "Grad encoder.fc1.bias: 0.00020433173631317914\n", + "Grad encoder.encoder.0.weight: 0.00011194762191735208\n", + "Grad encoder.encoder.0.bias: 0.0002184878831030801\n", + "Grad encoder.encoder.2.weight: 8.500271360389888e-05\n", + "Grad encoder.encoder.2.bias: 0.0003204170207027346\n", + "Grad encoder.encoder.4.weight: 0.00019355895346961915\n", + "Grad encoder.encoder.4.bias: 0.0008710600668564439\n", + "Grad decoder.fc1.0.weight: 7.443393405992538e-05\n", + "Grad decoder.fc1.0.bias: 0.0004530092410277575\n", + "Grad decoder.fc1.2.weight: 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_memory_unit.bias_hh_l1: 6.556895823450759e-05\n", + "Data X Sample: tensor([[2.4240, 2.9101, 3.2817, 3.3585, 3.5771, 3.6021, 3.7531, 3.6371, 3.9373,\n", + " 3.9714, 3.9432, 4.1014, 3.9769, 3.8389, 3.7753, 3.6650, 3.5630, 3.4799,\n", + " 3.5787, 3.5446, 3.4056, 3.3925, 3.1785, 2.9795, 2.9541, 2.8652, 2.8078,\n", + " 2.8183, 2.7406, 2.8002, 2.8556, 2.8379, 2.8623, 2.0619, 3.4953, 3.8575,\n", + " 4.2437, 4.4975, 5.0074, 4.8316, 2.0856, 1.1590, 1.2550, 1.9748, 2.9176,\n", + " 3.1221, 2.1825, 2.9557]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.9052e-01, -2.3283e-01, 5.5303e-01, -4.6678e-01, 7.3588e-02,\n", + " -1.1076e+00, 5.8715e-01, -1.5538e-03, -1.0921e-01, 4.0173e-02,\n", + " -3.0860e-02, 4.9494e-01, -6.8061e-01, -3.8104e-01, 8.1828e-01,\n", + " -9.3019e-03, 5.0297e-02, 4.6561e-01, -3.5035e-01, 5.2282e-01,\n", + " -5.1371e-01, -2.9497e-01, -4.8103e+00, -7.1561e-02, -8.4679e-02,\n", + " -1.7120e-01, 1.9755e+00, -1.5291e-01, -5.4970e-01, -7.1039e-01,\n", + " -2.7139e-02, 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"Grad _memory_unit.bias_hh_l0: 2.535258317948319e-05\n", + "Grad _memory_unit.weight_ih_l1: 6.585195478692185e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00014065427239984274\n", + "Grad _memory_unit.bias_hh_l1: 7.483565423171967e-05\n", + "Data X Sample: tensor([[1.3929, 1.6604, 1.7596, 1.8935, 1.9601, 1.9005, 2.0291, 2.1209, 3.4613,\n", + " 4.1752, 4.0003, 4.0333, 3.9658, 3.9179, 3.6971, 3.6281, 3.5803, 3.4683,\n", + " 3.4052, 3.3270, 3.4743, 3.3542, 2.8966, 2.8898, 2.8603, 2.9462, 2.9756,\n", + " 2.9407, 2.9974, 3.1867, 3.2371, 3.3685, 3.7357, 2.7874, 4.4513, 4.6504,\n", + " 4.7549, 5.2872, 5.6919, 5.4444, 1.1263, 0.6373, 0.6609, 1.1080, 1.7205,\n", + " 1.9099, 1.1762, 1.7437]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.5689, -0.0041, -1.9641, -0.4437, 0.1229, 1.0359, 0.6884, 0.6634,\n", + " -0.5272, 0.4653, 0.3439, -0.8116, -0.4421, -0.9547, -2.6971, -0.7339,\n", + " 0.1892, -0.2998, 0.3813, 0.1799, 0.7523, 0.0084, -0.0079, 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-8.1815e-02, 1.7469e-02,\n", + " 2.3190e-02, 9.3612e-03, -1.4874e-03, -5.6396e-02, -1.5139e-01,\n", + " -1.0015e-01, -1.8946e-01, -1.6140e-01, -2.3412e-01, -1.4852e-01,\n", + " -1.5885e-01, 3.0523e-02, 1.1674e-02, 8.2898e-03, 2.7280e-01,\n", + " 3.1274e-01, 3.2544e-01, 2.6156e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0008531895000487566\n", + "Grad encoder.fc1.bias: 0.0011104431468993425\n", + "Grad encoder.encoder.0.weight: 0.00022743357112631202\n", + "Grad encoder.encoder.0.bias: 0.0010013466235250235\n", + "Grad encoder.encoder.2.weight: 0.00012277369387447834\n", + "Grad encoder.encoder.2.bias: 0.0010425727814435959\n", + "Grad encoder.encoder.4.weight: 0.0002870334719773382\n", + "Grad encoder.encoder.4.bias: 0.0021897461265325546\n", + "Grad decoder.fc1.0.weight: 0.00010196249058935791\n", + "Grad decoder.fc1.0.bias: 0.0011587943881750107\n", + "Grad decoder.fc1.2.weight: 0.0001212576826219447\n", + "Grad decoder.fc1.2.bias: 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-0.2001, -0.0182, 0.0611, 0.0384, 0.2839,\n", + " 0.3310, 0.2841, 0.2218]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003708342555910349\n", + "Grad encoder.fc1.bias: 0.0006662430241703987\n", + "Grad encoder.encoder.0.weight: 0.00010948805720545352\n", + "Grad encoder.encoder.0.bias: 0.0004957953933626413\n", + "Grad encoder.encoder.2.weight: 7.732550147920847e-05\n", + "Grad encoder.encoder.2.bias: 0.0004170956090092659\n", + "Grad encoder.encoder.4.weight: 0.00017071992624551058\n", + "Grad encoder.encoder.4.bias: 0.0007706442847847939\n", + "Grad decoder.fc1.0.weight: 7.538394856965169e-05\n", + "Grad decoder.fc1.0.bias: 0.0005257611046545208\n", + "Grad decoder.fc1.2.weight: 7.454489241354167e-05\n", + "Grad decoder.fc1.2.bias: 0.0005219774320721626\n", + "Grad decoder.fc1.4.weight: 7.92621067375876e-05\n", + "Grad decoder.fc1.4.bias: 0.0006519003072753549\n", + "Grad decoder.fc2.weight: 0.00017673340335022658\n", + "Grad decoder.fc2.bias: 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0.0285, 0.2782,\n", + " 0.3196, 0.2821, 0.2336]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0005420913221314549\n", + "Grad encoder.fc1.bias: 0.0003833825176116079\n", + "Grad encoder.encoder.0.weight: 0.0001474158198107034\n", + "Grad encoder.encoder.0.bias: 0.00039620581082999706\n", + "Grad encoder.encoder.2.weight: 7.705356983933598e-05\n", + "Grad encoder.encoder.2.bias: 0.0004314732796046883\n", + "Grad encoder.encoder.4.weight: 0.00016359443543478847\n", + "Grad encoder.encoder.4.bias: 0.0006327557493932545\n", + "Grad decoder.fc1.0.weight: 7.107359124347568e-05\n", + "Grad decoder.fc1.0.bias: 0.0005078084650449455\n", + "Grad decoder.fc1.2.weight: 9.609197149984539e-05\n", + "Grad decoder.fc1.2.bias: 0.0006388876354321837\n", + "Grad decoder.fc1.4.weight: 8.65147594595328e-05\n", + "Grad decoder.fc1.4.bias: 0.0008134876843541861\n", + "Grad decoder.fc2.weight: 0.00017594230303075165\n", + "Grad decoder.fc2.bias: 0.0017699048621580005\n", + "Grad _memory_unit.weight_ih_l0: 1.7744563592714258e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 4.9721551476977766e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.680903344298713e-05\n", + "Grad _memory_unit.weight_ih_l1: 8.325416274601594e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00012373871868476272\n", + "Grad _memory_unit.bias_hh_l1: 6.613649020437151e-05\n", + "Data X Sample: tensor([[1.2666, 1.4157, 1.7160, 1.7798, 1.8645, 2.0965, 2.1785, 2.2714, 3.8788,\n", + " 4.2905, 4.3365, 4.2007, 4.0968, 3.9425, 3.7677, 3.8620, 3.6998, 3.6192,\n", + " 3.6530, 3.5837, 3.4498, 3.4032, 2.9978, 2.9050, 2.9203, 3.0481, 3.1062,\n", + " 3.1242, 3.0182, 3.2686, 3.2825, 3.3464, 3.8061, 2.8240, 4.6940, 4.9666,\n", + " 5.1836, 5.5337, 6.0785, 5.7480, 1.0213, 0.6273, 0.5982, 1.1080, 1.7205,\n", + " 1.7383, 1.1286, 1.6342]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.9312, -0.5477, -1.0009, -0.3865, 0.1928, -0.7733, 0.3747, 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1.9082e-01, 1.3311e-01,\n", + " 8.2084e-02, 1.2825e-01, 1.0551e-01, 7.9527e-04, 1.2452e-01,\n", + " 3.9336e-02, 2.6154e-03, 9.4014e-02, 4.1025e-01, 4.9947e-01,\n", + " 4.9895e-01, 4.2769e-01, 4.8388e-01, 4.9355e-01, 2.5884e-01,\n", + " 1.6439e-01, -1.8181e-01, 4.3820e-03, 2.3186e-02, -6.1173e-01,\n", + " -5.6625e-01, -3.8969e-01, -3.9649e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00030772091122344136\n", + "Grad encoder.fc1.bias: 0.0003241970553062856\n", + "Grad encoder.encoder.0.weight: 7.599530363222584e-05\n", + "Grad encoder.encoder.0.bias: 0.00031806505285203457\n", + "Grad encoder.encoder.2.weight: 5.200849045650102e-05\n", + "Grad encoder.encoder.2.bias: 0.0003569266991689801\n", + "Grad encoder.encoder.4.weight: 0.00010464074875926599\n", + "Grad encoder.encoder.4.bias: 0.000637983379419893\n", + "Grad decoder.fc1.0.weight: 4.521714072325267e-05\n", + "Grad decoder.fc1.0.bias: 0.00040910401730798185\n", + "Grad decoder.fc1.2.weight: 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2.9597,\n", + " 3.1364, 3.0667, 3.2129, 3.1776, 3.2358, 3.9249, 2.8560, 4.5788, 5.0007,\n", + " 5.1797, 5.3906, 6.1863, 5.8814, 1.0547, 0.5954, 0.6225, 1.1625, 1.6332,\n", + " 1.9270, 1.2102, 1.7593]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.4078, -0.7576, 1.9552, 0.2874, 0.5016, 0.3822, 0.4540, 0.4206,\n", + " 0.7239, 0.7485, 0.3583, -0.3509, -0.5456, -0.5862, -0.2621, -0.6957,\n", + " 0.3278, 0.1202, -1.1398, -0.8411, -0.7530, -0.3558, -0.9534, 0.1617,\n", + " -1.0422, 0.1642, -0.9028, 0.4427, -0.6119, 0.0695, -0.8853, 0.6076,\n", + " -0.9505, -1.0944, -1.0882, -0.0362, 0.0000, -0.3175, 0.0000, -0.0310,\n", + " 0.4654, -0.3723, 0.0067]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1840, -0.2325, -0.0656, -0.1157, -0.0027, 0.1822, 0.1380, 0.2492,\n", + " 0.1570, 0.1797, 0.2180, -0.1390, -0.1054, -0.2562, -0.2332, -0.2163,\n", + " -0.1509, -0.0457, -0.0924, -0.1354, -0.0286, 0.0016, 0.0244, -0.0391,\n", + " 0.0190, -0.0307, -0.1198, -0.1006, -0.2296, -0.2011, -0.2789, -0.1407,\n", + " -0.2993, -0.2010, -0.1301, -0.2063, -0.1087, -0.0068, 0.0467, 0.2929,\n", + " 0.3005, 0.3110, 0.2306]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0004354354168754071\n", + "Grad encoder.fc1.bias: 0.0006352941272780299\n", + "Grad encoder.encoder.0.weight: 9.836346725933254e-05\n", + "Grad encoder.encoder.0.bias: 0.00046240107621997595\n", + "Grad encoder.encoder.2.weight: 6.265267438720912e-05\n", + "Grad encoder.encoder.2.bias: 0.0004180520190857351\n", + "Grad encoder.encoder.4.weight: 0.00013180315727367997\n", + "Grad encoder.encoder.4.bias: 0.0006855344399809837\n", + "Grad decoder.fc1.0.weight: 6.0149686760269105e-05\n", + "Grad decoder.fc1.0.bias: 0.0005132233491167426\n", + "Grad decoder.fc1.2.weight: 7.133425970096141e-05\n", + "Grad decoder.fc1.2.bias: 0.0005717385793104768\n", + "Grad decoder.fc1.4.weight: 6.767200829926878e-05\n", + "Grad decoder.fc1.4.bias: 0.0006313803605735302\n", + "Grad decoder.fc2.weight: 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"Data Y Sample: tensor([[-0.6105, -0.0544, -0.6038, -0.5132, 0.1258, -0.0597, 0.5839, 0.7846,\n", + " -0.6831, -0.5538, -0.4960, -0.0365, 0.6825, -0.8384, 0.0769, 0.2192,\n", + " -0.9069, 1.0146, 0.9827, 3.3165, 1.9075, 1.6917, 2.2034, -0.3548,\n", + " 0.4019, 1.5782, 0.6382, -0.6550, -0.1726, 0.8011, -0.0556, -0.1781,\n", + " -1.3026, -0.7336, 2.1503, 0.3211, -0.9268, -0.7806, -0.4768, -0.2400,\n", + " 0.1108, -0.8559, 0.5018]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1834, -0.2224, -0.0676, -0.1326, -0.0104, 0.1614, 0.1312, 0.2409,\n", + " 0.1386, 0.1705, 0.2078, -0.1438, -0.1015, -0.2618, -0.2165, -0.1849,\n", + " -0.1535, -0.0430, -0.0939, -0.1139, -0.0291, -0.0015, 0.0130, -0.0368,\n", + " 0.0374, -0.0183, -0.0993, -0.1034, -0.2125, -0.1938, -0.2582, -0.1590,\n", + " -0.2793, -0.2010, -0.1195, -0.1816, -0.0937, -0.0083, 0.0455, 0.2777,\n", + " 0.2798, 0.3010, 0.2344]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0004368327499832958\n", + "Grad encoder.fc1.bias: 0.0002664321509655565\n", + "Grad encoder.encoder.0.weight: 9.799128019949421e-05\n", + "Grad encoder.encoder.0.bias: 0.00025680361432023346\n", + "Grad encoder.encoder.2.weight: 6.400088750524446e-05\n", + "Grad encoder.encoder.2.bias: 0.00031682325061410666\n", + "Grad encoder.encoder.4.weight: 0.00014046119758859277\n", + "Grad encoder.encoder.4.bias: 0.0007298764539882541\n", + "Grad decoder.fc1.0.weight: 4.937214180245064e-05\n", + "Grad decoder.fc1.0.bias: 0.0004640529805328697\n", + "Grad decoder.fc1.2.weight: 6.02737745794002e-05\n", + "Grad decoder.fc1.2.bias: 0.0005509894108399749\n", + "Grad decoder.fc1.4.weight: 6.247924466151744e-05\n", + "Grad decoder.fc1.4.bias: 0.0006453775567933917\n", + "Grad decoder.fc2.weight: 0.0001446790702175349\n", + "Grad decoder.fc2.bias: 0.0016958472551777959\n", + "Grad _memory_unit.weight_ih_l0: 1.0749433386081364e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 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_memory_unit.bias_hh_l0: 3.470955562079325e-05\n", + "Grad _memory_unit.weight_ih_l1: 5.001805220672395e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.000131197230075486\n", + "Grad _memory_unit.bias_hh_l1: 6.980068428674713e-05\n", + "Data X Sample: tensor([[-0.0021, 0.0015, 0.0030, 0.0087, -0.0102, 0.0074, -0.0077, 0.0014,\n", + " 0.0049, 0.0063, -0.0127, 0.0039, -0.0067, -0.0300, 0.0000, -0.0109,\n", + " 0.0189, 0.0213, 0.0145, 0.0167, 0.0147, 0.0260, 0.0120, 0.0134,\n", + " 0.0131, 0.0078, 0.0480, 0.0286, -0.0069, 0.0164, -0.0070, 0.0166,\n", + " -0.0022, -0.0252, -0.0025, 0.0073, 0.0000, 0.0000, -0.0634, -0.0170,\n", + " 0.0334, 0.0000, 0.0040, 0.0057, 0.0159, 0.0000, 0.0000, -0.0469]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 2.1424, 0.2475, 2.4353, 0.6455, 0.8320, -0.6513, -1.4234, -1.3675,\n", + " -0.5139, -0.8814, -0.8362, 0.1323, 1.0887, 1.5048, 1.7583, 1.7221,\n", + " 0.5972, -0.4375, 0.1900, 0.8883, -0.1908, -1.1005, 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" 2.3665, 2.4195, 2.4757, 2.4663, 2.5279, 2.5042, 2.5619, 2.6119, 2.6320,\n", + " 2.4961, 2.3799, 2.5317, 2.5022, 2.4345, 2.2044, 1.4280, 1.9193, 1.7780,\n", + " 1.6577, 1.4868, 1.6797, 1.5689, 1.2457, 0.8205, 0.7255, 1.3319, 1.8751,\n", + " 1.9728, 1.4142, 2.0252]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.4676, 0.7677, 0.1031, 0.3292, 0.0036, 0.0330, -0.6573, -0.5469,\n", + " -0.2682, -0.6219, -0.7808, -0.6444, 0.8182, 0.2775, -0.4816, -0.1929,\n", + " 0.0710, 0.0590, 0.1355, 0.9290, -0.6044, -0.9330, 0.2784, 0.1992,\n", + " 0.7066, 0.3562, -0.3249, 0.2803, 0.1126, 0.1538, 0.7952, -0.1482,\n", + " 1.7273, -0.0226, 2.2705, 0.4374, 0.0540, 0.1545, -0.0068, -0.4493,\n", + " -0.6892, -0.4113, -0.9778]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4646, 0.3806, 0.2065, 0.3358, 0.0769, -0.1930, -0.4786, -0.5021,\n", + " -0.2708, -0.3511, -0.3108, 0.3001, 0.2043, 0.3375, 0.1892, 0.3526,\n", + " 0.2640, 0.0876, 0.1455, 0.1593, 0.0407, 0.0563, 0.1415, 0.0579,\n", + " 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-0.0081, 0.0086, 0.0079, 0.0286, 0.0000, 0.0235]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.0182, 0.6265, 0.2968, -0.2021, -1.7067, -1.8048, -1.7675, -1.9899,\n", + " -1.3981, -0.9235, -1.1741, 1.1666, 0.1821, 1.0332, 0.1230, 1.2346,\n", + " -1.2329, 0.0367, -0.0512, 0.0133, 0.2908, 0.3188, 0.4751, 0.1144,\n", + " -0.2191, 0.3177, 0.5861, 0.7946, 1.0027, 0.6505, 1.4520, 0.0359,\n", + " 0.4656, 0.3858, 1.3317, -0.7436, 0.4768, 0.0705, -0.5970, -1.1708,\n", + " -1.0132, -0.8126, 0.3078]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.7880, 0.5608, 0.2052, 0.4309, -0.0314, -0.3813, -0.8967, -0.9436,\n", + " -0.5997, -0.6324, -0.6587, 0.4008, 0.3700, 0.6179, 0.2844, 0.5782,\n", + " 0.3617, 0.1628, 0.1894, 0.2069, -0.0250, 0.1830, 0.2372, 0.1108,\n", + " 0.0993, -0.0369, 0.0206, 0.1788, 0.6164, 0.6365, 0.5648, 0.4899,\n", + " 0.6255, 0.5613, 0.2622, 0.3100, -0.2150, 0.0038, -0.0637, -0.6642,\n", + " -0.6137, -0.5463, -0.5306]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0004215018998365849\n", + "Grad encoder.fc1.bias: 0.0006370748160406947\n", + "Grad encoder.encoder.0.weight: 9.889598732115701e-05\n", + "Grad encoder.encoder.0.bias: 0.0005086350138299167\n", + "Grad encoder.encoder.2.weight: 6.632106669712812e-05\n", + "Grad encoder.encoder.2.bias: 0.0004998972872272134\n", + "Grad encoder.encoder.4.weight: 0.0001620128023205325\n", + "Grad encoder.encoder.4.bias: 0.0009537470759823918\n", + "Grad decoder.fc1.0.weight: 7.246325549203902e-05\n", + "Grad decoder.fc1.0.bias: 0.0006668033311143517\n", + "Grad decoder.fc1.2.weight: 8.606589835835621e-05\n", + "Grad decoder.fc1.2.bias: 0.0006374482763931155\n", + "Grad decoder.fc1.4.weight: 6.844146992079914e-05\n", + "Grad decoder.fc1.4.bias: 0.0005975963431410491\n", + "Grad decoder.fc2.weight: 0.0001571996253915131\n", + "Grad decoder.fc2.bias: 0.001503262552432716\n", + "Grad _memory_unit.weight_ih_l0: 1.9410250388318673e-05\n", + "Grad 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4.0106,\n", + " 4.3205, 4.2921, 4.4792, 3.9392, 3.9670, 3.9896, 3.6637, 3.6558, 3.5554,\n", + " 3.5250, 3.5390, 3.5234, 3.4507, 2.8966, 2.8936, 2.8209, 2.8679, 2.9517,\n", + " 2.9447, 2.9627, 3.1310, 3.3000, 3.3298, 3.7973, 2.7348, 4.5861, 4.8499,\n", + " 5.0892, 5.4728, 5.9328, 5.9438, 1.0929, 0.6074, 0.6023, 1.1252, 1.7165,\n", + " 1.9270, 1.1626, 1.5873]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.2100, 0.4104, 0.8781, 0.2520, 1.3591, -1.4886, 0.4099, 0.7798,\n", + " 0.6775, 0.7211, 0.8061, -3.6802, 0.1422, -0.5170, -0.4260, -0.4251,\n", + " -0.3692, 0.6757, -0.3996, -0.8727, 0.4399, -0.2183, 12.7800, 1.4877,\n", + " 1.4605, 1.9213, -0.2122, -0.6568, -1.5618, -0.9462, -0.4776, -0.7272,\n", + " -0.7450, -0.9343, -0.9296, -0.5872, -0.8471, 0.8024, 0.6434, 0.7612,\n", + " 0.6558, 0.5654, 0.2792]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2171, -0.2266, -0.0639, -0.1649, 0.0140, 0.1666, 0.1423, 0.2756,\n", + " 0.1506, 0.2438, 0.1659, -0.2026, -0.0809, -0.3134, -0.2064, -0.1516,\n", + " -0.2343, -0.0146, -0.0880, -0.0745, -0.0091, -0.0009, -0.0203, -0.0725,\n", + " 0.0496, 0.0069, -0.0432, -0.0985, -0.1780, -0.2403, -0.2432, -0.1978,\n", + " -0.2708, -0.2324, -0.0914, -0.0870, -0.0329, 0.0096, 0.0239, 0.2828,\n", + " 0.2841, 0.2838, 0.2962]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0013989679282531142\n", + "Grad encoder.fc1.bias: 0.0008234913111664355\n", + "Grad encoder.encoder.0.weight: 0.00030178489396348596\n", + "Grad encoder.encoder.0.bias: 0.0006436303956434131\n", + "Grad encoder.encoder.2.weight: 0.00013561999367084354\n", + "Grad encoder.encoder.2.bias: 0.0006655758479610085\n", + "Grad encoder.encoder.4.weight: 0.0003593007568269968\n", + "Grad encoder.encoder.4.bias: 0.001679014298133552\n", + "Grad decoder.fc1.0.weight: 0.00010370231757406145\n", + "Grad decoder.fc1.0.bias: 0.0009503270266577601\n", + "Grad decoder.fc1.2.weight: 9.689340367913246e-05\n", + "Grad decoder.fc1.2.bias: 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" 1.4375, 1.1608, 1.0649, 0.9987, 1.0309, 0.6213, 0.6306, 1.1654, 1.5817,\n", + " 1.7155, 1.1082, 1.6968]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.8978, 1.2879, 1.8162, -1.4594, -0.1854, 0.1384, -0.2956, -0.6189,\n", + " 1.1809, 1.8157, 1.3661, -0.6465, 0.8372, -0.7884, -0.5286, -0.5426,\n", + " -0.8389, 1.1744, 0.7651, 1.0685, 1.6311, 1.1657, 1.1453, 1.1706,\n", + " 0.9664, 0.5716, -0.2306, 0.1715, 1.3402, -0.1184, -0.4857, 0.5508,\n", + " 0.9459, -0.5134, 1.3524, -0.5395, 0.3306, -0.6423, 0.7285, -0.0721,\n", + " 0.3927, 0.0972, -0.7808]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2536, 0.2275, 0.1544, 0.1602, -0.0182, -0.0872, -0.3070, -0.3234,\n", + " -0.1478, -0.2099, -0.1735, 0.1662, 0.1149, 0.1501, 0.0986, 0.2441,\n", + " 0.1515, 0.0970, 0.0682, 0.0738, 0.0058, 0.0320, 0.1285, 0.0942,\n", + " 0.0263, 0.0255, -0.0378, 0.0362, 0.1895, 0.1628, 0.1668, 0.1240,\n", + " 0.1765, 0.1569, 0.1492, 0.1077, -0.0342, -0.0237, -0.0241, -0.2041,\n", + " -0.1522, 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grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00022149283904582262\n", + "Grad encoder.fc1.bias: 0.0010257198009639978\n", + "Grad encoder.encoder.0.weight: 7.587873551528901e-05\n", + "Grad encoder.encoder.0.bias: 0.0009622488869354129\n", + "Grad encoder.encoder.2.weight: 5.5664633691776544e-05\n", + "Grad encoder.encoder.2.bias: 0.0008316109888255596\n", + "Grad encoder.encoder.4.weight: 0.00013112061424180865\n", + "Grad encoder.encoder.4.bias: 0.0020865313708782196\n", + "Grad decoder.fc1.0.weight: 7.40416653570719e-05\n", + "Grad decoder.fc1.0.bias: 0.0008911382756195962\n", + "Grad decoder.fc1.2.weight: 8.682620682520792e-05\n", + "Grad decoder.fc1.2.bias: 0.0007076876354403794\n", + "Grad decoder.fc1.4.weight: 5.843223334522918e-05\n", + "Grad decoder.fc1.4.bias: 0.0005240793107077479\n", + "Grad decoder.fc2.weight: 0.0001672563812462613\n", + "Grad decoder.fc2.bias: 0.001675963751040399\n", + "Grad _memory_unit.weight_ih_l0: 2.0443552784854546e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 0.0001551549503346905\n", + "Grad _memory_unit.bias_hh_l0: 8.385513501707464e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.312637596129207e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00027445529121905565\n", + "Grad _memory_unit.bias_hh_l1: 0.00014807652041781694\n", + "Data X Sample: tensor([[1.2338, 1.3808, 1.5327, 1.6333, 1.7006, 1.8018, 1.9305, 2.0286, 3.4223,\n", + " 3.7818, 3.9749, 3.8580, 3.6130, 3.4953, 3.3390, 3.2931, 3.1982, 3.2652,\n", + " 3.3020, 3.1392, 3.2191, 3.1583, 2.8725, 2.7810, 2.7420, 2.8783, 2.9570,\n", + " 2.9733, 2.7545, 3.0262, 3.0271, 3.1723, 3.2517, 2.2885, 3.8826, 4.2905,\n", + " 4.6547, 5.0381, 5.4193, 5.3281, 0.9259, 0.5218, 0.5457, 0.9271, 1.3201,\n", + " 1.6011, 0.9994, 1.5482]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.4680, -0.6576, 0.4491, 0.0139, 0.6964, 0.3724, 0.8281, 0.5250,\n", + " 0.0517, 0.4447, 0.9180, 0.1586, 0.0558, -0.6873, 0.2785, -0.1226,\n", + " -1.1688, 1.0594, 0.0536, 0.3688, -0.8784, -0.6729, -1.0348, -0.0179,\n", + " -0.7516, -0.2689, -0.7863, -0.2772, -0.3795, 0.2081, 0.9079, 0.5331,\n", + " -1.2545, -1.0065, 0.4923, -0.1605, -0.4351, -0.4206, 0.7114, 0.2620,\n", + " 0.7765, 0.1807, 0.0211]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1879, -0.1656, -0.0350, -0.1895, -0.0164, 0.0907, 0.1429, 0.1979,\n", + " 0.1292, 0.1678, 0.0888, -0.1042, -0.0147, -0.1921, -0.1501, -0.1332,\n", + " -0.1859, -0.0213, -0.0070, -0.0514, -0.0482, 0.0084, 0.0063, -0.0288,\n", + " -0.0207, 0.0535, -0.0207, -0.0226, -0.0826, -0.1828, -0.1302, -0.1745,\n", + " -0.1789, -0.2018, -0.0393, -0.0534, 0.0217, -0.0336, -0.0197, 0.2000,\n", + " 0.2474, 0.2298, 0.2814]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00021413571084849536\n", + "Grad encoder.fc1.bias: 0.0008643647888675332\n", + "Grad encoder.encoder.0.weight: 8.021266694413498e-05\n", + "Grad encoder.encoder.0.bias: 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_memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.0002733059518504888\n", + "Grad _memory_unit.bias_hh_l1: 0.00014756723248865455\n", + "Data X Sample: tensor([[1.9954, 2.5416, 2.6852, 2.9212, 2.9095, 3.0983, 3.1753, 3.2211, 3.3344,\n", + " 3.3127, 3.4229, 3.3847, 3.4021, 3.3808, 3.1927, 3.2425, 3.2753, 3.2652,\n", + " 3.3144, 3.2545, 3.2019, 3.2088, 2.9375, 2.8764, 2.7345, 2.7347, 2.6027,\n", + " 2.7000, 2.3972, 2.2762, 2.1627, 2.1388, 2.2660, 1.4921, 2.3605, 2.5052,\n", + " 2.6292, 2.7669, 2.9157, 2.8712, 1.7850, 0.9698, 0.9802, 1.7251, 2.3626,\n", + " 2.8591, 1.8833, 2.5100]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.0447, -0.6315, -0.2524, -0.2673, -0.0287, 0.8062, -0.0219, 0.0226,\n", + " -0.3577, -0.3666, -0.3686, 1.1302, 0.7126, -1.1840, 0.1351, 1.0283,\n", + " -0.0235, -0.4510, 0.0890, 0.3863, 0.1483, 0.1929, 0.1283, 0.0326,\n", + " 0.0755, -0.1055, 0.2045, 0.2996, 0.2348, -0.9073, 1.2135, 1.3257,\n", + " 0.4687, 0.7250, -0.7193, 3.0809, 0.8233, -0.7806, 0.0545, -0.3290,\n", + " -0.2669, 1.1019, -0.0619]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.0966, -0.0582, 0.0163, -0.1243, -0.0753, -0.0331, 0.0256, 0.0683,\n", + " 0.0581, 0.0727, -0.0092, -0.0543, 0.0347, -0.0786, -0.0496, -0.0252,\n", + " -0.1034, -0.0081, -0.0049, -0.0152, -0.0479, 0.0142, 0.0143, -0.0407,\n", + " -0.0221, 0.0433, 0.0244, -0.0339, 0.0376, -0.0637, -0.0567, -0.1156,\n", + " -0.0989, -0.0988, 0.0291, 0.0021, 0.0318, -0.0294, -0.0325, 0.1024,\n", + " 0.1385, 0.1152, 0.1186]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003212385345250368\n", + "Grad encoder.fc1.bias: 0.00010013885184889659\n", + "Grad encoder.encoder.0.weight: 8.042576519073918e-05\n", + "Grad encoder.encoder.0.bias: 0.00011061925033573061\n", + "Grad encoder.encoder.2.weight: 4.0853890823200345e-05\n", + "Grad encoder.encoder.2.bias: 0.0001338633883278817\n", + "Grad encoder.encoder.4.weight: 9.262468665838242e-05\n", + "Grad encoder.encoder.4.bias: 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2.2189, 2.3043,\n", + " 2.3064, 2.3380, 2.4091, 2.3458, 2.2384, 2.0780, 2.2442, 2.1479, 2.2419,\n", + " 2.2529, 2.3432, 2.3898, 2.4602, 2.3761, 2.4546, 2.4962, 2.5649, 2.5574,\n", + " 2.5940, 2.3209, 2.3548, 2.4252, 2.4179, 2.2638, 1.4212, 1.8776, 1.7828,\n", + " 1.6479, 1.4788, 1.6100, 1.5661, 1.2361, 0.6970, 0.6993, 1.2888, 1.8077,\n", + " 1.8984, 1.3326, 1.8766]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 5.1368e-02, 8.7720e-01, 1.2910e+00, 1.5132e-01, 8.2399e-01,\n", + " 1.0191e+00, -9.6747e-04, -3.4737e-01, 2.2632e-01, -4.4506e-01,\n", + " -7.0873e-01, 8.0875e-01, 2.3410e-01, -2.2658e-01, -5.8374e-03,\n", + " 2.9943e-01, 5.3673e-01, -1.0560e+00, 2.7546e-01, 8.1587e-01,\n", + " 2.7979e-01, -1.4743e-01, 4.2982e-01, 2.4822e-01, 9.1281e-01,\n", + " -5.5906e-01, 7.9646e-01, -1.1682e+00, 9.9613e-01, 6.1152e-01,\n", + " -9.0903e-01, 3.3850e-01, 3.1938e-01, 1.1012e+00, 1.1862e-01,\n", + " -7.6430e-01, 9.7765e-01, 1.5113e-01, -2.2862e+00, -8.2436e-02,\n", + " -3.4148e-01, -3.6701e-01, -1.0467e+00]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2892, 0.2899, 0.1927, 0.2389, 0.0075, -0.0941, -0.3762, -0.4031,\n", + " -0.1368, -0.2178, -0.1856, 0.2365, 0.1671, 0.1533, 0.1274, 0.2751,\n", + " 0.2133, 0.1396, 0.0891, 0.0766, 0.0503, 0.0495, 0.1753, 0.1047,\n", + " 0.0497, 0.0350, -0.0342, 0.0790, 0.2304, 0.2102, 0.2313, 0.1740,\n", + " 0.2281, 0.2166, 0.1963, 0.1450, -0.0419, -0.0045, -0.0389, -0.2491,\n", + " -0.1743, -0.2419, -0.2292]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003724546986632049\n", + "Grad encoder.fc1.bias: 0.0001980496454052627\n", + "Grad encoder.encoder.0.weight: 0.00011553228250704706\n", + "Grad encoder.encoder.0.bias: 0.00021479889983311296\n", + "Grad encoder.encoder.2.weight: 6.469937216024846e-05\n", + "Grad encoder.encoder.2.bias: 0.0002700967015698552\n", + "Grad encoder.encoder.4.weight: 0.00018711974553298205\n", + "Grad encoder.encoder.4.bias: 0.0007449454860761762\n", + "Grad decoder.fc1.0.weight: 8.048766176216304e-05\n", + "Grad decoder.fc1.0.bias: 0.0007577134529128671\n", + "Grad decoder.fc1.2.weight: 8.940070983953774e-05\n", + "Grad decoder.fc1.2.bias: 0.0009333777707070112\n", + "Grad decoder.fc1.4.weight: 7.321823795791715e-05\n", + "Grad decoder.fc1.4.bias: 0.0009789939504116774\n", + "Grad decoder.fc2.weight: 0.00015576234727632254\n", + "Grad decoder.fc2.bias: 0.0016038191970437765\n", + "Grad _memory_unit.weight_ih_l0: 2.820408553816378e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 9.98637406155467e-05\n", + "Grad _memory_unit.bias_hh_l0: 5.1415234338492155e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.039164635585621e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.0002044484717771411\n", + "Grad _memory_unit.bias_hh_l1: 0.00010727374319685623\n", + "Data X Sample: tensor([[1.3335, 1.5308, 1.6844, 1.8258, 1.9089, 1.9830, 2.0999, 2.4659, 4.0814,\n", + " 4.1199, 4.2033, 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device='cuda:0')\n", + "Prediction Sample: tensor([[-1.7096e-01, -1.6609e-01, -2.5842e-02, -1.6262e-01, -9.6694e-03,\n", + " 1.1664e-01, 1.4441e-01, 2.0433e-01, 1.5235e-01, 1.9257e-01,\n", + " 1.0336e-01, -1.3071e-01, -2.8933e-02, -2.0440e-01, -1.6837e-01,\n", + " -1.6864e-01, -2.0922e-01, -2.6681e-02, -2.7487e-02, -8.2723e-02,\n", + " -5.4162e-02, -9.6808e-03, -1.4362e-04, -3.5191e-02, -1.2240e-02,\n", + " 3.9453e-02, -3.9871e-02, -6.5754e-02, -1.0381e-01, -1.8680e-01,\n", + " -1.6074e-01, -1.6558e-01, -1.9738e-01, -2.1607e-01, -6.8080e-02,\n", + " -5.4317e-02, 4.9084e-03, -2.2463e-02, -2.3327e-02, 2.1940e-01,\n", + " 2.6049e-01, 2.3389e-01, 2.5929e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003363981086295098\n", + "Grad encoder.fc1.bias: 0.0012604028452187777\n", + "Grad encoder.encoder.0.weight: 0.00010168748849537224\n", + "Grad encoder.encoder.0.bias: 0.0009078203584067523\n", + "Grad encoder.encoder.2.weight: 7.246808672789484e-05\n", + "Grad 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_memory_unit.bias_hh_l1: 9.155456791631877e-05\n", + "Data X Sample: tensor([[1.3738, 1.5876, 1.7476, 1.8126, 1.8372, 1.8887, 1.9412, 2.0514, 1.9528,\n", + " 2.0521, 2.1128, 2.0507, 2.0439, 2.0476, 1.9847, 1.9871, 1.9120, 1.9866,\n", + " 2.0568, 2.0513, 2.0757, 2.1218, 2.1568, 2.1759, 2.1865, 2.1809, 2.1738,\n", + " 2.2636, 2.0399, 2.1026, 2.1312, 2.0310, 2.0834, 1.4509, 2.0173, 1.8728,\n", + " 1.6204, 1.3755, 1.2613, 1.2058, 1.1072, 0.6492, 0.6326, 1.1855, 1.7125,\n", + " 1.8527, 1.1898, 1.6655]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.6456, 1.2744, -0.4102, 0.2020, -0.5356, -0.2872, 0.1739, -1.1598,\n", + " 0.5707, 0.8485, 0.9582, -0.1742, -0.5852, 0.9795, 0.3500, 0.8314,\n", + " 0.2278, 0.5685, -0.3189, 0.3022, -0.0403, 0.7893, 1.2583, -0.2922,\n", + " -0.3433, 0.7083, -1.1719, -0.7532, -1.7569, -0.3458, 0.6909, -0.5814,\n", + " 0.4646, 0.4182, 1.3005, 0.2582, -0.6473, -0.7806, 0.0545, 0.2092,\n", + " -0.6319, 0.4465, -0.3711]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3458, 0.3539, 0.2310, 0.2775, 0.0153, -0.1147, -0.4561, -0.4724,\n", + " -0.1797, -0.2723, -0.2316, 0.2791, 0.2037, 0.1993, 0.1565, 0.3184,\n", + " 0.2632, 0.1664, 0.1061, 0.1077, 0.0589, 0.0733, 0.2078, 0.1299,\n", + " 0.0677, 0.0500, -0.0273, 0.1055, 0.2925, 0.2791, 0.2865, 0.2282,\n", + " 0.2741, 0.2594, 0.2102, 0.1793, -0.0410, 0.0111, -0.0472, -0.2962,\n", + " -0.2156, -0.3018, -0.2914]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003045727498829365\n", + "Grad encoder.fc1.bias: 0.000813398277387023\n", + "Grad encoder.encoder.0.weight: 7.845486106816679e-05\n", + "Grad encoder.encoder.0.bias: 0.0005315853049978614\n", + "Grad encoder.encoder.2.weight: 5.568192864302546e-05\n", + "Grad encoder.encoder.2.bias: 0.0004865557129960507\n", + "Grad encoder.encoder.4.weight: 0.0001483411033404991\n", + "Grad encoder.encoder.4.bias: 0.000965812592767179\n", + "Grad decoder.fc1.0.weight: 5.687697193934582e-05\n", + "Grad decoder.fc1.0.bias: 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3.4228, 3.2731, 2.9857, 2.8210, 2.8922, 2.9985, 3.0342,\n", + " 3.0467, 3.0876, 3.2555, 3.3560, 3.3906, 3.9227, 2.8927, 4.6351, 4.9788,\n", + " 4.9555, 5.5390, 6.0532, 5.8558, 1.0882, 0.5875, 0.5901, 1.0620, 1.5738,\n", + " 1.8756, 1.1898, 1.7202]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1617, 0.4253, -0.2380, 0.3095, -0.8703, 0.9001, -1.0300, 1.0232,\n", + " -0.7960, -0.6944, -0.6996, -0.6912, -0.0404, -0.5897, -0.5959, -0.8958,\n", + " -0.4009, 0.2655, -0.6308, 0.4167, -2.2219, -0.3597, -0.1795, -0.4803,\n", + " -0.2191, -1.3416, 0.0343, 0.0352, -0.0886, -0.6207, 0.5785, 0.1458,\n", + " -1.0741, 0.5418, 1.0551, -0.2810, -0.6417, -0.4031, 0.0545, -0.0869,\n", + " -0.4496, -1.0710, 0.7183]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2069, -0.2172, -0.0327, -0.1849, -0.0037, 0.1564, 0.1907, 0.2616,\n", + " 0.1939, 0.2421, 0.1607, -0.1797, -0.0529, -0.2703, -0.2336, -0.2512,\n", + " -0.2774, -0.0556, -0.0601, -0.1235, -0.0696, -0.0216, -0.0101, -0.0411,\n", + " -0.0048, 0.0448, -0.0453, -0.1097, -0.1328, -0.2287, -0.2258, -0.1915,\n", + " -0.2540, -0.2919, -0.1252, -0.0720, -0.0054, -0.0137, -0.0204, 0.3062,\n", + " 0.3265, 0.2878, 0.2916]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0010067534167319536\n", + "Grad encoder.fc1.bias: 0.0007717600674368441\n", + "Grad encoder.encoder.0.weight: 0.00021009072952438146\n", + "Grad encoder.encoder.0.bias: 0.0005184687906876206\n", + "Grad encoder.encoder.2.weight: 0.0001018588081933558\n", + "Grad encoder.encoder.2.bias: 0.00041547237196937203\n", + "Grad encoder.encoder.4.weight: 0.0002460474497638643\n", + "Grad encoder.encoder.4.bias: 0.0008740008343011141\n", + "Grad decoder.fc1.0.weight: 5.491467527463101e-05\n", + "Grad decoder.fc1.0.bias: 0.00043342442950233817\n", + "Grad decoder.fc1.2.weight: 5.3812822443433106e-05\n", + "Grad decoder.fc1.2.bias: 0.0005025584250688553\n", + "Grad decoder.fc1.4.weight: 5.035087451688014e-05\n", + "Grad decoder.fc1.4.bias: 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1.6729,\n", + " 1.7898, 1.2510, 1.8062]], device='cuda:0')\n", + "Data Y Sample: tensor([[-3.7961e-01, -2.0753e-01, 5.1526e-01, 1.4492e-01, 6.3922e-01,\n", + " 6.8372e-01, 1.9613e-01, -1.2085e-01, 1.2011e+00, 7.7821e-04,\n", + " 4.2756e-01, 9.0208e-01, 8.4082e-01, -3.1753e-01, 4.3004e-01,\n", + " 1.0264e-01, -4.4831e-01, -2.1606e-01, 6.4929e-01, 1.8331e+00,\n", + " -7.2175e-02, 4.7231e-01, -3.6082e-01, -1.5449e+00, -1.0423e+00,\n", + " 3.1006e-01, -1.3063e-01, 1.5543e-01, 5.8778e-01, 5.0659e-01,\n", + " 2.0748e-01, 8.8426e-01, 2.1155e-01, -8.4883e-01, 7.5894e-01,\n", + " -1.5264e+00, 6.2893e-01, 8.5322e-01, 9.2924e-02, 2.6379e-01,\n", + " -1.5803e-01, -4.2479e-01, 5.8607e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2272, -0.2403, -0.0411, -0.1975, -0.0018, 0.1792, 0.1966, 0.2910,\n", + " 0.1998, 0.2552, 0.1901, -0.2033, -0.0760, -0.3026, -0.2573, -0.2862,\n", + " -0.3072, -0.0713, -0.0782, -0.1342, -0.0836, -0.0315, -0.0107, -0.0449,\n", + " 0.0144, 0.0359, -0.0490, -0.1399, -0.1548, -0.2445, -0.2529, -0.2030,\n", + " -0.2787, -0.3167, -0.1523, -0.0900, -0.0175, -0.0076, -0.0174, 0.3426,\n", + " 0.3483, 0.3095, 0.3058]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00048384180990979075\n", + "Grad encoder.fc1.bias: 0.00016490643611177802\n", + "Grad encoder.encoder.0.weight: 0.00010984869004460052\n", + "Grad encoder.encoder.0.bias: 0.00016824417980387807\n", + "Grad encoder.encoder.2.weight: 7.394146814476699e-05\n", + "Grad encoder.encoder.2.bias: 0.00029190845089033246\n", + "Grad encoder.encoder.4.weight: 0.000197027504327707\n", + "Grad encoder.encoder.4.bias: 0.0008666841313242912\n", + "Grad decoder.fc1.0.weight: 7.245206506922841e-05\n", + "Grad decoder.fc1.0.bias: 0.0006084320484660566\n", + "Grad decoder.fc1.2.weight: 9.350523760076612e-05\n", + "Grad decoder.fc1.2.bias: 0.0008149496279656887\n", + "Grad decoder.fc1.4.weight: 7.886580715421587e-05\n", + "Grad decoder.fc1.4.bias: 0.0008746909443289042\n", + "Grad decoder.fc2.weight: 0.00016620181850157678\n", + "Grad decoder.fc2.bias: 0.0021034523379057646\n", + "Grad _memory_unit.weight_ih_l0: 3.403236041776836e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 0.00011680374154821038\n", + "Grad _memory_unit.bias_hh_l0: 6.074219345464371e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.0420767466712277e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.0002334678720217198\n", + "Grad _memory_unit.bias_hh_l1: 0.0001240208512172103\n", + "Data X Sample: tensor([[ 0.0149, 0.0160, 0.0255, 0.0175, 0.0171, 0.0309, 0.0185, 0.0128,\n", + " 0.0317, 0.0221, 0.0539, 0.0117, 0.0200, 0.0191, 0.0151, 0.0123,\n", + " -0.1305, -0.1489, -0.1879, -0.1730, -0.1914, -0.1225, -0.1976, -0.1871,\n", + " -0.1464, -0.3082, -0.3037, -0.5017, -0.0798, -0.1015, -0.1085, -0.0497,\n", + " -0.0176, -0.0618, -0.0319, 0.0073, -0.0157, -0.0212, -0.0507, -0.0113,\n", + " -0.0095, -0.0020, -0.0061, 0.0029, 0.0357, 0.0000, -0.0272, -0.0235]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 6.0292e-01, 1.0372e+00, -3.5242e-01, 3.1193e-01, -3.9384e-01,\n", + " 4.5728e-01, -5.8107e-01, -7.9866e-01, -2.6063e-01, -2.3154e-01,\n", + " -8.9347e-02, 3.4406e-01, 3.4162e-02, 3.0642e-01, 5.4604e-01,\n", + " 4.4885e-01, 6.0203e-01, -4.8929e-01, 9.1836e-01, 8.3930e-01,\n", + " -5.8706e-01, 5.6069e-01, 4.2866e-04, 1.2299e+00, 9.2359e-01,\n", + " 6.3169e-01, -4.3260e-01, 5.0708e-03, -5.7170e-01, 1.0540e+00,\n", + " -4.7387e-01, 3.9210e-01, 7.1893e-01, 5.7717e-01, 9.4283e-01,\n", + " -1.9581e-01, -5.0849e-01, 3.9452e-01, 4.0193e-03, -5.7140e-02,\n", + " -2.2672e-01, -1.6614e-01, -4.4950e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 5.7717e-01, 5.1334e-01, 2.0117e-01, 3.4810e-01, -7.6557e-02,\n", + " -2.6241e-01, -7.1783e-01, -7.5227e-01, -3.6840e-01, -4.7356e-01,\n", + " -4.8792e-01, 3.7802e-01, 2.6830e-01, 3.7906e-01, 1.9958e-01,\n", + " 3.7169e-01, 3.4510e-01, 1.5508e-01, 1.7257e-01, 1.0620e-01,\n", + " 7.6416e-02, 1.3448e-01, 2.0356e-01, 9.4252e-02, 6.9603e-02,\n", + " -1.6167e-04, 3.4462e-02, 2.1503e-01, 5.6959e-01, 6.0464e-01,\n", + " 4.7266e-01, 4.3767e-01, 4.6157e-01, 4.0913e-01, 2.7829e-01,\n", + " 2.4276e-01, -6.8595e-02, 6.6354e-02, -4.8257e-02, -5.0475e-01,\n", + " -4.4628e-01, -4.6036e-01, -3.9789e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002492075436748564\n", + "Grad encoder.fc1.bias: 0.0007603903068229556\n", + "Grad encoder.encoder.0.weight: 8.69055584189482e-05\n", + "Grad encoder.encoder.0.bias: 0.0006649699062108994\n", + "Grad encoder.encoder.2.weight: 7.563199324067682e-05\n", + "Grad encoder.encoder.2.bias: 0.0007319383439607918\n", + "Grad encoder.encoder.4.weight: 0.00022719110711477697\n", + "Grad encoder.encoder.4.bias: 0.0019641369581222534\n", + "Grad decoder.fc1.0.weight: 0.00010125015251105651\n", + "Grad decoder.fc1.0.bias: 0.0011308202520012856\n", + "Grad decoder.fc1.2.weight: 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2.8051,\n", + " 2.9937, 2.9072, 3.0688, 3.1706, 3.1972, 3.5399, 2.6638, 4.4023, 4.7793,\n", + " 4.7471, 5.2978, 5.7806, 5.6402, 1.2122, 0.6870, 0.7053, 1.1941, 1.7720,\n", + " 1.8527, 1.3054, 1.8766]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.3959, -1.1721, 0.3464, 0.1694, 1.0457, 1.5148, 0.0023, 0.4768,\n", + " -0.1360, 0.7357, 0.6178, -0.1277, -1.1198, -0.8409, -0.8757, 0.1001,\n", + " -0.3064, -0.4941, 0.2021, -0.1421, -0.1174, -0.2472, -0.0883, 0.8411,\n", + " 0.0681, 0.6164, 0.6217, 0.0215, 0.7271, -0.3636, -0.3598, 0.3602,\n", + " -0.7837, -1.1710, -0.2851, -0.4467, 0.8892, 0.7579, -0.4066, 0.9766,\n", + " 0.8432, 0.8313, 0.0890]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.3007, -0.3226, -0.0689, -0.2612, -0.0137, 0.2171, 0.2445, 0.3692,\n", + " 0.2261, 0.2985, 0.2721, -0.2584, -0.1276, -0.3997, -0.3288, -0.3920,\n", + " -0.3887, -0.1280, -0.1179, -0.1585, -0.1262, -0.0521, -0.0300, -0.0538,\n", + " 0.0491, 0.0350, -0.0495, -0.2044, -0.1987, -0.3029, 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"Data Y Sample: tensor([[-6.0360e-01, -2.2594e-01, -8.5454e-01, -4.4239e-01, 8.0261e-02,\n", + " -2.1865e-01, -3.2649e-03, -9.8278e-02, 6.0494e-01, 4.9749e-01,\n", + " 6.2094e-01, -2.6278e-03, -6.1185e+00, -1.2165e+00, -4.5739e-01,\n", + " -9.5540e-01, -4.0171e-01, -7.4770e-01, -1.2657e-01, -3.8523e-01,\n", + " 3.8845e-02, 1.9348e-01, 1.3760e-01, -2.2704e-01, -3.9000e-01,\n", + " -1.8735e-01, 6.8338e-01, 3.2690e-02, 1.3327e-01, -3.7699e-01,\n", + " -3.2661e-01, -1.9833e-01, -5.6717e-01, -4.5678e-01, 8.4585e-02,\n", + " -2.8685e-01, -7.4931e-01, -7.2314e-01, 1.2050e+00, 5.3598e-01,\n", + " 5.1287e-01, 6.6658e-01, 4.2245e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.3299, -0.3641, -0.0957, -0.2952, -0.0427, 0.2195, 0.2481, 0.3924,\n", + " 0.2269, 0.3101, 0.3024, -0.2804, -0.1598, -0.4383, -0.3463, -0.4316,\n", + " -0.4235, -0.1701, -0.1463, -0.1653, -0.1543, -0.0751, -0.0511, -0.0656,\n", + " 0.0767, 0.0369, -0.0358, -0.2373, -0.2047, -0.3217, -0.3553, -0.2940,\n", + " 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"Grad decoder.fc1.2.weight: 5.2978422900196165e-05\n", + "Grad decoder.fc1.2.bias: 0.0005048370803706348\n", + "Grad decoder.fc1.4.weight: 5.17014559591189e-05\n", + "Grad decoder.fc1.4.bias: 0.0006335160578601062\n", + "Grad decoder.fc2.weight: 0.00014179584104567766\n", + "Grad decoder.fc2.bias: 0.0019405506318435073\n", + "Grad _memory_unit.weight_ih_l0: 6.259606834646547e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 4.122590326005593e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.2241109036258422e-05\n", + "Grad _memory_unit.weight_ih_l1: 4.231626007822342e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 8.091728523140773e-05\n", + "Grad _memory_unit.bias_hh_l1: 4.3555784941418096e-05\n", + "Data X Sample: tensor([[1.5011, 1.6357, 1.8107, 1.9438, 1.9840, 2.0434, 2.0260, 2.0996, 2.1481,\n", + " 2.1768, 2.2587, 2.2338, 2.1993, 2.1539, 2.1260, 2.1129, 2.1148, 2.1936,\n", + " 2.2488, 2.2503, 2.2401, 2.2168, 2.3134, 2.3572, 2.3554, 2.2854, 2.4109,\n", + " 2.3452, 2.2411, 2.3155, 2.2082, 2.3157, 2.2572, 1.5379, 2.0982, 1.9117,\n", + " 1.6853, 1.4868, 1.5276, 1.5633, 1.2075, 0.6572, 0.7073, 1.2084, 1.7244,\n", + " 1.9671, 1.2850, 1.7906]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.0723, 0.2854, -0.1023, 0.1251, 0.9170, 0.1799, -0.3888, -0.1029,\n", + " 0.0756, -0.3351, 0.0446, -0.1172, 0.3669, 1.0021, 0.8575, 0.1350,\n", + " 0.4006, 1.3026, 1.4054, 1.4973, 1.4814, 0.9587, 0.0631, 0.2984,\n", + " 0.3862, 0.2228, 0.1814, -0.1427, 0.1280, 0.7291, 0.1570, 0.7699,\n", + " 0.2792, 0.4141, -0.2652, -0.0512, 0.4915, 0.7357, -0.0156, -0.0555,\n", + " -0.2417, 0.0228, -0.4240]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3171, 0.3074, 0.1670, 0.2323, 0.0058, -0.1229, -0.3376, -0.3356,\n", + " -0.1275, -0.2105, -0.1975, 0.1767, 0.0910, 0.1847, 0.1612, 0.2340,\n", + " 0.1800, 0.1024, 0.0670, 0.0913, 0.1226, 0.0729, 0.1117, 0.0819,\n", + " 0.0922, 0.0287, 0.0107, 0.1082, 0.1909, 0.2674, 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"Data Y Sample: tensor([[-0.2262, 0.3375, -0.2161, 0.7368, 0.8495, 0.0855, 0.3249, 0.0246,\n", + " -0.4197, -0.4960, -0.4171, 0.6210, 0.5356, 0.6509, -0.3231, 0.1340,\n", + " -0.1157, -0.7772, -0.5580, -0.6896, 0.6856, 0.5126, 0.8152, -0.5161,\n", + " -0.0533, 0.0086, -0.4467, -1.0828, -0.3474, 0.1569, 0.5459, 0.3701,\n", + " 0.3752, 0.8513, -1.0649, 0.0080, 0.5692, 0.7816, -0.0156, -0.1977,\n", + " -0.1756, -0.6170, -0.4398]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3370, 0.3177, 0.1591, 0.2460, 0.0110, -0.1365, -0.3432, -0.3485,\n", + " -0.1307, -0.2209, -0.2087, 0.1819, 0.0881, 0.1997, 0.1792, 0.2476,\n", + " 0.1785, 0.1004, 0.0725, 0.0957, 0.1305, 0.0782, 0.1091, 0.0789,\n", + " 0.0998, 0.0260, 0.0147, 0.1198, 0.1915, 0.2855, 0.2205, 0.1549,\n", + " 0.2104, 0.2036, 0.1706, 0.1953, -0.0567, -0.0069, 0.0013, -0.2750,\n", + " -0.1848, -0.2347, -0.2299]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0008149745408445597\n", + "Grad 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"Grad _memory_unit.bias_hh_l0: 3.340201510582119e-05\n", + "Grad _memory_unit.weight_ih_l1: 8.943125067162327e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00016439361206721514\n", + "Grad _memory_unit.bias_hh_l1: 8.600404544267803e-05\n", + "Data X Sample: tensor([[1.6602, 1.8672, 2.0451, 2.2762, 2.4297, 2.5458, 2.6037, 2.7654, 2.6802,\n", + " 2.7708, 2.8297, 2.8141, 2.7474, 2.6883, 2.5673, 2.6982, 2.5944, 2.5669,\n", + " 2.6639, 2.6259, 2.5321, 2.5811, 2.5833, 2.5596, 2.6069, 2.6067, 2.5254,\n", + " 2.5083, 2.2931, 2.3483, 2.4042, 2.3930, 2.2748, 1.5333, 1.9487, 1.9020,\n", + " 1.7089, 1.6723, 1.7114, 1.6399, 1.2838, 0.7348, 0.7538, 1.3692, 1.9742,\n", + " 2.2358, 1.3870, 2.1425]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.4363, 1.1282, 0.6256, 0.4635, 0.0962, -0.2408, -0.3212, -0.6872,\n", + " -0.2825, -0.5121, -0.2366, 0.1588, 0.7582, 0.1956, 0.3367, -0.6556,\n", + " -0.5049, -0.7524, -0.9027, -0.0240, -0.5065, 0.2869, 0.4640, 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_memory_unit.bias_hh_l1: 0.00013364262122195214\n", + "Data X Sample: tensor([[1.4714, 1.8017, 2.1893, 2.3833, 2.3887, 2.6209, 2.7100, 2.6944, 2.7803,\n", + " 2.7582, 2.8678, 2.8278, 2.6653, 2.7783, 2.7842, 2.7269, 2.6903, 2.8126,\n", + " 2.8745, 2.9235, 2.8781, 2.9669, 2.6532, 2.4374, 2.4267, 2.3089, 2.1658,\n", + " 2.1820, 1.7762, 1.7751, 1.6693, 1.5972, 1.3596, 0.8811, 1.2182, 1.2526,\n", + " 1.3470, 1.6856, 1.8952, 1.8668, 1.2218, 0.8045, 0.7498, 1.4955, 2.1010,\n", + " 2.3731, 1.6521, 2.1034]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.2168, -0.0731, 0.3410, 0.7768, -0.5587, -0.9638, -3.0592, -3.2014,\n", + " 0.5377, -0.7317, -0.5890, 0.3692, -0.2340, -0.3256, -0.1265, -1.4132,\n", + " -0.5183, 0.2224, 0.3934, -0.1526, -0.6191, 0.0497, -0.2195, 1.1961,\n", + " 0.7446, -0.8004, 0.9828, 0.1526, 1.0993, 2.0771, 1.2541, 0.6928,\n", + " 1.8865, -0.7297, 0.0685, -0.4562, -0.2284, -0.6931, 0.0000, 0.6594,\n", + " 0.9046, 0.7610, 1.2247]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.1035, 0.0997, 0.0724, -0.0065, -0.0384, -0.0633, -0.1151, -0.0877,\n", + " -0.0555, -0.0863, -0.0798, 0.0124, 0.0472, 0.0756, 0.0461, 0.0558,\n", + " 0.0177, 0.0298, 0.0159, 0.0173, -0.0291, 0.0234, 0.0267, 0.0467,\n", + " 0.0330, 0.0174, 0.0303, 0.0412, 0.0913, 0.1012, 0.0610, 0.0095,\n", + " 0.0024, 0.0089, 0.0181, 0.0377, -0.0167, 0.0077, 0.0068, -0.0607,\n", + " -0.0075, 0.0034, -0.0056]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00014548504259437323\n", + "Grad encoder.fc1.bias: 0.00033581035677343607\n", + "Grad encoder.encoder.0.weight: 4.249139965395443e-05\n", + "Grad encoder.encoder.0.bias: 0.0002607990463729948\n", + "Grad encoder.encoder.2.weight: 4.233224899508059e-05\n", + "Grad encoder.encoder.2.bias: 0.00030054806848056614\n", + "Grad encoder.encoder.4.weight: 0.00010061071952804923\n", + "Grad encoder.encoder.4.bias: 0.0005672269035130739\n", + "Grad decoder.fc1.0.weight: 4.9576799938222393e-05\n", + "Grad 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"Data Y Sample: tensor([[ 0.4855, 0.2181, 0.9733, 0.7077, -0.1928, 0.5302, -1.0823, -0.6461,\n", + " -0.3067, -0.3466, -0.4009, 4.8659, 0.5937, -0.4133, -0.2873, -0.8093,\n", + " 1.3650, 0.0475, 0.1713, 0.5408, 0.0537, 0.0321, 0.1026, 0.5960,\n", + " -1.4812, 0.3718, -0.3522, -0.0533, -0.6023, -0.1526, -0.5875, 0.3440,\n", + " 0.1849, 0.3687, 0.2335, 0.6354, -0.1484, -0.6330, -0.5562, -0.6359,\n", + " -0.1870, -0.5946, -0.2127]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.3283, -0.2463, -0.0802, -0.1627, 0.0699, 0.1342, 0.2075, 0.4093,\n", + " 0.2835, 0.4429, 0.2529, -0.2862, -0.2254, -0.3110, -0.2257, -0.4582,\n", + " -0.4044, -0.2159, -0.2209, -0.1766, -0.1204, -0.1965, -0.0418, -0.0787,\n", + " -0.1247, -0.0127, 0.0250, -0.1055, -0.2073, -0.3156, -0.2993, -0.2351,\n", + " -0.4535, -0.3878, -0.3950, -0.1401, 0.0036, 0.0654, -0.0516, 0.3819,\n", + " 0.4716, 0.4029, 0.3398]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0009491491364315152\n", + 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0.0066, 0.0295, -0.2417,\n", + " -0.2351, -0.2399, -0.2563]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00045568187488242984\n", + "Grad encoder.fc1.bias: 0.0004161708347965032\n", + "Grad encoder.encoder.0.weight: 9.715995838632807e-05\n", + "Grad encoder.encoder.0.bias: 0.0003548411186784506\n", + "Grad encoder.encoder.2.weight: 6.165385275380686e-05\n", + "Grad encoder.encoder.2.bias: 0.00037706081639043987\n", + "Grad encoder.encoder.4.weight: 0.00011806209658971056\n", + "Grad encoder.encoder.4.bias: 0.0006264219991862774\n", + "Grad decoder.fc1.0.weight: 4.7654037189204246e-05\n", + "Grad decoder.fc1.0.bias: 0.0003910026862286031\n", + "Grad decoder.fc1.2.weight: 5.8583846112014726e-05\n", + "Grad decoder.fc1.2.bias: 0.00044671521754935384\n", + "Grad decoder.fc1.4.weight: 5.558194243349135e-05\n", + "Grad decoder.fc1.4.bias: 0.0005458295927383006\n", + "Grad decoder.fc2.weight: 0.0001270012289751321\n", + "Grad decoder.fc2.bias: 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-0.0972, 0.0182,\n", + " 0.5276, 0.7782, 0.5688, -0.0867, 0.5069, 0.6423, -1.0094, 0.1579,\n", + " 1.0282, -0.3895, 0.6341]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1589, -0.2110, -0.0923, -0.1873, -0.1294, 0.0377, 0.1045, 0.1575,\n", + " 0.1311, 0.0610, 0.1126, -0.0353, -0.0595, -0.1022, -0.0811, -0.1324,\n", + " -0.1619, -0.0164, -0.0257, -0.1178, -0.1133, -0.0210, -0.0122, 0.0202,\n", + " -0.0050, 0.0120, 0.0591, -0.0144, -0.0151, -0.1161, -0.0992, -0.1243,\n", + " -0.1068, -0.1581, -0.0183, -0.0690, -0.0050, 0.0566, 0.0238, 0.1777,\n", + " 0.2173, 0.1986, 0.1613]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.000350313464878127\n", + "Grad encoder.fc1.bias: 0.0006387180183082819\n", + "Grad encoder.encoder.0.weight: 0.00010903382644755766\n", + "Grad encoder.encoder.0.bias: 0.0006346584996208549\n", + "Grad encoder.encoder.2.weight: 8.344883099198341e-05\n", + "Grad encoder.encoder.2.bias: 0.000725307036191225\n", + "Grad 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"Data X Sample: tensor([[1.5838, 1.8439, 2.0060, 2.1209, 2.2624, 2.3852, 2.4651, 2.5156, 2.6143,\n", + " 2.6397, 2.6425, 2.6505, 2.5144, 2.5738, 2.5496, 2.3659, 2.3963, 2.4895,\n", + " 2.5957, 2.4474, 2.5346, 2.5765, 2.5134, 2.5538, 2.5243, 2.5806, 2.4722,\n", + " 2.5491, 2.3243, 2.4203, 2.4287, 2.3599, 2.3298, 1.4692, 2.0148, 1.8680,\n", + " 1.7089, 1.5901, 1.6100, 1.5661, 1.2743, 0.7090, 0.7680, 1.2802, 2.0297,\n", + " 2.1672, 1.3870, 1.8922]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.1544, 0.8737, -0.3191, 0.1554, 1.2082, 0.0928, -0.0023, -0.1940,\n", + " -0.2737, -0.0738, -0.1473, -0.4104, 0.2125, -0.8065, 0.1188, 0.3578,\n", + " -0.0346, -0.6491, -0.4092, 0.4494, 0.1402, 0.6625, 0.2144, -0.7240,\n", + " -0.6393, 0.0588, 0.6760, 0.6744, -0.3405, 0.5157, 0.1414, 0.0458,\n", + " 0.2768, -0.4383, 1.8271, 0.1561, -1.0105, -0.6596, -2.2862, -0.1284,\n", + " -0.0484, 0.0591, -0.2049]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4194, 0.3243, 0.1954, 0.3033, 0.0809, 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"Data Y Sample: tensor([[ 1.2984, 1.2089, 0.0270, -0.4788, -1.1795, -1.2442, -1.4062, -0.8554,\n", + " -1.6349, -0.9069, -1.8811, 1.2708, 1.2125, -0.4429, 0.5672, 0.0528,\n", + " 0.4319, -0.3541, 0.0538, -0.3028, -0.1918, 0.1080, -0.3900, 1.7458,\n", + " 0.3441, -0.3988, -0.6771, 0.0921, 0.1616, 0.5446, 0.8427, 0.0761,\n", + " -0.0066, -0.4097, -0.5276, -0.7236, 0.5193, 0.6895, -0.2578, -1.6191,\n", + " -1.3394, -1.6718, -0.9327]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3325, 0.2768, 0.1573, 0.2426, 0.0541, -0.0968, -0.3348, -0.3725,\n", + " -0.1661, -0.2567, -0.2513, 0.1423, 0.2044, 0.2103, 0.2040, 0.2809,\n", + " 0.2322, 0.1012, 0.0685, 0.0949, 0.0131, 0.0714, 0.1014, 0.0299,\n", + " 0.0609, -0.0246, 0.0342, 0.0454, 0.1757, 0.2954, 0.2294, 0.2080,\n", + " 0.2361, 0.2859, 0.2218, 0.2052, -0.0498, -0.0080, 0.0185, -0.2975,\n", + " -0.2896, -0.2889, -0.2534]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0014553151559084654\n", + "Grad 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"Grad decoder.fc1.0.weight: 7.238844409584999e-05\n", + "Grad decoder.fc1.0.bias: 0.00040203292155638337\n", + "Grad decoder.fc1.2.weight: 0.00011321317288093269\n", + "Grad decoder.fc1.2.bias: 0.0007480132044292986\n", + "Grad decoder.fc1.4.weight: 0.00011975243978668004\n", + "Grad decoder.fc1.4.bias: 0.0009147932869382203\n", + "Grad decoder.fc2.weight: 0.0001926388795254752\n", + "Grad decoder.fc2.bias: 0.002081856830045581\n", + "Grad _memory_unit.weight_ih_l0: 2.5565603209543042e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 6.0403934185160324e-05\n", + "Grad _memory_unit.bias_hh_l0: 3.205807297490537e-05\n", + "Grad _memory_unit.weight_ih_l1: 9.32876173465047e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00011005732085322961\n", + "Grad _memory_unit.bias_hh_l1: 5.81754429731518e-05\n", + "Data X Sample: tensor([[2.5693, 3.0091, 3.2457, 3.4001, 3.3756, 3.8216, 3.7654, 3.9650, 3.9788,\n", + " 3.9967, 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" 4.1591, 4.5319, 5.1468, 4.8997, 1.8470, 1.1391, 1.0731, 2.1097, 3.0841,\n", + " 3.0535, 2.1145, 2.7524]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1655, -0.2004, 0.3434, -0.5956, -0.3248, -1.1160, 0.3228, 0.1673,\n", + " 0.7130, 0.4869, 0.2570, -0.4708, -0.0694, -0.1021, -0.0182, 0.0330,\n", + " 0.5874, 0.1708, -0.1056, 0.0532, -0.2304, 0.9713, -0.5294, -0.5523,\n", + " -0.1441, -0.3404, -0.2736, -0.0292, -0.4790, -0.9116, 0.0233, 0.6409,\n", + " -0.2613, -0.0544, 0.1499, 0.0817, 0.0000, -0.1765, -0.0197, 0.1901,\n", + " 0.3379, -0.1030, -0.0398]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 1.0128e-01, 8.4880e-02, -2.4748e-02, 1.8391e-04, -6.3405e-02,\n", + " -3.9683e-02, -1.2153e-01, -8.1165e-02, -2.1263e-02, -7.9891e-02,\n", + " -3.9245e-02, -1.6706e-02, -7.3498e-03, 7.8905e-02, 8.8636e-02,\n", + " 1.2206e-01, 8.9763e-03, -2.4568e-02, -2.7734e-02, -8.8114e-03,\n", + " 3.0290e-03, 7.8092e-03, 4.3055e-05, -1.7260e-02, 3.8815e-02,\n", + " -3.8356e-02, 4.7070e-03, -5.7953e-02, 4.2154e-02, 1.0551e-01,\n", + " 2.2642e-02, 1.6404e-02, 9.7838e-02, 5.8176e-02, 7.0443e-02,\n", + " 6.4230e-02, -4.6079e-02, -2.5122e-02, 3.2780e-02, -1.6229e-02,\n", + " -3.5030e-02, -3.3571e-02, -7.7929e-02]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0004946964327245951\n", + "Grad encoder.fc1.bias: 0.000790851772762835\n", + "Grad encoder.encoder.0.weight: 0.00011429807636886835\n", + "Grad encoder.encoder.0.bias: 0.0006088404916226864\n", + "Grad encoder.encoder.2.weight: 8.380615327041596e-05\n", + "Grad encoder.encoder.2.bias: 0.0006205091485753655\n", + "Grad encoder.encoder.4.weight: 0.00018573766283225268\n", + "Grad encoder.encoder.4.bias: 0.0014591615181416273\n", + "Grad decoder.fc1.0.weight: 8.89385337359272e-05\n", + "Grad decoder.fc1.0.bias: 0.0007666099118068814\n", + "Grad decoder.fc1.2.weight: 9.497077553533018e-05\n", + "Grad decoder.fc1.2.bias: 0.0006934618577361107\n", + "Grad decoder.fc1.4.weight: 7.796170393703505e-05\n", 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1.1855, 1.6531,\n", + " 1.8927, 1.2646, 1.7202]], device='cuda:0')\n", + "Data Y Sample: tensor([[-3.3756e-01, -5.7971e-01, -7.4326e-01, -5.3742e-01, -2.4590e-01,\n", + " -1.8076e-01, 2.0166e-01, 4.3634e-01, 1.6875e-01, 3.1390e-01,\n", + " 1.9796e-01, -4.0189e-01, 6.6080e-02, -5.4444e-01, -1.6948e-01,\n", + " -9.0179e-01, -1.6388e+00, -6.4610e-01, -2.0362e+00, 6.5652e-02,\n", + " -7.3538e-01, -1.6647e+00, 2.8697e-01, 1.3629e+00, 3.9335e-04,\n", + " 8.3151e-01, -6.1367e-01, -4.8515e-01, -9.7621e-01, -1.2043e+00,\n", + " -3.7085e-01, -4.1489e-01, -1.0231e+00, -4.4898e-01, 1.3043e-02,\n", + " -1.4394e-01, -1.9668e-02, -7.7253e-01, -5.9701e-01, 5.3494e-01,\n", + " 5.6081e-01, 3.3493e-01, 7.1889e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1649, -0.1375, -0.1317, -0.0640, 0.1109, 0.1715, 0.1601, 0.2496,\n", + " 0.1781, 0.2700, 0.2108, -0.2776, -0.2001, -0.2820, -0.1683, -0.2049,\n", + " -0.1920, 0.0075, -0.1387, -0.1083, 0.0007, -0.0952, -0.0590, -0.0110,\n", + " 0.0230, 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grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00012691153096966445\n", + "Grad encoder.fc1.bias: 0.0007022771169431508\n", + "Grad encoder.encoder.0.weight: 3.399873821763322e-05\n", + "Grad encoder.encoder.0.bias: 0.00041478578350506723\n", + "Grad encoder.encoder.2.weight: 3.869056308758445e-05\n", + "Grad encoder.encoder.2.bias: 0.0003453789686318487\n", + "Grad encoder.encoder.4.weight: 0.00011816294863820076\n", + "Grad encoder.encoder.4.bias: 0.0009220218635164201\n", + "Grad decoder.fc1.0.weight: 5.259669342194684e-05\n", + "Grad decoder.fc1.0.bias: 0.00043754198122769594\n", + "Grad decoder.fc1.2.weight: 7.924989040475339e-05\n", + "Grad decoder.fc1.2.bias: 0.0005850490415468812\n", + "Grad decoder.fc1.4.weight: 6.784760626032948e-05\n", + "Grad decoder.fc1.4.bias: 0.0006134251598268747\n", + "Grad decoder.fc2.weight: 0.00015307158173527569\n", + "Grad decoder.fc2.bias: 0.0017963272985070944\n", + "Grad _memory_unit.weight_ih_l0: 1.1550563613127451e-05\n", + "Grad 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" 4.9614, 5.4728, 6.2370, 6.0346, 1.1884, 0.7030, 0.7013, 1.2544, 1.7641,\n", + " 1.9556, 1.3258, 2.0330]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.0080, -0.0418, 0.0375, -0.6569, 0.2350, 0.4071, 0.4791, 0.6214,\n", + " 0.4497, 0.2614, 0.1120, -0.0524, -0.7640, -1.3322, -0.0834, -0.2734,\n", + " -0.3778, 0.4998, 3.7983, 0.4144, -4.5177, -0.8879, 0.2000, 0.9251,\n", + " -0.7827, -3.4318, -0.0170, 0.0189, 0.4095, -0.5933, -0.5629, -0.9544,\n", + " 0.1856, -0.8846, 0.1755, -0.4183, -0.3467, 0.9007, -0.7546, 0.4111,\n", + " 0.4548, 1.0926, -0.2091]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1770, -0.2148, -0.1712, -0.2232, -0.0033, 0.0871, 0.1823, 0.2792,\n", + " 0.2130, 0.1962, 0.2631, -0.2238, -0.1853, -0.2581, -0.1681, -0.2208,\n", + " -0.2224, -0.0121, -0.0591, -0.1061, -0.0552, -0.0477, -0.0741, 0.0261,\n", + " 0.0771, -0.0074, -0.0010, -0.1045, -0.1961, -0.2298, -0.2623, -0.2114,\n", + " -0.2485, -0.3153, -0.1974, -0.1297, -0.0613, 0.0584, 0.0946, 0.2696,\n", 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"Data X Sample: tensor([[2.4452, 2.9509, 3.1585, 3.3301, 3.4371, 3.7053, 3.7130, 3.8046, 4.0301,\n", + " 3.7518, 3.9717, 3.9846, 3.7794, 3.7243, 3.5660, 3.4544, 3.3869, 3.5205,\n", + " 3.3226, 3.2750, 3.3639, 3.2654, 2.9399, 2.9356, 2.6970, 2.7921, 2.7865,\n", + " 2.6919, 2.6782, 2.7806, 2.7366, 2.8517, 2.9657, 2.0963, 3.6032, 4.0472,\n", + " 4.4187, 4.7386, 5.2989, 5.1863, 2.0236, 1.1451, 1.1075, 2.0724, 2.8978,\n", + " 3.0535, 2.0193, 2.8931]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1503, -0.2109, -0.5963, 1.1628, 0.2477, 0.7847, 0.1021, 0.1102,\n", + " 0.2170, -0.1663, 0.1039, -0.8484, 0.3611, 0.6615, -0.1635, -0.3519,\n", + " -0.9256, -0.2836, 0.3536, -0.1744, 0.6306, 0.9745, -0.6136, -0.1432,\n", + " -0.1173, 0.7924, 0.3185, 0.4808, -0.2584, 0.0416, -0.4685, -1.4301,\n", + " -0.4223, -1.8095, 0.8528, 1.2114, 0.4206, 0.0000, -0.5988, -0.2823,\n", + " 0.0216, 0.0358, -0.6846]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1765, -0.2018, -0.1299, -0.1835, 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"Data Y Sample: tensor([[-0.1990, -0.9318, -0.1369, -0.2154, -1.2907, 0.4378, 0.4008, 0.3802,\n", + " 0.5952, 0.2424, 0.2855, -0.5385, -0.0905, 0.0113, -1.0464, -0.4210,\n", + " -0.6968, -0.0219, -0.2116, -0.5550, 2.2870, -0.5428, -0.0449, -0.8173,\n", + " -1.2778, -0.5734, 0.2541, -0.1708, -0.2681, -0.9533, 0.2138, -0.9561,\n", + " -0.4514, -0.7454, -0.5829, -0.6738, 0.7316, -0.7204, -0.0231, 0.5075,\n", + " 0.4012, 0.2722, 0.2565]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2135, -0.1651, -0.0885, -0.0492, 0.0569, 0.0516, 0.1261, 0.1815,\n", + " 0.2777, 0.2180, 0.1659, -0.1119, -0.1223, -0.1836, -0.1691, -0.2488,\n", + " -0.1585, -0.0920, -0.0924, -0.1375, -0.0512, -0.0211, -0.0308, -0.0162,\n", + " -0.0438, -0.0188, -0.0117, -0.1545, -0.1561, -0.2320, -0.2811, -0.1386,\n", + " -0.2780, -0.2448, -0.1304, -0.1178, 0.0143, 0.0111, 0.0199, 0.2840,\n", + " 0.2873, 0.2720, 0.2139]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00025516620371490717\n", 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_memory_unit.bias_hh_l1: 0.00010578206274658442\n", + "Data X Sample: tensor([[2.1132, 2.5183, 2.7844, 2.8316, 2.9112, 3.1557, 3.0382, 3.1033, 3.2465,\n", + " 3.2384, 3.1977, 3.1880, 3.1935, 3.2718, 3.0818, 3.1385, 3.1699, 3.2304,\n", + " 3.2607, 3.0146, 2.9934, 2.9133, 2.8267, 2.7199, 2.5769, 2.5832, 2.4215,\n", + " 2.5083, 2.0468, 2.1714, 2.2082, 2.1112, 1.9822, 1.3708, 2.0026, 2.4249,\n", + " 2.7177, 2.9444, 3.2072, 3.0386, 1.7563, 1.0316, 1.0287, 1.7682, 2.4499,\n", + " 2.8477, 1.9445, 2.5491]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.1993, 0.1155, 0.5188, 0.0785, 0.9138, 0.8763, 0.8417, 0.4072,\n", + " 1.0726, 0.2070, 0.2656, 1.5186, 0.7019, 0.2055, -0.4889, -0.4822,\n", + " 0.7467, -0.1758, -0.5607, -0.5561, -1.0420, -0.7280, -0.6592, -1.4325,\n", + " -1.2638, -0.2836, 0.1018, -0.9774, -0.1180, -0.1660, 0.0258, 1.1571,\n", + " -0.7034, -0.5300, 0.1038, -0.8173, 0.0849, -0.7231, 0.0382, 0.2377,\n", + " -0.3740, 0.0342, -0.5158]], device='cuda:0')\n", + "Prediction 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"Grad encoder.encoder.4.weight: 0.00020589606720022857\n", + "Grad encoder.encoder.4.bias: 0.0022268809843808413\n", + "Grad decoder.fc1.0.weight: 7.417236338369548e-05\n", + "Grad decoder.fc1.0.bias: 0.0008863523253239691\n", + "Grad decoder.fc1.2.weight: 7.301615551114082e-05\n", + "Grad decoder.fc1.2.bias: 0.0006868981290608644\n", + "Grad decoder.fc1.4.weight: 5.9278987464495e-05\n", + "Grad decoder.fc1.4.bias: 0.0006869367207400501\n", + "Grad decoder.fc2.weight: 0.00013606040738523006\n", + "Grad decoder.fc2.bias: 0.0018825288861989975\n", + "Grad _memory_unit.weight_ih_l0: 1.5007884940132499e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 0.0001495116885052994\n", + "Grad _memory_unit.bias_hh_l0: 7.963394455146044e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.1422293027862906e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00021861516870558262\n", + "Grad _memory_unit.bias_hh_l1: 0.000116930459626019\n", + "Data X Sample: tensor([[1.5297, 1.8192, 1.9053, 2.0422, 2.0865, 2.1554, 2.2463, 2.2174, 2.3043,\n", + " 2.3759, 2.4078, 2.4207, 2.2770, 2.3120, 2.3025, 2.4383, 2.3130, 2.3445,\n", + " 2.4305, 2.4083, 2.4806, 2.4403, 2.4291, 2.5157, 2.6031, 2.7059, 2.5947,\n", + " 2.5206, 2.3521, 2.3876, 2.4392, 2.4870, 2.1604, 1.4234, 1.9511, 1.7050,\n", + " 1.6164, 1.5292, 1.6543, 1.5576, 1.2409, 0.7309, 0.7235, 1.3376, 1.8235,\n", + " 2.0700, 1.3734, 1.9079]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.3573, 0.6627, 0.0155, -0.1624, -0.2833, -0.7264, -0.9152, -0.8976,\n", + " 0.5173, 0.6740, 0.5818, -0.4119, 0.9253, 0.1039, 0.8999, 1.5972,\n", + " 0.7928, -3.5731, 0.3792, -0.3107, -1.5054, 0.1714, -1.1545, -0.1302,\n", + " -0.5737, -0.5549, 0.5244, -0.4905, 0.3376, -1.0465, 0.1075, -0.1388,\n", + " -0.3869, -0.5769, 0.7913, 1.1322, -0.0333, -0.3755, -0.9499, -0.9779,\n", + " -0.3807, -0.2168, 0.6511]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3189, 0.3321, 0.1107, 0.2720, 0.0992, -0.1208, -0.3086, -0.3203,\n", + " -0.1737, -0.2462, -0.2232, 0.1959, 0.1534, 0.3163, 0.2232, 0.3144,\n", + " 0.2317, 0.0748, 0.0405, 0.1172, 0.0717, 0.0675, 0.0295, 0.0394,\n", + " 0.0079, 0.0327, -0.0069, 0.0077, 0.1736, 0.2522, 0.2668, 0.1827,\n", + " 0.2691, 0.2515, 0.2241, 0.2128, -0.0807, -0.0321, -0.0100, -0.2183,\n", + " -0.2128, -0.2717, -0.2931]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0009013103554025292\n", + "Grad encoder.fc1.bias: 0.0006673817988485098\n", + "Grad encoder.encoder.0.weight: 0.00020075388601981103\n", + "Grad encoder.encoder.0.bias: 0.0007293131784535944\n", + "Grad encoder.encoder.2.weight: 0.00015213183360174298\n", + "Grad encoder.encoder.2.bias: 0.0008593447273597121\n", + "Grad encoder.encoder.4.weight: 0.0003968144883401692\n", + "Grad encoder.encoder.4.bias: 0.0020516645163297653\n", + "Grad decoder.fc1.0.weight: 8.447462460026145e-05\n", + "Grad decoder.fc1.0.bias: 0.0008264606585726142\n", + "Grad 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2.9222, 2.8888, 2.8664,\n", + " 2.9733, 3.0078, 3.1081, 3.1076, 3.1999, 3.5487, 2.6707, 4.3434, 4.6285,\n", + " 4.8474, 5.3562, 5.8694, 5.5891, 1.0309, 0.6373, 0.6103, 1.1855, 1.6531,\n", + " 1.7784, 1.1762, 1.6108]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.6375, -0.8204, 0.4047, -0.0496, 0.8004, 0.1315, 0.8357, 0.8983,\n", + " 0.5247, 0.7862, 0.5017, -0.3087, 0.3435, -0.3861, 0.1195, 2.1341,\n", + " 0.3027, 0.7004, -2.2028, -0.7548, -0.4131, 5.0150, -0.0804, 0.2907,\n", + " -0.8434, -0.1976, 0.1583, -0.4056, -0.4615, 0.1470, -1.3773, -0.7752,\n", + " -0.1943, 0.0519, -0.4089, 0.3185, -0.6001, 0.7428, -0.8437, 0.6270,\n", + " 0.6256, 0.5209, 0.2421]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1826, -0.1452, -0.0567, -0.0137, 0.0977, 0.1156, 0.1240, 0.1862,\n", + " 0.2126, 0.1641, 0.1460, -0.1492, -0.1426, -0.2213, -0.1706, -0.2160,\n", + " -0.1396, -0.0723, -0.1142, -0.1161, -0.0438, -0.0319, -0.0401, -0.0060,\n", + " 0.0021, -0.0356, -0.0481, -0.2019, -0.1994, -0.2333, -0.3065, -0.1148,\n", + " -0.3064, -0.2543, -0.1201, -0.1191, -0.0025, 0.0303, 0.0427, 0.2424,\n", + " 0.2244, 0.2306, 0.1850]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00039458947139792144\n", + "Grad encoder.fc1.bias: 0.0009411359205842018\n", + "Grad encoder.encoder.0.weight: 8.601066656410694e-05\n", + "Grad encoder.encoder.0.bias: 0.000560096581466496\n", + "Grad encoder.encoder.2.weight: 5.721832349081524e-05\n", + "Grad encoder.encoder.2.bias: 0.000556666636839509\n", + "Grad encoder.encoder.4.weight: 0.00016459646576549858\n", + "Grad encoder.encoder.4.bias: 0.001265268074348569\n", + "Grad decoder.fc1.0.weight: 5.068501195637509e-05\n", + "Grad decoder.fc1.0.bias: 0.0005243204068392515\n", + "Grad decoder.fc1.2.weight: 6.605169619433582e-05\n", + "Grad decoder.fc1.2.bias: 0.0008525897283107042\n", + "Grad decoder.fc1.4.weight: 6.349280738504604e-05\n", + "Grad decoder.fc1.4.bias: 0.0008746524108573794\n", + "Grad decoder.fc2.weight: 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"Data Y Sample: tensor([[-0.6885, -0.4487, 0.2363, -0.3563, -1.2117, -0.2457, 0.5588, 0.3841,\n", + " 0.5265, 0.5060, 0.6976, -1.0525, -0.1335, -1.7282, -0.2763, -0.2084,\n", + " 0.8269, -0.4932, -0.7092, 1.4815, 0.6846, -3.2422, 0.2455, 0.8229,\n", + " 0.6213, -2.1575, -1.6734, -0.7222, -0.9858, 0.0272, -0.3659, -1.6406,\n", + " -1.6424, -0.8248, -0.4980, -0.0691, 0.0213, 0.1123, 0.9794, 0.7320,\n", + " 0.7059, 0.8519, 0.8650]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2067, -0.1595, -0.0557, 0.0097, 0.1484, 0.1557, 0.1434, 0.2054,\n", + " 0.2258, 0.1729, 0.1506, -0.1758, -0.1598, -0.2657, -0.2035, -0.2491,\n", + " -0.1333, -0.0815, -0.1386, -0.1209, -0.0497, -0.0319, -0.0387, -0.0033,\n", + " 0.0174, -0.0499, -0.0683, -0.2409, -0.2424, -0.2646, -0.3513, -0.1171,\n", + " -0.3567, -0.2833, -0.1330, -0.1279, 0.0046, 0.0242, 0.0552, 0.2652,\n", + " 0.2251, 0.2480, 0.2055]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0004903246299363673\n", + "Grad 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-0.5540,\n", + " -0.2586, -0.3737, 0.5434, 0.2119, -0.0429, -1.2999, -1.6969, -1.4301,\n", + " -2.0646, -1.7473, -1.7296, -1.6051, -0.9002, -0.3276, -1.0346, 6.3265,\n", + " 6.0156, 5.5054, 2.6091]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.0519, -0.0511, -0.0218, -0.0508, -0.0258, 0.0286, 0.0206, 0.0763,\n", + " 0.0733, 0.0548, 0.0320, -0.0316, -0.0658, -0.0328, -0.0156, -0.0580,\n", + " -0.0742, -0.0230, -0.0158, -0.0504, -0.0243, -0.0035, -0.0254, -0.0067,\n", + " -0.0226, -0.0152, -0.0170, -0.0648, -0.0567, -0.1007, -0.0920, -0.0534,\n", + " -0.1052, -0.0769, -0.0088, -0.0445, -0.0317, 0.0247, -0.0079, 0.0576,\n", + " 0.1098, 0.0892, 0.0802]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0005534035735763609\n", + "Grad encoder.fc1.bias: 0.0004579251108225435\n", + "Grad encoder.encoder.0.weight: 0.00015823598369024694\n", + "Grad encoder.encoder.0.bias: 0.0005740289343520999\n", + "Grad encoder.encoder.2.weight: 0.00014872741303406656\n", + 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"Grad _memory_unit.bias_hh_l1: 0.00010560108057688922\n", + "Data X Sample: tensor([[2.4590, 2.7936, 2.9842, 3.1071, 3.2049, 3.3561, 3.5451, 3.4880, 3.5419,\n", + " 3.7439, 3.7560, 3.7450, 3.7594, 3.6098, 3.6996, 3.4476, 3.4372, 3.3890,\n", + " 3.4300, 3.3791, 3.2461, 3.2042, 2.9592, 2.7867, 2.6951, 2.6354, 2.7146,\n", + " 2.6796, 2.5360, 2.4957, 2.6106, 2.6777, 2.6027, 1.8674, 3.2012, 3.5851,\n", + " 3.7461, 3.8932, 4.2467, 4.1904, 1.9520, 1.0973, 1.0873, 1.9519, 2.6917,\n", + " 2.9678, 1.9513, 2.8696]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.0100, -0.4204, -1.2983, -0.5521, -1.1200, 0.0592, 0.8080, 0.7599,\n", + " 0.8397, 0.8184, 0.7939, 0.5792, 0.5470, 0.7250, 0.3964, -0.0340,\n", + " -0.3453, -0.6858, -0.5940, -0.4168, -1.0672, -0.3220, -0.8437, -0.1249,\n", + " 0.2589, 0.0297, 1.9186, 2.0600, 0.3128, -0.8886, -0.4504, 0.4890,\n", + " -0.2816, 0.0114, 0.5128, 0.4058, -0.0457, 0.3945, 2.2782, 0.7936,\n", + " 0.7582, 0.8852, 0.9141]], device='cuda:0')\n", + "Prediction 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-0.5140, -1.0382, 0.0962, 0.0110, -0.5085, 0.1511, -0.0156, 0.8508,\n", + " 0.9169, 0.5031, 0.3217]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1698, -0.2340, -0.0623, -0.2625, -0.1298, 0.0152, 0.0892, 0.1629,\n", + " 0.1191, 0.1509, 0.1827, -0.0987, -0.1626, -0.1288, -0.0738, -0.1795,\n", + " -0.1736, -0.0797, -0.1124, -0.0766, -0.0568, -0.0839, -0.0995, -0.0159,\n", + " 0.0059, 0.0043, 0.1159, -0.0464, -0.0489, -0.1263, -0.1119, -0.1575,\n", + " -0.1571, -0.2023, -0.0947, -0.1241, -0.0398, 0.0203, -0.0235, 0.1970,\n", + " 0.2317, 0.2230, 0.1810]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.000558976549655199\n", + "Grad encoder.fc1.bias: 0.0003236634947825223\n", + "Grad encoder.encoder.0.weight: 0.00011556764366105199\n", + "Grad encoder.encoder.0.bias: 0.00036621594335883856\n", + "Grad encoder.encoder.2.weight: 9.021183359436691e-05\n", + "Grad encoder.encoder.2.bias: 0.00043101131450384855\n", + "Grad encoder.encoder.4.weight: 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"Prediction Sample: tensor([[-0.2071, -0.2645, -0.1309, -0.3131, -0.1970, -0.0077, 0.0645, 0.1978,\n", + " 0.1148, 0.1930, 0.1884, -0.0991, -0.1324, -0.1377, -0.0569, -0.2533,\n", + " -0.1429, -0.1152, -0.0405, -0.1228, -0.0825, -0.0847, -0.1224, -0.0712,\n", + " -0.0099, 0.0312, 0.1518, 0.0457, -0.0191, -0.0586, -0.0656, -0.1771,\n", + " -0.1181, -0.1622, -0.0866, -0.1467, -0.0482, -0.0181, -0.0666, 0.2544,\n", + " 0.2684, 0.2409, 0.2426]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0006319148233160377\n", + "Grad encoder.fc1.bias: 0.0005374071188271046\n", + "Grad encoder.encoder.0.weight: 0.00011680572788463905\n", + "Grad encoder.encoder.0.bias: 0.00048312090802937746\n", + "Grad encoder.encoder.2.weight: 0.00011498804087750614\n", + "Grad encoder.encoder.2.bias: 0.000541044631972909\n", + "Grad encoder.encoder.4.weight: 0.0002261543122585863\n", + "Grad encoder.encoder.4.bias: 0.0010529201244935393\n", + "Grad decoder.fc1.0.weight: 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-0.5369, -0.5240,\n", + " -0.2994, -0.3321, -0.3304, 0.2909, 0.2458, 0.3586, 0.2093, 0.2931,\n", + " 0.2718, 0.1697, 0.1152, 0.1770, 0.0696, 0.0734, 0.1110, 0.0495,\n", + " 0.0185, 0.0805, 0.0611, 0.1600, 0.3500, 0.4692, 0.3473, 0.2875,\n", + " 0.3504, 0.3934, 0.2111, 0.1690, -0.0715, 0.0110, 0.0504, -0.3754,\n", + " -0.3130, -0.3363, -0.3094]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00018663359514903277\n", + "Grad encoder.fc1.bias: 0.00021991919493302703\n", + "Grad encoder.encoder.0.weight: 4.995681956643239e-05\n", + "Grad encoder.encoder.0.bias: 0.00018884397286456078\n", + "Grad encoder.encoder.2.weight: 4.504418029682711e-05\n", + "Grad encoder.encoder.2.bias: 0.00024400537949986756\n", + "Grad encoder.encoder.4.weight: 0.00013450402184389532\n", + "Grad encoder.encoder.4.bias: 0.0006437161355279386\n", + "Grad decoder.fc1.0.weight: 5.502960993908346e-05\n", + "Grad decoder.fc1.0.bias: 0.0004041032516397536\n", + "Grad decoder.fc1.2.weight: 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+ " -2.0922e-01, -6.4126e-01, -6.3383e-01, -3.5925e-01, -4.0696e-01,\n", + " -3.9730e-01, 3.4638e-01, 2.8020e-01, 4.2710e-01, 2.3872e-01,\n", + " 3.3783e-01, 3.4946e-01, 1.8799e-01, 1.3100e-01, 2.0797e-01,\n", + " 8.0640e-02, 8.6869e-02, 1.2386e-01, 5.9585e-02, 2.4673e-02,\n", + " 9.2361e-02, 7.9315e-02, 1.8345e-01, 4.3478e-01, 5.6162e-01,\n", + " 4.2205e-01, 3.6322e-01, 4.2892e-01, 4.6844e-01, 2.6163e-01,\n", + " 2.0571e-01, -8.9568e-02, -3.2742e-04, 6.3703e-02, -4.6463e-01,\n", + " -3.9073e-01, -4.0250e-01, -3.7027e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002805717522278428\n", + "Grad encoder.fc1.bias: 0.0003211317234672606\n", + "Grad encoder.encoder.0.weight: 6.7629232944455e-05\n", + "Grad encoder.encoder.0.bias: 0.0002629398659337312\n", + "Grad encoder.encoder.2.weight: 4.545782576315105e-05\n", + "Grad encoder.encoder.2.bias: 0.0002896138757932931\n", + "Grad encoder.encoder.4.weight: 0.00013205087452661246\n", + "Grad 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3.1263, 3.1799, 3.3276, 3.1831,\n", + " 3.4912, 3.5657, 3.4802, 3.3267, 3.2200, 3.2179, 3.1823, 3.1935, 3.3213,\n", + " 3.2318, 3.3735, 3.1013, 3.2057, 3.1568, 2.8783, 2.7458, 2.6197, 2.4908,\n", + " 2.5777, 2.1821, 2.1223, 1.9702, 1.9067, 1.6632, 1.1008, 1.6815, 1.8825,\n", + " 1.9783, 2.2607, 2.6812, 2.7123, 1.6466, 0.9420, 0.9681, 1.7165, 2.3904,\n", + " 2.8591, 1.9377, 2.5412]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.4450, 0.6184, 0.1440, -0.5044, -1.1846, 0.2434, -0.8753, -0.4276,\n", + " 0.2679, -0.1827, 0.1962, 0.1914, 0.7273, 1.3509, -0.8846, 0.4743,\n", + " -0.1987, -5.2901, -1.0105, 0.0400, 0.2820, 0.5226, 1.3364, 1.1017,\n", + " 1.0260, 0.2780, 0.2058, 0.2039, 0.6784, -0.2870, 0.4509, 0.0872,\n", + " 0.7727, 0.3473, 1.0009, 0.1063, 0.5815, 0.4465, -0.5252, -0.1079,\n", + " -0.1455, -0.4502, 0.2195]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1505, -0.2093, -0.1262, -0.2332, -0.0879, 0.0160, 0.0546, 0.2114,\n", + " 0.1130, 0.1016, 0.1323, -0.0938, 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"Grad encoder.encoder.0.weight: 7.72334897192195e-05\n", + "Grad encoder.encoder.0.bias: 0.00026241177693009377\n", + "Grad encoder.encoder.2.weight: 5.24056376889348e-05\n", + "Grad encoder.encoder.2.bias: 0.00034622076782397926\n", + "Grad encoder.encoder.4.weight: 0.00017755641601979733\n", + "Grad encoder.encoder.4.bias: 0.0013269721530377865\n", + "Grad decoder.fc1.0.weight: 6.0418631619540974e-05\n", + "Grad decoder.fc1.0.bias: 0.0005329512059688568\n", + "Grad decoder.fc1.2.weight: 7.88337056292221e-05\n", + "Grad decoder.fc1.2.bias: 0.0008280489128082991\n", + "Grad decoder.fc1.4.weight: 8.129671186907217e-05\n", + "Grad decoder.fc1.4.bias: 0.001066812896169722\n", + "Grad decoder.fc2.weight: 0.00014903392002452165\n", + "Grad decoder.fc2.bias: 0.0023212023079395294\n", + "Grad _memory_unit.weight_ih_l0: 1.7295376892434433e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.984972136095166e-05\n", + "Grad _memory_unit.bias_hh_l0: 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_memory_unit.weight_ih_l1: 6.12215262663085e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 9.137645247392356e-05\n", + "Grad _memory_unit.bias_hh_l1: 4.956435441272333e-05\n", + "Data X Sample: tensor([[1.5106, 1.8119, 1.8933, 2.0313, 2.0899, 2.3395, 2.3742, 2.4290, 2.5118,\n", + " 2.5576, 2.5442, 2.5980, 2.5255, 2.5083, 2.5244, 2.4165, 2.3633, 2.4431,\n", + " 2.4842, 2.4474, 2.4291, 2.3821, 2.3881, 2.4088, 2.3798, 2.4500, 2.4082,\n", + " 2.4431, 2.2654, 2.2533, 2.3972, 2.3378, 2.2440, 1.5104, 1.9536, 1.8315,\n", + " 1.6577, 1.4921, 1.6290, 1.6001, 1.2743, 0.7468, 0.7457, 1.2314, 1.8235,\n", + " 2.0242, 1.3190, 1.8844]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1634, -1.2428, 0.1773, -0.1434, 1.1878, -0.1396, 1.1960, -0.6881,\n", + " 0.4946, 0.3906, 0.5484, 0.1541, 0.6958, 0.6181, -0.8154, -0.4528,\n", + " -0.2171, 0.7743, -1.8780, -1.3054, 0.5517, -1.6247, -1.6690, -1.2336,\n", + " -1.0735, -0.5507, 0.3797, -0.4631, -0.2649, -0.9492, 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_memory_unit.bias_hh_l1: 3.6043340514879674e-05\n", + "Data X Sample: tensor([[1.5075, 1.8235, 1.8768, 1.9395, 2.0575, 2.1200, 2.1693, 2.2444, 2.2653,\n", + " 2.3222, 2.2682, 2.4363, 2.2415, 2.2521, 2.2521, 2.2318, 2.2013, 2.2961,\n", + " 2.3417, 2.3674, 2.4045, 2.4709, 2.4652, 2.4202, 2.4905, 2.4970, 2.5574,\n", + " 2.5124, 2.3903, 2.4596, 2.3727, 2.3433, 2.1934, 1.3845, 1.8580, 1.7926,\n", + " 1.6145, 1.4788, 1.4832, 1.5292, 1.2409, 0.7050, 0.6892, 1.2544, 1.9028,\n", + " 1.9899, 1.3530, 1.9861]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.5123, 0.8958, 0.3684, -0.5754, -0.3527, -0.1572, -0.7681, -0.4400,\n", + " -0.4171, -0.6999, -0.5782, -0.6828, 1.7373, 1.3203, 0.2115, 0.6605,\n", + " 0.1345, 1.6649, 0.4948, 0.4948, 0.0541, -1.3924, -0.4965, 1.5132,\n", + " 0.2097, 1.8786, -0.5146, 0.8306, 0.6753, 0.9608, 1.1809, -0.0871,\n", + " 1.2243, 0.6832, 1.2525, -0.7024, 0.0000, -0.1625, -0.5596, -0.7212,\n", + " -0.2021, -0.6885, -0.8491]], device='cuda:0')\n", + "Prediction 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0.7882, 1.3577, 2.0297,\n", + " 2.1500, 1.5298, 2.2285]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.4785, 0.0754, 0.0261, 1.3494, 1.3026, 0.3636, -0.3633, -0.5379,\n", + " -0.4664, -0.5805, -0.8237, 0.3591, 0.4270, 0.5358, 0.2242, 0.1596,\n", + " -0.3058, 0.1141, -0.2930, 0.2381, -0.5905, -0.1839, -0.0980, -0.1564,\n", + " 0.3763, -0.3932, -0.2414, -0.3113, -0.1021, -0.4724, 0.2642, 0.3050,\n", + " 0.2704, -0.3068, -0.5147, 0.8943, 0.4768, -0.0459, 1.0291, -0.5908,\n", + " -0.3779, -0.8253, -0.9722]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2943, 0.2708, 0.1045, 0.1831, 0.1113, -0.1069, -0.3231, -0.3263,\n", + " -0.1639, -0.2255, -0.2059, 0.1814, 0.1456, 0.2145, 0.1649, 0.2791,\n", + " 0.1944, 0.1062, 0.0582, 0.1375, 0.0584, 0.1073, 0.0717, 0.0371,\n", + " 0.0706, 0.1069, -0.0131, 0.0332, 0.1964, 0.2446, 0.1776, 0.1756,\n", + " 0.2539, 0.2193, 0.1392, 0.1319, -0.0385, 0.0174, 0.0471, -0.2246,\n", + " -0.2132, -0.2729, -0.2535]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00044381868792697787\n", + "Grad encoder.fc1.bias: 0.0009844000451266766\n", + "Grad encoder.encoder.0.weight: 9.976981527870521e-05\n", + "Grad encoder.encoder.0.bias: 0.0008062258129939437\n", + "Grad encoder.encoder.2.weight: 6.028631833032705e-05\n", + "Grad encoder.encoder.2.bias: 0.0007225234294310212\n", + "Grad encoder.encoder.4.weight: 0.00017061442486010492\n", + "Grad encoder.encoder.4.bias: 0.0016444094944745302\n", + "Grad decoder.fc1.0.weight: 6.058991129975766e-05\n", + "Grad decoder.fc1.0.bias: 0.0006691760499961674\n", + "Grad decoder.fc1.2.weight: 6.828641198808327e-05\n", + "Grad decoder.fc1.2.bias: 0.0007641349220648408\n", + "Grad decoder.fc1.4.weight: 5.5575394071638584e-05\n", + "Grad decoder.fc1.4.bias: 0.0007226807647384703\n", + "Grad decoder.fc2.weight: 0.00011961717245867476\n", + "Grad decoder.fc2.bias: 0.0016227563610300422\n", + "Grad _memory_unit.weight_ih_l0: 1.5929730579955503e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 8.239247836172581e-05\n", + "Grad _memory_unit.bias_hh_l0: 4.278451524442062e-05\n", + "Grad _memory_unit.weight_ih_l1: 6.925055458850693e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.0001354870619252324\n", + "Grad _memory_unit.bias_hh_l1: 7.113444007700309e-05\n", + "Data X Sample: tensor([[2.6616, 2.8883, 2.9812, 2.7682, 2.7148, 2.9333, 3.6067, 3.7407, 3.9178,\n", + " 4.0141, 4.0352, 4.0060, 3.9192, 3.6371, 3.5307, 3.6008, 3.4875, 3.3832,\n", + " 3.5229, 3.3661, 3.3516, 3.1598, 2.8484, 2.8382, 2.6557, 2.7503, 2.8078,\n", + " 2.8468, 2.7857, 2.9214, 2.8381, 2.9540, 3.1285, 2.3182, 3.8385, 4.1859,\n", + " 4.3970, 4.7280, 5.3179, 5.2657, 1.9377, 1.1690, 1.0368, 1.9031, 2.4261,\n", + " 2.6647, 1.7269, 2.4709]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1257, -0.9588, -1.4588, -1.0897, -0.6066, -1.1504, 0.5278, 0.7586,\n", + " 1.0236, 0.2417, 1.0471, -1.0176, 0.9836, 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_memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00022545753745362163\n", + "Grad _memory_unit.bias_hh_l1: 0.0001216827513417229\n", + "Data X Sample: tensor([[1.6443, 2.1440, 2.5830, 2.7463, 2.9385, 2.8714, 3.0305, 3.0806, 3.1611,\n", + " 3.1926, 3.1120, 3.2231, 3.0737, 3.1518, 2.9557, 3.0018, 3.0268, 3.0679,\n", + " 3.1430, 3.1299, 3.1063, 3.1598, 2.8749, 2.7504, 2.5525, 2.4630, 2.3523,\n", + " 2.3941, 1.9913, 2.0502, 1.8232, 1.7658, 1.5796, 1.0298, 1.5197, 1.6661,\n", + " 1.7895, 2.0857, 2.3262, 2.3832, 1.6704, 0.8125, 0.8670, 1.6706, 2.4499,\n", + " 2.6304, 1.7269, 2.6820]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.4088, -0.7581, -0.8879, 0.2952, -0.0431, 0.0642, 0.5471, -0.1036,\n", + " -0.3912, -0.2452, -0.2844, -0.4783, 0.8625, -0.4759, 0.7407, -0.1151,\n", + " -0.6822, -0.1214, 0.1206, 0.1055, 0.1642, 10.0214, 0.9628, 0.4491,\n", + " -0.1393, 1.2003, -0.2379, -0.0220, -0.3369, -0.8111, 1.0987, 0.6558,\n", + " 0.6509, 0.1105, -0.1869, 0.2327, 0.8233, -0.6423, -0.9129, 1.4205,\n", + " -0.1537, 0.0205, -1.2399]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1759, -0.1970, -0.1210, -0.1881, -0.0994, -0.0326, 0.0235, 0.1617,\n", + " 0.1238, 0.1110, 0.1413, -0.0686, -0.0220, -0.1169, -0.0563, -0.1427,\n", + " -0.1483, -0.0277, -0.0124, -0.0523, -0.0189, 0.0131, -0.0095, -0.0540,\n", + " 0.0563, 0.0776, 0.0233, 0.0138, -0.0631, -0.0948, -0.0883, -0.1187,\n", + " -0.0635, -0.0989, -0.0659, -0.1196, -0.0415, 0.0051, 0.0215, 0.1552,\n", + " 0.2163, 0.1647, 0.1509]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 8.161600271705538e-05\n", + "Grad encoder.fc1.bias: 0.0007651750929653645\n", + "Grad encoder.encoder.0.weight: 3.232157541788183e-05\n", + "Grad encoder.encoder.0.bias: 0.000585421803407371\n", + "Grad encoder.encoder.2.weight: 3.116471998509951e-05\n", + "Grad encoder.encoder.2.bias: 0.0004865519003942609\n", + "Grad encoder.encoder.4.weight: 9.118453453993425e-05\n", + "Grad 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grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003308141604065895\n", + "Grad encoder.fc1.bias: 0.0006360691040754318\n", + "Grad encoder.encoder.0.weight: 7.438813190674409e-05\n", + "Grad encoder.encoder.0.bias: 0.000512756290845573\n", + "Grad encoder.encoder.2.weight: 5.1235390856163576e-05\n", + "Grad encoder.encoder.2.bias: 0.0005494856741279364\n", + "Grad encoder.encoder.4.weight: 0.00013924669474363327\n", + "Grad encoder.encoder.4.bias: 0.001767525216564536\n", + "Grad decoder.fc1.0.weight: 5.383100869948976e-05\n", + "Grad decoder.fc1.0.bias: 0.0006825770251452923\n", + "Grad decoder.fc1.2.weight: 7.654396176803857e-05\n", + "Grad decoder.fc1.2.bias: 0.0008900286629796028\n", + "Grad decoder.fc1.4.weight: 6.625651440117508e-05\n", + "Grad decoder.fc1.4.bias: 0.0008498555980622768\n", + "Grad decoder.fc2.weight: 0.00013616059732157737\n", + "Grad decoder.fc2.bias: 0.0018738603685051203\n", + "Grad _memory_unit.weight_ih_l0: 1.2495781447796617e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 0.0001033572043525055\n", + "Grad _memory_unit.bias_hh_l0: 5.312689972925e-05\n", + "Grad _memory_unit.weight_ih_l1: 6.166723323985934e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00016050582053139806\n", + "Grad _memory_unit.bias_hh_l1: 8.4290535596665e-05\n", + "Data X Sample: tensor([[2.5810, 2.9640, 3.2321, 3.4154, 3.5020, 3.5992, 3.6853, 3.8032, 3.8373,\n", + " 3.9193, 3.9400, 4.0216, 3.6818, 3.6098, 3.5735, 3.6404, 3.5850, 3.5554,\n", + " 3.4279, 3.2880, 3.2731, 3.2394, 2.9399, 2.8039, 2.7946, 2.8339, 2.8611,\n", + " 2.8958, 2.6643, 2.8952, 2.8801, 2.9512, 2.9921, 2.0734, 3.5860, 4.0351,\n", + " 4.2810, 4.5160, 5.0327, 4.8969, 2.0236, 1.2088, 1.1075, 1.9375, 2.9295,\n", + " 3.2422, 2.1757, 3.1120]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1850, -1.1412, 1.2860, 0.1183, -0.2140, -1.6565, -0.2094, 0.6006,\n", + " -0.5669, -0.6288, -0.4235, -0.9494, -0.0673, 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-0.1522, -0.0369, -0.0235, 0.0482, 0.2702,\n", + " 0.3106, 0.2937, 0.2218]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00016900477930903435\n", + "Grad encoder.fc1.bias: 0.0012451919028535485\n", + "Grad encoder.encoder.0.weight: 5.341223732102662e-05\n", + "Grad encoder.encoder.0.bias: 0.0008208960643969476\n", + "Grad encoder.encoder.2.weight: 4.4098829675931484e-05\n", + "Grad encoder.encoder.2.bias: 0.000523763766977936\n", + "Grad encoder.encoder.4.weight: 0.0001567088474985212\n", + "Grad encoder.encoder.4.bias: 0.0008472880581393838\n", + "Grad decoder.fc1.0.weight: 6.74352704663761e-05\n", + "Grad decoder.fc1.0.bias: 0.0005040277610532939\n", + "Grad decoder.fc1.2.weight: 0.00011237028229516\n", + "Grad decoder.fc1.2.bias: 0.0008410086738876998\n", + "Grad decoder.fc1.4.weight: 8.966089080786332e-05\n", + "Grad decoder.fc1.4.bias: 0.0008145521860569715\n", + "Grad decoder.fc2.weight: 0.00019271463679615408\n", + "Grad decoder.fc2.bias: 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_memory_unit.bias_hh_l1: 3.6965902836527675e-05\n", + "Data X Sample: tensor([[2.7274, 2.6290, 2.1638, 2.2456, 2.3068, 2.2040, 3.2138, 4.1212, 4.1082,\n", + " 4.1531, 4.2223, 4.2845, 3.9392, 3.8416, 3.7122, 3.7444, 3.6039, 3.4838,\n", + " 3.4217, 3.4628, 3.3909, 3.3144, 3.1110, 2.8230, 2.7326, 2.6876, 2.8105,\n", + " 2.7979, 2.7060, 2.8592, 2.8031, 2.8158, 3.0911, 2.3709, 3.9733, 4.3026,\n", + " 4.6153, 5.0699, 5.5525, 5.5324, 1.7325, 0.9260, 0.8003, 1.3347, 1.7046,\n", + " 2.0986, 1.4482, 2.2597]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.2693, 0.1060, -1.0441, -1.2441, -1.2060, -0.2651, -0.1094, 0.7519,\n", + " 0.3277, 0.7503, 0.4855, -0.6557, -0.2396, -0.7274, -1.0423, -0.5764,\n", + " -1.6823, 1.8713, 0.7270, 0.9174, 0.7898, 1.5503, 1.0333, 0.2243,\n", + " -0.3465, 0.1269, 0.6215, -0.5968, 0.1608, -0.0374, -0.1981, -0.6211,\n", + " -0.2418, -0.4781, -1.6410, -1.4446, -0.5085, -0.7055, -0.0156, 0.8425,\n", + " 0.8755, 0.9833, 1.2121]], device='cuda:0')\n", + "Prediction 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" 2.3149, 2.3432, 2.3530, 2.4188, 2.4194, 2.3840, 2.4530, 2.4787, 2.4855,\n", + " 2.4145, 2.1856, 2.3745, 2.4287, 2.4234, 2.1780, 1.4097, 1.9119, 1.7099,\n", + " 1.6184, 1.4815, 1.5783, 1.5633, 1.1597, 0.7607, 0.7033, 1.2802, 1.8116,\n", + " 1.9556, 1.2646, 1.8766]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.8099, -1.4260, -0.5747, 0.5569, -0.0454, 0.5544, 0.3277, -0.0312,\n", + " 0.2479, 0.2316, 0.1128, -0.0028, -0.5138, -0.4021, 0.2714, -0.0941,\n", + " 0.3084, -0.5807, 0.1690, -0.0738, 0.9195, 1.3876, 0.1493, 1.3518,\n", + " 1.1267, 1.2585, 2.1436, 2.3679, -1.0817, 0.7523, 1.4184, -0.5040,\n", + " -0.4707, 0.0536, -0.5736, -1.6908, -0.4229, -0.7806, -0.3730, -0.5820,\n", + " 0.1475, -0.0862, -0.7842]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2838, 0.2736, 0.1395, 0.1420, 0.0385, -0.0972, -0.3136, -0.3459,\n", + " -0.1883, -0.2116, -0.1817, 0.1266, 0.1697, 0.2064, 0.1503, 0.2048,\n", + " 0.1584, 0.0516, 0.0590, 0.1046, 0.0660, 0.0374, 0.0024, 0.0869,\n", + " 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1.9028,\n", + " 2.2587, 1.4618, 2.2285]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.1352, -0.9526, -0.0758, -0.0711, -0.0710, -1.2668, -0.1118, -0.3167,\n", + " 0.0196, 0.0191, 0.0158, 0.9455, 0.1588, -0.2146, -1.2322, 0.0655,\n", + " -0.2653, -0.2487, -0.3942, -0.4868, -0.3435, -1.1378, -0.1238, -0.5084,\n", + " 0.1937, -0.0687, 0.5060, -0.6149, -1.0586, 2.4230, -0.8728, 1.3464,\n", + " -0.8940, -0.7912, 0.5673, -1.4876, -0.0333, 0.7428, 0.2870, -0.7985,\n", + " 0.0885, 0.0391, -0.3200]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2470, 0.2465, 0.1280, 0.0940, 0.0222, -0.1055, -0.2882, -0.2964,\n", + " -0.1738, -0.1972, -0.1669, 0.1032, 0.1505, 0.1985, 0.1410, 0.1748,\n", + " 0.1389, 0.0360, 0.0480, 0.0996, 0.0514, 0.0444, -0.0020, 0.0682,\n", + " 0.0664, 0.1082, 0.0081, 0.0331, 0.2047, 0.2151, 0.1419, 0.1450,\n", + " 0.1725, 0.1028, 0.1273, 0.0198, -0.0110, 0.0266, 0.0258, -0.1701,\n", + " -0.1476, -0.1836, -0.2049]], device='cuda:0',\n", + " grad_fn=)\n", + 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"Grad _memory_unit.bias_ih_l0: 0.00015242763038259\n", + "Grad _memory_unit.bias_hh_l0: 7.90246922406368e-05\n", + "Grad _memory_unit.weight_ih_l1: 9.166915333480574e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.0002143047022400424\n", + "Grad _memory_unit.bias_hh_l1: 0.00011348249245202169\n", + "Data X Sample: tensor([[1.4597, 1.8148, 2.0060, 2.1319, 2.2043, 2.2732, 2.3017, 2.3935, 2.4874,\n", + " 2.3822, 2.4807, 2.4850, 2.3746, 2.3120, 2.3529, 2.2606, 2.2391, 2.3038,\n", + " 2.3851, 2.3767, 2.5444, 2.4571, 2.4074, 2.5042, 2.4755, 2.4134, 2.3869,\n", + " 2.5328, 1.9913, 2.0601, 1.8092, 1.7658, 1.5136, 1.0069, 1.3996, 1.3377,\n", + " 1.3628, 1.2960, 1.3057, 1.3590, 1.1979, 0.7428, 0.8023, 1.3290, 2.0415,\n", + " 2.1558, 1.4414, 2.1190]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.2414, -0.1038, 0.0292, -0.1347, -0.5832, 1.2942, 0.1295, -0.0313,\n", + " 0.6497, -0.0614, -0.1214, -0.9085, -0.5178, -0.3498, -0.3180, -0.6873,\n", + " 0.1146, 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_memory_unit.bias_ih_l1: 0.00017144645971711725\n", + "Grad _memory_unit.bias_hh_l1: 8.79178405739367e-05\n", + "Data X Sample: tensor([[1.5170, 1.8527, 2.0751, 2.2849, 2.4041, 2.5532, 2.5590, 2.4985, 2.5338,\n", + " 2.5291, 2.4712, 2.5064, 2.2814, 2.2193, 2.0049, 2.0965, 2.1211, 2.0775,\n", + " 2.0217, 1.9043, 1.9089, 1.8264, 1.7832, 1.7312, 1.6122, 1.6220, 1.5291,\n", + " 1.5784, 1.2350, 1.2675, 1.1408, 1.1219, 1.0472, 0.7255, 1.1520, 1.2842,\n", + " 1.3785, 1.6644, 2.0093, 1.9690, 1.2218, 0.7707, 0.7963, 1.3893, 2.1407,\n", + " 2.3445, 1.4414, 2.0721]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.7463, -1.1944, -1.0738, -1.1336, -0.4284, -0.0384, 1.1794, 2.2579,\n", + " 1.2079, 1.7362, 2.4761, -1.6475, -1.2392, -2.1282, -0.8180, -1.7708,\n", + " -1.1039, -0.5688, 0.3471, -0.2985, -0.4377, 0.1915, -0.0725, -0.2389,\n", + " -0.3226, -0.3661, 0.2997, 1.1793, -0.6730, -1.0401, -1.4046, -0.3500,\n", + " -2.4295, -1.5759, -1.3861, -1.4915, 0.1438, 0.6522, -0.1564, 5.4730,\n", + " 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"Grad _memory_unit.bias_ih_l1: 7.767796341795474e-05\n", + "Grad _memory_unit.bias_hh_l1: 4.1088333091465756e-05\n", + "Data X Sample: tensor([[1.4130, 1.5585, 1.8137, 1.9635, 2.0165, 2.1362, 2.1662, 3.1899, 4.1155,\n", + " 4.3663, 4.3112, 4.4111, 3.9925, 3.9152, 3.9014, 3.9139, 3.8413, 3.5264,\n", + " 3.6448, 3.4870, 3.5013, 3.4231, 3.0267, 2.9356, 2.8021, 2.8470, 2.9916,\n", + " 2.9570, 2.8239, 3.0950, 3.2231, 3.3436, 3.7159, 2.7027, 4.4881, 4.9423,\n", + " 5.1758, 5.4065, 6.0088, 5.7849, 1.0595, 0.6313, 0.6427, 1.1080, 1.6332,\n", + " 1.9728, 1.3054, 1.9626]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.0062e-01, 3.0751e-03, 5.1721e-01, -1.2500e-01, -2.7964e-01,\n", + " 3.1213e-01, 9.2191e-02, 1.2214e-01, 1.8091e+00, 3.3639e-01,\n", + " 1.1633e-01, 5.7217e-01, 1.1334e-01, 2.4027e-01, -9.0411e-01,\n", + " -6.0617e-03, -9.8730e-02, -6.4060e-01, 1.2259e+00, 6.3805e-01,\n", + " 2.7950e-02, 1.2802e+00, 7.5689e-01, -6.8096e-01, 3.2860e-01,\n", + " 6.4565e-01, -5.5648e-01, -2.6795e-02, 3.1499e-01, -5.5344e-01,\n", + " -3.7191e-01, 2.4979e-01, -2.4723e-01, -4.9619e-01, 1.2319e-03,\n", + " -3.9233e-01, 0.0000e+00, -8.0041e-01, -3.7122e-01, 2.8152e-01,\n", + " 1.4481e-01, 6.1257e-02, -3.5388e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2077, -0.2228, -0.0337, -0.1201, 0.0536, 0.1719, 0.1757, 0.2092,\n", + " 0.1377, 0.2109, 0.2427, -0.1588, -0.1187, -0.2895, -0.2471, -0.2730,\n", + " -0.2258, -0.0332, -0.0382, -0.0484, -0.0596, -0.0854, -0.0090, -0.0273,\n", + " -0.0405, 0.0249, -0.1362, -0.1312, -0.2344, -0.2013, -0.2747, -0.1551,\n", + " -0.3052, -0.3143, -0.1777, -0.2424, -0.0534, -0.0351, 0.0976, 0.2546,\n", + " 0.2720, 0.2765, 0.2160]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0006078635342419147\n", + "Grad encoder.fc1.bias: 0.0019193857442587614\n", + "Grad encoder.encoder.0.weight: 0.00014842147356830537\n", + "Grad encoder.encoder.0.bias: 0.001161487540230155\n", + "Grad encoder.encoder.2.weight: 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device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4688, 0.4083, 0.1849, 0.2759, 0.0492, -0.1762, -0.5019, -0.5685,\n", + " -0.3119, -0.3236, -0.2762, 0.2474, 0.2453, 0.3604, 0.2526, 0.3090,\n", + " 0.2551, 0.0549, 0.1363, 0.1590, 0.0751, 0.0314, 0.0587, 0.0983,\n", + " 0.0585, 0.0839, 0.0418, 0.1371, 0.3287, 0.3953, 0.3436, 0.2916,\n", + " 0.3677, 0.3131, 0.2035, 0.1295, -0.0096, -0.0246, 0.0280, -0.3699,\n", + " -0.3376, -0.3679, -0.3278]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00035610125632956624\n", + "Grad encoder.fc1.bias: 0.0004568150034174323\n", + "Grad encoder.encoder.0.weight: 8.354723104275763e-05\n", + "Grad encoder.encoder.0.bias: 0.0004185232101008296\n", + "Grad encoder.encoder.2.weight: 7.074574386933818e-05\n", + "Grad encoder.encoder.2.bias: 0.0005764828529208899\n", + "Grad encoder.encoder.4.weight: 0.00016529082495253533\n", + "Grad encoder.encoder.4.bias: 0.0011464809067547321\n", + "Grad decoder.fc1.0.weight: 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0.8650, 1.3548, 1.9187,\n", + " 2.1786, 1.5230, 1.9626]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.2427, 0.1967, -1.1274, -1.1882, -1.6379, -1.2308, 0.3393, 0.4015,\n", + " 0.8116, 0.7077, 0.8208, -0.4862, -0.6580, -1.2558, -1.1579, -0.9126,\n", + " -0.8872, 0.1419, -0.8658, -0.0161, -0.5931, -0.4391, 0.9074, -0.7086,\n", + " 0.2238, 0.0326, 1.6790, -0.7781, -1.1339, 0.3888, -0.1818, -0.5385,\n", + " 0.2161, -0.6135, -0.6120, -0.9436, 0.4915, 0.1511, -0.0156, 0.8585,\n", + " 0.7921, 0.9398, 1.0060]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1637, -0.2238, -0.0522, -0.1576, -0.0653, 0.0745, 0.1266, 0.1892,\n", + " 0.1293, 0.1441, 0.2115, -0.1311, -0.0887, -0.2329, -0.2031, -0.2308,\n", + " -0.2122, -0.0551, -0.0452, -0.0597, -0.0414, -0.0356, -0.0485, -0.0303,\n", + " 0.0046, 0.0522, -0.0526, -0.0966, -0.1488, -0.1583, -0.2135, -0.1343,\n", + " -0.1891, -0.2413, -0.1235, -0.1965, -0.0516, -0.0247, 0.0696, 0.2240,\n", + " 0.2404, 0.2602, 0.1888]], 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"Grad _memory_unit.bias_ih_l0: 4.794920823769644e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.5116321921814233e-05\n", + "Grad _memory_unit.weight_ih_l1: 4.467799044505227e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00010416000441182405\n", + "Grad _memory_unit.bias_hh_l1: 5.519711339729838e-05\n", + "Data X Sample: tensor([[0.0721, 0.0757, 0.0977, 0.1224, 0.1315, 0.1429, 0.1618, 0.1704, 0.1782,\n", + " 0.1896, 0.1776, 0.1831, 0.1886, 0.1854, 0.1891, 0.2215, 0.2359, 0.2534,\n", + " 0.2457, 0.2455, 0.2895, 0.2572, 0.2651, 0.2577, 0.2609, 0.2873, 0.2691,\n", + " 0.3100, 0.1839, 0.2391, 0.1960, 0.2211, 0.1826, 0.1304, 0.1985, 0.1581,\n", + " 0.1809, 0.1564, 0.1585, 0.1589, 0.0573, 0.0418, 0.0364, 0.0517, 0.1269,\n", + " 0.0801, 0.1088, 0.1173]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.1382, -0.2173, -0.9464, 1.2257, 1.0903, 0.4363, -0.3572, -0.2170,\n", + " -0.2688, -0.1283, -0.2556, -0.0418, 1.1452, 0.3096, 0.3205, 0.5408,\n", + " 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"Data X Sample: tensor([[1.4141, 1.7376, 1.9895, 2.0947, 2.0865, 2.0876, 2.0938, 2.1252, 3.3393,\n", + " 4.0978, 4.3556, 4.2806, 4.1789, 4.1142, 4.0098, 3.7457, 3.7014, 3.5766,\n", + " 3.7542, 3.6766, 3.6264, 3.4109, 2.9978, 2.8955, 2.9260, 2.9227, 3.0049,\n", + " 3.1691, 2.9453, 3.1671, 3.3175, 3.3547, 3.8017, 2.8034, 4.5689, 4.9836,\n", + " 5.1187, 5.5496, 6.0405, 5.9580, 1.1645, 0.6930, 0.6972, 1.2658, 1.7205,\n", + " 1.9613, 1.3734, 1.9001]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1063, 0.2393, 0.0540, 1.2078, 0.6621, -0.7359, 0.3116, 0.7303,\n", + " -0.7730, -0.5686, -0.4951, -0.1005, 0.7578, -0.4390, 0.6106, 0.2528,\n", + " -0.2311, -0.1228, 0.3846, 0.4850, 0.0801, 0.2440, 1.0272, 1.1259,\n", + " 0.8525, 0.3749, -0.7505, -0.1525, -0.5278, 0.4055, 0.6638, 0.6716,\n", + " 0.1949, 0.0125, 0.7100, 0.5335, -0.0333, 0.0940, 0.7936, 0.6807,\n", + " -1.3877, 1.2168, -0.0931]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2003, -0.2726, -0.0436, -0.1312, -0.0190, 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"Grad encoder.encoder.4.bias: 0.001486700726673007\n", + "Grad decoder.fc1.0.weight: 5.7423472753725946e-05\n", + "Grad decoder.fc1.0.bias: 0.0006213709712028503\n", + "Grad decoder.fc1.2.weight: 6.89644948579371e-05\n", + "Grad decoder.fc1.2.bias: 0.000814524304587394\n", + "Grad decoder.fc1.4.weight: 6.305850547505543e-05\n", + "Grad decoder.fc1.4.bias: 0.0008493616478517652\n", + "Grad decoder.fc2.weight: 0.00016390910604968667\n", + "Grad decoder.fc2.bias: 0.0021137415897101164\n", + "Grad _memory_unit.weight_ih_l0: 1.93503474292811e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 7.68761383369565e-05\n", + "Grad _memory_unit.bias_hh_l0: 4.096197517355904e-05\n", + "Grad _memory_unit.weight_ih_l1: 8.32255227578571e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00012035395775455981\n", + "Grad _memory_unit.bias_hh_l1: 6.447338091675192e-05\n", + "Data X Sample: tensor([[1.5891, 1.8425, 1.9023, 1.9985, 1.9772, 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"Data Y Sample: tensor([[ 7.6810e-01, 5.3031e-01, 1.2212e+00, 9.0612e-01, 1.0613e+00,\n", + " 6.0058e-01, -4.6584e-01, -7.8579e-01, -2.2881e-01, -7.1935e-01,\n", + " -3.8215e-01, -6.8673e-01, 3.6492e-01, -2.7608e-02, -1.6245e-01,\n", + " 1.0586e+00, 8.3578e-01, 2.9029e-02, -5.2438e-02, 1.1196e+00,\n", + " 3.6635e-01, 6.6670e-01, -2.6383e-01, 3.7769e-01, 1.6996e+00,\n", + " -4.2560e-01, 1.5980e+00, 1.0534e+00, 5.9862e-01, 8.4410e-01,\n", + " 1.2134e+00, 1.0543e+00, -5.5750e-02, 2.8805e-01, 8.1434e-01,\n", + " -1.3338e-01, -1.0781e-03, -1.0105e-01, 1.1148e+00, -7.3260e-01,\n", + " -4.9435e-01, -3.5065e-02, 1.9511e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3323, 0.3035, 0.1709, 0.1936, 0.0999, -0.1114, -0.2879, -0.3597,\n", + " -0.1474, -0.2276, -0.2008, 0.2307, 0.1616, 0.2468, 0.2277, 0.2663,\n", + " 0.1648, 0.0316, 0.1373, 0.0886, 0.1014, 0.0715, 0.0050, -0.0201,\n", + " 0.0215, -0.0131, -0.0515, 0.0061, 0.1553, 0.2597, 0.1831, 0.1162,\n", + " 0.2711, 0.2583, 0.1326, 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"Grad encoder.encoder.0.weight: 7.592637848574668e-05\n", + "Grad encoder.encoder.0.bias: 0.00033067952608689666\n", + "Grad encoder.encoder.2.weight: 4.7404071665368974e-05\n", + "Grad encoder.encoder.2.bias: 0.00032189866760745645\n", + "Grad encoder.encoder.4.weight: 0.00011434355837991461\n", + "Grad encoder.encoder.4.bias: 0.0006936212885193527\n", + "Grad decoder.fc1.0.weight: 5.006569699617103e-05\n", + "Grad decoder.fc1.0.bias: 0.0004840912588406354\n", + "Grad decoder.fc1.2.weight: 7.11815373506397e-05\n", + "Grad decoder.fc1.2.bias: 0.0007395449792966247\n", + "Grad decoder.fc1.4.weight: 6.210264109540731e-05\n", + "Grad decoder.fc1.4.bias: 0.0006455582333728671\n", + "Grad decoder.fc2.weight: 0.00014502737030852586\n", + "Grad decoder.fc2.bias: 0.001535559305921197\n", + "Grad _memory_unit.weight_ih_l0: 1.02054254966788e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.758595994440839e-05\n", + "Grad _memory_unit.bias_hh_l0: 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"Grad _memory_unit.bias_ih_l0: 5.752030847361311e-05\n", + "Grad _memory_unit.bias_hh_l0: 3.118141466984525e-05\n", + "Grad _memory_unit.weight_ih_l1: 9.498537110630423e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00013008160749450326\n", + "Grad _memory_unit.bias_hh_l1: 6.823597504990175e-05\n", + "Data X Sample: tensor([[1.7154, 1.8148, 2.0571, 2.1516, 2.3255, 2.4692, 2.5313, 2.5198, 2.6412,\n", + " 2.6460, 2.6679, 2.6389, 2.6476, 2.5465, 2.5345, 2.4383, 2.4388, 2.5630,\n", + " 2.5668, 2.4065, 2.4438, 2.4495, 2.4508, 2.4336, 2.4117, 2.5414, 2.5174,\n", + " 2.5695, 2.2897, 2.3155, 2.2677, 2.2189, 1.9382, 1.2495, 1.7305, 1.6709,\n", + " 1.5594, 1.5477, 1.6353, 1.4101, 1.2886, 0.7229, 0.7639, 1.3032, 2.0614,\n", + " 2.2072, 1.4414, 2.0877]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.3188, -0.1457, 0.0578, -0.3665, -0.3081, 0.3656, 0.6085, 0.1191,\n", + " 0.1931, -0.1546, -0.2459, 0.1305, 1.6684, 0.4060, -0.2002, 0.8738,\n", + " -0.4253, 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"Data Y Sample: tensor([[-0.2232, -0.2817, -1.2631, -0.5556, 0.6588, -0.3931, 0.5086, 0.5055,\n", + " -0.0242, 0.6948, 0.4168, -0.8099, -1.7270, -0.5325, -0.3183, -0.1737,\n", + " 0.2040, 0.8808, -1.7492, -1.4316, 0.9400, -0.5130, 0.0667, -0.7868,\n", + " 0.5811, 0.6431, 0.7997, -0.0170, -0.4841, -0.4584, -0.2513, -0.1722,\n", + " -0.5768, 0.0022, 0.0578, -0.7557, 0.7063, 0.8238, 0.3483, 0.6369,\n", + " 0.8441, 0.7224, 0.2953]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1678, -0.1622, -0.0081, -0.0995, 0.0826, 0.1562, 0.1224, 0.2086,\n", + " 0.1203, 0.2005, 0.1619, -0.1304, -0.1440, -0.2182, -0.1077, -0.1979,\n", + " -0.1516, -0.0294, -0.0654, -0.0603, -0.0326, -0.0348, -0.0096, -0.0464,\n", + " -0.1146, -0.0034, -0.0626, -0.0489, -0.1623, -0.1703, -0.1906, -0.1891,\n", + " -0.2625, -0.2155, -0.1024, -0.1558, -0.0193, 0.0077, 0.0362, 0.2055,\n", + " 0.2533, 0.2785, 0.1855]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0005277061718516052\n", + 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2.3480, 3.9807, 4.3148,\n", + " 4.6901, 4.9931, 5.6349, 5.4331, 1.6847, 0.8842, 0.8488, 1.3376, 1.8632,\n", + " 2.1215, 1.4142, 2.0564]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.1291, 0.6694, -0.0613, 0.6827, 0.0375, -0.0517, -1.2443, -1.1233,\n", + " -0.4239, -0.4666, -0.0765, 0.1367, 0.2664, -0.3701, 0.3249, -0.3169,\n", + " -0.4518, -3.1872, 0.2702, 0.2410, 0.6948, 0.4218, 0.1188, 0.3539,\n", + " 0.0283, 0.0606, -0.8016, 0.7573, 0.4814, 1.1423, 0.1174, 0.5519,\n", + " 0.7375, 1.0070, -1.3377, 1.2756, 0.6291, -0.8227, 0.5847, 0.1433,\n", + " 0.1413, 0.2049, 0.4927]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2846, -0.3291, -0.0825, -0.2288, 0.0309, 0.1607, 0.2002, 0.3367,\n", + " 0.2256, 0.2588, 0.2355, -0.1676, -0.1721, -0.3449, -0.1704, -0.3066,\n", + " -0.2289, -0.0731, -0.0824, -0.1315, -0.1132, -0.0207, -0.0555, -0.0727,\n", + " -0.0539, 0.0115, -0.0178, -0.0365, -0.1807, -0.2554, -0.2358, -0.3069,\n", + " -0.2898, -0.3154, -0.1372, -0.1504, -0.0144, 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"Grad _memory_unit.weight_ih_l0: 3.8157184462761506e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 0.00016435395809821784\n", + "Grad _memory_unit.bias_hh_l0: 8.428923320025206e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.164124387287302e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00029193219961598516\n", + "Grad _memory_unit.bias_hh_l1: 0.00014981839922256768\n", + "Data X Sample: tensor([[1.1669, 1.5381, 1.6514, 1.7798, 1.8577, 1.8504, 2.0383, 2.0925, 2.0700,\n", + " 2.1421, 2.2016, 2.1792, 2.0994, 2.1021, 2.0453, 2.0787, 1.9513, 2.0833,\n", + " 2.0630, 2.0847, 2.0316, 2.0897, 2.0869, 2.0404, 1.9294, 2.0608, 1.9740,\n", + " 1.9536, 1.7589, 1.8832, 1.9177, 1.9288, 1.8568, 1.2587, 1.8653, 1.7488,\n", + " 1.4808, 1.2695, 1.1156, 1.0100, 1.0213, 0.6452, 0.6144, 1.1051, 1.6134,\n", + " 1.8241, 1.1422, 1.6733]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 2.0829e-01, 2.7954e-01, 5.9361e-02, 3.7304e-01, 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"Grad encoder.encoder.4.bias: 0.0014137588441371918\n", + "Grad decoder.fc1.0.weight: 4.509317295742221e-05\n", + "Grad decoder.fc1.0.bias: 0.0006291613099165261\n", + "Grad decoder.fc1.2.weight: 7.11731263436377e-05\n", + "Grad decoder.fc1.2.bias: 0.0009619033080525696\n", + "Grad decoder.fc1.4.weight: 6.18721533101052e-05\n", + "Grad decoder.fc1.4.bias: 0.001040775328874588\n", + "Grad decoder.fc2.weight: 0.0001296094706049189\n", + "Grad decoder.fc2.bias: 0.0023333176504820585\n", + "Grad _memory_unit.weight_ih_l0: 1.2776406947523355e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 6.443250458687544e-05\n", + "Grad _memory_unit.bias_hh_l0: 3.3326996344840154e-05\n", + "Grad _memory_unit.weight_ih_l1: 4.7236180762411095e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00011274334246991202\n", + "Grad _memory_unit.bias_hh_l1: 5.746787792304531e-05\n", + "Data X Sample: tensor([[2.7062, 2.9553, 3.2261, 3.3323, 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"Grad encoder.encoder.4.weight: 0.00011910797184100375\n", + "Grad encoder.encoder.4.bias: 0.0010346374474465847\n", + "Grad decoder.fc1.0.weight: 4.526169504970312e-05\n", + "Grad decoder.fc1.0.bias: 0.0004191711195744574\n", + "Grad decoder.fc1.2.weight: 5.166833580005914e-05\n", + "Grad decoder.fc1.2.bias: 0.0005644322955049574\n", + "Grad decoder.fc1.4.weight: 4.5444532588589936e-05\n", + "Grad decoder.fc1.4.bias: 0.0006499442970380187\n", + "Grad decoder.fc2.weight: 0.00014840386575087905\n", + "Grad decoder.fc2.bias: 0.002347469562664628\n", + "Grad _memory_unit.weight_ih_l0: 9.04082571651088e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 4.944976171827875e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.684776518435683e-05\n", + "Grad _memory_unit.weight_ih_l1: 4.9261029744229745e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 8.883207192411646e-05\n", + "Grad _memory_unit.bias_hh_l1: 4.780357994604856e-05\n", + "Data X Sample: tensor([[2.5640, 3.0805, 3.1780, 3.4700, 3.5481, 3.5388, 3.7962, 3.8699, 3.9080,\n", + " 3.9240, 3.8512, 3.8814, 3.7949, 3.6589, 3.5962, 3.4613, 3.3539, 3.4645,\n", + " 3.4052, 3.3847, 3.2854, 3.1965, 2.9134, 2.7561, 2.7552, 2.7242, 2.7492,\n", + " 2.7694, 2.7337, 2.7544, 2.8906, 2.8739, 2.9063, 2.1054, 3.6204, 3.9986,\n", + " 4.2456, 4.5690, 5.1278, 5.0728, 1.8995, 1.0714, 1.1378, 2.0896, 2.9493,\n", + " 3.3166, 2.2165, 3.1199]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.4605, -0.2994, 0.3895, 0.0726, 0.0575, 0.6105, 0.1231, 0.0758,\n", + " 0.1178, -0.0045, 0.1591, -0.0350, -0.7411, -0.5724, -0.8031, -0.3801,\n", + " -0.0420, -0.6117, 0.4663, -0.7052, 0.7183, -2.8068, 1.1359, -0.0577,\n", + " 0.7521, 0.1356, 0.2393, -0.4797, -0.7371, -0.0697, 0.1144, 0.4446,\n", + " -0.1416, -0.5851, -0.2070, -0.6211, 0.1137, -0.6411, -0.1229, 0.0180,\n", + " 0.2827, -0.3193, 0.7817]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.3413, -0.4204, -0.1099, -0.4204, -0.2317, -0.0151, 0.0869, 0.3258,\n", + " 0.1790, 0.1951, 0.2231, -0.1703, -0.1376, -0.3078, -0.1324, -0.3118,\n", + " -0.3722, -0.1741, -0.1053, -0.1679, -0.1083, 0.0212, -0.0912, -0.1186,\n", + " 0.0346, -0.0246, 0.1627, 0.0050, -0.0838, -0.1754, -0.1534, -0.2348,\n", + " -0.1328, -0.2527, -0.1422, -0.1372, -0.0134, 0.0270, -0.0010, 0.3967,\n", + " 0.4144, 0.3692, 0.3129]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003108908422291279\n", + "Grad encoder.fc1.bias: 0.0013282820582389832\n", + "Grad encoder.encoder.0.weight: 8.108666224870831e-05\n", + "Grad encoder.encoder.0.bias: 0.001071844482794404\n", + "Grad encoder.encoder.2.weight: 5.485278234118596e-05\n", + "Grad encoder.encoder.2.bias: 0.0008253740379586816\n", + "Grad encoder.encoder.4.weight: 0.00011774049198720604\n", + "Grad encoder.encoder.4.bias: 0.0016641592374071479\n", + "Grad decoder.fc1.0.weight: 5.7993234804598615e-05\n", + "Grad decoder.fc1.0.bias: 0.0006781074916943908\n", + "Grad 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2.7158, 2.6955, 2.6347,\n", + " 2.7286, 2.8690, 2.9673, 2.9676, 3.0424, 3.4189, 2.5540, 4.2086, 4.7185,\n", + " 4.9083, 5.5178, 6.1356, 5.9523, 2.1954, 1.2128, 1.3379, 2.3221, 3.2506,\n", + " 3.6940, 2.4680, 3.5968]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.0396, 0.0376, -0.7580, -0.0760, 0.0918, 0.3058, -0.3033, 0.3292,\n", + " -0.0406, 0.3367, 0.0419, 1.7234, -0.7247, 1.0926, -0.0758, 0.5475,\n", + " -0.8522, -0.8899, -0.2223, 1.0768, -0.7091, 0.0641, -0.4691, -1.5674,\n", + " 2.2043, 0.0706, 0.5285, -0.3518, 0.6142, -0.5149, -0.0221, -0.4786,\n", + " 2.3936, 0.2183, 0.2772, -0.6399, 1.0570, 0.3924, -0.4508, 0.3048,\n", + " 0.2459, 0.1226, 0.3891]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.3934, -0.4972, -0.1367, -0.4849, -0.2720, -0.0197, 0.1127, 0.3788,\n", + " 0.2026, 0.2103, 0.2706, -0.2044, -0.1644, -0.3637, -0.1605, -0.3651,\n", + " -0.4245, -0.2081, -0.1337, -0.1896, -0.1271, 0.0208, -0.1054, -0.1242,\n", + " 0.0561, -0.0250, 0.1931, -0.0101, -0.1003, 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"Data Y Sample: tensor([[-0.1643, 0.4040, -0.9346, 0.4747, 0.8409, 0.5467, 1.0722, -0.2885,\n", + " 0.1631, -0.0331, 0.2501, -0.0203, 0.2366, 0.4475, -1.0594, -0.0343,\n", + " -0.0584, 0.2407, 0.5164, 0.1060, 0.3371, 1.7088, 0.0566, 0.2472,\n", + " -0.5168, 0.5098, 0.3200, -4.0733, -0.9570, -0.0832, 0.2249, 0.4644,\n", + " 3.5113, 0.5645, 0.8421, -0.8495, 0.1697, -0.7141, -0.2296, -1.1618,\n", + " -0.1606, -0.7812, 0.5014]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1875, -0.2496, -0.0106, -0.1173, 0.0586, 0.1651, 0.1863, 0.2381,\n", + " 0.1806, 0.1812, 0.1691, -0.1794, -0.1342, -0.2632, -0.1396, -0.2657,\n", + " -0.2092, -0.0329, -0.1010, -0.1140, -0.0515, 0.0025, -0.0545, -0.0477,\n", + " -0.0695, -0.0043, -0.0428, -0.0957, -0.1779, -0.1962, -0.2344, -0.1906,\n", + " -0.3008, -0.2627, -0.1156, -0.1031, -0.0089, -0.0033, 0.0619, 0.2556,\n", + " 0.3069, 0.3007, 0.2023]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0008933219942264259\n", + "Grad 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"Grad _memory_unit.bias_hh_l0: 2.384481740591582e-05\n", + "Grad _memory_unit.weight_ih_l1: 8.112712748697959e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00010929739073617384\n", + "Grad _memory_unit.bias_hh_l1: 5.9665013395715505e-05\n", + "Data X Sample: tensor([[1.6040, 1.8425, 2.0526, 2.0860, 2.1582, 2.3469, 2.3372, 2.3608, 2.3605,\n", + " 2.4896, 2.4807, 2.4324, 2.4257, 2.3748, 2.2975, 2.2865, 2.2532, 2.4122,\n", + " 2.3748, 2.3581, 2.4217, 2.4510, 2.4845, 2.4641, 2.4155, 2.4996, 2.4588,\n", + " 2.4961, 2.0815, 2.1190, 1.9527, 1.9675, 1.6500, 1.0664, 1.5075, 1.4545,\n", + " 1.4277, 1.3781, 1.3691, 1.3363, 1.2170, 0.7667, 0.7437, 1.3433, 1.9504,\n", + " 2.0528, 1.3938, 1.9079]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.1269, 0.0320, -0.2689, 0.3747, -0.2730, 0.0455, -0.2142, -0.0796,\n", + " 0.6805, -0.0815, -0.0975, 0.1864, 0.0563, 0.2211, -0.0369, 0.5616,\n", + " 0.5509, 0.5787, -0.5401, -0.1202, -0.1718, -1.4747, 0.4220, 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grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002060645492747426\n", + "Grad encoder.fc1.bias: 0.001495281234383583\n", + "Grad encoder.encoder.0.weight: 8.917690138332546e-05\n", + "Grad encoder.encoder.0.bias: 0.0010534476023167372\n", + "Grad encoder.encoder.2.weight: 5.896637594560161e-05\n", + "Grad encoder.encoder.2.bias: 0.0005723091890104115\n", + "Grad encoder.encoder.4.weight: 0.00010564352123765275\n", + "Grad encoder.encoder.4.bias: 0.0010080835781991482\n", + "Grad decoder.fc1.0.weight: 4.042763976030983e-05\n", + "Grad decoder.fc1.0.bias: 0.0004235572414472699\n", + "Grad decoder.fc1.2.weight: 5.445029819384217e-05\n", + "Grad decoder.fc1.2.bias: 0.0005446228897199035\n", + "Grad decoder.fc1.4.weight: 5.2874536777380854e-05\n", + "Grad decoder.fc1.4.bias: 0.0006862554000690579\n", + "Grad decoder.fc2.weight: 0.00014058178931009024\n", + "Grad decoder.fc2.bias: 0.0015654423041269183\n", + "Grad _memory_unit.weight_ih_l0: 1.1353277841408271e-05\n", + "Grad 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-0.0581, 0.4504,\n", + " 0.2963, 0.6524, 1.0192, -0.1291, 0.0501, 0.0940, 0.7285, 0.4512,\n", + " 1.0477, 1.2342, 0.0243]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 2.1370e-01, 8.7128e-02, 9.5044e-02, 1.2449e-01, 1.3341e-02,\n", + " 1.9479e-04, -1.5976e-01, -1.3983e-01, -3.6916e-02, -5.8617e-02,\n", + " -3.4507e-02, 7.5908e-02, 1.2829e-01, 1.1471e-01, 2.1098e-02,\n", + " 9.0717e-02, 1.6157e-02, 8.3637e-02, 4.5777e-02, 1.0934e-02,\n", + " 4.7416e-02, 2.4483e-02, -3.9514e-02, 6.8284e-02, -1.8859e-02,\n", + " 5.1702e-02, -4.2035e-02, 2.3106e-02, 7.6328e-02, 1.0143e-01,\n", + " 7.3789e-02, 6.6243e-02, 8.8396e-02, 7.3012e-02, 2.4526e-02,\n", + " 2.5261e-02, -3.4726e-02, 5.9172e-02, -3.4323e-02, -5.7328e-02,\n", + " -2.8581e-03, -4.6188e-02, -1.2010e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0017579521518200636\n", + "Grad encoder.fc1.bias: 0.0013136807829141617\n", + "Grad encoder.encoder.0.weight: 0.0002743263030424714\n", + "Grad 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0.2984,\n", + " 0.2463, 0.2645, 0.2064, -0.1957, -0.1525, -0.2774, -0.1463, -0.3003,\n", + " -0.2086, -0.0292, -0.1077, -0.0899, -0.0514, -0.0309, -0.0332, -0.0592,\n", + " -0.1077, 0.0004, -0.0758, -0.1393, -0.2399, -0.2391, -0.2852, -0.1860,\n", + " -0.3820, -0.2993, -0.1732, -0.1382, -0.0140, -0.0261, 0.0797, 0.2739,\n", + " 0.3285, 0.3201, 0.2137]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0009601713973097503\n", + "Grad encoder.fc1.bias: 0.0009960706811398268\n", + "Grad encoder.encoder.0.weight: 0.00016878460883162916\n", + "Grad encoder.encoder.0.bias: 0.0008927572052925825\n", + "Grad encoder.encoder.2.weight: 9.266294364351779e-05\n", + "Grad encoder.encoder.2.bias: 0.0008677688892930746\n", + "Grad encoder.encoder.4.weight: 0.0001925661927089095\n", + "Grad encoder.encoder.4.bias: 0.0016906217206269503\n", + "Grad decoder.fc1.0.weight: 7.573189213871956e-05\n", + "Grad decoder.fc1.0.bias: 0.0007958424394018948\n", + "Grad decoder.fc1.2.weight: 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2.2404,\n", + " 2.2310, 1.8976, 1.9291, 1.8127, 1.6718, 1.4674, 0.9886, 1.4045, 1.3548,\n", + " 1.4631, 1.5292, 1.7874, 1.8101, 1.3650, 0.7986, 0.7801, 1.4610, 1.9583,\n", + " 2.3387, 1.5910, 2.2050]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.1740, 0.3016, -0.3504, 0.1329, -0.0656, -0.4933, -0.4270, -0.1175,\n", + " 0.1419, -0.3034, -0.3684, 0.1748, -0.1860, 0.2356, 1.1534, -0.4985,\n", + " 1.0061, -0.3601, -1.9760, 0.7556, 0.5794, -0.5960, 0.3584, 0.4379,\n", + " -3.4119, -0.9387, 0.6048, -0.0128, 0.3304, -0.0375, 0.2833, 0.4249,\n", + " 0.2024, 0.6400, 1.0708, -0.2631, -0.2391, 0.4911, -0.2895, 0.0259,\n", + " -0.1724, 0.0897, 0.2026]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3093, 0.1879, 0.1286, 0.1895, 0.0290, -0.0493, -0.2456, -0.2433,\n", + " -0.1104, -0.1304, -0.1192, 0.1202, 0.1855, 0.1905, 0.0803, 0.2027,\n", + " 0.0843, 0.1154, 0.0690, 0.0421, 0.0451, 0.0169, -0.0278, 0.0797,\n", + " -0.0016, 0.0362, -0.0484, 0.0147, 0.1493, 0.1954, 0.1368, 0.1244,\n", + " 0.1742, 0.1463, 0.0634, 0.0887, -0.0270, 0.0748, -0.0482, -0.1238,\n", + " -0.0993, -0.1448, -0.2066]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0001485870743636042\n", + "Grad encoder.fc1.bias: 9.00708109838888e-05\n", + "Grad encoder.encoder.0.weight: 4.516951958066784e-05\n", + "Grad encoder.encoder.0.bias: 0.00014940132678020746\n", + "Grad encoder.encoder.2.weight: 3.76424977730494e-05\n", + "Grad encoder.encoder.2.bias: 0.0002223688061349094\n", + "Grad encoder.encoder.4.weight: 8.41284854686819e-05\n", + "Grad encoder.encoder.4.bias: 0.00044706626795232296\n", + "Grad decoder.fc1.0.weight: 3.474006734904833e-05\n", + "Grad decoder.fc1.0.bias: 0.0003211519797332585\n", + "Grad decoder.fc1.2.weight: 4.608662857208401e-05\n", + "Grad decoder.fc1.2.bias: 0.00043591385474428535\n", + "Grad decoder.fc1.4.weight: 4.619215542334132e-05\n", + "Grad decoder.fc1.4.bias: 0.0005199479637667537\n", + "Grad decoder.fc2.weight: 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" -1.5229, -1.6519, -1.0322, 0.1118, -1.7689, -1.8337, -1.6511, -0.4472,\n", + " -1.1590, -1.5214, -1.7268, -1.0625, -0.3740, 0.2376, 0.0382, 0.4722,\n", + " 0.4693, 0.6601, 1.2575]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2529, -0.3524, -0.0794, -0.1576, 0.0375, 0.1642, 0.2642, 0.3681,\n", + " 0.3161, 0.3064, 0.2791, -0.1932, -0.1418, -0.3234, -0.2015, -0.3600,\n", + " -0.2568, -0.0622, -0.0734, -0.1231, -0.0954, -0.0143, -0.0455, -0.0588,\n", + " -0.1146, 0.0185, -0.0459, -0.1219, -0.2292, -0.2651, -0.3057, -0.2401,\n", + " -0.3771, -0.3556, -0.2351, -0.1655, -0.0144, -0.0218, 0.0538, 0.3447,\n", + " 0.4120, 0.4014, 0.3029]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0009381849085912108\n", + "Grad encoder.fc1.bias: 0.00042284466326236725\n", + "Grad encoder.encoder.0.weight: 0.00017795937310438603\n", + "Grad encoder.encoder.0.bias: 0.00042481167474761605\n", + "Grad encoder.encoder.2.weight: 0.00012645646347664297\n", + "Grad 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Sample: tensor([[-0.2698, -0.3722, -0.1139, -0.2851, -0.1329, 0.0481, 0.2032, 0.3438,\n", + " 0.2737, 0.2672, 0.2819, -0.1432, -0.1154, -0.2785, -0.1966, -0.3328,\n", + " -0.2864, -0.1196, -0.0585, -0.1380, -0.1165, -0.0066, -0.0595, -0.0649,\n", + " -0.0696, 0.0135, 0.0498, -0.0658, -0.1384, -0.2032, -0.2456, -0.2293,\n", + " -0.2403, -0.3146, -0.2224, -0.1795, -0.0427, -0.0026, 0.0072, 0.3618,\n", + " 0.4075, 0.3841, 0.3114]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.000545170099940151\n", + "Grad encoder.fc1.bias: 0.0004256887477822602\n", + "Grad encoder.encoder.0.weight: 9.679226786829531e-05\n", + "Grad encoder.encoder.0.bias: 0.00047375046415254474\n", + "Grad encoder.encoder.2.weight: 6.399626727215946e-05\n", + "Grad encoder.encoder.2.bias: 0.00046311665209941566\n", + "Grad encoder.encoder.4.weight: 0.00011704354255925864\n", + "Grad encoder.encoder.4.bias: 0.0010681357234716415\n", + "Grad decoder.fc1.0.weight: 5.4204530897550285e-05\n", + "Grad 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" 3.2765, 2.2301, 3.1355]], device='cuda:0')\n", + "Data Y Sample: tensor([[-8.4225e-01, -1.5706e+00, -1.4398e+00, -4.0087e-01, 6.1345e-02,\n", + " -3.1837e-01, -1.7336e-01, 5.4405e-01, 5.4163e-01, 7.1846e-01,\n", + " -1.6129e-02, -1.1059e-01, 3.5316e-01, -1.9560e-01, -8.7357e-01,\n", + " -6.4385e-03, -5.3468e-03, -2.3710e+00, 1.0880e+00, -7.0561e-04,\n", + " -1.0066e+00, 9.1737e-01, 1.8661e+00, 4.5708e+00, -1.7966e+00,\n", + " -8.4390e-01, -2.0313e-01, -2.2693e-01, 1.9789e-01, -8.2682e-01,\n", + " -2.4504e-01, 1.0645e+00, 6.0628e-01, 6.9400e-01, 2.0362e-01,\n", + " 4.6878e-02, 4.9267e-01, 8.4972e-01, 0.0000e+00, 7.0293e-02,\n", + " 6.7675e-01, 2.2396e-01, 4.2385e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1061, -0.1820, -0.0216, -0.0980, -0.0236, 0.0807, 0.1288, 0.2194,\n", + " 0.1667, 0.1947, 0.1706, -0.1336, -0.0747, -0.1733, -0.1334, -0.2043,\n", + " -0.1805, -0.0249, -0.0354, -0.0770, -0.0181, 0.0032, -0.0457, -0.0545,\n", + " -0.1095, 0.0345, -0.0294, -0.0516, 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"Grad decoder.fc1.2.bias: 0.0011215991107746959\n", + "Grad decoder.fc1.4.weight: 9.288357250625268e-05\n", + "Grad decoder.fc1.4.bias: 0.0008132326183840632\n", + "Grad decoder.fc2.weight: 0.00018954419647343457\n", + "Grad decoder.fc2.bias: 0.0021930711809545755\n", + "Grad _memory_unit.weight_ih_l0: 2.6718576918938197e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 8.88041831785813e-05\n", + "Grad _memory_unit.bias_hh_l0: 4.76609384350013e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.1875045856868383e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.0001757343125063926\n", + "Grad _memory_unit.bias_hh_l1: 9.595397568773478e-05\n", + "Data X Sample: tensor([[1.4905, 1.5818, 1.8362, 1.9504, 2.0165, 2.0802, 1.9982, 2.0159, 3.2807,\n", + " 4.1341, 4.4127, 4.3176, 4.2454, 3.9397, 3.8232, 3.7922, 3.7423, 3.6153,\n", + " 3.5271, 3.4423, 3.5332, 3.4002, 3.0749, 2.8840, 2.9260, 2.9776, 2.9783,\n", + " 3.0793, 3.0008, 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" 1.6715, 1.5053, 1.5402, 1.5349, 1.3172, 0.6711, 0.7599, 1.2371, 1.8751,\n", + " 2.0814, 1.2782, 1.9470]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.5309, 0.8591, 0.4374, 1.0085, 0.1868, 0.3250, -0.6329, -1.0670,\n", + " -0.4465, -0.5037, -0.6059, 0.6928, -0.3399, -0.3933, 0.8394, 0.3829,\n", + " 0.4893, 0.1389, 0.4481, 1.1031, -0.1645, -0.2561, -0.0760, -1.4061,\n", + " 0.0783, 0.3309, 0.5368, -0.5167, 0.0245, -0.2270, 0.7878, 0.0990,\n", + " 0.3911, 0.0706, 0.4730, 0.0222, -1.0293, -0.7725, 0.2253, -0.4282,\n", + " -0.4924, 0.0029, 0.0642]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3245, 0.3855, 0.1074, 0.1668, 0.0904, -0.2235, -0.3710, -0.3955,\n", + " -0.2264, -0.2198, -0.2587, 0.1665, 0.1094, 0.2751, 0.2416, 0.2809,\n", + " 0.2837, 0.1376, 0.0386, 0.0875, 0.0709, 0.0848, 0.0316, -0.0311,\n", + " 0.0629, 0.1148, 0.0130, -0.0143, 0.1550, 0.2546, 0.2353, 0.2018,\n", + " 0.2366, 0.2819, 0.1680, 0.1769, -0.0322, 0.0285, -0.0194, -0.3117,\n", + " -0.2543, 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"Grad encoder.encoder.4.weight: 0.0004727027553599328\n", + "Grad encoder.encoder.4.bias: 0.005454637110233307\n", + "Grad decoder.fc1.0.weight: 0.0001380294852424413\n", + "Grad decoder.fc1.0.bias: 0.0016750055365264416\n", + "Grad decoder.fc1.2.weight: 0.00013501712237484753\n", + "Grad decoder.fc1.2.bias: 0.0018987912917509675\n", + "Grad decoder.fc1.4.weight: 0.0001151005199062638\n", + "Grad decoder.fc1.4.bias: 0.0014419823419302702\n", + "Grad decoder.fc2.weight: 0.00022743886802345514\n", + "Grad decoder.fc2.bias: 0.002339758211746812\n", + "Grad _memory_unit.weight_ih_l0: 5.253390918369405e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 0.0003350506885908544\n", + "Grad _memory_unit.bias_hh_l0: 0.00017114670481532812\n", + "Grad _memory_unit.weight_ih_l1: 2.1992742404108867e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.0004463199875317514\n", + "Grad _memory_unit.bias_hh_l1: 0.000235947867622599\n", + "Data X Sample: tensor([[2.7168, 2.8591, 2.4237, 2.1166, 2.3290, 2.3911, 3.6575, 3.9934, 4.0838,\n", + " 4.1989, 4.2097, 4.2007, 4.0879, 3.9888, 3.7728, 3.6322, 3.6699, 3.5708,\n", + " 3.6241, 3.5651, 3.4080, 3.3542, 3.0074, 2.9165, 2.7983, 2.8156, 2.7625,\n", + " 2.7326, 2.8274, 2.8985, 2.9011, 3.0314, 3.2495, 2.3343, 3.9537, 4.3659,\n", + " 4.7667, 5.0752, 5.6792, 5.6402, 1.8375, 0.9439, 0.9014, 1.4294, 1.9107,\n", + " 2.0871, 1.4686, 2.1034]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.3494, 0.8452, -0.6016, 0.4172, 0.8637, -0.3335, 0.0351, 0.0986,\n", + " -0.4908, -0.0825, -0.5126, 0.8755, 0.4125, -1.1724, -0.4632, -0.6655,\n", + " -0.3952, 0.1058, 0.5934, -0.8234, -1.0903, 2.0512, -1.0495, -0.4298,\n", + " 0.0612, -2.0254, -1.0056, 0.0256, -0.5191, -0.8357, -0.6531, -0.8046,\n", + " -0.0944, -0.0182, -0.0962, -1.0041, 0.5815, -0.0911, 0.8307, 0.0134,\n", + " 0.3203, 0.1335, 0.6088]], device='cuda:0')\n", + "Prediction Sample: tensor([[-6.6657e-02, -1.1082e-01, -4.7642e-02, -5.5628e-02, -6.2003e-02,\n", + " 4.1127e-02, 1.0005e-02, 1.1735e-01, 4.8188e-02, 9.7988e-02,\n", + " 1.2034e-01, -6.5063e-02, -7.3027e-02, -1.1281e-01, -8.0851e-02,\n", + " -9.4169e-02, -1.2062e-01, -4.9502e-02, -7.0551e-02, -6.9390e-02,\n", + " -1.3827e-02, 3.2343e-02, -7.8453e-03, -4.1084e-02, 4.6529e-06,\n", + " 2.2801e-02, -1.7519e-02, -3.9483e-02, -5.4406e-02, -4.5634e-02,\n", + " -1.4775e-01, -8.2144e-02, -8.1837e-02, -1.1564e-01, -7.8031e-02,\n", + " -1.3230e-01, -5.1549e-02, 7.7174e-03, -1.1360e-02, 1.7814e-01,\n", + " 1.7040e-01, 1.8762e-01, 9.3463e-02]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0014803351368755102\n", + "Grad encoder.fc1.bias: 0.0008962852298282087\n", + "Grad encoder.encoder.0.weight: 0.0002429447486065328\n", + "Grad encoder.encoder.0.bias: 0.0009524596389383078\n", + "Grad encoder.encoder.2.weight: 0.00021549765369854867\n", + "Grad encoder.encoder.2.bias: 0.0014353995211422443\n", + "Grad encoder.encoder.4.weight: 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"Data Y Sample: tensor([[ 1.1858, 0.2781, -0.5097, 0.9697, 1.3342, 0.5725, -0.3830, -0.3241,\n", + " -0.3512, -0.4986, -0.5739, 0.3423, 0.4207, 0.7288, 0.8908, 1.0562,\n", + " 0.6248, 3.7087, -0.5899, 0.5129, -0.0138, 0.3804, 0.5946, 0.9866,\n", + " -0.6673, 0.0793, -0.7187, -0.3336, -0.3776, 0.1685, 1.5315, -0.1196,\n", + " 0.5171, 1.1075, 4.2054, 1.1830, -0.0868, -0.4035, 1.0291, -0.3235,\n", + " -0.6913, -0.3813, -1.0527]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3542, 0.3827, 0.1260, 0.2367, 0.1368, -0.1336, -0.4274, -0.4327,\n", + " -0.1871, -0.2430, -0.2406, 0.2378, 0.1286, 0.2299, 0.1680, 0.2942,\n", + " 0.2251, 0.1790, 0.0826, 0.0798, 0.1299, 0.0870, 0.0686, 0.0319,\n", + " 0.0958, 0.1340, -0.0711, 0.0053, 0.1594, 0.2602, 0.2674, 0.2135,\n", + " 0.2856, 0.3281, 0.1894, 0.1727, -0.0229, 0.0228, -0.0309, -0.3122,\n", + " -0.2787, -0.3106, -0.3178]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.001481112907640636\n", + "Grad encoder.fc1.bias: 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" -1.8368, -0.4825, 1.3747, -0.4978, -0.0167, -0.4897, -0.0263, 0.1115,\n", + " -0.6950, -0.7524, -0.1409, 0.3605, -0.6001, -0.6782, 0.0000, 0.6977,\n", + " 0.2444, 0.1566, -0.0197]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1611, -0.1679, -0.0408, -0.0635, 0.0294, 0.0898, 0.1055, 0.2236,\n", + " 0.1266, 0.2084, 0.2153, -0.1151, -0.1569, -0.2241, -0.1358, -0.2140,\n", + " -0.1456, -0.0684, -0.0730, -0.0515, -0.0369, 0.0064, -0.0303, -0.0457,\n", + " -0.0274, 0.0786, -0.0685, -0.0865, -0.1468, -0.1525, -0.2316, -0.1736,\n", + " -0.2262, -0.2296, -0.1494, -0.1773, -0.0440, -0.0171, 0.0259, 0.2645,\n", + " 0.2928, 0.2979, 0.2098]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0008572554215788841\n", + "Grad encoder.fc1.bias: 0.0004677243414334953\n", + "Grad encoder.encoder.0.weight: 0.0001686006726231426\n", + "Grad encoder.encoder.0.bias: 0.0006801994750276208\n", + "Grad encoder.encoder.2.weight: 0.00012226292164996266\n", + "Grad 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1.9385,\n", + " 2.0300, 1.4006, 2.0643]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.3485, 0.6292, 0.2535, 0.8222, -0.6031, -1.1139, 0.1747, -0.2143,\n", + " -0.1202, -0.3868, -0.0313, -6.7571, 0.3640, -1.2299, 0.0125, -0.1591,\n", + " -0.2767, -0.0474, 1.1169, 0.2413, 0.4672, -0.2753, 0.8295, 0.0149,\n", + " 0.2539, 0.2268, -0.0623, -0.0231, 0.1112, -0.4413, -0.4002, 0.5188,\n", + " -0.2948, 0.3869, -1.0053, 0.2766, -0.0107, 0.5779, 0.5046, -0.2210,\n", + " -0.1183, -0.4708, -0.4820]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2153, 0.2430, 0.0724, 0.1559, 0.0784, -0.0676, -0.2623, -0.2462,\n", + " -0.0804, -0.1342, -0.1175, 0.1385, 0.0629, 0.1181, 0.0788, 0.1847,\n", + " 0.1289, 0.1284, 0.0619, 0.0313, 0.0719, 0.0347, 0.0304, 0.0315,\n", + " 0.0588, 0.1256, -0.0969, -0.0143, 0.0741, 0.1239, 0.1351, 0.1076,\n", + " 0.1644, 0.1857, 0.1193, 0.0651, -0.0113, -0.0007, -0.0135, -0.1616,\n", + " -0.1381, -0.1374, -0.1547]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad 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"Grad _memory_unit.bias_ih_l1: 0.00032676756381988525\n", + "Grad _memory_unit.bias_hh_l1: 0.0001686066680122167\n", + "Data X Sample: tensor([[2.4070, 2.8795, 3.0969, 3.2776, 3.3534, 3.6228, 3.8239, 3.6825, 3.7982,\n", + " 3.7755, 3.7877, 3.6671, 3.4421, 3.5389, 3.3718, 3.1823, 3.2768, 3.3542,\n", + " 3.4692, 3.2378, 3.3467, 3.3879, 3.1038, 2.9947, 2.8828, 2.9044, 2.8025,\n", + " 2.8713, 2.5707, 2.5939, 2.5441, 2.6998, 2.8557, 2.0436, 3.1595, 3.4197,\n", + " 3.6832, 4.0814, 4.6461, 4.4458, 1.8995, 1.0535, 1.0651, 1.9088, 2.6600,\n", + " 2.9906, 2.1077, 3.1746]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.2800, -0.7235, -0.3814, -0.7395, -1.1631, 0.2165, -0.1287, 0.5570,\n", + " 1.2151, 0.9844, 0.0421, -0.9204, 0.4286, -0.4482, -0.8657, 0.3239,\n", + " 0.1821, -0.1542, 0.9896, -0.8066, 0.5152, 1.4586, -0.9989, 1.5473,\n", + " 1.0903, -1.1662, 0.2276, 1.0511, 0.3337, -0.4165, 0.3905, -0.2341,\n", + " -0.3578, 0.1395, 0.4880, 0.5906, -0.2169, 0.8248, 0.4914, -0.4067,\n", + " 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"Grad decoder.fc1.0.weight: 5.829736983287148e-05\n", + "Grad decoder.fc1.0.bias: 0.0004024941590614617\n", + "Grad decoder.fc1.2.weight: 8.998638077173382e-05\n", + "Grad decoder.fc1.2.bias: 0.0007685378659516573\n", + "Grad decoder.fc1.4.weight: 7.021587225608528e-05\n", + "Grad decoder.fc1.4.bias: 0.0007449304684996605\n", + "Grad decoder.fc2.weight: 0.0001382703921990469\n", + "Grad decoder.fc2.bias: 0.002220863476395607\n", + "Grad _memory_unit.weight_ih_l0: 1.339304890279891e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 5.60924454475753e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.9473307222360745e-05\n", + "Grad _memory_unit.weight_ih_l1: 4.744141733681317e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00010481662320671603\n", + "Grad _memory_unit.bias_hh_l1: 5.436774154077284e-05\n", + "Data X Sample: tensor([[1.4990, 1.9080, 2.0992, 2.2959, 2.4092, 2.4471, 2.4574, 2.6050, 2.5948,\n", + " 2.7219, 2.7948, 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-0.4931, 0.2066, 0.9761, 1.7951,\n", + " 1.4111, 0.7014, 1.8256]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1501, -0.1924, -0.0404, -0.1291, -0.0629, -0.0070, 0.1352, 0.2346,\n", + " 0.1485, 0.1874, 0.2223, -0.1202, -0.1067, -0.1801, -0.1418, -0.2409,\n", + " -0.1641, -0.0673, -0.0338, -0.0888, -0.0504, 0.0338, -0.0763, -0.0166,\n", + " -0.0399, 0.0873, -0.0433, -0.0644, -0.1044, -0.1301, -0.2076, -0.1574,\n", + " -0.1515, -0.2504, -0.1383, -0.1489, -0.0286, -0.0231, 0.0269, 0.2955,\n", + " 0.3716, 0.3327, 0.2249]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 7.775046105962247e-05\n", + "Grad encoder.fc1.bias: 0.00026591395726427436\n", + "Grad encoder.encoder.0.weight: 2.8434697014745325e-05\n", + "Grad encoder.encoder.0.bias: 0.0002342657680856064\n", + "Grad encoder.encoder.2.weight: 2.7022126232623123e-05\n", + "Grad encoder.encoder.2.bias: 0.00020887611026410013\n", + "Grad encoder.encoder.4.weight: 9.35925345402211e-05\n", + "Grad 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" 1.6951, 1.5504, 1.5656, 1.5377, 1.1788, 0.7109, 0.6407, 1.2142, 1.7522,\n", + " 1.9385, 1.2714, 1.7359]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.0967, -0.2987, -0.2944, 0.0204, 0.5779, 0.0582, -0.1871, -0.3666,\n", + " -1.0765, -0.1817, -0.3582, 0.1120, 0.9661, 0.3923, 0.5268, 0.1601,\n", + " 1.2704, 0.0308, 0.7210, -0.5250, -1.0147, -0.0942, -0.6728, -0.2178,\n", + " -0.4838, -0.0568, 0.9112, -0.3637, 0.2102, 0.5718, 1.5093, -0.3004,\n", + " -0.1550, 0.3812, 0.9094, 0.0561, -0.0317, 0.0761, -1.6134, -0.3790,\n", + " -0.3726, -0.2370, -0.2526]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3695, 0.3949, 0.1319, 0.2204, 0.1031, -0.1754, -0.4216, -0.3999,\n", + " -0.1496, -0.2502, -0.2653, 0.1879, 0.1547, 0.3089, 0.1861, 0.2869,\n", + " 0.2423, 0.2114, 0.1215, 0.1137, 0.0786, 0.0885, 0.0506, 0.0258,\n", + " 0.0704, 0.1410, -0.0891, 0.0114, 0.1909, 0.2452, 0.2375, 0.1895,\n", + " 0.2666, 0.3010, 0.1526, 0.1489, 0.0074, 0.0216, -0.0206, -0.2922,\n", + " -0.2359, 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" grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00013881016639061272\n", + "Grad encoder.fc1.bias: 0.0003650807775557041\n", + "Grad encoder.encoder.0.weight: 4.894323501503095e-05\n", + "Grad encoder.encoder.0.bias: 0.00028847192879766226\n", + "Grad encoder.encoder.2.weight: 4.47482307208702e-05\n", + "Grad encoder.encoder.2.bias: 0.00022441762848757207\n", + "Grad encoder.encoder.4.weight: 0.00013270482304506004\n", + "Grad encoder.encoder.4.bias: 0.0006103509804233909\n", + "Grad decoder.fc1.0.weight: 5.31069454154931e-05\n", + "Grad decoder.fc1.0.bias: 0.00030981292366050184\n", + "Grad decoder.fc1.2.weight: 7.250357884913683e-05\n", + "Grad decoder.fc1.2.bias: 0.0006112637929618359\n", + "Grad decoder.fc1.4.weight: 7.274436939042062e-05\n", + "Grad decoder.fc1.4.bias: 0.0007301353616639972\n", + "Grad decoder.fc2.weight: 0.00017071857291739434\n", + "Grad decoder.fc2.bias: 0.0023368322290480137\n", + "Grad _memory_unit.weight_ih_l0: 9.762665285961702e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.13086093228776e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.656924723647535e-05\n", + "Grad _memory_unit.weight_ih_l1: 4.871319106314331e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 6.842259608674794e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.578697214834392e-05\n", + "Data X Sample: tensor([[1.4682, 1.7391, 1.8392, 1.9810, 2.2163, 2.3322, 2.4604, 2.4148, 2.5606,\n", + " 2.5923, 2.6425, 2.7090, 2.5255, 2.5356, 2.4967, 2.4069, 2.3617, 2.5302,\n", + " 2.5400, 2.5645, 2.4389, 2.4724, 2.3279, 2.3000, 2.3197, 2.3272, 2.3656,\n", + " 2.3697, 2.1752, 2.2173, 2.2607, 2.2411, 2.2110, 1.3823, 1.9487, 1.7609,\n", + " 1.6695, 1.5159, 1.6543, 1.5803, 1.1550, 0.6811, 0.7154, 1.2199, 1.7641,\n", + " 1.9385, 1.2918, 1.9235]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.5607, -0.3171, 0.6329, -0.7736, 0.9486, 3.1551, -0.8965, -0.6263,\n", + " 0.6127, 0.6668, 0.8863, -0.0562, -1.4392, 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_memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00011139112029923126\n", + "Grad _memory_unit.bias_hh_l1: 5.8409535995451733e-05\n", + "Data X Sample: tensor([[1.6411, 1.8148, 2.0661, 2.0641, 2.1770, 2.2320, 2.2648, 2.3424, 2.3751,\n", + " 2.4201, 2.3792, 2.4363, 2.4678, 2.3475, 2.2848, 2.3577, 2.3130, 2.3348,\n", + " 2.4636, 2.4957, 2.5395, 2.4770, 2.5182, 2.5214, 2.5787, 2.6276, 2.5467,\n", + " 2.5246, 2.4145, 2.5055, 2.5162, 2.5340, 2.2792, 1.4898, 1.9683, 1.9142,\n", + " 1.7364, 1.5928, 1.5466, 1.4668, 1.3077, 0.7946, 0.7599, 1.3778, 1.9940,\n", + " 2.0643, 1.3666, 2.0486]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.1049, 0.0979, -0.4898, 1.4146, 2.1566, 1.5330, 0.2140, -0.2461,\n", + " 0.2076, -0.3434, -0.3039, 1.2502, -0.7006, 0.4426, 0.9649, 0.7697,\n", + " -0.4924, -1.1908, -1.8606, -1.6769, -1.0020, -1.6686, -2.0299, -0.9517,\n", + " -1.2261, -0.5351, -0.7728, -0.8504, -0.3082, -0.5908, 0.5552, 1.0555,\n", + " -0.2681, -1.1597, 0.0396, 0.6668, -0.2060, 0.0000, 1.1148, -0.4913,\n", + " -0.3317, -0.5096, -0.5482]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4626, 0.4822, 0.1474, 0.2744, 0.1537, -0.1920, -0.5035, -0.4860,\n", + " -0.1908, -0.3308, -0.3242, 0.2339, 0.1946, 0.3898, 0.2632, 0.3732,\n", + " 0.3478, 0.2515, 0.1370, 0.1576, 0.0977, 0.1215, 0.0992, 0.0438,\n", + " 0.0921, 0.1590, -0.1242, -0.0253, 0.2264, 0.3016, 0.2799, 0.2218,\n", + " 0.3567, 0.3563, 0.1907, 0.2092, -0.0237, -0.0047, 0.0026, -0.3846,\n", + " -0.3410, -0.3853, -0.4142]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0001502223894931376\n", + "Grad encoder.fc1.bias: 0.0004500873328652233\n", + "Grad encoder.encoder.0.weight: 6.827512697782367e-05\n", + "Grad encoder.encoder.0.bias: 0.0003822381841018796\n", + "Grad encoder.encoder.2.weight: 5.126883843331598e-05\n", + "Grad encoder.encoder.2.bias: 0.00028553028823807836\n", + "Grad encoder.encoder.4.weight: 0.00012674074969254434\n", + "Grad encoder.encoder.4.bias: 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1.6670, 1.6143, 1.2600, 0.7906, 0.7841, 1.4151, 1.9306,\n", + " 2.1272, 1.4074, 1.9939]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1445, -0.0778, 0.2049, 0.4700, -0.3836, -0.1730, -0.1843, -1.1485,\n", + " 0.1936, 0.1143, 0.3114, 1.1332, 0.5873, 0.7346, 0.3825, -0.6303,\n", + " 1.0220, -0.7215, -1.2324, -0.7514, -0.9770, -1.0993, -0.8555, -0.7075,\n", + " -0.6714, -0.9164, 1.1983, 0.0239, -0.6060, 0.4980, 0.7949, -0.1001,\n", + " -0.9302, 1.8612, -0.2669, -0.2837, -0.4229, -0.4031, 0.2870, 1.0074,\n", + " -0.2248, 0.1693, 0.8493]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4178, 0.4265, 0.1327, 0.2476, 0.1372, -0.1745, -0.4434, -0.4300,\n", + " -0.1666, -0.2944, -0.2916, 0.2091, 0.1755, 0.3532, 0.2622, 0.3460,\n", + " 0.3091, 0.2078, 0.1181, 0.1335, 0.0756, 0.1044, 0.0921, 0.0393,\n", + " 0.0816, 0.1331, -0.1192, -0.0344, 0.2053, 0.2478, 0.2437, 0.1930,\n", + " 0.3299, 0.3002, 0.1735, 0.1843, -0.0389, -0.0110, 0.0068, -0.3379,\n", + " -0.3105, -0.3469, -0.3650]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00013861339539289474\n", + "Grad encoder.fc1.bias: 0.0007381124305538833\n", + "Grad encoder.encoder.0.weight: 5.769758718088269e-05\n", + "Grad encoder.encoder.0.bias: 0.00040071713738143444\n", + "Grad encoder.encoder.2.weight: 4.4769723899662495e-05\n", + "Grad encoder.encoder.2.bias: 0.0004224329604767263\n", + "Grad encoder.encoder.4.weight: 0.000118507698061876\n", + "Grad encoder.encoder.4.bias: 0.0008979098638519645\n", + "Grad decoder.fc1.0.weight: 5.633436012431048e-05\n", + "Grad decoder.fc1.0.bias: 0.0004036366590298712\n", + "Grad decoder.fc1.2.weight: 6.50398142170161e-05\n", + "Grad decoder.fc1.2.bias: 0.000452846463304013\n", + "Grad decoder.fc1.4.weight: 7.052467117318884e-05\n", + "Grad decoder.fc1.4.bias: 0.000620034639723599\n", + "Grad decoder.fc2.weight: 0.0001848852843977511\n", + "Grad decoder.fc2.bias: 0.0023083628620952368\n", + "Grad _memory_unit.weight_ih_l0: 1.4795569768466521e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 2.7399006285122596e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.4941033441573381e-05\n", + "Grad _memory_unit.weight_ih_l1: 5.404379407991655e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 7.642581476829946e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.939969974453561e-05\n", + "Data X Sample: tensor([[1.5382, 1.6750, 1.8347, 2.0072, 2.0831, 2.1819, 2.1538, 2.1933, 3.5419,\n", + " 4.4801, 4.4063, 4.3994, 4.0679, 3.9016, 3.9291, 3.6992, 3.6369, 3.7004,\n", + " 3.6324, 3.5502, 3.3982, 3.2226, 2.8821, 2.8153, 2.8528, 2.9697, 3.0529,\n", + " 3.0916, 3.1257, 3.2031, 3.1881, 3.3713, 3.9447, 2.8904, 4.5886, 4.8693,\n", + " 5.1187, 5.5072, 6.0025, 5.9211, 1.1645, 0.6552, 0.6427, 1.1769, 1.7878,\n", + " 2.0128, 1.3326, 1.9313]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.3991, -0.1735, 0.9027, 0.8725, 0.4435, 0.7583, 0.2797, -0.0349,\n", + " -0.9268, 0.2755, -0.3343, -0.2068, 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_memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 7.100452785380185e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.706240386236459e-05\n", + "Data X Sample: tensor([[ 0.0021, 0.0015, 0.0105, 0.0153, 0.0051, 0.0236, 0.0216, 0.0043,\n", + " 0.0293, 0.0095, 0.0286, 0.0097, 0.0111, 0.0136, 0.0177, 0.0027,\n", + " -0.0928, -0.1199, -0.1425, -0.1134, -0.1349, -0.0980, -0.1518, -0.1241,\n", + " -0.1145, -0.2168, -0.1891, -0.3181, -0.0243, -0.0622, -0.0630, -0.0249,\n", + " -0.0220, -0.0366, -0.0172, 0.0146, 0.0039, 0.0080, -0.0444, 0.0113,\n", + " 0.0286, 0.0100, 0.0222, 0.0115, 0.0198, -0.0057, -0.0272, -0.0156]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.0887, 0.3611, 1.0823, -1.2877, 0.7039, -0.1413, -0.8185, -1.1584,\n", + " 0.5535, -0.5948, -0.6405, 0.0588, 0.0783, 0.3360, 0.3629, 1.1363,\n", + " 0.6671, 0.2340, -0.2753, -0.1605, -0.2167, -0.8059, -0.2281, -0.5883,\n", + " 0.7551, -0.3622, 0.3950, 0.5063, 0.4538, 1.3913, 0.3930, 0.7191,\n", + " -0.0048, 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-1.4720e-01, -5.5032e-01, -2.4083e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4709, 0.4945, 0.1490, 0.2999, 0.1869, -0.1885, -0.4793, -0.4804,\n", + " -0.1817, -0.3332, -0.3175, 0.2490, 0.1848, 0.3862, 0.3279, 0.3802,\n", + " 0.3738, 0.2262, 0.1268, 0.1607, 0.1124, 0.1265, 0.1239, 0.0499,\n", + " 0.1025, 0.1544, -0.1373, -0.0635, 0.2138, 0.2754, 0.2834, 0.2188,\n", + " 0.3967, 0.3546, 0.2130, 0.2277, -0.0838, -0.0452, 0.0290, -0.4051,\n", + " -0.3741, -0.4260, -0.4154]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0005771437427029014\n", + "Grad encoder.fc1.bias: 0.0005464407149702311\n", + "Grad encoder.encoder.0.weight: 0.00025148154236376286\n", + "Grad encoder.encoder.0.bias: 0.0007122684037312865\n", + "Grad encoder.encoder.2.weight: 0.00014921012916602194\n", + "Grad encoder.encoder.2.bias: 0.0006759044481441379\n", + "Grad encoder.encoder.4.weight: 0.00030407169833779335\n", + "Grad encoder.encoder.4.bias: 0.0015823248540982604\n", + 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"Grad decoder.fc1.4.weight: 3.660362563095987e-05\n", + "Grad decoder.fc1.4.bias: 0.0005224447231739759\n", + "Grad decoder.fc2.weight: 0.00010187607404077426\n", + "Grad decoder.fc2.bias: 0.0016955643659457564\n", + "Grad _memory_unit.weight_ih_l0: 4.94792857352877e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.115745494142175e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.6399575542891398e-05\n", + "Grad _memory_unit.weight_ih_l1: 3.1652252800995484e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 5.581937148235738e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.8896927688037977e-05\n", + "Data X Sample: tensor([[1.7228, 2.1236, 2.3666, 2.4599, 2.6756, 2.7786, 2.8934, 3.0124, 3.0049,\n", + " 3.0315, 3.0359, 2.8433, 2.8074, 2.8083, 2.6001, 2.5519, 2.6180, 2.6598,\n", + " 2.5689, 2.5088, 2.4904, 2.4326, 2.3761, 2.3000, 2.2353, 2.1783, 2.0965,\n", + " 2.1739, 1.7311, 1.7325, 1.7568, 1.6608, 1.5752, 1.1168, 1.7452, 1.8971,\n", + " 2.0845, 2.3879, 2.8269, 2.7577, 1.4604, 0.8364, 0.8448, 1.5357, 2.2754,\n", + " 2.3445, 1.5230, 2.5803]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.9123, -1.2316, -1.0640, -1.1213, -1.0319, -0.1679, 0.0229, 0.3909,\n", + " 0.6834, 0.5539, 0.4814, -3.3511, -0.1253, -0.2576, 1.1678, -0.0056,\n", + " 0.1700, -0.4695, -0.3916, -0.5446, -0.2508, -1.3283, -0.2862, -0.1131,\n", + " 0.4802, 0.0804, 0.4560, -0.0667, 1.0804, -0.0140, 0.6519, -0.0933,\n", + " 2.6930, -0.8179, 0.1409, 0.7625, 0.9799, 0.4269, -0.7513, 0.6189,\n", + " 1.7117, 1.1114, 0.8420]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1973, -0.2229, -0.0155, -0.1977, -0.1209, -0.0088, 0.1543, 0.2060,\n", + " 0.1583, 0.1899, 0.1949, -0.0926, -0.1145, -0.1952, -0.1406, -0.2570,\n", + " -0.2255, -0.1011, 0.0048, -0.1074, -0.0851, -0.0088, -0.0534, -0.0079,\n", + " -0.0093, 0.0092, 0.0500, -0.0535, -0.0756, -0.1518, -0.1896, -0.1110,\n", + " -0.1455, -0.2452, -0.1171, -0.1311, -0.0214, 0.0390, -0.0108, 0.3218,\n", + " 0.3436, 0.2880, 0.2495]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00010727009794209152\n", + "Grad encoder.fc1.bias: 0.00031853115069679916\n", + "Grad encoder.encoder.0.weight: 4.174625064479187e-05\n", + "Grad encoder.encoder.0.bias: 0.00025308088515885174\n", + "Grad encoder.encoder.2.weight: 2.8353213565424085e-05\n", + "Grad encoder.encoder.2.bias: 0.000289703079033643\n", + "Grad encoder.encoder.4.weight: 7.346653728745878e-05\n", + "Grad encoder.encoder.4.bias: 0.0008995672105811536\n", + "Grad decoder.fc1.0.weight: 3.340798139106482e-05\n", + "Grad decoder.fc1.0.bias: 0.0003695912891998887\n", + "Grad decoder.fc1.2.weight: 4.837541564484127e-05\n", + "Grad decoder.fc1.2.bias: 0.0005393699393607676\n", + "Grad decoder.fc1.4.weight: 4.8859368689591065e-05\n", + "Grad decoder.fc1.4.bias: 0.0006577310850843787\n", + "Grad decoder.fc2.weight: 0.00013058257172815502\n", + "Grad decoder.fc2.bias: 0.0020683722104877234\n", + "Grad 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"Data X Sample: tensor([[1.9138, 1.6459, 1.8557, 2.0903, 2.1804, 2.0655, 1.7810, 3.2027, 4.2791,\n", + " 4.4580, 4.3873, 4.0820, 4.2210, 3.9970, 3.7829, 3.6869, 3.6951, 3.6772,\n", + " 3.5498, 3.5427, 3.4252, 3.4139, 2.9038, 2.7619, 2.7495, 2.7582, 2.7785,\n", + " 2.7694, 2.8621, 2.9574, 2.9781, 3.0977, 3.4937, 2.5242, 4.3483, 4.8693,\n", + " 5.0814, 5.4993, 6.1483, 6.0005, 1.1025, 0.6791, 0.6750, 1.1338, 1.7839,\n", + " 2.0357, 1.3190, 1.9157]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.0282, -0.1321, -0.2304, -0.8896, 0.3456, 0.3883, 0.2279, 0.2240,\n", + " -0.5215, -0.1417, -0.3665, -0.0951, 0.4512, -0.0570, 0.4439, 1.0651,\n", + " 1.7065, 0.5032, 0.4715, 0.9119, -0.2020, -0.1526, 0.2473, 0.2544,\n", + " 0.7143, 0.5363, 1.2401, -0.1911, -0.1438, 0.3484, 0.7297, 0.1062,\n", + " 1.1880, 0.0883, -0.1222, -1.2797, 0.2309, 0.0182, 0.5227, -0.1167,\n", + " 0.2473, 0.1688, 0.8658]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2582, -0.3180, -0.0250, -0.1591, -0.0640, 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device='cuda:0')\n", + "Prediction Sample: tensor([[-1.9951e-01, -2.2707e-01, -4.4240e-02, -1.3764e-01, -4.0764e-02,\n", + " 8.7965e-02, 1.5041e-01, 2.2968e-01, 1.5007e-01, 1.8988e-01,\n", + " 1.7111e-01, -1.5736e-01, -1.7511e-01, -2.4158e-01, -1.4994e-01,\n", + " -2.4376e-01, -1.9160e-01, -6.1185e-02, -6.3551e-02, -5.5680e-02,\n", + " -6.0931e-02, -1.3052e-04, -4.3572e-02, -2.3967e-02, 4.0693e-02,\n", + " 4.7307e-02, 2.4791e-02, -1.1419e-01, -1.6131e-01, -1.9846e-01,\n", + " -2.4233e-01, -1.1821e-01, -2.4576e-01, -2.4161e-01, -1.0814e-01,\n", + " -1.2685e-01, -3.1351e-02, 5.4483e-02, 4.8190e-02, 2.6546e-01,\n", + " 2.8366e-01, 3.0132e-01, 2.1801e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003942765761166811\n", + "Grad encoder.fc1.bias: 0.00028660098905675113\n", + "Grad encoder.encoder.0.weight: 0.00015282296226359904\n", + "Grad encoder.encoder.0.bias: 0.0003806836321018636\n", + "Grad encoder.encoder.2.weight: 0.00010263350850436836\n", + "Grad 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"Grad _memory_unit.bias_hh_l1: 4.8967507609631866e-05\n", + "Data X Sample: tensor([[-0.0032, 0.0073, 0.0015, 0.0109, -0.0034, 0.0250, 0.0139, 0.0099,\n", + " 0.0220, 0.0095, 0.0286, -0.0019, -0.0022, 0.0164, 0.0000, 0.0109,\n", + " -0.0676, -0.0696, -0.0971, -0.0967, -0.1031, -0.0704, -0.0868, -0.0840,\n", + " -0.0694, -0.1515, -0.1225, -0.2651, -0.0382, -0.0197, -0.0385, -0.0359,\n", + " -0.0154, -0.0389, 0.0074, 0.0268, -0.0020, 0.0027, 0.0380, 0.0113,\n", + " 0.0382, 0.0060, -0.0101, 0.0086, -0.0198, 0.0114, -0.0476, -0.0235]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.1600, 1.0805, 0.1507, -0.2474, -0.5991, -1.9280, -1.0442, -1.2462,\n", + " -0.7246, -0.3896, -0.5847, 0.5985, 0.5064, 0.3742, -0.3297, -0.3780,\n", + " 1.2956, -0.0069, 1.8112, 0.3276, 0.3945, -0.0761, -0.3347, -0.5229,\n", + " -1.3975, -1.7956, 0.2508, 0.0556, 0.2100, 0.8295, 1.0886, 0.2559,\n", + " -0.2943, 0.2307, 1.6810, -0.2867, 0.4497, 0.0000, 0.9794, -0.6297,\n", + " -0.6818, -0.4712, 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1.6204, 1.5822, 1.5783, 1.4696, 1.2791, 0.7528, 0.7882, 1.3003, 2.1288,\n", + " 2.2473, 1.6589, 2.0955]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.0827, 0.3530, -0.6742, 0.4220, 0.7073, -0.7015, 0.0572, -0.0885,\n", + " -0.8931, -0.3852, -0.1790, 0.7719, 0.3759, -0.1212, 0.5242, 1.0168,\n", + " 0.4534, 0.3813, 0.4039, 0.0684, 0.1585, 0.0079, 0.3467, -0.0904,\n", + " -0.0024, -0.3054, -0.7903, -0.1178, -0.5532, -0.3637, -0.6941, 0.3787,\n", + " 0.2351, -0.2217, 1.0426, 0.6598, -0.5893, 0.1123, 0.9794, -0.3188,\n", + " -0.1248, -0.3514, -0.5628]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 4.6022e-01, 4.0418e-01, 7.5851e-02, 3.3694e-01, 1.8031e-01,\n", + " -7.5501e-02, -3.9707e-01, -4.4907e-01, -2.7218e-01, -2.9070e-01,\n", + " -3.3903e-01, 2.5396e-01, 2.0846e-01, 3.2545e-01, 2.8652e-01,\n", + " 3.4916e-01, 2.5830e-01, 1.2088e-01, 1.1133e-01, 1.7664e-01,\n", + " 9.6117e-02, 8.2320e-02, 8.2567e-02, 4.2916e-02, 3.8964e-02,\n", + " 4.1311e-02, 2.2563e-04, 6.9248e-03, 2.0561e-01, 3.2404e-01,\n", + " 2.7020e-01, 2.0281e-01, 2.9427e-01, 4.0802e-01, 1.8363e-01,\n", + " 2.1636e-01, -6.2203e-02, -1.2640e-02, 7.8023e-03, -3.4679e-01,\n", + " -2.7977e-01, -3.0523e-01, -3.0861e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.000198719761101529\n", + "Grad encoder.fc1.bias: 0.00022508419351652265\n", + "Grad encoder.encoder.0.weight: 7.486958929803222e-05\n", + "Grad encoder.encoder.0.bias: 0.0002758558257482946\n", + "Grad encoder.encoder.2.weight: 7.096190529409796e-05\n", + "Grad encoder.encoder.2.bias: 0.00041888616397045553\n", + "Grad encoder.encoder.4.weight: 0.00014619901776313782\n", + "Grad encoder.encoder.4.bias: 0.0013736285036429763\n", + "Grad decoder.fc1.0.weight: 5.713117934647016e-05\n", + "Grad decoder.fc1.0.bias: 0.000631254049949348\n", + "Grad decoder.fc1.2.weight: 8.894770871847868e-05\n", + "Grad decoder.fc1.2.bias: 0.0009823654545471072\n", + "Grad decoder.fc1.4.weight: 6.999984179856256e-05\n", + "Grad 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grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00021623091015499085\n", + "Grad encoder.fc1.bias: 0.0007857728051021695\n", + "Grad encoder.encoder.0.weight: 8.378004713449627e-05\n", + "Grad encoder.encoder.0.bias: 0.0005428490694612265\n", + "Grad encoder.encoder.2.weight: 6.20502614765428e-05\n", + "Grad encoder.encoder.2.bias: 0.00044914433965459466\n", + "Grad encoder.encoder.4.weight: 0.00013478792971000075\n", + "Grad encoder.encoder.4.bias: 0.0010251663625240326\n", + "Grad decoder.fc1.0.weight: 5.2369745390024036e-05\n", + "Grad decoder.fc1.0.bias: 0.00041724054608494043\n", + "Grad decoder.fc1.2.weight: 7.28793820599094e-05\n", + "Grad decoder.fc1.2.bias: 0.0007456464227288961\n", + "Grad decoder.fc1.4.weight: 5.500916813616641e-05\n", + "Grad decoder.fc1.4.bias: 0.0007237840909510851\n", + "Grad decoder.fc2.weight: 0.00011653800902422518\n", + "Grad decoder.fc2.bias: 0.0020102562848478556\n", + "Grad _memory_unit.weight_ih_l0: 1.1875065865751822e-05\n", + "Grad 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-0.7806, -0.4768, 0.4652,\n", + " 0.8849, 0.2741, -0.2774]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4284, 0.3814, 0.0798, 0.3029, 0.1788, -0.0511, -0.3660, -0.4026,\n", + " -0.2455, -0.2688, -0.3080, 0.2303, 0.1952, 0.2913, 0.2530, 0.3462,\n", + " 0.2444, 0.0934, 0.1032, 0.1755, 0.0839, 0.0804, 0.0782, 0.0353,\n", + " 0.0382, 0.0315, -0.0197, -0.0025, 0.1709, 0.2928, 0.2348, 0.1804,\n", + " 0.2749, 0.3513, 0.1610, 0.2049, -0.0400, -0.0190, 0.0052, -0.3114,\n", + " -0.2472, -0.2729, -0.2806]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0007647468009963632\n", + "Grad encoder.fc1.bias: 0.000572955294046551\n", + "Grad encoder.encoder.0.weight: 0.0002799526846501976\n", + "Grad encoder.encoder.0.bias: 0.0006778957322239876\n", + "Grad encoder.encoder.2.weight: 0.00018293879111297429\n", + "Grad encoder.encoder.2.bias: 0.0006520127062685788\n", + "Grad encoder.encoder.4.weight: 0.00034515210427343845\n", + "Grad encoder.encoder.4.bias: 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1.4738, 1.9094, 1.8388,\n", + " 1.7226, 1.6432, 1.5402, 1.5235, 1.2791, 0.7806, 0.7437, 1.3118, 1.9543,\n", + " 2.2415, 1.4142, 2.0799]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.9862, 0.2550, 0.0958, 0.0147, -0.8049, 0.5431, -0.9905, -0.2429,\n", + " -1.7641, -0.6462, -0.5584, -0.2818, -0.2671, 0.4441, -0.3588, 1.0245,\n", + " 0.2700, -0.8003, -0.1769, -1.6890, -0.3921, -0.6122, 0.6858, -0.1770,\n", + " 0.2003, 1.0768, -0.0293, 0.2971, 0.8917, 0.3027, 0.6543, -0.1788,\n", + " 0.0736, 0.1526, -0.7485, -0.4052, 0.5472, -0.6596, 0.0040, -0.2351,\n", + " -0.1616, -0.1560, 0.0381]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3770, 0.3413, 0.0806, 0.2543, 0.1589, -0.0343, -0.3258, -0.3460,\n", + " -0.2139, -0.2311, -0.2658, 0.1943, 0.1701, 0.2539, 0.2096, 0.3194,\n", + " 0.2165, 0.0677, 0.0898, 0.1561, 0.0670, 0.0748, 0.0623, 0.0294,\n", + " 0.0387, 0.0317, -0.0310, -0.0086, 0.1448, 0.2485, 0.1980, 0.1521,\n", + " 0.2444, 0.2882, 0.1328, 0.1781, -0.0184, -0.0176, -0.0038, -0.2590,\n", + " -0.2027, -0.2314, -0.2451]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003196911420673132\n", + "Grad encoder.fc1.bias: 0.000399062322685495\n", + "Grad encoder.encoder.0.weight: 0.00011848308349726722\n", + "Grad encoder.encoder.0.bias: 0.00043898363946937025\n", + "Grad encoder.encoder.2.weight: 8.696077566128224e-05\n", + "Grad encoder.encoder.2.bias: 0.0004708229098469019\n", + "Grad encoder.encoder.4.weight: 0.00016953480371739715\n", + "Grad encoder.encoder.4.bias: 0.0012655913596972823\n", + "Grad decoder.fc1.0.weight: 5.034386049374007e-05\n", + "Grad decoder.fc1.0.bias: 0.0005626826314255595\n", + "Grad decoder.fc1.2.weight: 5.1056515076197684e-05\n", + "Grad decoder.fc1.2.bias: 0.0007378467125818133\n", + "Grad decoder.fc1.4.weight: 5.38824824616313e-05\n", + "Grad decoder.fc1.4.bias: 0.0008985669119283557\n", + "Grad decoder.fc2.weight: 0.00013503871741704643\n", + "Grad decoder.fc2.bias: 0.002445068210363388\n", + "Grad 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_memory_unit.weight_ih_l1: 3.380494263183209e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 8.560213609598577e-05\n", + "Grad _memory_unit.bias_hh_l1: 4.440036354935728e-05\n", + "Data X Sample: tensor([[1.3504, 1.5279, 1.7010, 1.8673, 1.9038, 2.1230, 1.9767, 1.8214, 3.1782,\n", + " 4.2874, 4.3016, 4.3897, 3.9547, 3.8661, 3.7879, 3.7963, 3.7611, 3.5902,\n", + " 3.6324, 3.5279, 3.5332, 3.2930, 2.9978, 2.8783, 2.8865, 3.0115, 2.9410,\n", + " 2.8468, 3.0772, 3.1343, 3.1811, 3.3574, 3.7973, 2.8538, 4.6645, 4.9763,\n", + " 5.0833, 5.4039, 6.0849, 5.9466, 1.1407, 0.6333, 0.6063, 1.1281, 1.6332,\n", + " 1.8756, 1.1898, 1.7124]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.7295, -0.5652, 0.0782, 0.8992, 0.6974, 0.0300, -0.1947, 0.5001,\n", + " 1.5130, -0.2875, 0.3482, 0.5159, 0.3071, -1.0189, 0.6654, -0.3364,\n", + " 0.1254, 0.3773, 0.7609, -0.2290, 0.0732, 0.0200, 0.3064, 0.3201,\n", + " -0.0416, 0.0035, 0.2316, -0.2121, -0.3849, -0.0803, 0.8078, 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-0.0121, -0.2055,\n", + " -0.1583, -0.1915, -0.2054]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00022508038091473281\n", + "Grad encoder.fc1.bias: 0.0007304234895855188\n", + "Grad encoder.encoder.0.weight: 8.786765101831406e-05\n", + "Grad encoder.encoder.0.bias: 0.0005037967930547893\n", + "Grad encoder.encoder.2.weight: 6.267084245337173e-05\n", + "Grad encoder.encoder.2.bias: 0.000421680771978572\n", + "Grad encoder.encoder.4.weight: 0.000132810790091753\n", + "Grad encoder.encoder.4.bias: 0.0009820915292948484\n", + "Grad decoder.fc1.0.weight: 4.075859396834858e-05\n", + "Grad decoder.fc1.0.bias: 0.00039642228512093425\n", + "Grad decoder.fc1.2.weight: 4.03786834795028e-05\n", + "Grad decoder.fc1.2.bias: 0.0006760062533430755\n", + "Grad decoder.fc1.4.weight: 4.967951463186182e-05\n", + "Grad decoder.fc1.4.bias: 0.0008796870242804289\n", + "Grad decoder.fc2.weight: 0.00010775355622172356\n", + "Grad decoder.fc2.bias: 0.0019513142760843039\n", + "Grad 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-1.9208,\n", + " -1.1049, -0.6770, 0.9041, 0.7780, 0.3203, 0.1011, 0.4611, 0.5388,\n", + " -0.9108, -0.4697, -0.4178, -0.2174, -0.9997, -0.8232, -0.1690, 0.2640,\n", + " 0.0734, -0.4141, 0.4670, 2.2864, 0.8419, 1.5990, 1.6308, 1.1717,\n", + " 0.5255, 0.3376, 1.1621, -0.6478, 0.4999, -0.4865, 0.3461, -0.7349,\n", + " 0.9049, 0.5563, -0.2406]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2675, -0.2433, -0.0901, -0.2098, -0.1461, -0.0051, 0.1051, 0.2213,\n", + " 0.1499, 0.2417, 0.1872, -0.0465, -0.1504, -0.1758, -0.1344, -0.2623,\n", + " -0.1796, -0.1451, 0.0029, -0.0516, -0.1038, 0.0313, -0.0365, -0.0585,\n", + " 0.0080, 0.0142, 0.0430, -0.0452, -0.1000, -0.1533, -0.1265, -0.1428,\n", + " -0.1265, -0.2120, -0.1254, -0.1397, 0.0270, 0.0098, -0.0442, 0.3404,\n", + " 0.3521, 0.2735, 0.2884]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00015034536772873253\n", + "Grad encoder.fc1.bias: 0.0001154080091509968\n", + "Grad encoder.encoder.0.weight: 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Sample: tensor([[-0.0011, 0.0029, 0.0105, 0.0131, -0.0017, 0.0118, 0.0139, 0.0156,\n", + " 0.0220, 0.0142, 0.0286, 0.0039, 0.0155, 0.0354, 0.0126, 0.0109,\n", + " -0.0692, -0.0735, -0.0805, -0.0725, -0.1006, -0.0551, -0.0819, -0.0592,\n", + " -0.0713, -0.1149, -0.1012, -0.1591, -0.0520, -0.0295, -0.0070, -0.0055,\n", + " -0.0132, -0.0343, 0.0000, 0.0389, 0.0000, 0.0000, 0.0634, -0.0057,\n", + " 0.0239, 0.0060, 0.0040, 0.0201, 0.0079, 0.0457, -0.0476, 0.0313]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[-3.0519, 0.3995, 1.5879, 0.1634, 1.5751, -1.0401, -0.0207, -0.7700,\n", + " 0.6821, 0.6840, 1.0154, 0.2329, 0.6055, -0.5633, 0.5689, 0.7168,\n", + " 0.5427, 0.2254, -0.4111, -1.0683, 0.5079, -0.1898, -0.6111, -0.3540,\n", + " 1.4992, -0.1921, -1.3559, -3.1031, -1.7347, -1.4826, -1.5491, -1.0408,\n", + " 0.1813, 0.8339, -0.1993, 0.6024, 0.0059, 0.0940, 0.0545, -1.4009,\n", + " -0.2454, -0.5046, -0.7669]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.6019, 0.4612, 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2.6092, 2.6407, 2.7529, 2.7412,\n", + " 2.7816, 2.6747, 2.8592, 2.8626, 3.0231, 3.3177, 2.4624, 3.9415, 4.2807,\n", + " 4.4993, 4.8632, 5.3877, 5.2090, 0.9832, 0.5317, 0.5436, 1.0018, 1.4549,\n", + " 1.5782, 1.1014, 1.4935]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.0953, -0.0528, 0.5273, 1.4612, 0.1060, 0.0701, -0.2653, -0.0236,\n", + " 0.3385, 0.0953, -0.0326, 0.5923, -1.1609, -1.0805, -1.3045, -0.3266,\n", + " 0.7653, 0.4342, -1.1803, -0.1047, -0.6090, 1.3425, -1.2692, -0.1244,\n", + " 0.2940, 1.8093, -1.3144, -1.4093, 0.3306, 0.3731, 0.2838, -0.2244,\n", + " -0.4284, -1.1538, -0.2693, -0.9535, 0.2315, -0.5781, 0.7114, 0.6371,\n", + " 0.8872, 0.1363, -0.8751]], device='cuda:0')\n", + "Prediction Sample: tensor([[-2.6540e-01, -2.6863e-01, -7.4386e-02, -2.0869e-01, -6.1706e-02,\n", + " 6.4023e-02, 1.3826e-01, 2.3033e-01, 1.8886e-01, 2.5110e-01,\n", + " 2.1271e-01, -5.8699e-02, -1.6281e-01, -2.1241e-01, -1.5479e-01,\n", + " -2.8741e-01, -1.6798e-01, -1.1902e-01, 1.7397e-02, -6.3299e-02,\n", + " -1.3096e-01, 3.9172e-02, -2.0551e-02, -6.0569e-02, 7.8799e-03,\n", + " 3.2867e-02, 3.8936e-05, -7.1154e-02, -1.6152e-01, -2.1056e-01,\n", + " -1.5734e-01, -1.6138e-01, -1.8374e-01, -2.4806e-01, -1.6154e-01,\n", + " -1.3671e-01, 2.9389e-02, 3.7421e-03, -2.0185e-02, 3.2374e-01,\n", + " 3.5225e-01, 2.8858e-01, 2.9925e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00032844519591890275\n", + "Grad encoder.fc1.bias: 0.0007835719152353704\n", + "Grad encoder.encoder.0.weight: 0.0001347015640931204\n", + "Grad encoder.encoder.0.bias: 0.0007031607674434781\n", + "Grad encoder.encoder.2.weight: 0.00011433747567934915\n", + "Grad encoder.encoder.2.bias: 0.0006090163951739669\n", + "Grad encoder.encoder.4.weight: 0.00023536162916570902\n", + "Grad encoder.encoder.4.bias: 0.0015558333834633231\n", + "Grad decoder.fc1.0.weight: 7.985960837686434e-05\n", + "Grad decoder.fc1.0.bias: 0.0007223297143355012\n", + "Grad decoder.fc1.2.weight: 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2.6373,\n", + " 2.7408, 2.6088, 2.8166, 2.7331, 2.8766, 3.0999, 2.2496, 3.7331, 4.1080,\n", + " 4.3990, 4.8367, 5.4764, 5.2657, 2.0522, 1.2148, 1.1398, 1.9031, 2.8225,\n", + " 2.8191, 1.8425, 2.4943]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.5046, -0.7526, -0.8748, -0.6211, -0.1423, 0.3017, -0.1168, -0.5318,\n", + " -2.0346, -0.1632, 0.4610, 0.6743, 1.5043, 0.7970, -0.4769, -0.1092,\n", + " 0.3246, -0.0490, 2.6633, 0.6075, -0.2500, 0.4750, -1.4976, -0.1660,\n", + " -0.2914, 0.5464, 0.1536, 0.3242, 0.4623, -0.5487, -0.7810, 0.9701,\n", + " -0.9948, 0.3311, 1.1833, -0.0081, 1.0050, -0.0140, -0.7911, 0.0949,\n", + " -0.3052, 0.1061, 1.0851]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2346, -0.2200, -0.0597, -0.2318, -0.1433, -0.0383, 0.0517, 0.1693,\n", + " 0.1156, 0.2097, 0.1631, -0.0380, -0.1033, -0.1568, -0.1003, -0.1977,\n", + " -0.1841, -0.1259, 0.0155, -0.0487, -0.0805, 0.0320, -0.0308, -0.0548,\n", + " 0.0056, 0.0011, 0.0684, 0.0022, -0.0451, -0.1250, -0.0853, -0.1179,\n", + " -0.0797, -0.1858, -0.1187, -0.1108, 0.0549, 0.0317, -0.0500, 0.3032,\n", + " 0.3186, 0.2323, 0.2532]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0009511564276181161\n", + "Grad encoder.fc1.bias: 0.0005010843742638826\n", + "Grad encoder.encoder.0.weight: 0.0003397881519049406\n", + "Grad encoder.encoder.0.bias: 0.0007656397647224367\n", + "Grad encoder.encoder.2.weight: 0.00022904948855284601\n", + "Grad encoder.encoder.2.bias: 0.0008879097877070308\n", + "Grad encoder.encoder.4.weight: 0.00041578366653993726\n", + "Grad encoder.encoder.4.bias: 0.00276179239153862\n", + "Grad decoder.fc1.0.weight: 0.00011404085671529174\n", + "Grad decoder.fc1.0.bias: 0.001035475404933095\n", + "Grad decoder.fc1.2.weight: 7.102404197212309e-05\n", + "Grad decoder.fc1.2.bias: 0.0012890361249446869\n", + "Grad decoder.fc1.4.weight: 6.179902266012505e-05\n", + "Grad decoder.fc1.4.bias: 0.0012807443272322416\n", + "Grad decoder.fc2.weight: 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"Data Y Sample: tensor([[-0.4655, -1.3143, 0.4263, -1.1704, -2.0395, -0.4369, 0.2077, 0.4910,\n", + " 0.5144, 0.5843, 0.7010, -0.2757, -0.2222, -0.9194, -0.7631, -1.1544,\n", + " -0.7063, -0.3744, 0.7426, -0.9395, -0.0577, -1.4678, -0.3471, 0.0379,\n", + " -0.2841, -0.2633, 0.8873, 0.2045, -0.6865, -0.0619, -0.4567, -0.1771,\n", + " -1.3811, -0.6343, -0.8537, -0.9445, 0.0000, -0.6143, 0.0000, 0.7401,\n", + " 0.8786, 0.7597, 0.4988]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2812, -0.2925, -0.0756, -0.2073, -0.0344, 0.0900, 0.1573, 0.2541,\n", + " 0.2120, 0.2691, 0.2291, -0.0703, -0.1812, -0.2335, -0.1608, -0.3127,\n", + " -0.1754, -0.1147, 0.0155, -0.0742, -0.1427, 0.0397, -0.0202, -0.0699,\n", + " 0.0032, 0.0375, -0.0170, -0.0857, -0.1936, -0.2418, -0.1741, -0.1736,\n", + " -0.2179, -0.2741, -0.1821, -0.1432, 0.0340, 0.0036, -0.0155, 0.3380,\n", + " 0.3742, 0.3027, 0.3090]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00019328613416291773\n", + 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device='cuda:0')\n", + "Prediction Sample: tensor([[ 5.3468e-01, 4.1093e-01, 1.4987e-01, 2.8478e-01, 1.3743e-01,\n", + " -1.2815e-01, -5.4184e-01, -6.0633e-01, -3.2609e-01, -3.4148e-01,\n", + " -2.7276e-01, 2.9322e-01, 2.4253e-01, 3.5047e-01, 2.5489e-01,\n", + " 3.6111e-01, 3.6473e-01, 1.3421e-01, 1.3402e-01, 2.1986e-01,\n", + " 1.4104e-01, 9.7331e-02, 5.6722e-02, 8.0913e-02, 8.3459e-02,\n", + " 6.2310e-02, -8.1871e-02, 9.8476e-02, 2.9800e-01, 4.1295e-01,\n", + " 3.4591e-01, 2.6653e-01, 3.9590e-01, 4.0469e-01, 1.7430e-01,\n", + " 2.3600e-01, 3.7654e-02, 2.5929e-02, -1.0952e-04, -3.9800e-01,\n", + " -3.5077e-01, -3.6068e-01, -3.3116e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0005459233070723712\n", + "Grad encoder.fc1.bias: 0.0006855481187812984\n", + "Grad encoder.encoder.0.weight: 0.00018474487296771258\n", + "Grad encoder.encoder.0.bias: 0.0006308969459496439\n", + "Grad encoder.encoder.2.weight: 0.00013591132301371545\n", + "Grad 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_memory_unit.bias_hh_l1: 7.097997877281159e-05\n", + "Data X Sample: tensor([[1.1977, 1.3356, 1.4605, 1.5765, 1.5913, 1.7650, 1.9844, 1.8881, 3.3906,\n", + " 3.7834, 3.8163, 3.8249, 3.7750, 3.5308, 3.3844, 3.3163, 3.2438, 3.2362,\n", + " 3.3061, 3.0369, 3.0817, 3.0970, 2.7929, 2.6989, 2.7064, 2.7242, 2.9543,\n", + " 2.7979, 2.7962, 2.8657, 2.9431, 3.1004, 3.1087, 2.3572, 3.8704, 4.2880,\n", + " 4.6035, 5.0938, 5.3940, 5.4501, 0.8925, 0.5536, 0.5517, 0.9644, 1.4866,\n", + " 1.5096, 1.0742, 1.4544]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 9.4182e-02, 1.9066e-01, -7.0604e-01, 7.5355e-01, -4.0627e-01,\n", + " 4.9594e-01, -1.7437e-01, 1.1428e-01, -2.0074e-03, 1.3583e-01,\n", + " -5.8682e-02, -1.2205e+00, -1.1393e+00, -6.2187e-01, -1.7932e-01,\n", + " -3.2120e-01, -4.1710e-02, 3.1686e+00, 8.7986e-01, -8.9530e-01,\n", + " -4.6012e-01, 3.3000e-01, 4.7604e-01, 2.8349e+00, 8.1620e-01,\n", + " 9.6599e-01, 4.8683e-01, -1.1266e+00, -1.5768e-02, -2.1460e-01,\n", + " -1.2087e-01, 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-1.8131e+00, 1.8831e-01,\n", + " 1.0311e-01, -1.0460e-04, -7.1424e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3487, 0.2614, 0.1413, 0.1982, 0.1128, -0.1102, -0.3375, -0.3951,\n", + " -0.1802, -0.2106, -0.1935, 0.1395, 0.1620, 0.2557, 0.1698, 0.2843,\n", + " 0.1773, 0.1036, 0.0783, 0.1273, 0.1234, 0.0705, 0.0491, 0.0523,\n", + " 0.0473, 0.0319, -0.0737, 0.0249, 0.1818, 0.2553, 0.2008, 0.1476,\n", + " 0.2297, 0.2415, 0.1061, 0.1788, 0.0436, 0.0340, -0.0078, -0.2517,\n", + " -0.2496, -0.2160, -0.2369]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00011400080984458327\n", + "Grad encoder.fc1.bias: 0.0010329849319532514\n", + "Grad encoder.encoder.0.weight: 4.595361679093912e-05\n", + "Grad encoder.encoder.0.bias: 0.0008731384878046811\n", + "Grad encoder.encoder.2.weight: 4.122842801734805e-05\n", + "Grad encoder.encoder.2.bias: 0.0006393225630745292\n", + "Grad encoder.encoder.4.weight: 0.0001142780965892598\n", + "Grad encoder.encoder.4.bias: 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Sample: tensor([[-0.1899, -0.2085, -0.0540, -0.2539, -0.1874, -0.0692, 0.0340, 0.1771,\n", + " 0.0808, 0.1870, 0.1510, -0.1149, -0.0833, -0.1689, -0.1007, -0.1623,\n", + " -0.2280, -0.1147, -0.0313, -0.0606, -0.0514, -0.0155, -0.0656, -0.0494,\n", + " 0.0019, 0.0016, 0.0942, 0.0007, 0.0291, -0.0698, -0.0959, -0.0973,\n", + " -0.0580, -0.1736, -0.1356, -0.0890, 0.0396, 0.0333, -0.0606, 0.2875,\n", + " 0.2826, 0.2585, 0.2208]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.000877309706993401\n", + "Grad encoder.fc1.bias: 0.0005373663152568042\n", + "Grad encoder.encoder.0.weight: 0.0002890598843805492\n", + "Grad encoder.encoder.0.bias: 0.0008563207811675966\n", + "Grad encoder.encoder.2.weight: 0.00019575122860260308\n", + "Grad encoder.encoder.2.bias: 0.0009161455673165619\n", + "Grad encoder.encoder.4.weight: 0.0002974968811031431\n", + "Grad encoder.encoder.4.bias: 0.0026016058400273323\n", + "Grad decoder.fc1.0.weight: 7.617256051162258e-05\n", + "Grad 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-2.7837e-01, -2.4399e-01, -1.3859e-01, -7.8755e-02, -8.8193e-02,\n", + " -1.0509e-01, -2.6943e-02, -6.0036e-02, -6.4150e-02, -8.7246e-05,\n", + " 3.3809e-02, 7.3783e-02, -6.5587e-02, -4.6077e-02, -1.3838e-01,\n", + " -1.9162e-01, -1.5604e-01, -1.3554e-01, -2.4408e-01, -1.8705e-01,\n", + " -1.5750e-01, -4.1457e-03, -4.2172e-03, -3.5308e-02, 3.7838e-01,\n", + " 3.6581e-01, 3.7516e-01, 2.9284e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003305419231764972\n", + "Grad encoder.fc1.bias: 0.00021637865575030446\n", + "Grad encoder.encoder.0.weight: 0.00010742098675109446\n", + "Grad encoder.encoder.0.bias: 0.00029789749532938004\n", + "Grad encoder.encoder.2.weight: 7.249397458508611e-05\n", + "Grad encoder.encoder.2.bias: 0.00035346540971659124\n", + "Grad encoder.encoder.4.weight: 0.00011734809959307313\n", + "Grad encoder.encoder.4.bias: 0.0010806581703945994\n", + "Grad decoder.fc1.0.weight: 3.709081283886917e-05\n", + "Grad decoder.fc1.0.bias: 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1.6055,\n", + " 1.8584, 1.1626, 1.7671]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.3558, -0.1834, 1.2807, -0.3908, 0.0910, -0.0064, 0.1316, 0.3776,\n", + " 0.5771, 0.4400, 0.3213, 0.8428, -0.8711, 0.1363, -1.1952, 0.3151,\n", + " -0.5003, -0.3460, -0.2940, -0.4217, -0.3530, -0.1952, 1.0969, -0.3641,\n", + " 0.0921, 0.2080, -0.6346, -0.3495, -0.8023, -0.5580, -0.3950, -0.8401,\n", + " -0.4130, 0.0123, 1.3024, -0.7877, 0.7923, 0.0000, 0.0000, 0.5198,\n", + " 0.3049, 0.3331, 0.1530]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2220, -0.2756, -0.0851, -0.1899, -0.0343, 0.1157, 0.2021, 0.3445,\n", + " 0.2116, 0.2518, 0.2653, -0.2206, -0.1767, -0.2987, -0.1818, -0.2884,\n", + " -0.2104, -0.0599, -0.0884, -0.0958, -0.1099, -0.0213, -0.0581, -0.0720,\n", + " -0.0019, 0.0828, -0.0506, -0.1524, -0.1750, -0.2164, -0.2678, -0.1673,\n", + " -0.2626, -0.2831, -0.2351, -0.1847, -0.0126, 0.0039, 0.0077, 0.3249,\n", + " 0.3564, 0.4013, 0.2681]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002052594645647332\n", + "Grad encoder.fc1.bias: 0.0001202352432301268\n", + "Grad encoder.encoder.0.weight: 6.377507816068828e-05\n", + "Grad encoder.encoder.0.bias: 0.0001570275635458529\n", + "Grad encoder.encoder.2.weight: 4.593094126903452e-05\n", + "Grad encoder.encoder.2.bias: 0.0001934032334247604\n", + "Grad encoder.encoder.4.weight: 7.885103696025908e-05\n", + "Grad encoder.encoder.4.bias: 0.0005726065719500184\n", + "Grad decoder.fc1.0.weight: 3.0177830922184512e-05\n", + "Grad decoder.fc1.0.bias: 0.00026013568276539445\n", + "Grad decoder.fc1.2.weight: 4.654717486118898e-05\n", + "Grad decoder.fc1.2.bias: 0.0006245611584745347\n", + "Grad decoder.fc1.4.weight: 4.041303327539936e-05\n", + "Grad decoder.fc1.4.bias: 0.0005554945673793554\n", + "Grad decoder.fc2.weight: 0.00013927309191785753\n", + "Grad decoder.fc2.bias: 0.0022726557217538357\n", + "Grad _memory_unit.weight_ih_l0: 5.395254447648767e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 1.4344342162075918e-05\n", + "Grad _memory_unit.bias_hh_l0: 8.164861355908215e-06\n", + "Grad _memory_unit.weight_ih_l1: 3.070051661779871e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 4.2849755118368194e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.2230327886063606e-05\n", + "Data X Sample: tensor([[0.0393, 0.0626, 0.0691, 0.0743, 0.0854, 0.0899, 0.1171, 0.1036, 0.1196,\n", + " 0.1295, 0.1015, 0.1383, 0.1265, 0.1281, 0.1211, 0.1518, 0.1714, 0.1799,\n", + " 0.2024, 0.2157, 0.2061, 0.1960, 0.2217, 0.2080, 0.1989, 0.2586, 0.2717,\n", + " 0.3712, 0.1041, 0.2260, 0.1750, 0.1769, 0.1408, 0.1167, 0.1569, 0.1581,\n", + " 0.1337, 0.1352, 0.2028, 0.1220, 0.0764, 0.0498, 0.0485, 0.0459, 0.0912,\n", + " 0.0801, 0.0816, 0.0938]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.6985, 1.5681, -0.1240, -0.8124, 0.6218, 0.2860, 0.8409, -1.0039,\n", + " 0.5102, 0.8293, 1.1034, 1.1617, -0.9147, 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0.0893, -0.1800, -0.6028, -0.3476,\n", + " -0.8179, -0.3803, 0.4338]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1873, -0.2473, -0.0787, -0.1863, -0.0442, 0.0930, 0.1701, 0.2995,\n", + " 0.1848, 0.2272, 0.2293, -0.1971, -0.1484, -0.2633, -0.1613, -0.2582,\n", + " -0.2025, -0.0560, -0.0733, -0.0893, -0.0932, -0.0210, -0.0581, -0.0731,\n", + " -0.0099, 0.0624, -0.0345, -0.1265, -0.1507, -0.1954, -0.2381, -0.1494,\n", + " -0.2311, -0.2466, -0.2034, -0.1662, -0.0135, 0.0067, 0.0065, 0.2871,\n", + " 0.3241, 0.3606, 0.2415]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003186904941685498\n", + "Grad encoder.fc1.bias: 0.0012222534278407693\n", + "Grad encoder.encoder.0.weight: 9.636346658226103e-05\n", + "Grad encoder.encoder.0.bias: 0.0008542332798242569\n", + "Grad encoder.encoder.2.weight: 6.856117397546768e-05\n", + "Grad encoder.encoder.2.bias: 0.0007142227841541171\n", + "Grad encoder.encoder.4.weight: 0.00014116137754172087\n", + "Grad 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0.0579, -0.4262,\n", + " -0.4343, -0.3971, -0.4232]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003139438049402088\n", + "Grad encoder.fc1.bias: 0.000585022266022861\n", + "Grad encoder.encoder.0.weight: 0.00011727923265425488\n", + "Grad encoder.encoder.0.bias: 0.0005559074925258756\n", + "Grad encoder.encoder.2.weight: 8.349171548616141e-05\n", + "Grad encoder.encoder.2.bias: 0.0005203622858971357\n", + "Grad encoder.encoder.4.weight: 0.00016064284136518836\n", + "Grad encoder.encoder.4.bias: 0.0009373007342219353\n", + "Grad decoder.fc1.0.weight: 5.0289112550672144e-05\n", + "Grad decoder.fc1.0.bias: 0.00037164456443861127\n", + "Grad decoder.fc1.2.weight: 6.542756455019116e-05\n", + "Grad decoder.fc1.2.bias: 0.0008465952123515308\n", + "Grad decoder.fc1.4.weight: 5.3829273383598775e-05\n", + "Grad decoder.fc1.4.bias: 0.0008707253728061914\n", + "Grad decoder.fc2.weight: 0.00013349764049053192\n", + "Grad decoder.fc2.bias: 0.001828240929171443\n", + "Grad 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"Data Y Sample: tensor([[-0.4151, 0.1628, 0.2779, -0.4328, -0.4883, -0.1318, -0.0414, 0.2148,\n", + " 0.6777, 0.3264, 0.2644, -1.3860, -1.6863, 0.2297, -0.9390, -0.2156,\n", + " -0.3535, -1.2947, -0.1733, -0.1003, -0.2863, -1.0709, -0.1756, 1.9703,\n", + " 1.8541, -1.3661, 0.5400, 0.5610, 0.3607, -0.1778, 0.2245, -0.6190,\n", + " -0.0624, -0.9702, -0.0513, 0.6487, -1.0293, -0.6782, -0.5970, 0.5167,\n", + " 0.3646, 0.7861, 0.6727]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2075, -0.2465, -0.0363, -0.1030, 0.0994, 0.2008, 0.1753, 0.2910,\n", + " 0.2076, 0.2669, 0.1843, -0.2665, -0.2071, -0.3442, -0.1663, -0.2394,\n", + " -0.2130, -0.0112, -0.1399, -0.1017, -0.0863, -0.0954, -0.0289, -0.1181,\n", + " -0.0041, 0.0278, -0.0909, -0.2511, -0.2188, -0.2846, -0.3300, -0.1181,\n", + " -0.3705, -0.2845, -0.1812, -0.1837, -0.0054, 0.0296, 0.0588, 0.2587,\n", + " 0.2727, 0.3171, 0.1915]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0004091041046194732\n", + 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0.1511, -0.0156, -0.1307,\n", + " -0.2865, -0.1779, -0.2315]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4192, 0.3202, 0.1614, 0.2766, 0.0809, -0.1690, -0.3810, -0.4289,\n", + " -0.2070, -0.2795, -0.2742, 0.1555, 0.1668, 0.2558, 0.2191, 0.3084,\n", + " 0.2066, 0.1503, 0.0865, 0.0758, 0.0714, -0.0150, 0.0486, 0.0085,\n", + " 0.0182, 0.0286, -0.0455, 0.0254, 0.2061, 0.2480, 0.2229, 0.1696,\n", + " 0.2322, 0.3045, 0.2109, 0.1627, -0.0483, 0.0466, -0.0440, -0.2983,\n", + " -0.2909, -0.2447, -0.2802]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00012787376181222498\n", + "Grad encoder.fc1.bias: 0.00016677254461683333\n", + "Grad encoder.encoder.0.weight: 4.13334091717843e-05\n", + "Grad encoder.encoder.0.bias: 0.0001370642421534285\n", + "Grad encoder.encoder.2.weight: 2.274625512654893e-05\n", + "Grad encoder.encoder.2.bias: 0.00016137922648340464\n", + "Grad encoder.encoder.4.weight: 4.6879766159690917e-05\n", + "Grad encoder.encoder.4.bias: 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"Grad _memory_unit.weight_ih_l0: 1.0349036529078148e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 2.616678102640435e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.399513530486729e-05\n", + "Grad _memory_unit.weight_ih_l1: 4.2516958274063654e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 5.257268639979884e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.8165397452539764e-05\n", + "Data X Sample: tensor([[1.4926, 1.6124, 1.6559, 1.7383, 1.9448, 2.0066, 2.0691, 2.1067, 2.1701,\n", + " 3.6507, 4.0732, 4.1696, 4.0790, 3.7952, 3.6668, 3.4626, 3.4498, 3.4142,\n", + " 3.5869, 3.4312, 3.3958, 3.4139, 3.0195, 2.8230, 2.7552, 2.8548, 2.8797,\n", + " 2.8958, 2.9488, 3.0393, 3.0936, 3.2110, 3.5333, 2.5997, 4.3704, 4.6261,\n", + " 4.9083, 5.2607, 5.8821, 5.6402, 1.0452, 0.5875, 0.6184, 1.0161, 1.5579,\n", + " 1.6411, 1.1830, 1.6029]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.9752, -0.4666, -0.5710, -0.3170, 0.3978, 0.5850, 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"Grad _memory_unit.bias_hh_l1: 3.711294993991032e-05\n", + "Data X Sample: tensor([[2.6595, 2.8213, 3.1149, 3.1617, 3.2920, 3.4636, 3.4773, 3.6186, 3.8714,\n", + " 3.7755, 3.7592, 3.7879, 3.7861, 3.6889, 3.6643, 3.5871, 3.5284, 3.5534,\n", + " 3.5229, 3.5242, 3.3124, 3.2807, 2.8749, 2.9337, 2.7176, 2.6746, 2.7119,\n", + " 2.6878, 2.6782, 2.6692, 2.7576, 2.7716, 2.7831, 2.0253, 3.3091, 3.6970,\n", + " 4.0332, 4.1238, 4.4749, 4.4259, 1.9759, 1.1272, 1.1136, 1.8858, 2.6996,\n", + " 2.9735, 1.9241, 2.8306]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 2.3898, 0.3894, 0.0119, 0.0254, -1.0964, -0.8045, -0.1312, 0.6422,\n", + " -0.5600, -0.4710, -0.2976, -0.7770, -0.5739, -0.3697, -0.4659, -0.8210,\n", + " -0.0529, 0.5431, -0.8561, -0.2930, -0.4742, 0.5710, 1.1420, 0.3205,\n", + " 0.6058, -0.0752, -0.6555, 0.0814, 0.3718, -1.6298, 0.5534, -1.9418,\n", + " 0.6174, -0.4706, 1.2937, -0.4340, 0.8233, -0.7806, -0.4768, 0.0684,\n", + " 0.4068, 0.1843, -1.5100]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2079, -0.2645, -0.1096, -0.2620, -0.2489, -0.0767, 0.0414, 0.2262,\n", + " 0.0891, 0.1997, 0.1719, -0.1289, -0.0929, -0.2000, -0.1383, -0.2251,\n", + " -0.2393, -0.1333, -0.0394, -0.1120, -0.0519, -0.0252, -0.0729, -0.0669,\n", + " 0.0236, -0.0209, 0.0811, -0.0179, -0.0214, -0.1159, -0.1396, -0.1470,\n", + " -0.0861, -0.1829, -0.1014, -0.1275, -0.0070, -0.0114, -0.0428, 0.3209,\n", + " 0.3355, 0.3089, 0.2459]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.000119678137707524\n", + "Grad encoder.fc1.bias: 0.0014439936494454741\n", + "Grad encoder.encoder.0.weight: 6.495595880551264e-05\n", + "Grad encoder.encoder.0.bias: 0.0011972220381721854\n", + "Grad encoder.encoder.2.weight: 6.018800922902301e-05\n", + "Grad encoder.encoder.2.bias: 0.001020955853164196\n", + "Grad encoder.encoder.4.weight: 0.0001665887248236686\n", + "Grad encoder.encoder.4.bias: 0.0030648107640445232\n", + "Grad decoder.fc1.0.weight: 8.28736592666246e-05\n", + "Grad decoder.fc1.0.bias: 0.0009727091528475285\n", + "Grad decoder.fc1.2.weight: 9.488279465585947e-05\n", + "Grad decoder.fc1.2.bias: 0.0011996040120720863\n", + "Grad decoder.fc1.4.weight: 7.752339297439903e-05\n", + "Grad decoder.fc1.4.bias: 0.0010637976229190826\n", + "Grad decoder.fc2.weight: 0.00019837157742585987\n", + "Grad decoder.fc2.bias: 0.0018000972922891378\n", + "Grad _memory_unit.weight_ih_l0: 2.0157416656729765e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 0.00017197171109728515\n", + "Grad _memory_unit.bias_hh_l0: 8.896073268260807e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.022938795358641e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00027878856053575873\n", + "Grad _memory_unit.bias_hh_l1: 0.00014619604917243123\n", + "Data X Sample: tensor([[1.5647, 1.7303, 1.9083, 1.9788, 2.1275, 2.2953, 2.4081, 2.4233, 2.5533,\n", + " 2.5813, 2.5664, 2.6486, 2.5899, 2.4593, 2.4689, 2.4014, 2.3696, 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_memory_unit.weight_ih_l0: 8.763724508753512e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 5.505268563865684e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.982758087455295e-05\n", + "Grad _memory_unit.weight_ih_l1: 5.513459655048791e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00011636752606136724\n", + "Grad _memory_unit.bias_hh_l1: 6.103504711063579e-05\n", + "Data X Sample: tensor([[1.7387, 1.8920, 2.0451, 2.1822, 2.1480, 2.2674, 2.3326, 2.3622, 2.3922,\n", + " 2.4422, 2.5220, 2.5064, 2.4478, 2.3666, 2.4135, 2.4001, 2.3947, 2.4547,\n", + " 2.4512, 2.5255, 2.5149, 2.5459, 2.5616, 2.5519, 2.5844, 2.7268, 2.6400,\n", + " 2.6796, 2.3903, 2.4039, 2.4147, 2.4041, 2.1516, 1.4349, 1.9609, 1.7877,\n", + " 1.6794, 1.5742, 1.5339, 1.5122, 1.3841, 0.7627, 0.7518, 1.2687, 1.9147,\n", + " 2.1672, 1.4346, 2.0955]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 2.9787e-01, 1.1706e-01, 4.9050e-01, 5.6912e-01, -6.5620e-01,\n", + " -3.0011e-01, -4.7681e-01, -4.3763e-01, 2.1799e-02, -4.3448e-01,\n", + " -4.2963e-01, 2.7341e-01, 4.5099e-01, -7.6737e-01, 1.2830e-01,\n", + " 5.4907e-02, -1.1654e-02, 2.8027e-01, 2.1979e-01, -2.7596e-02,\n", + " 3.7718e-01, -4.1321e+00, 2.6759e-01, -9.4034e-01, -6.6599e-02,\n", + " -9.8641e-02, -3.7348e-03, 1.9078e-01, 8.2640e-02, 1.8274e-01,\n", + " -7.2141e-01, 1.3292e+00, 4.4560e-02, 4.4064e-01, -5.5109e-01,\n", + " -1.6444e-01, 0.0000e+00, 7.6718e-01, -1.9703e-02, -3.5796e-01,\n", + " -1.8229e-01, -6.5499e-01, 6.6668e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4912, 0.3772, 0.1541, 0.3681, 0.1191, -0.1574, -0.4061, -0.4570,\n", + " -0.2309, -0.3085, -0.3001, 0.2019, 0.1999, 0.3002, 0.2461, 0.3344,\n", + " 0.2901, 0.1045, 0.1091, 0.0830, 0.0643, 0.0324, 0.0298, 0.0153,\n", + " 0.0135, 0.0593, -0.1338, 0.0006, 0.1750, 0.2622, 0.2644, 0.1570,\n", + " 0.2708, 0.3305, 0.2370, 0.2268, -0.0903, 0.0182, -0.0364, -0.3504,\n", + " -0.3181, -0.3022, -0.3042]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0001752872485667467\n", + "Grad encoder.fc1.bias: 0.00020213978132233024\n", + "Grad encoder.encoder.0.weight: 5.621779564535245e-05\n", + "Grad encoder.encoder.0.bias: 0.0002571367076598108\n", + "Grad encoder.encoder.2.weight: 4.290915603633039e-05\n", + "Grad encoder.encoder.2.bias: 0.0003350428305566311\n", + "Grad encoder.encoder.4.weight: 8.350670395884663e-05\n", + "Grad encoder.encoder.4.bias: 0.0009490516968071461\n", + "Grad decoder.fc1.0.weight: 3.468879731371999e-05\n", + "Grad decoder.fc1.0.bias: 0.0003731604665517807\n", + "Grad decoder.fc1.2.weight: 3.511802788125351e-05\n", + "Grad decoder.fc1.2.bias: 0.0004631312913261354\n", + "Grad decoder.fc1.4.weight: 3.5855766327586025e-05\n", + "Grad decoder.fc1.4.bias: 0.0006101182661950588\n", + "Grad decoder.fc2.weight: 0.00012562023766804487\n", + "Grad decoder.fc2.bias: 0.0017865594709292054\n", + "Grad _memory_unit.weight_ih_l0: 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-0.6050, -0.8549, 0.6253, 0.5727, -0.5075, 1.1356, 0.5006,\n", + " 0.5701, 1.3932, 0.6763, -0.5156, 0.6304, 1.3965, -0.1337, -0.0052,\n", + " -0.6359, -0.6738, -0.4697, -1.2639, -0.9495, -0.9608, -0.5961, 0.2539,\n", + " -0.6477, 0.0039, -1.3430, -0.5741, -0.9576, -0.3276, -0.2715, -0.9109,\n", + " -0.6407, -1.1520, -0.1456]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2201, -0.2970, -0.0445, -0.1472, 0.0637, 0.1991, 0.2362, 0.3025,\n", + " 0.2421, 0.2499, 0.2190, -0.1922, -0.1311, -0.3184, -0.2361, -0.3185,\n", + " -0.2079, -0.0855, -0.0400, -0.1704, -0.1098, -0.0382, -0.0516, -0.0686,\n", + " 0.0088, -0.0318, -0.1370, -0.2173, -0.2655, -0.3249, -0.3202, -0.2135,\n", + " -0.3579, -0.3010, -0.1954, -0.1639, 0.0132, -0.0487, 0.0309, 0.2888,\n", + " 0.3535, 0.3384, 0.2552]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 8.750002598389983e-05\n", + "Grad encoder.fc1.bias: 0.00021082365128677338\n", + "Grad encoder.encoder.0.weight: 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"Grad encoder.encoder.0.bias: 0.00024397679953835905\n", + "Grad encoder.encoder.2.weight: 3.956608270527795e-05\n", + "Grad encoder.encoder.2.bias: 0.0002769716957118362\n", + "Grad encoder.encoder.4.weight: 7.024001388344914e-05\n", + "Grad encoder.encoder.4.bias: 0.0009699015645310283\n", + "Grad decoder.fc1.0.weight: 4.0736969822319224e-05\n", + "Grad decoder.fc1.0.bias: 0.0004907406400889158\n", + "Grad decoder.fc1.2.weight: 4.9268950533587486e-05\n", + "Grad decoder.fc1.2.bias: 0.001030094106681645\n", + "Grad decoder.fc1.4.weight: 4.363457628642209e-05\n", + "Grad decoder.fc1.4.bias: 0.00082686438690871\n", + "Grad decoder.fc2.weight: 0.00011656437709461898\n", + "Grad decoder.fc2.bias: 0.0019580908119678497\n", + "Grad _memory_unit.weight_ih_l0: 9.215157660946716e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.4366130421403795e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.777505167410709e-05\n", + "Grad _memory_unit.weight_ih_l1: 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" -1.4794e-01, -2.6634e-01, -1.8064e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.1415, 0.0643, 0.0264, 0.0615, -0.0656, -0.0872, -0.2070, -0.1171,\n", + " -0.1461, -0.0857, -0.1132, 0.0084, 0.0620, 0.0659, 0.0507, 0.0768,\n", + " 0.0376, -0.0022, 0.0224, 0.0068, 0.0072, 0.0415, -0.0280, -0.0175,\n", + " 0.0104, 0.0097, -0.0194, 0.0239, 0.0790, 0.1046, 0.0699, 0.0248,\n", + " 0.0884, 0.0934, 0.0918, 0.0559, -0.0333, -0.0017, -0.0459, -0.0505,\n", + " -0.0229, -0.0294, -0.0565]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0011154480744153261\n", + "Grad encoder.fc1.bias: 0.0005278855096548796\n", + "Grad encoder.encoder.0.weight: 0.0003656214685179293\n", + "Grad encoder.encoder.0.bias: 0.0009063543984666467\n", + "Grad encoder.encoder.2.weight: 0.00020632859377656132\n", + "Grad encoder.encoder.2.bias: 0.0008903080015443265\n", + "Grad encoder.encoder.4.weight: 0.00028809296782128513\n", + "Grad encoder.encoder.4.bias: 0.002736845752224326\n", 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"Grad decoder.fc1.0.bias: 0.0010466580279171467\n", + "Grad decoder.fc1.2.weight: 8.867510769050568e-05\n", + "Grad decoder.fc1.2.bias: 0.0016656226944178343\n", + "Grad decoder.fc1.4.weight: 8.721105405129492e-05\n", + "Grad decoder.fc1.4.bias: 0.0018972596153616905\n", + "Grad decoder.fc2.weight: 0.00018406970775686204\n", + "Grad decoder.fc2.bias: 0.0034340082202106714\n", + "Grad _memory_unit.weight_ih_l0: 1.0527834092499688e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 4.759724106406793e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.4802086045383476e-05\n", + "Grad _memory_unit.weight_ih_l1: 6.594812930416083e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00016127791604958475\n", + "Grad _memory_unit.bias_hh_l1: 8.239227463491261e-05\n", + "Data X Sample: tensor([[1.3250, 1.5483, 1.6679, 1.7820, 1.7979, 1.9889, 2.1015, 2.1223, 2.0114,\n", + " 3.4280, 4.1621, 4.0722, 3.9569, 3.7053, 3.5887, 3.5392, 3.4812, 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_memory_unit.bias_ih_l0: 1.5054913092171773e-05\n", + "Grad _memory_unit.bias_hh_l0: 7.695902240811847e-06\n", + "Grad _memory_unit.weight_ih_l1: 1.7859669014796964e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 3.9265258237719536e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.027950540650636e-05\n", + "Data X Sample: tensor([[1.5053, 1.6619, 1.8993, 2.0029, 2.0387, 1.9653, 1.9243, 2.5454, 4.0325,\n", + " 4.3695, 4.3905, 4.3410, 4.0768, 3.8770, 3.9443, 3.8346, 3.7344, 3.7720,\n", + " 3.5828, 3.5632, 3.5626, 3.4017, 2.9183, 2.7371, 2.8715, 2.8626, 2.9330,\n", + " 2.7979, 2.9904, 3.0491, 3.1181, 3.2607, 3.8369, 2.8629, 4.5469, 4.8110,\n", + " 5.1679, 5.4728, 6.0278, 5.7991, 1.1836, 0.6831, 0.7053, 1.1654, 1.7205,\n", + " 1.9556, 1.2714, 1.8219]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.7014, -0.3002, -0.1198, -0.0036, 0.8791, 0.4037, 0.3739, 0.2934,\n", + " -0.1394, 0.6248, 0.0565, 0.2705, -0.5137, -0.4050, 0.4826, 0.7908,\n", + " 0.7580, 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"Grad _memory_unit.bias_ih_l1: 5.4550615459447727e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.80753865808947e-05\n", + "Data X Sample: tensor([[1.6835, 1.8905, 2.0060, 2.1975, 2.2811, 2.4780, 2.5822, 2.6519, 2.5606,\n", + " 2.6571, 2.6520, 2.6272, 2.6565, 2.5547, 2.4059, 2.4725, 2.4372, 2.4044,\n", + " 2.5709, 2.4995, 2.5861, 2.6393, 2.5761, 2.5252, 2.5956, 2.5597, 2.4855,\n", + " 2.5858, 2.2966, 2.4039, 2.3727, 2.3129, 2.1824, 1.3891, 1.8751, 1.7877,\n", + " 1.6715, 1.6087, 1.5973, 1.4611, 1.3220, 0.7488, 0.7599, 1.3003, 1.7601,\n", + " 2.2930, 1.3258, 2.1034]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.6104e-01, 4.5188e-01, -1.3380e-01, 1.4864e+00, 5.1602e-01,\n", + " -1.6702e-02, -2.4289e-02, -4.2424e-01, -2.8217e-01, -4.9993e-01,\n", + " -4.5852e-01, -3.4616e-01, 6.3251e-02, 3.8091e-02, -6.9865e-01,\n", + " 2.3419e-01, 1.5468e-03, -5.9655e-01, -7.5859e-02, 6.5158e-01,\n", + " 1.2889e+00, -5.4556e-01, 1.0199e-01, 5.3473e-02, 2.0236e+00,\n", + " 3.6257e-01, -6.0340e-02, -5.0334e-01, 8.9125e-02, -6.6143e-01,\n", + " 1.3207e-01, -8.9816e-01, 7.3372e-01, 7.7012e-01, 7.4299e-01,\n", + " 8.7515e-02, -8.9580e-01, -8.6851e-02, 1.1339e+00, -2.9433e-01,\n", + " -2.6578e-01, 1.5296e-01, -1.0718e-02]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4934, 0.4170, 0.1527, 0.3784, 0.1763, -0.1447, -0.4159, -0.4617,\n", + " -0.2572, -0.3190, -0.2996, 0.2037, 0.1936, 0.3111, 0.2588, 0.3743,\n", + " 0.3414, 0.0730, 0.1483, 0.1108, 0.0591, 0.1158, 0.0218, 0.0233,\n", + " 0.0278, 0.0996, -0.1842, -0.0052, 0.1480, 0.2731, 0.2681, 0.1810,\n", + " 0.3174, 0.3546, 0.2401, 0.2601, -0.0394, -0.0078, -0.0137, -0.3605,\n", + " -0.3320, -0.3322, -0.3057]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 9.862417209660634e-05\n", + "Grad encoder.fc1.bias: 5.410632002167404e-05\n", + "Grad encoder.encoder.0.weight: 2.4653107175254263e-05\n", + "Grad encoder.encoder.0.bias: 5.940813571214676e-05\n", + "Grad encoder.encoder.2.weight: 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_memory_unit.bias_ih_l1: 6.240567017812282e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.234883479308337e-05\n", + "Data X Sample: tensor([[1.2550, 1.4405, 1.5372, 1.6596, 1.7160, 1.9270, 2.0306, 2.1195, 3.4565,\n", + " 3.9493, 4.1652, 3.9670, 3.8637, 3.8443, 3.6820, 3.5201, 3.4938, 3.5070,\n", + " 3.3619, 3.2973, 3.4007, 3.2394, 2.9640, 2.7695, 2.8040, 2.8731, 2.9224,\n", + " 3.0304, 3.0737, 3.0000, 3.2126, 3.3105, 3.5333, 2.5585, 4.2527, 4.7137,\n", + " 4.8474, 5.3482, 5.7489, 5.7055, 1.0882, 0.5696, 0.5396, 0.9989, 1.4033,\n", + " 1.5897, 1.1286, 1.6108]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.4494, -1.1157, -0.7270, -1.0743, 0.0489, 0.3586, 0.0580, 0.3533,\n", + " 0.6567, 0.6306, 0.5234, -0.9194, -0.4070, -1.5123, -1.0779, -1.4650,\n", + " -1.6297, 0.1443, 0.8376, 0.1040, -0.5480, 0.0368, 0.9067, -0.5932,\n", + " 0.5939, 1.5485, -0.0191, -1.0371, 0.3565, -0.6115, -0.2674, 0.1754,\n", + " -1.3866, -1.0969, -0.8553, -0.7836, 0.0000, 0.5129, 0.9794, 0.7863,\n", + " 0.7243, 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1.6543, 1.6228, 1.2361, 0.7587, 0.7356, 1.2429, 1.8235,\n", + " 2.0128, 1.4006, 2.0330]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.1229, 0.6186, 0.7128, 0.3283, -0.7466, -0.2866, -1.6486, -1.8147,\n", + " -0.5031, -1.1682, 0.0115, 0.1520, -0.4707, 0.7598, 0.8544, 0.1555,\n", + " 0.0325, 0.0174, 0.5162, -0.1919, 0.3319, 0.0891, 2.7081, -0.8603,\n", + " 0.4692, 0.8402, 0.2940, 0.2670, 0.8133, 1.2898, 0.8121, 1.7803,\n", + " 0.9363, 0.2025, 0.7923, 0.3390, -0.4777, 0.5703, -0.4557, -1.0157,\n", + " -0.6556, -0.3177, 1.2992]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.5085, 0.4400, 0.1737, 0.3874, 0.1806, -0.1675, -0.4500, -0.4940,\n", + " -0.2796, -0.3436, -0.3211, 0.2132, 0.2139, 0.3349, 0.2682, 0.3989,\n", + " 0.3608, 0.0830, 0.1596, 0.1208, 0.0636, 0.1251, 0.0240, 0.0251,\n", + " 0.0373, 0.0969, -0.1829, 0.0049, 0.1791, 0.3064, 0.2990, 0.2032,\n", + " 0.3401, 0.3784, 0.2353, 0.2615, -0.0196, 0.0030, 0.0016, -0.3798,\n", + " -0.3509, -0.3577, -0.3281]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0005357627524062991\n", + "Grad encoder.fc1.bias: 0.000251430319622159\n", + "Grad encoder.encoder.0.weight: 0.0001487150730099529\n", + "Grad encoder.encoder.0.bias: 0.00036757535417564213\n", + "Grad encoder.encoder.2.weight: 8.011160389287397e-05\n", + "Grad encoder.encoder.2.bias: 0.0003592756111174822\n", + "Grad encoder.encoder.4.weight: 0.00013310287613421679\n", + "Grad encoder.encoder.4.bias: 0.0011106745805591345\n", + "Grad decoder.fc1.0.weight: 3.750503310584463e-05\n", + "Grad decoder.fc1.0.bias: 0.00043747812742367387\n", + "Grad decoder.fc1.2.weight: 3.002535959240049e-05\n", + "Grad decoder.fc1.2.bias: 0.0006922875763848424\n", + "Grad decoder.fc1.4.weight: 3.06731672026217e-05\n", + "Grad decoder.fc1.4.bias: 0.0006770134204998612\n", + "Grad decoder.fc2.weight: 0.0001065024989657104\n", + "Grad decoder.fc2.bias: 0.001306968042626977\n", + "Grad _memory_unit.weight_ih_l0: 1.1207471288798843e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 4.784714474226348e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.4414515792159364e-05\n", + "Grad _memory_unit.weight_ih_l1: 4.232657374814153e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00010544220276642591\n", + "Grad _memory_unit.bias_hh_l1: 5.279872857499868e-05\n", + "Data X Sample: tensor([[-0.0106, 0.0087, 0.0105, -0.0175, -0.0154, 0.0015, 0.0031, 0.0057,\n", + " 0.0073, 0.0047, -0.0159, -0.0078, -0.0022, 0.0136, 0.0076, -0.0027,\n", + " -0.0016, -0.0019, 0.0021, 0.0019, 0.0196, 0.0015, 0.0145, 0.0095,\n", + " -0.0019, 0.0052, 0.0160, 0.0367, -0.0139, 0.0393, 0.0070, -0.0055,\n", + " -0.0066, -0.0023, 0.0074, 0.0316, -0.0020, 0.0106, 0.0063, -0.0142,\n", + " 0.0286, 0.0239, 0.0040, 0.0057, 0.0000, 0.0515, 0.0340, -0.0313]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 2.1207, 1.2177, 0.6232, 1.0234, 0.9681, 0.6045, -1.6891, -1.2837,\n", + " -1.1048, -0.6664, -0.6086, 0.3980, -0.1800, 0.4464, 0.0862, 0.4005,\n", + " 0.3693, 0.2573, -1.9026, -0.0378, -1.5818, 0.9175, -3.1601, 0.5572,\n", + " -0.2235, 0.9064, 0.2080, 0.9262, 1.5842, 1.0027, 0.7913, 0.2212,\n", + " -0.2182, 1.0750, 0.8334, 0.4834, 0.0000, -0.8399, -0.7678, -0.5866,\n", + " -0.5815, -0.8215, -0.8911]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 1.0727e+00, 7.3145e-01, 2.1075e-01, 4.9861e-01, -9.2903e-01,\n", + " -1.0953e+00, -1.6884e+00, -1.4794e+00, -1.2571e+00, -1.1867e+00,\n", + " -1.1508e+00, 4.9092e-01, 6.3733e-01, 7.7097e-01, 5.5227e-01,\n", + " 5.5667e-01, 6.0485e-01, -2.7102e-01, 5.4748e-02, 4.6561e-02,\n", + " -4.2558e-02, -1.2810e-02, -5.8461e-02, 5.2122e-02, -6.3811e-02,\n", + " -1.3808e-01, 3.6794e-01, 7.7161e-01, 1.3661e+00, 1.2086e+00,\n", + " 1.0174e+00, 6.5450e-01, 8.7159e-01, 6.7085e-01, 5.1178e-01,\n", + " 2.8191e-01, -1.2583e-01, 1.0282e-02, -1.3735e-03, -9.2970e-01,\n", + " -8.4443e-01, -7.5580e-01, -4.1096e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002489947946742177\n", + "Grad encoder.fc1.bias: 0.000949978013522923\n", + "Grad encoder.encoder.0.weight: 8.883709961082786e-05\n", + "Grad encoder.encoder.0.bias: 0.0005085988086648285\n", + "Grad encoder.encoder.2.weight: 5.006224091630429e-05\n", + "Grad encoder.encoder.2.bias: 0.000488822057377547\n", + "Grad encoder.encoder.4.weight: 0.00010102657688548788\n", + "Grad encoder.encoder.4.bias: 0.0009809067705646157\n", + "Grad decoder.fc1.0.weight: 4.247910692356527e-05\n", + "Grad decoder.fc1.0.bias: 0.00049471331294626\n", + "Grad decoder.fc1.2.weight: 4.4191343476995826e-05\n", + "Grad decoder.fc1.2.bias: 0.0003729587479028851\n", + "Grad decoder.fc1.4.weight: 4.0857132262317464e-05\n", + "Grad decoder.fc1.4.bias: 0.0003952828119508922\n", + "Grad decoder.fc2.weight: 0.00016239986871369183\n", + "Grad decoder.fc2.bias: 0.0018686861731112003\n", + "Grad _memory_unit.weight_ih_l0: 8.62810429680394e-06\n", + "Grad 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-0.8035, -1.0700, -1.2376,\n", + " -0.5776, 2.8329, -0.2912, -1.5660, -0.2256, -0.8792, -0.1261, -1.3064,\n", + " -0.9939, -0.5986, 0.5369, -1.5083, -0.0502, -0.4615, -0.6322, -0.2597,\n", + " 0.2498, -1.0478, 0.5525, -0.5354, 0.2309, 0.0000, -0.5562, 0.4764,\n", + " 0.9254, 0.3260, 0.2825]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2182, -0.2469, -0.0561, -0.1612, 0.0969, 0.1778, 0.1735, 0.2432,\n", + " 0.1874, 0.2066, 0.1955, -0.1449, -0.1186, -0.2688, -0.1811, -0.2650,\n", + " -0.1619, -0.0719, -0.0194, -0.1274, -0.0905, -0.0343, -0.0562, -0.0571,\n", + " -0.0428, -0.0049, -0.0850, -0.1309, -0.2402, -0.2848, -0.2343, -0.1904,\n", + " -0.3126, -0.2893, -0.2220, -0.1837, 0.0453, -0.0278, 0.0830, 0.2234,\n", + " 0.3074, 0.2877, 0.2261]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0005369551945477724\n", + "Grad encoder.fc1.bias: 0.0005661228788085282\n", + "Grad encoder.encoder.0.weight: 0.0001597180962562561\n", + "Grad encoder.encoder.0.bias: 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_memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 6.689013389404863e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.4757442335831e-05\n", + "Data X Sample: tensor([[1.5403, 1.7420, 1.9309, 1.9832, 2.0387, 2.0788, 2.1246, 2.1919, 2.3116,\n", + " 2.3490, 2.2809, 2.2766, 2.2969, 2.1675, 2.0680, 2.2045, 2.1573, 2.1955,\n", + " 2.2488, 2.2781, 2.3432, 2.4464, 2.5038, 2.4641, 2.3873, 2.5544, 2.4722,\n", + " 2.4390, 2.2099, 2.3679, 2.3097, 2.4262, 2.2902, 1.4807, 2.0099, 1.7901,\n", + " 1.6990, 1.5080, 1.5402, 1.5547, 1.2457, 0.7070, 0.7437, 1.2658, 1.7482,\n", + " 1.9099, 1.3326, 1.8532]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.1495, 0.5174, -0.4749, 1.0744, 0.7185, -0.2238, -0.3105, -0.3323,\n", + " -0.3794, -0.4729, -0.1681, 1.8429, 1.6936, 0.8519, -0.0542, 1.0737,\n", + " 0.3550, 1.2059, 1.0949, 0.8689, 1.4085, 1.5846, 0.7319, 0.8736,\n", + " 0.8870, 0.2306, -0.3114, -0.0481, -0.5770, 0.8236, 0.9933, 0.5761,\n", + " 0.2149, 0.0851, 0.5151, 1.0301, -0.6306, 0.4473, 0.0000, -0.1729,\n", + " -0.1347, -0.0331, -0.4595]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.5291, 0.4673, 0.1847, 0.4025, 0.2058, -0.1855, -0.4730, -0.5226,\n", + " -0.2953, -0.3740, -0.3444, 0.2224, 0.2285, 0.3465, 0.2895, 0.4284,\n", + " 0.3716, 0.0948, 0.1702, 0.1331, 0.0704, 0.1410, 0.0328, 0.0221,\n", + " 0.0457, 0.0852, -0.1855, -0.0071, 0.1937, 0.3337, 0.3312, 0.2183,\n", + " 0.3595, 0.4093, 0.2441, 0.2734, -0.0119, 0.0018, 0.0410, -0.4025,\n", + " -0.3823, -0.3862, -0.3545]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002405154809821397\n", + "Grad encoder.fc1.bias: 0.0004555532359518111\n", + "Grad encoder.encoder.0.weight: 9.187762043438852e-05\n", + "Grad encoder.encoder.0.bias: 0.0003548967943061143\n", + "Grad encoder.encoder.2.weight: 6.0405509429983795e-05\n", + "Grad encoder.encoder.2.bias: 0.00031060725450515747\n", + "Grad encoder.encoder.4.weight: 0.00013535929610952735\n", + "Grad encoder.encoder.4.bias: 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4.3035,\n", + " 4.2289, 4.3365, 4.2514, 3.9925, 3.8579, 3.8030, 3.8428, 3.7312, 3.5805,\n", + " 3.6695, 3.5149, 3.4767, 3.5226, 2.9520, 2.8115, 2.7158, 2.8130, 2.8531,\n", + " 2.9121, 2.8933, 2.9476, 2.9466, 3.0811, 3.3265, 2.3938, 4.1596, 4.5361,\n", + " 4.8611, 5.3429, 6.0215, 5.8303, 1.3268, 0.6950, 0.6710, 1.2199, 1.8394,\n", + " 1.8870, 1.2918, 1.8297]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.7587e-01, 5.3072e-02, 2.1880e+00, -8.2929e-02, 8.2470e-01,\n", + " 7.6295e-01, 4.6302e-01, 1.1767e-01, 4.3985e-01, -1.5965e-02,\n", + " 2.9159e-01, -1.7120e-01, -9.1447e-01, -5.7465e-03, 6.9478e-01,\n", + " -8.9036e-01, -1.2431e-03, -6.0272e-01, -2.7925e-01, -5.1712e-01,\n", + " -8.7320e-01, 4.7687e-01, -2.2248e-01, 1.8834e-01, 1.2682e+00,\n", + " 2.6001e+00, -6.8375e-01, -2.0213e+00, -3.2284e+00, -2.4730e+00,\n", + " -2.0795e+00, -1.1037e+00, -9.8268e-01, -1.8601e+00, -5.3388e+00,\n", + " -4.6913e-01, 6.3666e-02, 1.0045e-01, -7.3667e-01, 1.2178e-01,\n", + " 8.5770e-02, 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-8.9580e-03, 1.6795e-01, 2.5971e-01,\n", + " 2.5812e-01, 1.6916e-01, 2.7376e-01, 3.0628e-01, 1.9087e-01,\n", + " 1.9834e-01, -9.1580e-03, -7.8009e-04, 4.9871e-02, -3.0643e-01,\n", + " -2.9256e-01, -2.7082e-01, -2.5832e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002909241011366248\n", + "Grad encoder.fc1.bias: 0.0007854810683056712\n", + "Grad encoder.encoder.0.weight: 9.531915566185489e-05\n", + "Grad encoder.encoder.0.bias: 0.0005490155890583992\n", + "Grad encoder.encoder.2.weight: 5.416182102635503e-05\n", + "Grad encoder.encoder.2.bias: 0.0004155667847953737\n", + "Grad encoder.encoder.4.weight: 9.956280700862408e-05\n", + "Grad encoder.encoder.4.bias: 0.0006028131465427577\n", + "Grad decoder.fc1.0.weight: 4.902435466647148e-05\n", + "Grad decoder.fc1.0.bias: 0.0004035423626191914\n", + "Grad decoder.fc1.2.weight: 5.3813899285160005e-05\n", + "Grad decoder.fc1.2.bias: 0.0004303809837438166\n", + "Grad decoder.fc1.4.weight: 4.994399205315858e-05\n", 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"Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 0.00024645280791446567\n", + "Grad _memory_unit.bias_hh_l0: 0.00012832926586270332\n", + "Grad _memory_unit.weight_ih_l1: 1.6083529772004113e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.0004086281405761838\n", + "Grad _memory_unit.bias_hh_l1: 0.00021324898989405483\n", + "Data X Sample: tensor([[ 0.0074, 0.0000, 0.0060, 0.0044, 0.0000, 0.0059, 0.0046, 0.0028,\n", + " 0.0073, 0.0000, -0.0159, -0.0097, 0.0067, -0.0055, 0.0000, -0.0027,\n", + " -0.0047, 0.0019, 0.0021, 0.0000, -0.0049, 0.0031, 0.0096, -0.0057,\n", + " 0.0113, -0.0052, 0.0346, 0.0326, -0.0139, 0.0098, -0.0175, 0.0111,\n", + " -0.0132, -0.0160, 0.0147, 0.0170, 0.0039, 0.0186, 0.0127, -0.0170,\n", + " 0.0000, 0.0100, 0.0000, -0.0029, 0.0159, 0.0057, 0.0204, -0.0235]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.7051, 1.0785, 1.6576, 0.9009, 1.7479, -0.1543, -0.6211, -1.2007,\n", + " -1.7362, 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"Data Y Sample: tensor([[ 0.9100, 1.1301, -2.0823, 1.3797, 0.9453, 0.2215, -0.6111, -0.8495,\n", + " -0.7102, -0.7608, -0.4837, -0.2817, 0.9264, 0.8982, 0.1301, 0.8739,\n", + " 1.1603, 0.8967, -0.0815, -0.1548, -0.5654, -0.0257, 1.1803, 0.1289,\n", + " -0.1026, 0.3208, -1.2214, 0.7289, 0.5033, 0.4385, 1.1971, -0.4926,\n", + " 1.2363, 1.4185, 0.7530, 1.4035, 0.9958, -0.6532, 0.5659, -0.6547,\n", + " -0.9056, -0.2387, -0.4755]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3459, 0.3149, 0.1142, 0.2631, 0.1486, -0.1638, -0.3098, -0.3652,\n", + " -0.1914, -0.2586, -0.2454, 0.1372, 0.1181, 0.2014, 0.2040, 0.2710,\n", + " 0.1934, 0.0724, 0.1284, 0.0710, 0.0477, 0.1047, 0.0128, -0.0302,\n", + " 0.0156, 0.0405, -0.1204, -0.0160, 0.1475, 0.2313, 0.2278, 0.1564,\n", + " 0.2389, 0.2754, 0.1683, 0.1889, -0.0276, 0.0024, 0.0783, -0.2652,\n", + " -0.2630, -0.2315, -0.2138]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00015249352145474404\n", + "Grad 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3.3568, 3.4228, 3.3619, 2.8869, 2.7371, 2.6820, 2.8235, 2.7279,\n", + " 2.7245, 2.8343, 3.0491, 3.0271, 3.0811, 3.4541, 2.5585, 4.2724, 4.6845,\n", + " 4.8887, 5.0991, 5.8060, 5.6487, 1.0309, 0.5576, 0.5841, 1.0018, 1.4588,\n", + " 1.7097, 1.0810, 1.5404]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.5898, -0.8005, -1.9984, -0.3417, 0.1139, -0.5862, 0.7889, 0.9558,\n", + " 0.7998, 0.7407, 0.3320, -0.1586, 0.9549, -0.6375, -2.0425, -0.2281,\n", + " -1.0832, -1.6415, -1.1254, 1.5659, -1.0684, 0.5940, -0.8406, -0.3500,\n", + " 0.3651, 0.3273, -1.9020, -2.1335, -1.8526, -1.9746, -2.6022, -1.8364,\n", + " -2.6945, -2.1246, 0.7999, -0.8810, 0.4852, 0.0000, 1.1604, 0.5744,\n", + " 0.4567, 0.2194, 0.8646]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2094, -0.2534, -0.0611, -0.1795, 0.0226, 0.1209, 0.1591, 0.2482,\n", + " 0.1681, 0.1687, 0.2164, -0.1451, -0.1552, -0.2862, -0.1531, -0.2900,\n", + " -0.2095, -0.0731, -0.0427, -0.1404, -0.0699, -0.0195, -0.0713, -0.0718,\n", + " 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1.8592,\n", + " 2.0357, 1.3938, 1.9313]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.4892, 0.2067, 1.0081, -0.0203, -0.1128, -0.4567, -0.1822, -0.2380,\n", + " -0.2221, -0.2801, -0.2180, 0.3449, 0.6873, 0.0999, 0.8147, 0.0688,\n", + " -0.4272, 0.0305, -1.3755, -0.9042, 0.2631, -0.9252, -0.8500, -1.3026,\n", + " -1.1315, -0.2532, 1.0501, 0.4017, -0.1403, -0.5762, 1.0256, 0.3875,\n", + " 0.0080, 1.2099, 1.1101, -0.1676, 0.3652, 0.0000, 0.0073, -0.3485,\n", + " -0.4793, 0.0499, -0.2222]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3736, 0.3136, 0.1325, 0.2885, 0.1348, -0.1809, -0.3427, -0.4111,\n", + " -0.2106, -0.2695, -0.2609, 0.1459, 0.1545, 0.2286, 0.2140, 0.2798,\n", + " 0.1918, 0.0917, 0.1265, 0.0753, 0.0397, 0.1043, 0.0156, -0.0137,\n", + " 0.0132, 0.0389, -0.0948, 0.0172, 0.1971, 0.2619, 0.2424, 0.1745,\n", + " 0.2502, 0.3003, 0.1771, 0.1961, -0.0294, 0.0205, 0.0500, -0.2734,\n", + " -0.2837, -0.2304, -0.2403]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad 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"Grad _memory_unit.bias_ih_l0: 3.8940601370995864e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.0312545530032367e-05\n", + "Grad _memory_unit.weight_ih_l1: 4.0796508073981386e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 7.689256017329171e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.909470615326427e-05\n", + "Data X Sample: tensor([[1.5117, 1.8163, 1.9384, 2.1056, 2.2197, 2.3528, 2.3295, 2.4176, 2.5069,\n", + " 2.5339, 2.4427, 2.5142, 2.4656, 2.4811, 2.3731, 2.3768, 2.3963, 2.4160,\n", + " 2.4347, 2.4567, 2.4340, 2.4525, 2.5062, 2.5004, 2.5712, 2.6250, 2.5094,\n", + " 2.5940, 2.2411, 2.3450, 2.3237, 2.3267, 2.1736, 1.4372, 1.8776, 1.7245,\n", + " 1.5968, 1.5318, 1.6036, 1.5945, 1.2838, 0.7109, 0.7215, 1.2687, 1.7720,\n", + " 2.2301, 1.4210, 2.0174]], device='cuda:0')\n", + "Data Y Sample: tensor([[-7.8352e-02, 3.5252e-01, -1.9205e-01, 6.7102e-01, -3.0667e-02,\n", + " -7.2114e-01, -1.0562e-01, -3.6877e-01, 1.8932e-01, -2.7444e-02,\n", + " -3.7440e-01, 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-2.4511e-01,\n", + " -2.0829e-01, -3.6341e-01, 1.7293e-01, 9.1353e-01, -6.0974e-01,\n", + " -5.1351e-01, 3.4389e-01, -3.8524e-02, -9.1211e-01, -5.6653e-01,\n", + " 1.3125e-02, -2.8552e-01, -7.3354e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2502, -0.2901, -0.0530, -0.1897, 0.0461, 0.1417, 0.1889, 0.2838,\n", + " 0.2087, 0.2001, 0.2358, -0.1699, -0.2003, -0.3448, -0.1677, -0.3299,\n", + " -0.2301, -0.0824, -0.0684, -0.1516, -0.0819, -0.0216, -0.0755, -0.0810,\n", + " -0.0739, 0.0194, -0.0226, -0.1252, -0.1914, -0.2591, -0.2433, -0.1936,\n", + " -0.2957, -0.3375, -0.2009, -0.2164, 0.0276, 0.0333, 0.0792, 0.2940,\n", + " 0.3567, 0.3449, 0.2520]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00024403256247751415\n", + "Grad encoder.fc1.bias: 0.0004395553842186928\n", + "Grad encoder.encoder.0.weight: 7.13501067366451e-05\n", + "Grad encoder.encoder.0.bias: 0.0005146136973053217\n", + "Grad encoder.encoder.2.weight: 5.140374560141936e-05\n", + "Grad 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_memory_unit.bias_hh_l1: 5.823415995109826e-05\n", + "Data X Sample: tensor([[1.5584, 1.7595, 1.9955, 2.0597, 2.1548, 2.3130, 2.2925, 2.3921, 2.4410,\n", + " 2.5323, 2.5823, 2.5181, 2.5166, 2.4211, 2.4589, 2.4137, 2.3523, 2.3793,\n", + " 2.4223, 2.3749, 2.5002, 2.5719, 2.6604, 2.5252, 2.6688, 2.6511, 2.6054,\n", + " 2.7163, 2.4041, 2.3745, 2.2327, 2.3571, 2.2880, 1.5058, 1.9536, 1.7634,\n", + " 1.6459, 1.5027, 1.5846, 1.6058, 1.2504, 0.7468, 0.7579, 1.2687, 1.9662,\n", + " 2.0814, 1.4550, 1.8688]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.3647, 0.8460, 0.7215, -0.1193, -0.8281, 0.3836, -0.4639, -0.4989,\n", + " -0.7261, -0.1508, -0.1235, 0.7825, 0.0768, 0.5707, 0.5242, 0.3877,\n", + " -0.0754, -1.2123, 1.1478, -0.1678, -0.0472, 0.5270, -0.5147, -0.2513,\n", + " -0.6993, 0.2839, 0.1484, 0.3317, 1.1030, 0.7237, -0.3831, 0.1725,\n", + " 0.9970, 0.5741, 0.5613, 0.2246, -0.8350, -0.8198, 0.0040, -0.2541,\n", + " -0.0532, -0.2489, -1.1287]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3982, 0.3383, 0.1497, 0.3027, 0.1355, -0.1976, -0.3657, -0.4424,\n", + " -0.2242, -0.2791, -0.2736, 0.1462, 0.1871, 0.2582, 0.2271, 0.2933,\n", + " 0.1969, 0.0977, 0.1400, 0.0870, 0.0414, 0.1078, 0.0238, -0.0174,\n", + " 0.0095, 0.0401, -0.0836, 0.0419, 0.2314, 0.2912, 0.2657, 0.1907,\n", + " 0.2590, 0.3164, 0.1869, 0.2062, -0.0308, 0.0358, 0.0379, -0.2942,\n", + " -0.2919, -0.2492, -0.2680]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 7.448741234838963e-05\n", + "Grad encoder.fc1.bias: 0.000449951970949769\n", + "Grad encoder.encoder.0.weight: 3.04388340737205e-05\n", + "Grad encoder.encoder.0.bias: 0.00032408128026872873\n", + "Grad encoder.encoder.2.weight: 3.6086094041820616e-05\n", + "Grad encoder.encoder.2.bias: 0.0003640295471996069\n", + "Grad encoder.encoder.4.weight: 9.857883560471237e-05\n", + "Grad encoder.encoder.4.bias: 0.001062565017491579\n", + "Grad decoder.fc1.0.weight: 4.142578472965397e-05\n", + "Grad decoder.fc1.0.bias: 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2.3407, 2.3561, 2.2700, 2.2866, 2.2972, 2.3690, 2.3789,\n", + " 2.3329, 2.1266, 2.2860, 2.2152, 2.1029, 1.7270, 1.1076, 1.5442, 1.4934,\n", + " 1.4316, 1.4205, 1.4008, 1.3391, 1.2600, 0.7428, 0.7457, 1.2744, 1.8711,\n", + " 2.0242, 1.4278, 2.1737]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.8038, 1.2073, -0.3587, 0.9084, 1.7604, -0.1937, -1.3271, -0.8686,\n", + " -0.1475, -0.7203, -2.8798, 0.1481, 1.3386, 0.1004, 1.0828, 2.0752,\n", + " 0.0716, 0.1552, 0.7386, 0.4815, -0.1952, 0.6689, 0.0383, 0.6739,\n", + " 0.6271, -0.6236, -0.3474, -0.0874, 0.1740, 0.5566, -1.5106, 0.4119,\n", + " -0.5866, -0.1366, 0.9758, -0.9551, -0.4931, 0.6423, -1.0672, -0.9008,\n", + " -1.2416, -0.0663, 0.2611]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3481, 0.2727, 0.1265, 0.2493, 0.0875, -0.1737, -0.3282, -0.3851,\n", + " -0.2010, -0.2347, -0.2419, 0.1141, 0.1639, 0.2248, 0.1847, 0.2434,\n", + " 0.1524, 0.0792, 0.1164, 0.0640, 0.0279, 0.0881, 0.0160, -0.0093,\n", + " -0.0077, 0.0290, -0.0551, 0.0534, 0.2225, 0.2580, 0.2236, 0.1709,\n", + " 0.2158, 0.2596, 0.1619, 0.1672, -0.0333, 0.0369, 0.0133, -0.2506,\n", + " -0.2357, -0.1925, -0.2229]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 6.269059667829424e-05\n", + "Grad encoder.fc1.bias: 0.00021093207760713995\n", + "Grad encoder.encoder.0.weight: 3.495153214316815e-05\n", + "Grad encoder.encoder.0.bias: 0.00019476933812256902\n", + "Grad encoder.encoder.2.weight: 3.401605135877617e-05\n", + "Grad encoder.encoder.2.bias: 0.0002515114319976419\n", + "Grad encoder.encoder.4.weight: 8.566182805225253e-05\n", + "Grad encoder.encoder.4.bias: 0.0006284393602982163\n", + "Grad decoder.fc1.0.weight: 4.19590032834094e-05\n", + "Grad decoder.fc1.0.bias: 0.0004047477850690484\n", + "Grad decoder.fc1.2.weight: 4.2919142288155854e-05\n", + "Grad decoder.fc1.2.bias: 0.0004814612620975822\n", + "Grad decoder.fc1.4.weight: 3.925905184587464e-05\n", + "Grad decoder.fc1.4.bias: 0.0005443991394713521\n", + "Grad decoder.fc2.weight: 0.0001297176640946418\n", + "Grad decoder.fc2.bias: 0.0022017667070031166\n", + "Grad _memory_unit.weight_ih_l0: 9.487984243605752e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 2.0710274839075282e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.1532207281561568e-05\n", + "Grad _memory_unit.weight_ih_l1: 3.6485976124822628e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 5.6653207138879225e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.8790911528631113e-05\n", + "Data X Sample: tensor([[ 0.0064, 0.0204, 0.0135, 0.0153, 0.0137, 0.0206, 0.0169, 0.0142,\n", + " 0.0391, 0.0126, 0.0286, 0.0234, 0.0133, 0.0164, -0.0025, 0.0150,\n", + " -0.1085, -0.1315, -0.1507, -0.1525, -0.1472, -0.1072, -0.1831, -0.1737,\n", + " -0.1426, -0.2978, -0.3064, -0.5384, -0.1075, -0.0753, -0.1225, -0.0387,\n", + " -0.0440, -0.0549, -0.0392, 0.0024, -0.0079, -0.0265, -0.0254, -0.0227,\n", + " -0.0191, 0.0040, -0.0141, 0.0000, 0.0040, 0.0572, 0.0476, 0.0547]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 7.7951e-01, 1.4982e+00, 9.3185e-01, -1.4913e-01, 7.5483e-01,\n", + " -9.6971e-01, -1.7970e-01, -9.8373e-01, -4.1926e-01, -6.0157e-01,\n", + " -3.3077e-01, 1.5570e+00, 6.4801e-01, 1.6436e+00, 6.7951e-01,\n", + " -6.3247e-01, 7.0403e-01, -1.8974e-01, 7.8995e-01, 1.3911e+00,\n", + " 5.7383e-01, 2.8052e-01, 1.2149e+00, 8.8629e-01, -6.6687e-01,\n", + " 1.1754e-02, -2.4289e-01, -7.0769e-01, -1.1258e-01, 3.2459e-01,\n", + " 5.4114e-01, -6.2376e-01, 3.2287e-01, 4.4609e-01, 3.9511e-01,\n", + " 3.1387e-01, -2.6554e-04, -6.5324e-01, 1.1148e+00, -5.0483e-01,\n", + " -5.5248e-01, -2.6734e-01, -3.3642e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.6915, 0.5127, 0.2368, 0.5464, 0.1890, -0.3129, -0.6291, -0.7752,\n", + " -0.3518, -0.4461, -0.3964, 0.2721, 0.3533, 0.4526, 0.3667, 0.4601,\n", + " 0.3600, 0.1589, 0.2433, 0.1606, 0.0566, 0.1317, 0.0412, 0.0443,\n", + " 0.0168, 0.0798, -0.1131, 0.1466, 0.4225, 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"Grad encoder.encoder.2.bias: 0.0003738493542186916\n", + "Grad encoder.encoder.4.weight: 0.0001264843303943053\n", + "Grad encoder.encoder.4.bias: 0.0011306700762361288\n", + "Grad decoder.fc1.0.weight: 4.834051287616603e-05\n", + "Grad decoder.fc1.0.bias: 0.0005605543265119195\n", + "Grad decoder.fc1.2.weight: 8.130734204314649e-05\n", + "Grad decoder.fc1.2.bias: 0.0009816065430641174\n", + "Grad decoder.fc1.4.weight: 7.350370287895203e-05\n", + "Grad decoder.fc1.4.bias: 0.0011757508618757129\n", + "Grad decoder.fc2.weight: 0.0001656824751989916\n", + "Grad decoder.fc2.bias: 0.002277415245771408\n", + "Grad _memory_unit.weight_ih_l0: 2.312702235940378e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.8852936995681375e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.148375460819807e-05\n", + "Grad _memory_unit.weight_ih_l1: 5.126971700519789e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 8.528972102794796e-05\n", + "Grad _memory_unit.bias_hh_l1: 4.342986721894704e-05\n", + "Data X Sample: tensor([[1.4968, 1.7260, 1.8452, 1.9110, 2.1036, 2.2438, 2.2509, 2.4034, 2.5045,\n", + " 2.4170, 2.5188, 2.4889, 2.5544, 2.3339, 2.3807, 2.4479, 2.3947, 2.3541,\n", + " 2.3830, 2.4232, 2.4045, 2.4418, 2.4580, 2.4336, 2.4999, 2.4526, 2.4109,\n", + " 2.4920, 2.2307, 2.2729, 2.2117, 2.2438, 2.1714, 1.4234, 1.9266, 1.7488,\n", + " 1.6046, 1.4576, 1.4959, 1.5576, 1.2313, 0.6791, 0.7013, 1.1941, 1.8751,\n", + " 1.9156, 1.3326, 1.9939]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.7170, 1.4135, 0.5858, 0.3813, 0.7423, -0.5735, -0.1410, -0.5129,\n", + " -1.3435, -0.7386, -0.7445, 0.8839, 1.6708, 0.4928, 6.5892, 1.0510,\n", + " 0.4817, -0.1936, -0.2252, 1.2521, -0.8458, 0.2628, 0.8067, 1.6691,\n", + " 0.1560, -0.0506, -2.5457, -0.8154, 1.2234, 0.5032, -0.2155, -0.2750,\n", + " 0.3745, 0.6112, -0.6654, -1.1773, -0.4969, -0.6532, 0.7551, -0.5181,\n", + " -0.5902, -0.3384, -0.2562]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3803, 0.3181, 0.1421, 0.2705, 0.0862, -0.1977, -0.3622, -0.4204,\n", + " -0.2089, -0.2515, -0.2605, 0.1293, 0.2065, 0.2660, 0.2126, 0.2703,\n", + " 0.1914, 0.0905, 0.1414, 0.0781, 0.0311, 0.0852, 0.0232, -0.0114,\n", + " -0.0040, 0.0351, -0.0591, 0.0652, 0.2451, 0.2810, 0.2466, 0.1747,\n", + " 0.2440, 0.2910, 0.1715, 0.1765, -0.0347, 0.0357, 0.0039, -0.2781,\n", + " -0.2496, -0.2218, -0.2612]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003168605617247522\n", + "Grad encoder.fc1.bias: 0.0014011814491823316\n", + "Grad encoder.encoder.0.weight: 7.992469909368083e-05\n", + "Grad encoder.encoder.0.bias: 0.0010114352917298675\n", + "Grad encoder.encoder.2.weight: 5.3003626817371696e-05\n", + "Grad encoder.encoder.2.bias: 0.0007789003429934382\n", + "Grad encoder.encoder.4.weight: 0.00012655499449465424\n", + "Grad encoder.encoder.4.bias: 0.0015004584565758705\n", + "Grad decoder.fc1.0.weight: 5.397541099227965e-05\n", + "Grad 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-0.0104,\n", + " 0.0014, 0.0369, -0.0655, 0.0690, 0.2584, 0.2983, 0.2626, 0.1808,\n", + " 0.2645, 0.3143, 0.1773, 0.1877, -0.0333, 0.0348, 0.0068, -0.2956,\n", + " -0.2681, -0.2415, -0.2844]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00021913996897637844\n", + "Grad encoder.fc1.bias: 0.00016694256919436157\n", + "Grad encoder.encoder.0.weight: 7.185559661593288e-05\n", + "Grad encoder.encoder.0.bias: 0.00017049946472980082\n", + "Grad encoder.encoder.2.weight: 5.646365389111452e-05\n", + "Grad encoder.encoder.2.bias: 0.00023412419250234962\n", + "Grad encoder.encoder.4.weight: 0.00013680057600140572\n", + "Grad encoder.encoder.4.bias: 0.0009509457158856094\n", + "Grad decoder.fc1.0.weight: 4.820380127057433e-05\n", + "Grad decoder.fc1.0.bias: 0.00039514905074611306\n", + "Grad decoder.fc1.2.weight: 5.840853918925859e-05\n", + "Grad decoder.fc1.2.bias: 0.0006826023454777896\n", + "Grad decoder.fc1.4.weight: 6.597262108698487e-05\n", + "Grad 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1.2917, 1.8275,\n", + " 2.2873, 1.3190, 2.1190]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.4196, 0.0899, -0.1235, 0.1668, 0.2701, 0.1674, -0.0777, -0.3020,\n", + " -0.2650, -0.5799, -0.5047, 0.2503, -1.3486, 0.2985, 1.1666, 0.0686,\n", + " 0.1126, -0.0826, 0.2498, -0.5546, 0.7470, 0.1225, -0.5617, 0.3811,\n", + " -1.6004, 0.1980, -1.3134, -0.1029, -0.1405, 0.6108, -0.0855, 0.5522,\n", + " 1.6130, 0.3850, 0.6831, -1.4560, 0.0213, 0.3857, -0.3712, -0.3566,\n", + " -0.3755, -0.6083, -0.7960]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 4.0344e-01, 3.4564e-01, 1.4993e-01, 2.8635e-01, 8.9219e-02,\n", + " -2.1072e-01, -3.8601e-01, -4.4801e-01, -2.1888e-01, -2.6721e-01,\n", + " -2.7531e-01, 1.4224e-01, 2.3064e-01, 2.9624e-01, 2.2985e-01,\n", + " 2.9221e-01, 2.1072e-01, 9.7151e-02, 1.5250e-01, 8.5164e-02,\n", + " 3.3283e-02, 8.5472e-02, 3.1332e-02, -6.1272e-03, -5.3238e-05,\n", + " 3.5852e-02, -5.9929e-02, 7.1578e-02, 2.6465e-01, 3.0309e-01,\n", + " 2.6230e-01, 1.8281e-01, 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-8.8688e-01, 8.2843e-01, 3.5056e-01, 1.0846e+00, 2.6110e-01,\n", + " 7.6695e-01, -2.9030e-01, 3.6525e-01, 1.0034e-01, 8.3696e-01,\n", + " 5.3411e-01, 2.3139e-02, 2.4294e-01, 9.5095e-01, 1.0123e+00,\n", + " -8.9711e-01, 4.4285e-01, -2.7838e-02, 2.0835e+00, 1.5900e+00,\n", + " 6.9763e-01, -9.0427e-01, 3.7251e-01, 2.8501e-01, -6.2917e-02,\n", + " -7.8759e-03, -1.0781e-03, 8.2377e-01, 0.0000e+00, -6.7209e-01,\n", + " -6.1610e-01, -7.0032e-01, -2.1594e+00]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 1.1481, 0.7452, 0.2335, 0.7548, -0.3519, -0.8579, -1.2959, -1.4461,\n", + " -0.8742, -0.9553, -0.9000, 0.5173, 0.7505, 0.8485, 0.5395, 0.6389,\n", + " 0.4508, 0.0653, 0.3237, 0.0704, 0.0495, 0.0936, 0.0971, 0.1084,\n", + " -0.0768, -0.0574, 0.1866, 0.6405, 1.1632, 1.1506, 0.9344, 0.6563,\n", + " 0.8218, 0.8080, 0.4904, 0.4314, -0.1646, 0.0253, 0.0399, -0.9735,\n", + " -0.8536, -0.6420, -0.5686]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 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_memory_unit.bias_ih_l1: 9.014546958496794e-05\n", + "Grad _memory_unit.bias_hh_l1: 4.749670915771276e-05\n", + "Data X Sample: tensor([[1.3823, 1.5512, 1.7430, 1.7930, 1.8611, 2.1082, 2.0753, 2.0088, 3.4296,\n", + " 4.0899, 4.3270, 4.2728, 4.0701, 4.0297, 3.8913, 3.6199, 3.5944, 3.6637,\n", + " 3.5291, 3.6766, 3.5160, 3.4078, 3.0484, 2.8401, 2.8715, 2.9253, 2.9224,\n", + " 2.8876, 2.9627, 3.1540, 3.1076, 3.3989, 3.9271, 2.8469, 4.6547, 4.9155,\n", + " 5.2190, 5.3853, 6.0532, 5.9296, 1.1454, 0.5855, 0.6204, 1.1108, 1.5302,\n", + " 1.7612, 1.1694, 1.6420]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1141, -0.7943, -2.1526, -0.6438, -0.5735, 0.3039, 0.1846, 0.2900,\n", + " 0.8539, 1.0012, -0.1797, -1.5789, 0.4812, 0.1735, -0.2962, 0.7295,\n", + " -0.1291, 1.0124, -4.3250, -0.4628, 0.6534, -0.6408, 0.4137, 0.8073,\n", + " 0.8184, -0.2858, 0.4120, -0.5492, 0.4091, -0.3397, -0.2088, -0.4396,\n", + " 0.8425, -0.0592, 0.8654, -0.6136, -1.0138, 0.6895, -0.5562, 0.4958,\n", + " 0.1069, 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decoder.fc1.0.weight: 5.188567592995241e-05\n", + "Grad decoder.fc1.0.bias: 0.0005878929514437914\n", + "Grad decoder.fc1.2.weight: 5.1037888624705374e-05\n", + "Grad decoder.fc1.2.bias: 0.0008133469382300973\n", + "Grad decoder.fc1.4.weight: 5.5314882047241554e-05\n", + "Grad decoder.fc1.4.bias: 0.000875667785294354\n", + "Grad decoder.fc2.weight: 0.00012357225932646543\n", + "Grad decoder.fc2.bias: 0.001888554310426116\n", + "Grad _memory_unit.weight_ih_l0: 9.761924047779758e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 4.547799107967876e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.291022610734217e-05\n", + "Grad _memory_unit.weight_ih_l1: 4.612035809259396e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00010724848107201979\n", + "Grad _memory_unit.bias_hh_l1: 5.508763933903538e-05\n", + "Data X Sample: tensor([[2.4420, 2.8154, 3.0488, 3.2426, 3.2902, 3.3296, 3.5543, 3.5803, 3.6444,\n", + " 3.7439, 3.9432, 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"Prediction Sample: tensor([[-2.1771e-01, -2.5000e-01, -8.3399e-02, -2.4897e-01, -1.9532e-01,\n", + " -3.2338e-02, 4.4059e-02, 2.1795e-01, 1.1387e-01, 1.3928e-01,\n", + " 1.8105e-01, -1.2938e-01, -1.2984e-01, -2.1809e-01, -1.2899e-01,\n", + " -2.3236e-01, -1.8650e-01, -7.7054e-02, -5.4889e-02, -8.6499e-02,\n", + " -1.7698e-02, 3.4509e-02, -4.1446e-02, -5.6161e-02, -6.6891e-03,\n", + " 2.3763e-02, 8.9725e-02, 2.8558e-02, -4.4047e-02, -1.2012e-01,\n", + " -1.5214e-01, -1.5129e-01, -1.2115e-01, -1.9587e-01, -1.0783e-01,\n", + " -1.5834e-01, -5.0341e-02, -2.7077e-04, 5.1283e-03, 2.5723e-01,\n", + " 3.0042e-01, 3.0047e-01, 2.1581e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 4.841342888539657e-05\n", + "Grad encoder.fc1.bias: 4.010951306554489e-05\n", + "Grad encoder.encoder.0.weight: 1.0356885468354449e-05\n", + "Grad encoder.encoder.0.bias: 3.090325481025502e-05\n", + "Grad encoder.encoder.2.weight: 9.822485480981413e-06\n", + "Grad encoder.encoder.2.bias: 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_memory_unit.bias_hh_l1: 1.1500599612190854e-05\n", + "Data X Sample: tensor([[1.5096, 1.7187, 1.9234, 2.0269, 2.1377, 2.2777, 2.4065, 2.4034, 2.4556,\n", + " 2.5939, 2.5696, 2.5882, 2.5166, 2.5438, 2.3933, 2.4602, 2.3790, 2.3928,\n", + " 2.4161, 2.4083, 2.4389, 2.5551, 2.4628, 2.5557, 2.4868, 2.6511, 2.4881,\n", + " 2.5124, 2.2688, 2.4170, 2.3622, 2.3737, 2.2682, 1.4875, 1.9045, 1.8193,\n", + " 1.6420, 1.4603, 1.5909, 1.5491, 1.2027, 0.7408, 0.6750, 1.3462, 1.7482,\n", + " 1.9328, 1.2578, 1.9001]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.5833, 0.6647, 1.2787, 0.9894, 0.0772, 0.8288, 0.2446, -0.4694,\n", + " 0.6182, 0.8745, 0.9775, 0.1233, 0.2871, -1.2220, -0.7744, 0.0923,\n", + " -0.3060, 0.1881, -0.3938, -0.1702, -0.0665, -0.0174, 2.0786, -0.0081,\n", + " -0.1650, -1.1221, -0.4115, -0.6265, 0.3702, -0.5469, 0.0276, -0.7412,\n", + " 0.2714, -0.5712, 0.3794, -0.5635, -0.8287, 0.8274, 0.5660, -1.1253,\n", + " -0.4347, 0.3461, -0.9883]], device='cuda:0')\n", + "Prediction 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"Grad encoder.encoder.2.weight: 3.670363730634563e-05\n", + "Grad encoder.encoder.2.bias: 0.0004893731093034148\n", + "Grad encoder.encoder.4.weight: 0.0001330909872194752\n", + "Grad encoder.encoder.4.bias: 0.0016562113305553794\n", + "Grad decoder.fc1.0.weight: 5.2133789722574875e-05\n", + "Grad decoder.fc1.0.bias: 0.0006426357431337237\n", + "Grad decoder.fc1.2.weight: 7.000578625593334e-05\n", + "Grad decoder.fc1.2.bias: 0.0009311960311606526\n", + "Grad decoder.fc1.4.weight: 6.184709491208196e-05\n", + "Grad decoder.fc1.4.bias: 0.0009251153096556664\n", + "Grad decoder.fc2.weight: 0.00013372056127991527\n", + "Grad decoder.fc2.bias: 0.0014783490914851427\n", + "Grad _memory_unit.weight_ih_l0: 1.6720257917768322e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 0.00013175691128708422\n", + "Grad _memory_unit.bias_hh_l0: 6.5820429881569e-05\n", + "Grad _memory_unit.weight_ih_l1: 6.541150469274726e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00021795670909341425\n", + "Grad _memory_unit.bias_hh_l1: 0.00011127575999125838\n", + "Data X Sample: tensor([[-0.0032, 0.0117, -0.0030, 0.0000, -0.0017, -0.0029, -0.0046, 0.0142,\n", + " -0.0171, 0.0032, -0.0063, 0.0058, 0.0022, 0.0000, -0.0151, 0.0068,\n", + " -0.0031, 0.0135, 0.0103, 0.0130, 0.0172, 0.0000, -0.0024, -0.0191,\n", + " -0.0094, 0.0235, 0.0186, 0.0245, -0.0208, 0.0360, -0.0105, 0.0111,\n", + " 0.0088, -0.0137, -0.0049, 0.0389, 0.0157, 0.0053, 0.0380, 0.0000,\n", + " 0.0048, 0.0159, 0.0040, 0.0086, 0.0198, 0.0000, 0.0204, -0.0313]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.8178, 1.6954, -0.4459, 0.1397, -0.1354, -0.2006, -0.9791, -1.0028,\n", + " -0.4959, -0.7578, -0.8291, -0.9153, 0.0813, 0.8941, 1.1233, 1.2938,\n", + " 0.3436, -1.2027, -0.1674, 0.4254, -0.5433, 2.4943, -0.5557, -0.4741,\n", + " 0.4209, 3.8107, 0.6125, 0.4349, 0.7895, 0.9394, 1.0522, 0.2167,\n", + " 1.0828, 1.0719, 0.7275, 0.4690, -0.0107, -0.0869, 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-0.2921,\n", + " -0.7823, -0.8884, -0.4468, 0.8210, 0.3174, 1.1078, 0.4917, 0.7349,\n", + " -0.1645, 0.5426, 0.1275, -0.0642, 1.5110, 1.0769, 1.0519, 1.0199,\n", + " 1.0992, -0.3021, 0.0436, -0.9647, 0.5782, -0.1387, 0.0475, 0.3327,\n", + " 0.6180, 0.7478, -0.0385, 0.6202, 0.0000, 0.7357, -1.1818, -0.1496,\n", + " 0.1439, -0.1197, -0.3365]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.5065, 0.5141, 0.1809, 0.3293, 0.1780, -0.1922, -0.4487, -0.4963,\n", + " -0.2306, -0.3031, -0.3052, 0.2124, 0.2445, 0.3518, 0.2427, 0.4130,\n", + " 0.3856, 0.1470, 0.1386, 0.1518, 0.0614, 0.0744, 0.1070, 0.0264,\n", + " 0.1077, 0.0966, -0.0910, -0.0044, 0.2714, 0.3396, 0.3435, 0.1611,\n", + " 0.3637, 0.3567, 0.2069, 0.2362, -0.0541, -0.0420, -0.0098, -0.4231,\n", + " -0.3396, -0.3554, -0.4149]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00010802822362165898\n", + "Grad encoder.fc1.bias: 0.000366476975614205\n", + "Grad encoder.encoder.0.weight: 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-0.4886,\n", + " -0.2254, -0.3108, -0.3088, 0.2099, 0.2360, 0.3543, 0.2282, 0.4113,\n", + " 0.3905, 0.1452, 0.1291, 0.1537, 0.0524, 0.0759, 0.1140, 0.0349,\n", + " 0.1120, 0.1021, -0.0758, -0.0007, 0.2720, 0.3440, 0.3491, 0.1673,\n", + " 0.3657, 0.3542, 0.2084, 0.2461, -0.0473, -0.0548, -0.0072, -0.4283,\n", + " -0.3417, -0.3482, -0.4127]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003253932227380574\n", + "Grad encoder.fc1.bias: 0.00018298628856427968\n", + "Grad encoder.encoder.0.weight: 8.452977635897696e-05\n", + "Grad encoder.encoder.0.bias: 0.0002585992042440921\n", + "Grad encoder.encoder.2.weight: 6.048174691386521e-05\n", + "Grad encoder.encoder.2.bias: 0.0003243404207751155\n", + "Grad encoder.encoder.4.weight: 0.0001279400021303445\n", + "Grad encoder.encoder.4.bias: 0.0007682036375626922\n", + "Grad decoder.fc1.0.weight: 5.532407158170827e-05\n", + "Grad decoder.fc1.0.bias: 0.00043364684097468853\n", + "Grad decoder.fc1.2.weight: 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2.9623,\n", + " 2.8876, 3.0390, 3.2817, 3.2161, 3.3049, 3.9117, 2.8927, 4.6498, 4.9228,\n", + " 5.1246, 5.5205, 5.9645, 5.9835, 1.0166, 0.5835, 0.5841, 1.0965, 1.5421,\n", + " 1.7955, 1.1898, 1.7124]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.1795, 0.5574, 0.8423, 0.9508, 0.9746, -0.0861, -0.2265, -0.1276,\n", + " 0.8215, -0.1320, -0.2269, -0.2459, 0.3557, 0.6810, 0.5039, 1.0727,\n", + " -0.3481, -0.6195, 0.0828, 0.0349, -0.8579, -0.1793, 0.5273, -0.4620,\n", + " -0.8040, 0.2101, -0.8808, -0.1480, 0.2965, 0.4629, -0.1785, -0.6912,\n", + " 2.2160, 1.1384, 1.2661, 1.4661, 0.1281, -0.3276, 0.3857, -1.3227,\n", + " 0.1696, 0.3761, 0.3722]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2127, -0.2587, -0.0479, -0.1987, -0.0315, 0.1468, 0.1756, 0.2640,\n", + " 0.2301, 0.2085, 0.2264, -0.1529, -0.1713, -0.2910, -0.1869, -0.2707,\n", + " -0.1895, -0.0376, -0.0978, -0.1028, -0.0424, -0.0256, -0.0393, -0.0321,\n", + " -0.0194, 0.0394, 0.0076, -0.0907, -0.1618, -0.1807, 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"Data Y Sample: tensor([[ 0.6506, 0.6731, 0.4013, 0.1751, -0.2800, -0.6858, -1.4069, -2.3466,\n", + " -1.5323, -1.8478, -1.5542, -3.7984, -1.1191, -0.3208, 0.4618, -0.3309,\n", + " -1.2007, -0.3909, -0.2562, 0.0413, 0.5426, -0.6114, 0.8749, -0.1265,\n", + " -0.2248, 0.1101, 0.1259, 0.1346, -0.4601, 0.4121, 0.9410, 1.1233,\n", + " 0.4552, 1.0929, -0.2048, 0.3071, -0.0558, -0.8266, 0.6318, -0.8341,\n", + " -1.0465, 0.6656, 0.4207]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4684, 0.4848, 0.1690, 0.2853, 0.1558, -0.1799, -0.4342, -0.4642,\n", + " -0.2100, -0.3087, -0.3036, 0.2043, 0.2208, 0.3404, 0.2109, 0.3951,\n", + " 0.3845, 0.1422, 0.1219, 0.1581, 0.0519, 0.0848, 0.1195, 0.0313,\n", + " 0.1127, 0.1049, -0.0665, -0.0048, 0.2498, 0.3339, 0.3411, 0.1639,\n", + " 0.3605, 0.3421, 0.2042, 0.2490, -0.0397, -0.0626, -0.0028, -0.4185,\n", + " -0.3331, -0.3402, -0.4006]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0005564961466006935\n", + "Grad 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"Grad _memory_unit.bias_hh_l1: 5.896300353924744e-05\n", + "Data X Sample: tensor([[1.3748, 1.3997, 1.5026, 1.7624, 2.0319, 2.0876, 2.2432, 2.8123, 4.3230,\n", + " 4.2589, 4.3905, 4.3059, 4.1079, 4.0570, 3.8434, 3.7526, 3.7202, 3.6676,\n", + " 3.5374, 3.5837, 3.6902, 3.5425, 3.0267, 2.9737, 2.8246, 2.9070, 2.9890,\n", + " 3.0997, 3.1986, 3.2620, 3.2510, 3.3602, 3.7533, 2.7462, 4.5224, 4.9131,\n", + " 5.1994, 5.5390, 5.9645, 5.8700, 0.9259, 0.5357, 0.5538, 0.9558, 1.5103,\n", + " 1.6983, 1.1014, 1.6655]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.6988e-03, 3.0510e-01, 5.5131e-01, 8.5796e-01, 6.8334e-01,\n", + " 4.3826e-01, 3.1800e-01, 1.5836e-03, -6.8821e-01, 1.2837e-02,\n", + " -8.4343e-01, -8.7525e-02, 6.5094e-01, 2.6213e-01, -3.0653e-01,\n", + " 6.0198e-01, 8.5607e-01, 8.9136e-01, 1.1122e+00, 1.0593e+00,\n", + " 2.1065e+00, 6.5720e-01, 9.3341e-01, 2.6447e-01, -9.2203e-01,\n", + " 1.2770e-01, -7.9265e-01, -7.7788e-01, 4.9583e-01, 3.9251e-01,\n", + " 7.3845e-01, -1.8977e-01, 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_memory_unit.bias_hh_l1: 3.0196952138794586e-05\n", + "Data X Sample: tensor([[1.5287, 1.7740, 1.9955, 2.0400, 2.1531, 2.2880, 2.3588, 2.3878, 2.4264,\n", + " 2.5481, 2.4966, 2.5765, 2.4967, 2.4620, 2.4109, 2.3563, 2.3649, 2.3851,\n", + " 2.3397, 2.3804, 2.4070, 2.4816, 2.4749, 2.3668, 2.4267, 2.4787, 2.3949,\n", + " 2.4227, 2.1856, 2.3024, 2.2082, 2.2411, 2.1692, 1.4326, 1.9291, 1.7731,\n", + " 1.6086, 1.4735, 1.5339, 1.5462, 1.1932, 0.7408, 0.7296, 1.2687, 1.8354,\n", + " 2.0242, 1.5230, 1.8844]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 9.0502e-01, 5.9396e-01, -2.4133e-01, 9.9373e-02, -3.6660e-01,\n", + " -1.7139e+00, -8.3035e-01, -2.2166e-01, -6.5095e-01, -3.3627e-01,\n", + " -4.0872e-01, 1.1664e-01, 5.0750e-01, -5.5263e-02, 9.0130e-01,\n", + " 1.2466e+00, 1.3118e-02, 1.2628e-02, 2.3040e-01, 4.4281e-01,\n", + " 1.9071e-01, -8.9047e-01, 8.9297e-01, 8.6976e-01, 9.8495e-01,\n", + " -3.0366e-01, -4.5087e-01, -1.7305e-01, 6.3821e-01, -7.7804e-04,\n", + " 1.4757e-01, 2.3959e-01, 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"Data X Sample: tensor([[2.3137, 2.7324, 3.0969, 3.0524, 3.1673, 3.3311, 3.3278, 3.3389, 3.4906,\n", + " 3.6491, 3.7909, 3.6477, 3.6840, 3.5580, 3.4020, 3.3382, 3.4010, 3.4258,\n", + " 3.3577, 3.2396, 3.2927, 3.1675, 2.9809, 2.8077, 2.6876, 2.7320, 2.5627,\n", + " 2.5124, 2.4284, 2.5644, 2.5721, 2.4759, 2.5191, 1.7324, 2.8924, 3.1668,\n", + " 3.4885, 3.7130, 3.9679, 4.0117, 1.8518, 1.1471, 1.0812, 1.9691, 2.7947,\n", + " 2.9849, 1.7677, 2.9557]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1669, -1.8937, 0.0989, 0.8973, 0.0253, 0.5840, -0.4015, -0.0576,\n", + " -0.5409, -0.3212, -0.5257, 0.8394, 0.8927, -0.1187, 0.6676, 0.2872,\n", + " 0.2337, -0.1071, -1.3093, 1.4188, -0.4240, 0.4026, 1.1885, 0.6905,\n", + " 0.0693, -0.8837, -0.2927, 0.8998, 0.8761, 0.1629, 0.4281, -0.4760,\n", + " -0.2367, -0.8313, -0.1303, -0.3831, 0.9141, 0.5763, 0.0545, -0.7724,\n", + " -0.6019, 0.2045, -0.2516]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.4496, -0.4245, -0.1781, -0.4416, 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-0.2358, -0.2974, -0.2234,\n", + " -0.3199, -0.3772, -0.2183, -0.1911, 0.0074, -0.0254, -0.0354, 0.3703,\n", + " 0.4063, 0.3730, 0.2720]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.000431907013989985\n", + "Grad encoder.fc1.bias: 0.00029084496782161295\n", + "Grad encoder.encoder.0.weight: 0.00011834163160528988\n", + "Grad encoder.encoder.0.bias: 0.00038793275598436594\n", + "Grad encoder.encoder.2.weight: 8.461771358270198e-05\n", + "Grad encoder.encoder.2.bias: 0.0004979083314538002\n", + "Grad encoder.encoder.4.weight: 0.0001887847320176661\n", + "Grad encoder.encoder.4.bias: 0.0014954505022615194\n", + "Grad decoder.fc1.0.weight: 9.529452654533088e-05\n", + "Grad decoder.fc1.0.bias: 0.0009415089152753353\n", + "Grad decoder.fc1.2.weight: 0.00011743261711671948\n", + "Grad decoder.fc1.2.bias: 0.0012151178671047091\n", + "Grad decoder.fc1.4.weight: 9.065716585610062e-05\n", + "Grad decoder.fc1.4.bias: 0.0013137104688212276\n", + "Grad decoder.fc2.weight: 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"Data Y Sample: tensor([[-1.2420, -0.6739, -0.4227, 0.2969, -0.0174, 0.1707, -0.0821, 0.5170,\n", + " 1.5336, 0.9732, 0.5526, 0.0427, -0.8918, -1.1126, -2.1076, -1.0702,\n", + " -0.4147, -0.1364, -0.5388, 0.6872, 0.5483, -1.9390, -0.8952, 0.8405,\n", + " 0.8903, -0.6337, -0.8542, 0.1820, -0.5569, -0.2822, -1.5998, 0.6097,\n", + " -1.9815, -0.0622, 0.2386, -0.5504, -1.0365, 0.0103, 0.0000, 0.7843,\n", + " 0.6853, 1.0145, 0.7034]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2648, -0.3084, -0.0324, -0.1870, -0.0023, 0.2122, 0.2327, 0.3137,\n", + " 0.2845, 0.2729, 0.2677, -0.1634, -0.2065, -0.3489, -0.2254, -0.3278,\n", + " -0.2271, -0.0412, -0.1041, -0.1115, -0.0556, -0.0518, -0.0383, -0.0325,\n", + " -0.0368, 0.0466, -0.0125, -0.1342, -0.2270, -0.2163, -0.2677, -0.1955,\n", + " -0.2943, -0.3499, -0.1965, -0.1758, 0.0144, -0.0162, -0.0361, 0.3340,\n", + " 0.3678, 0.3289, 0.2509]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0004149672749917954\n", + "Grad encoder.fc1.bias: 0.0005391199374571443\n", + "Grad encoder.encoder.0.weight: 0.00010130981536349282\n", + "Grad encoder.encoder.0.bias: 0.0007019131444394588\n", + "Grad encoder.encoder.2.weight: 7.016723975539207e-05\n", + "Grad encoder.encoder.2.bias: 0.0008365639369003475\n", + "Grad encoder.encoder.4.weight: 0.0001640402479097247\n", + "Grad encoder.encoder.4.bias: 0.0038525816053152084\n", + "Grad decoder.fc1.0.weight: 8.316325693158433e-05\n", + "Grad decoder.fc1.0.bias: 0.0014307466335594654\n", + "Grad decoder.fc1.2.weight: 7.892826397437602e-05\n", + "Grad decoder.fc1.2.bias: 0.0011101269628852606\n", + "Grad decoder.fc1.4.weight: 5.676724322256632e-05\n", + "Grad decoder.fc1.4.bias: 0.000725570076610893\n", + "Grad decoder.fc2.weight: 0.00013571401359513402\n", + "Grad decoder.fc2.bias: 0.001770977396517992\n", + "Grad _memory_unit.weight_ih_l0: 1.708178206172306e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 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"Grad _memory_unit.bias_hh_l1: 5.881694596610032e-05\n", + "Data X Sample: tensor([[2.2193, 2.5926, 2.6717, 2.8381, 2.8770, 2.9274, 3.0983, 3.1075, 3.2172,\n", + " 3.2384, 3.2167, 3.3536, 3.3466, 3.3863, 3.2785, 3.1891, 3.1164, 3.2169,\n", + " 3.2565, 3.0927, 3.0915, 2.9807, 2.7761, 2.7180, 2.5619, 2.5544, 2.3763,\n", + " 2.4553, 2.1093, 2.2107, 2.1907, 2.2051, 2.0790, 1.4212, 2.2771, 2.5392,\n", + " 2.7236, 2.9418, 2.9474, 2.9988, 1.7181, 0.9519, 0.9297, 1.7452, 2.4776,\n", + " 2.6818, 1.8357, 2.5725]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.0114, -0.0336, -0.6353, 0.3626, -1.1866, 0.7485, 0.1751, -0.0520,\n", + " -0.4084, -0.3306, -0.2804, 0.8152, -1.0411, -0.1500, -0.1754, -1.1893,\n", + " 0.3384, 0.3303, 1.0773, 1.9196, 0.8950, -0.3269, -0.2664, 0.0088,\n", + " 0.0309, -0.3200, 0.1287, -0.4193, -0.6294, 0.7331, 0.6774, -0.3877,\n", + " 0.7143, -2.3290, -0.0343, 1.4991, -0.6417, 0.7428, 0.2870, -0.5959,\n", + " 0.1556, -0.7708, 1.6293]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.0452, -0.0374, -0.0115, -0.0755, -0.1346, -0.0684, -0.1320, -0.0387,\n", + " -0.0202, -0.0234, -0.0526, -0.0069, -0.0102, 0.0189, -0.0228, -0.0188,\n", + " -0.0332, -0.0120, -0.0338, 0.0002, 0.0034, 0.0380, 0.0172, -0.0371,\n", + " 0.0362, 0.0224, 0.0799, 0.0242, 0.0688, 0.0850, 0.0330, 0.0224,\n", + " 0.0636, 0.0220, 0.0204, 0.0420, -0.0450, -0.0178, -0.0115, -0.0045,\n", + " 0.0483, 0.0471, -0.0394]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.000254761049291119\n", + "Grad encoder.fc1.bias: 0.002164149656891823\n", + "Grad encoder.encoder.0.weight: 0.00010735519754234701\n", + "Grad encoder.encoder.0.bias: 0.0013442201307043433\n", + "Grad encoder.encoder.2.weight: 7.605952851008624e-05\n", + "Grad encoder.encoder.2.bias: 0.0009867019252851605\n", + "Grad encoder.encoder.4.weight: 0.00017092392954509705\n", + "Grad encoder.encoder.4.bias: 0.0019755379762500525\n", + "Grad decoder.fc1.0.weight: 7.400050526484847e-05\n", + 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0.7229, 0.7255, 1.2945, 1.8473,\n", + " 2.1786, 1.4822, 2.0643]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.1684, 0.9304, 0.5666, -0.0740, 0.3022, -0.3352, 0.0331, -0.1912,\n", + " -0.8942, -0.4585, -0.3428, 0.4725, 0.3361, 1.3565, 1.1187, 0.3458,\n", + " 0.8371, 0.5430, -1.3648, 1.1407, 0.7214, -0.0806, 0.7629, 0.3697,\n", + " 0.3511, 0.4460, -0.1336, 1.1918, -0.3876, 0.1401, 1.1194, 0.4654,\n", + " 1.4930, -0.0879, 0.1564, 1.1351, -1.0105, 0.0761, -0.0156, -0.2002,\n", + " -0.2255, -0.7465, -0.6876]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 3.2149e-01, 3.3547e-01, 1.7962e-01, 1.6123e-01, 6.2380e-02,\n", + " -1.2932e-01, -3.6460e-01, -3.6077e-01, -1.8512e-01, -2.5940e-01,\n", + " -2.5892e-01, 1.3774e-01, 1.2201e-01, 2.4687e-01, 1.1745e-01,\n", + " 2.5504e-01, 2.5435e-01, 9.6858e-02, 6.6747e-02, 1.6172e-01,\n", + " 5.1179e-02, 8.5227e-02, 9.4176e-02, 1.4754e-02, 9.5887e-02,\n", + " 1.0175e-01, -2.6683e-02, 2.4632e-03, 1.9458e-01, 2.9685e-01,\n", + " 2.8463e-01, 1.6295e-01, 2.6321e-01, 2.6433e-01, 1.4659e-01,\n", + " 2.0010e-01, 2.2371e-02, -2.3322e-02, 1.2336e-04, -3.0555e-01,\n", + " -2.3719e-01, -2.4934e-01, -2.9817e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.000659980985801667\n", + "Grad encoder.fc1.bias: 0.0009300693636760116\n", + "Grad encoder.encoder.0.weight: 0.00011876900680363178\n", + "Grad encoder.encoder.0.bias: 0.000686915242113173\n", + "Grad encoder.encoder.2.weight: 6.952609692234546e-05\n", + "Grad encoder.encoder.2.bias: 0.0005691215046681464\n", + "Grad encoder.encoder.4.weight: 0.00014118946273811162\n", + "Grad encoder.encoder.4.bias: 0.0012589984107762575\n", + "Grad decoder.fc1.0.weight: 6.027424751664512e-05\n", + "Grad decoder.fc1.0.bias: 0.0006126323714852333\n", + "Grad decoder.fc1.2.weight: 5.166661867406219e-05\n", + "Grad decoder.fc1.2.bias: 0.0008318689651787281\n", + "Grad decoder.fc1.4.weight: 4.797039582626894e-05\n", + "Grad decoder.fc1.4.bias: 0.0008291444974020123\n", + "Grad decoder.fc2.weight: 0.0001323236501775682\n", + "Grad decoder.fc2.bias: 0.0019274818478152156\n", + "Grad _memory_unit.weight_ih_l0: 1.0630355063767638e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 5.167405106476508e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.7832502382807434e-05\n", + "Grad _memory_unit.weight_ih_l1: 6.3398142629012e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00011024457489838824\n", + "Grad _memory_unit.bias_hh_l1: 5.72144563193433e-05\n", + "Data X Sample: tensor([[1.4767, 1.7172, 1.9234, 1.9963, 2.0933, 2.3278, 2.3726, 2.4787, 2.5704,\n", + " 2.5418, 2.5854, 2.5142, 2.5544, 2.5138, 2.4311, 2.4548, 2.3617, 2.4102,\n", + " 2.4801, 2.3730, 2.5027, 2.4005, 2.4556, 2.3420, 2.3929, 2.3977, 2.4642,\n", + " 2.4553, 2.1023, 2.2598, 2.2957, 2.2825, 2.2330, 1.4578, 1.9242, 1.8071,\n", + " 1.6361, 1.5504, 1.6480, 1.5774, 1.1788, 0.7149, 0.7053, 1.2630, 1.7839,\n", + " 1.9613, 1.2306, 2.0564]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.5812, 0.8651, -0.1888, 0.0514, -0.4423, -0.9744, -0.3453, -0.3910,\n", + " -0.7696, 0.1050, -0.2307, 1.0932, 0.6083, 0.3495, 0.0972, 0.4293,\n", + " -0.1312, -0.1274, 0.7661, 0.8131, 0.8391, 1.0035, 0.3207, 1.2824,\n", + " 0.3534, -0.3230, 0.3345, -0.0770, 0.1038, 0.8733, 0.2450, 0.7851,\n", + " 0.6201, 0.0328, 1.6790, -0.6519, -0.6306, -0.5802, -0.0059, -0.3304,\n", + " -0.2301, -0.1428, -0.3429]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3005, 0.3100, 0.1694, 0.1563, 0.0555, -0.1182, -0.3420, -0.3414,\n", + " -0.1646, -0.2333, -0.2362, 0.1275, 0.1061, 0.2224, 0.1020, 0.2336,\n", + " 0.2371, 0.0915, 0.0577, 0.1524, 0.0531, 0.0762, 0.0891, 0.0149,\n", + " 0.0877, 0.1046, -0.0341, -0.0008, 0.1764, 0.2672, 0.2636, 0.1508,\n", + " 0.2487, 0.2460, 0.1395, 0.1855, 0.0218, -0.0242, -0.0055, -0.2845,\n", + " -0.2210, -0.2230, -0.2705]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0007411243277601898\n", 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"Grad _memory_unit.bias_hh_l1: 0.00010362493776483461\n", + "Data X Sample: tensor([[1.5287, 1.7740, 1.8813, 2.0116, 2.1821, 2.2880, 2.3156, 2.4432, 2.4434,\n", + " 2.5734, 2.5156, 2.5006, 2.5078, 2.5138, 2.4387, 2.4151, 2.4152, 2.4276,\n", + " 2.4429, 2.4567, 2.4757, 2.5291, 2.4146, 2.4049, 2.4361, 2.4265, 2.3549,\n", + " 2.4512, 2.1544, 2.3024, 2.2712, 2.2632, 2.2836, 1.5127, 1.9560, 1.8047,\n", + " 1.6243, 1.5027, 1.5593, 1.6228, 1.2600, 0.7109, 0.6871, 1.2314, 1.8196,\n", + " 2.0071, 1.2986, 1.9861]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.4783, 0.7684, -0.1437, 0.6640, 0.4276, 0.2098, -0.1956, -0.5044,\n", + " -0.0680, -0.2828, -0.3801, 0.9622, -0.6134, -0.0610, 0.8015, 0.0956,\n", + " -0.2985, 0.2915, 0.3531, -0.8425, 0.4568, -0.1513, 0.3367, -1.1593,\n", + " 0.0928, -0.6449, -0.0262, 0.1530, -0.0873, 0.3612, 0.7344, -0.6493,\n", + " 0.9184, 0.9037, 0.1975, -0.7555, 0.4768, 0.0705, -0.8437, -0.3672,\n", + " -0.2541, -0.4048, -0.3211]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2994, 0.3022, 0.1669, 0.1811, 0.0612, -0.1063, -0.3405, -0.3513,\n", + " -0.1458, -0.2146, -0.2172, 0.1309, 0.1039, 0.2053, 0.0957, 0.2278,\n", + " 0.2415, 0.1006, 0.0577, 0.1576, 0.0675, 0.0712, 0.0960, 0.0230,\n", + " 0.0851, 0.1186, -0.0547, 0.0009, 0.1609, 0.2463, 0.2584, 0.1510,\n", + " 0.2579, 0.2542, 0.1464, 0.1797, 0.0242, -0.0246, -0.0166, -0.2796,\n", + " -0.2250, -0.2141, -0.2589]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0006526046781800687\n", + "Grad encoder.fc1.bias: 0.00026010669535025954\n", + "Grad encoder.encoder.0.weight: 0.00015932272071950138\n", + "Grad encoder.encoder.0.bias: 0.00038178657996468246\n", + "Grad encoder.encoder.2.weight: 8.219460141845047e-05\n", + "Grad encoder.encoder.2.bias: 0.000371138914488256\n", + "Grad encoder.encoder.4.weight: 0.00014470696623902768\n", + "Grad encoder.encoder.4.bias: 0.00141645479016006\n", + "Grad decoder.fc1.0.weight: 4.8918322136159986e-05\n", + "Grad 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" 3.4672, 3.4646, 3.4964, 3.2287, 2.8556, 2.7848, 2.8058, 2.8966, 2.9863,\n", + " 3.0018, 2.9627, 3.1605, 3.1531, 3.2828, 3.7093, 2.7714, 4.6376, 4.9861,\n", + " 5.0008, 5.3456, 5.8377, 5.8331, 1.1741, 0.6711, 0.6892, 1.2515, 1.7601,\n", + " 1.9671, 1.2782, 1.9001]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.4108, -0.9922, -0.7612, -1.0786, 0.1469, 0.2200, 0.8795, 0.3015,\n", + " 0.2804, 0.5470, 0.7506, 0.3958, -0.7570, -1.0738, -0.0419, 0.0108,\n", + " -1.1222, 0.1980, -0.3188, -0.6872, -0.8594, -0.4930, -2.0781, -0.9901,\n", + " 0.1485, 0.3511, -0.0502, -0.7699, 0.9069, -0.5347, 0.4570, -0.3123,\n", + " -1.1599, -0.7548, -1.2488, -0.7188, 0.9776, -0.0751, -1.8131, 0.9072,\n", + " 0.8487, 0.6494, 0.3849]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2137, -0.2314, 0.0180, -0.1395, 0.0826, 0.2690, 0.2146, 0.2330,\n", + " 0.2234, 0.2377, 0.2048, -0.1396, -0.1916, -0.2849, -0.1954, -0.2391,\n", + " -0.2187, 0.0080, -0.0763, -0.0767, -0.0290, -0.0801, -0.0213, 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device='cuda:0')\n", + "Prediction Sample: tensor([[-2.3333e-01, -2.6118e-01, -1.3153e-02, -1.3580e-01, 7.4136e-02,\n", + " 2.7890e-01, 2.2618e-01, 2.7557e-01, 2.5586e-01, 2.6308e-01,\n", + " 2.3273e-01, -1.4526e-01, -2.0081e-01, -3.0131e-01, -2.1524e-01,\n", + " -2.6028e-01, -2.1064e-01, 8.3248e-03, -6.8702e-02, -8.1285e-02,\n", + " -3.7223e-02, -6.0680e-02, -3.1523e-02, 1.9353e-02, -3.6865e-02,\n", + " 6.4476e-02, -6.8485e-02, -1.4064e-01, -2.4119e-01, -2.3628e-01,\n", + " -2.4984e-01, -1.5066e-01, -2.9673e-01, -3.3499e-01, -1.8206e-01,\n", + " -1.9616e-01, 2.2433e-04, 5.2922e-02, -1.1969e-03, 2.6052e-01,\n", + " 2.9659e-01, 2.8798e-01, 2.2247e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0001058907582773827\n", + "Grad encoder.fc1.bias: 0.0002886835136450827\n", + "Grad encoder.encoder.0.weight: 5.313080328051001e-05\n", + "Grad encoder.encoder.0.bias: 0.000311072391923517\n", + "Grad encoder.encoder.2.weight: 4.0883773181121796e-05\n", + "Grad 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"Grad encoder.encoder.4.bias: 0.0006622240762226284\n", + "Grad decoder.fc1.0.weight: 3.85388448194135e-05\n", + "Grad decoder.fc1.0.bias: 0.0003264942788518965\n", + "Grad decoder.fc1.2.weight: 5.4288666433421895e-05\n", + "Grad decoder.fc1.2.bias: 0.0004448586842045188\n", + "Grad decoder.fc1.4.weight: 4.4361455366015434e-05\n", + "Grad decoder.fc1.4.bias: 0.00039422325789928436\n", + "Grad decoder.fc2.weight: 0.00012174541916465387\n", + "Grad decoder.fc2.bias: 0.0017263037152588367\n", + "Grad _memory_unit.weight_ih_l0: 9.84455255093053e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 5.008863809052855e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.5362285668961704e-05\n", + "Grad _memory_unit.weight_ih_l1: 3.7169872939557536e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 6.396816024789587e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.385108720976859e-05\n", + "Data X Sample: tensor([[2.5704, 3.1490, 3.5462, 3.7127, 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-0.0554, -0.0728, -0.0307, -0.5243,\n", + " -0.5190, -0.5197, -0.5041]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0008487023878842592\n", + "Grad encoder.fc1.bias: 0.0006754741189070046\n", + "Grad encoder.encoder.0.weight: 0.0001892601721920073\n", + "Grad encoder.encoder.0.bias: 0.0008465233258903027\n", + "Grad encoder.encoder.2.weight: 0.0001628254831302911\n", + "Grad encoder.encoder.2.bias: 0.0013010376133024693\n", + "Grad encoder.encoder.4.weight: 0.0003733128251042217\n", + "Grad encoder.encoder.4.bias: 0.005646419711410999\n", + "Grad decoder.fc1.0.weight: 9.20533129828982e-05\n", + "Grad decoder.fc1.0.bias: 0.0007742572342976928\n", + "Grad decoder.fc1.2.weight: 0.00012021084694424644\n", + "Grad decoder.fc1.2.bias: 0.0008676366414874792\n", + "Grad decoder.fc1.4.weight: 9.337392111774534e-05\n", + "Grad decoder.fc1.4.bias: 0.0007224680157378316\n", + "Grad decoder.fc2.weight: 0.00017683778423815966\n", + "Grad decoder.fc2.bias: 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"Grad decoder.fc1.4.bias: 0.0007238400867208838\n", + "Grad decoder.fc2.weight: 0.00012958697334397584\n", + "Grad decoder.fc2.bias: 0.0014898329973220825\n", + "Grad _memory_unit.weight_ih_l0: 1.407362287864089e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 5.757085091318004e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.9279099180712365e-05\n", + "Grad _memory_unit.weight_ih_l1: 6.037187631591223e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00011413956235628575\n", + "Grad _memory_unit.bias_hh_l1: 5.907573358854279e-05\n", + "Data X Sample: tensor([[1.5520, 1.6560, 1.9143, 2.0226, 2.1770, 2.2585, 2.1107, 2.3012, 3.7762,\n", + " 4.3711, 4.3334, 4.1423, 4.1500, 4.0624, 3.8030, 3.7649, 3.7611, 3.5728,\n", + " 3.5704, 3.4442, 3.5773, 3.3313, 3.0195, 2.9298, 2.9485, 2.9906, 2.9517,\n", + " 3.0018, 3.0459, 3.2129, 3.2161, 3.4431, 3.7247, 2.8194, 4.5273, 4.8718,\n", + " 5.0676, 5.5019, 5.8313, 5.6345, 1.1932, 0.7129, 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3.9616, 3.7349, 3.6404, 3.5614, 3.5438,\n", + " 3.4837, 3.4739, 3.4743, 3.3404, 2.9375, 2.7638, 2.8415, 2.7895, 2.8131,\n", + " 2.8672, 2.9037, 3.1343, 3.0866, 3.2497, 3.7599, 2.7714, 4.4954, 4.7283,\n", + " 5.1364, 5.5708, 5.8377, 5.8076, 1.1932, 0.6412, 0.6588, 1.2228, 1.8037,\n", + " 1.8413, 1.2170, 1.9548]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.0055, -0.3715, 0.4144, 1.2409, -0.1049, 1.0519, 0.0327, 0.0597,\n", + " 0.0527, 0.3241, 0.2685, -0.4186, 0.6401, -1.0256, -0.4766, 0.0394,\n", + " -0.5879, 0.3952, 0.6873, 0.3647, -0.5086, 0.5931, 0.1683, -0.3789,\n", + " 0.2555, 0.5026, -0.1514, -0.6639, -0.7501, -0.9139, 0.3319, 0.3230,\n", + " 0.2274, -0.3634, -0.0711, 0.4679, -1.0138, -0.0140, -0.7911, 0.0856,\n", + " -0.1090, -0.2890, 1.0446]], device='cuda:0')\n", + "Prediction Sample: tensor([[-2.0443e-01, -2.3238e-01, -1.3881e-01, -1.8424e-01, -1.5919e-01,\n", + " 7.3777e-03, 1.0750e-01, 3.0942e-01, 9.9513e-02, 1.2788e-01,\n", + " 2.4487e-01, -1.5899e-01, -1.3460e-01, -1.9417e-01, -1.5445e-01,\n", + " -1.9431e-01, -1.2019e-01, -3.8520e-02, -1.1432e-01, -5.2816e-02,\n", + " -3.9331e-02, 2.2692e-02, -4.6589e-02, 2.8336e-02, 2.4811e-02,\n", + " 1.0763e-01, 6.0201e-02, 1.4715e-02, -1.4420e-01, -1.3093e-01,\n", + " -1.9752e-01, -2.2169e-01, -1.8048e-01, -2.0951e-01, -1.1721e-01,\n", + " -2.2977e-01, -9.0133e-02, -1.1802e-04, 4.3039e-02, 2.5306e-01,\n", + " 2.9656e-01, 3.8918e-01, 2.0837e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0021486128680408\n", + "Grad encoder.fc1.bias: 0.0013195722131058574\n", + "Grad encoder.encoder.0.weight: 0.0004321392625570297\n", + "Grad encoder.encoder.0.bias: 0.0014794659800827503\n", + "Grad encoder.encoder.2.weight: 0.00033536410774104297\n", + "Grad encoder.encoder.2.bias: 0.0015787844313308597\n", + "Grad encoder.encoder.4.weight: 0.0005928489845246077\n", + "Grad encoder.encoder.4.bias: 0.0022403448820114136\n", + "Grad decoder.fc1.0.weight: 0.00013286534522194415\n", + "Grad 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"Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 0.0006243293173611164\n", + "Grad _memory_unit.bias_hh_l0: 0.000317291880492121\n", + "Grad _memory_unit.weight_ih_l1: 3.1169442081591114e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.0005289202672429383\n", + "Grad _memory_unit.bias_hh_l1: 0.00026621061260811985\n", + "Data X Sample: tensor([[1.5966, 1.8891, 2.0706, 2.1690, 2.3187, 2.4530, 2.5267, 2.6391, 2.6900,\n", + " 2.6286, 2.7028, 2.6603, 2.6520, 2.5302, 2.4488, 2.4137, 2.4010, 2.5263,\n", + " 2.4801, 2.5887, 2.6327, 2.6347, 2.5303, 2.5920, 2.6219, 2.6223, 2.5894,\n", + " 2.6062, 2.3625, 2.4563, 2.4532, 2.4069, 2.2660, 1.5035, 1.9536, 1.8290,\n", + " 1.7502, 1.6538, 1.6733, 1.5491, 1.3459, 0.7627, 0.7619, 1.3519, 2.0376,\n", + " 2.2187, 1.3870, 2.1659]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.6712, 0.3852, 0.4777, 0.0902, 0.3872, -0.3272, -0.4547, -0.6400,\n", + " 0.0552, 0.1009, -0.0251, -0.9357, 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1.1358, 1.9519, 2.6837,\n", + " 3.0592, 2.0941, 2.9400]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 2.8552e-01, -8.0987e-01, 1.1153e+00, -5.3759e-01, 7.1691e-01,\n", + " 1.7533e-01, -7.2148e-02, -1.7305e-01, -5.5026e-01, -4.8243e-01,\n", + " 2.7277e-01, 2.9688e-01, 1.7560e-01, -1.9946e-01, 1.7641e+00,\n", + " 9.0965e-01, 1.8369e+00, -2.1111e+00, 2.8222e+00, 2.4130e+00,\n", + " 1.7608e+00, 4.6939e-01, 1.9003e+00, 2.7814e+00, 4.1895e+00,\n", + " 4.7872e-02, 5.1048e-01, -1.5291e-01, -5.0785e-01, -4.0964e-01,\n", + " -2.9661e-01, 1.1323e+00, -3.8217e-01, 4.6092e-02, 1.4853e+00,\n", + " -8.4280e-01, -8.4715e-01, 8.0236e-01, 4.4037e-01, -1.8539e-01,\n", + " -2.4162e-03, 5.0485e-02, -5.9398e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-1.9701e-01, -2.4712e-01, -1.2102e-01, -1.7098e-01, -1.6557e-01,\n", + " 4.2215e-02, 1.1573e-01, 3.1284e-01, 1.3105e-01, 1.5797e-01,\n", + " 2.6478e-01, -1.5841e-01, -1.1876e-01, -1.7023e-01, -1.4729e-01,\n", + " -1.9942e-01, -1.4674e-01, -3.7619e-02, -8.5953e-02, -7.6695e-02,\n", + " -2.8319e-02, 9.6770e-03, -6.7427e-02, 2.0874e-02, 6.0615e-03,\n", + " 8.6682e-02, 3.0842e-02, 8.2054e-05, -1.4369e-01, -1.2913e-01,\n", + " -1.9809e-01, -1.7894e-01, -1.6014e-01, -2.2736e-01, -1.2383e-01,\n", + " -2.3422e-01, -6.6783e-02, 3.6458e-02, 1.7593e-02, 2.8333e-01,\n", + " 3.3705e-01, 3.7712e-01, 2.0723e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0006842942675575614\n", + "Grad encoder.fc1.bias: 0.0005348306731320918\n", + "Grad encoder.encoder.0.weight: 0.00016113181482069194\n", + "Grad encoder.encoder.0.bias: 0.0006146724335849285\n", + "Grad encoder.encoder.2.weight: 9.076163405552506e-05\n", + "Grad encoder.encoder.2.bias: 0.0005396895576268435\n", + "Grad encoder.encoder.4.weight: 0.0002345499087823555\n", + "Grad encoder.encoder.4.bias: 0.0016372798709198833\n", + "Grad decoder.fc1.0.weight: 7.498475315514952e-05\n", + "Grad decoder.fc1.0.bias: 0.0006221185903996229\n", + "Grad decoder.fc1.2.weight: 8.892182086128742e-05\n", + "Grad decoder.fc1.2.bias: 0.0008439375669695437\n", + "Grad decoder.fc1.4.weight: 6.436280091293156e-05\n", + "Grad decoder.fc1.4.bias: 0.0005875579663552344\n", + "Grad decoder.fc2.weight: 0.00013091602886561304\n", + "Grad decoder.fc2.bias: 0.0020031018648296595\n", + "Grad _memory_unit.weight_ih_l0: 1.4788050975766964e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 7.535057375207543e-05\n", + "Grad _memory_unit.bias_hh_l0: 4.174049536231905e-05\n", + "Grad _memory_unit.weight_ih_l1: 7.688307960052043e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00011466875730548054\n", + "Grad _memory_unit.bias_hh_l1: 6.144677172414958e-05\n", + "Data X Sample: tensor([[1.8469, 1.6560, 1.9008, 2.0575, 2.1924, 2.1318, 2.2016, 3.5590, 4.2107,\n", + " 4.3253, 4.4952, 4.3916, 4.2654, 4.0924, 3.9089, 3.7403, 3.6338, 3.7256,\n", + " 3.6489, 3.5576, 3.4866, 3.3833, 3.0050, 2.7447, 2.7458, 2.8391, 2.8584,\n", + " 2.8387, 2.8170, 3.0393, 3.0866, 3.2110, 3.5047, 2.6249, 4.4709, 4.7380,\n", + " 5.0991, 5.5894, 6.2941, 6.0516, 1.1550, 0.6432, 0.6952, 1.2630, 1.7918,\n", + " 1.9442, 1.3530, 1.7984]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.2721, -0.3447, -0.6611, -0.5379, -0.7145, -0.2167, 0.2078, 0.2543,\n", + " 0.4786, 0.0472, 0.4834, 0.7765, -0.6548, -0.2258, 0.4044, 0.2914,\n", + " 0.4775, 0.8837, -0.6575, 0.8905, 0.0415, 0.4288, 0.2464, -1.3032,\n", + " 0.5322, -0.0484, 0.3054, -0.6518, -1.6701, -0.1275, -0.8074, -0.1473,\n", + " -0.3803, -0.2537, -0.3597, -0.8325, 0.7686, 0.7579, -0.4066, 0.3833,\n", + " 0.2564, 0.3094, 0.1925]], device='cuda:0')\n", + "Prediction Sample: tensor([[-2.2723e-01, -2.4214e-01, -1.2156e-01, -1.3924e-01, -1.3580e-01,\n", + " 7.0888e-02, 1.3379e-01, 3.5271e-01, 1.3498e-01, 1.7840e-01,\n", + " 2.7917e-01, -1.7074e-01, -1.4541e-01, -1.8657e-01, -1.4468e-01,\n", + " -1.9512e-01, -1.4683e-01, -3.5177e-02, -1.1149e-01, -8.6351e-02,\n", + " -3.4328e-02, -5.2377e-03, -6.6529e-02, 2.6095e-02, 1.7331e-04,\n", + " 8.2065e-02, 9.4011e-03, -3.5458e-02, -1.7016e-01, -1.2801e-01,\n", + " -2.2019e-01, -1.9545e-01, -1.8351e-01, -2.5540e-01, -1.3199e-01,\n", + " -2.5883e-01, -5.2568e-02, 3.3130e-02, 1.0489e-02, 3.1414e-01,\n", + " 3.5838e-01, 3.8878e-01, 2.0256e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00126581359654665\n", + "Grad encoder.fc1.bias: 0.0008942248532548547\n", + "Grad encoder.encoder.0.weight: 0.00022009620442986488\n", + "Grad encoder.encoder.0.bias: 0.0009943144395947456\n", + "Grad encoder.encoder.2.weight: 0.0001337862340733409\n", + "Grad encoder.encoder.2.bias: 0.001148765441030264\n", + "Grad encoder.encoder.4.weight: 0.00037459618761204183\n", + "Grad encoder.encoder.4.bias: 0.0045216279104352\n", + "Grad decoder.fc1.0.weight: 0.00010150405432796106\n", + "Grad decoder.fc1.0.bias: 0.0009970009559765458\n", + "Grad decoder.fc1.2.weight: 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2.9756,\n", + " 3.0141, 2.8898, 3.0753, 3.1426, 3.2524, 3.5465, 2.5288, 4.2920, 4.6577,\n", + " 4.9870, 5.1865, 5.7426, 5.6629, 1.1359, 0.6651, 0.6851, 1.1367, 1.5262,\n", + " 1.7441, 1.1762, 1.6499]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.9680, -0.5982, -0.1486, 0.1519, 0.1926, -0.6466, 0.2324, 0.7525,\n", + " -0.0788, 0.6936, 0.2498, 1.2040, -0.9426, -0.6301, 0.3600, 0.0359,\n", + " -0.2650, -0.0725, -0.4641, 0.4659, -1.4356, -0.3754, -0.9781, -1.0149,\n", + " 0.9542, 0.5670, 0.6609, -0.4235, 0.8712, -0.9395, 0.2142, -0.6127,\n", + " -0.6608, 0.4971, 0.3385, -0.5483, -0.7301, -0.0140, 0.7332, 0.7364,\n", + " 0.1309, 0.6764, 0.2660]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2311, -0.2338, -0.1088, -0.1101, -0.1030, 0.0963, 0.1311, 0.3634,\n", + " 0.1431, 0.1987, 0.2754, -0.1694, -0.1546, -0.1883, -0.1437, -0.1895,\n", + " -0.1446, -0.0352, -0.1090, -0.0895, -0.0363, -0.0162, -0.0710, 0.0238,\n", + " 0.0019, 0.0736, -0.0096, -0.0530, -0.1775, -0.1355, -0.2293, -0.1901,\n", + " -0.1976, -0.2632, -0.1412, -0.2597, -0.0416, 0.0454, 0.0017, 0.3209,\n", + " 0.3640, 0.3836, 0.1962]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0006946527282707393\n", + "Grad encoder.fc1.bias: 0.0006281316746026278\n", + "Grad encoder.encoder.0.weight: 0.00021879642736166716\n", + "Grad encoder.encoder.0.bias: 0.001010384876281023\n", + "Grad encoder.encoder.2.weight: 0.00015506338968407363\n", + "Grad encoder.encoder.2.bias: 0.0012265502009540796\n", + "Grad encoder.encoder.4.weight: 0.00044969096779823303\n", + "Grad encoder.encoder.4.bias: 0.00525397714227438\n", + "Grad decoder.fc1.0.weight: 0.00011554345837794244\n", + "Grad decoder.fc1.0.bias: 0.0009500047308392823\n", + "Grad decoder.fc1.2.weight: 0.00011670451203826815\n", + "Grad decoder.fc1.2.bias: 0.000992494635283947\n", + "Grad decoder.fc1.4.weight: 0.00010890084377024323\n", + "Grad decoder.fc1.4.bias: 0.0009911067318171263\n", + "Grad decoder.fc2.weight: 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"Data Y Sample: tensor([[-0.3964, -0.0811, 0.8360, -0.5013, 0.5611, -0.1102, 0.2008, 0.0662,\n", + " 0.6789, 0.0467, 0.3053, -0.1070, 0.6914, -0.1846, -1.4929, -0.5806,\n", + " 0.7834, -0.6465, -0.3138, 0.1705, 0.2574, 0.4761, 1.0261, -0.4998,\n", + " -1.2869, -0.6827, -0.0450, 0.1520, -0.1050, -0.5593, -0.1155, 0.9632,\n", + " -0.6915, -0.4960, 0.3846, 0.0203, -0.8819, 0.6361, 0.2630, 0.0182,\n", + " 0.4235, 0.3346, -0.1003]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2402, -0.2315, -0.1059, -0.0878, -0.0769, 0.1220, 0.1375, 0.3808,\n", + " 0.1596, 0.2225, 0.2835, -0.1699, -0.1655, -0.1982, -0.1458, -0.1910,\n", + " -0.1420, -0.0364, -0.1068, -0.0878, -0.0404, -0.0281, -0.0772, 0.0169,\n", + " 0.0046, 0.0696, -0.0252, -0.0753, -0.1951, -0.1512, -0.2462, -0.1946,\n", + " -0.2147, -0.2768, -0.1547, -0.2649, -0.0355, 0.0557, 0.0023, 0.3297,\n", + " 0.3753, 0.3892, 0.2040]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0014626093907281756\n", + "Grad 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grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0015197766479104757\n", + "Grad encoder.fc1.bias: 0.001321011921390891\n", + "Grad encoder.encoder.0.weight: 0.0005073532811366022\n", + "Grad encoder.encoder.0.bias: 0.0014969112817198038\n", + "Grad encoder.encoder.2.weight: 0.0003085724310949445\n", + "Grad encoder.encoder.2.bias: 0.0012576603330671787\n", + "Grad encoder.encoder.4.weight: 0.0007865051738917828\n", + "Grad encoder.encoder.4.bias: 0.0027619502507150173\n", + "Grad decoder.fc1.0.weight: 0.00021308095892891288\n", + "Grad decoder.fc1.0.bias: 0.0008286102674901485\n", + "Grad decoder.fc1.2.weight: 0.00014922804257366806\n", + "Grad decoder.fc1.2.bias: 0.001033162698149681\n", + "Grad decoder.fc1.4.weight: 0.00010498370102141052\n", + "Grad decoder.fc1.4.bias: 0.0008257250301539898\n", + "Grad decoder.fc2.weight: 0.00012064634211128578\n", + "Grad decoder.fc2.bias: 0.0026349907275289297\n", + "Grad _memory_unit.weight_ih_l0: 2.0930949176545255e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 6.010293145664036e-05\n", + "Grad _memory_unit.bias_hh_l0: 3.24499087582808e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.3771757949143648e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.0001317660789936781\n", + "Grad _memory_unit.bias_hh_l1: 7.250986527651548e-05\n", + "Data X Sample: tensor([[1.4512, 1.6590, 1.7941, 1.9613, 1.9738, 2.0891, 2.1307, 2.3168, 3.8592,\n", + " 4.2494, 4.3556, 4.2709, 4.2787, 4.0188, 3.9140, 3.7293, 3.6731, 3.6327,\n", + " 3.5477, 3.4033, 3.5185, 3.3649, 3.0171, 2.7122, 2.6632, 2.8574, 2.7465,\n", + " 2.8020, 2.8968, 3.0426, 3.1146, 3.1447, 3.7093, 2.7554, 4.5126, 4.8061,\n", + " 5.0204, 5.3827, 5.8821, 5.6062, 1.1216, 0.6572, 0.6629, 1.1252, 1.6491,\n", + " 1.8927, 1.3190, 1.7515]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.5075, -0.6937, -0.8007, -0.9462, 0.7177, -1.7838, 0.4786, 0.3195,\n", + " -0.8555, 0.0664, 0.2074, -0.1504, 3.7170, 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_memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 7.950705185066909e-05\n", + "Grad _memory_unit.bias_hh_l1: 4.237825851305388e-05\n", + "Data X Sample: tensor([[1.3271, 1.3837, 1.7250, 1.9438, 2.0797, 2.1465, 2.1693, 2.1096, 3.0610,\n", + " 3.8940, 4.3429, 4.2105, 3.9126, 3.8770, 3.7324, 3.5871, 3.5300, 3.6734,\n", + " 3.6654, 3.4312, 3.4203, 3.4889, 2.9905, 2.7657, 2.7627, 2.9044, 2.9756,\n", + " 3.0671, 3.1396, 3.2064, 3.3175, 3.3436, 3.7797, 2.8011, 4.5518, 5.0663,\n", + " 5.1522, 5.4754, 5.9391, 5.8615, 1.0691, 0.6233, 0.6285, 1.1252, 1.6332,\n", + " 1.9556, 1.2510, 1.9079]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.4297, -0.4367, 0.3658, -0.6519, 0.0233, -0.0589, 1.0193, 0.6316,\n", + " 0.3646, 0.4220, 1.0213, -0.7732, -0.9970, -1.2338, -0.5725, -1.8489,\n", + " -1.4116, 0.1377, -0.4130, 0.2050, 0.6439, -0.0774, 1.0891, 0.8952,\n", + " 0.5671, 0.4738, -1.3512, 0.9023, -0.9835, -0.7063, -1.3503, -0.5058,\n", + " -1.0769, -0.8360, -0.5295, -0.8994, -0.3727, -0.6596, -0.0059, 0.9357,\n", + " 1.0103, 1.1943, 0.6334]], device='cuda:0')\n", + "Prediction Sample: tensor([[-2.1718e-01, -1.0707e-01, -3.8613e-02, 4.2795e-02, 1.2483e-01,\n", + " 1.6643e-01, 1.2887e-01, 2.3680e-01, 1.3503e-01, 2.2337e-01,\n", + " 9.8997e-02, -9.9671e-02, -1.8918e-01, -1.9704e-01, -8.6988e-02,\n", + " -1.0960e-01, -8.2746e-02, -7.9218e-03, -6.5348e-02, -7.1354e-02,\n", + " -5.9272e-02, -8.6289e-02, -1.3074e-02, -6.2165e-02, 1.7450e-02,\n", + " -1.5318e-02, -9.1592e-02, -1.7102e-01, -1.7133e-01, -2.0264e-01,\n", + " -1.9086e-01, -1.5384e-01, -2.9330e-01, -1.8842e-01, -1.1460e-01,\n", + " -1.3217e-01, 2.6555e-02, -7.6298e-05, -1.7598e-02, 2.1355e-01,\n", + " 2.2772e-01, 1.9836e-01, 1.3535e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00021295525948517025\n", + "Grad encoder.fc1.bias: 0.00022118199558462948\n", + "Grad encoder.encoder.0.weight: 7.167021976783872e-05\n", + "Grad encoder.encoder.0.bias: 0.0002963124425150454\n", + "Grad encoder.encoder.2.weight: 5.3543390095001087e-05\n", + "Grad encoder.encoder.2.bias: 0.0003480065206531435\n", + "Grad encoder.encoder.4.weight: 0.00016947474796324968\n", + "Grad encoder.encoder.4.bias: 0.0010403692722320557\n", + "Grad decoder.fc1.0.weight: 5.8005469327326864e-05\n", + "Grad decoder.fc1.0.bias: 0.00031486095394939184\n", + "Grad decoder.fc1.2.weight: 6.95200651534833e-05\n", + "Grad decoder.fc1.2.bias: 0.0004768098588101566\n", + "Grad decoder.fc1.4.weight: 6.178088369779289e-05\n", + "Grad decoder.fc1.4.bias: 0.0004853595746681094\n", + "Grad decoder.fc2.weight: 0.00010575380292721093\n", + "Grad decoder.fc2.bias: 0.0018878791015595198\n", + "Grad _memory_unit.weight_ih_l0: 1.008485287457006e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.328645834699273e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.7574886442162097e-05\n", + "Grad _memory_unit.weight_ih_l1: 4.374349828140112e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 5.469274037750438e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.8436732463887893e-05\n", + "Data X Sample: tensor([[2.6532, 3.0121, 3.1420, 3.4132, 3.4371, 3.6390, 3.6313, 3.8443, 3.8470,\n", + " 4.1120, 3.8766, 3.9339, 3.9214, 3.7134, 3.7576, 3.5461, 3.6039, 3.3948,\n", + " 3.5023, 3.3345, 3.3344, 3.2042, 2.9014, 2.8497, 2.7758, 2.8731, 2.8931,\n", + " 2.9529, 2.7892, 2.8526, 3.0271, 2.9402, 3.0339, 2.1649, 3.6620, 3.8624,\n", + " 4.2102, 4.4365, 5.0581, 4.8174, 1.9472, 1.1331, 1.0812, 1.9949, 2.9929,\n", + " 3.1793, 2.2436, 3.1746]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.9598, -1.2717, 0.6687, -0.1989, 1.1680, 1.3966, 0.0397, 1.0420,\n", + " 0.4184, 0.8672, 0.8558, -0.7712, -0.9349, -0.6061, -0.8689, -0.5382,\n", + " -1.0346, -0.6608, -1.2936, 0.4453, 0.4088, 0.7419, 0.5596, 0.3680,\n", + " -1.0284, -0.3673, -1.2085, -0.3061, -0.1641, -0.3183, -0.3511, -0.8729,\n", + " -0.8121, -0.3720, -0.1532, -0.2915, 0.1343, 0.8959, -0.9960, 0.9864,\n", + " 1.0918, 0.7617, 0.7598]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1729, -0.1252, -0.0564, -0.0597, -0.0146, 0.0608, 0.0924, 0.1678,\n", + " 0.0721, 0.1278, 0.1018, -0.0912, -0.1267, -0.1473, -0.0805, -0.0941,\n", + " -0.0997, -0.0239, -0.0782, -0.0649, -0.0464, -0.0487, -0.0127, -0.0350,\n", + " -0.0032, 0.0048, -0.0136, -0.0827, -0.0933, -0.1011, -0.1159, -0.1292,\n", + " -0.1818, -0.1596, -0.0693, -0.1182, 0.0165, -0.0213, -0.0336, 0.2038,\n", + " 0.2133, 0.1741, 0.1117]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00013929628767073154\n", + "Grad encoder.fc1.bias: 0.00019827476353384554\n", + "Grad encoder.encoder.0.weight: 4.793656262336299e-05\n", + "Grad encoder.encoder.0.bias: 0.00020586213213391602\n", + "Grad encoder.encoder.2.weight: 4.829241515835747e-05\n", + "Grad encoder.encoder.2.bias: 0.0002308691618964076\n", + "Grad encoder.encoder.4.weight: 0.00014181676669977605\n", + "Grad encoder.encoder.4.bias: 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3.8202,\n", + " 4.3221, 4.3207, 4.4890, 4.1034, 3.9343, 3.9014, 3.7799, 3.7014, 3.6579,\n", + " 3.6282, 3.4423, 3.5185, 3.4032, 3.0122, 2.8020, 2.9429, 3.0141, 2.9117,\n", + " 2.9978, 3.1292, 3.3275, 3.2510, 3.4099, 3.8391, 2.7645, 4.5616, 4.8182,\n", + " 5.1325, 5.4145, 6.0532, 5.8388, 1.2409, 0.6930, 0.6730, 1.2429, 1.7720,\n", + " 2.0300, 1.3054, 1.7671]], device='cuda:0')\n", + "Data Y Sample: tensor([[-3.1812e-01, -6.2249e-01, 1.1702e+00, 6.2866e-02, 5.6402e-01,\n", + " 7.9191e-01, 5.0075e-01, 1.5178e-01, 5.3426e-01, 4.5412e-01,\n", + " 4.6988e-01, -3.1267e-03, -6.9120e-01, -2.0195e-01, -2.7115e-01,\n", + " -9.8759e-01, -1.9477e+00, 7.7627e-01, 3.4778e-01, 5.3474e-01,\n", + " -1.8585e+00, 5.4538e-01, 5.0055e-02, -2.3796e-01, 2.0083e-01,\n", + " 4.4971e-01, -7.5819e-01, 8.6161e-02, -7.2899e-01, -1.1983e+00,\n", + " -1.7470e+00, -1.9369e-01, -1.4665e-01, -1.3902e-01, -5.8427e-01,\n", + " -2.8219e-01, -1.9440e-03, 1.1226e-01, 9.7944e-01, 3.8333e-01,\n", + " 7.0596e-01, 4.8910e-01, 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"Prediction Sample: tensor([[ 0.5941, 0.4705, 0.1525, 0.4650, 0.3202, -0.1719, -0.4874, -0.6027,\n", + " -0.3590, -0.5510, -0.4533, 0.2390, 0.1616, 0.4715, 0.2681, 0.5623,\n", + " 0.3409, 0.0238, 0.1800, 0.1876, 0.0708, 0.1803, 0.1136, 0.0124,\n", + " 0.0351, -0.0268, -0.1723, -0.0304, 0.2458, 0.5645, 0.3873, 0.2715,\n", + " 0.5473, 0.4779, 0.2253, 0.4519, -0.0828, -0.0860, 0.1008, -0.5984,\n", + " -0.5255, -0.5278, -0.4640]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0007590772584080696\n", + "Grad encoder.fc1.bias: 0.00047970673767849803\n", + "Grad encoder.encoder.0.weight: 0.00021402505808509886\n", + "Grad encoder.encoder.0.bias: 0.000602056214120239\n", + "Grad encoder.encoder.2.weight: 0.00017781727365218103\n", + "Grad encoder.encoder.2.bias: 0.0006694078911095858\n", + "Grad encoder.encoder.4.weight: 0.0004828921228181571\n", + "Grad encoder.encoder.4.bias: 0.0017802942311391234\n", + "Grad decoder.fc1.0.weight: 0.00015035743126645684\n", + "Grad 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"Grad decoder.fc1.0.weight: 5.689619138138369e-05\n", + "Grad decoder.fc1.0.bias: 0.00037852226523682475\n", + "Grad decoder.fc1.2.weight: 8.669567614560947e-05\n", + "Grad decoder.fc1.2.bias: 0.0007126879645511508\n", + "Grad decoder.fc1.4.weight: 8.031995821511373e-05\n", + "Grad decoder.fc1.4.bias: 0.0007186300354078412\n", + "Grad decoder.fc2.weight: 8.08907498139888e-05\n", + "Grad decoder.fc2.bias: 0.0017619197024032474\n", + "Grad _memory_unit.weight_ih_l0: 1.3079658856440801e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 5.9651378251146525e-05\n", + "Grad _memory_unit.bias_hh_l0: 3.106399526586756e-05\n", + "Grad _memory_unit.weight_ih_l1: 5.4413912948803045e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00010501368524273857\n", + "Grad _memory_unit.bias_hh_l1: 5.3916937758913264e-05\n", + "Data X Sample: tensor([[ 0.0000, -0.0073, 0.0090, 0.0000, -0.0068, 0.0103, 0.0092, 0.0057,\n", + " 0.0220, 0.0237, 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" 0.1278, 0.1299, 0.1838, 0.1362, 0.0334, 0.0060, 0.0222, 0.0057, 0.0674,\n", + " 0.0743, 0.0068, 0.0626]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 9.9602e-01, 5.8305e-01, -1.3308e-01, 3.4645e-01, -4.1884e-01,\n", + " -1.6562e-01, -8.7787e-01, -1.1908e+00, -2.0146e-01, -8.1464e-01,\n", + " -4.6450e-01, 5.8226e-01, 3.1630e-01, 9.1665e-01, 3.5581e-01,\n", + " 1.7702e-01, 8.4923e-01, 7.4480e-01, 1.9913e-01, 4.9869e-04,\n", + " 9.6757e-01, 7.0931e-02, 1.9691e-01, 1.0136e+00, -7.6783e-01,\n", + " 7.3207e-01, 2.0277e-01, 7.8574e-01, 1.6567e+00, 1.0974e+00,\n", + " 6.9855e-01, 5.8604e-01, 1.0662e+00, 5.5704e-01, 1.0629e+00,\n", + " -1.7440e-01, -7.7889e-01, -8.6851e-02, 5.0463e-01, -4.5678e-01,\n", + " -5.1896e-01, -5.7862e-01, -4.5221e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.6252, 0.4219, 0.1742, 0.3688, -0.1017, -0.4151, -0.7437, -0.8389,\n", + " -0.3989, -0.5366, -0.4688, 0.3046, 0.2486, 0.5054, 0.2151, 0.3738,\n", + " 0.3517, 0.0215, 0.1694, 0.0708, -0.0161, 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_memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 7.429011020576581e-05\n", + "Grad _memory_unit.bias_hh_l0: 3.942684270441532e-05\n", + "Grad _memory_unit.weight_ih_l1: 5.95749315834837e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.0001026992395054549\n", + "Grad _memory_unit.bias_hh_l1: 5.4265437938738614e-05\n", + "Data X Sample: tensor([[1.4247, 1.5235, 1.6664, 1.7624, 1.7979, 1.9786, 2.0660, 2.1550, 3.9105,\n", + " 4.2952, 4.1208, 4.1969, 4.0879, 4.0024, 3.7526, 3.6049, 3.5080, 3.5728,\n", + " 3.4878, 3.5074, 3.5283, 3.4859, 2.9905, 2.7943, 2.8828, 2.9358, 2.8984,\n", + " 3.0549, 3.0425, 3.0655, 3.3035, 3.2939, 3.6917, 2.8469, 4.5984, 4.9034,\n", + " 5.1443, 5.5417, 5.8123, 5.7395, 1.0357, 0.6811, 0.6023, 1.0448, 1.5302,\n", + " 1.6240, 1.1558, 1.7515]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.4921, 0.3317, 1.7156, 1.3818, 1.2212, -0.1993, 0.4497, -0.0413,\n", + " -0.2426, -1.1672, -0.0892, 1.6874, 0.4279, 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"Grad encoder.encoder.4.bias: 0.0004817539011128247\n", + "Grad decoder.fc1.0.weight: 4.0268394513987005e-05\n", + "Grad decoder.fc1.0.bias: 0.00022504905064124614\n", + "Grad decoder.fc1.2.weight: 4.67567442683503e-05\n", + "Grad decoder.fc1.2.bias: 0.0005088719772174954\n", + "Grad decoder.fc1.4.weight: 4.8492227506358176e-05\n", + "Grad decoder.fc1.4.bias: 0.00046575136366300285\n", + "Grad decoder.fc2.weight: 0.0001166662186733447\n", + "Grad decoder.fc2.bias: 0.0014271275140345097\n", + "Grad _memory_unit.weight_ih_l0: 6.850944828329375e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 2.084821244352497e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.0877753084059805e-05\n", + "Grad _memory_unit.weight_ih_l1: 3.894816472893581e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 3.96468021790497e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.149396823369898e-05\n", + "Data X Sample: tensor([[1.5913, 1.6881, 1.9219, 1.8236, 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-6.5961e-01, -5.9407e-03, 4.4232e-01,\n", + " 5.8553e-01, 4.0688e-01, 2.1987e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2893, 0.1971, -0.0769, 0.4027, 0.1120, -0.0899, -0.3570, -0.4225,\n", + " -0.1638, -0.2746, -0.2223, 0.1147, 0.0689, 0.2278, 0.3000, 0.2047,\n", + " 0.1951, 0.1723, 0.2388, 0.0154, 0.1411, 0.0012, 0.0137, 0.0179,\n", + " 0.0446, -0.0551, -0.1554, 0.1642, 0.2052, 0.2580, 0.3282, 0.2449,\n", + " 0.3248, 0.3925, 0.2516, 0.3606, -0.0167, -0.0450, 0.0740, -0.3254,\n", + " -0.2908, -0.3195, -0.3378]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0005154883256182075\n", + "Grad encoder.fc1.bias: 0.00020793266594409943\n", + "Grad encoder.encoder.0.weight: 0.000147571467095986\n", + "Grad encoder.encoder.0.bias: 0.00032628647750243545\n", + "Grad encoder.encoder.2.weight: 0.00010463544458616525\n", + "Grad encoder.encoder.2.bias: 0.0003804790903814137\n", + "Grad encoder.encoder.4.weight: 0.00027745665283873677\n", + "Grad 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"Grad decoder.fc1.4.bias: 0.0015601133927702904\n", + "Grad decoder.fc2.weight: 0.00017901537648867816\n", + "Grad decoder.fc2.bias: 0.002633551834151149\n", + "Grad _memory_unit.weight_ih_l0: 1.0553574611549266e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 1.9429273379500955e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.0656745871528983e-05\n", + "Grad _memory_unit.weight_ih_l1: 9.46681211644318e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 8.412773604504764e-05\n", + "Grad _memory_unit.bias_hh_l1: 4.451767017599195e-05\n", + "Data X Sample: tensor([[2.6913, 2.1469, 1.8708, 1.9263, 2.1138, 2.0979, 3.0952, 4.1425, 4.2644,\n", + " 4.2700, 4.2953, 4.2455, 4.1167, 3.8579, 3.8837, 3.8346, 3.8067, 3.6908,\n", + " 3.6035, 3.5130, 3.4473, 3.2915, 3.1713, 2.9241, 2.8659, 2.6850, 2.7732,\n", + " 2.7653, 2.7094, 2.9574, 2.9186, 3.0452, 3.3661, 2.4075, 4.2111, 4.6042,\n", + " 4.8061, 5.4436, 5.9771, 5.9523, 1.3268, 0.6950, 0.6588, 1.1309, 1.6808,\n", + " 1.7955, 1.2646, 1.7984]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.3383, 0.1447, 0.8288, 0.4006, 0.4581, -0.1402, -0.6049, -0.9582,\n", + " -0.3016, -0.8149, -1.3588, 1.0691, 0.1801, -0.6926, 0.7869, 0.3652,\n", + " 0.7541, 1.0260, -1.9692, -0.3949, -1.3364, -0.0895, -0.3586, 5.0909,\n", + " 2.0896, 1.1115, 0.3568, 0.4828, -0.0775, 0.6701, -0.1002, -0.3432,\n", + " 0.2482, 0.8079, -1.0272, 0.6368, 0.4844, -0.2862, -0.9248, 0.1088,\n", + " -0.3054, -0.0586, 1.4193]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2447, -0.2593, -0.0770, -0.1862, -0.1341, 0.0706, 0.1267, 0.2427,\n", + " 0.1201, 0.1614, 0.2414, -0.1293, -0.1091, -0.2094, -0.1639, -0.2072,\n", + " -0.1993, -0.0617, -0.0773, -0.1253, -0.0864, -0.0183, -0.0671, -0.0430,\n", + " 0.0093, -0.0275, 0.0092, -0.0588, -0.1012, -0.1184, -0.1586, -0.0900,\n", + " -0.1461, -0.2252, -0.0771, -0.1754, -0.0364, 0.0293, -0.0314, 0.3776,\n", + " 0.3499, 0.2695, 0.2097]], 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device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.5403, 0.4338, 0.0873, 0.4791, 0.1140, -0.2149, -0.4980, -0.6491,\n", + " -0.4187, -0.5016, -0.4495, 0.1671, 0.1992, 0.4579, 0.3196, 0.3915,\n", + " 0.3769, 0.1576, 0.1032, 0.1432, 0.0829, 0.1620, 0.1561, 0.1843,\n", + " 0.1063, 0.0706, -0.0578, 0.0237, 0.4053, 0.4188, 0.3637, 0.2643,\n", + " 0.3922, 0.4009, 0.3186, 0.2925, -0.0672, -0.0278, 0.0570, -0.5432,\n", + " -0.5225, -0.4872, -0.4510]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00038614682853221893\n", + "Grad encoder.fc1.bias: 0.0010255704401060939\n", + "Grad encoder.encoder.0.weight: 0.00010132720490219072\n", + "Grad encoder.encoder.0.bias: 0.0008074961369857192\n", + "Grad encoder.encoder.2.weight: 8.524313307134435e-05\n", + "Grad encoder.encoder.2.bias: 0.0006361798150464892\n", + "Grad encoder.encoder.4.weight: 0.00024400382244493812\n", + "Grad encoder.encoder.4.bias: 0.0010321688605472445\n", + "Grad decoder.fc1.0.weight: 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2.4235, 2.4124, 2.3680, 2.4489,\n", + " 2.6019, 2.5757, 2.7333, 2.6730, 2.6074, 2.7065, 2.7007, 2.7007, 2.6347,\n", + " 2.7490, 2.3417, 2.5186, 2.3307, 2.2880, 2.0724, 1.2816, 1.7624, 1.7074,\n", + " 1.6066, 1.6220, 1.5783, 1.4781, 1.3984, 0.7309, 0.8084, 1.3720, 1.9187,\n", + " 2.2301, 1.5026, 2.0643]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.6265e+00, 1.2772e+00, 4.0198e-01, 1.4241e+00, 2.0632e+00,\n", + " 8.0103e-02, -1.1090e+00, -1.1107e+00, -8.3325e-01, -9.6978e-01,\n", + " -6.3165e-01, 1.3078e+00, 9.7246e-01, 1.7035e+00, -1.2414e+00,\n", + " 1.4533e+00, 7.8954e-01, 6.9981e-01, 1.8588e-01, 2.1151e-01,\n", + " 7.9629e-01, 1.5908e-01, -1.4150e+00, -7.4059e-01, -1.1796e+00,\n", + " 6.5422e-01, -7.1679e-05, -6.3650e-01, 1.0064e+00, 4.6706e-01,\n", + " 1.2551e+00, 4.8386e-01, -1.6583e-02, 1.1452e+00, 8.4877e-01,\n", + " 1.0913e-01, 9.8389e-01, -1.3986e-02, 1.1137e+00, -8.1774e-01,\n", + " -1.0004e+00, -1.4354e-01, -3.7373e-02]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.5572, 0.4588, 0.0997, 0.4955, 0.1268, -0.2271, -0.5311, -0.6665,\n", + " -0.4489, -0.5431, -0.4778, 0.1711, 0.1892, 0.4718, 0.3247, 0.3924,\n", + " 0.3903, 0.1738, 0.1170, 0.1600, 0.0970, 0.1704, 0.1498, 0.2103,\n", + " 0.1236, 0.0813, -0.0473, 0.0279, 0.4249, 0.4464, 0.4001, 0.2810,\n", + " 0.3974, 0.4279, 0.3411, 0.3142, -0.0505, -0.0252, 0.0739, -0.5697,\n", + " -0.5339, -0.5311, -0.4782]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003359587281011045\n", + "Grad encoder.fc1.bias: 0.00036877457750961185\n", + "Grad encoder.encoder.0.weight: 7.593237387482077e-05\n", + "Grad encoder.encoder.0.bias: 0.00037948100361973047\n", + "Grad encoder.encoder.2.weight: 6.854714592918754e-05\n", + "Grad encoder.encoder.2.bias: 0.000516177446115762\n", + "Grad encoder.encoder.4.weight: 0.00017413287423551083\n", + "Grad encoder.encoder.4.bias: 0.0012100903550162911\n", + "Grad decoder.fc1.0.weight: 7.644448487553746e-05\n", + "Grad decoder.fc1.0.bias: 0.00042259282781742513\n", + "Grad decoder.fc1.2.weight: 6.938070873729885e-05\n", + "Grad decoder.fc1.2.bias: 0.0005123774171806872\n", + "Grad decoder.fc1.4.weight: 6.500209565274417e-05\n", + "Grad decoder.fc1.4.bias: 0.0004425555525813252\n", + "Grad decoder.fc2.weight: 0.00012792545021511614\n", + "Grad decoder.fc2.bias: 0.0023153850343078375\n", + "Grad _memory_unit.weight_ih_l0: 1.2215376955282409e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 4.855516453972086e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.5597670173738152e-05\n", + "Grad _memory_unit.weight_ih_l1: 6.373765700118383e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 7.398608431685716e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.8572736229980364e-05\n", + "Data X Sample: tensor([[2.0888, 2.4193, 2.8144, 2.9868, 3.0273, 3.1631, 3.2369, 3.2467, 3.2343,\n", + " 3.2447, 3.2580, 3.3263, 3.1758, 3.1572, 3.1726, 3.1536, 3.1542, 3.3097,\n", + " 3.3990, 3.2266, 3.1676, 3.1169, 2.9424, 2.7733, 2.6050, 2.6511, 2.5228,\n", + " 2.6592, 2.1613, 2.1714, 2.2852, 2.2936, 2.0240, 1.3639, 1.9658, 2.2693,\n", + " 2.5250, 2.8013, 3.0741, 2.9563, 1.7134, 0.9838, 0.9984, 1.7194, 2.5529,\n", + " 2.9678, 1.8425, 2.8540]], device='cuda:0')\n", + "Data Y Sample: tensor([[-2.5785e-03, 2.9416e-01, 3.9700e-01, 3.8724e-01, 1.1726e+00,\n", + " 2.4516e+00, 4.9382e-01, -1.7949e-01, 1.7484e-01, 1.4138e-01,\n", + " -5.9534e-03, 3.3871e-02, -1.5263e+00, 5.4413e-01, 7.9672e-01,\n", + " 2.7842e-01, 4.0542e-01, 1.0185e+00, -1.8362e-01, -4.3057e-01,\n", + " -6.0991e-01, 6.5107e-01, 2.1624e+00, 7.9256e-01, -5.4322e-01,\n", + " 4.7668e-01, -1.7548e-01, 5.2134e-01, -5.3603e-01, -8.7865e-01,\n", + " -8.7296e-01, -9.1144e+00, -7.5881e-01, 1.4454e-01, 3.1288e-01,\n", + " 3.2158e-01, 2.7304e-01, 7.0514e-02, 8.5538e-01, -1.4235e-01,\n", + " -1.2153e-01, 1.3957e-01, 2.2247e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.3526, -0.3966, -0.1852, -0.3968, -0.2715, -0.1021, 0.0958, 0.3273,\n", + " 0.1969, 0.3212, 0.3301, -0.1859, -0.1476, -0.2950, -0.1767, -0.3574,\n", + " -0.3225, -0.1796, -0.0998, -0.1101, -0.0906, -0.0189, -0.0486, -0.0761,\n", + " 0.0229, 0.0273, 0.2259, 0.0807, -0.0628, -0.2366, -0.1525, -0.2004,\n", + " -0.2064, -0.3070, -0.1451, -0.2065, -0.0253, -0.0272, -0.0474, 0.5126,\n", + " 0.5501, 0.4525, 0.4171]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00019576915656216443\n", + "Grad encoder.fc1.bias: 0.0004973660688847303\n", + "Grad encoder.encoder.0.weight: 6.436524563468993e-05\n", + "Grad encoder.encoder.0.bias: 0.0004982062382623553\n", + "Grad encoder.encoder.2.weight: 5.719221371691674e-05\n", + "Grad encoder.encoder.2.bias: 0.0005405483534559608\n", + "Grad encoder.encoder.4.weight: 0.00022730376804247499\n", + "Grad encoder.encoder.4.bias: 0.0020375829190015793\n", + "Grad decoder.fc1.0.weight: 0.00013654131907969713\n", + "Grad decoder.fc1.0.bias: 0.0009174965089187026\n", + "Grad decoder.fc1.2.weight: 0.00014243062469176948\n", + "Grad decoder.fc1.2.bias: 0.0016729880589991808\n", + "Grad decoder.fc1.4.weight: 0.0001224843435920775\n", + "Grad decoder.fc1.4.bias: 0.0009765776339918375\n", + "Grad decoder.fc2.weight: 0.00021870044292882085\n", + "Grad decoder.fc2.bias: 0.0025161183439195156\n", + "Grad _memory_unit.weight_ih_l0: 2.1319176084944047e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 0.00011097179958596826\n", + "Grad _memory_unit.bias_hh_l0: 5.720133049180731e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.7383612430421636e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00021548912627622485\n", + "Grad _memory_unit.bias_hh_l1: 0.0001170346440630965\n", + "Data X Sample: tensor([[1.1096, 1.3240, 1.5808, 1.7252, 1.8099, 1.9580, 2.0321, 2.0812, 3.5126,\n", + " 4.1578, 4.4444, 4.3176, 4.2543, 3.8825, 3.8913, 3.7744, 3.7234, 3.7507,\n", + " 3.5931, 3.5837, 3.4988, 3.3711, 2.9544, 2.8172, 2.7833, 2.8522, 2.8797,\n", + " 2.9407, 2.9488, 3.0491, 3.0761, 3.3160, 3.8479, 2.8103, 4.7136, 5.1028,\n", + " 5.2230, 5.6583, 6.1609, 6.1622, 0.9784, 0.5317, 0.5881, 0.9903, 1.4549,\n", + " 1.7212, 1.1490, 1.7202]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.6763, -0.6382, -0.7224, -0.5828, -0.3501, -0.2792, 0.1336, 0.3816,\n", + " 1.2403, 0.6889, 0.5734, -1.3505, -0.7512, -0.3267, -0.3001, -1.0043,\n", + " -0.1418, 0.2468, -0.8152, 0.0798, -0.1579, 0.0818, -0.6388, -0.2729,\n", + " 0.1506, -0.3884, -0.7264, 0.3704, -0.0545, 0.0344, -0.2748, 0.0688,\n", + " -0.7488, -1.0712, -0.4217, -0.7355, -0.9937, 0.1545, -0.7678, 0.7257,\n", + " 0.5893, 0.9727, -0.1408]], device='cuda:0')\n", + "Prediction Sample: tensor([[-2.3961e-01, -1.9462e-01, -5.9095e-02, -1.3723e-01, -1.6853e-02,\n", + " 6.2709e-02, 1.1044e-01, 1.8084e-01, 9.7117e-02, 1.4129e-01,\n", + " 1.6852e-01, -1.6178e-01, -1.4171e-01, -2.6020e-01, -1.6281e-01,\n", + " -1.7384e-01, -1.2519e-01, -1.1218e-02, -9.0986e-02, -5.1421e-02,\n", + " -1.0765e-01, -2.0606e-02, -3.3841e-02, -8.4857e-03, 4.7679e-02,\n", + " 3.1258e-02, 1.4619e-02, -6.5381e-02, -1.2721e-01, -1.8436e-01,\n", + " -1.6389e-01, -1.6946e-01, -2.4357e-01, -2.4787e-01, -9.7747e-02,\n", + " -1.5468e-01, 8.1654e-03, 1.0672e-02, -1.2737e-04, 2.0996e-01,\n", + " 2.1381e-01, 2.4353e-01, 2.2410e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00011200710287084803\n", + "Grad encoder.fc1.bias: 0.0007848583627492189\n", + "Grad encoder.encoder.0.weight: 4.717058254755102e-05\n", + "Grad encoder.encoder.0.bias: 0.000820531218778342\n", + "Grad encoder.encoder.2.weight: 5.3887128160567954e-05\n", + "Grad encoder.encoder.2.bias: 0.0007182714180089533\n", + "Grad encoder.encoder.4.weight: 0.00019805264309979975\n", + "Grad encoder.encoder.4.bias: 0.0018206968670710921\n", + "Grad decoder.fc1.0.weight: 0.00010613854828989133\n", + "Grad decoder.fc1.0.bias: 0.0006338909734040499\n", + "Grad decoder.fc1.2.weight: 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2.5920,\n", + " 2.6348, 2.3660, 2.3352, 2.1137, 2.2245, 2.0504, 1.3914, 2.1399, 2.3349,\n", + " 2.5997, 2.7934, 3.1565, 2.8229, 1.6609, 0.9599, 0.9862, 1.7796, 2.4776,\n", + " 2.7219, 1.8357, 2.5412]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.2857, -0.0651, -0.4668, 0.0355, 0.7468, -2.1262, 0.1971, 0.1425,\n", + " -0.2089, -0.1120, -0.1501, -0.7092, -1.9915, -0.7780, -0.4270, -1.2686,\n", + " -0.5894, -0.3155, -0.2112, -0.6636, -0.0867, -1.0720, -0.4827, 0.2432,\n", + " -0.3098, -0.6149, 0.0553, 0.4715, -0.8224, -0.3906, -1.2326, 0.1314,\n", + " -0.2674, -0.0768, 0.4418, 0.3159, 0.0213, 0.5129, 0.5046, -0.3164,\n", + " -0.2353, -0.5078, 0.1095]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2862, -0.3351, -0.1613, -0.3262, -0.2330, -0.0733, 0.0621, 0.2675,\n", + " 0.1521, 0.2379, 0.2520, -0.1652, -0.1261, -0.2454, -0.1525, -0.2865,\n", + " -0.2651, -0.1301, -0.0892, -0.0882, -0.0794, -0.0104, -0.0475, -0.0673,\n", + " 0.0318, 0.0295, 0.1834, 0.0650, -0.0703, 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decoder.fc2.weight: 0.0001360093301627785\n", + "Grad decoder.fc2.bias: 0.002428218023851514\n", + "Grad _memory_unit.weight_ih_l0: 1.280841479456285e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 1.4823979654465802e-05\n", + "Grad _memory_unit.bias_hh_l0: 9.858950761554297e-06\n", + "Grad _memory_unit.weight_ih_l1: 1.0998068319167942e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 7.799390004947782e-05\n", + "Grad _memory_unit.bias_hh_l1: 4.5041117118671536e-05\n", + "Data X Sample: tensor([[2.5036, 2.9654, 3.2382, 3.3651, 3.5583, 3.4931, 3.7962, 3.7932, 3.7884,\n", + " 3.8545, 3.8607, 3.8093, 3.7794, 3.6889, 3.6542, 3.4750, 3.5001, 3.5147,\n", + " 3.5291, 3.3605, 3.2019, 3.1751, 2.9183, 2.8077, 2.7420, 2.7399, 2.7146,\n", + " 2.7653, 2.7129, 2.7675, 2.8556, 2.8490, 2.8777, 2.1581, 3.5738, 3.9986,\n", + " 4.1906, 4.5955, 4.9693, 4.9763, 1.9997, 1.0853, 1.1297, 2.0609, 2.9969,\n", + " 3.0878, 2.3252, 2.9869]], 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"Grad _memory_unit.bias_ih_l0: 1.6113626770675182e-05\n", + "Grad _memory_unit.bias_hh_l0: 9.42081805987982e-06\n", + "Grad _memory_unit.weight_ih_l1: 5.529839654627722e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 6.612436118302867e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.489645314402878e-05\n", + "Data X Sample: tensor([[1.2635, 1.4711, 1.6168, 1.6661, 1.7467, 1.7930, 1.8750, 1.8867, 1.9089,\n", + " 1.9746, 2.0049, 1.9592, 1.9596, 1.8949, 1.8410, 1.9365, 1.9120, 1.9131,\n", + " 1.9473, 1.9992, 2.0389, 2.0514, 2.0989, 2.0060, 2.0382, 2.1339, 2.0512,\n", + " 2.2024, 1.7936, 1.9553, 1.8057, 1.6773, 1.5290, 1.0207, 1.6031, 1.5201,\n", + " 1.3215, 1.2456, 1.3501, 1.2824, 0.9736, 0.5895, 0.6124, 1.0879, 1.5976,\n", + " 1.6354, 1.0878, 1.5717]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.8365, 0.5727, 0.3075, 1.0442, 0.8491, 0.3769, -0.7619, -0.7180,\n", + " -0.0596, -0.7863, -0.5126, -1.0170, 0.1996, 1.2263, 1.4197, 0.8628,\n", + " 1.4751, 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device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3233, 0.4005, 0.1046, 0.2564, 0.1255, -0.1503, -0.3665, -0.3878,\n", + " -0.1919, -0.2708, -0.2472, 0.1306, 0.0711, 0.2650, 0.1334, 0.2194,\n", + " 0.2477, 0.1145, 0.0762, 0.1084, 0.1161, 0.1115, 0.0052, 0.1068,\n", + " 0.1124, 0.1316, -0.0386, -0.0380, 0.1822, 0.1788, 0.2972, 0.1430,\n", + " 0.2390, 0.3112, 0.2102, 0.2080, 0.0566, 0.0119, 0.0341, -0.2898,\n", + " -0.2012, -0.3685, -0.2997]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00010394409764558077\n", + "Grad encoder.fc1.bias: 0.0005178162828087807\n", + "Grad encoder.encoder.0.weight: 3.4944136132253334e-05\n", + "Grad encoder.encoder.0.bias: 0.0004073863383382559\n", + "Grad encoder.encoder.2.weight: 2.5319775886600837e-05\n", + "Grad encoder.encoder.2.bias: 0.0003150523407384753\n", + "Grad encoder.encoder.4.weight: 8.816008630674332e-05\n", + "Grad encoder.encoder.4.bias: 0.0007045083912089467\n", + "Grad decoder.fc1.0.weight: 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0.8125, 0.8124, 1.3319, 1.9781,\n", + " 2.0814, 1.4210, 2.0955]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.3714, -0.7288, 0.1479, 0.3285, -0.1676, -0.3427, 0.0452, -0.3680,\n", + " -0.8173, -0.5738, -0.6093, -3.1065, -0.3169, 0.0322, -0.4824, 0.1217,\n", + " -0.0813, -0.3622, -1.8554, 0.5252, -1.1090, 0.6233, 0.4087, -0.0206,\n", + " 1.9690, -1.4820, -0.8339, 0.0859, -0.1864, -0.0147, 0.0744, 0.4822,\n", + " 0.8007, 0.6884, -0.4940, 0.5330, -0.4532, 0.1123, -0.3712, -0.4760,\n", + " -0.3561, -0.7516, -0.6552]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4082, 0.5233, 0.1279, 0.3256, 0.1563, -0.1942, -0.4550, -0.4921,\n", + " -0.2583, -0.3636, -0.3218, 0.1621, 0.1013, 0.3365, 0.1859, 0.2959,\n", + " 0.3162, 0.1463, 0.1014, 0.1437, 0.1454, 0.1361, 0.0088, 0.1395,\n", + " 0.1480, 0.1550, -0.0471, -0.0438, 0.2463, 0.2616, 0.3803, 0.1898,\n", + " 0.3109, 0.4041, 0.2677, 0.2691, 0.0694, 0.0195, 0.0612, -0.3727,\n", + " -0.2821, -0.4618, -0.3869]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0005414197221398354\n", + "Grad encoder.fc1.bias: 0.0003876802511513233\n", + "Grad encoder.encoder.0.weight: 0.000130846441606991\n", + "Grad encoder.encoder.0.bias: 0.0005704703507944942\n", + "Grad encoder.encoder.2.weight: 9.440052963327616e-05\n", + "Grad encoder.encoder.2.bias: 0.0006481430027633905\n", + "Grad encoder.encoder.4.weight: 0.0002862701076082885\n", + "Grad encoder.encoder.4.bias: 0.0021812336053699255\n", + "Grad decoder.fc1.0.weight: 0.00014710606774315238\n", + "Grad decoder.fc1.0.bias: 0.0008118587429635227\n", + "Grad decoder.fc1.2.weight: 0.00011915886716451496\n", + "Grad decoder.fc1.2.bias: 0.0015728978905826807\n", + "Grad decoder.fc1.4.weight: 9.332734771305695e-05\n", + "Grad decoder.fc1.4.bias: 0.000954765360802412\n", + "Grad decoder.fc2.weight: 0.00014476136129815131\n", + "Grad decoder.fc2.bias: 0.0020710628014057875\n", + "Grad _memory_unit.weight_ih_l0: 2.6000594516517594e-05\n", + "Grad 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-5.1649e-01,\n", + " -8.0109e-01, 4.1242e-01, -8.4229e-01, 4.0796e-01, -1.5896e-01,\n", + " 9.6079e-04, 2.9119e-01, -1.3495e+00, -9.7538e-01, 1.6418e-01,\n", + " 5.9154e-01, -1.2780e+00, -2.6095e+00, 8.2497e-01, 1.0356e-01,\n", + " 1.5257e-01, 2.3836e-02, 4.6518e-01, 2.3027e-01, 8.0973e-01,\n", + " 1.2694e-01, -6.3572e-03, 1.0307e+00, 6.3086e-01, 9.5190e-01,\n", + " 1.1496e-01, -1.0781e-03, -7.8147e-01, -1.0390e-02, -5.0527e-01,\n", + " -7.7079e-01, -6.4465e-01, -7.9699e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4035, 0.5116, 0.1174, 0.3230, 0.1539, -0.1781, -0.4319, -0.4915,\n", + " -0.2300, -0.3384, -0.2988, 0.1791, 0.1022, 0.3142, 0.1750, 0.2970,\n", + " 0.2978, 0.1381, 0.1019, 0.1267, 0.1384, 0.1301, 0.0161, 0.1160,\n", + " 0.1373, 0.1439, -0.0571, -0.0454, 0.2263, 0.2538, 0.3669, 0.1808,\n", + " 0.3089, 0.4029, 0.2590, 0.2589, 0.0607, 0.0190, 0.0554, -0.3602,\n", + " -0.2834, -0.4354, -0.3640]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 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_memory_unit.bias_hh_l0: 2.673614289960824e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.088926183001604e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00013414172281045467\n", + "Grad _memory_unit.bias_hh_l1: 7.111747254384682e-05\n", + "Data X Sample: tensor([[1.6082, 1.8294, 1.9624, 2.1363, 2.1599, 2.1318, 2.2355, 2.2884, 2.4361,\n", + " 2.3822, 2.4395, 2.3837, 2.2881, 2.2957, 2.2949, 2.2797, 2.2454, 2.2942,\n", + " 2.3066, 2.3358, 2.3751, 2.4357, 2.3905, 2.3191, 2.3573, 2.4082, 2.4295,\n", + " 2.3900, 2.1925, 2.3221, 2.2642, 2.3212, 2.3650, 1.5310, 1.8923, 1.7853,\n", + " 1.6735, 1.6379, 1.6480, 1.4299, 1.2982, 0.7508, 0.7538, 1.2859, 2.0455,\n", + " 2.1729, 1.3802, 1.8922]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.5236, 0.2130, -0.2157, 0.3283, 1.6485, 0.0073, -0.5003, -0.1860,\n", + " -0.0612, -0.5413, -0.2559, 0.4608, 1.1245, -0.2463, 1.0079, -0.1679,\n", + " -0.1843, 1.8105, 0.3134, 0.0393, 0.2689, -0.1616, -0.3077, -0.3124,\n", 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grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0005510776536539197\n", + "Grad encoder.fc1.bias: 0.000611260300502181\n", + "Grad encoder.encoder.0.weight: 0.00012351984332781285\n", + "Grad encoder.encoder.0.bias: 0.0007197523955255747\n", + "Grad encoder.encoder.2.weight: 0.00010928744450211525\n", + "Grad encoder.encoder.2.bias: 0.0010682707652449608\n", + "Grad encoder.encoder.4.weight: 0.0003116279549431056\n", + "Grad encoder.encoder.4.bias: 0.0024982194881886244\n", + "Grad decoder.fc1.0.weight: 0.00019188977603334934\n", + "Grad decoder.fc1.0.bias: 0.001149331103079021\n", + "Grad decoder.fc1.2.weight: 0.00018341478426009417\n", + "Grad decoder.fc1.2.bias: 0.0019624284468591213\n", + "Grad decoder.fc1.4.weight: 0.0001818773162085563\n", + "Grad decoder.fc1.4.bias: 0.0013461776543408632\n", + "Grad decoder.fc2.weight: 0.0002842087997123599\n", + "Grad decoder.fc2.bias: 0.0028257695958018303\n", + "Grad _memory_unit.weight_ih_l0: 5.5360949772875756e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 0.00011089500912930816\n", + "Grad _memory_unit.bias_hh_l0: 7.477153849322349e-05\n", + "Grad _memory_unit.weight_ih_l1: 3.212779120076448e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00029237568378448486\n", + "Grad _memory_unit.bias_hh_l1: 0.00016890940605662763\n", + "Data X Sample: tensor([[1.9976, 2.4149, 2.6251, 2.9168, 2.9624, 2.9893, 3.2416, 3.0976, 3.1880,\n", + " 3.2274, 3.2643, 3.3224, 3.1980, 3.1354, 3.0061, 3.0702, 3.0677, 3.0679,\n", + " 3.1636, 3.0276, 2.7505, 2.8368, 2.6146, 2.5214, 2.4305, 2.3716, 2.3443,\n", + " 2.4023, 1.9289, 2.0797, 2.0157, 1.9592, 1.8502, 1.2449, 1.9560, 2.3374,\n", + " 2.5663, 2.8782, 3.0044, 3.0527, 1.6704, 0.9659, 0.9984, 1.6878, 2.5331,\n", + " 2.7333, 1.8765, 2.7367]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.4882, 0.8294, 0.2174, -0.2298, -1.1145, -0.9778, -2.3204, -3.2579,\n", + " -0.8322, -0.9602, -0.4778, 0.0356, -1.4848, 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0.7916, -0.2505, -0.2541,\n", + " -0.2008, -0.8470, -0.7207]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3525, 0.4258, 0.1071, 0.3134, 0.0679, -0.1108, -0.3893, -0.4280,\n", + " -0.2706, -0.3117, -0.3215, 0.1708, 0.1819, 0.2833, 0.2246, 0.2884,\n", + " 0.1412, 0.1109, 0.1065, 0.1398, 0.1462, 0.0517, 0.0237, 0.0775,\n", + " 0.0274, 0.0617, 0.0303, 0.0266, 0.2172, 0.3714, 0.2763, 0.2820,\n", + " 0.2981, 0.3463, 0.2570, 0.2837, 0.0545, 0.0434, -0.0271, -0.3700,\n", + " -0.3056, -0.3587, -0.3741]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0009497166029177606\n", + "Grad encoder.fc1.bias: 0.0009827845497056842\n", + "Grad encoder.encoder.0.weight: 0.00021258631022647023\n", + "Grad encoder.encoder.0.bias: 0.0010769678046926856\n", + "Grad encoder.encoder.2.weight: 0.0001539408985991031\n", + "Grad encoder.encoder.2.bias: 0.0011139577254652977\n", + "Grad encoder.encoder.4.weight: 0.0005296330200508237\n", + "Grad encoder.encoder.4.bias: 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"Grad _memory_unit.bias_hh_l1: 0.00019079689809586853\n", + "Data X Sample: tensor([[1.5711, 1.9080, 2.0090, 2.1603, 2.1770, 2.2202, 2.2894, 2.3551, 2.4776,\n", + " 2.3680, 2.4490, 2.5201, 2.4456, 2.3475, 2.3126, 2.3057, 2.2611, 2.3580,\n", + " 2.4594, 2.3916, 2.4511, 2.5995, 2.4580, 2.5634, 2.5187, 2.6041, 2.5281,\n", + " 2.5654, 2.2966, 2.5579, 2.3727, 2.5561, 2.4553, 1.6042, 2.1104, 1.9944,\n", + " 1.7816, 1.6564, 1.6163, 1.5661, 1.2791, 0.7289, 0.8064, 1.2917, 1.9543,\n", + " 2.1043, 1.3802, 2.0330]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.4198, 0.4283, 1.5060, -0.9346, 0.1568, 1.4720, -1.0899, -0.4591,\n", + " 0.2234, -0.0338, 0.2786, 0.4754, -0.5291, 2.0161, 1.0211, -0.0526,\n", + " -0.0517, 0.0085, -0.5128, -0.2171, -0.8344, 0.1734, -0.5333, 0.6437,\n", + " -1.1680, -0.5290, 1.4204, 1.2236, 0.9319, 0.2190, -0.0674, 0.5917,\n", + " -0.0398, 0.9194, 0.3067, -0.2218, -0.8287, 0.0940, -0.5775, -1.7867,\n", + " -0.4665, -0.3448, 0.2330]], device='cuda:0')\n", + 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2.7402, 2.7686, 2.8904,\n", + " 2.9570, 3.0321, 3.2850, 3.2930, 3.3491, 3.7423, 2.8171, 4.5935, 4.9180,\n", + " 5.1620, 5.5072, 5.9074, 5.8899, 1.0977, 0.6373, 0.6204, 1.1482, 1.5658,\n", + " 1.6926, 1.1626, 1.5873]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.1592, 0.6803, -0.1549, 0.4979, 0.8747, -0.1718, 0.2755, 0.8200,\n", + " 0.4425, 0.1253, 0.5811, 0.6239, 0.1767, 0.2078, 0.1800, 0.1722,\n", + " 0.9983, -1.0702, 0.9083, -0.0614, -1.1286, 0.4312, 1.2283, 1.4545,\n", + " -0.2740, -0.2887, -0.7298, -1.0769, -0.1941, 0.3139, -0.8216, -0.8431,\n", + " -0.3690, 0.5002, 0.4108, 0.1836, 1.0050, 0.0182, 0.1391, 0.3798,\n", + " 0.4073, 1.0845, 0.7154]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1460, -0.1718, -0.0365, -0.1324, -0.0366, 0.0646, 0.0946, 0.1570,\n", + " 0.0791, 0.1295, 0.1427, -0.1166, -0.0974, -0.1742, -0.1276, -0.1703,\n", + " -0.2005, -0.0467, -0.0525, -0.0712, -0.0459, -0.0281, -0.0479, -0.0487,\n", + " -0.0227, 0.0267, -0.0321, -0.0554, -0.1181, -0.1316, -0.1414, -0.1363,\n", + " -0.1921, -0.1953, -0.1348, -0.1201, -0.0062, 0.0189, -0.0144, 0.2229,\n", + " 0.2526, 0.2081, 0.1671]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00023036377388052642\n", + "Grad encoder.fc1.bias: 0.00022960580827202648\n", + "Grad encoder.encoder.0.weight: 8.533158688805997e-05\n", + "Grad encoder.encoder.0.bias: 0.00021286493574734777\n", + "Grad encoder.encoder.2.weight: 7.882587669882923e-05\n", + "Grad encoder.encoder.2.bias: 0.00027880878769792616\n", + "Grad encoder.encoder.4.weight: 0.00021468805789481848\n", + "Grad encoder.encoder.4.bias: 0.0007797097787261009\n", + "Grad decoder.fc1.0.weight: 7.749173528281972e-05\n", + "Grad decoder.fc1.0.bias: 0.00025359453866258264\n", + "Grad decoder.fc1.2.weight: 7.065730460453779e-05\n", + "Grad decoder.fc1.2.bias: 0.0003419697459321469\n", + "Grad decoder.fc1.4.weight: 6.7220586061012e-05\n", + "Grad decoder.fc1.4.bias: 0.0004509931313805282\n", + "Grad decoder.fc2.weight: 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"Data Y Sample: tensor([[ 0.6036, -0.0904, 0.0135, 0.0378, -0.7645, -0.6915, -0.8020, -0.7337,\n", + " -0.8603, -0.6393, -0.7973, -2.5333, 0.3399, 0.6425, 0.3529, 0.4478,\n", + " -2.9208, -1.1614, -1.0501, -1.2972, -0.5193, -0.1049, -0.2665, 0.5257,\n", + " -1.1733, -0.8949, -0.5294, 0.1088, -0.3853, -0.2498, 0.5621, -0.2616,\n", + " -0.3951, -0.3205, -0.3085, -0.3345, -0.5002, 0.0936, -0.2890, -0.6313,\n", + " -0.5441, -0.2042, -0.5083]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2600, 0.1808, 0.0490, 0.1805, 0.1154, -0.0647, -0.2188, -0.2858,\n", + " -0.0717, -0.1571, -0.1660, 0.1377, 0.0873, 0.1643, 0.1429, 0.1178,\n", + " 0.1192, 0.0013, 0.0494, 0.0880, 0.0273, 0.0419, 0.0762, 0.0437,\n", + " 0.0474, 0.0572, -0.0954, 0.0108, 0.0708, 0.1300, 0.1139, 0.1707,\n", + " 0.2154, 0.2380, 0.1106, 0.1223, -0.0194, -0.0079, -0.0331, -0.1683,\n", + " -0.1329, -0.1546, -0.1675]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00020947253506164998\n", + "Grad 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"Grad _memory_unit.bias_hh_l0: 1.506255466665607e-05\n", + "Grad _memory_unit.weight_ih_l1: 3.5846630908054067e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 6.312828918453306e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.349766484461725e-05\n", + "Data X Sample: tensor([[1.4608, 1.5643, 1.6063, 1.7492, 1.9106, 2.0449, 2.1261, 2.4474, 3.9837,\n", + " 4.1910, 4.1557, 4.2553, 3.9281, 3.8252, 3.7475, 3.5543, 3.5630, 3.5554,\n", + " 3.5704, 3.4814, 3.4718, 3.2501, 2.7929, 2.8077, 2.8359, 2.9723, 2.9943,\n", + " 3.1283, 3.1500, 3.2850, 3.2301, 3.3519, 3.7181, 2.7462, 4.3729, 4.7283,\n", + " 4.9988, 5.3615, 5.8187, 5.5579, 1.0404, 0.5915, 0.5942, 1.0104, 1.5579,\n", + " 1.6926, 1.2578, 1.7671]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.3973, 0.4485, 0.8115, 0.5328, 1.1816, 0.2683, 0.2626, -0.1076,\n", + " -0.6494, -0.1795, -0.2544, -0.5129, -0.2940, 0.5980, -2.0825, 0.9890,\n", + " 0.4431, -1.3717, -0.0084, -0.3726, -0.5107, 0.1127, 4.8071, 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Sample: tensor([[-0.1130, -0.1403, -0.0302, -0.1469, -0.0897, 0.0168, 0.0422, 0.1071,\n", + " 0.0386, 0.0933, 0.0939, -0.0904, -0.0617, -0.1128, -0.1002, -0.1289,\n", + " -0.1771, -0.0580, -0.0542, -0.0703, -0.0317, -0.0269, -0.0441, -0.0488,\n", + " -0.0400, 0.0154, 0.0110, 0.0017, -0.0605, -0.0801, -0.0851, -0.0856,\n", + " -0.1295, -0.1377, -0.0905, -0.0929, 0.0079, 0.0107, -0.0409, 0.1779,\n", + " 0.2135, 0.1472, 0.1158]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002232654660474509\n", + "Grad encoder.fc1.bias: 0.0004784799530170858\n", + "Grad encoder.encoder.0.weight: 5.329601845005527e-05\n", + "Grad encoder.encoder.0.bias: 0.0004371557733975351\n", + "Grad encoder.encoder.2.weight: 4.125286068301648e-05\n", + "Grad encoder.encoder.2.bias: 0.00044462509686127305\n", + "Grad encoder.encoder.4.weight: 0.00014945300063118339\n", + "Grad encoder.encoder.4.bias: 0.0014116172678768635\n", + "Grad decoder.fc1.0.weight: 7.621527038281783e-05\n", + "Grad 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"Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.4922682971227914e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.9042068743146956e-05\n", + "Grad _memory_unit.weight_ih_l1: 4.786194949701894e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00011482801346573979\n", + "Grad _memory_unit.bias_hh_l1: 5.844133556820452e-05\n", + "Data X Sample: tensor([[1.6029, 1.7638, 2.0165, 2.1625, 2.2948, 2.3012, 2.3557, 2.4488, 2.4434,\n", + " 2.4486, 2.3983, 2.5123, 2.4878, 2.3748, 2.2773, 2.3057, 2.2249, 2.3483,\n", + " 2.4202, 2.5608, 2.5100, 2.5658, 2.4917, 2.5920, 2.5694, 2.6694, 2.5920,\n", + " 2.6103, 2.3972, 2.3876, 2.2852, 2.4207, 2.1670, 1.4395, 1.8604, 1.7609,\n", + " 1.6459, 1.4868, 1.6290, 1.6030, 1.3363, 0.7647, 0.7397, 1.3319, 1.9424,\n", + " 2.1329, 1.4346, 2.0955]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.5716, -0.0926, -1.2703, 0.3775, 0.2843, 0.5829, 0.7158, -1.1696,\n", + " 0.6965, 0.5414, 0.7099, 1.2188, 0.3974, 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2.3576,\n", + " 2.2922, 1.9844, 2.0142, 1.8582, 1.8183, 1.5070, 0.9886, 1.3726, 1.3718,\n", + " 1.3903, 1.3198, 1.3881, 1.3476, 1.3554, 0.7806, 0.7296, 1.3433, 1.9781,\n", + " 2.0300, 1.3054, 1.9704]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 3.8721e-01, 1.9971e-01, -4.8914e-01, 7.5651e-01, 4.9677e-01,\n", + " 6.7987e-01, -3.0364e-01, -1.9300e-02, -8.5773e-02, -1.1085e-01,\n", + " -3.0296e-01, 3.7039e+00, 1.0203e+00, 1.1571e-03, -6.0759e-01,\n", + " -1.0241e-01, 1.1540e+00, 4.1655e-01, 6.7537e-01, 5.2834e-01,\n", + " 5.2353e-01, 1.0869e+00, 5.9797e-01, 4.0377e-01, -1.2407e-01,\n", + " 3.1738e-01, 1.0757e+00, 3.5216e-01, -3.4166e-01, 5.7192e-01,\n", + " 3.3145e-01, 3.8904e-01, -1.7909e-01, 3.8988e-01, -5.7237e-01,\n", + " 8.0931e-02, -4.9686e-01, 8.2377e-01, 1.1148e+00, -2.0396e-01,\n", + " -2.3401e-01, -2.6451e-01, 3.0270e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4548, 0.3346, 0.1006, 0.4280, 0.2056, -0.1028, -0.3655, -0.4931,\n", + " -0.1498, -0.1698, -0.1933, 0.1882, 0.2196, 0.2233, 0.1416, 0.3142,\n", + " 0.2252, 0.1186, 0.0717, 0.0630, 0.1398, 0.0406, 0.1199, 0.0747,\n", + " -0.0171, 0.0040, -0.2036, 0.0805, 0.1102, 0.1583, 0.1636, 0.1847,\n", + " 0.3323, 0.3170, 0.2580, 0.1887, -0.0365, -0.0606, -0.0584, -0.2915,\n", + " -0.3054, -0.2375, -0.2916]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00013240022235549986\n", + "Grad encoder.fc1.bias: 0.000903741572983563\n", + "Grad encoder.encoder.0.weight: 4.0566934330854565e-05\n", + "Grad encoder.encoder.0.bias: 0.0007915450260043144\n", + "Grad encoder.encoder.2.weight: 3.8767262594774365e-05\n", + "Grad encoder.encoder.2.bias: 0.0007115191547200084\n", + "Grad encoder.encoder.4.weight: 0.0001149490854004398\n", + "Grad encoder.encoder.4.bias: 0.0015738935908302665\n", + "Grad decoder.fc1.0.weight: 7.133444887585938e-05\n", + "Grad decoder.fc1.0.bias: 0.0006635983590967953\n", + "Grad decoder.fc1.2.weight: 9.76809678832069e-05\n", + "Grad 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3.3805, 3.5232, 3.9337, 2.8812, 4.5665, 4.9593,\n", + " 5.1758, 5.6212, 6.0405, 5.9495, 1.1693, 0.6890, 0.6427, 1.1826, 1.7006,\n", + " 1.9156, 1.3938, 1.8610]], device='cuda:0')\n", + "Data Y Sample: tensor([[-2.2650e-01, -4.5591e-01, 8.4236e-04, -4.9159e-01, -2.0662e-01,\n", + " 5.1921e-01, -4.2526e-01, -5.4427e-03, 5.7118e-02, 4.4368e-01,\n", + " 3.6133e-01, 1.9546e+00, 3.2955e-01, -4.9263e-01, -1.2395e+00,\n", + " 1.9962e-01, -8.6480e-01, -1.1049e+00, -8.1200e-01, -7.3217e-01,\n", + " 2.9180e-01, 1.1819e+00, -2.4481e-01, -7.4409e-01, -4.0013e-01,\n", + " 2.0042e-01, -2.5481e-01, 1.3306e-02, 2.4194e-02, -4.3304e-01,\n", + " -3.0757e-01, -1.0192e-01, 2.0962e-01, -2.9412e-01, -5.6483e-01,\n", + " -3.9841e-01, -7.4337e-01, 7.0514e-02, 1.0291e+00, 6.0802e-01,\n", + " 7.8636e-01, 8.0910e-01, 3.3757e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.4337, -0.3545, -0.1070, -0.2717, -0.1674, 0.0576, 0.2029, 0.3580,\n", + " 0.0472, 0.1658, 0.3857, -0.2575, -0.1989, -0.3750, -0.2675, -0.2374,\n", + " -0.2466, -0.1338, -0.1994, -0.1190, -0.1399, -0.0267, -0.0459, 0.0266,\n", + " 0.0232, 0.0101, 0.0133, -0.0829, -0.1262, -0.1111, -0.2451, -0.2682,\n", + " -0.2409, -0.4029, -0.1185, -0.2769, 0.0065, -0.0566, 0.0201, 0.5588,\n", + " 0.4310, 0.4449, 0.3194]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00039821036625653505\n", + "Grad encoder.fc1.bias: 0.00045960897114127874\n", + "Grad encoder.encoder.0.weight: 8.756585884839296e-05\n", + "Grad encoder.encoder.0.bias: 0.00037224902189336717\n", + "Grad encoder.encoder.2.weight: 5.952655919827521e-05\n", + "Grad encoder.encoder.2.bias: 0.00035215954994782805\n", + "Grad encoder.encoder.4.weight: 0.00019058605539612472\n", + "Grad encoder.encoder.4.bias: 0.000933339586481452\n", + "Grad decoder.fc1.0.weight: 8.723112841835245e-05\n", + "Grad decoder.fc1.0.bias: 0.0003890639345627278\n", + "Grad decoder.fc1.2.weight: 0.00014682242181152105\n", + "Grad decoder.fc1.2.bias: 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-0.2543, 0.0038, -0.0489, 0.0155, 0.5172,\n", + " 0.4104, 0.4137, 0.3003]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0004991550231352448\n", + "Grad encoder.fc1.bias: 0.0007995129562914371\n", + "Grad encoder.encoder.0.weight: 0.00011740827903850004\n", + "Grad encoder.encoder.0.bias: 0.0007452672580257058\n", + "Grad encoder.encoder.2.weight: 7.272283255588263e-05\n", + "Grad encoder.encoder.2.bias: 0.000702913966961205\n", + "Grad encoder.encoder.4.weight: 0.00019349568174220622\n", + "Grad encoder.encoder.4.bias: 0.0012303527910262346\n", + "Grad decoder.fc1.0.weight: 7.611982437083498e-05\n", + "Grad decoder.fc1.0.bias: 0.0004859489854425192\n", + "Grad decoder.fc1.2.weight: 0.00011582579463720322\n", + "Grad decoder.fc1.2.bias: 0.001097323140129447\n", + "Grad decoder.fc1.4.weight: 9.860037243925035e-05\n", + "Grad decoder.fc1.4.bias: 0.000997132621705532\n", + "Grad decoder.fc2.weight: 0.0001368939265375957\n", + "Grad decoder.fc2.bias: 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-1.0312e+00, -7.4456e-01, -2.2611e-01,\n", + " 4.0660e-01, 5.7540e-01, 7.0789e-01, 1.2996e+00, 7.4270e-01,\n", + " 6.4651e-01, 1.3435e+00, 4.3370e-01, 5.6908e-02, 1.6615e-01,\n", + " -1.0081e-01, -4.9224e-02, -2.7167e-01, -1.3272e+00, 1.2500e+00,\n", + " 3.3712e-01, -1.1767e-01, 1.0142e+00, 9.8108e-01, 1.0708e+00,\n", + " 6.9882e-01, 6.4717e-01, 1.6666e-01, -6.0506e-02, -6.3506e-01,\n", + " -3.1042e-01, -5.6559e-01, -4.4978e-01, -6.1529e-01, -9.4480e-01,\n", + " -3.5556e-01, -1.0781e-03, -6.5324e-01, -1.0390e-02, 7.7740e-01,\n", + " 7.4193e-01, 9.8069e-01, 8.2097e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.4179, -0.3596, -0.1143, -0.2834, -0.1733, 0.0509, 0.1813, 0.3453,\n", + " 0.0668, 0.1836, 0.3732, -0.2438, -0.1917, -0.3559, -0.2586, -0.2528,\n", + " -0.2557, -0.1486, -0.1864, -0.1247, -0.1288, -0.0283, -0.0502, 0.0115,\n", + " 0.0170, 0.0029, 0.0274, -0.0541, -0.1212, -0.1231, -0.2358, -0.2503,\n", + " -0.2363, -0.3838, -0.1188, -0.2649, -0.0038, -0.0472, 0.0149, 0.5458,\n", + " 0.4354, 0.4293, 0.3144]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0004373282426968217\n", + "Grad encoder.fc1.bias: 0.000590287265367806\n", + "Grad encoder.encoder.0.weight: 0.00010366748756496236\n", + "Grad encoder.encoder.0.bias: 0.0003614212619140744\n", + "Grad encoder.encoder.2.weight: 7.157016807468608e-05\n", + "Grad encoder.encoder.2.bias: 0.0003554942086338997\n", + "Grad encoder.encoder.4.weight: 0.0002387544373050332\n", + "Grad encoder.encoder.4.bias: 0.0007902182405814528\n", + "Grad decoder.fc1.0.weight: 8.536290260963142e-05\n", + "Grad decoder.fc1.0.bias: 0.0003518152516335249\n", + "Grad decoder.fc1.2.weight: 8.686508954269812e-05\n", + "Grad decoder.fc1.2.bias: 0.00042351585580036044\n", + "Grad decoder.fc1.4.weight: 6.476140697486699e-05\n", + "Grad decoder.fc1.4.bias: 0.00042877139640040696\n", + "Grad decoder.fc2.weight: 0.0001325742923654616\n", + "Grad decoder.fc2.bias: 0.0019486320670694113\n", + "Grad _memory_unit.weight_ih_l0: 1.2585408512677532e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.276941060903482e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.774240081431344e-05\n", + "Grad _memory_unit.weight_ih_l1: 4.365474978840211e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 7.408937381114811e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.8389167457353324e-05\n", + "Data X Sample: tensor([[1.4205, 1.5599, 1.6829, 1.8148, 1.9636, 2.1524, 2.1831, 2.1678, 3.7616,\n", + " 4.4327, 4.4381, 4.4013, 4.1744, 4.0570, 3.9543, 3.7991, 3.6668, 3.6927,\n", + " 3.5353, 3.7064, 3.5356, 3.3971, 2.9737, 2.9375, 2.7627, 2.8940, 2.9117,\n", + " 2.8672, 3.0286, 3.1736, 3.1881, 3.3105, 3.9469, 2.8354, 4.7356, 4.8450,\n", + " 5.1699, 5.6212, 6.2370, 5.9920, 1.0929, 0.6193, 0.6023, 1.1194, 1.5976,\n", + " 1.7726, 1.2238, 1.5560]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.6875, -0.6718, -0.2397, -0.8399, -0.6731, 0.4109, 0.8736, 0.5966,\n", + " 0.7248, 0.4091, 0.6977, -0.7679, -2.3935, -0.5635, -0.9736, -0.6556,\n", + " -0.7157, -0.4194, -0.1748, 0.0832, -1.0409, 0.8011, 0.3134, 0.1767,\n", + " -0.3861, -0.0441, 1.6962, 0.0088, 0.0462, -1.0062, -2.0029, -0.9271,\n", + " -1.0863, -0.9395, 0.4711, 0.4333, 0.5692, 0.8959, 0.0040, 0.6388,\n", + " 0.8883, 1.0018, 0.3376]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.3963, -0.3337, -0.0984, -0.2554, -0.1334, 0.0685, 0.2039, 0.3503,\n", + " 0.0758, 0.1725, 0.3651, -0.2506, -0.1798, -0.3503, -0.2635, -0.2329,\n", + " -0.2467, -0.1231, -0.1792, -0.1207, -0.1111, -0.0137, -0.0533, 0.0221,\n", + " 0.0061, 0.0323, -0.0070, -0.0753, -0.1298, -0.1213, -0.2392, -0.2623,\n", + " -0.2496, -0.3938, -0.1297, -0.2522, 0.0019, -0.0387, 0.0233, 0.5094,\n", + " 0.4165, 0.4291, 0.2960]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 8.338225597981364e-05\n", + "Grad encoder.fc1.bias: 0.00016619949019514024\n", + "Grad encoder.encoder.0.weight: 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" 2.9275e-01, -2.1839e-01, -9.7447e-01, 4.7051e-01, 2.5238e-01,\n", + " 1.6069e+00, 6.6251e-01, -1.5452e-01, 4.2404e-01, -3.8420e-01,\n", + " 8.4640e-01, -3.3194e-01, 8.0775e-01, 3.0986e-01, 5.3151e-01,\n", + " -4.9071e-02, -1.0105e+00, -3.1845e-01, 4.0193e-03, -1.4236e-01,\n", + " 1.5690e-01, -3.4591e-01, -3.2658e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3670, 0.2937, 0.1000, 0.2675, 0.1864, -0.0870, -0.2986, -0.3468,\n", + " -0.1553, -0.1671, -0.2098, 0.1088, 0.1842, 0.1964, 0.0983, 0.2691,\n", + " 0.1674, 0.0639, 0.0338, 0.0831, 0.0995, 0.0690, 0.0845, 0.0458,\n", + " -0.0325, 0.0072, -0.1139, 0.0373, 0.0853, 0.1683, 0.1641, 0.1363,\n", + " 0.2320, 0.2267, 0.2033, 0.1827, -0.0660, -0.0495, -0.0272, -0.2585,\n", + " -0.2242, -0.1896, -0.2266]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 9.780137042980641e-05\n", + "Grad encoder.fc1.bias: 0.00010427532834000885\n", + "Grad encoder.encoder.0.weight: 3.436138649703935e-05\n", + "Grad 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tensor([[1.4321, 1.7755, 1.9234, 1.9635, 2.1497, 2.2806, 2.4481, 2.4716, 2.4068,\n", + " 2.5907, 2.6520, 2.5843, 2.5677, 2.5274, 2.4790, 2.4684, 2.4293, 2.5011,\n", + " 2.5276, 2.5497, 2.6033, 2.4403, 2.4556, 2.4030, 2.4399, 2.4552, 2.4269,\n", + " 2.3860, 2.1301, 2.2435, 2.1592, 2.3046, 2.1934, 1.4875, 1.9143, 1.7877,\n", + " 1.6184, 1.4735, 1.5593, 1.6087, 1.2266, 0.6771, 0.6851, 1.2974, 1.8354,\n", + " 1.8984, 1.2986, 2.0017]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.0833, -0.0815, 0.2685, -0.0810, 0.1500, -0.9216, -0.8040, -0.5280,\n", + " -1.6491, -0.1299, -0.5148, 1.3103, 0.7533, -0.9263, 0.4696, -0.0717,\n", + " 0.1294, 0.5898, 2.0337, -0.1496, 0.1933, 0.2662, -0.3074, 0.1440,\n", + " -0.2340, -0.1090, -0.5985, -0.4102, -0.8396, 0.4853, 1.0083, 0.9625,\n", + " 0.2369, 0.0480, 0.6946, 0.2651, 0.7170, 0.0000, -1.8131, -0.1268,\n", + " -0.3424, -0.3431, -0.5137]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 2.5022e-01, 1.8821e-01, 6.2812e-02, 1.4661e-01, 1.1706e-01,\n", + " -7.7478e-02, -2.2393e-01, -2.3926e-01, -9.1507e-02, -9.6067e-02,\n", + " -1.4708e-01, 6.4375e-02, 1.2821e-01, 1.2106e-01, 5.0306e-02,\n", + " 1.6361e-01, 8.6226e-02, 1.7857e-02, 1.5725e-02, 4.1415e-02,\n", + " 6.1378e-02, 4.4358e-02, 3.8754e-02, 1.8029e-04, -2.8252e-02,\n", + " 2.1094e-02, -6.5932e-02, 3.9531e-02, 6.3785e-02, 1.1261e-01,\n", + " 1.3574e-01, 7.7827e-02, 1.4406e-01, 1.4648e-01, 1.3652e-01,\n", + " 1.2852e-01, -5.9911e-02, -3.6209e-02, -2.7165e-02, -1.5798e-01,\n", + " -1.1760e-01, -1.0820e-01, -1.2140e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0011427316348999739\n", + "Grad encoder.fc1.bias: 0.0009869445348158479\n", + "Grad encoder.encoder.0.weight: 0.0002724466903600842\n", + "Grad encoder.encoder.0.bias: 0.0010262320283800364\n", + "Grad encoder.encoder.2.weight: 0.00015262837405316532\n", + "Grad encoder.encoder.2.bias: 0.0009283513063564897\n", + "Grad encoder.encoder.4.weight: 0.000492711435072124\n", + "Grad 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2.3557, 2.4358, 2.5667, 2.6924,\n", + " 2.6776, 2.6584, 2.6233, 2.6298, 2.6010, 2.4286, 2.4602, 2.4309, 2.4934,\n", + " 2.5173, 2.5497, 2.4904, 2.5735, 2.5303, 2.4469, 2.4511, 2.5962, 2.5068,\n", + " 2.5817, 2.4145, 2.2893, 2.3447, 2.3903, 1.9690, 1.2976, 1.7550, 1.7172,\n", + " 1.6204, 1.5822, 1.5783, 1.4696, 1.2791, 0.7528, 0.7882, 1.3003, 2.1288,\n", + " 2.2473, 1.6589, 2.0955]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.5087, 0.0434, -0.7113, 0.3169, -0.2079, 1.1198, -0.7216, -0.5147,\n", + " 0.1517, -0.8263, -0.8283, 0.3684, -1.4994, 0.6274, 1.5134, 0.1254,\n", + " -0.6033, 0.0233, 0.4775, -0.1543, 0.2042, -0.5477, 0.9882, -0.2615,\n", + " 0.5684, -1.2068, -0.3904, 0.2247, 0.0269, 0.0261, 1.0336, 0.3579,\n", + " 0.1523, -0.0037, -1.3977, -0.3570, 0.4796, -0.1310, 0.0000, -0.8592,\n", + " -0.5952, -0.6952, -0.7628]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2986, 0.2411, 0.0894, 0.1763, 0.1546, -0.0696, -0.2469, -0.2685,\n", + " -0.1281, -0.1375, -0.1840, 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2.8538, 4.6008, 4.9277,\n", + " 4.9831, 5.4118, 6.0088, 5.7877, 1.1645, 0.6492, 0.7053, 1.1769, 1.6610,\n", + " 1.7784, 1.1422, 1.6889]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.4075, 0.4912, 1.0243, 0.4403, 0.3536, 0.2615, 0.2004, 0.0666,\n", + " -0.3785, -0.7153, 0.5375, -0.0732, 1.1206, -1.1138, -0.3842, 1.4878,\n", + " 0.1013, 0.7374, 0.2941, -0.1203, -0.5735, 3.4187, 0.3391, 1.6119,\n", + " 0.7833, -0.8364, -0.4707, 0.5270, -0.1062, 0.0254, -0.7785, 0.0263,\n", + " 0.0579, 0.1023, -0.1355, 0.3486, -0.2445, -0.7406, -0.7513, -0.2878,\n", + " -0.3307, -0.2466, -0.4456]], device='cuda:0')\n", + "Prediction Sample: tensor([[-2.7179e-01, -2.4836e-01, -6.6828e-02, -2.0142e-01, -7.0136e-02,\n", + " 5.7637e-02, 1.4679e-01, 2.5622e-01, 9.1292e-02, 1.5874e-01,\n", + " 2.4330e-01, -1.8805e-01, -1.2858e-01, -2.5023e-01, -2.0804e-01,\n", + " -1.8402e-01, -2.1177e-01, -9.3062e-02, -1.2209e-01, -9.4308e-02,\n", + " -5.7459e-02, -1.0282e-02, -5.3009e-02, -6.8026e-03, -2.2883e-02,\n", + " 4.1750e-02, -7.5175e-03, -3.5006e-02, -1.0283e-01, -1.2273e-01,\n", + " -1.7378e-01, -2.0545e-01, -2.2725e-01, -3.0215e-01, -1.2771e-01,\n", + " -1.5993e-01, 3.1734e-04, -8.7310e-03, 1.1328e-02, 3.3083e-01,\n", + " 3.1700e-01, 3.0900e-01, 2.1450e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00017275972641073167\n", + "Grad encoder.fc1.bias: 9.475127444602549e-05\n", + "Grad encoder.encoder.0.weight: 4.6438839490292594e-05\n", + "Grad encoder.encoder.0.bias: 0.00011712787090800703\n", + "Grad encoder.encoder.2.weight: 3.358966569066979e-05\n", + "Grad encoder.encoder.2.bias: 0.00015628476103302091\n", + "Grad encoder.encoder.4.weight: 0.00011609109060373157\n", + "Grad encoder.encoder.4.bias: 0.0005642040632665157\n", + "Grad decoder.fc1.0.weight: 3.677857603179291e-05\n", + "Grad decoder.fc1.0.bias: 0.0001650820777285844\n", + "Grad decoder.fc1.2.weight: 3.961992479162291e-05\n", + "Grad decoder.fc1.2.bias: 0.00033561832970008254\n", + "Grad 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1.6420, 1.5133, 1.5529, 1.6143, 1.1741, 0.7050, 0.6972, 1.2486, 1.7641,\n", + " 1.9671, 1.2986, 1.9939]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1547, 1.0719, 0.5111, 0.5156, 0.0080, -0.8353, -0.0665, -0.5744,\n", + " 0.4042, 0.1954, 0.2362, 0.7847, 0.5980, 0.9359, 0.0209, 0.3369,\n", + " 0.2921, -0.7583, -0.4084, -0.0499, 0.3895, 0.5465, 0.1350, 0.6929,\n", + " -0.9413, -0.3847, 0.7174, 0.4359, 0.1958, -0.5849, 0.5115, 1.3261,\n", + " 0.4634, 0.6123, 0.1647, -0.6427, 0.4246, 0.2302, -0.4768, 0.8621,\n", + " -0.9353, -1.0614, -0.2463]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3990, 0.3300, 0.1159, 0.2541, 0.2018, -0.0952, -0.3013, -0.3245,\n", + " -0.1942, -0.2200, -0.2418, 0.1045, 0.1631, 0.2162, 0.1000, 0.3018,\n", + " 0.1471, 0.0375, 0.0457, 0.1265, 0.1221, 0.0851, 0.0774, 0.0614,\n", + " -0.0405, 0.0476, -0.0983, 0.0411, 0.1007, 0.2125, 0.1978, 0.1229,\n", + " 0.2344, 0.2260, 0.1948, 0.2423, -0.0816, -0.0564, -0.0127, -0.2825,\n", + " -0.2370, -0.2410, 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-0.1185, -0.3021,\n", + " 0.3878, 0.6923, 0.5300, 0.7728, 0.5019, 0.4816, 1.2763, 1.3190,\n", + " -0.1767, -0.2977, 0.5010, -0.0391, 0.5275, 0.0259, -0.2609, -0.1453,\n", + " 0.2987, 0.3380, -0.4639, -0.7857, -0.1426, -1.9272, 0.3459, -0.0433,\n", + " -1.0149, -0.1589, -0.1843, -0.0087, -0.9268, 0.3805, 0.5660, 0.2865,\n", + " -0.3876, -0.7155, 0.6495]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.5577, 0.5077, 0.2682, 0.3782, 0.2646, -0.1692, -0.5523, -0.6030,\n", + " -0.3376, -0.3471, -0.3380, 0.2393, 0.2928, 0.3493, 0.1517, 0.4257,\n", + " 0.2909, 0.1352, 0.1272, 0.2053, 0.1605, 0.1051, 0.0365, 0.0920,\n", + " 0.0345, 0.0617, -0.0646, 0.1286, 0.2824, 0.4225, 0.4420, 0.2657,\n", + " 0.3666, 0.4290, 0.2941, 0.3038, -0.0472, 0.0509, -0.0227, -0.4436,\n", + " -0.3999, -0.4110, -0.4001]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00030173445702530444\n", + "Grad encoder.fc1.bias: 0.00047372974222525954\n", + "Grad encoder.encoder.0.weight: 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X Sample: tensor([[ 0.0117, -0.0044, 0.0090, 0.0087, -0.0085, 0.0074, 0.0123, 0.0114,\n", + " 0.0195, 0.0142, 0.0254, 0.0214, 0.0155, -0.0027, 0.0050, 0.0041,\n", + " -0.0472, -0.0658, -0.0764, -0.0688, -0.0932, -0.0551, -0.0747, -0.0706,\n", + " -0.0544, -0.0993, -0.1039, -0.1917, -0.0659, -0.0066, -0.0210, -0.0166,\n", + " -0.0066, -0.0435, -0.0025, 0.0438, 0.0079, 0.0159, 0.0317, 0.0028,\n", + " 0.0286, 0.0100, 0.0162, -0.0029, 0.0079, 0.0286, -0.0136, -0.0313]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.2985e+00, 1.2780e+00, 3.8357e-01, 2.5693e+00, 1.5159e-01,\n", + " 9.6219e-01, -5.9078e-01, -9.7552e-01, 3.6543e-01, -6.4351e-01,\n", + " -4.3739e-01, 4.1250e-01, 9.2413e-01, -6.2575e-01, -1.0314e-01,\n", + " 1.1270e-01, -7.2765e-01, 4.6550e-01, -3.8780e-01, 4.0826e-01,\n", + " 6.3307e-01, -3.8319e-01, 4.7756e-01, -1.7553e+00, -6.3947e-01,\n", + " 2.9459e-02, 5.6201e-01, -2.2504e-01, -5.0151e-01, -1.3893e-01,\n", + " 8.1758e-01, -4.3832e-01, 4.5153e-01, 3.6593e-01, -8.3266e-02,\n", + " -1.7780e-01, -1.0781e-03, -6.5324e-01, 1.7270e-02, -6.9164e-01,\n", + " -7.5806e-01, -6.5578e-01, -8.0928e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.5597, 0.5067, 0.2689, 0.3708, 0.2612, -0.1690, -0.5497, -0.6048,\n", + " -0.3316, -0.3486, -0.3364, 0.2423, 0.3077, 0.3453, 0.1608, 0.4178,\n", + " 0.2926, 0.1233, 0.1336, 0.2038, 0.1622, 0.0982, 0.0259, 0.0842,\n", + " 0.0402, 0.0869, -0.0593, 0.1314, 0.2881, 0.4184, 0.4499, 0.2648,\n", + " 0.3658, 0.4353, 0.2911, 0.3000, -0.0534, 0.0545, -0.0254, -0.4381,\n", + " -0.4019, -0.4264, -0.4010]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 2.3961103579495102e-05\n", + "Grad encoder.fc1.bias: 0.0006596907041966915\n", + "Grad encoder.encoder.0.weight: 1.4414093129744288e-05\n", + "Grad encoder.encoder.0.bias: 0.0007158748339861631\n", + "Grad encoder.encoder.2.weight: 2.1382775230449624e-05\n", + "Grad encoder.encoder.2.bias: 0.0006832189974375069\n", + "Grad 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"Data X Sample: tensor([[1.3218, 1.3924, 1.6589, 1.8498, 2.1053, 3.7966, 4.1398, 4.0346, 4.2449,\n", + " 4.1436, 4.0320, 3.9184, 3.7261, 3.6126, 3.5988, 3.3806, 3.3743, 3.3310,\n", + " 3.3680, 3.3475, 3.1799, 3.0986, 2.7014, 2.5767, 2.6407, 2.7007, 2.7945,\n", + " 2.9284, 2.8655, 2.9378, 3.0761, 3.0811, 3.2781, 2.3869, 4.0910, 4.4875,\n", + " 4.9162, 5.2475, 5.8884, 5.7452, 1.0404, 0.6054, 0.5962, 1.1166, 1.5698,\n", + " 1.6926, 1.2646, 1.8062]], device='cuda:0')\n", + "Data Y Sample: tensor([[-4.9098e-01, -1.3021e-01, 5.3787e-01, 3.3716e-01, -3.6634e-01,\n", + " -1.9860e-01, 2.1630e-01, 5.2811e-03, 1.1786e-01, 4.0073e-01,\n", + " 3.5483e-01, -3.5573e-01, 2.1081e+00, -1.1832e+00, -1.1858e-01,\n", + " 4.1618e-01, -2.6146e-01, -4.9797e-01, 4.2661e-01, 4.4317e-01,\n", + " 3.3518e-01, 8.1836e-02, 3.0733e-01, -6.7824e-04, 2.8115e-01,\n", + " -1.0127e-01, -2.7319e-02, -1.6254e-01, 1.5873e-01, 2.6064e-01,\n", + " -3.3653e-01, 3.2841e-01, -5.7398e-01, -3.9130e-01, -2.3949e-01,\n", + " -1.1943e-01, -4.4044e-01, 6.3434e-01, -8.8267e-01, -2.3661e-01,\n", + " -3.4234e-01, -2.6739e-01, 8.5156e-02]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1678, -0.1872, -0.0455, -0.1711, -0.0533, 0.0391, 0.0774, 0.1668,\n", + " 0.0905, 0.1433, 0.1368, -0.1202, -0.0794, -0.1566, -0.1467, -0.1559,\n", + " -0.1919, -0.0865, -0.0674, -0.0794, -0.0297, -0.0240, -0.0535, -0.0439,\n", + " -0.0412, 0.0304, 0.0177, 0.0169, -0.0684, -0.1166, -0.1126, -0.1315,\n", + " -0.1851, -0.1978, -0.1137, -0.0932, -0.0104, 0.0102, -0.0117, 0.2064,\n", + " 0.2450, 0.1931, 0.1471]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00010368175571784377\n", + "Grad encoder.fc1.bias: 0.00011815862671937793\n", + "Grad encoder.encoder.0.weight: 2.5081921194214374e-05\n", + "Grad encoder.encoder.0.bias: 0.00016783177852630615\n", + "Grad encoder.encoder.2.weight: 2.0482852050918154e-05\n", + "Grad encoder.encoder.2.bias: 0.00016057011089287698\n", + "Grad encoder.encoder.4.weight: 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3.0449,\n", + " 3.0222, 3.1014, 3.2195, 3.3805, 3.3215, 3.8017, 2.8538, 4.6425, 5.0542,\n", + " 5.1148, 5.5019, 6.0595, 5.9211, 0.9975, 0.6074, 0.5699, 1.0276, 1.5421,\n", + " 1.6125, 1.1898, 1.7046]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.4085, -0.6805, -0.5922, 0.4536, -0.8790, -1.1819, 0.2148, 0.7770,\n", + " -0.3229, 0.5483, 0.7389, -0.4864, -1.3013, -0.2198, -0.2770, -0.3679,\n", + " -0.8222, -0.0896, -0.2143, -1.2710, -0.2424, -0.5362, -0.7051, 0.0621,\n", + " -1.3081, -1.5186, 0.4931, 1.0331, -0.8427, 0.4537, -0.1355, 0.0720,\n", + " -0.0126, -0.1670, -0.5908, -0.8151, -0.5085, -0.0751, 0.0040, 0.9849,\n", + " 0.7943, 0.4609, 0.3534]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1771, -0.1940, -0.0479, -0.1726, -0.0494, 0.0480, 0.0855, 0.1765,\n", + " 0.0955, 0.1501, 0.1452, -0.1214, -0.0839, -0.1660, -0.1536, -0.1618,\n", + " -0.1952, -0.0872, -0.0689, -0.0826, -0.0320, -0.0260, -0.0523, -0.0434,\n", + " -0.0366, 0.0329, 0.0177, 0.0118, -0.0724, -0.1216, 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"Data Y Sample: tensor([[-0.0446, -0.3856, -0.5571, 1.7655, 0.0150, 0.0579, -0.9936, -0.0684,\n", + " -0.2038, 0.2542, 0.5408, -0.1228, -0.5906, 0.1307, -0.6149, -0.3680,\n", + " -1.1345, 4.4548, -0.0165, 0.1391, -0.9184, -0.2187, -0.8846, 4.4171,\n", + " -0.6052, 1.5163, 0.7713, 0.4159, -0.3321, 0.5720, -0.2264, 0.8664,\n", + " 0.3428, 0.0272, 0.5745, -0.2645, 0.9799, -0.7393, 0.8448, -0.3132,\n", + " -0.2614, -0.7009, -0.0331]], device='cuda:0')\n", + "Prediction Sample: tensor([[-2.5065e-01, -2.4751e-01, -6.9997e-02, -1.8754e-01, -3.7224e-02,\n", + " 9.3116e-02, 1.4488e-01, 2.5110e-01, 1.2771e-01, 1.9282e-01,\n", + " 2.2026e-01, -1.5019e-01, -1.2675e-01, -2.3113e-01, -2.0310e-01,\n", + " -1.9642e-01, -2.1530e-01, -8.6187e-02, -9.2692e-02, -1.0055e-01,\n", + " -4.6564e-02, -3.0516e-02, -3.9192e-02, -2.5197e-02, 3.1294e-03,\n", + " 4.7267e-02, 8.8052e-03, -2.3718e-02, -1.0201e-01, -1.4332e-01,\n", + " -1.7977e-01, -1.9341e-01, -2.3007e-01, -2.8177e-01, -1.5122e-01,\n", + " 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"Grad decoder.fc2.bias: 0.002722624223679304\n", + "Grad _memory_unit.weight_ih_l0: 2.9391218049568124e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 5.328063707565889e-05\n", + "Grad _memory_unit.bias_hh_l0: 3.623057637014426e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.6960591892711818e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00021847570315003395\n", + "Grad _memory_unit.bias_hh_l1: 0.00011155496758874506\n", + "Data X Sample: tensor([[2.5301, 2.9290, 3.1976, 3.3410, 3.5583, 3.6905, 3.7792, 3.8017, 3.9422,\n", + " 3.9493, 3.9305, 4.0839, 3.8415, 3.6153, 3.5887, 3.4503, 3.3822, 3.5089,\n", + " 3.3928, 3.3177, 3.2437, 3.2057, 2.8773, 2.8230, 2.7420, 2.7817, 2.7599,\n", + " 2.7897, 2.8413, 2.9149, 2.9501, 2.8628, 3.0317, 2.2084, 3.6032, 3.9962,\n", + " 4.1847, 4.6114, 5.1468, 4.9735, 1.9997, 1.1252, 1.0711, 2.1729, 2.9176,\n", + " 3.0592, 2.0261, 3.1981]], device='cuda:0')\n", + "Data Y Sample: 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-0.1470, -0.1168, -0.0242, 0.0031, 0.0216, 0.2704,\n", + " 0.2810, 0.2563, 0.1931]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.000634056399576366\n", + "Grad encoder.fc1.bias: 0.0007975613116286695\n", + "Grad encoder.encoder.0.weight: 0.00012527633225545287\n", + "Grad encoder.encoder.0.bias: 0.0007025166414678097\n", + "Grad encoder.encoder.2.weight: 8.239185262937099e-05\n", + "Grad encoder.encoder.2.bias: 0.0006073237163946033\n", + "Grad encoder.encoder.4.weight: 0.0002474469074513763\n", + "Grad encoder.encoder.4.bias: 0.0013150940649211407\n", + "Grad decoder.fc1.0.weight: 8.275423897430301e-05\n", + "Grad decoder.fc1.0.bias: 0.00041531980969011784\n", + "Grad decoder.fc1.2.weight: 8.35466489661485e-05\n", + "Grad decoder.fc1.2.bias: 0.0008184026228263974\n", + "Grad decoder.fc1.4.weight: 7.090366125339642e-05\n", + "Grad decoder.fc1.4.bias: 0.0008891312754712999\n", + "Grad decoder.fc2.weight: 0.00012007250916212797\n", + "Grad decoder.fc2.bias: 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_memory_unit.bias_hh_l0: 6.481671880465001e-05\n", + "Grad _memory_unit.weight_ih_l1: 2.181932177336421e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00024309512809850276\n", + "Grad _memory_unit.bias_hh_l1: 0.00012375263031572104\n", + "Data X Sample: tensor([[1.5308, 1.7828, 1.9204, 2.0379, 2.2231, 2.1642, 2.2170, 2.2260, 2.3702,\n", + " 2.4106, 2.4173, 2.3701, 2.2614, 2.2793, 2.2621, 2.2934, 2.2375, 2.2729,\n", + " 2.3066, 2.2726, 2.3088, 2.4081, 2.3495, 2.4164, 2.2991, 2.4604, 2.4615,\n", + " 2.5002, 2.2099, 2.2860, 2.0542, 2.0338, 1.6478, 1.0573, 1.5442, 1.4812,\n", + " 1.4218, 1.4020, 1.3501, 1.3136, 1.3077, 0.7448, 0.7316, 1.1883, 1.8552,\n", + " 2.0643, 1.3598, 1.9079]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.3605, -0.1712, 0.0540, -0.0330, 0.2612, -0.1736, -0.6825, -0.6806,\n", + " -0.1196, -0.1312, -0.4492, 0.0933, 2.9566, 0.2445, 0.8344, 0.1641,\n", + " 0.7673, -0.4591, -2.7713, -1.0587, 0.3408, -0.1061, -1.0309, -0.8336,\n", + " -0.4358, -0.2961, 0.6915, -0.1253, 1.3921, 0.7249, 0.9037, -0.0762,\n", + " 0.8457, 0.3620, 0.7245, 0.0796, 1.0359, -0.0911, 1.1113, -0.3757,\n", + " -0.2732, -0.5274, 0.1631]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4567, 0.3298, 0.0911, 0.2548, 0.0913, -0.1576, -0.3536, -0.4629,\n", + " -0.1630, -0.2463, -0.2582, 0.1782, 0.1849, 0.2481, 0.1733, 0.2368,\n", + " 0.1645, 0.0724, 0.0342, 0.1011, 0.1050, 0.0098, 0.0409, 0.0016,\n", + " 0.0026, 0.0959, -0.0530, 0.0728, 0.2218, 0.2539, 0.2738, 0.1822,\n", + " 0.2970, 0.3003, 0.1716, 0.2575, -0.0934, -0.0514, 0.0144, -0.2926,\n", + " -0.3105, -0.3303, -0.2772]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00033996516140177846\n", + "Grad encoder.fc1.bias: 0.0011601285077631474\n", + "Grad encoder.encoder.0.weight: 7.916430331533775e-05\n", + "Grad encoder.encoder.0.bias: 0.0007127125863917172\n", + "Grad encoder.encoder.2.weight: 7.908888073870912e-05\n", + "Grad 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"Grad _memory_unit.bias_ih_l0: 2.385172774665989e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.2814438377972692e-05\n", + "Grad _memory_unit.weight_ih_l1: 3.4555778256617486e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 5.85981979384087e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.0530492949765176e-05\n", + "Data X Sample: tensor([[1.5393, 1.6284, 1.7686, 1.9395, 1.9943, 2.1849, 2.1985, 2.8179, 4.2156,\n", + " 4.4185, 4.2953, 4.2144, 3.9925, 3.8307, 3.8560, 3.6254, 3.6117, 3.6424,\n", + " 3.4279, 3.5874, 3.6068, 3.4155, 2.9737, 2.8382, 2.8040, 2.9201, 2.9383,\n", + " 2.8958, 2.9731, 3.1605, 3.0761, 3.2994, 3.6917, 2.7691, 4.5150, 4.8499,\n", + " 5.0362, 5.4622, 5.8884, 5.7792, 1.1216, 0.6592, 0.6285, 1.1654, 1.7680,\n", + " 1.9042, 1.2782, 1.9157]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.2131, 0.6049, 1.0497, -0.0028, 1.5649, 1.8653, 0.1504, 0.0968,\n", + " -0.2695, -0.8789, -0.8419, 0.0794, 0.3452, 0.0808, 0.8151, 0.4436,\n", + " 0.9283, 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"Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.0511720979120582e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.7602951629669406e-05\n", + "Grad _memory_unit.weight_ih_l1: 3.784479076784919e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 7.464415102731436e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.822206053882837e-05\n", + "Data X Sample: tensor([[2.1005, 2.5139, 2.7919, 2.9300, 3.0888, 3.2397, 3.2585, 3.3602, 3.2612,\n", + " 3.2653, 3.3722, 3.4179, 3.2157, 3.1900, 3.2961, 3.2589, 3.1558, 3.2575,\n", + " 3.4424, 3.3307, 3.1799, 3.1322, 2.9640, 2.8230, 2.7420, 2.7347, 2.5654,\n", + " 2.5654, 2.2168, 2.2566, 2.2747, 2.2880, 2.0570, 1.4280, 2.0197, 2.2668,\n", + " 2.5859, 2.8093, 3.1058, 2.8797, 1.6847, 0.9897, 1.0469, 1.6533, 2.6322,\n", + " 2.8820, 1.8697, 2.6429]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 3.3780e-03, -4.9136e-01, -5.2693e-02, -6.0280e-01, 6.0562e-01,\n", + " 5.7361e-01, 1.7588e-01, 1.5618e-01, 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_memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 5.2828690968453884e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.7365324058337137e-05\n", + "Data X Sample: tensor([[1.5149, 1.8949, 2.1653, 2.3658, 2.5031, 2.4825, 2.5806, 2.5610, 2.3897,\n", + " 2.5655, 2.4300, 2.3428, 2.3169, 2.2657, 2.0553, 2.0554, 2.0724, 2.0640,\n", + " 1.9597, 1.9341, 1.9187, 1.8570, 1.7664, 1.7006, 1.6535, 1.6272, 1.5930,\n", + " 1.6233, 1.1969, 1.2609, 1.2003, 1.1855, 1.0450, 0.7621, 1.1741, 1.2794,\n", + " 1.4454, 1.7068, 1.9903, 1.9746, 1.2791, 0.7548, 0.7700, 1.4725, 2.1129,\n", + " 2.3044, 1.6045, 2.2441]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.4963, -1.3903, -0.8165, -1.3645, -0.8328, 0.3123, 1.4673, 1.5473,\n", + " 1.0352, 1.8329, 3.9918, -0.2799, -0.8658, -2.1535, -1.6335, -1.5052,\n", + " -1.5085, -1.2613, -0.3210, -0.6531, -0.4894, -0.5101, -0.1072, 0.0992,\n", + " -0.3552, -0.1343, 0.5494, 0.1986, -0.1172, -0.8828, -1.3238, -0.8973,\n", + " -1.9989, -2.2481, -0.4596, 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"Data Y Sample: tensor([[-0.6640, -0.7094, -0.2872, -1.0267, -0.0421, -0.9732, 0.9202, 0.9730,\n", + " 0.1568, 0.5462, 0.3858, 0.1789, -0.1017, -0.3169, 0.3432, -0.2547,\n", + " 0.0872, -0.1350, -1.4777, -0.4518, 0.0756, 0.9852, 0.4336, 1.6707,\n", + " -1.0864, -0.7714, 0.2366, 0.3695, 0.1371, 0.4042, 0.2064, -0.2605,\n", + " 0.2680, -0.7518, -0.1113, -0.7475, 0.0000, -0.0459, 0.2253, 0.4699,\n", + " 0.7143, 0.6645, 0.1019]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2428, -0.2908, -0.0680, -0.1177, 0.0889, 0.2059, 0.2132, 0.3189,\n", + " 0.2215, 0.2764, 0.2710, -0.1583, -0.1916, -0.2847, -0.2232, -0.2656,\n", + " -0.2312, -0.0659, -0.0328, -0.1000, -0.0356, -0.0510, -0.0573, -0.0278,\n", + " 0.0532, 0.0532, -0.0861, -0.1184, -0.2114, -0.2439, -0.2751, -0.2568,\n", + " -0.3313, -0.3466, -0.2379, -0.1569, -0.0668, -0.0072, 0.0849, 0.3031,\n", + " 0.3234, 0.3514, 0.2604]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002632199029903859\n", + "Grad 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"Data Y Sample: tensor([[ 0.1260, 0.7761, 1.3236, 0.0170, -0.5736, 0.2325, -0.3191, -0.2927,\n", + " -0.0262, -0.3946, -0.1202, 2.5654, -0.5945, 0.0864, -0.0214, 1.0356,\n", + " 1.4567, 0.4854, -0.9873, -0.1789, 0.9865, -1.5975, -0.4685, 0.0956,\n", + " 0.1780, 0.0822, 0.8532, 0.1168, -0.0086, -0.2237, 0.3976, -0.0262,\n", + " 0.4421, -0.1992, 1.1904, 0.3484, -0.3467, 0.2376, 0.0382, -0.3646,\n", + " -0.5265, 0.5466, 0.5647]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2554, 0.2578, 0.1454, 0.0666, 0.0661, -0.0277, -0.2318, -0.2301,\n", + " -0.1258, -0.1599, -0.1979, 0.1277, 0.1161, 0.2184, 0.1441, 0.1969,\n", + " 0.1919, 0.0282, 0.0905, 0.0594, 0.0970, 0.0503, 0.0231, -0.0607,\n", + " 0.0021, 0.0342, -0.0680, -0.0347, 0.0680, 0.1856, 0.1303, 0.1088,\n", + " 0.1879, 0.1579, 0.0772, 0.1491, -0.0195, 0.0167, 0.0216, -0.2069,\n", + " -0.1618, -0.2154, -0.2387]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.000251409481279552\n", + "Grad 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"Grad decoder.fc1.0.bias: 0.00025880106841214\n", + "Grad decoder.fc1.2.weight: 5.313671863405034e-05\n", + "Grad decoder.fc1.2.bias: 0.00048336436157114804\n", + "Grad decoder.fc1.4.weight: 5.05269126733765e-05\n", + "Grad decoder.fc1.4.bias: 0.0004047393158543855\n", + "Grad decoder.fc2.weight: 0.00015589952818118036\n", + "Grad decoder.fc2.bias: 0.0018213054863736033\n", + "Grad _memory_unit.weight_ih_l0: 5.822575985803269e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 2.2972459191805683e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.3747048797085881e-05\n", + "Grad _memory_unit.weight_ih_l1: 3.1641875466448255e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 6.135024887043983e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.244969775550999e-05\n", + "Data X Sample: tensor([[1.5000, 1.5818, 1.6905, 1.8520, 2.0182, 2.1333, 2.2093, 3.4199, 4.4573,\n", + " 4.1941, 4.2953, 4.3897, 4.0612, 3.9479, 3.8232, 3.6350, 3.5882, 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-0.0447, -0.0056,\n", + " 0.0195, 0.0367, -0.0818, -0.1350, -0.2528, -0.2410, -0.2778, -0.2494,\n", + " -0.3268, -0.3433, -0.2301, -0.1706, -0.0574, -0.0087, 0.0680, 0.3104,\n", + " 0.3254, 0.3614, 0.2697]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 8.393726602662355e-05\n", + "Grad encoder.fc1.bias: 0.00015844317385926843\n", + "Grad encoder.encoder.0.weight: 3.1661082175560296e-05\n", + "Grad encoder.encoder.0.bias: 0.0001379195600748062\n", + "Grad encoder.encoder.2.weight: 2.3833503291825764e-05\n", + "Grad encoder.encoder.2.bias: 0.00014416906924452633\n", + "Grad encoder.encoder.4.weight: 7.66659650253132e-05\n", + "Grad encoder.encoder.4.bias: 0.0004324961337260902\n", + "Grad decoder.fc1.0.weight: 3.705814742716029e-05\n", + "Grad decoder.fc1.0.bias: 0.00017207543714903295\n", + "Grad decoder.fc1.2.weight: 5.886891813133843e-05\n", + "Grad decoder.fc1.2.bias: 0.00025980552891269326\n", + "Grad decoder.fc1.4.weight: 7.487561379093677e-05\n", + "Grad 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"Data Y Sample: tensor([[-1.0172, -0.7685, -0.7449, -0.8885, -1.1741, 0.2053, 1.0283, 0.3519,\n", + " 0.2208, 0.4854, 0.8983, -1.6349, -0.1166, -0.8599, -0.3740, -1.0098,\n", + " -0.9776, -1.3089, -1.0560, -0.9604, -0.4423, 0.5341, -0.6438, -0.6096,\n", + " -1.5273, -0.1656, 0.8786, -0.1521, 0.3355, -0.8195, -1.1964, -1.0526,\n", + " -0.2676, -0.8556, -0.8568, 0.4520, 0.9776, 0.7816, 0.0040, 0.8718,\n", + " 0.8444, 0.9464, 0.8813]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2779, -0.3295, -0.0543, -0.1333, 0.0578, 0.2239, 0.2447, 0.3307,\n", + " 0.2525, 0.2923, 0.3130, -0.1717, -0.1931, -0.3092, -0.2269, -0.2967,\n", + " -0.2545, -0.0840, -0.0580, -0.1262, -0.0267, -0.0671, -0.0477, 0.0184,\n", + " 0.0175, 0.0368, -0.0873, -0.1624, -0.2737, -0.2525, -0.2882, -0.2717,\n", + " -0.3471, -0.3733, -0.2330, -0.1853, -0.0374, -0.0054, 0.0687, 0.3438,\n", + " 0.3431, 0.3868, 0.2824]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0004191974876448512\n", + 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" grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0006762339035049081\n", + "Grad encoder.fc1.bias: 0.0009822876891121268\n", + "Grad encoder.encoder.0.weight: 0.000128403480630368\n", + "Grad encoder.encoder.0.bias: 0.0009121668990701437\n", + "Grad encoder.encoder.2.weight: 9.78614843916148e-05\n", + "Grad encoder.encoder.2.bias: 0.0008306960226036608\n", + "Grad encoder.encoder.4.weight: 0.00027758313808590174\n", + "Grad encoder.encoder.4.bias: 0.0018047299236059189\n", + "Grad decoder.fc1.0.weight: 0.00010650210606399924\n", + "Grad decoder.fc1.0.bias: 0.0006225104443728924\n", + "Grad decoder.fc1.2.weight: 9.764046990312636e-05\n", + "Grad decoder.fc1.2.bias: 0.0009180845227092505\n", + "Grad decoder.fc1.4.weight: 8.267063094535843e-05\n", + "Grad decoder.fc1.4.bias: 0.00078048394061625\n", + "Grad decoder.fc2.weight: 0.00012897142732981592\n", + "Grad decoder.fc2.bias: 0.0018744149710983038\n", + "Grad _memory_unit.weight_ih_l0: 2.820648114720825e-05\n", + "Grad 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_memory_unit.bias_ih_l0: 7.101167284417897e-05\n", + "Grad _memory_unit.bias_hh_l0: 4.506950426730327e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.399374013999477e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.0001527851418359205\n", + "Grad _memory_unit.bias_hh_l1: 8.429079025518149e-05\n", + "Data X Sample: tensor([[ 0.0064, 0.0073, 0.0255, 0.0109, 0.0068, 0.0368, 0.0200, 0.0071,\n", + " 0.0317, 0.0300, 0.0412, 0.0292, 0.0266, 0.0164, 0.0101, 0.0178,\n", + " -0.1179, -0.1412, -0.1631, -0.1618, -0.1840, -0.1133, -0.1807, -0.1641,\n", + " -0.1389, -0.2978, -0.2531, -0.4854, -0.0520, -0.0688, -0.0700, -0.0525,\n", + " -0.0484, -0.0664, -0.0123, 0.0146, 0.0039, 0.0027, -0.0190, -0.0227,\n", + " -0.0048, 0.0020, 0.0061, 0.0000, 0.0277, 0.0515, -0.0408, 0.0313]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.6502, 0.8127, 0.5308, 0.6623, -0.2477, 0.9170, -0.9162, -0.7829,\n", + " -0.7166, -0.5879, -0.4726, 1.6715, 2.3229, 2.1032, 0.4260, 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encoder.encoder.2.weight: 1.7633552488405257e-05\n", + "Grad encoder.encoder.2.bias: 0.0001549942244309932\n", + "Grad encoder.encoder.4.weight: 4.764142431668006e-05\n", + "Grad encoder.encoder.4.bias: 0.000587293179705739\n", + "Grad decoder.fc1.0.weight: 3.0189918106771074e-05\n", + "Grad decoder.fc1.0.bias: 0.0002682118793018162\n", + "Grad decoder.fc1.2.weight: 3.2714615372242406e-05\n", + "Grad decoder.fc1.2.bias: 0.00021287862909957767\n", + "Grad decoder.fc1.4.weight: 2.78251500276383e-05\n", + "Grad decoder.fc1.4.bias: 0.00019672114285640419\n", + "Grad decoder.fc2.weight: 7.703772280365229e-05\n", + "Grad decoder.fc2.bias: 0.00202537071891129\n", + "Grad _memory_unit.weight_ih_l0: 4.046444246341707e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 2.0920844690408558e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.1211904165975284e-05\n", + "Grad _memory_unit.weight_ih_l1: 2.4192793262045598e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 6.0450980527093634e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.064873089897446e-05\n", + "Data X Sample: tensor([[1.3207, 1.4099, 1.6709, 1.6705, 1.7518, 1.9462, 2.1508, 2.0570, 2.8779,\n", + " 3.9398, 4.1938, 4.0274, 3.9747, 3.8579, 3.6895, 3.3970, 3.4309, 3.3871,\n", + " 3.3453, 3.3493, 3.3222, 3.2960, 2.9207, 2.7924, 2.7495, 2.8235, 2.8291,\n", + " 2.8917, 2.9349, 2.9804, 3.1006, 3.1751, 3.4783, 2.5402, 4.2282, 4.5993,\n", + " 4.9634, 5.1680, 5.6729, 5.7963, 1.0166, 0.5835, 0.6103, 1.0735, 1.4747,\n", + " 1.6526, 1.0946, 1.6577]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.2621, -0.5171, 0.0358, 0.0170, 0.1088, 0.1615, 0.2109, 0.4369,\n", + " 1.3546, 0.9656, 0.5534, -0.3335, -0.8387, -1.0469, -0.3339, -0.5783,\n", + " -1.2869, -0.8726, -0.7525, -0.7921, 0.1376, 0.6734, 3.8961, 0.6439,\n", + " 0.3015, -0.4373, -0.7834, -0.4016, -0.2092, 0.1091, -0.1446, -0.9126,\n", + " -0.6962, -0.8739, 0.5858, 0.4228, 0.9953, -0.0911, -0.7681, 0.9306,\n", + " 1.2830, 1.0179, 1.7444]], device='cuda:0')\n", + "Prediction Sample: tensor([[-2.8942e-01, -2.6973e-01, -8.4826e-02, -2.1509e-01, -8.6993e-02,\n", + " 9.7578e-02, 1.9120e-01, 3.2431e-01, 1.2923e-01, 1.8046e-01,\n", + " 2.5506e-01, -1.6454e-01, -1.5433e-01, -2.8361e-01, -1.7169e-01,\n", + " -2.3642e-01, -2.4218e-01, -9.4909e-02, -1.1266e-01, -1.0901e-01,\n", + " -5.0823e-02, -4.0623e-02, -5.3620e-02, 1.0215e-02, 2.0686e-02,\n", + " 3.6396e-02, -2.9442e-02, -9.9729e-02, -1.6072e-01, -1.5619e-01,\n", + " -2.1163e-01, -1.8345e-01, -2.4826e-01, -3.1496e-01, -1.1610e-01,\n", + " -1.7664e-01, 8.5841e-04, -1.6247e-04, 3.3079e-02, 3.4864e-01,\n", + " 3.2317e-01, 3.4139e-01, 2.2895e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00027754975599236786\n", + "Grad encoder.fc1.bias: 0.00016479901387356222\n", + "Grad encoder.encoder.0.weight: 7.062193617457524e-05\n", + "Grad encoder.encoder.0.bias: 0.00022616000205744058\n", + "Grad encoder.encoder.2.weight: 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6.022361048962921e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.067535726586357e-05\n", + "Data X Sample: tensor([[1.3176, 1.4711, 1.6829, 1.8979, 1.8321, 1.9226, 1.9998, 2.0712, 2.9194,\n", + " 3.7155, 4.2192, 4.1774, 3.9303, 3.6698, 3.7198, 3.4312, 3.3979, 3.4219,\n", + " 3.5828, 3.2787, 3.2657, 3.2807, 2.8966, 2.7829, 2.7664, 2.8679, 2.9090,\n", + " 2.9121, 2.9904, 2.9967, 3.1391, 3.2414, 3.4431, 2.5151, 4.2601, 4.5945,\n", + " 4.8454, 5.1971, 5.8757, 5.6799, 1.0882, 0.5875, 0.5982, 1.1051, 1.6293,\n", + " 1.7726, 1.2374, 1.5638]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1529, 0.0895, -0.4106, -0.4293, -0.5144, 0.8776, -0.2807, -0.4021,\n", + " 0.1612, -0.1552, 0.0561, 0.4215, 0.2681, 1.4122, 1.0544, 1.2403,\n", + " -0.5615, -0.4538, -0.8541, 0.3024, 1.5653, -1.3737, -0.1444, 1.2884,\n", + " 0.9415, -0.1889, -0.3522, -0.1554, 0.1694, 0.7260, 0.5053, 0.6135,\n", + " 0.4687, 0.6203, 0.0832, 0.6663, 0.6904, -0.7406, 0.8448, -0.0643,\n", + " -0.2107, 0.2475, -0.9384]], device='cuda:0')\n", + "Prediction Sample: tensor([[-2.8238e-01, -2.6438e-01, -8.3313e-02, -2.1253e-01, -8.5600e-02,\n", + " 9.4317e-02, 1.8429e-01, 3.1590e-01, 1.2727e-01, 1.7724e-01,\n", + " 2.4754e-01, -1.6026e-01, -1.5239e-01, -2.7536e-01, -1.6885e-01,\n", + " -2.3351e-01, -2.4053e-01, -9.2217e-02, -1.0716e-01, -1.0564e-01,\n", + " -5.0633e-02, -4.0728e-02, -5.3582e-02, 5.4230e-03, 1.8035e-02,\n", + " 3.4263e-02, -3.0637e-02, -9.5332e-02, -1.5891e-01, -1.5553e-01,\n", + " -2.0780e-01, -1.7734e-01, -2.4572e-01, -3.0833e-01, -1.1584e-01,\n", + " -1.7438e-01, -9.0630e-05, 6.1050e-04, 2.9920e-02, 3.3978e-01,\n", + " 3.1784e-01, 3.3451e-01, 2.2576e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 8.213576802518219e-05\n", + "Grad encoder.fc1.bias: 4.6992110583232716e-05\n", + "Grad encoder.encoder.0.weight: 1.5461213479284197e-05\n", + "Grad encoder.encoder.0.bias: 5.013900954509154e-05\n", + "Grad encoder.encoder.2.weight: 1.9806999262073077e-05\n", + "Grad encoder.encoder.2.bias: 9.78104944806546e-05\n", + "Grad encoder.encoder.4.weight: 5.124281597090885e-05\n", + "Grad encoder.encoder.4.bias: 0.00022484560031443834\n", + "Grad decoder.fc1.0.weight: 2.73562254733406e-05\n", + "Grad decoder.fc1.0.bias: 0.00012095551937818527\n", + "Grad decoder.fc1.2.weight: 3.325831858091988e-05\n", + "Grad decoder.fc1.2.bias: 0.00019298959523439407\n", + "Grad decoder.fc1.4.weight: 3.596546230255626e-05\n", + "Grad decoder.fc1.4.bias: 0.00025918541359715164\n", + "Grad decoder.fc2.weight: 8.742200589040294e-05\n", + "Grad decoder.fc2.bias: 0.0020553406793624163\n", + "Grad _memory_unit.weight_ih_l0: 2.3482305095967604e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 5.712454367312603e-06\n", + "Grad _memory_unit.bias_hh_l0: 3.440816726651974e-06\n", + "Grad _memory_unit.weight_ih_l1: 1.348220166619285e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 1.9983461243100464e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.0263884178129956e-05\n", + "Data X Sample: tensor([[1.5053, 1.6939, 1.8633, 1.9526, 1.9772, 2.0670, 2.0907, 2.1294, 3.9007,\n", + " 4.3158, 4.2287, 4.4383, 4.3498, 4.0761, 3.8106, 3.9071, 3.9246, 3.7623,\n", + " 3.4692, 3.7194, 3.6510, 3.6818, 3.2460, 2.9966, 2.8640, 3.0298, 2.9730,\n", + " 3.0345, 3.0043, 3.2260, 3.2825, 3.3270, 3.7445, 2.7851, 4.4635, 4.8523,\n", + " 5.3174, 5.5655, 5.9328, 5.9495, 1.1932, 0.6512, 0.6649, 1.2084, 1.8275,\n", + " 1.8584, 1.3054, 1.8297]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.5769, 0.4720, -0.6067, -0.1398, 0.3657, -1.0254, -0.5710, 0.6080,\n", + " -0.7542, -0.5832, -0.7170, -1.3970, 0.0100, -0.2725, -0.4335, 0.1333,\n", + " -1.2783, -0.0509, 0.2965, -1.0064, -0.3001, -0.4017, -0.2771, -0.2109,\n", + " 3.2098, -0.8101, -0.2850, 1.5256, 0.8937, -0.5495, -0.8901, -0.1244,\n", + " 0.1190, 0.4808, 0.1485, -0.6664, -0.8131, -0.4031, -0.2265, -0.0826,\n", + " -0.5650, 0.0135, -0.1971]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2852, -0.2652, -0.0840, -0.2134, -0.0848, 0.0953, 0.1851, 0.3173,\n", + " 0.1276, 0.1775, 0.2499, -0.1607, -0.1550, -0.2764, -0.1707, -0.2339,\n", + " -0.2432, -0.0914, -0.1065, -0.1042, -0.0527, -0.0402, -0.0538, 0.0042,\n", + " 0.0170, 0.0339, -0.0343, -0.0974, -0.1630, -0.1574, -0.2100, -0.1784,\n", + " -0.2483, -0.3113, -0.1179, -0.1775, -0.0012, 0.0007, 0.0285, 0.3417,\n", + " 0.3185, 0.3381, 0.2294]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00015458885172847658\n", + "Grad encoder.fc1.bias: 0.00029047319549135864\n", + "Grad encoder.encoder.0.weight: 3.210831346223131e-05\n", + "Grad encoder.encoder.0.bias: 0.0003344374126754701\n", + "Grad encoder.encoder.2.weight: 2.78431034530513e-05\n", + "Grad encoder.encoder.2.bias: 0.0004478422924876213\n", + "Grad encoder.encoder.4.weight: 8.31179422675632e-05\n", + "Grad encoder.encoder.4.bias: 0.0011943000135943294\n", + "Grad decoder.fc1.0.weight: 4.4683634769171476e-05\n", + "Grad decoder.fc1.0.bias: 0.0004627193557098508\n", + "Grad decoder.fc1.2.weight: 6.135379953775555e-05\n", + "Grad decoder.fc1.2.bias: 0.0003859630087390542\n", + "Grad decoder.fc1.4.weight: 6.024262256687507e-05\n", + "Grad decoder.fc1.4.bias: 0.00048447868903167546\n", + "Grad decoder.fc2.weight: 0.00011159384303027764\n", + "Grad decoder.fc2.bias: 0.0017623627791181207\n", + "Grad _memory_unit.weight_ih_l0: 4.980428911949275e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.615727473516017e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.8568876839708537e-05\n", + "Grad _memory_unit.weight_ih_l1: 3.323769760754658e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 9.522608888801187e-05\n", + "Grad _memory_unit.bias_hh_l1: 4.907891707262024e-05\n", + "Data X Sample: tensor([[ 0.0159, 0.0087, 0.0285, 0.0087, -0.0017, 0.0309, 0.0247, 0.0256,\n", + " 0.0342, 0.0363, 0.0508, 0.0058, 0.0200, 0.0055, 0.0126, 0.0137,\n", + " -0.1352, -0.1547, -0.1838, -0.1730, -0.1865, -0.1102, -0.2000, -0.1775,\n", + " -0.1577, -0.3108, -0.2957, -0.5384, -0.0902, -0.0688, -0.0840, -0.0636,\n", + " -0.0396, -0.0526, -0.0343, -0.0097, -0.0177, -0.0504, -0.0190, -0.0057,\n", + " 0.0095, -0.0179, -0.0081, -0.0287, 0.0000, 0.0229, 0.0408, -0.0235]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.6386, 0.0242, -1.1856, 1.0551, 0.2689, -0.6681, -0.8542, -0.6695,\n", + " 0.0164, -1.2116, -0.3662, 1.4654, 0.2647, 0.1995, -0.9054, 0.1193,\n", + " -0.5363, -1.2946, 1.1252, -0.3309, -0.1095, -0.5726, -1.1067, -0.2555,\n", + " -0.0124, -0.4716, 2.0301, 1.8758, 1.8089, 1.8559, 1.7673, 1.3743,\n", + " 0.9478, 1.4642, 1.0811, 0.5994, 0.7329, 0.3075, -0.1229, -0.3989,\n", + " -0.4483, -0.2901, -1.7962]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.5540, 0.5202, 0.1563, 0.3432, 0.0950, -0.1482, -0.5668, -0.6304,\n", + " -0.3889, -0.4713, -0.4312, 0.2831, 0.1882, 0.3508, 0.2285, 0.3405,\n", + " 0.3276, 0.1478, 0.1828, 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+ "Grad decoder.fc1.4.bias: 0.0003339533577673137\n", + "Grad decoder.fc2.weight: 0.00016196767683140934\n", + "Grad decoder.fc2.bias: 0.0018109852680936456\n", + "Grad _memory_unit.weight_ih_l0: 1.4771457244933117e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 5.2739189413841814e-05\n", + "Grad _memory_unit.bias_hh_l0: 3.27581146848388e-05\n", + "Grad _memory_unit.weight_ih_l1: 7.895674571045674e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00011509243631735444\n", + "Grad _memory_unit.bias_hh_l1: 6.136228330433369e-05\n", + "Data X Sample: tensor([[1.4152, 1.5162, 1.6859, 1.8367, 1.7860, 1.8696, 2.0106, 1.9591, 3.3173,\n", + " 4.1705, 4.3112, 4.4286, 4.2188, 3.9179, 3.7475, 3.6678, 3.6479, 3.6463,\n", + " 3.5250, 3.3828, 3.5430, 3.3481, 2.9158, 2.8153, 2.8246, 3.0455, 2.9623,\n", + " 3.0793, 2.9800, 3.1834, 3.2790, 3.3574, 3.9293, 2.8675, 4.6278, 4.9155,\n", + " 5.1168, 5.6503, 6.1863, 5.9126, 1.0834, 0.5954, 0.6508, 1.1137, 1.6253,\n", + " 1.7726, 1.1490, 1.6889]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.3684, 0.1468, 1.6955, 0.6487, 0.3189, 0.7502, 0.2389, -0.2295,\n", + " -0.3168, -0.4131, -1.1623, -0.6366, 0.0588, 0.1414, 0.2512, -0.0257,\n", + " 0.3389, 0.2227, -0.3944, 1.0546, 0.5501, -0.0444, -0.6791, 0.8474,\n", + " -0.6341, 1.7606, -0.7293, -1.6159, 0.0133, -0.7997, 0.1789, -0.7665,\n", + " 0.1439, 0.1987, 0.6898, -0.1698, 0.4743, 0.5488, 0.1500, -1.1628,\n", + " -0.5589, -0.5031, -0.0132]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2588, -0.2468, -0.0780, -0.2041, -0.0820, 0.0875, 0.1626, 0.2887,\n", + " 0.1203, 0.1673, 0.2217, -0.1466, -0.1430, -0.2507, -0.1594, -0.2217,\n", + " -0.2383, -0.0841, -0.0890, -0.0954, -0.0497, -0.0410, -0.0532, -0.0042,\n", + " 0.0075, 0.0298, -0.0328, -0.0840, -0.1537, -0.1528, -0.1898, -0.1598,\n", + " -0.2354, -0.2856, -0.1129, -0.1648, -0.0057, 0.0061, 0.0187, 0.3110,\n", + " 0.2999, 0.3117, 0.2156]], 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_memory_unit.weight_ih_l1: 4.719371190731181e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.0001259021955775097\n", + "Grad _memory_unit.bias_hh_l1: 6.475318514276296e-05\n", + "Data X Sample: tensor([[2.1535, 2.5212, 2.7182, 2.9059, 2.9710, 3.0983, 3.0844, 3.2211, 3.1635,\n", + " 3.3206, 3.4039, 3.3555, 3.4754, 3.2881, 3.1902, 3.2944, 3.2674, 3.3677,\n", + " 3.3288, 3.2285, 3.1700, 3.2088, 2.8146, 2.8649, 2.6313, 2.6223, 2.4855,\n", + " 2.5817, 2.2029, 2.1780, 2.1907, 2.1830, 2.0702, 1.3960, 2.1840, 2.3909,\n", + " 2.6587, 2.9338, 3.1122, 2.9676, 1.7563, 0.9778, 0.9984, 1.7280, 2.5212,\n", + " 2.7962, 1.8765, 2.6116]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.4817, 0.3376, -0.2418, -0.8041, 1.6371, -0.4515, -0.5937, -0.4796,\n", + " 0.1365, -0.6276, -0.3008, 0.5485, -0.4932, -0.6612, 0.1852, -0.5809,\n", + " -1.0146, -0.0406, 0.0940, -2.6867, -1.2037, -0.2954, 0.2353, -1.9389,\n", + " -0.5755, 2.0283, -2.0799, -0.5586, -1.5043, -0.3682, 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"Data X Sample: tensor([[1.5425, 1.7653, 1.9083, 1.9766, 2.0797, 2.2305, 2.4004, 2.5283, 2.4849,\n", + " 2.5923, 2.6838, 2.5298, 2.5344, 2.4974, 2.5093, 2.4329, 2.3963, 2.4683,\n", + " 2.4491, 2.4288, 2.4242, 2.4204, 2.4532, 2.3763, 2.4455, 2.4630, 2.4375,\n", + " 2.5083, 2.2584, 2.3155, 2.3867, 2.2438, 2.2506, 1.5173, 1.9511, 1.7950,\n", + " 1.6577, 1.5080, 1.6163, 1.5774, 1.2027, 0.6771, 0.6993, 1.2515, 1.7720,\n", + " 1.9671, 1.3190, 1.9626]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.3941, 0.5756, 0.8487, 0.4138, 0.7495, -0.6806, -1.0196, -0.9523,\n", + " -0.5429, -0.6205, -0.7075, 1.0965, 0.2313, 1.0490, 1.0801, 0.6506,\n", + " 0.4243, 0.2546, 0.0708, 0.5915, 0.2101, 0.1333, -0.3045, -0.2499,\n", + " -2.4224, -0.9011, -0.6468, 0.1375, -0.3470, 0.3761, -0.6088, -0.1831,\n", + " 0.1038, -0.2755, -0.1119, 1.0937, -0.5235, 0.2376, 1.1238, -0.4122,\n", + " -0.7093, -0.6846, -0.7761]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4962, 0.5099, 0.1644, 0.3034, 0.1483, 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_memory_unit.bias_hh_l1: 3.529564128257334e-05\n", + "Data X Sample: tensor([[2.7274, 2.4600, 2.0195, 2.4074, 2.3324, 2.1480, 2.8626, 3.9778, 4.1863,\n", + " 4.2779, 4.0732, 4.1404, 4.0146, 3.8934, 3.7576, 3.6268, 3.5646, 3.7024,\n", + " 3.5704, 3.4758, 3.4105, 3.3328, 3.0339, 2.9317, 2.8021, 2.7582, 2.7998,\n", + " 2.8183, 2.8031, 2.8395, 2.9606, 2.9816, 3.2715, 2.4556, 4.0616, 4.4291,\n", + " 4.7746, 5.2819, 5.6539, 5.6118, 1.4175, 0.8065, 0.7437, 1.2515, 1.8592,\n", + " 2.3330, 1.5502, 2.0721]], device='cuda:0')\n", + "Data Y Sample: tensor([[-3.3664e-01, 4.7447e-01, 2.2548e-01, -4.5941e-01, 5.7411e-01,\n", + " -8.9172e-01, -1.1782e-01, 7.2749e-02, 5.9110e-01, -8.9880e-02,\n", + " 8.2543e-02, -1.2006e-01, 1.3718e-01, 4.3117e-01, 1.3466e+00,\n", + " 4.4360e-02, 4.9006e-01, -1.1803e-01, -1.4344e+00, -1.2843e+00,\n", + " 4.5180e+00, -5.9147e-01, -5.1867e-01, -9.2924e-01, -1.1327e+00,\n", + " -6.5576e-01, -7.7868e-02, 1.3812e+00, -2.7227e-01, -2.2139e-01,\n", + " 2.0682e-01, 1.6050e-01, 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_memory_unit.bias_hh_l1: 3.90400382457301e-05\n", + "Data X Sample: tensor([[1.5075, 1.6415, 1.8137, 2.0860, 2.0711, 2.2585, 2.3588, 2.4105, 2.5313,\n", + " 2.5323, 2.5220, 2.6330, 2.4745, 2.4320, 2.5194, 2.4342, 2.4089, 2.3638,\n", + " 2.4161, 2.3377, 2.3898, 2.4801, 2.3881, 2.3553, 2.4230, 2.4447, 2.3656,\n", + " 2.4390, 2.2133, 2.3155, 2.2187, 2.2438, 2.2220, 1.4372, 1.9119, 1.7658,\n", + " 1.6636, 1.4868, 1.5846, 1.5945, 1.1788, 0.6890, 0.6831, 1.2888, 1.7641,\n", + " 2.0014, 1.4618, 1.8610]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.3538, -0.0411, -0.5334, 0.7068, 0.1530, 0.5062, 0.7642, -0.2500,\n", + " 0.6842, 0.6702, 0.8757, 1.0247, 0.7068, -0.5002, 1.3828, 0.4165,\n", + " 0.3023, -1.1603, 0.6821, -1.0103, 0.4832, -0.4575, -0.7934, -0.7799,\n", + " -0.5579, -0.5135, -0.3383, 0.4816, 1.1646, -0.2845, -0.0507, -0.1462,\n", + " 0.8686, 0.0410, 0.6518, -0.4823, 0.6097, 0.0940, 0.2870, 1.2796,\n", + " 0.7729, 0.4695, -0.1875]], device='cuda:0')\n", + "Prediction Sample: 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+ "Grad encoder.encoder.2.weight: 2.2314266971079633e-05\n", + "Grad encoder.encoder.2.bias: 0.00023017296916805208\n", + "Grad encoder.encoder.4.weight: 6.133475108072162e-05\n", + "Grad encoder.encoder.4.bias: 0.0005026580183766782\n", + "Grad decoder.fc1.0.weight: 2.9751045076409355e-05\n", + "Grad decoder.fc1.0.bias: 0.0002203222829848528\n", + "Grad decoder.fc1.2.weight: 4.7437206376343966e-05\n", + "Grad decoder.fc1.2.bias: 0.00030815083300694823\n", + "Grad decoder.fc1.4.weight: 5.081060589873232e-05\n", + "Grad decoder.fc1.4.bias: 0.0004282363806851208\n", + "Grad decoder.fc2.weight: 0.0001450089766876772\n", + "Grad decoder.fc2.bias: 0.0016117951599881053\n", + "Grad _memory_unit.weight_ih_l0: 3.7516365409828722e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 1.5766076103318483e-05\n", + "Grad _memory_unit.bias_hh_l0: 8.582385817135219e-06\n", + "Grad _memory_unit.weight_ih_l1: 2.214498408648069e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 4.9664449761621654e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.5644261768320575e-05\n", + "Data X Sample: tensor([[-0.0085, 0.0000, 0.0075, 0.0022, -0.0034, 0.0000, 0.0077, -0.0043,\n", + " 0.0122, 0.0063, 0.0095, -0.0039, 0.0111, 0.0191, 0.0000, 0.0000,\n", + " -0.0236, -0.0077, -0.0083, -0.0056, -0.0025, -0.0122, -0.0048, -0.0134,\n", + " 0.0000, -0.0104, 0.0213, 0.0408, -0.0069, 0.0557, 0.0280, 0.0000,\n", + " 0.0044, -0.0137, -0.0025, 0.0219, 0.0236, 0.0186, -0.0190, 0.0113,\n", + " -0.0095, 0.0159, 0.0121, -0.0057, 0.0317, 0.0114, -0.0068, 0.0078]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.5219, 1.1781, -0.4428, 1.3200, -0.1872, -0.9967, -1.3431, -1.5073,\n", + " -0.1234, -0.9422, -0.4887, 0.3707, 0.2747, 0.8902, 0.6858, 0.0509,\n", + " 0.5588, 2.0616, -1.0332, -2.1393, -0.4924, 9.7704, -0.7969, -0.5836,\n", + " 1.2764, -1.4640, 1.7673, -1.2217, 0.9525, 1.0439, 1.3359, 0.9501,\n", + " 0.1762, 0.6295, 0.3618, 0.9593, 0.7063, -0.1011, 0.0173, -0.5375,\n", + " -0.2887, -0.6227, -0.0437]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.8636, 0.5570, 0.1566, 0.3520, -0.5007, -0.6381, -1.1191, -1.0968,\n", + " -0.7944, -0.8275, -0.7974, 0.4272, 0.4196, 0.6163, 0.3117, 0.4542,\n", + " 0.4215, 0.0972, 0.1572, 0.2242, 0.0357, 0.0381, 0.0606, 0.0255,\n", + " 0.0735, -0.0591, 0.3041, 0.5380, 0.8923, 0.9656, 0.7326, 0.5977,\n", + " 0.6778, 0.6196, 0.3544, 0.2351, -0.0470, 0.0488, 0.0454, -0.6856,\n", + " -0.6307, -0.4969, -0.5147]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00013707243488170207\n", + "Grad encoder.fc1.bias: 0.0005298670148476958\n", + "Grad encoder.encoder.0.weight: 3.0149700251058675e-05\n", + "Grad encoder.encoder.0.bias: 0.00034773992956615984\n", + "Grad encoder.encoder.2.weight: 2.5093002477660775e-05\n", + "Grad encoder.encoder.2.bias: 0.00031038737506605685\n", + "Grad encoder.encoder.4.weight: 7.834732241462916e-05\n", + "Grad encoder.encoder.4.bias: 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-0.4594, -0.4144, 0.3839, 0.3516, 0.4333, 0.2658, 0.3902,\n", + " 0.3396, 0.1334, 0.1721, 0.1856, 0.0479, 0.0466, 0.0664, 0.0448,\n", + " 0.0351, -0.0398, 0.0272, 0.2165, 0.4155, 0.5708, 0.4421, 0.4164,\n", + " 0.4613, 0.4822, 0.3015, 0.1995, -0.0455, 0.1176, -0.0326, -0.4289,\n", + " -0.4515, -0.4060, -0.4591]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00013829876843374223\n", + "Grad encoder.fc1.bias: 0.0005700218607671559\n", + "Grad encoder.encoder.0.weight: 2.5623268811614253e-05\n", + "Grad encoder.encoder.0.bias: 0.00038065374246798456\n", + "Grad encoder.encoder.2.weight: 2.431491702736821e-05\n", + "Grad encoder.encoder.2.bias: 0.00035308621590957046\n", + "Grad encoder.encoder.4.weight: 8.908224117476493e-05\n", + "Grad encoder.encoder.4.bias: 0.00085215870058164\n", + "Grad decoder.fc1.0.weight: 4.232545688864775e-05\n", + "Grad decoder.fc1.0.bias: 0.00036508121411316097\n", + "Grad decoder.fc1.2.weight: 5.229139787843451e-05\n", + "Grad 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" -0.3136, -0.8565, -0.7743, -1.4516, 1.2207, -0.2187, -0.2612, 1.3449,\n", + " 4.7723, 0.2205, -0.2010, 0.2220, 0.7406, 0.5703, 0.0000, -0.9628,\n", + " -0.5620, -1.7499, -0.3932]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.3374, -0.3067, -0.1168, -0.2360, -0.0667, 0.1175, 0.2379, 0.3860,\n", + " 0.1612, 0.2321, 0.3303, -0.1806, -0.1632, -0.3358, -0.1733, -0.2762,\n", + " -0.2659, -0.1012, -0.1145, -0.1037, -0.0942, -0.0615, -0.0299, 0.0006,\n", + " -0.0040, 0.0370, -0.0586, -0.1498, -0.2361, -0.2066, -0.2623, -0.2282,\n", + " -0.2690, -0.3606, -0.1667, -0.2256, -0.0090, 0.0233, 0.0381, 0.4098,\n", + " 0.3787, 0.4084, 0.2608]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0001353219267912209\n", + "Grad encoder.fc1.bias: 8.968195470515639e-05\n", + "Grad encoder.encoder.0.weight: 3.2555173675064e-05\n", + "Grad encoder.encoder.0.bias: 7.762882160022855e-05\n", + "Grad encoder.encoder.2.weight: 2.7884567316505127e-05\n", + "Grad 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0.6184, 1.0161, 1.5460,\n", + " 1.7441, 1.0606, 1.6733]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.0337, -0.4487, -0.6721, 0.2393, 0.5724, 0.2350, 0.7502, 0.8869,\n", + " 0.2684, 0.8671, 0.6222, -1.3752, -0.6915, -1.2115, 0.0150, -0.1899,\n", + " -0.4585, -4.1691, -1.4597, 0.2443, -1.2445, -0.5110, -0.4698, -0.2320,\n", + " -3.8050, -1.0163, 0.0156, 0.7583, -0.1485, -0.3610, -0.4541, -0.2800,\n", + " 0.0658, -0.7011, 0.1146, 0.3745, 0.4852, 0.0000, -0.2505, 0.3737,\n", + " 0.3050, 0.0626, 0.7279]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.3091, -0.2915, -0.1015, -0.2264, -0.0681, 0.1030, 0.2137, 0.3563,\n", + " 0.1582, 0.2266, 0.2991, -0.1642, -0.1552, -0.3073, -0.1551, -0.2714,\n", + " -0.2596, -0.0955, -0.1023, -0.1003, -0.0869, -0.0684, -0.0293, -0.0096,\n", + " -0.0158, 0.0333, -0.0461, -0.1288, -0.2134, -0.2015, -0.2380, -0.2057,\n", + " -0.2493, -0.3266, -0.1629, -0.2066, -0.0120, 0.0251, 0.0316, 0.3779,\n", + " 0.3625, 0.3799, 0.2425]], 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grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00019437860464677215\n", + "Grad encoder.fc1.bias: 8.500729745719582e-05\n", + "Grad encoder.encoder.0.weight: 3.711608223966323e-05\n", + "Grad encoder.encoder.0.bias: 0.00016510591376572847\n", + "Grad encoder.encoder.2.weight: 3.9268863474717364e-05\n", + "Grad encoder.encoder.2.bias: 0.00019625035929493606\n", + "Grad encoder.encoder.4.weight: 0.00011290074326097965\n", + "Grad encoder.encoder.4.bias: 0.001206949818879366\n", + "Grad decoder.fc1.0.weight: 4.246201933710836e-05\n", + "Grad decoder.fc1.0.bias: 0.00026265467749908566\n", + "Grad decoder.fc1.2.weight: 4.75974811706692e-05\n", + "Grad decoder.fc1.2.bias: 0.0007984554395079613\n", + "Grad decoder.fc1.4.weight: 3.805584128713235e-05\n", + "Grad decoder.fc1.4.bias: 0.00032465512049384415\n", + "Grad decoder.fc2.weight: 0.00014509502216242254\n", + "Grad decoder.fc2.bias: 0.0015907678753137589\n", + "Grad _memory_unit.weight_ih_l0: 1.717071791063063e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 7.472449215129018e-05\n", + "Grad _memory_unit.bias_hh_l0: 4.017946412204765e-05\n", + "Grad _memory_unit.weight_ih_l1: 9.669491191743873e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 8.335624443134293e-05\n", + "Grad _memory_unit.bias_hh_l1: 4.713689486379735e-05\n", + "Data X Sample: tensor([[2.0230, 2.4265, 2.8189, 2.8294, 2.8702, 2.9244, 3.0490, 3.0167, 3.2172,\n", + " 3.2305, 3.2960, 3.3088, 3.2335, 3.2827, 3.1599, 3.1645, 3.1385, 3.0698,\n", + " 3.0253, 3.0202, 2.8535, 2.7357, 2.6387, 2.6073, 2.4342, 2.3664, 2.2937,\n", + " 2.2840, 2.0086, 1.9684, 1.9247, 1.8873, 1.8040, 1.2060, 1.9756, 2.3617,\n", + " 2.7000, 2.8305, 3.0361, 3.0244, 1.7611, 1.0535, 1.0145, 1.8198, 2.3468,\n", + " 2.7905, 1.7881, 2.6507]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.0302, -0.6319, 0.1089, -0.3466, 0.3737, 0.1575, 0.2481, -0.1110,\n", + " 0.0977, 0.0649, 0.0574, -0.2449, 1.6615, 1.6066, 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_memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.0001582019467605278\n", + "Grad _memory_unit.bias_hh_l1: 8.263898780569434e-05\n", + "Data X Sample: tensor([[1.5870, 1.7755, 1.9204, 2.1100, 2.0899, 2.1274, 2.2879, 2.5440, 3.9862,\n", + " 4.2463, 4.3239, 4.2611, 4.1700, 4.1224, 4.0502, 3.6514, 3.5646, 3.6231,\n", + " 3.6613, 3.5279, 3.6608, 3.3971, 2.9520, 2.8249, 2.8903, 3.0194, 3.1088,\n", + " 3.0467, 3.1535, 3.2588, 3.3525, 3.3961, 3.8523, 2.8721, 4.5788, 4.8328,\n", + " 5.0322, 5.3906, 5.9391, 5.9041, 1.2122, 0.7030, 0.6791, 1.2228, 1.8037,\n", + " 2.0757, 1.3054, 1.8766]], device='cuda:0')\n", + "Data Y Sample: tensor([[-4.1370e-01, -5.1709e-01, -5.7145e-01, -2.9021e-01, -6.3420e-01,\n", + " 1.6095e-01, -2.8289e-01, 3.0501e-01, 1.8057e-01, 1.0021e-02,\n", + " 5.6242e-01, -3.0900e-01, -4.3464e-01, -5.2527e-01, -1.0877e-01,\n", + " -7.5255e-01, -8.7363e-02, -1.6543e+00, -2.3155e-01, 1.0882e+00,\n", + " -1.8657e-01, 7.7280e+00, 2.6090e-01, 7.0910e-01, -1.6119e-01,\n", + " -1.8443e+00, 2.4933e-01, 6.0655e-01, 4.7021e-01, 7.5262e-03,\n", + " 5.5096e-01, 1.4966e-01, 1.5528e-02, -2.1974e-01, -1.0830e-01,\n", + " -3.4610e-01, -8.6812e-02, 4.1551e-01, -2.5051e-01, 4.5450e-01,\n", + " 7.1302e-01, 3.1837e-01, 1.8538e+00]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2898, -0.2801, -0.0884, -0.2211, -0.0694, 0.0903, 0.1958, 0.3321,\n", + " 0.1582, 0.2239, 0.2771, -0.1527, -0.1487, -0.2894, -0.1457, -0.2715,\n", + " -0.2502, -0.0920, -0.0947, -0.0944, -0.0806, -0.0684, -0.0296, -0.0210,\n", + " -0.0228, 0.0297, -0.0357, -0.1090, -0.1972, -0.1985, -0.2183, -0.1902,\n", + " -0.2372, -0.3030, -0.1589, -0.1914, -0.0130, 0.0239, 0.0259, 0.3541,\n", + " 0.3529, 0.3606, 0.2340]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00030212377896532416\n", + "Grad encoder.fc1.bias: 0.00017751673294696957\n", + "Grad encoder.encoder.0.weight: 3.842420846922323e-05\n", + "Grad encoder.encoder.0.bias: 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_memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00021235179156064987\n", + "Grad _memory_unit.bias_hh_l1: 0.00011344220547471195\n", + "Data X Sample: tensor([[1.1446, 1.3007, 1.4110, 1.5940, 1.8355, 1.8769, 2.0861, 2.0386, 3.3832,\n", + " 4.0188, 4.0193, 3.9670, 3.8837, 3.7516, 3.5861, 3.4818, 3.4671, 3.4935,\n", + " 3.4424, 3.3624, 3.3663, 3.3129, 2.9062, 2.7581, 2.7758, 2.7921, 2.7945,\n", + " 2.8836, 2.9002, 3.0295, 3.2301, 3.1115, 3.4277, 2.5334, 4.2454, 4.5337,\n", + " 4.9300, 5.2687, 5.7997, 5.6345, 0.9354, 0.5516, 0.5255, 0.9472, 1.4152,\n", + " 1.5039, 1.0946, 1.5247]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.5479, -1.0352, 0.3281, 0.5091, 0.9745, 0.5823, 0.7972, 0.7698,\n", + " 1.5493, 1.1332, 0.6336, -0.4415, -0.9805, -0.2046, 0.6243, -1.3895,\n", + " -0.9696, -0.7796, -1.3136, -0.2362, 0.0907, -0.7587, -0.4549, 0.0567,\n", + " 1.4415, -0.4072, 0.2251, 0.5551, -0.4288, -0.1482, 0.0128, -0.5914,\n", + " -0.4978, -0.7142, 0.0998, 0.4091, -0.0868, -0.0459, 1.0291, 0.6387,\n", + " 0.8115, 0.6271, 0.6587]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2828, -0.2760, -0.0846, -0.2187, -0.0686, 0.0868, 0.1902, 0.3234,\n", + " 0.1573, 0.2219, 0.2695, -0.1490, -0.1462, -0.2843, -0.1439, -0.2715,\n", + " -0.2469, -0.0895, -0.0907, -0.0918, -0.0786, -0.0675, -0.0296, -0.0249,\n", + " -0.0258, 0.0292, -0.0331, -0.1031, -0.1936, -0.1974, -0.2125, -0.1848,\n", + " -0.2335, -0.2955, -0.1566, -0.1867, -0.0126, 0.0223, 0.0239, 0.3458,\n", + " 0.3483, 0.3540, 0.2298]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0024186954833567142\n", + "Grad encoder.fc1.bias: 0.002088290173560381\n", + "Grad encoder.encoder.0.weight: 0.00037054874701425433\n", + "Grad encoder.encoder.0.bias: 0.0020328606478869915\n", + "Grad encoder.encoder.2.weight: 0.00037529479595832527\n", + "Grad encoder.encoder.2.bias: 0.003297791350632906\n", + "Grad encoder.encoder.4.weight: 0.0010995528427883983\n", + "Grad 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Sample: tensor([[-0.2846, -0.2742, -0.0746, -0.2166, -0.0525, 0.0886, 0.1936, 0.3222,\n", + " 0.1629, 0.2289, 0.2708, -0.1514, -0.1533, -0.2964, -0.1593, -0.2844,\n", + " -0.2368, -0.0812, -0.0937, -0.0892, -0.0723, -0.0667, -0.0196, -0.0355,\n", + " -0.0354, 0.0341, -0.0386, -0.0990, -0.1963, -0.1995, -0.2110, -0.1808,\n", + " -0.2454, -0.2967, -0.1521, -0.1793, -0.0109, 0.0130, 0.0271, 0.3449,\n", + " 0.3505, 0.3609, 0.2354]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 5.0008282414637506e-05\n", + "Grad encoder.fc1.bias: 0.0005734062287956476\n", + "Grad encoder.encoder.0.weight: 1.7459928130847402e-05\n", + "Grad encoder.encoder.0.bias: 0.0003486394416540861\n", + "Grad encoder.encoder.2.weight: 1.1109177648904733e-05\n", + "Grad encoder.encoder.2.bias: 0.0004924915265291929\n", + "Grad encoder.encoder.4.weight: 3.0419547329074703e-05\n", + "Grad encoder.encoder.4.bias: 0.0010666018351912498\n", + "Grad decoder.fc1.0.weight: 2.683697130123619e-05\n", + "Grad 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0.7235, 1.3893, 1.8671,\n", + " 2.2015, 1.4686, 2.1816]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.2636, 0.0378, -0.4131, 0.0790, 0.6254, 0.1558, -0.5630, -0.3223,\n", + " -0.0617, -0.5365, -0.1487, 0.3519, 1.1807, 0.2393, 0.3429, 0.6774,\n", + " 0.9687, 0.2223, 0.6077, -0.1020, -0.1991, 0.2678, -0.2294, -0.1726,\n", + " -0.3322, 0.4160, -0.5449, -0.4028, 0.0122, 0.3571, -0.5475, -0.0850,\n", + " 0.9842, 0.6557, 1.1077, 0.5514, -0.0457, 0.1511, -0.0156, -0.0125,\n", + " -0.3276, -0.3403, 0.2005]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 3.4605e-01, 2.8970e-01, 1.5424e-01, 1.8608e-01, -7.9975e-03,\n", + " -1.5947e-01, -3.5945e-01, -3.9713e-01, -2.5585e-01, -2.4368e-01,\n", + " -2.5784e-01, 1.9024e-01, 1.5767e-01, 2.7033e-01, 1.6590e-01,\n", + " 2.4929e-01, 1.8757e-01, 5.8281e-03, 1.0022e-01, 1.2583e-01,\n", + " 4.8731e-02, 6.9340e-02, 4.8082e-02, -4.5362e-02, -1.3845e-04,\n", + " -4.1509e-02, 7.6149e-03, 1.9066e-02, 1.9524e-01, 2.7833e-01,\n", + " 2.2054e-01, 2.2860e-01, 2.7609e-01, 2.4076e-01, 1.9761e-01,\n", + " 1.9941e-01, -9.5011e-02, -1.4887e-02, 7.6616e-03, -2.9434e-01,\n", + " -2.2331e-01, -2.2019e-01, -2.4537e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00010089199349749833\n", + "Grad encoder.fc1.bias: 0.00046373065561056137\n", + "Grad encoder.encoder.0.weight: 2.0671428501373157e-05\n", + "Grad encoder.encoder.0.bias: 0.0002728993131313473\n", + "Grad encoder.encoder.2.weight: 1.4044406270841137e-05\n", + "Grad encoder.encoder.2.bias: 0.00023292392143048346\n", + "Grad encoder.encoder.4.weight: 4.154044290771708e-05\n", + "Grad encoder.encoder.4.bias: 0.0006522401235997677\n", + "Grad decoder.fc1.0.weight: 3.186120738973841e-05\n", + "Grad decoder.fc1.0.bias: 0.0002700706827454269\n", + "Grad decoder.fc1.2.weight: 5.228769441600889e-05\n", + "Grad decoder.fc1.2.bias: 0.0002915970399044454\n", + "Grad decoder.fc1.4.weight: 5.4669217206537724e-05\n", + "Grad decoder.fc1.4.bias: 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+ " 3.1850, 2.2708, 3.1746]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.0422, -0.1457, 1.3328, 0.3942, 0.9791, 0.6609, 0.4345, 0.2854,\n", + " 0.8089, 0.2986, 0.2754, -0.8579, 0.2614, -0.7171, -0.9725, -0.4459,\n", + " -1.0491, -0.2548, -0.5797, 0.0633, 1.0415, -0.0507, 1.1225, -0.7498,\n", + " -0.0019, -1.1320, 1.0900, -0.5703, -0.2777, -0.2474, 0.9439, 0.9750,\n", + " 0.0610, 0.3782, 0.6913, 0.0191, 0.4717, -0.7231, -0.7546, 0.0300,\n", + " 0.5294, -0.0786, 0.0393]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2468, -0.2477, -0.0669, -0.1996, -0.0472, 0.0743, 0.1602, 0.2781,\n", + " 0.1522, 0.2135, 0.2270, -0.1311, -0.1362, -0.2629, -0.1450, -0.2701,\n", + " -0.2148, -0.0746, -0.0777, -0.0786, -0.0570, -0.0673, -0.0207, -0.0494,\n", + " -0.0424, 0.0332, -0.0271, -0.0726, -0.1680, -0.1842, -0.1792, -0.1517,\n", + " -0.2281, -0.2590, -0.1367, -0.1497, -0.0124, 0.0187, 0.0199, 0.2992,\n", + " 0.3193, 0.3163, 0.2098]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0008574227686040103\n", + "Grad encoder.fc1.bias: 0.0008425078704021871\n", + "Grad encoder.encoder.0.weight: 0.00012209579290356487\n", + "Grad encoder.encoder.0.bias: 0.0009504157933406532\n", + "Grad encoder.encoder.2.weight: 0.00012178744509583339\n", + "Grad encoder.encoder.2.bias: 0.0015043254243209958\n", + "Grad encoder.encoder.4.weight: 0.00037406489718705416\n", + "Grad encoder.encoder.4.bias: 0.0034487834200263023\n", + "Grad decoder.fc1.0.weight: 0.00014427732094191015\n", + "Grad decoder.fc1.0.bias: 0.0010047941468656063\n", + "Grad decoder.fc1.2.weight: 0.00012087014329154044\n", + "Grad decoder.fc1.2.bias: 0.0019391519017517567\n", + "Grad decoder.fc1.4.weight: 7.423922215821221e-05\n", + "Grad decoder.fc1.4.bias: 0.000777136068791151\n", + "Grad decoder.fc2.weight: 0.00014934001956135035\n", + "Grad decoder.fc2.bias: 0.0016598777147009969\n", + "Grad _memory_unit.weight_ih_l0: 5.2824954764219e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 0.00012604770017787814\n", + "Grad _memory_unit.bias_hh_l0: 8.584005990996957e-05\n", + "Grad _memory_unit.weight_ih_l1: 2.6893525500781834e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00034415925620123744\n", + "Grad _memory_unit.bias_hh_l1: 0.00017908219888340682\n", + "Data X Sample: tensor([[1.6114, 1.8483, 2.1533, 2.3134, 2.3341, 2.4471, 2.5282, 2.4588, 2.6387,\n", + " 2.6255, 2.5696, 2.6856, 2.5965, 2.4484, 2.5194, 2.5642, 2.4906, 2.5379,\n", + " 2.6081, 2.5887, 2.5763, 2.6699, 2.5544, 2.6512, 2.6820, 2.7634, 2.6746,\n", + " 2.7245, 2.5394, 2.4236, 2.4357, 2.3903, 2.1406, 1.3777, 1.8849, 1.7974,\n", + " 1.6892, 1.6140, 1.6353, 1.5264, 1.3316, 0.7966, 0.8124, 1.4065, 2.0019,\n", + " 2.2244, 1.5366, 2.1503]], device='cuda:0')\n", + "Data Y Sample: tensor([[-8.7137e-02, 5.5568e-01, 3.8859e-01, -2.8454e-01, -3.1676e-01,\n", + " -2.0553e-01, -3.6771e-01, 3.9617e-01, -2.1368e-01, 8.1407e-02,\n", + " 9.5284e-03, 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"Grad encoder.fc1.bias: 0.001187005778774619\n", + "Grad encoder.encoder.0.weight: 0.0002111559733748436\n", + "Grad encoder.encoder.0.bias: 0.0011127575999125838\n", + "Grad encoder.encoder.2.weight: 0.00019340074504725635\n", + "Grad encoder.encoder.2.bias: 0.0022771458607167006\n", + "Grad encoder.encoder.4.weight: 0.0006171857239678502\n", + "Grad encoder.encoder.4.bias: 0.005081559531390667\n", + "Grad decoder.fc1.0.weight: 0.00021738919895142317\n", + "Grad decoder.fc1.0.bias: 0.0015083479229360819\n", + "Grad decoder.fc1.2.weight: 0.00016332787345163524\n", + "Grad decoder.fc1.2.bias: 0.002300878521054983\n", + "Grad decoder.fc1.4.weight: 9.644971578381956e-05\n", + "Grad decoder.fc1.4.bias: 0.0012451432412490249\n", + "Grad decoder.fc2.weight: 0.00013524926907848567\n", + "Grad decoder.fc2.bias: 0.0025660551618784666\n", + "Grad _memory_unit.weight_ih_l0: 9.4054383225739e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 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-2.7248e-02, -1.1544e-03,\n", + " -2.2343e-01, -4.7944e-01, -1.3217e+00, -3.1408e-01, 1.1564e-02,\n", + " 3.8107e-01, 1.0114e-01, 8.7056e-01, 7.8146e-01, 1.3881e+00,\n", + " -1.6057e-01, -4.0246e-02, 4.5433e-01, 1.1186e+00, 3.1662e-01,\n", + " 6.8695e-01, -2.9976e-01, 1.2582e+00, 8.1670e-01, 4.2783e-01,\n", + " 1.1837e+00, -5.7539e-01, 9.3982e-02, 2.8698e-01, -8.4486e-01,\n", + " -4.7939e-01, -7.4733e-01, 6.3154e-02]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 3.5506e-01, 3.1130e-01, 1.7547e-01, 2.1794e-01, 3.6151e-04,\n", + " -1.5801e-01, -3.7783e-01, -4.3106e-01, -2.4547e-01, -2.3292e-01,\n", + " -2.4542e-01, 2.0402e-01, 1.7341e-01, 2.6035e-01, 1.7425e-01,\n", + " 2.6970e-01, 2.2401e-01, 1.0668e-02, 1.1507e-01, 1.3764e-01,\n", + " 4.9949e-02, 6.6029e-02, 4.3141e-02, -5.0812e-02, -4.3605e-04,\n", + " -1.8869e-02, -1.7638e-02, 2.2503e-02, 1.9747e-01, 2.7435e-01,\n", + " 2.2695e-01, 2.3368e-01, 2.9696e-01, 2.6190e-01, 2.1270e-01,\n", + " 2.0868e-01, -9.9049e-02, -2.2626e-02, -1.3682e-02, -3.0307e-01,\n", + " -2.4432e-01, -2.3572e-01, -2.5066e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 5.3975942137185484e-05\n", + "Grad encoder.fc1.bias: 8.544311276637018e-05\n", + "Grad encoder.encoder.0.weight: 1.328528560406994e-05\n", + "Grad encoder.encoder.0.bias: 3.925084456568584e-05\n", + "Grad encoder.encoder.2.weight: 1.0572881365078501e-05\n", + "Grad encoder.encoder.2.bias: 5.051283733337186e-05\n", + "Grad encoder.encoder.4.weight: 3.9861275581642985e-05\n", + "Grad encoder.encoder.4.bias: 7.96906097093597e-05\n", + "Grad decoder.fc1.0.weight: 1.6486161257489584e-05\n", + "Grad decoder.fc1.0.bias: 7.251584611367434e-05\n", + "Grad decoder.fc1.2.weight: 2.9609163902932778e-05\n", + "Grad decoder.fc1.2.bias: 0.00013395679707173258\n", + "Grad decoder.fc1.4.weight: 3.753980126930401e-05\n", + "Grad decoder.fc1.4.bias: 0.00024067613412626088\n", + "Grad decoder.fc2.weight: 9.16933422558941e-05\n", + "Grad decoder.fc2.bias: 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-2.3156e-01, -2.2563e-01, -2.3733e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0007895961171016097\n", + "Grad encoder.fc1.bias: 0.0010661701671779156\n", + "Grad encoder.encoder.0.weight: 0.00012788191088475287\n", + "Grad encoder.encoder.0.bias: 0.0007851527188904583\n", + "Grad encoder.encoder.2.weight: 9.378790855407715e-05\n", + "Grad encoder.encoder.2.bias: 0.0012334397761151195\n", + "Grad encoder.encoder.4.weight: 0.00029460963560268283\n", + "Grad encoder.encoder.4.bias: 0.003145765047520399\n", + "Grad decoder.fc1.0.weight: 0.00012915514525957406\n", + "Grad decoder.fc1.0.bias: 0.0010108929127454758\n", + "Grad decoder.fc1.2.weight: 0.0001059384667314589\n", + "Grad decoder.fc1.2.bias: 0.0017392849549651146\n", + "Grad decoder.fc1.4.weight: 7.663727592444047e-05\n", + "Grad decoder.fc1.4.bias: 0.0008098422549664974\n", + "Grad decoder.fc2.weight: 0.00014835673209745437\n", + "Grad decoder.fc2.bias: 0.001546387793496251\n", + "Grad 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-0.6359,\n", + " -0.0511, -0.9116, -0.4419, 0.3459, -1.6485, -0.4350, 1.0057, 0.6991,\n", + " -0.7412, -0.1020, 0.5224, 1.2201, 0.1475, 0.6728, -0.1046, -0.8212,\n", + " -0.1818, 0.4640, -0.4956, 0.1054, 0.3270, 0.7172, 1.2233, 0.0991,\n", + " 0.3793, 0.5829, -1.2996, 0.9986, 0.8291, -0.3710, 0.0382, -0.4174,\n", + " -0.5289, -0.4849, -0.3886]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3123, 0.2728, 0.1653, 0.1993, -0.0009, -0.1332, -0.3429, -0.3824,\n", + " -0.2011, -0.1826, -0.2066, 0.1769, 0.1606, 0.2113, 0.1444, 0.2285,\n", + " 0.1987, 0.0061, 0.1107, 0.1236, 0.0448, 0.0441, 0.0317, -0.0571,\n", + " -0.0087, -0.0021, -0.0249, 0.0242, 0.1651, 0.2205, 0.1929, 0.2078,\n", + " 0.2608, 0.2348, 0.1916, 0.1899, -0.0971, -0.0218, -0.0308, -0.2616,\n", + " -0.2036, -0.2040, -0.2136]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 2.376341581111774e-05\n", + "Grad encoder.fc1.bias: 9.308928565587848e-05\n", + "Grad encoder.encoder.0.weight: 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2.1418,\n", + " 2.1535, 1.9011, 2.0077, 1.9807, 1.9979, 2.0262, 1.4028, 2.0271, 1.9555,\n", + " 1.7816, 1.4656, 1.3564, 1.2455, 1.0834, 0.6293, 0.6750, 1.1424, 1.6134,\n", + " 1.8927, 1.1082, 1.7593]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.5141, 1.2982, -0.3004, 0.4300, -0.0261, 0.6622, 0.6997, -0.2132,\n", + " -0.9587, -0.3541, -0.2455, 0.7820, -0.2006, 0.8955, 1.6836, 0.8519,\n", + " 0.0609, -0.2445, -0.0765, 0.3488, 0.0678, -0.3800, -0.0432, -0.5704,\n", + " -0.9841, 0.2130, 0.7071, 1.0461, 0.5704, 1.4621, 0.7100, -0.2769,\n", + " 0.4432, 0.2880, 0.3209, 0.1468, -0.5085, 0.4473, 0.0040, -0.0590,\n", + " -0.2998, -0.5066, -0.7783]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3979, 0.3984, 0.2373, 0.3360, 0.0617, -0.1180, -0.4340, -0.5020,\n", + " -0.2373, -0.2431, -0.2416, 0.2390, 0.2297, 0.2434, 0.1856, 0.3370,\n", + " 0.3125, 0.0388, 0.1700, 0.1737, 0.0733, 0.0522, 0.0387, -0.0575,\n", + " 0.0077, 0.0098, -0.0893, 0.0176, 0.1828, 0.2869, 0.2562, 0.2681,\n", 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"Grad _memory_unit.bias_hh_l1: 1.8377591914031655e-05\n", + "Data X Sample: tensor([[1.6835, 1.8905, 2.0060, 2.1975, 2.2811, 2.4780, 2.5822, 2.6519, 2.5606,\n", + " 2.6571, 2.6520, 2.6272, 2.6565, 2.5547, 2.4059, 2.4725, 2.4372, 2.4044,\n", + " 2.5709, 2.4995, 2.5861, 2.6393, 2.5761, 2.5252, 2.5956, 2.5597, 2.4855,\n", + " 2.5858, 2.2966, 2.4039, 2.3727, 2.3129, 2.1824, 1.3891, 1.8751, 1.7877,\n", + " 1.6715, 1.6087, 1.5973, 1.4611, 1.3220, 0.7488, 0.7599, 1.3003, 1.7601,\n", + " 2.2930, 1.3258, 2.1034]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.7039, 0.3052, -1.0029, 0.1125, 0.1011, -0.5213, -0.8192, -0.9451,\n", + " -0.2616, -0.9404, -0.5421, -0.4100, 0.9016, -0.1766, 0.4123, 0.0781,\n", + " 0.2479, -1.1955, -0.3541, -0.4245, 0.4493, -0.4119, 0.1009, -0.8473,\n", + " -0.2585, -1.5080, -0.4897, 0.7431, 0.8179, 1.0910, 0.6646, 0.2710,\n", + " 1.1995, 0.5708, -0.4751, -0.5541, -0.5002, 0.8631, 0.2630, -0.6063,\n", + " -0.8372, -0.2570, -0.3733]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4450, 0.4903, 0.2837, 0.4074, 0.1191, -0.0939, -0.4659, -0.5205,\n", + " -0.2468, -0.2739, -0.2612, 0.2609, 0.2673, 0.2469, 0.2022, 0.3897,\n", + " 0.3841, 0.0419, 0.2021, 0.2202, 0.0956, 0.0367, 0.0345, -0.0698,\n", + " 0.0276, 0.0415, -0.1400, 0.0035, 0.1605, 0.3022, 0.2791, 0.2912,\n", + " 0.4368, 0.4012, 0.3080, 0.3160, -0.1249, -0.0506, -0.0781, -0.3810,\n", + " -0.3582, -0.3832, -0.3386]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.000610621296800673\n", + "Grad encoder.fc1.bias: 0.0004112633760087192\n", + "Grad encoder.encoder.0.weight: 8.636160055175424e-05\n", + "Grad encoder.encoder.0.bias: 0.0005248755333013833\n", + "Grad encoder.encoder.2.weight: 7.016578456386924e-05\n", + "Grad encoder.encoder.2.bias: 0.000805218587629497\n", + "Grad encoder.encoder.4.weight: 0.00020206710905767977\n", + "Grad encoder.encoder.4.bias: 0.0027856635861098766\n", + "Grad decoder.fc1.0.weight: 9.493224933976308e-05\n", + "Grad 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2.9031, 2.6763, 2.6119, 2.4988,\n", + " 2.4268, 2.4631, 2.5088, 2.5197, 2.4759, 2.4531, 1.7438, 3.0223, 3.5000,\n", + " 3.5888, 3.8561, 4.1136, 4.1621, 1.8756, 1.1172, 1.0287, 1.8944, 2.7194,\n", + " 3.3394, 1.9581, 2.9869]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1967, -0.6324, -1.1680, 0.1539, -0.6622, -1.1096, 0.4497, 0.4902,\n", + " 1.2011, 0.3056, 0.7605, -0.9260, -0.8253, 0.0859, -0.2926, -0.1924,\n", + " -0.6588, -0.3739, -0.4258, -0.5144, 0.1351, -0.1826, -0.1252, -0.1460,\n", + " 0.0178, -0.1755, 1.0456, 0.7953, -0.7680, -1.1620, -0.7797, 0.3068,\n", + " -1.2009, -0.7785, -0.6238, -0.5769, -0.5085, -0.0751, 2.2782, 0.6829,\n", + " 0.9626, 0.6368, 0.7857]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1912, -0.2080, -0.0364, -0.1610, -0.0047, 0.0977, 0.1243, 0.2296,\n", + " 0.1506, 0.1801, 0.1431, -0.1119, -0.1189, -0.2325, -0.1092, -0.2486,\n", + " -0.1776, -0.0775, -0.0628, -0.0648, -0.0469, -0.0769, -0.0113, -0.0775,\n", + " -0.0390, 0.0236, -0.0050, 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_memory_unit.bias_hh_l1: 7.476442988263443e-05\n", + "Data X Sample: tensor([[1.2338, 1.3254, 1.4560, 1.6049, 1.7109, 1.8165, 1.9489, 1.9406, 3.4101,\n", + " 3.9588, 3.8480, 3.8541, 3.6507, 3.3944, 3.4424, 3.3286, 3.2910, 3.2807,\n", + " 3.2152, 3.2136, 3.2584, 3.1782, 2.8146, 2.8058, 2.7326, 2.8339, 2.8611,\n", + " 2.9039, 2.8031, 2.9509, 3.0761, 3.0839, 3.1813, 2.3251, 3.8581, 4.3488,\n", + " 4.6094, 4.8791, 5.3243, 5.3310, 0.8782, 0.5078, 0.5052, 0.9357, 1.4707,\n", + " 1.4581, 1.0130, 1.5091]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.0039, -0.5402, 0.4849, 1.4644, 0.3968, -0.6171, 1.1698, 0.4220,\n", + " 0.9516, 0.0823, 0.0198, 0.1484, -0.2322, 0.2513, 0.0932, 0.2455,\n", + " 0.6119, 0.2598, -0.0501, 0.0117, -0.0082, -0.2493, 0.0375, -0.0100,\n", + " -0.0759, 0.1445, -0.2449, -0.9644, 0.3016, 0.2005, 1.1624, -0.0063,\n", + " -0.2618, -0.2385, 0.3707, 1.1699, 0.9799, 0.4269, -0.7513, -0.7047,\n", + " -0.1288, -0.8849, -0.3695]], device='cuda:0')\n", + "Prediction Sample: 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-0.0547, -0.1710, 0.0385, 0.1486, 0.1924, 0.3090,\n", + " 0.1891, 0.2139, 0.2150, -0.1442, -0.1611, -0.3090, -0.1343, -0.2916,\n", + " -0.1952, -0.0770, -0.0982, -0.0764, -0.0692, -0.0824, -0.0011, -0.0602,\n", + " -0.0365, 0.0385, -0.0206, -0.0828, -0.1841, -0.1855, -0.1723, -0.1602,\n", + " -0.2596, -0.3026, -0.1553, -0.1283, -0.0193, 0.0122, 0.0362, 0.2917,\n", + " 0.3237, 0.3343, 0.2422]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0004986696876585484\n", + "Grad encoder.fc1.bias: 0.0002907251473516226\n", + "Grad encoder.encoder.0.weight: 0.00011689508392009884\n", + "Grad encoder.encoder.0.bias: 0.00036719435593113303\n", + "Grad encoder.encoder.2.weight: 6.880837463540956e-05\n", + "Grad encoder.encoder.2.bias: 0.00035627090255729854\n", + "Grad encoder.encoder.4.weight: 0.0002004921407205984\n", + "Grad encoder.encoder.4.bias: 0.0009249073336832225\n", + "Grad decoder.fc1.0.weight: 5.730744305765256e-05\n", + "Grad decoder.fc1.0.bias: 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3.2461, 3.1812, 2.9399, 2.8382, 2.7439, 2.7529, 2.7785,\n", + " 2.6837, 2.7094, 2.8264, 2.8976, 2.8739, 2.9569, 2.1786, 3.5591, 3.9670,\n", + " 4.2161, 4.6088, 5.1088, 5.0075, 1.9997, 1.0893, 1.1156, 2.0064, 2.7908,\n", + " 3.3509, 2.1689, 3.1590]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 5.1285e-01, -9.3311e-01, 1.6673e+00, -2.7542e-02, 1.9830e+00,\n", + " 7.3957e-01, -5.1458e-02, -3.4580e-01, 3.2358e-01, -1.3358e-03,\n", + " 1.4074e-01, -2.8515e-02, -1.7442e+00, -5.2637e-02, 3.8752e-01,\n", + " 1.0330e+00, -7.0680e-01, 7.7021e-01, -1.1107e+00, 1.1384e+00,\n", + " -3.4164e-02, 2.0955e-01, -9.0180e-01, -5.1049e-01, 8.9278e-01,\n", + " 5.2610e-01, 6.0141e-01, -2.9673e-01, -1.2601e-03, -7.7300e-01,\n", + " -3.7012e-01, -3.2242e-01, -1.4588e-01, -7.5951e-01, 2.3437e-01,\n", + " 4.6134e-01, 0.0000e+00, -6.7160e-01, 1.3913e-01, -1.0716e-01,\n", + " -3.5338e-01, -6.7685e-01, 1.1764e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1853, -0.2041, -0.0406, -0.1569, 0.0030, 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decoder.fc1.2.weight: 3.0487226467812434e-05\n", + "Grad decoder.fc1.2.bias: 0.0002012473705690354\n", + "Grad decoder.fc1.4.weight: 3.8004665839252993e-05\n", + "Grad decoder.fc1.4.bias: 0.0003037331625819206\n", + "Grad decoder.fc2.weight: 9.911628876579925e-05\n", + "Grad decoder.fc2.bias: 0.0019990308210253716\n", + "Grad _memory_unit.weight_ih_l0: 6.089178896218073e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 1.6468966350657865e-05\n", + "Grad _memory_unit.bias_hh_l0: 9.119816240854561e-06\n", + "Grad _memory_unit.weight_ih_l1: 1.6161683333848487e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 3.605867823353037e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.854501897469163e-05\n", + "Data X Sample: tensor([[1.3950, 1.6124, 1.9429, 1.8914, 2.0080, 2.1775, 2.2740, 1.9193, 3.1220,\n", + " 3.9635, 4.3492, 4.3118, 4.1966, 3.8389, 3.7047, 3.7252, 3.6636, 3.5979,\n", + " 3.5436, 3.6116, 3.3933, 3.3359, 2.9930, 2.8592, 2.7890, 2.9671, 2.9250,\n", + " 3.1160, 2.9731, 3.1671, 3.3140, 3.1861, 3.8391, 2.7897, 4.6596, 4.9374,\n", + " 5.1148, 5.5602, 6.2433, 5.9381, 1.1693, 0.6771, 0.7296, 1.2113, 1.6967,\n", + " 1.8527, 1.2102, 1.6811]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.3070, -1.0147, 0.1277, -0.2096, -0.7212, -0.5519, -0.0601, 0.5510,\n", + " 0.7739, 0.4648, 0.6296, 0.5767, 0.2268, -0.1437, -0.0052, -0.0932,\n", + " -0.2748, 0.4068, 0.8462, 0.8663, -0.4651, 0.1202, -1.0579, -0.3321,\n", + " 0.9050, 0.5996, 0.9139, -0.0409, 0.7258, -0.1222, -0.3894, -1.1690,\n", + " -0.1266, -0.3027, -0.4817, -1.7743, -0.0107, 0.7579, -0.1041, 0.5224,\n", + " 0.5788, 0.6123, 0.8418]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2298, -0.2354, -0.0512, -0.1680, 0.0215, 0.1234, 0.1557, 0.2726,\n", + " 0.1629, 0.1891, 0.1793, -0.1257, -0.1404, -0.2660, -0.1164, -0.2626,\n", + " -0.1845, -0.0662, -0.0830, -0.0739, -0.0607, -0.0767, -0.0055, -0.0597,\n", + " -0.0385, 0.0342, -0.0051, -0.0548, 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decoder.fc2.weight: 8.404254913330078e-05\n", + "Grad decoder.fc2.bias: 0.001972515368834138\n", + "Grad _memory_unit.weight_ih_l0: 3.074916321565979e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 8.372157026315108e-06\n", + "Grad _memory_unit.bias_hh_l0: 4.541129783319775e-06\n", + "Grad _memory_unit.weight_ih_l1: 8.671785280967015e-07\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 1.830163091653958e-05\n", + "Grad _memory_unit.bias_hh_l1: 9.18088699108921e-06\n", + "Data X Sample: tensor([[2.5725, 2.9858, 3.2111, 3.3170, 3.6027, 3.6080, 3.7284, 3.7790, 3.7811,\n", + " 3.8972, 3.9812, 3.9963, 3.7195, 3.7134, 3.5307, 3.4312, 3.4639, 3.5612,\n", + " 3.4878, 3.3010, 3.2829, 3.2976, 2.9351, 2.8764, 2.7721, 2.8208, 2.7092,\n", + " 2.7326, 2.7406, 2.7740, 2.9151, 2.8739, 3.0449, 2.1443, 3.5640, 4.0618,\n", + " 4.1670, 4.6644, 5.2165, 5.0699, 1.9663, 1.1371, 1.1277, 2.0121, 2.8701,\n", + " 3.2937, 2.0873, 2.9400]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.4286, -0.7870, -0.1667, -0.8360, -1.1230, 0.7870, 0.5872, 0.0708,\n", + " -0.3360, 0.1374, 0.2308, -0.2318, -0.1824, -0.4579, -1.5157, -0.3726,\n", + " -1.1029, -0.9321, 0.1632, -1.5270, -0.7351, -0.9303, -0.6807, -1.1945,\n", + " 0.1527, -0.8956, -0.3063, 0.7756, 0.1124, 0.5399, 0.7194, 0.1923,\n", + " -0.4221, -0.0276, 0.0377, -0.8251, 0.0213, -0.0869, 0.6035, 0.2400,\n", + " 0.1762, 0.1444, 0.0918]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1671, -0.1925, -0.0380, -0.1530, -0.0008, 0.0852, 0.0962, 0.2029,\n", + " 0.1305, 0.1557, 0.1146, -0.0938, -0.1039, -0.1955, -0.0872, -0.2213,\n", + " -0.1696, -0.0562, -0.0511, -0.0662, -0.0417, -0.0704, -0.0148, -0.0685,\n", + " -0.0436, 0.0246, 0.0116, -0.0135, -0.1152, -0.1429, -0.1032, -0.0933,\n", + " -0.1887, -0.1998, -0.1135, -0.0771, -0.0177, 0.0257, 0.0062, 0.1887,\n", + " 0.2492, 0.2185, 0.1643]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 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-1.7999, -1.4050,\n", + " -0.4161, -0.9279, -1.1147, 0.6393, 0.4981, 0.3573, -0.8834, 0.2785,\n", + " 0.2961, -1.5495, -1.2339, 0.7606, -0.2548, 2.0216, -0.0275, 0.8604,\n", + " 0.0944, 0.8363, 0.4078, -0.5669, 0.6114, 0.3241, 1.0339, 0.3166,\n", + " 0.6463, 1.2889, 1.0155, 0.8955, 0.7316, 0.7450, -0.0231, -1.1705,\n", + " -0.8531, -0.6611, -1.2262]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.5604, 0.5042, 0.2312, 0.3739, 0.1705, -0.2023, -0.5650, -0.6817,\n", + " -0.3540, -0.4350, -0.4031, 0.3192, 0.3200, 0.4092, 0.2881, 0.4564,\n", + " 0.3901, 0.2179, 0.1380, 0.1202, 0.1032, 0.0454, 0.0861, 0.0370,\n", + " 0.0866, -0.0377, -0.0471, 0.1083, 0.3454, 0.4896, 0.4006, 0.3538,\n", + " 0.4718, 0.4802, 0.2817, 0.4117, 0.0147, 0.0567, -0.0259, -0.4541,\n", + " -0.4980, -0.4832, -0.4844]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00047499273205175996\n", + "Grad encoder.fc1.bias: 0.0003880714066326618\n", + "Grad encoder.encoder.0.weight: 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-0.1197, -0.2882, -0.2809,\n", + " -0.1631, -0.2031, -0.2284, 0.1320, 0.1298, 0.2414, 0.1461, 0.1911,\n", + " 0.1218, 0.0954, 0.0392, 0.0663, 0.0206, 0.0627, 0.0735, 0.0407,\n", + " 0.0305, -0.0033, 0.0344, 0.0671, 0.1712, 0.2078, 0.1905, 0.1688,\n", + " 0.1771, 0.1898, 0.1020, 0.2047, -0.0125, 0.0157, 0.0543, -0.2222,\n", + " -0.1547, -0.1773, -0.1989]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 9.239038627129048e-05\n", + "Grad encoder.fc1.bias: 0.00045777004561387\n", + "Grad encoder.encoder.0.weight: 2.188203507103026e-05\n", + "Grad encoder.encoder.0.bias: 0.0005603526951745152\n", + "Grad encoder.encoder.2.weight: 2.4818418751237914e-05\n", + "Grad encoder.encoder.2.bias: 0.00047475710744038224\n", + "Grad encoder.encoder.4.weight: 0.00011105075100203976\n", + "Grad encoder.encoder.4.bias: 0.001414584694430232\n", + "Grad decoder.fc1.0.weight: 4.235510277794674e-05\n", + "Grad decoder.fc1.0.bias: 0.00035417996696196496\n", + "Grad decoder.fc1.2.weight: 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0.0150, 0.0261, 0.0773, 0.0612, -0.0069, 0.0066, -0.0175, 0.0193,\n", + " -0.0088, 0.0046, 0.0098, 0.0462, 0.0079, 0.0186, 0.0063, -0.0255,\n", + " 0.0191, 0.0100, 0.0202, 0.0057, 0.0159, 0.0172, 0.0408, -0.0235]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.1003e-01, 1.6922e-02, -1.4864e-03, -1.4421e+00, -2.9638e+00,\n", + " -2.1367e+00, -3.0840e+00, -2.7229e+00, -1.7699e+00, -1.7456e+00,\n", + " -1.5235e+00, 3.4040e-01, 1.5185e+00, 8.3073e-01, 1.0414e+00,\n", + " 1.1954e+00, 1.6853e-01, -7.6667e-01, -4.6717e-01, 4.6098e-01,\n", + " -1.7474e-01, 1.4275e-01, 3.4252e-01, 3.6173e-03, 2.9673e-01,\n", + " 5.8774e-02, 3.6965e-02, 6.2265e-01, 1.6565e+00, 1.8916e+00,\n", + " 1.2692e+00, 5.9963e-01, 1.0301e+00, 1.5136e+00, 1.3665e-01,\n", + " -1.4756e-01, 4.8520e-01, 9.3114e-01, -8.4372e-01, -1.4565e+00,\n", + " -1.1776e+00, -6.5248e-01, 2.2275e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.9836, 0.6937, 0.1187, 0.3138, -0.7425, -0.8667, -1.5120, -1.2674,\n", + " -1.0053, -1.0499, -1.0564, 0.3861, 0.5097, 0.8577, 0.4364, 0.5960,\n", + " 0.4715, 0.1720, -0.0269, 0.3174, 0.0824, 0.1407, -0.0065, 0.1847,\n", + " 0.2199, 0.0740, 0.5479, 0.7406, 1.2923, 1.1038, 1.0124, 0.7598,\n", + " 0.8507, 0.7067, 0.4637, 0.4977, 0.0323, -0.0439, 0.0902, -0.9878,\n", + " -0.7321, -0.6081, -0.5556]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00026727814110927284\n", + "Grad encoder.fc1.bias: 0.0008832664461806417\n", + "Grad encoder.encoder.0.weight: 4.854859798797406e-05\n", + "Grad encoder.encoder.0.bias: 0.0006152503774501383\n", + "Grad encoder.encoder.2.weight: 3.891807500622235e-05\n", + "Grad encoder.encoder.2.bias: 0.0005954147782176733\n", + "Grad encoder.encoder.4.weight: 0.00011725326476152986\n", + "Grad encoder.encoder.4.bias: 0.0012255553156137466\n", + "Grad decoder.fc1.0.weight: 5.719056207453832e-05\n", + "Grad decoder.fc1.0.bias: 0.0005133751546964049\n", + "Grad decoder.fc1.2.weight: 5.206893911235966e-05\n", + "Grad 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2.4252, 2.4511, 2.1054, 1.3891, 1.8751, 1.7731,\n", + " 1.6263, 1.4762, 1.5846, 1.5718, 1.3268, 0.7209, 0.7215, 1.2716, 1.8473,\n", + " 1.9613, 1.3190, 1.9079]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.4021, 0.6851, 1.0738, -0.4961, 1.4314, 0.7263, -0.1180, -0.2666,\n", + " -0.7558, -0.5176, -0.6356, -0.7489, 0.0369, -1.2767, -1.7985, 1.2490,\n", + " -0.5688, 0.5126, -0.7181, -1.5253, -0.5340, 0.5348, -0.6347, -0.4536,\n", + " 0.1291, -0.7723, -0.2855, 0.4397, 0.3710, 0.9326, -0.2517, 1.0304,\n", + " -0.0122, 0.6109, -0.6256, 1.3940, 0.0000, -0.7725, 1.1604, -0.4129,\n", + " -0.4905, -0.3745, -0.8282]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2338, 0.1435, 0.0278, 0.1062, 0.0548, -0.1055, -0.2711, -0.2623,\n", + " -0.1398, -0.1708, -0.2012, 0.1211, 0.1161, 0.2160, 0.1238, 0.1556,\n", + " 0.0904, 0.0905, 0.0334, 0.0598, 0.0159, 0.0567, 0.0697, 0.0451,\n", + " 0.0228, 0.0023, 0.0361, 0.0756, 0.1595, 0.1881, 0.1780, 0.1609,\n", + " 0.1572, 0.1795, 0.0951, 0.1811, -0.0227, 0.0125, 0.0466, -0.1997,\n", + " -0.1264, -0.1456, -0.1632]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0006262038368731737\n", + "Grad encoder.fc1.bias: 0.0004421166086103767\n", + "Grad encoder.encoder.0.weight: 8.658748993184417e-05\n", + "Grad encoder.encoder.0.bias: 0.0005587100167758763\n", + "Grad encoder.encoder.2.weight: 8.018377411644906e-05\n", + "Grad encoder.encoder.2.bias: 0.000993414781987667\n", + "Grad encoder.encoder.4.weight: 0.00028757183463312685\n", + "Grad encoder.encoder.4.bias: 0.0031106597743928432\n", + "Grad decoder.fc1.0.weight: 0.00010801830649143085\n", + "Grad decoder.fc1.0.bias: 0.0007631219923496246\n", + "Grad decoder.fc1.2.weight: 8.64953181007877e-05\n", + "Grad decoder.fc1.2.bias: 0.001822298625484109\n", + "Grad decoder.fc1.4.weight: 8.621523011242971e-05\n", + "Grad decoder.fc1.4.bias: 0.0009229435818269849\n", + "Grad decoder.fc2.weight: 0.00016540767683181912\n", + "Grad decoder.fc2.bias: 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_memory_unit.bias_hh_l0: 0.00016417964070569724\n", + "Grad _memory_unit.weight_ih_l1: 4.305399488657713e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.000593008182477206\n", + "Grad _memory_unit.bias_hh_l1: 0.0003180456697009504\n", + "Data X Sample: tensor([[1.6178, 1.8265, 2.0361, 2.2368, 2.3136, 2.5664, 2.6838, 2.6178, 2.7388,\n", + " 2.7487, 2.7663, 2.7479, 2.6742, 2.6092, 2.5799, 2.6503, 2.5834, 2.6675,\n", + " 2.6907, 2.5962, 2.5468, 2.6577, 2.5375, 2.5786, 2.5074, 2.5936, 2.6933,\n", + " 2.5246, 2.3243, 2.3908, 2.3342, 2.3626, 2.3430, 1.5013, 1.9536, 1.8534,\n", + " 1.7816, 1.6803, 1.6607, 1.6143, 1.2552, 0.7886, 0.7377, 1.3778, 1.9900,\n", + " 2.3044, 1.5026, 2.0095]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.3347, 0.2708, -0.8270, 0.4695, 0.4008, -0.9323, -0.5043, -0.3544,\n", + " 0.1966, -0.3556, -0.3868, -0.5370, 0.1273, 0.5115, 0.5363, 0.6041,\n", + " 0.1413, -0.2423, 0.5688, 1.3381, 0.0835, 2.4540, -0.1239, -0.9415,\n", + " 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" -0.0265, 0.0444, 0.0214, 0.0079, -0.0842, -0.1317, -0.0931, -0.0776,\n", + " -0.1620, -0.1484, -0.0958, -0.0782, -0.0280, 0.0224, 0.0248, 0.1619,\n", + " 0.2287, 0.1928, 0.1536]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00015749444719403982\n", + "Grad encoder.fc1.bias: 0.003011858556419611\n", + "Grad encoder.encoder.0.weight: 4.9106321966974065e-05\n", + "Grad encoder.encoder.0.bias: 0.0022547896951436996\n", + "Grad encoder.encoder.2.weight: 4.5902903366368264e-05\n", + "Grad encoder.encoder.2.bias: 0.0014815698377788067\n", + "Grad encoder.encoder.4.weight: 0.0001818643359001726\n", + "Grad encoder.encoder.4.bias: 0.002491678111255169\n", + "Grad decoder.fc1.0.weight: 8.639368752483279e-05\n", + "Grad decoder.fc1.0.bias: 0.0007640690309926867\n", + "Grad decoder.fc1.2.weight: 0.00010892387945204973\n", + "Grad decoder.fc1.2.bias: 0.000642009952571243\n", + "Grad decoder.fc1.4.weight: 0.0001086265838239342\n", + "Grad decoder.fc1.4.bias: 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" 3.1507, 2.2029, 3.2450]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.8526e-01, -3.9220e-01, -9.6428e-02, -2.7245e-01, -5.7511e-02,\n", + " -4.5793e-01, 4.8686e-01, 3.8740e-02, -1.1093e-01, 3.9928e-01,\n", + " 2.2120e-01, -3.6204e-02, 4.9070e-01, -1.9815e-01, -2.0498e-01,\n", + " 4.2723e-01, 7.1668e-01, 1.2357e-02, -9.0271e-01, 3.2797e-01,\n", + " -2.9795e-01, 5.9134e-01, -2.5738e-02, -7.6712e-01, 2.1425e-01,\n", + " -9.7648e-01, -7.9911e-01, 2.6275e-01, -3.8890e-01, -8.9876e-01,\n", + " 3.5650e-04, 1.8780e-01, 1.4364e-01, -7.0500e-01, -9.8818e-01,\n", + " -1.2723e-01, -7.7889e-01, 0.0000e+00, 4.4225e-01, 2.2467e-01,\n", + " 1.2155e-01, -7.0706e-02, 1.6651e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1596, -0.2031, -0.0624, -0.1652, -0.0036, 0.0652, 0.0672, 0.1862,\n", + " 0.1287, 0.1617, 0.1243, -0.1041, -0.0989, -0.1623, -0.1030, -0.1825,\n", + " -0.1751, -0.0475, -0.0502, -0.0690, -0.0392, -0.0369, -0.0249, -0.0485,\n", + " -0.0239, 0.0507, 0.0149, -0.0045, 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-2.2573,\n", + " -1.8720, -1.7773, -1.0483, -0.5478, 0.1466, -1.8682, 0.4596, 0.0844,\n", + " -0.4552, 0.2660, -1.3526, 0.5069, 1.5845, 0.5895, 0.9661, -0.0105,\n", + " -0.1074, -0.4572, 0.0207, 0.4367, 0.6970, 1.4742, 0.6168, 0.5242,\n", + " 0.3581, 0.7328, 0.2508, -0.4494, -0.7353, 0.1432, 0.6318, -0.4342,\n", + " -0.5781, 0.1374, 0.8332]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3349, 0.3050, 0.0860, 0.2443, 0.1356, -0.1280, -0.3554, -0.3842,\n", + " -0.1651, -0.2307, -0.2167, 0.1556, 0.1429, 0.2684, 0.1652, 0.2517,\n", + " 0.2103, 0.0835, 0.0478, 0.1183, 0.0379, 0.0647, 0.0632, 0.0677,\n", + " 0.0396, 0.0971, -0.0510, 0.0398, 0.1837, 0.2434, 0.2385, 0.2028,\n", + " 0.2870, 0.2914, 0.1514, 0.2133, -0.0172, -0.0130, 0.0465, -0.2829,\n", + " -0.2430, -0.2555, -0.2261]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00011755799641832709\n", + "Grad encoder.fc1.bias: 0.00019758706912398338\n", + "Grad encoder.encoder.0.weight: 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_memory_unit.weight_ih_l1: 5.055665042164037e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 7.787905633449554e-05\n", + "Grad _memory_unit.bias_hh_l1: 4.287582851247862e-05\n", + "Data X Sample: tensor([[1.4756, 1.8367, 2.0436, 2.2500, 2.2846, 2.3705, 2.3988, 2.4545, 2.4727,\n", + " 2.3822, 2.3475, 2.3350, 2.1460, 2.0967, 1.8763, 1.8777, 1.8806, 1.8396,\n", + " 1.8854, 1.8448, 1.7592, 1.7468, 1.6001, 1.6205, 1.5108, 1.4653, 1.4092,\n", + " 1.4561, 1.0859, 1.1692, 1.0918, 1.0058, 0.9482, 0.6980, 1.0344, 1.1650,\n", + " 1.3962, 1.5292, 1.7494, 1.6626, 1.2600, 0.7548, 0.7559, 1.3232, 2.0019,\n", + " 2.3330, 1.4686, 1.9470]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.6597, -1.4131, -0.3663, -1.1426, -1.0169, 0.1802, 1.7652, 1.9722,\n", + " 0.9623, 2.3675, 3.8781, -1.3628, 0.1311, -1.4653, -0.7565, -1.6478,\n", + " -1.4536, -0.7895, -0.5489, -0.9041, 0.2388, -0.9278, -0.3970, -0.6827,\n", + " -0.2316, 0.0160, 0.6093, 0.7226, -0.6883, -0.8094, -1.1459, -0.8226,\n", + " -1.9017, -2.6351, -0.4696, -1.0723, 0.4513, -0.5083, -0.8612, 5.7908,\n", + " 5.7900, 6.1455, 3.0917]], device='cuda:0')\n", + "Prediction Sample: tensor([[-1.6506, -1.4109, -0.6763, -0.9774, -0.4969, 0.0790, 1.2971, 1.9876,\n", + " 1.5221, 2.5996, 3.5600, -0.6193, -0.6511, -1.4458, -1.1214, -2.0347,\n", + " -1.8419, -0.8293, -0.3488, -0.6428, -0.3637, -0.1382, -0.1480, -0.0880,\n", + " -0.1685, -0.0920, 0.5442, 0.0975, -0.5204, -0.9975, -1.3085, -1.3524,\n", + " -1.1097, -2.2919, -0.8177, -0.7431, 0.1375, -0.0331, -0.0730, 5.4994,\n", + " 5.2312, 3.8523, 2.6246]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003269864246249199\n", + "Grad encoder.fc1.bias: 0.0009294047486037016\n", + "Grad encoder.encoder.0.weight: 5.4453546908916906e-05\n", + "Grad encoder.encoder.0.bias: 0.00060326571110636\n", + "Grad encoder.encoder.2.weight: 4.0555612940806895e-05\n", + "Grad encoder.encoder.2.bias: 0.0006616865284740925\n", + "Grad encoder.encoder.4.weight: 0.00013754672545474023\n", + "Grad encoder.encoder.4.bias: 0.0014512891648337245\n", + "Grad decoder.fc1.0.weight: 6.462402961915359e-05\n", + "Grad decoder.fc1.0.bias: 0.000607526395469904\n", + "Grad decoder.fc1.2.weight: 6.147953536128625e-05\n", + "Grad decoder.fc1.2.bias: 0.0008502710843458772\n", + "Grad decoder.fc1.4.weight: 5.4640921007376164e-05\n", + "Grad decoder.fc1.4.bias: 0.0006097350269556046\n", + "Grad decoder.fc2.weight: 0.00014673039549961686\n", + "Grad decoder.fc2.bias: 0.0018795182695612311\n", + "Grad _memory_unit.weight_ih_l0: 1.3787515854346566e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 6.203496013768017e-05\n", + "Grad _memory_unit.bias_hh_l0: 3.7557052564807236e-05\n", + "Grad _memory_unit.weight_ih_l1: 7.5555267358140554e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00014970624761190265\n", + "Grad _memory_unit.bias_hh_l1: 7.77076929807663e-05\n", + "Data X Sample: tensor([[2.6733, 2.9451, 3.0759, 3.4329, 3.4285, 3.5815, 3.7361, 3.9252, 3.8470,\n", + " 3.8987, 4.0193, 3.8502, 3.7439, 3.6671, 3.6189, 3.5515, 3.4734, 3.5051,\n", + " 3.4052, 3.3456, 3.2216, 3.1506, 2.9086, 2.8840, 2.8265, 2.7555, 2.8105,\n", + " 2.7449, 2.7025, 2.8068, 2.8136, 2.9098, 2.9041, 2.1191, 3.6007, 3.9451,\n", + " 4.1945, 4.5478, 4.9883, 5.0643, 1.9854, 1.1570, 1.1196, 1.8801, 2.7828,\n", + " 3.3852, 2.0873, 2.9635]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.4169, -0.5768, 1.1548, 0.4477, 2.5533, 0.4876, 0.0128, -0.3418,\n", + " 0.2339, -0.2225, -0.0567, 0.7574, -4.7268, -0.1358, 0.3804, 0.7070,\n", + " -0.9661, -0.1892, 0.4952, 0.7827, -0.1596, -0.1147, -0.5687, -0.2857,\n", + " 0.8979, 0.3228, 0.1516, -0.8897, -0.8547, -0.4023, -0.7254, -1.0501,\n", + " -0.8639, -0.8061, -0.3814, 0.0339, -0.3398, 0.6895, -0.2811, 0.1068,\n", + " -0.3830, -0.6167, 0.1636]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1902, -0.2186, -0.0633, -0.1567, 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decoder.fc1.2.weight: 7.815775461494923e-05\n", + "Grad decoder.fc1.2.bias: 0.0005387614946812391\n", + "Grad decoder.fc1.4.weight: 7.075761095620692e-05\n", + "Grad decoder.fc1.4.bias: 0.0006948356167413294\n", + "Grad decoder.fc2.weight: 0.00012855698878411204\n", + "Grad decoder.fc2.bias: 0.0019505815580487251\n", + "Grad _memory_unit.weight_ih_l0: 1.2905704352306202e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 8.010086457943544e-05\n", + "Grad _memory_unit.bias_hh_l0: 4.0245417039841413e-05\n", + "Grad _memory_unit.weight_ih_l1: 5.19943205290474e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00015513777907472104\n", + "Grad _memory_unit.bias_hh_l1: 7.747911149635911e-05\n", + "Data X Sample: tensor([[1.7111, 2.0959, 2.1683, 2.2237, 2.3682, 2.4471, 2.4774, 2.5525, 2.5997,\n", + " 2.5955, 2.5981, 2.5629, 2.5211, 2.4456, 2.4614, 2.5026, 2.4749, 2.4489,\n", + " 2.5792, 2.6501, 2.6401, 2.7158, 2.7158, 2.7237, 2.7158, 2.7738, 2.7066,\n", + " 2.6388, 2.4215, 2.5710, 2.3657, 2.2770, 1.9822, 1.2427, 1.7697, 1.7123,\n", + " 1.6695, 1.6193, 1.5656, 1.4469, 1.4222, 0.8324, 0.7943, 1.4294, 2.0772,\n", + " 2.2415, 1.5434, 2.3770]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.0525, 0.9075, 0.0509, 0.2553, 0.5519, 0.2300, -0.2964, -0.2442,\n", + " 0.2176, -0.6756, -0.3212, 0.6147, 1.3535, -0.8435, 0.6225, 0.3199,\n", + " 1.3201, -0.3029, 0.2319, -0.1563, -0.8552, 0.7320, 0.4851, 0.4043,\n", + " 1.4587, 0.5290, -0.2222, 0.1391, 0.1194, 0.7865, 0.8079, -0.2709,\n", + " 0.1716, 0.6286, 0.5970, -0.2377, -0.5204, 0.4911, 0.9175, -0.3636,\n", + " -0.6476, -0.2872, -1.1575]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2868, 0.2610, 0.0885, 0.1896, 0.1105, -0.1066, -0.3003, -0.3095,\n", + " -0.1239, -0.1809, -0.1806, 0.1112, 0.1112, 0.2070, 0.1284, 0.1908,\n", + " 0.1690, 0.0524, 0.0363, 0.0977, 0.0344, 0.0385, 0.0392, 0.0442,\n", + " 0.0140, 0.1075, -0.0562, 0.0259, 0.1465, 0.1789, 0.1882, 0.1545,\n", + " 0.2345, 0.2332, 0.1053, 0.1611, -0.0292, -0.0119, 0.0328, -0.2265,\n", + " -0.1879, -0.2070, -0.1683]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 6.727861909894273e-05\n", + "Grad encoder.fc1.bias: 6.918722647242248e-05\n", + "Grad encoder.encoder.0.weight: 1.1124726370326243e-05\n", + "Grad encoder.encoder.0.bias: 8.305565279442817e-05\n", + "Grad encoder.encoder.2.weight: 1.2322087059146725e-05\n", + "Grad encoder.encoder.2.bias: 0.00015055746189318597\n", + "Grad encoder.encoder.4.weight: 4.0488128433935344e-05\n", + "Grad encoder.encoder.4.bias: 0.0004542265087366104\n", + "Grad decoder.fc1.0.weight: 1.7793761799111962e-05\n", + "Grad decoder.fc1.0.bias: 0.00013389342348091304\n", + "Grad decoder.fc1.2.weight: 2.9595161322504282e-05\n", + "Grad decoder.fc1.2.bias: 0.00019922660430893302\n", + "Grad decoder.fc1.4.weight: 2.726673483266495e-05\n", + "Grad decoder.fc1.4.bias: 0.00020951945043634623\n", + "Grad decoder.fc2.weight: 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"Data Y Sample: tensor([[ 0.3804, 0.8106, 0.2408, 0.2564, -0.1509, -0.7463, -0.0678, -0.3773,\n", + " -0.6727, -0.1133, -0.4265, 1.2840, 0.0734, 0.8929, -0.3113, 0.4458,\n", + " 0.6959, 0.8286, 0.4398, 0.8672, 1.4939, 1.4255, -0.9417, 0.4384,\n", + " 0.1284, 1.8902, -2.0123, 0.1828, 0.7810, 1.2234, -0.2469, -0.2178,\n", + " 0.6332, 0.4880, -0.4092, 0.2280, -1.0105, 0.1511, 0.0040, -0.3077,\n", + " -0.1372, -0.4098, -0.6131]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 2.5934e-01, 2.2443e-01, 7.7855e-02, 1.6347e-01, 9.3164e-02,\n", + " -9.5194e-02, -2.7453e-01, -2.7848e-01, -1.0563e-01, -1.5466e-01,\n", + " -1.6278e-01, 9.4901e-02, 9.6725e-02, 1.7916e-01, 1.1143e-01,\n", + " 1.5791e-01, 1.4031e-01, 4.5314e-02, 3.1722e-02, 8.2144e-02,\n", + " 2.9585e-02, 2.7495e-02, 3.4807e-02, 3.6133e-02, -2.4487e-04,\n", + " 1.0217e-01, -5.0398e-02, 2.8943e-02, 1.3218e-01, 1.5632e-01,\n", + " 1.6592e-01, 1.3626e-01, 2.0634e-01, 2.0806e-01, 8.7612e-02,\n", + " 1.4045e-01, -3.4522e-02, 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-0.0304, 0.3124, 0.7158, 0.3584, 0.2251, -0.1841, 0.0923,\n", + " -0.0226, 0.0215, 0.0399, 0.2248, 0.1247, 0.1187, 0.2604, 0.4390,\n", + " 0.2651, 0.6491, 1.0106, 0.3804, 0.4584, 3.7580, 1.0622, -0.3161,\n", + " 0.7071, 0.9737, 0.1463, 1.1506, 0.9899, 0.9474, 1.5963, 1.2838,\n", + " 0.4396, 0.4429, 0.4328, 0.9896, 0.4497, 0.5779, 0.9794, -0.0321,\n", + " -0.1750, -0.1484, -0.1604]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.1866, 0.1519, 0.0570, 0.0908, 0.0571, -0.0734, -0.2116, -0.1900,\n", + " -0.0600, -0.0913, -0.1137, 0.0513, 0.0527, 0.1158, 0.0707, 0.0846,\n", + " 0.0868, 0.0201, 0.0141, 0.0543, 0.0166, 0.0049, 0.0121, 0.0109,\n", + " -0.0125, 0.1069, -0.0407, 0.0204, 0.0911, 0.0878, 0.1103, 0.0902,\n", + " 0.1358, 0.1361, 0.0495, 0.0891, -0.0393, -0.0048, 0.0161, -0.1318,\n", + " -0.0850, -0.1041, -0.0762]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0030202760826796293\n", + "Grad encoder.fc1.bias: 0.0017595465760678053\n", + "Grad 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-1.7392e-01, 3.3649e-04, 5.7593e-01,\n", + " -1.2429e-01, 6.7410e-01, 7.7601e-01, 1.0437e+00, 9.7780e-01,\n", + " 6.0195e-02, -1.1187e+00, -5.1472e-01, 4.9012e-01, 4.3499e-02,\n", + " 4.8224e-01, -4.4310e-03, 3.4998e-01, 8.0129e-01, 5.1093e-01,\n", + " 2.3401e-01, -4.5720e-02, 1.5113e-01, -1.5613e-02, -3.5564e-01,\n", + " -2.9588e-01, -1.7546e-01, 1.7138e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3118, 0.3016, 0.1112, 0.2091, 0.1233, -0.1101, -0.3133, -0.3335,\n", + " -0.1316, -0.1976, -0.1893, 0.1220, 0.1170, 0.2112, 0.1435, 0.2096,\n", + " 0.2031, 0.0570, 0.0365, 0.1079, 0.0429, 0.0288, 0.0343, 0.0394,\n", + " 0.0080, 0.1285, -0.0799, 0.0172, 0.1506, 0.1894, 0.2002, 0.1484,\n", + " 0.2610, 0.2508, 0.1117, 0.1587, -0.0293, -0.0098, 0.0328, -0.2409,\n", + " -0.2177, -0.2331, -0.1800]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00018979130254592746\n", + "Grad encoder.fc1.bias: 0.000551072706002742\n", + "Grad encoder.encoder.0.weight: 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0.3084,\n", + " 0.2081, 0.2494, 0.2481, -0.1997, -0.1867, -0.2942, -0.1738, -0.2608,\n", + " -0.2169, -0.0597, -0.1009, -0.1018, -0.0742, -0.0090, -0.0188, -0.0574,\n", + " -0.0198, 0.1024, -0.0715, -0.1392, -0.2095, -0.2263, -0.2484, -0.2077,\n", + " -0.2785, -0.2757, -0.1687, -0.1547, -0.0504, 0.0230, 0.0884, 0.3104,\n", + " 0.3060, 0.3409, 0.2647]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00015214239829219878\n", + "Grad encoder.fc1.bias: 0.0008131069480441511\n", + "Grad encoder.encoder.0.weight: 3.993306017946452e-05\n", + "Grad encoder.encoder.0.bias: 0.0007197277154773474\n", + "Grad encoder.encoder.2.weight: 3.070786260650493e-05\n", + "Grad encoder.encoder.2.bias: 0.0006971168331801891\n", + "Grad encoder.encoder.4.weight: 0.00012397067621350288\n", + "Grad encoder.encoder.4.bias: 0.001606726087629795\n", + "Grad decoder.fc1.0.weight: 5.963912553852424e-05\n", + "Grad decoder.fc1.0.bias: 0.0006312718614935875\n", + "Grad decoder.fc1.2.weight: 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"Prediction Sample: tensor([[-0.2433, -0.2500, -0.0638, -0.1775, 0.0059, 0.1084, 0.1474, 0.2694,\n", + " 0.1437, 0.1965, 0.2069, -0.1712, -0.1618, -0.2489, -0.1499, -0.2180,\n", + " -0.1900, -0.0529, -0.1025, -0.1041, -0.0587, -0.0119, -0.0171, -0.0492,\n", + " -0.0209, 0.0879, -0.0380, -0.0911, -0.1522, -0.1662, -0.2023, -0.1794,\n", + " -0.2216, -0.2386, -0.1344, -0.1475, -0.0473, 0.0138, 0.0600, 0.2953,\n", + " 0.2824, 0.3016, 0.2281]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003271948080509901\n", + "Grad encoder.fc1.bias: 0.0003521722392179072\n", + "Grad encoder.encoder.0.weight: 5.75008089072071e-05\n", + "Grad encoder.encoder.0.bias: 0.00036128555075265467\n", + "Grad encoder.encoder.2.weight: 4.267881013220176e-05\n", + "Grad encoder.encoder.2.bias: 0.0003942537005059421\n", + "Grad encoder.encoder.4.weight: 0.00013695406960323453\n", + "Grad encoder.encoder.4.bias: 0.0010912007419392467\n", + "Grad decoder.fc1.0.weight: 4.992186586605385e-05\n", 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"Data Y Sample: tensor([[ 0.5307, 0.4902, 0.3031, 0.2651, 0.9753, 0.9968, 0.5934, 0.2533,\n", + " 0.0592, -0.1038, 0.4532, -1.3927, -0.8909, 0.6896, -0.0576, -0.0530,\n", + " 0.4940, 0.6182, 2.0906, 0.6775, 1.2368, -0.0811, 1.5325, 0.6217,\n", + " -1.2923, 1.2474, -1.0779, -0.8380, -1.1458, -1.0710, -0.3192, -0.0920,\n", + " -1.0762, -0.2745, 0.1807, -1.2749, 0.0235, -0.3643, -0.2715, 0.1434,\n", + " 0.2521, -0.1211, 0.5123]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2687, -0.2888, -0.0664, -0.1541, 0.0648, 0.1565, 0.1909, 0.3159,\n", + " 0.1975, 0.2473, 0.2495, -0.1924, -0.1821, -0.3040, -0.1811, -0.2738,\n", + " -0.2267, -0.0619, -0.1104, -0.1271, -0.0559, -0.0113, -0.0284, -0.0626,\n", + " -0.0069, 0.0821, -0.0692, -0.1449, -0.2083, -0.2369, -0.2731, -0.2260,\n", + " -0.2947, -0.3056, -0.1891, -0.1738, -0.0527, 0.0172, 0.0560, 0.3383,\n", + " 0.3334, 0.3642, 0.2836]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00010979387297993526\n", + "Grad 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_memory_unit.bias_ih_l1: 2.743207551247906e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.4092094716033898e-05\n", + "Data X Sample: tensor([[1.4905, 1.7099, 1.7806, 1.9766, 2.1463, 2.2438, 2.4512, 2.4886, 2.5240,\n", + " 2.6571, 2.4807, 2.5687, 2.4612, 2.5274, 2.4689, 2.4616, 2.5111, 2.4973,\n", + " 2.4305, 2.5013, 2.4315, 2.3668, 2.3664, 2.3286, 2.3610, 2.3533, 2.2724,\n", + " 2.4349, 2.1335, 2.2893, 2.1662, 2.2825, 2.2308, 1.4624, 1.9781, 1.7488,\n", + " 1.6400, 1.4974, 1.6226, 1.6087, 1.2218, 0.7030, 0.6851, 1.1453, 1.7839,\n", + " 2.0014, 1.3462, 1.7437]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.2079, 1.1093, -0.8115, 1.5844, 1.0441, 1.1841, -0.6563, -0.8619,\n", + " -0.4643, -0.7519, -0.8025, 0.1238, 1.3380, 1.3339, 0.7531, 1.3540,\n", + " 1.5542, 0.2075, 0.5475, 0.0610, 0.7786, 0.8030, 0.5127, 0.3034,\n", + " 1.6265, -1.0327, -0.0079, -0.0688, 0.5046, 0.5548, 0.2239, 0.6734,\n", + " 0.3013, 0.4778, 0.1769, 0.9883, 0.3997, 0.6228, -0.0231, -0.7836,\n", + " -0.8426, -0.4766, -0.5179]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3029, 0.2589, 0.1102, 0.1475, 0.0607, -0.0881, -0.2732, -0.2599,\n", + " -0.1431, -0.1786, -0.1961, 0.1059, 0.1106, 0.1975, 0.1726, 0.2098,\n", + " 0.1746, 0.0663, 0.0073, 0.0780, 0.0437, 0.0315, 0.0297, 0.0048,\n", + " 0.0153, 0.0801, -0.0571, -0.0130, 0.1827, 0.1661, 0.1466, 0.1085,\n", + " 0.1996, 0.1814, 0.0922, 0.1062, -0.0301, 0.0116, 0.0146, -0.1923,\n", + " -0.1758, -0.1757, -0.1464]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00010706264583859593\n", + "Grad encoder.fc1.bias: 0.00040730665205046535\n", + "Grad encoder.encoder.0.weight: 2.106418105540797e-05\n", + "Grad encoder.encoder.0.bias: 0.0003253471804782748\n", + "Grad encoder.encoder.2.weight: 2.0780667910003103e-05\n", + "Grad encoder.encoder.2.bias: 0.00027055031387135386\n", + "Grad encoder.encoder.4.weight: 7.306876068469137e-05\n", + "Grad encoder.encoder.4.bias: 0.0005817230558022857\n", + "Grad 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4.2001, 4.1423, 4.0280, 3.9970, 3.9997, 3.6979, 3.5866, 3.6946,\n", + " 3.5332, 3.3642, 3.4252, 3.3527, 2.8773, 2.7714, 2.7758, 2.8052, 2.8744,\n", + " 2.8713, 2.7684, 2.9542, 3.0516, 3.0645, 3.3397, 2.5677, 4.1768, 4.6018,\n", + " 4.8454, 5.1600, 5.9898, 5.8104, 1.1741, 0.6791, 0.6669, 1.2199, 1.7363,\n", + " 1.9728, 1.2714, 1.7828]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.8525, -0.1462, 0.1043, -0.1956, 0.0391, -0.9205, 0.6176, 0.8895,\n", + " 1.1925, 0.6517, 0.6824, -1.9703, -0.8515, -0.7576, 0.1541, -0.6950,\n", + " -1.1199, -0.7004, -1.7897, -1.8624, 0.4158, -1.8764, -0.5906, -0.2040,\n", + " -1.4022, -1.1445, -0.7134, -0.4871, -1.2820, -1.2546, -0.1930, -0.1925,\n", + " -0.1190, -0.3711, -0.0334, -0.6427, 0.9776, 0.1511, 0.0040, 0.8156,\n", + " 0.9605, 0.8330, 0.9675]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2326, -0.2688, -0.0613, -0.1523, 0.0457, 0.1293, 0.1572, 0.2773,\n", + " 0.1774, 0.2267, 0.2154, -0.1647, -0.1503, -0.2591, -0.1605, -0.2535,\n", 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_memory_unit.weight_ih_l0: 6.639659204665804e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 2.2118851120467298e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.1632544556050561e-05\n", + "Grad _memory_unit.weight_ih_l1: 2.291110376972938e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 5.612662062048912e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.8795020625693724e-05\n", + "Data X Sample: tensor([[1.4650, 1.7405, 1.9173, 1.9635, 2.0523, 2.3675, 2.4358, 2.4957, 2.6143,\n", + " 2.6002, 2.6362, 2.5571, 2.5455, 2.5765, 2.4185, 2.5614, 2.4969, 2.5553,\n", + " 2.5214, 2.4418, 2.3775, 2.3637, 2.4074, 2.4202, 2.4793, 2.4604, 2.3683,\n", + " 2.3370, 2.1023, 2.3221, 2.3202, 2.3820, 2.2726, 1.4715, 1.9364, 1.8412,\n", + " 1.6164, 1.5106, 1.5402, 1.5718, 1.2695, 0.6910, 0.7457, 1.2486, 1.7363,\n", + " 1.8298, 1.3394, 1.8297]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.0840, 1.4080, 0.2457, 0.9742, -0.6713, -0.9174, -0.9050, -0.5630,\n", + " -0.4519, -0.7545, -0.6923, 0.5146, 0.3970, 0.6490, -0.2847, 0.5885,\n", + " 0.4368, 0.3759, 0.1943, 2.0135, -0.2370, 0.1263, 0.6110, 0.6577,\n", + " -0.8267, 0.2556, 1.2426, 0.6881, 0.8990, 0.4526, 0.7076, -0.3164,\n", + " 0.9824, 0.0586, 1.4160, 0.7134, -0.5204, -0.5073, -0.1229, -0.3671,\n", + " -0.6549, -0.5158, -1.1453]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 2.7714e-01, 2.0280e-01, 8.9457e-02, 1.2760e-01, 4.4522e-02,\n", + " -7.6238e-02, -2.5467e-01, -2.3154e-01, -1.3888e-01, -1.5211e-01,\n", + " -1.8538e-01, 9.1407e-02, 1.0905e-01, 1.8689e-01, 1.5190e-01,\n", + " 1.8193e-01, 1.3281e-01, 6.2972e-02, 6.5301e-03, 6.0354e-02,\n", + " 3.6160e-02, 3.3724e-02, 4.0952e-02, 6.3596e-03, 8.5473e-05,\n", + " 5.1403e-02, -4.2400e-02, 1.3854e-03, 1.7698e-01, 1.5775e-01,\n", + " 1.2221e-01, 1.1064e-01, 1.7309e-01, 1.6230e-01, 8.5204e-02,\n", + " 9.8650e-02, -3.4055e-02, 1.6467e-02, 2.3333e-03, -1.7659e-01,\n", + " -1.4176e-01, -1.3850e-01, -1.3313e-01]], 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"Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 0.00014300484326668084\n", + "Grad _memory_unit.bias_hh_l0: 8.678528683958575e-05\n", + "Grad _memory_unit.weight_ih_l1: 2.0094768842682242e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00023806383251212537\n", + "Grad _memory_unit.bias_hh_l1: 0.0001305404002778232\n", + "Data X Sample: tensor([[ 0.0180, 0.0160, 0.0150, 0.0175, 0.0102, 0.0295, 0.0262, 0.0284,\n", + " 0.0269, 0.0253, 0.0698, 0.0136, 0.0133, 0.0409, 0.0177, 0.0109,\n", + " -0.1274, -0.1354, -0.1962, -0.1748, -0.1889, -0.1286, -0.2024, -0.1909,\n", + " -0.1708, -0.3030, -0.2957, -0.5588, -0.0555, -0.0655, -0.0875, -0.0525,\n", + " -0.0264, -0.0595, -0.0343, 0.0073, 0.0020, -0.0186, -0.0127, 0.0000,\n", + " -0.0048, 0.0020, 0.0101, 0.0057, 0.0040, 0.0343, 0.0204, -0.0156]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.4589, 0.3984, 0.3007, -0.3699, -0.3475, -0.6430, -0.0520, -0.0369,\n", + " 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_memory_unit.bias_hh_l0: 4.6480119635816664e-05\n", + "Grad _memory_unit.weight_ih_l1: 9.220422725775279e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00017066599684767425\n", + "Grad _memory_unit.bias_hh_l1: 8.93537508090958e-05\n", + "Data X Sample: tensor([[1.2125, 1.3444, 1.5387, 1.6421, 1.8679, 1.9889, 3.6991, 4.0331, 4.0423,\n", + " 4.1136, 4.1367, 3.9495, 3.7616, 3.6098, 3.5937, 3.3751, 3.3979, 3.3813,\n", + " 3.3846, 3.4181, 3.3786, 3.2486, 2.7640, 2.7714, 2.7927, 2.8052, 2.9064,\n", + " 2.9121, 3.0043, 3.1081, 3.1881, 3.1474, 3.3595, 2.5105, 4.0272, 4.6115,\n", + " 4.9064, 5.4304, 5.6349, 5.6601, 0.9593, 0.5755, 0.5780, 0.9903, 1.5302,\n", + " 1.5897, 0.9994, 1.6186]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.6009, -0.1158, 0.3102, -0.6206, 0.0668, 1.0708, 1.3407, 0.5155,\n", + " 0.3483, 0.4264, -0.4897, 0.4172, 0.0798, -1.2565, 0.1716, -0.6491,\n", + " 0.4846, -0.1716, -2.0963, 0.1801, -0.4727, -0.2399, -0.3357, 1.1236,\n", + " -0.2397, -0.2003, -1.0973, -0.5742, -0.4284, -0.9427, 0.9454, -0.7667,\n", + " -1.3316, -0.3567, 0.5667, 0.0586, -0.2633, 0.0532, 0.3857, -0.7124,\n", + " 0.0999, -0.6865, 0.3837]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1689, -0.2350, -0.0667, -0.1202, 0.0521, 0.1074, 0.1190, 0.2196,\n", + " 0.1707, 0.2112, 0.1609, -0.1283, -0.1170, -0.2006, -0.1297, -0.2461,\n", + " -0.1999, -0.0613, -0.0531, -0.1092, -0.0422, -0.0202, -0.0359, -0.0798,\n", + " -0.0176, 0.0296, -0.0418, -0.0855, -0.1536, -0.2098, -0.1947, -0.1602,\n", + " -0.2502, -0.2166, -0.1675, -0.1249, -0.0388, 0.0226, 0.0082, 0.2312,\n", + " 0.2754, 0.2571, 0.2108]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00019905994122382253\n", + "Grad encoder.fc1.bias: 0.0005988277844153345\n", + "Grad encoder.encoder.0.weight: 4.514705506153405e-05\n", + "Grad encoder.encoder.0.bias: 0.0008305851370096207\n", + "Grad encoder.encoder.2.weight: 3.6579061998054385e-05\n", + "Grad 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_memory_unit.bias_hh_l1: 8.356131729669869e-05\n", + "Data X Sample: tensor([[1.6984, 1.9677, 2.2104, 2.2325, 2.2897, 2.3219, 2.2987, 2.4233, 2.5338,\n", + " 2.5765, 2.5252, 2.5707, 2.4922, 2.4647, 2.3782, 2.4288, 2.3790, 2.5089,\n", + " 2.5482, 2.4902, 2.5984, 2.5582, 2.5953, 2.5729, 2.6820, 2.7373, 2.6666,\n", + " 2.6184, 2.3764, 2.3417, 2.3062, 2.2604, 1.9404, 1.2518, 1.7501, 1.6880,\n", + " 1.5928, 1.5848, 1.5656, 1.4242, 1.3459, 0.7846, 0.8124, 1.4237, 1.9861,\n", + " 2.2129, 1.4414, 2.1346]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.1460, 0.1300, 0.7552, -0.0827, 0.4839, -0.8755, -0.2564, -0.2361,\n", + " -0.5484, -0.4431, -0.2102, 0.0703, -0.0335, 0.3881, -0.4958, -0.2283,\n", + " 0.5732, 0.0577, -0.0777, 1.6892, -1.5073, -0.5839, -0.4181, -0.4117,\n", + " 0.0673, -0.0529, -1.0662, 0.2743, 0.5157, -0.3734, -0.9505, 0.2304,\n", + " 0.4064, 0.0467, -0.4230, -0.5197, -0.9937, 0.1545, 1.1148, -0.2906,\n", + " -0.3717, -0.1605, -0.5773]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 4.5375e-01, 3.6190e-01, 1.3868e-01, 2.8208e-01, 8.8535e-02,\n", + " -1.4768e-01, -4.0449e-01, -4.5099e-01, -2.6016e-01, -2.9844e-01,\n", + " -3.1470e-01, 2.0963e-01, 2.0965e-01, 3.3622e-01, 2.7119e-01,\n", + " 3.5592e-01, 2.5201e-01, 1.1138e-01, 3.5503e-02, 8.6625e-02,\n", + " 5.0210e-02, 7.9213e-02, 9.6991e-02, 4.0414e-02, 1.8236e-02,\n", + " 4.0527e-03, -7.8267e-02, 1.7951e-02, 3.3087e-01, 3.2554e-01,\n", + " 2.4920e-01, 2.2201e-01, 3.3126e-01, 3.1432e-01, 1.7460e-01,\n", + " 1.7746e-01, -4.9336e-05, 2.4807e-02, 6.4360e-03, -3.3222e-01,\n", + " -3.1880e-01, -2.7454e-01, -2.7682e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00012037756096106023\n", + "Grad encoder.fc1.bias: 0.00015059488941915333\n", + "Grad encoder.encoder.0.weight: 3.436012048041448e-05\n", + "Grad encoder.encoder.0.bias: 0.00017500326794106513\n", + "Grad encoder.encoder.2.weight: 1.9994495232822374e-05\n", + "Grad encoder.encoder.2.bias: 0.00012355286162346601\n", + "Grad encoder.encoder.4.weight: 5.879185482626781e-05\n", + "Grad encoder.encoder.4.bias: 0.00024229069822467864\n", + "Grad decoder.fc1.0.weight: 2.3376665922114626e-05\n", + "Grad decoder.fc1.0.bias: 0.00012374375364743173\n", + "Grad decoder.fc1.2.weight: 3.4184999094577506e-05\n", + "Grad decoder.fc1.2.bias: 0.0001593777269590646\n", + "Grad decoder.fc1.4.weight: 5.351267463993281e-05\n", + "Grad decoder.fc1.4.bias: 0.000338227953761816\n", + "Grad decoder.fc2.weight: 0.00011587818880798295\n", + "Grad decoder.fc2.bias: 0.0019119569333270192\n", + "Grad _memory_unit.weight_ih_l0: 2.0215193217154592e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 9.287859938922338e-06\n", + "Grad _memory_unit.bias_hh_l0: 4.8496572162548546e-06\n", + "Grad _memory_unit.weight_ih_l1: 9.946220416168217e-07\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 2.930836853920482e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.4862746866128873e-05\n", + "Data X Sample: tensor([[1.6050, 1.8134, 2.0075, 1.9919, 2.1206, 2.2320, 2.2679, 2.2785, 2.2628,\n", + " 2.3980, 2.4173, 2.4110, 2.2836, 2.3257, 2.2218, 2.1867, 2.2029, 2.3483,\n", + " 2.2715, 2.3618, 2.4291, 2.5321, 2.5062, 2.5042, 2.5018, 2.5597, 2.5547,\n", + " 2.5736, 2.3417, 2.2991, 2.4392, 2.4538, 2.1956, 1.4120, 1.9242, 1.7561,\n", + " 1.6853, 1.4921, 1.5466, 1.5973, 1.1884, 0.7309, 0.7619, 1.2744, 1.9464,\n", + " 1.9613, 1.3802, 1.9783]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.1687, 0.8298, 0.2994, 1.2228, 0.4861, 0.0708, -0.9397, -1.1907,\n", + " -0.9916, -0.8922, -0.7499, -0.5843, 0.2044, -0.3058, 0.3746, 0.8216,\n", + " -0.5360, -0.1837, 0.1491, 0.7727, 0.0373, -1.0272, -0.0332, -0.7809,\n", + " -0.3555, 0.0744, -0.3607, -0.4016, -0.1051, 0.0709, 0.4401, 0.4258,\n", + " 0.1117, 0.1612, 0.7586, 0.7440, -0.0868, 0.7916, -0.2505, -0.5618,\n", + " -0.5741, -0.0849, -0.5159]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.5093, 0.4109, 0.1507, 0.3264, 0.1047, -0.1635, -0.4369, -0.4970,\n", + " -0.2810, -0.3278, -0.3535, 0.2337, 0.2357, 0.3743, 0.2969, 0.3947,\n", + " 0.2826, 0.1262, 0.0489, 0.1005, 0.0574, 0.0886, 0.1077, 0.0489,\n", + " 0.0263, -0.0098, -0.1053, 0.0168, 0.3665, 0.3631, 0.2789, 0.2546,\n", + " 0.3640, 0.3569, 0.2023, 0.2076, 0.0136, 0.0333, 0.0060, -0.3760,\n", + " -0.3691, -0.3061, -0.3052]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0008757174946367741\n", + "Grad encoder.fc1.bias: 0.0007575757335871458\n", + "Grad encoder.encoder.0.weight: 0.00014649666263721883\n", + "Grad encoder.encoder.0.bias: 0.0010159939993172884\n", + "Grad encoder.encoder.2.weight: 8.674271521158516e-05\n", + "Grad encoder.encoder.2.bias: 0.0008796199690550566\n", + "Grad encoder.encoder.4.weight: 0.0002957089454866946\n", + "Grad encoder.encoder.4.bias: 0.002552919089794159\n", + "Grad decoder.fc1.0.weight: 8.816225454211235e-05\n", + "Grad decoder.fc1.0.bias: 0.0006223786040209234\n", + "Grad decoder.fc1.2.weight: 5.631199383060448e-05\n", + "Grad decoder.fc1.2.bias: 0.0007167127914726734\n", + "Grad decoder.fc1.4.weight: 5.450546450447291e-05\n", + "Grad decoder.fc1.4.bias: 0.0008462753612548113\n", + "Grad decoder.fc2.weight: 0.00012778674135915935\n", + "Grad decoder.fc2.bias: 0.001900321338325739\n", + "Grad _memory_unit.weight_ih_l0: 1.0874723557208199e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.131436096737161e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.6853127817739733e-05\n", + "Grad _memory_unit.weight_ih_l1: 6.1708356042800006e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00014001493400428444\n", + "Grad _memory_unit.bias_hh_l1: 7.033984002191573e-05\n", + "Data X Sample: tensor([[1.5032, 1.4551, 1.5883, 1.7055, 1.9994, 1.9845, 2.0106, 2.0116, 3.0342,\n", + " 3.7976, 4.0828, 4.0430, 3.8682, 3.7025, 3.5483, 3.6774, 3.5709, 3.3716,\n", + " 3.3061, 3.2973, 3.3811, 3.3297, 2.8652, 2.7886, 2.8302, 2.9514, 2.9170,\n", + " 2.8999, 2.8204, 3.1605, 3.2056, 3.1308, 3.4167, 2.6020, 4.2503, 4.5556,\n", + " 4.7648, 5.1706, 5.7236, 5.6629, 0.9450, 0.5735, 0.5901, 1.0247, 1.4430,\n", + " 1.5897, 1.2646, 1.7437]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 3.4184e-01, -1.3662e-01, -8.8402e-01, -7.1110e-01, -3.1822e-01,\n", + " 1.3554e-01, 3.3268e-01, 4.1518e-01, 2.9571e-01, 2.5591e-01,\n", + " 4.3413e-01, -3.9494e-01, -4.3551e-01, -1.3216e+00, 6.0547e-01,\n", + " -9.6728e-01, -8.9174e-01, -1.6728e-03, 2.4309e-01, 2.8205e-02,\n", + " -1.4498e-01, -1.3515e-02, -3.6253e-02, -3.4269e-01, -2.5295e-01,\n", + " -8.1407e-01, 1.2637e+00, 1.3826e-01, -5.1198e-04, 1.9965e-01,\n", + " -5.3236e-01, -7.3321e-01, -1.0845e+00, -9.7300e-01, 1.0220e-01,\n", + " -1.7799e+00, 9.9263e-01, -1.0105e-01, 1.7270e-02, 7.6865e-01,\n", + " 8.3932e-01, 5.5095e-01, 9.4022e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1492, -0.2274, -0.0661, -0.0971, 0.0723, 0.1184, 0.1188, 0.2093,\n", + " 0.1829, 0.2158, 0.1480, -0.1195, -0.1159, -0.1965, -0.1268, -0.2596,\n", + " -0.2005, -0.0618, -0.0378, -0.1106, -0.0436, -0.0142, -0.0429, -0.0839,\n", + " -0.0166, 0.0230, -0.0512, -0.0993, -0.1671, -0.2303, -0.1933, -0.1568,\n", + " -0.2660, -0.2084, -0.1805, -0.1149, -0.0365, 0.0281, 0.0029, 0.2112,\n", + " 0.2687, 0.2472, 0.2086]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00032408893457613885\n", + "Grad encoder.fc1.bias: 0.00014197651762515306\n", + "Grad encoder.encoder.0.weight: 6.591495184693485e-05\n", + "Grad encoder.encoder.0.bias: 0.00017945376748684794\n", + "Grad encoder.encoder.2.weight: 4.332152457209304e-05\n", + "Grad encoder.encoder.2.bias: 0.0001728421775624156\n", + "Grad encoder.encoder.4.weight: 0.00014441940584219992\n", + "Grad encoder.encoder.4.bias: 0.0006097516743466258\n", + "Grad decoder.fc1.0.weight: 3.704888149513863e-05\n", + "Grad decoder.fc1.0.bias: 0.0001124595946748741\n", + "Grad decoder.fc1.2.weight: 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2.5574,\n", + " 2.5410, 2.4874, 2.5284, 2.4462, 2.3765, 2.0944, 1.3823, 1.8188, 1.7609,\n", + " 1.6341, 1.5901, 1.5212, 1.4413, 1.2982, 0.7667, 0.7336, 1.3950, 1.9147,\n", + " 2.2301, 1.4074, 1.9235]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.6211, 0.3045, -0.2455, 0.8634, 0.3636, -0.1139, -0.2528, -0.0981,\n", + " -0.0836, -0.2734, -0.5720, 0.3400, -1.1838, 0.5896, -0.6819, 0.5304,\n", + " 0.5429, -0.3215, -0.8038, -1.8269, -1.4533, -1.7901, -1.7243, -1.9975,\n", + " -0.7930, -0.9780, -0.3271, -0.2793, -0.5499, 0.1539, 0.8125, 0.2288,\n", + " 0.0834, 0.5858, 1.1188, 0.6530, 0.4963, -0.1011, -1.0062, -0.3719,\n", + " -0.2130, -0.1962, 0.0240]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.5323, 0.4389, 0.1487, 0.3242, 0.1079, -0.1796, -0.4327, -0.4832,\n", + " -0.2508, -0.3091, -0.3692, 0.2232, 0.2308, 0.3794, 0.2930, 0.3758,\n", + " 0.2833, 0.1159, 0.0670, 0.1112, 0.0621, 0.0705, 0.0844, 0.0331,\n", + " 0.0416, -0.0090, -0.1563, 0.0086, 0.3664, 0.3427, 0.2877, 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"Grad _memory_unit.bias_hh_l0: 1.210693153552711e-05\n", + "Grad _memory_unit.weight_ih_l1: 2.2664844436803833e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 6.59800716675818e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.293363261036575e-05\n", + "Data X Sample: tensor([[1.6348, 1.8876, 1.9234, 2.0182, 2.1753, 2.3337, 2.5637, 2.4758, 2.5045,\n", + " 2.6097, 2.6965, 2.7187, 2.5721, 2.4756, 2.4916, 2.4876, 2.4026, 2.4180,\n", + " 2.3603, 2.3674, 2.3775, 2.4234, 2.4508, 2.3897, 2.4530, 2.4787, 2.4375,\n", + " 2.4553, 2.1301, 2.3483, 2.2677, 2.0780, 1.8106, 1.2106, 1.6717, 1.6272,\n", + " 1.5496, 1.5133, 1.4515, 1.4299, 1.2075, 0.7866, 0.7498, 1.3032, 1.8592,\n", + " 2.0357, 1.3938, 1.9313]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.2535, 0.5846, -0.0244, 0.0705, -0.3324, 0.0607, -1.2554, -0.1887,\n", + " -1.2185, 0.0814, -0.2569, -0.7556, 0.1066, -0.7790, 0.2234, 0.3069,\n", + " 0.6613, 0.0455, 0.2991, 0.0730, 0.5932, -0.4410, 0.8199, 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"Grad _memory_unit.bias_hh_l1: 4.249853373039514e-05\n", + "Data X Sample: tensor([[ 0.0053, 0.0117, 0.0075, 0.0131, 0.0034, 0.0250, 0.0185, 0.0284,\n", + " 0.0220, 0.0316, 0.0476, 0.0156, 0.0155, 0.0218, 0.0252, 0.0123,\n", + " -0.1116, -0.1257, -0.1404, -0.1079, -0.1497, -0.0949, -0.1181, -0.1012,\n", + " -0.0995, -0.1959, -0.1838, -0.2937, -0.0659, -0.0360, -0.0350, -0.0166,\n", + " 0.0066, -0.0137, 0.0147, 0.0316, 0.0079, 0.0027, -0.0254, 0.0142,\n", + " 0.0095, 0.0040, 0.0040, 0.0000, -0.0040, 0.0343, -0.0068, 0.0156]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[-1.3081, -0.3104, 0.1676, -0.2276, -0.5331, -0.9588, 0.2167, -0.8257,\n", + " 0.5010, 0.4981, 0.4779, -0.6620, 1.2478, -0.7765, 0.5808, 0.2202,\n", + " -0.7624, -0.4636, 0.1638, 0.3241, 0.7728, -0.1572, -0.2095, 2.0348,\n", + " 0.1935, 1.0226, -0.6180, 1.1516, -0.1208, 0.0896, 0.3108, -0.1935,\n", + " 0.9155, 0.4685, -0.4059, 1.1256, 0.7922, -0.3755, -0.4768, -0.6562,\n", + " 0.0768, 0.0575, 0.6044]], 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1.0839, 1.0015, 0.9975, 0.5636, 0.5578, 1.0046, 1.4469,\n", + " 1.6468, 1.1150, 1.5717]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.7906, -0.8831, -1.8274, -0.6982, -0.7149, -0.9777, -1.0405, -1.4039,\n", + " 0.8128, 1.2885, 1.0375, -1.0222, 0.8334, 1.2121, 0.1562, 1.1882,\n", + " -0.0621, -1.4794, -0.1795, 0.8243, 1.2813, 0.1360, 1.0409, -0.3046,\n", + " 1.0491, -1.3047, -0.9940, -0.1753, -0.5679, 0.2121, 0.1925, -0.9234,\n", + " -0.8710, 1.0133, 0.0284, 0.2945, -0.4229, 0.7428, 0.7285, -1.9370,\n", + " -0.2097, -0.8616, -1.2481]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3643, 0.3124, 0.1009, 0.1763, 0.0567, -0.1731, -0.3153, -0.3389,\n", + " -0.1266, -0.1924, -0.2279, 0.1672, 0.1111, 0.2426, 0.2024, 0.2210,\n", + " 0.1841, 0.0386, 0.0738, 0.0404, 0.0368, 0.0063, 0.0061, -0.0356,\n", + " 0.0394, 0.0479, -0.1359, 0.0232, 0.2317, 0.1769, 0.2043, 0.1818,\n", + " 0.2381, 0.2331, 0.1503, 0.1230, 0.0349, 0.0262, -0.0252, -0.2651,\n", + " -0.2379, -0.1996, -0.1500]], 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" 0.0810, 0.0825, 0.0696, -0.0395, -0.0913, -0.0856, -0.0425, -0.1361,\n", + " -0.0605, -0.0796, -0.0377, -0.0850, -0.0451, -0.0483, -0.0691, -0.0730,\n", + " 0.0077, 0.0956, -0.0248, -0.0181, -0.0291, -0.1235, -0.0586, -0.0560,\n", + " -0.0770, -0.1211, -0.0619, -0.0961, -0.0227, -0.0104, -0.0357, 0.1086,\n", + " 0.1550, 0.1417, 0.1372]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00011541610001586378\n", + "Grad encoder.fc1.bias: 0.0002537939581088722\n", + "Grad encoder.encoder.0.weight: 3.8114503695396706e-05\n", + "Grad encoder.encoder.0.bias: 0.00019713054643943906\n", + "Grad encoder.encoder.2.weight: 2.160594885936007e-05\n", + "Grad encoder.encoder.2.bias: 0.00017457846843171865\n", + "Grad encoder.encoder.4.weight: 6.286034476943314e-05\n", + "Grad encoder.encoder.4.bias: 0.00023715551651548594\n", + "Grad decoder.fc1.0.weight: 2.7081576263299212e-05\n", + "Grad decoder.fc1.0.bias: 0.00010078047489514574\n", + "Grad decoder.fc1.2.weight: 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_memory_unit.bias_hh_l1: 9.545472494210117e-06\n", + "Data X Sample: tensor([[2.5863, 2.9159, 3.1450, 3.3607, 3.4268, 3.7995, 3.7284, 3.7989, 4.0008,\n", + " 3.8103, 4.0447, 3.9612, 3.6951, 3.6862, 3.5962, 3.4503, 3.3806, 3.4142,\n", + " 3.3598, 3.4367, 3.3222, 3.1981, 2.9207, 2.7829, 2.7514, 2.6772, 2.7572,\n", + " 2.8061, 2.8239, 2.7839, 2.8941, 2.9264, 3.0273, 2.1512, 3.6130, 4.0035,\n", + " 4.4108, 4.6512, 5.3243, 5.1153, 1.8804, 1.1192, 1.1318, 1.8830, 2.9018,\n", + " 3.1393, 2.1621, 3.0651]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1059, -0.2226, -0.4923, 0.9907, -0.0145, -0.3639, 0.2161, -0.2173,\n", + " -0.3182, 0.0634, 0.2127, -0.3484, 0.7849, -0.0642, 0.3483, 0.0897,\n", + " 0.5516, 0.3149, -0.0698, 0.1500, -0.6575, 0.0753, 1.2210, 0.4378,\n", + " -0.0642, -1.7443, -0.2062, 0.5383, 0.4376, 0.8656, -0.3950, 0.4412,\n", + " -0.3668, 0.2015, -0.8254, -0.2242, 0.0000, -0.3227, -0.0231, 0.1162,\n", + " 0.0392, 0.2605, 0.6191]], device='cuda:0')\n", + "Prediction Sample: 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3.3697,\n", + " 3.2607, 3.2898, 3.3884, 3.3450, 2.8195, 2.8134, 2.9128, 2.9253, 3.0875,\n", + " 3.1527, 3.0425, 3.1933, 3.2056, 3.3326, 3.7247, 2.6775, 4.4047, 4.6820,\n", + " 4.8179, 5.2395, 5.6856, 5.6090, 1.1311, 0.6492, 0.6568, 1.2113, 1.8354,\n", + " 2.1157, 1.3190, 1.8766]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.5479, -1.2818, 0.2048, -0.0254, 0.6533, 0.1479, 0.2746, 0.7428,\n", + " 0.8001, 0.8653, 0.8434, -1.4456, -0.1172, -0.7911, 0.6489, -0.5133,\n", + " -0.3414, -0.0270, 0.1600, -0.0360, 1.2454, 0.5368, 0.4455, 0.4752,\n", + " 0.3429, -0.2774, 0.8092, -0.9568, -0.6452, -0.9934, 0.0916, -0.2428,\n", + " -0.7816, -1.0947, -0.4428, -0.7056, -0.9937, 0.8238, -1.0062, 0.7205,\n", + " 0.7787, 0.5578, 0.3246]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2219, -0.2808, -0.0664, -0.1727, -0.0329, 0.0666, 0.1355, 0.2741,\n", + " 0.1641, 0.1799, 0.2066, -0.1342, -0.1302, -0.2197, -0.1626, -0.2367,\n", + " -0.1759, -0.0550, -0.0763, -0.1169, -0.1079, 0.0116, -0.0271, -0.0302,\n", + " 0.0155, 0.0523, -0.0190, -0.0648, -0.1454, -0.1698, -0.1914, -0.1598,\n", + " -0.2427, -0.2577, -0.1502, -0.1574, -0.0039, -0.0068, 0.0187, 0.2901,\n", + " 0.2971, 0.3023, 0.2354]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002211552782682702\n", + "Grad encoder.fc1.bias: 0.00010026688687503338\n", + "Grad encoder.encoder.0.weight: 6.194297020556405e-05\n", + "Grad encoder.encoder.0.bias: 0.00014383310917764902\n", + "Grad encoder.encoder.2.weight: 4.290521610528231e-05\n", + "Grad encoder.encoder.2.bias: 0.00014254412963055074\n", + "Grad encoder.encoder.4.weight: 0.0001271062792511657\n", + "Grad encoder.encoder.4.bias: 0.00044842972420156\n", + "Grad decoder.fc1.0.weight: 3.688442666316405e-05\n", + "Grad decoder.fc1.0.bias: 8.937816164689139e-05\n", + "Grad decoder.fc1.2.weight: 2.7993432013317943e-05\n", + "Grad decoder.fc1.2.bias: 9.649747516959906e-05\n", + "Grad decoder.fc1.4.weight: 3.1536343158222735e-05\n", + "Grad 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1.9031, 3.0326,\n", + " 3.0535, 2.1485, 3.0182]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.7145, 0.3702, 0.6229, -0.3970, -1.0440, -1.0084, -2.6168, -2.0445,\n", + " -0.7298, -1.0066, -0.1171, 0.7515, -0.3838, -0.9549, 0.6044, 0.5358,\n", + " -0.0516, 0.0029, -0.1759, 0.2219, 0.2759, -0.9483, -1.6920, -1.7661,\n", + " -0.6407, -0.2860, 0.1419, 0.6753, 1.3788, 0.8638, 0.9017, -0.3519,\n", + " -0.3194, 0.4346, 0.2861, -0.8082, 0.7456, -0.2114, -1.1380, 0.5576,\n", + " 1.1977, 1.1251, 0.9950]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1718, -0.2350, -0.0530, -0.1709, -0.0615, 0.0306, 0.0843, 0.2129,\n", + " 0.1240, 0.1446, 0.1446, -0.1044, -0.0983, -0.1626, -0.1284, -0.1963,\n", + " -0.1635, -0.0478, -0.0533, -0.0996, -0.0817, -0.0010, -0.0299, -0.0379,\n", + " 0.0024, 0.0424, 0.0043, -0.0170, -0.0984, -0.1345, -0.1392, -0.1138,\n", + " -0.1888, -0.1946, -0.1184, -0.1243, -0.0045, 0.0016, 0.0016, 0.2288,\n", + " 0.2544, 0.2318, 0.1872]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00012640391651075333\n", + "Grad encoder.fc1.bias: 0.00018864944286178797\n", + "Grad encoder.encoder.0.weight: 3.886759077431634e-05\n", + "Grad encoder.encoder.0.bias: 0.0001330636441707611\n", + "Grad encoder.encoder.2.weight: 2.84802372334525e-05\n", + "Grad encoder.encoder.2.bias: 0.00016502542712260038\n", + "Grad encoder.encoder.4.weight: 9.549615060677752e-05\n", + "Grad encoder.encoder.4.bias: 0.0005239985184744\n", + "Grad decoder.fc1.0.weight: 3.183818989782594e-05\n", + "Grad decoder.fc1.0.bias: 0.00016543130914214998\n", + "Grad decoder.fc1.2.weight: 4.184562567388639e-05\n", + "Grad decoder.fc1.2.bias: 0.00018467902555130422\n", + "Grad decoder.fc1.4.weight: 5.5162621720228344e-05\n", + "Grad decoder.fc1.4.bias: 0.00044833417632617056\n", + "Grad decoder.fc2.weight: 0.00012587949458975345\n", + "Grad decoder.fc2.bias: 0.0023004382383078337\n", + "Grad _memory_unit.weight_ih_l0: 1.8534600485509145e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 1.0522329830564559e-05\n", + "Grad _memory_unit.bias_hh_l0: 5.612471795757301e-06\n", + "Grad _memory_unit.weight_ih_l1: 1.1234910743951332e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 3.796803503064439e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.9483875803416595e-05\n", + "Data X Sample: tensor([[1.5881, 1.6910, 1.7581, 1.9679, 2.0216, 2.2320, 2.1939, 2.3977, 3.9080,\n", + " 4.2763, 4.4063, 4.2942, 4.1522, 4.0706, 3.8383, 3.6869, 3.6243, 3.6289,\n", + " 3.5498, 3.6078, 3.6264, 3.5150, 3.1062, 2.9146, 2.9466, 2.9410, 2.9383,\n", + " 3.0712, 3.0043, 3.3112, 3.3665, 3.2911, 3.7775, 2.8263, 4.5738, 4.8450,\n", + " 5.1935, 5.4993, 6.0785, 5.9182, 1.2075, 0.6452, 0.6225, 1.1711, 1.5579,\n", + " 2.0471, 1.1558, 1.9626]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.5053, 0.2458, -0.4500, 0.4671, 0.9198, 1.7307, 0.5936, 0.1894,\n", + " 0.0783, -0.3435, -1.0961, 1.2348, 0.2873, 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_memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 4.9972382839769125e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.4963825126178563e-05\n", + "Data X Sample: tensor([[2.0888, 2.5474, 2.7393, 2.9453, 2.9505, 2.9539, 3.1876, 3.2396, 3.2465,\n", + " 3.3237, 3.4197, 3.4334, 3.3466, 3.2881, 3.2709, 3.2055, 3.1793, 3.3155,\n", + " 3.3309, 3.1968, 3.0842, 3.1077, 3.0869, 2.8516, 2.8002, 2.6641, 2.6080,\n", + " 2.6429, 2.2862, 2.2729, 2.3062, 2.3240, 2.1428, 1.4761, 2.2600, 2.4225,\n", + " 2.5210, 2.8278, 3.0044, 2.8201, 1.7420, 1.0136, 0.9903, 1.7021, 2.4419,\n", + " 2.9620, 1.7269, 2.6273]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.0176, -0.2145, 0.1661, -1.3085, 0.3534, 1.0622, 0.3190, 0.1630,\n", + " 0.2307, 0.0709, -0.2209, 0.1409, -1.5410, -0.0030, 0.1824, -0.7047,\n", + " 2.0748, 0.4471, 0.8405, 1.1414, -1.0208, 0.0936, -0.8349, -0.2851,\n", + " -0.8211, -0.3739, -1.1424, -1.2336, -1.0893, -0.2509, 0.2945, -0.3475,\n", + " 1.0674, -0.5355, -0.0116, -0.0999, 0.0000, 0.7579, 1.1339, -0.1257,\n", + " -0.1663, -0.1553, 0.4981]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2107, 0.1662, 0.0897, 0.0670, 0.0434, -0.1166, -0.2059, -0.1631,\n", + " -0.0714, -0.1396, -0.1214, 0.0626, 0.0288, 0.1324, 0.0863, 0.1180,\n", + " 0.0950, 0.0443, 0.0615, 0.0709, 0.0470, 0.0267, 0.0046, -0.0040,\n", + " 0.0647, 0.0720, -0.0379, 0.0363, 0.0721, 0.1294, 0.1234, 0.1129,\n", + " 0.1510, 0.1252, 0.0668, 0.0851, -0.0061, -0.0040, 0.0133, -0.1506,\n", + " -0.0975, -0.1481, -0.0845]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002389481960562989\n", + "Grad encoder.fc1.bias: 0.0008987977635115385\n", + "Grad encoder.encoder.0.weight: 7.00296150171198e-05\n", + "Grad encoder.encoder.0.bias: 0.0007003281498327851\n", + "Grad encoder.encoder.2.weight: 5.440394670586102e-05\n", + "Grad encoder.encoder.2.bias: 0.0006747347069904208\n", + "Grad encoder.encoder.4.weight: 0.000183615178684704\n", + "Grad encoder.encoder.4.bias: 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-1.1865e+00,\n", + " -9.6466e-01, -6.8366e-01, 3.2435e-02]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.9630, 0.7488, 0.3276, 0.4515, -0.3873, -0.7933, -1.2708, -1.2596,\n", + " -0.7746, -0.8512, -0.8480, 0.3342, 0.4753, 0.7483, 0.4779, 0.6104,\n", + " 0.4145, 0.2176, 0.1026, 0.2381, 0.1695, 0.0856, 0.0173, 0.1466,\n", + " 0.1993, 0.0182, 0.2662, 0.5596, 1.0344, 1.1065, 0.8797, 0.6419,\n", + " 0.8310, 0.7229, 0.3348, 0.4496, 0.0772, 0.1847, 0.0690, -0.8220,\n", + " -0.7605, -0.7253, -0.5885]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.000624944397713989\n", + "Grad encoder.fc1.bias: 0.0008925800211727619\n", + "Grad encoder.encoder.0.weight: 0.0001680007262621075\n", + "Grad encoder.encoder.0.bias: 0.0005835436168126762\n", + "Grad encoder.encoder.2.weight: 0.00011253470438532531\n", + "Grad encoder.encoder.2.bias: 0.0006622004439122975\n", + "Grad encoder.encoder.4.weight: 0.000352596485754475\n", + "Grad encoder.encoder.4.bias: 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0.0087, -0.0076, 0.0370, 0.2964,\n", + " 0.3285, 0.3478, 0.2894]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002758727059699595\n", + "Grad encoder.fc1.bias: 0.000313297554384917\n", + "Grad encoder.encoder.0.weight: 7.800771709298715e-05\n", + "Grad encoder.encoder.0.bias: 0.0002345418033655733\n", + "Grad encoder.encoder.2.weight: 5.234754644334316e-05\n", + "Grad encoder.encoder.2.bias: 0.0002441877732053399\n", + "Grad encoder.encoder.4.weight: 0.00019499112386256456\n", + "Grad encoder.encoder.4.bias: 0.0005544043378904462\n", + "Grad decoder.fc1.0.weight: 5.2484203479252756e-05\n", + "Grad decoder.fc1.0.bias: 0.00015944818733260036\n", + "Grad decoder.fc1.2.weight: 6.0527945606736466e-05\n", + "Grad decoder.fc1.2.bias: 0.00022143771639093757\n", + "Grad decoder.fc1.4.weight: 6.455018592532724e-05\n", + "Grad decoder.fc1.4.bias: 0.0004470953135751188\n", + "Grad decoder.fc2.weight: 0.00011516579979797825\n", + "Grad decoder.fc2.bias: 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_memory_unit.weight_ih_l1: 3.494278416837915e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00011408240970922634\n", + "Grad _memory_unit.bias_hh_l1: 5.824907566420734e-05\n", + "Data X Sample: tensor([[1.3006, 1.4827, 1.5597, 1.6661, 1.8509, 1.9108, 2.0861, 2.2998, 3.5273,\n", + " 3.9888, 3.8861, 3.9028, 3.6418, 3.5417, 3.5004, 3.3779, 3.3586, 3.3252,\n", + " 3.4217, 3.6376, 3.4817, 3.3297, 2.9255, 2.7352, 2.8227, 2.7529, 2.9756,\n", + " 2.9855, 2.9314, 3.1474, 3.1496, 3.3132, 3.3947, 2.4350, 3.9390, 4.4388,\n", + " 4.7097, 5.0196, 5.7870, 5.3820, 1.1407, 0.6014, 0.6225, 0.9472, 1.4430,\n", + " 1.6640, 1.1014, 1.5717]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.0637, -0.0426, 1.3122, -0.8724, -0.0595, -1.3554, -1.3176, -0.1479,\n", + " -0.2719, -0.2583, 0.5919, -0.1993, -0.5694, -0.6390, -0.1359, 0.1472,\n", + " -0.3973, -0.5812, 1.4873, -1.1866, -0.2678, -0.3323, 0.8355, -0.4433,\n", + " -1.0350, -0.2198, 1.1303, 1.3347, -0.1311, 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device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.5054, 0.3993, 0.2020, 0.2585, 0.0014, -0.2969, -0.5380, -0.5696,\n", + " -0.2532, -0.3157, -0.3095, 0.1682, 0.2344, 0.3839, 0.2521, 0.2541,\n", + " 0.2956, 0.1928, 0.0623, 0.1961, 0.0742, 0.0524, 0.0401, 0.0732,\n", + " 0.0754, 0.0697, 0.0039, 0.1376, 0.3831, 0.4243, 0.3889, 0.2880,\n", + " 0.3925, 0.3732, 0.1885, 0.1838, 0.0846, 0.0372, -0.0346, -0.3440,\n", + " -0.3103, -0.3594, -0.3265]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00010615461360430345\n", + "Grad encoder.fc1.bias: 0.0004520161892287433\n", + "Grad encoder.encoder.0.weight: 3.4171727747889236e-05\n", + "Grad encoder.encoder.0.bias: 0.0002813398605212569\n", + "Grad encoder.encoder.2.weight: 2.6627145416568965e-05\n", + "Grad encoder.encoder.2.bias: 0.0003388577315490693\n", + "Grad encoder.encoder.4.weight: 9.643963858252391e-05\n", + "Grad encoder.encoder.4.bias: 0.0011563487350940704\n", + "Grad decoder.fc1.0.weight: 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-0.7819, 0.3895, 0.1044, -0.8141, 0.0540, 0.7966, -0.7678, 0.9062,\n", + " 0.8255, 1.0012, 0.6968]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2541, -0.3047, -0.0723, -0.2136, -0.0679, 0.0685, 0.1337, 0.2813,\n", + " 0.1438, 0.1763, 0.2067, -0.1428, -0.1388, -0.2313, -0.1777, -0.2441,\n", + " -0.1887, -0.0653, -0.0906, -0.1186, -0.1159, 0.0013, -0.0067, -0.0092,\n", + " -0.0211, 0.0510, -0.0062, -0.0563, -0.1212, -0.1700, -0.1817, -0.1520,\n", + " -0.2022, -0.2650, -0.1399, -0.1658, -0.0006, -0.0316, 0.0278, 0.3048,\n", + " 0.3066, 0.3115, 0.2275]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0001599554088898003\n", + "Grad encoder.fc1.bias: 0.00012227035767864436\n", + "Grad encoder.encoder.0.weight: 3.736923827091232e-05\n", + "Grad encoder.encoder.0.bias: 0.00016008330567274243\n", + "Grad encoder.encoder.2.weight: 2.4274479073937982e-05\n", + "Grad encoder.encoder.2.bias: 0.00016659761604387313\n", + "Grad encoder.encoder.4.weight: 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decoder.fc1.2.weight: 7.424395880661905e-05\n", + "Grad decoder.fc1.2.bias: 0.0004150339518673718\n", + "Grad decoder.fc1.4.weight: 6.352084164973348e-05\n", + "Grad decoder.fc1.4.bias: 0.000724497833289206\n", + "Grad decoder.fc2.weight: 8.445179264526814e-05\n", + "Grad decoder.fc2.bias: 0.0026262737810611725\n", + "Grad _memory_unit.weight_ih_l0: 4.595351128955372e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 1.565597631270066e-05\n", + "Grad _memory_unit.bias_hh_l0: 8.073055141721852e-06\n", + "Grad _memory_unit.weight_ih_l1: 2.1302298591763247e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 6.882570596644655e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.482880492811091e-05\n", + "Data X Sample: tensor([[ 0.0191, 0.0102, 0.0270, 0.0044, 0.0068, 0.0309, 0.0154, 0.0256,\n", + " 0.0293, 0.0316, 0.0508, 0.0136, 0.0178, 0.0136, 0.0076, 0.0137,\n", + " -0.1447, -0.1489, -0.1797, -0.1637, -0.1889, -0.1087, -0.2024, 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" 4.1276, 4.3729, 4.6714, 4.7976, 1.8661, 1.0833, 1.0529, 1.8973, 2.7115,\n", + " 3.0821, 2.0873, 2.7524]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.6803, 2.0991, -0.2682, 1.1761, 0.1595, -0.6089, -0.8639, -0.2754,\n", + " -0.3721, -0.5268, -0.3218, -0.1509, 0.7438, -1.1497, -0.5273, 0.3319,\n", + " -1.2719, -0.8643, 0.7668, 0.8966, 0.3132, -0.7417, -0.1096, 0.2249,\n", + " 1.7484, -0.4148, 1.1287, 0.4356, -0.0395, -0.4316, 1.5183, 0.9875,\n", + " 0.9299, -0.9016, -0.6162, -0.9838, -0.0333, 0.0940, 0.2870, 0.3449,\n", + " 0.5405, 0.0249, -0.1386]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2168, -0.2620, -0.0698, -0.2033, -0.0838, 0.0573, 0.0972, 0.2323,\n", + " 0.1241, 0.1629, 0.1522, -0.1182, -0.1177, -0.1922, -0.1614, -0.2299,\n", + " -0.1908, -0.0765, -0.0712, -0.1178, -0.0938, -0.0143, -0.0138, -0.0149,\n", + " -0.0337, 0.0414, 0.0159, -0.0164, -0.0883, -0.1614, -0.1524, -0.1274,\n", + " -0.1759, -0.2182, -0.1303, -0.1344, -0.0093, -0.0161, 0.0054, 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_memory_unit.weight_ih_l0: 3.972250397055177e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 2.1868339899810962e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.1304677173029631e-05\n", + "Grad _memory_unit.weight_ih_l1: 2.053588332273648e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 6.104150088503957e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.0496301405946724e-05\n", + "Data X Sample: tensor([[1.4788, 1.6342, 1.6844, 1.8301, 1.9311, 2.0744, 2.0013, 3.6527, 4.2888,\n", + " 4.3474, 4.3651, 4.2767, 4.1212, 4.0161, 3.8358, 3.6432, 3.6589, 3.6289,\n", + " 3.5456, 3.4832, 3.4817, 3.4859, 3.0098, 2.9031, 2.8603, 2.8809, 2.9064,\n", + " 3.0059, 2.8759, 3.1474, 3.1601, 3.3713, 3.6323, 2.7096, 4.5518, 4.8912,\n", + " 5.1364, 5.4595, 6.0659, 5.7764, 1.2170, 0.6592, 0.5922, 1.1252, 1.6055,\n", + " 1.7555, 1.2782, 1.6029]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.0958e-02, -3.7320e-01, -1.3546e+00, -4.1525e-01, -3.0357e-01,\n", + " 5.9559e-01, 9.7960e-01, 2.5007e-01, 5.2689e-01, 1.5721e-01,\n", + " 1.8323e-01, 1.3563e-01, -1.1625e+00, -3.6035e-01, -3.0826e-01,\n", + " 8.8505e-01, 8.4276e-01, -5.6082e-01, 2.9876e-01, 3.2009e-01,\n", + " -5.0121e-02, 1.1533e+00, 2.5628e+00, -2.0429e-01, 2.7830e-01,\n", + " 4.1114e+00, -2.3431e-01, -6.8263e-01, -4.1523e-01, -9.8441e-01,\n", + " -1.9873e-02, 1.9410e-01, -1.0670e-01, -3.4404e-01, 1.2172e+00,\n", + " -2.9027e-01, -2.6554e-04, 8.4603e-02, -1.0390e-02, 4.1546e-01,\n", + " 4.5275e-01, 6.3908e-01, -2.1577e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.3155, -0.3403, -0.0920, -0.2326, -0.0728, 0.1067, 0.1721, 0.3335,\n", + " 0.1620, 0.2097, 0.2442, -0.1687, -0.1694, -0.2829, -0.2220, -0.2868,\n", + " -0.2154, -0.0957, -0.1173, -0.1472, -0.1387, -0.0030, 0.0006, 0.0157,\n", + " -0.0234, 0.0623, -0.0057, -0.0680, -0.1384, -0.2003, -0.2274, -0.1908,\n", + " -0.2314, -0.3197, -0.1679, -0.1861, -0.0117, -0.0384, 0.0267, 0.3540,\n", + " 0.3496, 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_memory_unit.weight_ih_l1: 1.108621972889523e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 4.204206197755411e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.06215372600127e-05\n", + "Data X Sample: tensor([[1.2741, 1.3779, 1.6514, 1.7886, 1.8970, 2.0081, 2.3172, 2.1067, 3.3735,\n", + " 4.0994, 4.3429, 4.3585, 4.0524, 3.9234, 3.8560, 3.5638, 3.6794, 3.4606,\n", + " 3.6200, 3.6469, 3.4817, 3.3726, 2.9640, 2.8439, 2.8434, 2.8652, 2.8904,\n", + " 2.9488, 3.0494, 3.3112, 3.4120, 3.4403, 3.8699, 2.9018, 4.7528, 4.8985,\n", + " 5.2407, 5.3403, 5.9264, 5.8416, 0.8782, 0.5636, 0.5901, 1.0907, 1.5302,\n", + " 1.7155, 1.1422, 1.6577]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.5811, -0.9406, -0.4162, -1.3028, -0.0155, 0.5095, 0.4229, 0.1866,\n", + " -0.2182, 0.1063, 0.4104, -0.4877, -0.6552, 0.1400, -0.7238, -0.7124,\n", + " -0.5874, -0.4237, 0.3506, -0.3201, 0.2976, -0.1610, -0.2861, -0.1719,\n", + " 0.4258, -0.1766, 0.0087, -0.7005, 0.3743, -0.7779, -0.0884, -0.4424,\n", + " -0.7788, -0.0810, -0.4106, -0.4261, -0.5893, 0.1123, 0.9794, 0.3375,\n", + " 0.5088, 0.3684, 0.6363]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.3571, -0.3721, -0.1063, -0.2459, -0.0703, 0.1342, 0.2057, 0.3783,\n", + " 0.1794, 0.2330, 0.2821, -0.1933, -0.1936, -0.3268, -0.2524, -0.3134,\n", + " -0.2289, -0.1093, -0.1359, -0.1568, -0.1596, -0.0011, 0.0084, 0.0289,\n", + " -0.0192, 0.0728, -0.0146, -0.0875, -0.1638, -0.2220, -0.2632, -0.2200,\n", + " -0.2579, -0.3652, -0.1874, -0.2096, -0.0135, -0.0455, 0.0309, 0.3970,\n", + " 0.3812, 0.4174, 0.3004]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00021216075401753187\n", + "Grad encoder.fc1.bias: 0.0003371468628756702\n", + "Grad encoder.encoder.0.weight: 5.504948057932779e-05\n", + "Grad encoder.encoder.0.bias: 0.00029596115928143263\n", + "Grad encoder.encoder.2.weight: 3.339747490826994e-05\n", + "Grad encoder.encoder.2.bias: 0.00022096914472058415\n", + "Grad encoder.encoder.4.weight: 0.00010061361535917968\n", + "Grad encoder.encoder.4.bias: 0.0005013598711229861\n", + "Grad decoder.fc1.0.weight: 3.824482701020315e-05\n", + "Grad decoder.fc1.0.bias: 0.00015192193677648902\n", + "Grad decoder.fc1.2.weight: 4.7000881750136614e-05\n", + "Grad decoder.fc1.2.bias: 0.00019641758990474045\n", + "Grad decoder.fc1.4.weight: 5.8276600611861795e-05\n", + "Grad decoder.fc1.4.bias: 0.0004734481335617602\n", + "Grad decoder.fc2.weight: 0.0001073401581379585\n", + "Grad decoder.fc2.bias: 0.0016422334592789412\n", + "Grad _memory_unit.weight_ih_l0: 3.1493359529122245e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 8.251160579675343e-06\n", + "Grad _memory_unit.bias_hh_l0: 4.278949290892342e-06\n", + "Grad _memory_unit.weight_ih_l1: 1.3095043414068641e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 3.5935936466557905e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.870280539151281e-05\n", + "Data X Sample: tensor([[1.4693, 1.4652, 1.5732, 1.7842, 1.8372, 1.8902, 2.0984, 2.0173, 3.7518,\n", + " 4.0409, 4.3651, 4.2942, 4.1855, 3.9370, 3.8686, 3.6979, 3.7501, 3.6037,\n", + " 3.5580, 3.6413, 3.5749, 3.6910, 3.2267, 3.0062, 2.8734, 2.9201, 2.7972,\n", + " 2.8346, 3.0355, 3.1540, 3.3560, 3.3630, 3.7665, 2.8126, 4.6253, 4.9399,\n", + " 5.1954, 5.5205, 6.0088, 5.9665, 1.0118, 0.5994, 0.5618, 1.1051, 1.5579,\n", + " 1.8184, 1.1830, 1.6499]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.8894e-01, -7.4265e-01, -6.5714e-01, -6.8658e-01, -7.3660e-01,\n", + " 6.8723e-01, 7.3109e-02, 5.4469e-01, 4.2005e-01, 4.6870e-01,\n", + " 7.8571e-02, -3.8288e-01, 1.3556e+00, -9.1537e-01, -6.8272e-01,\n", + " -5.8889e-01, 6.2600e-01, -1.4587e-01, 6.7631e+00, 2.5443e+00,\n", + " -7.3248e-01, -1.6912e+00, -2.0402e+00, 2.1710e-01, 5.3331e-01,\n", + " 3.5807e-01, 5.6895e-03, 2.8363e-01, -6.6626e-01, -6.0826e-01,\n", + " -5.7036e-01, -6.8529e-01, -5.3561e-01, -1.9238e-01, -4.1682e-01,\n", + " 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"Grad _memory_unit.bias_ih_l1: 2.985327591886744e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.5021944818727206e-05\n", + "Data X Sample: tensor([[1.5796, 1.7245, 1.8497, 1.9045, 2.0284, 2.2924, 2.2663, 2.4417, 2.3873,\n", + " 2.4249, 2.5442, 2.5337, 2.3879, 2.4565, 2.4942, 2.2852, 2.2737, 2.4199,\n", + " 2.3438, 2.4046, 2.5125, 2.5122, 2.4026, 2.5176, 2.4962, 2.5283, 2.4322,\n", + " 2.3778, 2.2203, 2.3024, 2.2677, 2.2549, 2.2792, 1.4532, 1.9315, 1.7658,\n", + " 1.6282, 1.4974, 1.5339, 1.6001, 1.2600, 0.6831, 0.6811, 1.2687, 1.8116,\n", + " 1.9213, 1.3190, 1.8844]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.0505, 1.6079, -0.2793, 0.5396, -0.5251, -1.1041, -1.3283, -1.1098,\n", + " -0.7263, -0.9161, -0.8775, 1.1187, 0.3985, 0.1238, 0.2868, 1.2922,\n", + " 0.6887, 0.3334, 0.1744, 0.1491, 1.1347, -0.5294, 0.1338, -0.1416,\n", + " -1.5549, 0.6318, 1.0196, 1.1537, 0.7665, 0.8307, 0.7268, 0.9142,\n", + " 0.8596, 0.7992, -0.0825, 0.7326, -0.6348, -0.3227, -0.0231, -0.7335,\n", + " 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_memory_unit.bias_ih_l0: 8.571647413191386e-06\n", + "Grad _memory_unit.bias_hh_l0: 4.486798388825264e-06\n", + "Grad _memory_unit.weight_ih_l1: 8.157170441336348e-07\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 2.162265263905283e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.0964145985781215e-05\n", + "Data X Sample: tensor([[1.6199, 1.8920, 1.9429, 2.1013, 2.3580, 2.4235, 2.5513, 2.7058, 2.7168,\n", + " 2.6397, 2.7885, 2.7031, 2.6165, 2.5138, 2.5143, 2.5354, 2.5158, 2.5205,\n", + " 2.5503, 2.5273, 2.4511, 2.4250, 2.4387, 2.4164, 2.4887, 2.5153, 2.5521,\n", + " 2.5165, 2.2688, 2.4236, 2.3202, 2.3212, 2.2176, 1.4440, 1.9609, 1.8850,\n", + " 1.6872, 1.6644, 1.6353, 1.5888, 1.2791, 0.7269, 0.7417, 1.3146, 1.9306,\n", + " 2.2530, 1.3666, 1.9157]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.3590, -0.5681, -0.3958, 0.1895, -1.2118, 0.4085, 0.1618, 0.6646,\n", + " -0.2678, -0.2198, -0.1527, -1.0844, -0.8103, -1.1903, 1.0239, -0.0333,\n", + " 0.2636, 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_memory_unit.bias_hh_l0: 6.562494490935933e-06\n", + "Grad _memory_unit.weight_ih_l1: 7.759751383673574e-07\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 2.1791234757984057e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.140647418651497e-05\n", + "Data X Sample: tensor([[1.6846, 1.8774, 2.0571, 2.2346, 2.2419, 2.3616, 2.4358, 2.4616, 2.4801,\n", + " 2.5291, 2.5664, 2.5181, 2.4035, 2.4593, 2.4160, 2.4069, 2.3696, 2.4199,\n", + " 2.4883, 2.5236, 2.6106, 2.6194, 2.6459, 2.6798, 2.6989, 2.7477, 2.6933,\n", + " 2.6552, 2.4284, 2.5808, 2.4007, 2.2908, 2.0064, 1.2655, 1.7722, 1.7245,\n", + " 1.6577, 1.6352, 1.5212, 1.4583, 1.3936, 0.7846, 0.8003, 1.3261, 1.9623,\n", + " 2.3788, 1.4822, 2.0095]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.7780e-01, 2.8853e-01, 1.0313e-01, 8.4885e-01, -6.1314e-01,\n", + " 1.0436e+00, 3.0909e-02, 1.0347e-01, -2.1647e-01, -2.7878e-01,\n", + " -4.4942e-01, 1.3522e+00, -1.2097e-01, -1.4561e-01, 2.6420e-01,\n", + " -6.5070e-01, -3.1149e+00, 7.8468e-01, 8.9712e-01, 4.7325e-02,\n", + " 6.8104e-01, -1.9253e-01, 2.5887e-02, -6.9781e-01, -1.3086e+00,\n", + " -3.8821e-01, -1.0050e+00, -5.9651e-01, -1.7559e-01, 4.0229e-01,\n", + " -2.2925e-03, -1.3098e-01, -7.4501e-01, -6.8725e-01, 4.2130e-01,\n", + " 1.1785e+00, -5.8615e-01, 2.3763e-01, 5.6860e-01, -2.7122e-01,\n", + " -5.5658e-01, -1.7062e-01, -1.2946e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4164, 0.3354, 0.0365, 0.2660, 0.0825, -0.1136, -0.3644, -0.4373,\n", + " -0.1781, -0.2614, -0.2669, 0.2184, 0.1826, 0.2709, 0.1375, 0.2871,\n", + " 0.2330, 0.0812, 0.0823, 0.1166, 0.0509, 0.0499, 0.1086, -0.0142,\n", + " 0.0257, 0.0249, -0.0568, 0.0168, 0.1814, 0.3031, 0.2193, 0.2148,\n", + " 0.2555, 0.2565, 0.1739, 0.1968, -0.0548, -0.0173, -0.0212, -0.2926,\n", + " -0.2829, -0.2991, -0.3024]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00018801141413860023\n", + "Grad encoder.fc1.bias: 0.0005282334750518203\n", + "Grad encoder.encoder.0.weight: 6.111682887421921e-05\n", + "Grad encoder.encoder.0.bias: 0.0004624478751793504\n", + "Grad encoder.encoder.2.weight: 3.9473259676015005e-05\n", + "Grad encoder.encoder.2.bias: 0.00044174870708957314\n", + "Grad encoder.encoder.4.weight: 0.00013986002886667848\n", + "Grad encoder.encoder.4.bias: 0.0007710956851951778\n", + "Grad decoder.fc1.0.weight: 5.025237987865694e-05\n", + "Grad decoder.fc1.0.bias: 0.0003211855364497751\n", + "Grad decoder.fc1.2.weight: 5.884069832973182e-05\n", + "Grad decoder.fc1.2.bias: 0.00031926314113661647\n", + "Grad decoder.fc1.4.weight: 5.759455598308705e-05\n", + "Grad decoder.fc1.4.bias: 0.0003807747852988541\n", + "Grad decoder.fc2.weight: 0.00012011644139420241\n", + "Grad decoder.fc2.bias: 0.001979992026463151\n", + "Grad _memory_unit.weight_ih_l0: 6.982490958762355e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 2.4447726900689304e-05\n", + "Grad _memory_unit.bias_hh_l0: 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_memory_unit.bias_hh_l1: 1.8584012650535442e-05\n", + "Data X Sample: tensor([[1.4396, 1.6080, 1.6935, 1.8367, 1.8748, 2.0154, 2.0352, 2.0556, 3.4345,\n", + " 4.1752, 4.2033, 4.0995, 3.8948, 3.7652, 3.7324, 3.6103, 3.5111, 3.5225,\n", + " 3.5064, 3.3865, 3.3933, 3.2869, 2.7496, 2.7924, 2.7664, 2.9253, 3.0049,\n", + " 3.0222, 3.0355, 3.1540, 3.3035, 3.2662, 3.6609, 2.6936, 4.4415, 4.8912,\n", + " 4.9634, 5.4569, 5.9074, 5.7196, 1.0691, 0.6233, 0.5982, 1.0850, 1.6887,\n", + " 1.7383, 1.2442, 1.5717]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.4942, -0.9827, -0.2991, -0.1817, 0.0172, 0.0311, -0.0621, 0.6739,\n", + " 0.2706, 0.5217, 0.6761, -0.5116, 2.3475, 0.4070, -0.1843, -0.6503,\n", + " -0.3085, -0.2021, -0.0330, 0.1176, -0.3939, 0.6179, 0.6704, 0.3596,\n", + " 0.4784, 0.1797, 0.7424, 0.0467, -0.1965, -0.0930, 0.2701, -0.9290,\n", + " -1.1542, -0.9745, -0.2213, -0.4029, 0.0540, -0.1011, -1.1206, 0.6362,\n", + " 0.6487, 0.4677, 0.8740]], device='cuda:0')\n", + "Prediction Sample: tensor([[-3.3360e-01, -3.6620e-01, -1.2740e-01, -2.1896e-01, -7.8477e-02,\n", + " 1.6922e-01, 2.0690e-01, 3.6437e-01, 2.0510e-01, 2.5682e-01,\n", + " 2.7491e-01, -2.1342e-01, -2.0406e-01, -3.4775e-01, -2.6030e-01,\n", + " -3.1861e-01, -2.4986e-01, -1.3291e-01, -1.3212e-01, -1.5949e-01,\n", + " -1.5085e-01, -5.8069e-02, -2.8701e-02, 4.7392e-04, -2.8002e-02,\n", + " 8.4121e-02, -3.6867e-02, -8.5447e-02, -2.1846e-01, -2.5410e-01,\n", + " -2.8978e-01, -2.5226e-01, -2.9966e-01, -3.6953e-01, -1.8615e-01,\n", + " -2.1783e-01, -2.7850e-02, -6.7647e-03, 1.3729e-04, 3.7680e-01,\n", + " 3.8957e-01, 4.2458e-01, 2.9663e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002720014890655875\n", + "Grad encoder.fc1.bias: 0.0003445831243880093\n", + "Grad encoder.encoder.0.weight: 7.67228048061952e-05\n", + "Grad encoder.encoder.0.bias: 0.0003710180753841996\n", + "Grad encoder.encoder.2.weight: 5.069900362286717e-05\n", + "Grad encoder.encoder.2.bias: 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_memory_unit.bias_hh_l1: 3.1498839234700426e-05\n", + "Data X Sample: tensor([[1.3844, 1.6007, 1.8001, 1.8586, 2.0080, 2.0140, 2.1693, 1.9804, 3.5028,\n", + " 4.3332, 4.3239, 4.3663, 4.2321, 3.9943, 3.8383, 3.6350, 3.6243, 3.6289,\n", + " 3.5539, 3.6004, 3.5062, 3.4246, 2.9665, 2.8764, 2.7514, 2.8940, 2.8877,\n", + " 2.9407, 2.9453, 3.1081, 3.2021, 3.3409, 3.8149, 2.8721, 4.5224, 4.9423,\n", + " 5.0381, 5.5443, 5.9581, 5.9977, 1.0595, 0.6393, 0.6609, 1.1395, 1.6213,\n", + " 1.8012, 1.1626, 1.7671]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.2765, -0.8500, -1.4990, -1.0419, 0.4489, 0.6153, 0.2104, 0.4135,\n", + " 0.2790, 0.5725, 0.5433, 0.0877, 0.3064, 0.4010, -0.8604, -0.5531,\n", + " -1.3425, -0.3836, -1.2447, -1.1085, -1.6494, -1.5091, -0.3409, -0.7788,\n", + " -0.5308, 0.9506, -0.0835, -0.3883, -0.2723, -0.2966, -0.2014, -0.9613,\n", + " -0.3145, -0.2010, 0.3232, -0.6725, -0.5204, -0.4903, -0.3491, 0.5643,\n", + " 0.7127, 0.5821, 0.4269]], device='cuda:0')\n", + "Prediction Sample: tensor([[-3.2553e-01, -3.5917e-01, -1.2573e-01, -2.1817e-01, -7.9041e-02,\n", + " 1.6649e-01, 2.0120e-01, 3.5491e-01, 2.0254e-01, 2.5366e-01,\n", + " 2.6805e-01, -2.0876e-01, -2.0049e-01, -3.4220e-01, -2.5413e-01,\n", + " -3.1340e-01, -2.4731e-01, -1.3400e-01, -1.2733e-01, -1.5752e-01,\n", + " -1.4587e-01, -5.5625e-02, -3.2662e-02, -3.5956e-03, -3.2103e-02,\n", + " 7.8891e-02, -3.4914e-02, -8.2263e-02, -2.1439e-01, -2.5100e-01,\n", + " -2.8627e-01, -2.4747e-01, -2.9449e-01, -3.6142e-01, -1.8244e-01,\n", + " -2.1562e-01, -2.7879e-02, -6.4777e-03, -3.7048e-04, 3.6938e-01,\n", + " 3.8480e-01, 4.1567e-01, 2.9035e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0005098152905702591\n", + "Grad encoder.fc1.bias: 0.0005292388377711177\n", + "Grad encoder.encoder.0.weight: 0.0001437556347809732\n", + "Grad encoder.encoder.0.bias: 0.0005878875381313264\n", + "Grad encoder.encoder.2.weight: 9.081844473257661e-05\n", + "Grad encoder.encoder.2.bias: 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"Grad _memory_unit.bias_ih_l1: 0.0001277086848858744\n", + "Grad _memory_unit.bias_hh_l1: 6.486443453468382e-05\n", + "Data X Sample: tensor([[2.5089, 2.8256, 3.0503, 3.0327, 3.3688, 3.3767, 3.6853, 3.7123, 3.6493,\n", + " 3.8814, 3.8734, 3.7820, 3.8504, 3.6507, 3.7223, 3.5078, 3.4419, 3.5089,\n", + " 3.4321, 3.3047, 3.2731, 3.1874, 3.0002, 2.8535, 2.7627, 2.6641, 2.7519,\n", + " 2.7204, 2.6019, 2.7380, 2.7156, 2.7771, 2.7413, 1.9521, 3.4047, 3.7773,\n", + " 3.8995, 4.1211, 4.5320, 4.3947, 1.9806, 1.1570, 1.0206, 2.0322, 2.7710,\n", + " 2.8420, 2.1757, 2.9166]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.0620, 0.0263, 0.2645, -0.3194, 0.1888, 0.9180, -0.0477, 0.0124,\n", + " -0.1503, 0.0276, -0.0309, -0.5000, -0.6984, 0.5083, 0.1344, 0.2701,\n", + " 0.1853, 0.6034, -1.3144, 0.0142, -0.1998, 0.0460, 0.3519, -0.3540,\n", + " -0.1428, -0.3507, -0.7507, -0.6858, 0.1868, -0.0486, -0.1689, 0.4898,\n", + " -0.4998, 0.1437, 0.4990, 0.5838, -0.4532, 0.0000, 1.1339, -0.0655,\n", + " 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"Grad decoder.fc1.0.weight: 4.355921191745438e-05\n", + "Grad decoder.fc1.0.bias: 0.00025096265017054975\n", + "Grad decoder.fc1.2.weight: 5.345923636923544e-05\n", + "Grad decoder.fc1.2.bias: 0.0003931279934477061\n", + "Grad decoder.fc1.4.weight: 5.800590952276252e-05\n", + "Grad decoder.fc1.4.bias: 0.0005839698133058846\n", + "Grad decoder.fc2.weight: 0.00011215317499591038\n", + "Grad decoder.fc2.bias: 0.002085787244141102\n", + "Grad _memory_unit.weight_ih_l0: 3.9786086745152716e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 1.3181080248614307e-05\n", + "Grad _memory_unit.bias_hh_l0: 7.292894224519841e-06\n", + "Grad _memory_unit.weight_ih_l1: 1.925980996020371e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 4.426016676006839e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.202601172029972e-05\n", + "Data X Sample: tensor([[1.5669, 1.8687, 1.9609, 2.0619, 2.1514, 2.2335, 2.3495, 2.4886, 2.5118,\n", + " 2.5418, 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"Grad encoder.encoder.0.bias: 0.000281050568446517\n", + "Grad encoder.encoder.2.weight: 4.718761920230463e-05\n", + "Grad encoder.encoder.2.bias: 0.0003674436593428254\n", + "Grad encoder.encoder.4.weight: 0.0001503162202425301\n", + "Grad encoder.encoder.4.bias: 0.0009938579751178622\n", + "Grad decoder.fc1.0.weight: 6.353836215566844e-05\n", + "Grad decoder.fc1.0.bias: 0.00043143052607774734\n", + "Grad decoder.fc1.2.weight: 0.00011500827531563118\n", + "Grad decoder.fc1.2.bias: 0.0005477939266711473\n", + "Grad decoder.fc1.4.weight: 5.55556507606525e-05\n", + "Grad decoder.fc1.4.bias: 0.000490837381221354\n", + "Grad decoder.fc2.weight: 9.981128823710606e-05\n", + "Grad decoder.fc2.bias: 0.0018416490638628602\n", + "Grad _memory_unit.weight_ih_l0: 4.507304765866138e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.5688506613951176e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.819194403651636e-05\n", + "Grad _memory_unit.weight_ih_l1: 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"Grad encoder.encoder.4.bias: 0.0009517064318060875\n", + "Grad decoder.fc1.0.weight: 6.616641621803865e-05\n", + "Grad decoder.fc1.0.bias: 0.00039372697938233614\n", + "Grad decoder.fc1.2.weight: 9.027465421240777e-05\n", + "Grad decoder.fc1.2.bias: 0.0009185806848108768\n", + "Grad decoder.fc1.4.weight: 6.395123637048528e-05\n", + "Grad decoder.fc1.4.bias: 0.0008496905793435872\n", + "Grad decoder.fc2.weight: 0.00010618795931804925\n", + "Grad decoder.fc2.bias: 0.0018784201238304377\n", + "Grad _memory_unit.weight_ih_l0: 1.0860561815206893e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 2.7983780455542728e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.722897468425799e-05\n", + "Grad _memory_unit.weight_ih_l1: 4.1951402636186685e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00011414126493036747\n", + "Grad _memory_unit.bias_hh_l1: 5.509277252713218e-05\n", + "Data X Sample: tensor([[1.5446, 1.6750, 1.8152, 1.9329, 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3.6269,\n", + " 3.6613, 3.4981, 3.4449, 3.4078, 3.0460, 2.8936, 2.9016, 2.9044, 2.9490,\n", + " 3.0304, 2.8759, 3.0262, 3.1041, 3.2635, 3.5949, 2.6798, 4.5297, 4.9739,\n", + " 5.1994, 5.5417, 6.2180, 6.0005, 1.1359, 0.6432, 0.6043, 1.1137, 1.6927,\n", + " 1.9156, 1.3054, 1.8062]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.0504, -0.0598, -0.0730, -0.8032, 0.3435, -0.1174, 0.3541, 0.3386,\n", + " 0.7500, 0.2707, 0.0664, -0.8404, -0.1176, 0.3604, 1.6510, -0.0319,\n", + " 0.0849, 0.0868, 0.9945, 0.2479, 2.1909, -0.3504, 0.8668, 0.4047,\n", + " 0.1087, 2.6985, 0.1546, -0.4839, -0.5720, -0.7571, -0.4129, -0.8077,\n", + " -0.0501, -0.7172, -0.0206, -0.9293, 0.4796, -0.1625, -0.5596, 0.2443,\n", + " 0.5748, 0.2561, 0.1782]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2490, -0.2931, -0.0905, -0.2105, -0.0725, 0.1300, 0.1528, 0.2723,\n", + " 0.1597, 0.2251, 0.1977, -0.1501, -0.1510, -0.2658, -0.1907, -0.2694,\n", + " -0.2093, -0.1079, -0.0697, -0.1406, -0.0913, -0.0488, 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1.1080, 1.7205,\n", + " 1.7383, 1.1286, 1.6342]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.9391e-01, -8.1508e-01, -1.1040e-01, -5.0022e-01, -6.2516e-01,\n", + " 3.2523e-01, 1.1447e-01, -1.2943e-02, 6.7558e-01, 2.5853e-01,\n", + " 7.2564e-01, 5.2661e-02, 4.5467e-01, -7.7529e-01, 8.3593e-04,\n", + " -9.0053e-01, 2.8255e-01, -4.7309e+00, 1.3646e+00, 3.1466e-01,\n", + " -5.2163e-02, 3.1032e-01, 1.0591e+00, -6.7917e-02, -3.3080e-01,\n", + " -1.6579e-01, 5.8619e-01, 1.4461e+00, 2.9773e-02, 5.3289e-02,\n", + " -5.1638e-01, -6.8420e-01, -6.0609e-01, -8.9792e-01, -4.2068e-01,\n", + " 9.0357e-03, 8.2914e-01, -7.2483e-01, 1.1238e+00, 6.1341e-01,\n", + " 6.9152e-01, 9.0996e-01, 8.1333e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2599, -0.3029, -0.0964, -0.2134, -0.0698, 0.1369, 0.1647, 0.2876,\n", + " 0.1663, 0.2343, 0.2100, -0.1594, -0.1574, -0.2760, -0.1971, -0.2757,\n", + " -0.2128, -0.1095, -0.0751, -0.1463, -0.0922, -0.0484, -0.0508, -0.0402,\n", + " -0.0529, 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-0.0126, -0.0447, -0.0668, -0.3322,\n", + " -0.3055, -0.2948, -0.3649]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0009827815229073167\n", + "Grad encoder.fc1.bias: 0.0016114383470267057\n", + "Grad encoder.encoder.0.weight: 0.00018042353622149676\n", + "Grad encoder.encoder.0.bias: 0.0012480092700570822\n", + "Grad encoder.encoder.2.weight: 0.00011625509796431288\n", + "Grad encoder.encoder.2.bias: 0.0011100503616034985\n", + "Grad encoder.encoder.4.weight: 0.00032876228215172887\n", + "Grad encoder.encoder.4.bias: 0.0020034208428114653\n", + "Grad decoder.fc1.0.weight: 0.0001158882150775753\n", + "Grad decoder.fc1.0.bias: 0.0006897993735037744\n", + "Grad decoder.fc1.2.weight: 0.00011072537745349109\n", + "Grad decoder.fc1.2.bias: 0.0013055324088782072\n", + "Grad decoder.fc1.4.weight: 8.170480577973649e-05\n", + "Grad decoder.fc1.4.bias: 0.0013059882912784815\n", + "Grad decoder.fc2.weight: 0.00014922481204848737\n", + "Grad decoder.fc2.bias: 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"Grad encoder.encoder.0.weight: 2.2611069653066806e-05\n", + "Grad encoder.encoder.0.bias: 2.588858660601545e-05\n", + "Grad encoder.encoder.2.weight: 1.686394534772262e-05\n", + "Grad encoder.encoder.2.bias: 4.511116640060209e-05\n", + "Grad encoder.encoder.4.weight: 5.5391421483363956e-05\n", + "Grad encoder.encoder.4.bias: 0.000134147223434411\n", + "Grad decoder.fc1.0.weight: 2.1815154468640685e-05\n", + "Grad decoder.fc1.0.bias: 6.630032294197008e-05\n", + "Grad decoder.fc1.2.weight: 3.768134774873033e-05\n", + "Grad decoder.fc1.2.bias: 0.00016938784392550588\n", + "Grad decoder.fc1.4.weight: 3.640924842329696e-05\n", + "Grad decoder.fc1.4.bias: 0.00042372976895421743\n", + "Grad decoder.fc2.weight: 7.700362039031461e-05\n", + "Grad decoder.fc2.bias: 0.001947836484760046\n", + "Grad _memory_unit.weight_ih_l0: 1.6036659644669271e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 7.022197678452358e-06\n", + "Grad _memory_unit.bias_hh_l0: 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"Data X Sample: tensor([[1.4841, 1.6153, 1.8152, 1.8542, 1.9892, 2.0876, 2.0814, 3.1402, 4.1521,\n", + " 4.1847, 4.3048, 4.3079, 4.0368, 4.0815, 3.8762, 3.6719, 3.6872, 3.6521,\n", + " 3.4899, 3.5390, 3.5013, 3.3052, 2.9640, 2.8115, 2.7908, 2.9149, 2.8691,\n", + " 2.8795, 2.9210, 3.1310, 3.3595, 3.3049, 3.7665, 2.8263, 4.6057, 4.7550,\n", + " 5.0027, 5.4993, 5.9201, 5.8672, 1.1550, 0.6691, 0.6750, 1.1596, 1.6848,\n", + " 1.8355, 1.2374, 1.7828]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.0811, 0.4488, -0.3432, 0.0269, 0.9583, 0.7202, 0.1865, 0.4459,\n", + " 0.6698, 0.1819, 0.0390, -1.2067, 0.4180, 0.0163, -0.7415, -0.4351,\n", + " -0.3730, -0.9574, -1.8775, 0.6923, -0.4257, -0.1396, 0.6139, -0.1499,\n", + " -0.8685, -0.5033, -1.5741, -1.4945, -1.0546, -0.9169, -1.0237, -0.5188,\n", + " -0.2252, -0.9233, -0.3467, -0.8537, -0.7416, 0.7348, -0.1229, 0.1744,\n", + " 0.3045, 0.0252, -0.3539]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1876, -0.2447, -0.0875, -0.2010, 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_memory_unit.bias_hh_l0: 9.220704669132829e-05\n", + "Grad _memory_unit.weight_ih_l1: 2.3924532797536813e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00042276812018826604\n", + "Grad _memory_unit.bias_hh_l1: 0.0002132972003892064\n", + "Data X Sample: tensor([[ 0.0085, 0.0131, 0.0210, 0.0087, 0.0051, 0.0192, 0.0247, 0.0099,\n", + " 0.0171, 0.0158, 0.0286, -0.0019, 0.0089, -0.0082, -0.0101, 0.0123,\n", + " -0.0487, -0.0754, -0.0929, -0.1023, -0.0883, -0.0643, -0.1446, -0.1508,\n", + " -0.1051, -0.2220, -0.2344, -0.4405, -0.0729, -0.0983, -0.0805, -0.0718,\n", + " -0.0770, -0.0595, -0.0539, -0.0341, -0.0315, -0.0424, -0.0507, -0.0113,\n", + " -0.0430, 0.0040, 0.0061, -0.0144, -0.0119, 0.0286, 0.0136, 0.0547]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 9.2592e-01, 1.0150e+00, 2.3872e+00, 1.0704e+00, -1.1228e-02,\n", + " -1.5727e+00, -1.3512e+00, -1.4450e+00, -1.3460e+00, -1.1605e+00,\n", + " -7.9645e-01, -1.0287e+00, 1.1159e-01, 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_memory_unit.bias_hh_l0: 4.837217602471355e-06\n", + "Grad _memory_unit.weight_ih_l1: 1.826110519687063e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 5.958205656497739e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.9449574867612682e-05\n", + "Data X Sample: tensor([[1.6549, 1.8964, 2.0015, 2.1931, 2.2709, 2.5001, 2.5513, 2.6490, 2.5997,\n", + " 2.7313, 2.6679, 2.7051, 2.5788, 2.4784, 2.4790, 2.5177, 2.4891, 2.4934,\n", + " 2.6329, 2.5032, 2.5149, 2.5520, 2.4845, 2.4927, 2.5600, 2.5597, 2.5148,\n", + " 2.5165, 2.2966, 2.3745, 2.3622, 2.3737, 2.1274, 1.3662, 1.8604, 1.7196,\n", + " 1.6636, 1.6008, 1.5529, 1.5122, 1.3029, 0.7906, 0.7235, 1.2917, 1.9623,\n", + " 2.0700, 1.4278, 2.1034]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.4894, -0.7940, -0.1920, 0.2395, -0.5505, 0.4178, 1.0359, 0.6573,\n", + " -0.0827, -0.3041, -0.0876, 0.6218, -0.7889, 0.0575, -0.2825, -0.2989,\n", + " -1.5450, 0.6842, -0.3380, 0.9117, -0.0042, -0.3494, 0.5854, 0.4766,\n", 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encoder.encoder.0.weight: 8.499275281792507e-05\n", + "Grad encoder.encoder.0.bias: 0.00043448235373944044\n", + "Grad encoder.encoder.2.weight: 7.145477866288275e-05\n", + "Grad encoder.encoder.2.bias: 0.0007198507664725184\n", + "Grad encoder.encoder.4.weight: 0.000229819372179918\n", + "Grad encoder.encoder.4.bias: 0.0017239218577742577\n", + "Grad decoder.fc1.0.weight: 7.787362119415775e-05\n", + "Grad decoder.fc1.0.bias: 0.0004959174548275769\n", + "Grad decoder.fc1.2.weight: 6.964857311686501e-05\n", + "Grad decoder.fc1.2.bias: 0.000980034121312201\n", + "Grad decoder.fc1.4.weight: 7.758704305160791e-05\n", + "Grad decoder.fc1.4.bias: 0.0011355557944625616\n", + "Grad decoder.fc2.weight: 0.00015549179806839675\n", + "Grad decoder.fc2.bias: 0.002425775397568941\n", + "Grad _memory_unit.weight_ih_l0: 2.0724943169625476e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 6.582046626135707e-05\n", + "Grad _memory_unit.bias_hh_l0: 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-1.4319, -1.7012, -0.2198, -0.5833, 1.4242,\n", + " 1.1210, -0.0127, -1.0841, 0.1921, -0.8287, 0.5417, -0.7130, -0.6816,\n", + " -0.1035, 0.8422, -0.6135]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2321, 0.2202, 0.1107, 0.1378, 0.0760, -0.0350, -0.2192, -0.2252,\n", + " -0.1233, -0.1367, -0.1524, 0.1390, 0.1542, 0.1930, 0.1266, 0.1993,\n", + " 0.1552, 0.0955, 0.0864, 0.0618, 0.0336, 0.0792, 0.0926, 0.0134,\n", + " 0.0577, 0.0272, -0.0608, 0.0139, 0.0540, 0.1335, 0.1280, 0.0779,\n", + " 0.1508, 0.1598, 0.1012, 0.0991, 0.0125, 0.0025, -0.0169, -0.2068,\n", + " -0.1668, -0.1779, -0.2371]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003240539226680994\n", + "Grad encoder.fc1.bias: 0.00038285148912109435\n", + "Grad encoder.encoder.0.weight: 5.3585536079481244e-05\n", + "Grad encoder.encoder.0.bias: 0.00027084158500656486\n", + "Grad encoder.encoder.2.weight: 3.768564420170151e-05\n", + "Grad encoder.encoder.2.bias: 0.00023443330428563058\n", + "Grad encoder.encoder.4.weight: 0.0001223197323270142\n", + "Grad encoder.encoder.4.bias: 0.0006416091928258538\n", + "Grad decoder.fc1.0.weight: 3.5878758353646845e-05\n", + "Grad decoder.fc1.0.bias: 0.0001861035416368395\n", + "Grad decoder.fc1.2.weight: 4.2322459194110706e-05\n", + "Grad decoder.fc1.2.bias: 0.00034414936089888215\n", + "Grad decoder.fc1.4.weight: 4.341309249866754e-05\n", + "Grad decoder.fc1.4.bias: 0.0005868154112249613\n", + "Grad decoder.fc2.weight: 9.098070586333051e-05\n", + "Grad decoder.fc2.bias: 0.0020139075350016356\n", + "Grad _memory_unit.weight_ih_l0: 4.711841484095203e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 2.493538886483293e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.3251199561636895e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.7036554709193297e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 3.2945070415735245e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.6444126231363043e-05\n", + "Data X Sample: tensor([[1.5000, 1.7667, 1.8768, 1.9263, 2.0199, 2.0228, 2.1646, 2.1323, 2.2238,\n", + " 2.2179, 2.2523, 2.2046, 2.2082, 2.2193, 2.2016, 2.1990, 2.1400, 2.1897,\n", + " 2.2364, 2.2335, 2.3236, 2.3668, 2.3640, 2.3305, 2.4643, 2.4604, 2.3549,\n", + " 2.3615, 2.2064, 2.2828, 2.4252, 2.3378, 2.2418, 1.5013, 2.0148, 1.8436,\n", + " 1.6833, 1.4682, 1.5719, 1.5491, 1.1884, 0.7229, 0.6791, 1.3376, 1.8116,\n", + " 1.8699, 1.2578, 1.8610]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.0047, 0.0555, 0.2579, 0.0328, 0.3670, -0.7540, 0.2200, -0.4570,\n", + " -0.7203, -0.0481, -0.3430, 1.1729, -0.7722, 0.4714, 0.4836, -0.0943,\n", + " 0.0829, -0.6617, 0.3855, -0.0787, -0.6684, 0.3811, -0.3830, 0.4413,\n", + " 0.0643, -0.2600, 1.4284, -1.1225, -1.0475, 0.8745, -0.0232, 0.3841,\n", + " 0.9764, 0.0625, 1.1639, -0.2194, -0.3148, -0.7055, 0.0040, -0.2054,\n", + " -0.3399, -0.1720, -0.3667]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2514, 0.2399, 0.1229, 0.1486, 0.0791, -0.0426, -0.2326, -0.2458,\n", + " -0.1350, -0.1541, -0.1694, 0.1499, 0.1608, 0.2112, 0.1423, 0.2146,\n", + " 0.1687, 0.1060, 0.0886, 0.0681, 0.0332, 0.0815, 0.0963, 0.0152,\n", + " 0.0634, 0.0250, -0.0608, 0.0169, 0.0666, 0.1474, 0.1453, 0.0917,\n", + " 0.1676, 0.1795, 0.1102, 0.1119, 0.0164, 0.0046, -0.0177, -0.2242,\n", + " -0.1820, -0.1945, -0.2531]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0005661282921209931\n", + "Grad encoder.fc1.bias: 0.0004714579554274678\n", + "Grad encoder.encoder.0.weight: 0.0001027665421133861\n", + "Grad encoder.encoder.0.bias: 0.0004136449424549937\n", + "Grad encoder.encoder.2.weight: 7.553414616268128e-05\n", + "Grad encoder.encoder.2.bias: 0.00045558513374999166\n", + "Grad encoder.encoder.4.weight: 0.00026789819821715355\n", + "Grad encoder.encoder.4.bias: 0.0014567140024155378\n", + "Grad decoder.fc1.0.weight: 8.598563726991415e-05\n", + "Grad decoder.fc1.0.bias: 0.00040631083538755774\n", + "Grad decoder.fc1.2.weight: 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2.9756,\n", + " 3.0426, 3.0321, 3.2358, 3.2860, 3.3768, 3.7599, 2.7737, 4.5788, 4.8791,\n", + " 5.1325, 5.5655, 6.0532, 5.8899, 1.1072, 0.6791, 0.6609, 1.2285, 1.8751,\n", + " 1.8756, 1.2102, 1.7984]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 7.4442e-01, 5.7557e-01, 5.8722e-01, 1.8544e+00, 7.7836e-01,\n", + " 5.8339e-01, 5.2409e-01, -1.3477e-01, -7.3603e-01, -3.5812e-01,\n", + " -1.6667e+00, -1.8045e-01, -9.2528e-02, 3.3277e-01, 2.2575e-01,\n", + " 9.1946e-01, 3.0279e-01, 2.3293e-01, -2.9041e-01, 3.2480e-01,\n", + " 3.8457e-01, 4.6525e-01, 4.6300e-01, 3.1476e-01, 3.6967e-02,\n", + " 2.0058e-01, -2.6737e-01, 2.8190e-01, -6.7186e-01, -3.8128e-01,\n", + " 2.8400e-01, -1.4855e-03, 1.0696e-01, 8.8774e-01, 6.1013e-02,\n", + " 2.5802e-01, -9.0653e-01, 6.5219e-01, -8.8267e-01, -7.6678e-01,\n", + " -5.9315e-01, -4.9175e-01, -2.9426e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2255, -0.2847, -0.0901, -0.2104, -0.0299, 0.1321, 0.1639, 0.2529,\n", + " 0.1656, 0.2266, 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_memory_unit.bias_hh_l1: 1.3201954061514698e-05\n", + "Data X Sample: tensor([[2.1312, 2.5489, 2.8114, 2.8600, 3.0888, 3.0600, 3.1337, 3.1516, 3.2954,\n", + " 3.3285, 3.2643, 3.2601, 3.1913, 3.2254, 3.2280, 3.2561, 3.2045, 3.3407,\n", + " 3.3061, 3.1634, 3.0915, 3.0710, 2.8821, 2.6836, 2.5468, 2.6093, 2.4748,\n", + " 2.5369, 2.1509, 2.1911, 2.2467, 2.1830, 2.0130, 1.3708, 2.0369, 2.3763,\n", + " 2.6626, 2.8437, 3.1629, 3.0471, 1.7754, 1.0017, 1.0307, 1.7194, 2.6124,\n", + " 2.7505, 1.9309, 2.7211]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.5248, 0.4639, -1.7575, 0.5277, -1.3045, 1.5737, 1.1191, 0.7314,\n", + " 1.2075, 0.7821, 0.9103, 0.1650, -0.8316, -0.8012, -0.7003, -1.1256,\n", + " -1.6280, -0.0783, 0.0054, -0.8318, -0.4366, 0.1579, -1.3680, 0.7038,\n", + " 0.3215, 0.4538, 0.9168, -0.1451, 0.1311, -1.2175, -1.0272, -1.4114,\n", + " -0.0632, -0.9714, -0.6486, 0.1463, 0.9776, -0.0751, 1.1723, 0.8475,\n", + " 0.8913, 0.6383, 0.0076]], device='cuda:0')\n", + "Prediction 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2.4992,\n", + " 2.4636, 2.4065, 2.3800, 2.3561, 2.3327, 2.3572, 2.3554, 2.3768, 2.3043,\n", + " 2.3574, 2.1544, 2.3155, 2.2362, 2.1582, 2.2484, 1.4509, 1.9242, 1.6977,\n", + " 1.5928, 1.4444, 1.5402, 1.5774, 1.1407, 0.6771, 0.6447, 1.2027, 1.7165,\n", + " 1.8355, 1.3326, 1.9470]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.6673, 0.2437, 0.8463, 0.5227, -0.7357, 0.3967, -0.4561, -0.6193,\n", + " 0.4708, -0.7616, -0.8905, -0.3128, 0.6231, 0.0507, 1.6476, 0.1791,\n", + " 1.2158, 1.0048, 0.2391, 0.3759, 0.0033, -0.2434, 0.0032, 0.0325,\n", + " -0.3212, -0.3691, 0.5161, 0.9004, 1.0815, 0.5486, 0.8131, 0.3487,\n", + " 0.2657, 0.2293, 1.2171, 1.7814, -0.4969, -0.3178, 0.0173, -0.5849,\n", + " -0.4808, -1.0026, -0.1829]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3483, 0.3268, 0.1672, 0.2246, 0.1132, -0.0558, -0.2979, -0.3537,\n", + " -0.1926, -0.2468, -0.2395, 0.2138, 0.1718, 0.2854, 0.2161, 0.2984,\n", + " 0.2400, 0.1605, 0.1227, 0.1028, 0.0284, 0.0772, 0.1016, 0.0284,\n", + " 0.0747, 0.0138, -0.0864, 0.0154, 0.1111, 0.2214, 0.2406, 0.1588,\n", + " 0.2588, 0.2801, 0.1662, 0.1865, 0.0170, 0.0163, -0.0028, -0.3019,\n", + " -0.2601, -0.2694, -0.3024]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002412644971627742\n", + "Grad encoder.fc1.bias: 0.0001841323246480897\n", + "Grad encoder.encoder.0.weight: 4.927626287098974e-05\n", + "Grad encoder.encoder.0.bias: 0.00017870235024020076\n", + "Grad encoder.encoder.2.weight: 2.548925840528682e-05\n", + "Grad encoder.encoder.2.bias: 0.00016697954561095685\n", + "Grad encoder.encoder.4.weight: 7.978677604114637e-05\n", + "Grad encoder.encoder.4.bias: 0.00034316343953832984\n", + "Grad decoder.fc1.0.weight: 2.846937786671333e-05\n", + "Grad decoder.fc1.0.bias: 0.00019187433645129204\n", + "Grad decoder.fc1.2.weight: 3.258304059272632e-05\n", + "Grad decoder.fc1.2.bias: 0.00021328004368115216\n", + "Grad decoder.fc1.4.weight: 4.1799270547926426e-05\n", + "Grad decoder.fc1.4.bias: 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"Grad _memory_unit.bias_ih_l0: 2.5467727027717046e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.11623828363372e-05\n", + "Grad _memory_unit.weight_ih_l1: 5.433717888081446e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 6.336078513413668e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.393507358850911e-05\n", + "Data X Sample: tensor([[1.3430, 1.6124, 1.8587, 1.9854, 2.0284, 2.0935, 2.1985, 2.4630, 3.8788,\n", + " 4.1531, 4.0891, 4.0274, 4.0280, 3.6753, 3.7223, 3.6623, 3.5190, 3.4857,\n", + " 3.4403, 3.3791, 3.4154, 3.3037, 2.9472, 2.8764, 2.9034, 3.0220, 3.0129,\n", + " 3.2098, 3.1292, 3.2653, 3.3700, 3.4016, 3.6653, 2.6203, 4.4145, 4.6407,\n", + " 4.8179, 5.2236, 5.7553, 5.5948, 1.1120, 0.6970, 0.6649, 1.2113, 1.7006,\n", + " 1.9671, 1.2102, 1.8141]], device='cuda:0')\n", + "Data Y Sample: tensor([[-4.9439e-03, 8.2781e-01, 6.4198e-01, 9.1527e-01, 6.5695e-01,\n", + " -5.1326e-01, -5.3007e-01, -2.4584e-01, 5.4793e-01, -5.5925e-01,\n", + " -1.6497e-01, 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_memory_unit.bias_ih_l0: 1.4977590581111144e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.1305588486720808e-05\n", + "Grad _memory_unit.weight_ih_l1: 3.1504168873652816e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 5.8209341659676284e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.9347907911869697e-05\n", + "Data X Sample: tensor([[1.2603, 1.5002, 1.5913, 1.7033, 1.7792, 1.9462, 2.0568, 1.9420, 3.2734,\n", + " 3.9809, 3.8829, 3.8074, 3.5353, 3.4626, 3.3793, 3.3218, 3.2470, 3.2207,\n", + " 3.3082, 3.2415, 3.2706, 3.1567, 2.7713, 2.7199, 2.7833, 2.8261, 2.9437,\n", + " 2.8672, 2.8898, 2.9902, 2.9046, 3.2055, 3.1901, 2.2862, 3.8851, 4.3245,\n", + " 4.5190, 5.0567, 5.3369, 5.3196, 1.0834, 0.5954, 0.5780, 1.0046, 1.4985,\n", + " 1.7269, 1.0674, 1.6186]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.2056, -0.7765, -0.7195, -0.2654, -0.1100, 0.7892, -0.0251, 0.3684,\n", + " -0.2134, -0.0798, 0.3368, -0.2547, 1.3851, -0.4338, 0.2501, 0.3512,\n", + " -0.6230, 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+ "Grad _memory_unit.bias_ih_l1: 5.741523273172788e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.88407154585002e-05\n", + "Data X Sample: tensor([[1.4947, 1.6823, 1.8497, 1.8170, 1.9465, 2.0140, 2.0953, 2.0812, 2.9219,\n", + " 3.7597, 4.0225, 4.0294, 3.9703, 3.7734, 3.7324, 3.4394, 3.3539, 3.4432,\n", + " 3.3102, 3.4014, 3.3516, 3.3496, 2.7929, 2.8439, 2.7495, 2.8156, 2.8584,\n", + " 2.8795, 3.0043, 3.0557, 3.0971, 3.2773, 3.4937, 2.5837, 4.3140, 4.7039,\n", + " 4.8218, 5.2978, 5.9771, 5.6799, 1.0977, 0.6213, 0.6750, 1.3032, 1.7086,\n", + " 1.7555, 1.1830, 1.9548]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.2830, -0.6898, -0.2064, -0.3871, -0.2265, 0.7746, 0.6833, 0.5565,\n", + " 0.2748, 0.5750, 0.5518, -1.0268, -0.2475, -0.9934, -0.8744, -1.6947,\n", + " -2.1588, 0.0086, -0.0100, 2.1861, -0.0227, -0.2232, -0.0894, 0.7957,\n", + " -0.5438, 0.1845, 0.2294, 0.8050, -0.5462, -0.0175, 0.5691, -1.2840,\n", + " -0.3011, -0.9295, 0.1223, -0.6674, 0.0000, 0.0000, 0.0000, 0.7401,\n", + " 1.1485, 1.0258, 0.5415]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2108, -0.2900, -0.0904, -0.1725, 0.0208, 0.1720, 0.1741, 0.2627,\n", + " 0.2052, 0.2542, 0.1880, -0.1659, -0.1709, -0.2565, -0.1753, -0.2936,\n", + " -0.2216, -0.0938, -0.0312, -0.1439, -0.0973, -0.0572, -0.0275, -0.0489,\n", + " -0.0452, 0.0052, -0.0687, -0.1294, -0.2324, -0.2727, -0.2577, -0.1907,\n", + " -0.2618, -0.2627, -0.1766, -0.1511, -0.0304, 0.0377, 0.0279, 0.2464,\n", + " 0.3173, 0.3088, 0.2309]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00035570835461840034\n", + "Grad encoder.fc1.bias: 0.00047118356451392174\n", + "Grad encoder.encoder.0.weight: 5.658468580804765e-05\n", + "Grad encoder.encoder.0.bias: 0.0005574360257014632\n", + "Grad encoder.encoder.2.weight: 4.708981578005478e-05\n", + "Grad encoder.encoder.2.bias: 0.0006182133802212775\n", + "Grad encoder.encoder.4.weight: 0.000164799319463782\n", + "Grad encoder.encoder.4.bias: 0.0015574272256344557\n", + "Grad 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-1.1216, -1.0314,\n", + " -0.3255, -0.5795, -0.5905, -0.3760, 0.5092, 0.6280, 0.1829, 0.9140,\n", + " 0.2635, 0.4087, 0.4412, 0.9588, 0.7989, 0.2556, 0.9766, 0.6818,\n", + " 0.3980, 0.2869, 1.1681, 0.2620, 0.7115, 1.3594, 1.5527, 0.0143,\n", + " 0.6215, 0.4848, 0.3764, 0.6252, -0.5893, -0.2645, -0.3712, -0.7537,\n", + " -0.3909, -0.5896, -0.3067]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.6693, 0.5795, 0.2520, 0.4300, 0.1000, -0.2271, -0.6361, -0.7956,\n", + " -0.3149, -0.4429, -0.3810, 0.3988, 0.2844, 0.4824, 0.4281, 0.4737,\n", + " 0.4886, 0.2621, 0.2018, 0.1864, 0.0135, 0.0774, 0.0758, 0.0572,\n", + " 0.1113, 0.0575, -0.1227, 0.1515, 0.4173, 0.4976, 0.5502, 0.3240,\n", + " 0.5171, 0.5224, 0.2626, 0.2574, 0.0149, 0.0675, -0.0322, -0.5242,\n", + " -0.5205, -0.4598, -0.4391]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002709923719521612\n", + "Grad encoder.fc1.bias: 0.00032828658004291356\n", + "Grad encoder.encoder.0.weight: 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_memory_unit.bias_ih_l1: 5.072785643278621e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.536482315917965e-05\n", + "Data X Sample: tensor([[1.6210, 1.8323, 2.0150, 2.1494, 2.3375, 2.4839, 2.6191, 2.6802, 2.6705,\n", + " 2.7140, 2.7218, 2.7830, 2.6343, 2.5438, 2.5698, 2.4356, 2.4246, 2.5205,\n", + " 2.4904, 2.4548, 2.5419, 2.5046, 2.4580, 2.5328, 2.4342, 2.5100, 2.4455,\n", + " 2.4227, 2.3764, 2.4465, 2.3132, 2.2687, 2.2088, 1.3685, 1.9585, 1.8412,\n", + " 1.7502, 1.6193, 1.6670, 1.5008, 1.2647, 0.7050, 0.7377, 1.2859, 1.9068,\n", + " 2.1901, 1.4550, 2.1346]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.1058, 1.5308, 0.0589, -0.1135, 0.2249, 0.9795, -0.8772, 0.4481,\n", + " -0.0366, 0.0890, 0.1083, -0.3315, -0.1791, 0.0387, -0.2114, -0.0427,\n", + " -0.5737, 0.1484, -1.0247, -0.0385, -0.5718, 0.9024, 0.4311, 0.4554,\n", + " -1.0330, 0.9759, 0.4851, 0.0045, -0.5146, -0.3949, -0.4068, 0.3172,\n", + " 0.9792, -0.2732, 0.3098, -1.3007, 0.8233, 0.0000, 0.7285, -0.3906,\n", + " -0.2754, -0.0395, -0.9296]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3810, 0.2955, 0.0836, 0.2191, 0.0956, -0.1167, -0.3375, -0.3595,\n", + " -0.2061, -0.2714, -0.2787, 0.1940, 0.1311, 0.2634, 0.2383, 0.2668,\n", + " 0.2109, 0.1520, 0.1074, 0.0992, -0.0193, 0.0636, 0.0899, 0.0341,\n", + " 0.0368, -0.0338, -0.0765, 0.0474, 0.1978, 0.2634, 0.2821, 0.1642,\n", + " 0.2743, 0.2629, 0.1460, 0.1918, -0.0602, -0.0074, 0.0101, -0.3079,\n", + " -0.2583, -0.2461, -0.2334]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 8.755487942835316e-05\n", + "Grad encoder.fc1.bias: 0.0001505102845840156\n", + "Grad encoder.encoder.0.weight: 2.5133003873634152e-05\n", + "Grad encoder.encoder.0.bias: 0.00014221746823750436\n", + "Grad encoder.encoder.2.weight: 1.921592775033787e-05\n", + "Grad encoder.encoder.2.bias: 0.00019066774984821677\n", + "Grad encoder.encoder.4.weight: 5.376733315642923e-05\n", + "Grad encoder.encoder.4.bias: 0.0007110515143722296\n", + "Grad decoder.fc1.0.weight: 3.418284541112371e-05\n", + "Grad decoder.fc1.0.bias: 0.0003139657201245427\n", + "Grad decoder.fc1.2.weight: 4.5833538024453446e-05\n", + "Grad decoder.fc1.2.bias: 0.00024429187760688365\n", + "Grad decoder.fc1.4.weight: 4.881938002654351e-05\n", + "Grad decoder.fc1.4.bias: 0.0002714006113819778\n", + "Grad decoder.fc2.weight: 8.428141882177442e-05\n", + "Grad decoder.fc2.bias: 0.001325098448432982\n", + "Grad _memory_unit.weight_ih_l0: 4.469162377063185e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 2.8471882615122013e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.476708257541759e-05\n", + "Grad _memory_unit.weight_ih_l1: 2.343537971682963e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 7.805154018569738e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.907585778506473e-05\n", + "Data X Sample: tensor([[1.4056, 1.7158, 1.8918, 1.8935, 2.0438, 2.1244, 2.0337, 1.9080, 2.9365,\n", + " 3.8845, 4.3175, 4.1793, 4.1411, 4.0161, 3.9165, 3.5857, 3.6196, 3.5631,\n", + " 3.6035, 3.5167, 3.4449, 3.3588, 2.8990, 2.7771, 2.7908, 2.7947, 2.8877,\n", + " 2.8305, 2.9419, 3.1212, 3.3035, 3.3602, 3.8787, 2.8629, 4.6180, 4.8815,\n", + " 5.1266, 5.4966, 5.9961, 5.7877, 1.1550, 0.6452, 0.6690, 1.1482, 1.7680,\n", + " 1.7612, 1.2034, 1.8375]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.5976, 0.9808, -1.9404, 1.5049, 0.8035, 0.5361, 0.7085, 0.9105,\n", + " -0.4401, -0.4287, 0.1871, 1.0133, 1.3955, 1.5676, 0.3722, 0.6655,\n", + " 2.0015, 0.9011, 0.3688, 0.5695, -0.1925, 0.0905, -0.0168, 0.2291,\n", + " -0.1796, -0.2519, -0.6063, -0.9335, -0.7861, -0.0861, -0.6678, -0.1749,\n", + " 0.0242, -0.0151, 0.4812, -1.2719, 0.0000, 0.3924, 0.5906, -0.7695,\n", + " -0.2711, -0.7401, -0.4083]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2536, -0.3067, -0.1138, -0.1943, -0.0061, 0.1448, 0.1820, 0.3079,\n", + " 0.1952, 0.2441, 0.2191, -0.1860, -0.2043, -0.2876, -0.1918, -0.2819,\n", + " -0.2284, -0.0969, -0.0527, -0.1301, -0.1220, -0.0558, -0.0427, -0.0296,\n", + " -0.0355, 0.0038, -0.0679, -0.1197, -0.2125, -0.2393, -0.2660, -0.2164,\n", + " -0.2590, -0.3109, -0.1813, -0.1514, -0.0339, 0.0101, 0.0477, 0.3084,\n", + " 0.3419, 0.3356, 0.2598]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00010828455560840666\n", + "Grad encoder.fc1.bias: 9.706008131615818e-05\n", + "Grad encoder.encoder.0.weight: 2.1441832359414548e-05\n", + "Grad encoder.encoder.0.bias: 0.00011608458589762449\n", + "Grad encoder.encoder.2.weight: 1.7073945855372585e-05\n", + "Grad encoder.encoder.2.bias: 0.0001584039709996432\n", + "Grad encoder.encoder.4.weight: 4.702839578385465e-05\n", + "Grad encoder.encoder.4.bias: 0.00029822165379300714\n", + "Grad decoder.fc1.0.weight: 1.6621230315649882e-05\n", + "Grad decoder.fc1.0.bias: 0.00012478663120418787\n", + "Grad decoder.fc1.2.weight: 2.9736431315541267e-05\n", + "Grad decoder.fc1.2.bias: 0.0001638146350160241\n", + "Grad 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" 4.8926, 5.2978, 5.7236, 5.6714, 1.1216, 0.6193, 0.6265, 1.0965, 1.5341,\n", + " 1.7955, 1.2510, 1.7124]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.1002e+00, -1.2987e+00, 4.6326e-01, 1.4619e+00, -2.2114e-01,\n", + " 1.0005e-01, -9.6380e-02, 2.3624e-01, 4.2294e-01, 8.8437e-01,\n", + " 7.1189e-01, 7.6763e-01, 6.3398e-01, 4.8238e-01, -1.3417e+00,\n", + " -2.5445e-03, 1.6981e-01, -5.7287e-01, -7.4444e-01, 2.7684e-01,\n", + " -1.7037e+00, -8.0606e-02, 1.5137e-01, -6.6308e-01, 2.6691e-01,\n", + " 5.7280e+00, 3.1111e-01, -9.5250e-01, -1.3881e-01, -1.8519e-01,\n", + " 3.5923e-02, 1.0518e-01, -1.3437e+00, -1.5662e-01, -6.6577e-01,\n", + " -7.1342e-02, 3.2988e-03, 9.3563e-02, 8.3070e-01, 6.3330e-01,\n", + " 7.2354e-01, 6.1601e-01, 8.6101e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2510, -0.2971, -0.1109, -0.2045, -0.0148, 0.1360, 0.1705, 0.2959,\n", + " 0.1813, 0.2361, 0.2062, -0.1733, -0.1838, -0.2715, -0.1849, -0.2703,\n", + " -0.2140, -0.0909, -0.0525, -0.1314, -0.1229, -0.0522, -0.0419, -0.0359,\n", + " -0.0316, -0.0041, -0.0509, -0.1058, -0.1949, -0.2192, -0.2398, -0.1995,\n", + " -0.2431, -0.2905, -0.1658, -0.1514, -0.0419, 0.0138, 0.0458, 0.2919,\n", + " 0.3281, 0.3258, 0.2581]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.000226707779802382\n", + "Grad encoder.fc1.bias: 0.00020584845333360136\n", + "Grad encoder.encoder.0.weight: 4.860808257944882e-05\n", + "Grad encoder.encoder.0.bias: 0.00021296425256878138\n", + "Grad encoder.encoder.2.weight: 3.915444540325552e-05\n", + "Grad encoder.encoder.2.bias: 0.0002779854694381356\n", + "Grad encoder.encoder.4.weight: 0.00012524686462711543\n", + "Grad encoder.encoder.4.bias: 0.0005343860830180347\n", + "Grad decoder.fc1.0.weight: 4.044962042826228e-05\n", + "Grad decoder.fc1.0.bias: 0.00022574496688321233\n", + "Grad decoder.fc1.2.weight: 4.071422517881729e-05\n", + "Grad decoder.fc1.2.bias: 0.0004006385861430317\n", + "Grad decoder.fc1.4.weight: 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1.6797, 1.5718, 1.2170, 0.7508, 0.6972, 1.2113, 1.8671,\n", + " 2.0014, 1.3530, 1.9626]], device='cuda:0')\n", + "Data Y Sample: tensor([[-6.2277e-02, -8.5551e-02, 5.2826e-01, 7.5042e-01, 1.0577e-01,\n", + " -5.4429e-01, -3.0361e-01, -1.1078e-01, -9.0744e-01, 9.9722e-02,\n", + " -2.4266e-01, -7.9012e-01, 6.7935e-01, -1.7118e-01, 1.3956e-01,\n", + " 3.7247e-01, 1.0152e+00, -2.0172e-03, 8.9241e-01, 1.0236e+00,\n", + " 5.6634e-02, -1.3120e-01, -1.1534e-01, 7.8265e-01, -5.5249e-02,\n", + " 5.2103e-02, 7.9708e-03, -5.6843e-01, -1.8776e-01, 6.4464e-01,\n", + " 8.2980e-01, 2.3500e-01, 1.5575e-01, 2.1944e-01, 3.5976e-01,\n", + " -3.5028e-01, 5.6924e-01, -7.0551e-01, 2.2782e+00, -1.5953e-01,\n", + " -2.5613e-01, 5.3509e-02, 1.9577e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3952, 0.3036, 0.0676, 0.2352, 0.1181, -0.1142, -0.3471, -0.3661,\n", + " -0.2135, -0.2947, -0.2862, 0.2098, 0.1436, 0.2724, 0.2490, 0.2833,\n", + " 0.2179, 0.1433, 0.1102, 0.0936, -0.0146, 0.0680, 0.0867, 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device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0008116684621199965\n", + "Grad encoder.fc1.bias: 0.0006218298804014921\n", + "Grad encoder.encoder.0.weight: 0.00012922947644256055\n", + "Grad encoder.encoder.0.bias: 0.0006954205455258489\n", + "Grad encoder.encoder.2.weight: 0.00010493901208974421\n", + "Grad encoder.encoder.2.bias: 0.0010970492148771882\n", + "Grad encoder.encoder.4.weight: 0.0003083937044721097\n", + "Grad encoder.encoder.4.bias: 0.0030734974425286055\n", + "Grad decoder.fc1.0.weight: 0.00010384871711721644\n", + "Grad decoder.fc1.0.bias: 0.0007649608887732029\n", + "Grad decoder.fc1.2.weight: 8.330638229381293e-05\n", + "Grad decoder.fc1.2.bias: 0.0011127113830298185\n", + "Grad decoder.fc1.4.weight: 7.117877248674631e-05\n", + "Grad decoder.fc1.4.bias: 0.000720604439266026\n", + "Grad decoder.fc2.weight: 0.00012545540812425315\n", + "Grad decoder.fc2.bias: 0.0018574638525024056\n", + "Grad _memory_unit.weight_ih_l0: 3.1485029467148706e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 7.385603385046124e-05\n", + "Grad _memory_unit.bias_hh_l0: 5.752992001362145e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.2627440810319968e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00023119497927837074\n", + "Grad _memory_unit.bias_hh_l1: 0.00011866271961480379\n", + "Data X Sample: tensor([[1.5796, 1.7857, 1.8753, 1.9898, 1.9943, 2.0567, 2.0337, 2.5511, 4.1229,\n", + " 4.3363, 4.3746, 4.3974, 4.1434, 4.0379, 3.7173, 3.7293, 3.7910, 3.5999,\n", + " 3.6200, 3.5167, 3.4473, 3.2471, 2.9424, 2.8859, 2.8002, 2.8626, 3.0103,\n", + " 3.0712, 3.0910, 3.1376, 3.2336, 3.2469, 3.9139, 2.7874, 4.5199, 4.8304,\n", + " 5.0715, 5.4304, 5.8440, 5.7849, 1.1311, 0.6851, 0.6791, 1.2429, 1.7641,\n", + " 2.0986, 1.3802, 1.8453]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.3832, -0.7763, -0.1057, -0.7803, -0.1194, 0.5050, 0.3623, 0.3661,\n", + " -0.0174, 0.2392, 0.1825, -0.1279, 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-0.0073, 0.0165, 0.0087, -0.0068, -0.0088, 0.0000, 0.0085,\n", + " -0.0024, -0.0047, -0.0317, -0.0117, -0.0155, -0.0164, -0.0177, 0.0178,\n", + " 0.0503, 0.0600, 0.0289, 0.0167, 0.0638, 0.0321, -0.0096, -0.0115,\n", + " 0.0094, 0.0131, 0.0080, 0.0000, 0.0416, 0.0033, -0.0070, -0.0083,\n", + " -0.0352, -0.0046, -0.0490, -0.0170, -0.0079, -0.0239, -0.0634, -0.0113,\n", + " -0.0382, 0.0040, 0.0081, -0.0115, 0.0159, -0.0057, -0.0204, -0.0547]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1117, 0.2114, -1.2218, 0.3479, -0.4161, -0.2291, -0.3578, -0.3973,\n", + " 0.0526, -0.5137, -0.4356, -0.4082, 0.2094, 0.3030, 1.2166, 0.3505,\n", + " 0.7135, 1.5066, -0.9460, 0.2975, 0.1910, 0.1223, -0.3374, 0.1554,\n", + " 0.4428, 0.1485, 0.5190, 0.2558, 0.0499, 1.0333, 1.1340, 0.7060,\n", + " -0.3493, 0.0492, -0.0637, -0.1143, -0.5085, -0.6596, 0.0000, -0.1221,\n", + " -0.1939, -0.1009, 0.1196]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.7837, 0.5327, 0.1404, 0.4006, -0.1863, -0.4855, -0.9188, -0.9448,\n", + " -0.5398, -0.6739, -0.6533, 0.4046, 0.3227, 0.5323, 0.4538, 0.3698,\n", + " 0.4520, 0.0793, 0.1436, 0.0760, 0.0027, 0.0543, 0.0222, 0.0614,\n", + " 0.0723, -0.0839, 0.1209, 0.3654, 0.7722, 0.7987, 0.7216, 0.4071,\n", + " 0.6442, 0.5268, 0.3045, 0.2368, 0.0029, 0.0607, 0.0642, -0.6232,\n", + " -0.5708, -0.4415, -0.3767]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00021393834322225302\n", + "Grad encoder.fc1.bias: 0.0007453424041159451\n", + "Grad encoder.encoder.0.weight: 3.7851226807106286e-05\n", + "Grad encoder.encoder.0.bias: 0.00041367398807778955\n", + "Grad encoder.encoder.2.weight: 2.9308499506441876e-05\n", + "Grad encoder.encoder.2.bias: 0.0005237628356553614\n", + "Grad encoder.encoder.4.weight: 0.00010453484719619155\n", + "Grad encoder.encoder.4.bias: 0.0012343170819804072\n", + "Grad decoder.fc1.0.weight: 4.953532334184274e-05\n", + "Grad decoder.fc1.0.bias: 0.0005636645946651697\n", + "Grad decoder.fc1.2.weight: 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2.4908,\n", + " 2.5858, 2.1544, 2.1452, 2.0297, 1.9509, 1.6324, 1.0802, 1.4658, 1.4861,\n", + " 1.4178, 1.3914, 1.4071, 1.3647, 1.2695, 0.7129, 0.7882, 1.3032, 1.9345,\n", + " 1.9556, 1.3258, 2.0486]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1700, 0.2505, -0.3755, 0.1905, -0.2399, -0.9207, -0.6723, -0.1978,\n", + " 0.4042, -0.3682, -0.3391, -0.6122, 0.9782, 0.8017, 1.4487, 0.1993,\n", + " 0.8028, 0.3205, 0.3169, 1.2142, 0.1330, -0.0828, 0.6163, 0.9089,\n", + " 0.9857, 1.0724, -1.4239, -0.4775, -0.5055, 0.0307, 0.2882, -0.5803,\n", + " 0.9163, -0.0038, 0.6357, -0.0316, -0.4285, -0.4903, 0.0000, -0.0652,\n", + " -0.1431, 0.1662, -0.3507]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 3.0161e-01, 2.2861e-01, 5.2192e-02, 1.7818e-01, 6.4947e-02,\n", + " -1.0622e-01, -2.9917e-01, -2.8337e-01, -1.5181e-01, -1.9743e-01,\n", + " -2.2688e-01, 1.4910e-01, 1.3124e-01, 2.0062e-01, 1.8284e-01,\n", + " 1.7937e-01, 1.6686e-01, 9.1471e-02, 6.5162e-02, 5.8388e-02,\n", + " -2.5205e-02, 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_memory_unit.bias_hh_l0: 1.06656289062812e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.8590158106235322e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 6.151913112262264e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.111887417617254e-05\n", + "Data X Sample: tensor([[1.7674, 1.8745, 2.1037, 2.2412, 2.4587, 2.4972, 2.5205, 2.6121, 2.7168,\n", + " 2.6997, 2.7980, 2.7479, 2.6920, 2.6556, 2.5976, 2.5724, 2.5174, 2.6269,\n", + " 2.5400, 2.6092, 2.6253, 2.6531, 2.6339, 2.5844, 2.6688, 2.7216, 2.6213,\n", + " 2.6511, 2.4770, 2.5808, 2.4287, 2.4234, 2.3452, 1.5196, 1.9658, 1.8971,\n", + " 1.7659, 1.6750, 1.6733, 1.6313, 1.3125, 0.7129, 0.7720, 1.3319, 1.9940,\n", + " 2.1844, 1.4346, 2.0330]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.0713, -0.1935, 0.9554, 0.0371, -0.0326, -0.0786, -0.4714, -0.2559,\n", + " -0.5163, 0.0396, -0.1553, -0.0314, 1.9428, 0.1439, -0.8195, -0.0216,\n", + " -0.4384, -0.7471, -0.5745, -0.4020, 0.4904, -0.4974, 0.4615, 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" 0.2028, 0.1368, 0.1388]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00012802748824469745\n", + "Grad encoder.fc1.bias: 0.00011140772403450683\n", + "Grad encoder.encoder.0.weight: 2.0763694919878617e-05\n", + "Grad encoder.encoder.0.bias: 0.0001003227080218494\n", + "Grad encoder.encoder.2.weight: 1.7209733414347284e-05\n", + "Grad encoder.encoder.2.bias: 0.00015872390940785408\n", + "Grad encoder.encoder.4.weight: 6.28435518592596e-05\n", + "Grad encoder.encoder.4.bias: 0.000619887956418097\n", + "Grad decoder.fc1.0.weight: 3.064162592636421e-05\n", + "Grad decoder.fc1.0.bias: 0.00024579904857091606\n", + "Grad decoder.fc1.2.weight: 3.939524322049692e-05\n", + "Grad decoder.fc1.2.bias: 0.0003174972953274846\n", + "Grad decoder.fc1.4.weight: 4.321250889915973e-05\n", + "Grad decoder.fc1.4.bias: 0.00036591614480130374\n", + "Grad decoder.fc2.weight: 7.240776903927326e-05\n", + "Grad decoder.fc2.bias: 0.00263466639444232\n", + "Grad _memory_unit.weight_ih_l0: 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device='cuda:0')\n", + "Data Y Sample: tensor([[ 6.6249e-01, -8.7853e-02, -4.4641e-01, 8.6446e-01, 1.2221e+00,\n", + " -1.0467e+00, 1.0783e-01, -2.4441e-01, 3.9722e-01, 3.2233e-01,\n", + " 5.3808e-01, -9.0758e-01, -9.2251e-01, -6.2854e-04, 1.2202e+00,\n", + " 4.9810e-01, -2.2518e-01, 1.8236e+00, 1.8000e+00, 2.0212e+00,\n", + " 1.2794e+00, 4.0622e-01, 1.0192e+00, 1.5331e+00, 5.6022e-01,\n", + " 7.7227e-01, 1.4327e-01, 5.9703e-01, -1.2215e+00, -9.5658e-01,\n", + " 2.9372e-02, 1.0134e+00, -3.5778e-01, -2.3845e-01, 7.3076e-01,\n", + " -3.1887e-01, 6.0965e-01, 7.4276e-01, -9.1288e-01, -9.4365e-01,\n", + " 5.7260e-01, -8.2204e-01, -1.1837e+00]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3563, 0.3501, 0.1739, 0.3087, 0.1246, -0.1342, -0.3755, -0.3586,\n", + " -0.1743, -0.2094, -0.2120, 0.1998, 0.2108, 0.2613, 0.2037, 0.2339,\n", + " 0.2854, 0.1065, 0.0272, 0.1563, 0.0523, 0.0347, 0.0347, 0.0390,\n", + " 0.0820, 0.0529, -0.0402, 0.0621, 0.1804, 0.1987, 0.2676, 0.2275,\n", + " 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"Grad decoder.fc2.bias: 0.0016208195593208075\n", + "Grad _memory_unit.weight_ih_l0: 2.22394555748906e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 1.522075581306126e-05\n", + "Grad _memory_unit.bias_hh_l0: 7.886704224802088e-06\n", + "Grad _memory_unit.weight_ih_l1: 1.1654256013571285e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 3.6314420867711306e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.8123193513019942e-05\n", + "Data X Sample: tensor([[1.5775, 1.8134, 1.9609, 2.1122, 2.1975, 2.2953, 2.3449, 2.3126, 2.3067,\n", + " 2.4375, 2.4300, 2.4519, 2.3857, 2.2193, 2.2495, 2.3194, 2.3287, 2.3599,\n", + " 2.4553, 2.5199, 2.5198, 2.5612, 2.5496, 2.4966, 2.5731, 2.6903, 2.5867,\n", + " 2.5613, 2.3348, 2.5644, 2.4462, 2.4925, 2.1054, 1.3708, 1.8678, 1.7001,\n", + " 1.5869, 1.4894, 1.5402, 1.5235, 1.2934, 0.7647, 0.7538, 1.2601, 1.9543,\n", + " 2.1100, 1.4210, 1.9079]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 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-0.0337, -0.0394, -0.2788,\n", + " -0.1916, -0.2254, -0.2767]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0005571332294493914\n", + "Grad encoder.fc1.bias: 0.00037952218553982675\n", + "Grad encoder.encoder.0.weight: 9.128211968345568e-05\n", + "Grad encoder.encoder.0.bias: 0.00039792637107893825\n", + "Grad encoder.encoder.2.weight: 7.283087325049564e-05\n", + "Grad encoder.encoder.2.bias: 0.0005661434261128306\n", + "Grad encoder.encoder.4.weight: 0.00020398995548021048\n", + "Grad encoder.encoder.4.bias: 0.0015054200775921345\n", + "Grad decoder.fc1.0.weight: 7.504955283366144e-05\n", + "Grad decoder.fc1.0.bias: 0.0004624114080797881\n", + "Grad decoder.fc1.2.weight: 6.517769361380488e-05\n", + "Grad decoder.fc1.2.bias: 0.0008089776965789497\n", + "Grad decoder.fc1.4.weight: 4.956359043717384e-05\n", + "Grad decoder.fc1.4.bias: 0.0006275980267673731\n", + "Grad decoder.fc2.weight: 0.00010952883894788101\n", + "Grad decoder.fc2.bias: 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-8.4696e-01, -3.4718e-01,\n", + " 5.0698e-01, 7.5261e-01, -6.8797e-01, 1.4786e+00, -6.7198e-02,\n", + " 8.8910e-01, -6.1235e-01, -5.7201e-01, -4.2527e-01, 1.9638e-04,\n", + " -6.0123e-01, 5.6237e-01, -8.3005e-01, -9.9747e-01, -1.9636e-01,\n", + " -9.4998e-01, 1.5963e-01, -3.3673e-01, -4.2937e-01, 6.7312e-02,\n", + " -5.4509e-01, -4.9954e-01, -6.1208e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2121, -0.2624, -0.0896, -0.1410, 0.0520, 0.1412, 0.1532, 0.2407,\n", + " 0.2006, 0.2392, 0.1906, -0.1473, -0.1601, -0.2446, -0.1775, -0.2815,\n", + " -0.2245, -0.0861, -0.0496, -0.1242, -0.0954, -0.0511, -0.0797, -0.0719,\n", + " -0.0125, -0.0261, -0.0286, -0.1172, -0.2079, -0.2417, -0.1917, -0.1606,\n", + " -0.2693, -0.2628, -0.1963, -0.1218, -0.0329, 0.0147, 0.0361, 0.2459,\n", + " 0.3057, 0.3198, 0.2475]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0001534540206193924\n", + "Grad encoder.fc1.bias: 0.00016303276061080396\n", + "Grad 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"Grad encoder.encoder.4.weight: 3.305214340798557e-05\n", + "Grad encoder.encoder.4.bias: 0.00010350917000323534\n", + "Grad decoder.fc1.0.weight: 1.2734840311168227e-05\n", + "Grad decoder.fc1.0.bias: 7.680218550376594e-05\n", + "Grad decoder.fc1.2.weight: 2.1855717932339758e-05\n", + "Grad decoder.fc1.2.bias: 0.00010960260988213122\n", + "Grad decoder.fc1.4.weight: 2.3816610337235034e-05\n", + "Grad decoder.fc1.4.bias: 0.00022134085884317756\n", + "Grad decoder.fc2.weight: 7.735807594144717e-05\n", + "Grad decoder.fc2.bias: 0.0014266122598201036\n", + "Grad _memory_unit.weight_ih_l0: 1.2683710792771308e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 5.274686373013537e-06\n", + "Grad _memory_unit.bias_hh_l0: 2.7532732929103076e-06\n", + "Grad _memory_unit.weight_ih_l1: 5.645139253829257e-07\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 1.4048009688849561e-05\n", + "Grad _memory_unit.bias_hh_l1: 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"Grad decoder.fc2.weight: 0.00014055265637580305\n", + "Grad decoder.fc2.bias: 0.0014668938238173723\n", + "Grad _memory_unit.weight_ih_l0: 1.7847074559540488e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 5.354218228603713e-05\n", + "Grad _memory_unit.bias_hh_l0: 3.6705692764371634e-05\n", + "Grad _memory_unit.weight_ih_l1: 8.576475011068396e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.0001153859484475106\n", + "Grad _memory_unit.bias_hh_l1: 6.1030583310639486e-05\n", + "Data X Sample: tensor([[2.6266, 3.0397, 3.2772, 3.3629, 3.4798, 3.4622, 3.5928, 3.7975, 3.8592,\n", + " 3.8719, 3.9146, 3.8814, 3.8460, 3.7680, 3.6441, 3.6336, 3.6180, 3.6463,\n", + " 3.5931, 3.4163, 3.5013, 3.3267, 2.9978, 2.9566, 2.8865, 2.7582, 2.7652,\n", + " 2.9284, 2.7580, 2.9640, 2.7926, 2.9181, 2.9371, 2.1672, 3.5762, 4.0229,\n", + " 4.1611, 4.3703, 4.7982, 4.7068, 1.9186, 1.1092, 1.1318, 2.0437, 2.8899,\n", + " 3.3166, 2.0601, 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-0.4715, 0.2732, 0.6967]], device='cuda:0')\n", + "Prediction Sample: tensor([[-2.0557e-01, -2.5607e-01, -9.6446e-02, -1.6803e-01, 1.8035e-03,\n", + " 1.0551e-01, 1.2843e-01, 2.2056e-01, 1.5108e-01, 2.0554e-01,\n", + " 1.8108e-01, -1.3666e-01, -1.4334e-01, -2.1825e-01, -1.6470e-01,\n", + " -2.5621e-01, -2.1807e-01, -7.6474e-02, -6.7442e-02, -1.2331e-01,\n", + " -9.4347e-02, -5.1881e-02, -7.4109e-02, -6.0466e-02, -1.9547e-02,\n", + " -1.8429e-02, 1.9197e-04, -8.9758e-02, -1.6323e-01, -1.8893e-01,\n", + " -1.7223e-01, -1.3982e-01, -2.2276e-01, -2.4804e-01, -1.5999e-01,\n", + " -1.1981e-01, -3.4219e-02, 6.6626e-03, 2.9602e-02, 2.4597e-01,\n", + " 2.9154e-01, 3.0035e-01, 2.1775e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0006642569787800312\n", + "Grad encoder.fc1.bias: 0.0003803225699812174\n", + "Grad encoder.encoder.0.weight: 7.878286123741418e-05\n", + "Grad encoder.encoder.0.bias: 0.00041840755147859454\n", + "Grad encoder.encoder.2.weight: 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0.2746,\n", + " 0.3107, 0.3341, 0.2356]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 7.230289338622242e-05\n", + "Grad encoder.fc1.bias: 0.0001340686867479235\n", + "Grad encoder.encoder.0.weight: 1.5507726857322268e-05\n", + "Grad encoder.encoder.0.bias: 0.00011027047003153712\n", + "Grad encoder.encoder.2.weight: 1.237664127984317e-05\n", + "Grad encoder.encoder.2.bias: 0.0001518665812909603\n", + "Grad encoder.encoder.4.weight: 4.51665255241096e-05\n", + "Grad encoder.encoder.4.bias: 0.00043789655319415033\n", + "Grad decoder.fc1.0.weight: 2.666226282599382e-05\n", + "Grad decoder.fc1.0.bias: 0.00021470728097483516\n", + "Grad decoder.fc1.2.weight: 3.164688314427622e-05\n", + "Grad decoder.fc1.2.bias: 0.00023128243628889322\n", + "Grad decoder.fc1.4.weight: 3.8451973523478955e-05\n", + "Grad decoder.fc1.4.bias: 0.0003514162381179631\n", + "Grad decoder.fc2.weight: 0.00015038800484035164\n", + "Grad decoder.fc2.bias: 0.0020534845534712076\n", + "Grad _memory_unit.weight_ih_l0: 3.6279363939684117e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 2.1261028450680897e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.187757334264461e-05\n", + "Grad _memory_unit.weight_ih_l1: 2.0500087885011453e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 5.004996637580916e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.609415605547838e-05\n", + "Data X Sample: tensor([[1.5541, 1.8672, 1.9339, 2.1078, 2.2811, 2.4368, 2.3742, 2.5170, 2.7168,\n", + " 2.6602, 2.6489, 2.6447, 2.5699, 2.5329, 2.4614, 2.4985, 2.4089, 2.4702,\n", + " 2.4037, 2.4418, 2.3849, 2.5474, 2.4387, 2.4202, 2.4267, 2.5074, 2.4269,\n", + " 2.4471, 2.3035, 2.4039, 2.3132, 2.3101, 2.3408, 1.4990, 2.0320, 1.8436,\n", + " 1.7049, 1.6167, 1.6353, 1.5349, 1.2791, 0.7050, 0.7579, 1.2285, 1.9147,\n", + " 2.0871, 1.4278, 2.1190]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.1210, 1.5516, 0.0306, -0.4372, -2.0431, -1.6624, -1.9957, -1.3710,\n", + " -1.6343, -1.7692, -1.5407, 0.0814, 0.0608, 1.6675, 0.2571, 0.0471,\n", + " -0.1781, -2.9060, 0.3803, -0.2193, -0.3053, 0.0350, -0.1272, 0.0595,\n", + " -0.1065, -0.1084, -0.6010, 0.0564, 0.1884, 0.5868, 1.0625, 0.7591,\n", + " 1.1693, -0.3369, 0.2196, -1.1030, -0.5685, -0.0140, -0.4773, -1.6367,\n", + " -1.6526, -1.0503, -0.4995]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4731, 0.4569, 0.2018, 0.4139, 0.1793, -0.1826, -0.4418, -0.5251,\n", + " -0.2556, -0.3158, -0.2799, 0.2518, 0.2787, 0.3596, 0.2434, 0.3310,\n", + " 0.3370, 0.1517, 0.0493, 0.1846, 0.0790, 0.0869, 0.0414, 0.0243,\n", + " 0.0569, 0.0258, -0.0565, 0.0276, 0.2569, 0.3259, 0.3088, 0.2908,\n", + " 0.3703, 0.3402, 0.2389, 0.2498, -0.0196, -0.0512, 0.0065, -0.3595,\n", + " -0.3185, -0.3536, -0.3935]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00037554092705249786\n", + "Grad encoder.fc1.bias: 0.00046489731175825\n", + "Grad encoder.encoder.0.weight: 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0.7918,\n", + " 0.9712, 0.4852, -0.3062, 0.5700, 0.3997, 0.0533, 0.0000, -0.4230,\n", + " -0.4817, -0.3732, -0.0904]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4346, 0.4100, 0.1780, 0.3588, 0.1440, -0.1876, -0.4162, -0.4853,\n", + " -0.2383, -0.2937, -0.2614, 0.2290, 0.2478, 0.3341, 0.2197, 0.2946,\n", + " 0.2953, 0.1304, 0.0472, 0.1608, 0.0598, 0.0773, 0.0341, 0.0119,\n", + " 0.0414, 0.0179, -0.0429, 0.0167, 0.2513, 0.3028, 0.2811, 0.2681,\n", + " 0.3326, 0.3067, 0.2064, 0.2232, -0.0256, -0.0402, 0.0106, -0.3238,\n", + " -0.2813, -0.3151, -0.3490]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00015557670849375427\n", + "Grad encoder.fc1.bias: 0.0002611532108858228\n", + "Grad encoder.encoder.0.weight: 1.8494845789973624e-05\n", + "Grad encoder.encoder.0.bias: 0.00023524541757069528\n", + "Grad encoder.encoder.2.weight: 1.553269976284355e-05\n", + "Grad encoder.encoder.2.bias: 0.00019424152560532093\n", + "Grad encoder.encoder.4.weight: 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tensor([[1.5064, 1.7274, 1.8016, 1.9395, 1.9926, 2.0434, 2.0753, 2.1536, 2.1871,\n", + " 2.2716, 2.1762, 2.2825, 2.1704, 2.1048, 2.1108, 2.0431, 2.0079, 2.1800,\n", + " 2.1538, 2.1647, 2.2647, 2.2902, 2.2748, 2.2962, 2.2653, 2.3612, 2.3336,\n", + " 2.3126, 2.1231, 2.2828, 2.1732, 2.2245, 2.2814, 1.5333, 2.2036, 1.8971,\n", + " 1.6479, 1.5027, 1.5846, 1.5462, 1.2027, 0.6691, 0.6791, 1.2457, 1.7284,\n", + " 1.8355, 1.3122, 1.8219]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 7.1597e-01, 2.8619e-01, 1.0035e+00, -1.3861e-01, -3.6136e-01,\n", + " -3.8891e-01, -4.9672e-01, -4.9191e-01, 2.4200e+00, -6.4884e-01,\n", + " -9.4816e-01, -3.7756e-01, -2.3252e-01, -7.5082e-03, 6.7570e-01,\n", + " 1.1488e-01, 7.1228e-01, 3.0337e-01, -6.9078e-01, -3.3420e+00,\n", + " -4.6887e-01, -1.8130e-01, 3.5630e-02, 4.9893e-01, 2.6528e-01,\n", + " -3.3611e-01, -1.3697e+00, -9.9016e-02, -4.0096e-02, 5.6693e-01,\n", + " 3.4566e-01, 5.9508e-01, -2.0510e-01, 5.2695e-01, 1.2468e+00,\n", + " 1.6259e+00, 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"Grad _memory_unit.bias_hh_l1: 3.064488555537537e-05\n", + "Data X Sample: tensor([[1.4597, 1.6706, 1.8377, 1.9438, 2.1189, 2.2953, 2.3649, 2.4190, 2.5191,\n", + " 2.5702, 2.5537, 2.5941, 2.5344, 2.5465, 2.4689, 2.4862, 2.3806, 2.4779,\n", + " 2.4574, 2.5088, 2.5321, 2.5382, 2.3953, 2.3458, 2.3629, 2.4839, 2.2963,\n", + " 2.3819, 2.2133, 2.2860, 2.2642, 2.1499, 2.1868, 1.4509, 1.9462, 1.8144,\n", + " 1.6951, 1.4841, 1.5719, 1.6030, 1.2122, 0.7289, 0.6629, 1.2457, 1.8671,\n", + " 1.8870, 1.3122, 1.9235]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.5789, 0.6977, 0.3235, 0.1395, -0.6455, 0.2203, -1.1089, -1.0837,\n", + " -0.5582, -0.5610, -0.7691, 0.2253, 0.0777, 0.3857, 0.3613, 0.0165,\n", + " 0.0814, -0.4006, 0.0724, -0.7132, -0.1044, 1.8815, -0.4043, 0.9688,\n", + " -0.1053, -0.2205, 0.0518, 0.5120, 0.6340, 1.2082, 0.1110, 0.3149,\n", + " 0.1782, 0.6441, 0.8689, 1.1706, 0.7316, 0.7450, 1.1574, -0.8333,\n", + " -0.8568, -0.7618, -0.3153]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3063, 0.2947, 0.1469, 0.1796, 0.0582, -0.2090, -0.3513, -0.3370,\n", + " -0.1602, -0.2474, -0.1838, 0.1927, 0.1368, 0.2488, 0.1437, 0.2024,\n", + " 0.1714, 0.0546, 0.0785, 0.1020, 0.0044, 0.0571, 0.0062, -0.0292,\n", + " 0.0472, -0.0018, -0.0145, -0.0562, 0.2046, 0.2041, 0.1890, 0.1854,\n", + " 0.2003, 0.2021, 0.0875, 0.1189, -0.0384, 0.0229, 0.0462, -0.1948,\n", + " -0.1570, -0.2074, -0.2038]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00031517742900177836\n", + "Grad encoder.fc1.bias: 0.0002034623030340299\n", + "Grad encoder.encoder.0.weight: 8.158264972735196e-05\n", + "Grad encoder.encoder.0.bias: 0.00025038968306034803\n", + "Grad encoder.encoder.2.weight: 5.067557140137069e-05\n", + "Grad encoder.encoder.2.bias: 0.0002585753973107785\n", + "Grad encoder.encoder.4.weight: 0.00017192214727401733\n", + "Grad encoder.encoder.4.bias: 0.0005208470392972231\n", + "Grad decoder.fc1.0.weight: 4.560767047223635e-05\n", + "Grad 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" 2.4450, 2.4827, 2.5002, 2.4893, 2.5110, 2.4927, 2.4605, 2.4761, 2.4775,\n", + " 2.5410, 2.2376, 2.3483, 2.2677, 2.3074, 2.2286, 1.4875, 1.9045, 1.8509,\n", + " 1.7207, 1.5663, 1.5529, 1.5689, 1.2695, 0.7070, 0.6791, 1.2945, 1.8394,\n", + " 2.2587, 1.3598, 1.8844]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.8587, 0.1170, 0.9406, 0.8504, 0.7226, -0.1715, -1.5152, -1.5624,\n", + " -1.3800, -2.3224, -0.9181, 0.7876, 0.8311, 0.1306, 0.1903, 0.9364,\n", + " -0.1052, 1.0989, 0.2755, 0.5632, 0.2962, 0.0998, -0.8385, -0.3722,\n", + " 0.7712, -0.2984, 1.2076, 0.6986, 0.8264, 1.8747, 0.5569, 1.5165,\n", + " 1.0310, 1.0847, 1.0706, 1.9428, -0.9148, 0.3176, -0.2102, -1.8882,\n", + " -1.1700, -0.8685, -0.1585]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3061, 0.2875, 0.1456, 0.1721, 0.0537, -0.2031, -0.3441, -0.3314,\n", + " -0.1585, -0.2444, -0.1853, 0.1879, 0.1345, 0.2472, 0.1352, 0.2016,\n", + " 0.1598, 0.0548, 0.0764, 0.0991, 0.0049, 0.0560, 0.0048, -0.0234,\n", + " 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"Grad decoder.fc1.0.weight: 0.00010232274507870898\n", + "Grad decoder.fc1.0.bias: 0.0007206606678664684\n", + "Grad decoder.fc1.2.weight: 6.59416982671246e-05\n", + "Grad decoder.fc1.2.bias: 0.0012627430260181427\n", + "Grad decoder.fc1.4.weight: 7.363977783825248e-05\n", + "Grad decoder.fc1.4.bias: 0.0007742258021607995\n", + "Grad decoder.fc2.weight: 0.00015758886002004147\n", + "Grad decoder.fc2.bias: 0.0018243398517370224\n", + "Grad _memory_unit.weight_ih_l0: 3.725208807736635e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 0.00011265421926509589\n", + "Grad _memory_unit.bias_hh_l0: 7.833029667381197e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.6204350686166435e-05\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00025464329519309103\n", + "Grad _memory_unit.bias_hh_l1: 0.00013416307047009468\n", + "Data X Sample: tensor([[1.7005, 1.9998, 2.0962, 2.2543, 2.2521, 2.3970, 2.4420, 2.5425, 2.4971,\n", + " 2.5576, 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" 5.0362, 5.4622, 5.8884, 5.7792, 1.1216, 0.6592, 0.6285, 1.1654, 1.7680,\n", + " 1.9042, 1.2782, 1.9157]], device='cuda:0')\n", + "Data Y Sample: tensor([[-5.4397e-01, -2.2488e-01, -1.9183e-01, -3.8667e-01, -1.0109e+00,\n", + " 2.8331e-01, 1.7370e-01, 3.4237e-01, 4.1999e-01, 1.0675e-01,\n", + " -2.3265e-01, -6.8471e+00, -7.9853e-02, -4.4374e-02, -8.2083e+00,\n", + " -9.2152e-01, -6.5568e-02, -6.4613e-01, -2.8206e-01, -1.8819e-01,\n", + " -1.2394e+00, -2.9503e-02, 5.3843e-03, -1.0081e-01, -6.5127e-03,\n", + " 1.0875e+00, 1.2745e-01, 2.8804e-01, -5.1171e-01, 1.2652e-01,\n", + " -3.5678e-01, -5.6805e-01, -2.2235e-01, -6.2432e-01, -5.4590e-01,\n", + " 7.8402e-02, 2.8984e-01, -1.3986e-02, 7.3323e-01, 2.7009e-02,\n", + " -7.9185e-03, 3.1206e-01, 1.4959e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2283, -0.2989, -0.0870, -0.1538, 0.0227, 0.1279, 0.1619, 0.2446,\n", + " 0.1957, 0.2451, 0.2255, -0.1529, -0.1787, -0.2600, -0.2176, -0.3050,\n", + " -0.2724, -0.0689, -0.0728, 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_memory_unit.bias_ih_l1: 7.355622801696882e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.7273512134561315e-05\n", + "Data X Sample: tensor([[ 0.0000, 0.0029, -0.0030, 0.0109, 0.0000, 0.0015, -0.0077, -0.0028,\n", + " 0.0000, 0.0063, -0.0063, 0.0039, -0.0022, 0.0000, -0.0151, 0.0027,\n", + " -0.0220, -0.0155, -0.0103, -0.0130, 0.0000, -0.0077, -0.0072, 0.0115,\n", + " -0.0075, -0.0209, -0.0053, -0.0449, -0.0347, 0.0164, 0.0000, -0.0055,\n", + " -0.0110, -0.0183, 0.0123, 0.0413, -0.0138, -0.0027, 0.0190, 0.0028,\n", + " 0.0143, 0.0239, 0.0141, -0.0258, 0.0238, 0.0457, -0.0340, -0.0313]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.7281, 0.8548, 0.6023, 0.9644, -0.2537, -0.1324, -1.2970, -1.2739,\n", + " -1.0805, -1.3796, -1.1917, 0.1072, 0.0998, 2.1879, 0.9514, 0.0596,\n", + " 1.4761, -0.3012, 0.4755, -0.6348, 0.4947, -0.3385, 0.2533, 0.3740,\n", + " -0.4153, 0.4526, 0.9758, 0.1113, 0.7250, 0.6211, 0.9175, 0.8909,\n", + " 1.0464, 0.4258, -1.0304, -1.5892, -0.9172, -0.6616, 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+ " -5.7438e-01, -6.4680e-01, 2.0349e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.8119, 0.6718, 0.2506, 0.5796, 0.1758, -0.3033, -0.6995, -0.8228,\n", + " -0.3726, -0.5403, -0.4826, 0.4275, 0.4204, 0.5637, 0.4221, 0.6079,\n", + " 0.3877, 0.2202, 0.1910, 0.2745, 0.1162, 0.1138, 0.2107, 0.1429,\n", + " 0.1093, 0.0035, -0.0894, 0.0790, 0.4886, 0.5751, 0.5501, 0.4120,\n", + " 0.5962, 0.5515, 0.3902, 0.3304, -0.0762, -0.0765, 0.0295, -0.5784,\n", + " -0.6613, -0.5872, -0.5181]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0007400411413982511\n", + "Grad encoder.fc1.bias: 0.0006593018770217896\n", + "Grad encoder.encoder.0.weight: 0.0001526575069874525\n", + "Grad encoder.encoder.0.bias: 0.00044198139221407473\n", + "Grad encoder.encoder.2.weight: 9.776874503586441e-05\n", + "Grad encoder.encoder.2.bias: 0.0004786300123669207\n", + "Grad encoder.encoder.4.weight: 0.0002930101181846112\n", + "Grad encoder.encoder.4.bias: 0.0008401891100220382\n", + 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3.8858, 4.2059, 4.5954, 4.4429, 1.8947, 1.0993, 1.1055, 2.0007, 2.8265,\n", + " 3.1164, 1.9105, 2.9948]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1417, -0.0655, -0.2849, -0.7060, 0.1275, -0.2653, -0.0379, -0.5124,\n", + " 0.0400, -0.2310, -0.0180, -0.6207, 0.3453, -0.2927, 0.6790, -0.6626,\n", + " -0.6168, -0.2092, 0.2848, -0.5284, 0.2071, 1.3516, 0.1224, -0.1544,\n", + " 6.6779, 0.6420, -0.2590, -0.1129, -1.1665, -0.4213, -0.0827, 0.2934,\n", + " -1.2308, -0.6981, -1.3594, -0.1145, -0.5002, 0.0936, 0.2630, -0.4462,\n", + " -0.4728, 0.2608, 0.0134]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.0780, -0.1421, -0.0481, -0.1867, -0.1174, -0.0204, -0.0151, 0.0971,\n", + " 0.0547, 0.0967, 0.0360, -0.0427, -0.0363, -0.0571, -0.0830, -0.1538,\n", + " -0.1840, -0.0578, -0.0089, -0.0677, -0.0415, -0.0570, -0.0505, -0.0547,\n", + " -0.0177, 0.0162, 0.0679, 0.0502, -0.0078, -0.0782, -0.0437, -0.0353,\n", + " -0.0906, -0.0794, -0.0548, -0.0602, -0.0317, 0.0193, -0.0253, 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"Data Y Sample: tensor([[-0.1461, 0.5160, 0.4678, -0.6354, -0.6193, -0.1179, 0.4445, 0.9344,\n", + " -0.7436, -0.6043, -0.5836, -0.1133, 0.4357, -0.8338, -0.2961, 0.1547,\n", + " 0.6070, -0.5965, 0.7470, 0.3580, 1.2752, 0.6873, 1.2126, 0.8847,\n", + " 0.2798, 0.1564, -0.0930, -0.5462, 0.1201, -0.5581, 0.6369, 1.2145,\n", + " -0.7695, -0.4234, 0.4712, 0.7204, -0.4229, -0.0920, 0.7285, -0.0631,\n", + " 0.9984, -1.2589, -1.2465]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2498, -0.2783, -0.0962, -0.2140, -0.0788, 0.0944, 0.1282, 0.2738,\n", + " 0.1308, 0.1930, 0.2271, -0.1606, -0.1320, -0.2203, -0.2059, -0.2654,\n", + " -0.2315, -0.0809, -0.1057, -0.1102, -0.0953, -0.0537, -0.0374, -0.0232,\n", + " 0.0176, 0.0358, 0.0144, -0.0631, -0.1346, -0.1319, -0.1947, -0.1753,\n", + " -0.2086, -0.2474, -0.1038, -0.1676, -0.0418, -0.0064, 0.0187, 0.3306,\n", + " 0.3155, 0.3280, 0.2332]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00032708951039239764\n", + "Grad 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device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2837, -0.3041, -0.1052, -0.2186, -0.0717, 0.1174, 0.1558, 0.3060,\n", + " 0.1443, 0.2106, 0.2617, -0.1816, -0.1474, -0.2484, -0.2300, -0.2832,\n", + " -0.2379, -0.0847, -0.1204, -0.1168, -0.1006, -0.0488, -0.0324, -0.0160,\n", + " 0.0248, 0.0394, 0.0060, -0.0807, -0.1584, -0.1432, -0.2228, -0.2020,\n", + " -0.2303, -0.2764, -0.1121, -0.1860, -0.0439, -0.0094, 0.0261, 0.3650,\n", + " 0.3388, 0.3641, 0.2525]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00014726723020430654\n", + "Grad encoder.fc1.bias: 0.00011746588279493153\n", + "Grad encoder.encoder.0.weight: 5.210947711020708e-05\n", + "Grad encoder.encoder.0.bias: 0.00013104663230478764\n", + "Grad encoder.encoder.2.weight: 4.633036951418035e-05\n", + "Grad encoder.encoder.2.bias: 0.00016724377928767353\n", + "Grad encoder.encoder.4.weight: 0.00010927855328191072\n", + "Grad encoder.encoder.4.bias: 0.0003856118710245937\n", + "Grad decoder.fc1.0.weight: 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"Grad decoder.fc2.weight: 5.566950494539924e-05\n", + "Grad decoder.fc2.bias: 0.0012393611250445247\n", + "Grad _memory_unit.weight_ih_l0: 7.528326477768132e-07\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 8.108199835987762e-07\n", + "Grad _memory_unit.bias_hh_l0: 5.61541980914626e-07\n", + "Grad _memory_unit.weight_ih_l1: 4.4555781641975045e-07\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 1.9674947907333262e-05\n", + "Grad _memory_unit.bias_hh_l1: 9.458385648031253e-06\n", + "Data X Sample: tensor([[1.4820, 1.6167, 1.6318, 1.8892, 1.9277, 2.0758, 2.0075, 1.8171, 2.9707,\n", + " 4.0267, 4.3397, 4.3916, 4.0324, 4.0515, 3.8434, 3.8004, 3.6935, 3.6269,\n", + " 3.5704, 3.6543, 3.5602, 3.4323, 3.0484, 2.8669, 2.9034, 3.0716, 2.9170,\n", + " 3.0018, 3.1778, 3.1605, 3.3840, 3.3934, 3.9447, 2.9293, 4.5935, 5.0931,\n", + " 5.1541, 5.5311, 6.0469, 5.8473, 1.0595, 0.6393, 0.5901, 1.0850, 1.7403,\n", + " 1.8527, 1.2850, 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_memory_unit.bias_ih_l1: 8.993330993689597e-05\n", + "Grad _memory_unit.bias_hh_l1: 4.458857438294217e-05\n", + "Data X Sample: tensor([[ 0.0095, 0.0160, 0.0240, 0.0197, 0.0154, 0.0295, 0.0247, 0.0199,\n", + " 0.0415, 0.0253, 0.0571, 0.0253, 0.0133, 0.0218, 0.0303, 0.0123,\n", + " -0.1148, -0.1567, -0.1838, -0.1692, -0.1718, -0.1163, -0.1976, -0.1947,\n", + " -0.1652, -0.3213, -0.3143, -0.5506, -0.0624, -0.1015, -0.0735, -0.0691,\n", + " -0.0308, -0.0481, -0.0049, -0.0097, 0.0039, -0.0398, -0.0127, -0.0085,\n", + " -0.0239, 0.0080, 0.0101, 0.0000, 0.0238, 0.0172, 0.0136, -0.0078]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[-0.3333, 0.9667, 2.3575, -0.9687, 0.0299, -0.2800, -0.3703, -0.4799,\n", + " 1.0622, 1.4790, 1.2817, 0.3940, -0.6913, 1.4699, 0.0954, 1.0066,\n", + " 0.5535, -0.4508, 1.1699, -0.1897, -0.0488, -0.8684, -0.6196, -1.9652,\n", + " -0.4625, 0.1834, 0.1676, -0.6105, -1.6213, -0.1454, -0.2568, 0.7511,\n", + " 0.2312, -0.1354, -0.7874, -0.3526, -0.0182, 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0.0399, -0.4005,\n", + " -0.3296, -0.3862, -0.3629]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00033501023426651955\n", + "Grad encoder.fc1.bias: 0.00038639793638139963\n", + "Grad encoder.encoder.0.weight: 9.028125350596383e-05\n", + "Grad encoder.encoder.0.bias: 0.00038436459726653993\n", + "Grad encoder.encoder.2.weight: 5.594871618086472e-05\n", + "Grad encoder.encoder.2.bias: 0.0003493581898510456\n", + "Grad encoder.encoder.4.weight: 0.0001721830340102315\n", + "Grad encoder.encoder.4.bias: 0.00047556168283335865\n", + "Grad decoder.fc1.0.weight: 5.116811371408403e-05\n", + "Grad decoder.fc1.0.bias: 0.0001908631093101576\n", + "Grad decoder.fc1.2.weight: 7.375179848168045e-05\n", + "Grad decoder.fc1.2.bias: 0.0002777580521069467\n", + "Grad decoder.fc1.4.weight: 9.847354522207752e-05\n", + "Grad decoder.fc1.4.bias: 0.00046526105143129826\n", + "Grad decoder.fc2.weight: 0.00010815657878993079\n", + "Grad decoder.fc2.bias: 0.0016438458114862442\n", + "Grad _memory_unit.weight_ih_l0: 9.19231115403818e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 1.0378403203503694e-05\n", + "Grad _memory_unit.bias_hh_l0: 6.061565272830194e-06\n", + "Grad _memory_unit.weight_ih_l1: 2.3914024041005177e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 3.4329492336837575e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.6883799617062323e-05\n", + "Data X Sample: tensor([[-0.0117, -0.0044, 0.0015, 0.0087, 0.0068, 0.0044, 0.0031, -0.0014,\n", + " 0.0195, 0.0032, -0.0317, -0.0175, -0.0089, 0.0082, 0.0076, -0.0014,\n", + " 0.0252, 0.0329, 0.0227, 0.0205, 0.0515, 0.0214, 0.0169, -0.0057,\n", + " 0.0206, 0.0209, 0.0186, 0.0612, -0.0104, 0.0000, 0.0175, 0.0083,\n", + " -0.0154, 0.0046, 0.0074, 0.0389, 0.0118, 0.0106, 0.0254, 0.0057,\n", + " -0.0048, 0.0199, 0.0243, 0.0115, 0.0238, -0.0343, -0.0340, -0.0704]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.8486, 1.1185, 1.0551, 1.7414, 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0.7149, 0.7599, 1.3032, 1.9464,\n", + " 2.0814, 1.4414, 2.0174]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.5088, 0.4467, -0.6127, 0.4714, -0.7029, -0.3683, 0.0211, 0.0168,\n", + " 0.1504, 0.1060, 0.2075, -0.1110, -0.8788, 0.3282, 0.4388, 0.6832,\n", + " -0.4332, 0.6001, 1.5344, 0.7379, 0.2135, 1.8721, 0.0097, -0.6615,\n", + " -1.0812, 0.2338, 0.5700, -0.3392, 1.4226, 0.9489, -0.6136, -0.1894,\n", + " 0.2980, -0.0253, 1.1435, 0.4279, 0.8233, 0.7428, 0.5660, 0.1979,\n", + " 0.4957, -0.0188, -0.2794]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4939, 0.4272, 0.1829, 0.3971, 0.2205, -0.1235, -0.4841, -0.5479,\n", + " -0.3543, -0.4127, -0.3496, 0.2512, 0.2423, 0.3715, 0.2625, 0.3732,\n", + " 0.3163, 0.1418, 0.2268, 0.2372, 0.1439, 0.0848, 0.1828, 0.1394,\n", + " 0.1053, 0.0158, -0.0505, 0.1136, 0.2505, 0.4500, 0.3931, 0.3378,\n", + " 0.4237, 0.4077, 0.3790, 0.3633, -0.1311, -0.0176, 0.0489, -0.4928,\n", + " -0.4396, -0.4497, -0.4577]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002802530361805111\n", + "Grad encoder.fc1.bias: 0.0006001554429531097\n", + "Grad encoder.encoder.0.weight: 7.515528704971075e-05\n", + "Grad encoder.encoder.0.bias: 0.0005941278068348765\n", + "Grad encoder.encoder.2.weight: 5.3542142268270254e-05\n", + "Grad encoder.encoder.2.bias: 0.00038990535540506244\n", + "Grad encoder.encoder.4.weight: 0.0001750728697516024\n", + "Grad encoder.encoder.4.bias: 0.0011126324534416199\n", + "Grad decoder.fc1.0.weight: 5.142722511664033e-05\n", + "Grad decoder.fc1.0.bias: 0.0002779457136057317\n", + "Grad decoder.fc1.2.weight: 7.312555680982769e-05\n", + "Grad decoder.fc1.2.bias: 0.00034499476896598935\n", + "Grad decoder.fc1.4.weight: 0.00010530592408031225\n", + "Grad decoder.fc1.4.bias: 0.000643824809230864\n", + "Grad decoder.fc2.weight: 0.0001648719480726868\n", + "Grad decoder.fc2.bias: 0.0032970039173960686\n", + "Grad _memory_unit.weight_ih_l0: 7.674746484553907e-06\n", + "Grad 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-0.0317, 0.1511, 0.0040, -0.1775,\n", + " -0.3200, -0.5123, -0.7294]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3891, 0.3388, 0.1346, 0.2637, 0.1605, -0.1257, -0.3934, -0.4213,\n", + " -0.2872, -0.3349, -0.2933, 0.1860, 0.1885, 0.3049, 0.2088, 0.2870,\n", + " 0.2513, 0.1043, 0.1758, 0.1934, 0.1009, 0.0681, 0.1240, 0.0885,\n", + " 0.1002, 0.0281, -0.0363, 0.0739, 0.2118, 0.3478, 0.3046, 0.2559,\n", + " 0.3302, 0.2970, 0.2844, 0.2912, -0.1080, -0.0048, 0.0407, -0.3731,\n", + " -0.3198, -0.3492, -0.3677]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0005193765973672271\n", + "Grad encoder.fc1.bias: 0.0007214441429823637\n", + "Grad encoder.encoder.0.weight: 0.00014414801262319088\n", + "Grad encoder.encoder.0.bias: 0.0004751607193611562\n", + "Grad encoder.encoder.2.weight: 7.375757559202611e-05\n", + "Grad encoder.encoder.2.bias: 0.0003671738086268306\n", + "Grad encoder.encoder.4.weight: 0.00017403331003151834\n", + "Grad encoder.encoder.4.bias: 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-3.2003e-01,\n", + " -2.8819e-01, 1.7370e-01, 1.8362e-01, 2.9609e-01, 2.0115e-01,\n", + " 2.6494e-01, 2.3390e-01, 9.7793e-02, 1.6066e-01, 1.7391e-01,\n", + " 8.0281e-02, 6.7441e-02, 1.1841e-01, 7.6656e-02, 9.1844e-02,\n", + " 1.9912e-02, -2.5701e-02, 7.3519e-02, 2.2211e-01, 3.3947e-01,\n", + " 2.8644e-01, 2.4055e-01, 3.1006e-01, 2.7568e-01, 2.5860e-01,\n", + " 2.7533e-01, -1.0411e-01, 2.2622e-04, 4.0587e-02, -3.4475e-01,\n", + " -2.9727e-01, -3.2204e-01, -3.4605e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00014958818792365491\n", + "Grad encoder.fc1.bias: 0.0004021063505206257\n", + "Grad encoder.encoder.0.weight: 3.672743696370162e-05\n", + "Grad encoder.encoder.0.bias: 0.0005101165734231472\n", + "Grad encoder.encoder.2.weight: 2.737727845669724e-05\n", + "Grad encoder.encoder.2.bias: 0.0003716585342772305\n", + "Grad encoder.encoder.4.weight: 0.00010194725473411381\n", + "Grad encoder.encoder.4.bias: 0.001179780694656074\n", + "Grad 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"Grad _memory_unit.bias_ih_l1: 7.377246220130473e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.662252856884152e-05\n", + "Data X Sample: tensor([[1.5138, 1.8876, 1.9008, 2.0116, 2.1667, 2.1804, 2.2802, 2.2742, 2.3653,\n", + " 2.3617, 2.3507, 2.3876, 2.3990, 2.3529, 2.3403, 2.3317, 2.2186, 2.3309,\n", + " 2.3500, 2.3972, 2.4389, 2.5260, 2.4580, 2.4851, 2.4868, 2.5727, 2.5467,\n", + " 2.5940, 2.2792, 2.5251, 2.4112, 2.3654, 2.1186, 1.4097, 1.8188, 1.7147,\n", + " 1.5732, 1.4762, 1.5719, 1.5320, 1.3125, 0.7050, 0.7053, 1.3118, 1.8552,\n", + " 2.0357, 1.3462, 1.8766]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.5188, 0.0929, -0.5745, 0.2507, -0.5432, 0.2916, -0.7109, -0.0790,\n", + " -1.1254, -0.8515, -0.2159, 0.0999, 0.3826, 0.2952, 0.7536, 0.3635,\n", + " 0.6357, -0.8018, 0.3410, 0.5826, -0.3959, -1.0630, -0.5258, 0.8022,\n", + " -0.5015, 1.3888, 0.1468, -0.4295, 1.0362, 0.7789, 0.9730, 1.1791,\n", + " 0.1392, 0.3241, 0.1237, 0.2218, 0.7170, 0.8959, 0.9946, -0.4688,\n", + " -0.3254, 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_memory_unit.bias_ih_l0: 2.91025980914128e-06\n", + "Grad _memory_unit.bias_hh_l0: 1.6431799849669915e-06\n", + "Grad _memory_unit.weight_ih_l1: 5.118299668538384e-07\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 1.4505110812024213e-05\n", + "Grad _memory_unit.bias_hh_l1: 7.123823706933763e-06\n", + "Data X Sample: tensor([[ 0.0095, 0.0015, 0.0255, 0.0153, 0.0051, 0.0339, 0.0231, 0.0227,\n", + " 0.0366, 0.0158, 0.0508, 0.0097, 0.0244, 0.0109, 0.0126, 0.0096,\n", + " -0.1321, -0.1412, -0.1859, -0.1581, -0.1742, -0.1010, -0.1566, -0.1641,\n", + " -0.1295, -0.2481, -0.2371, -0.4242, -0.0763, -0.0491, -0.0595, -0.0442,\n", + " -0.0022, -0.0412, -0.0294, 0.0292, 0.0098, 0.0027, -0.0444, 0.0028,\n", + " -0.0095, -0.0159, 0.0061, -0.0029, -0.0159, -0.0229, 0.0000, 0.0469]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.6446, -0.0594, -0.4215, -0.0545, -0.5665, -0.3186, 0.3576, -0.1527,\n", + " 0.8069, 1.3779, 1.2319, -0.1230, 0.2435, 0.0863, 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"Grad encoder.encoder.2.weight: 1.6959245840553194e-05\n", + "Grad encoder.encoder.2.bias: 4.547219577943906e-05\n", + "Grad encoder.encoder.4.weight: 3.973476850660518e-05\n", + "Grad encoder.encoder.4.bias: 8.431009337073192e-05\n", + "Grad decoder.fc1.0.weight: 1.3349283108254895e-05\n", + "Grad decoder.fc1.0.bias: 5.2310468163341284e-05\n", + "Grad decoder.fc1.2.weight: 2.0271972971386276e-05\n", + "Grad decoder.fc1.2.bias: 7.493087468901649e-05\n", + "Grad decoder.fc1.4.weight: 3.0714661988895386e-05\n", + "Grad decoder.fc1.4.bias: 0.0001322873868048191\n", + "Grad decoder.fc2.weight: 7.216675294330344e-05\n", + "Grad decoder.fc2.bias: 0.0016441551269963384\n", + "Grad _memory_unit.weight_ih_l0: 8.220033009820327e-07\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 4.148063453612849e-06\n", + "Grad _memory_unit.bias_hh_l0: 2.2043147964723175e-06\n", + "Grad _memory_unit.weight_ih_l1: 4.844654313274077e-07\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 1.2687352864304557e-05\n", + "Grad _memory_unit.bias_hh_l1: 6.381103958119638e-06\n", + "Data X Sample: tensor([[-0.0074, -0.0044, 0.0135, 0.0109, -0.0017, 0.0088, 0.0092, 0.0043,\n", + " 0.0098, 0.0205, 0.0159, 0.0058, 0.0133, 0.0273, 0.0151, -0.0041,\n", + " -0.0456, -0.0368, -0.0475, -0.0502, -0.0589, -0.0276, -0.0410, -0.0229,\n", + " -0.0188, -0.0470, -0.0293, -0.0489, -0.0069, -0.0098, -0.0350, 0.0442,\n", + " 0.0132, -0.0183, 0.0172, 0.0486, 0.0098, 0.0106, 0.0380, 0.0085,\n", + " 0.0286, -0.0020, 0.0121, 0.0144, -0.0159, 0.0400, -0.0272, 0.0156]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.9197, 0.7372, -1.5985, 0.8869, 0.1811, 0.6779, -0.3986, -0.4501,\n", + " -0.9388, -0.7004, -0.6302, 0.0669, 0.6198, 0.5973, 1.0304, -0.0413,\n", + " -0.4712, 0.4089, 0.4177, 1.0299, -0.5535, -0.1113, 0.3185, 0.5456,\n", + " -0.0760, 0.8978, 0.9790, 0.8279, 1.4064, 1.0478, 0.2169, 0.3628,\n", + " 0.5961, -0.1444, 0.1362, 0.6167, 0.2955, -0.9005, -0.0104, -0.7728,\n", + " -0.3724, -0.4083, -0.0634]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 8.4171e-01, 6.2226e-01, -2.6424e-02, 4.2236e-01, -4.0947e-01,\n", + " -7.6941e-01, -1.1209e+00, -1.2086e+00, -6.6208e-01, -7.7627e-01,\n", + " -7.6820e-01, 3.6301e-01, 4.8376e-01, 6.1441e-01, 3.6056e-01,\n", + " 4.9028e-01, 4.6217e-01, 3.7121e-02, 3.8359e-02, 1.4544e-01,\n", + " 8.1644e-02, 4.6046e-02, 9.3076e-02, -1.6369e-02, 5.1266e-02,\n", + " -6.7603e-05, 2.7285e-01, 5.5445e-01, 9.3190e-01, 8.7224e-01,\n", + " 7.9248e-01, 4.5641e-01, 6.4787e-01, 6.5886e-01, 3.8950e-01,\n", + " 3.8599e-01, 9.4560e-03, 8.7473e-03, 2.4317e-02, -7.5704e-01,\n", + " -7.0428e-01, -5.7098e-01, -4.6958e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 4.38668648712337e-05\n", + "Grad encoder.fc1.bias: 0.0004029479459859431\n", + "Grad encoder.encoder.0.weight: 1.327896006841911e-05\n", + "Grad encoder.encoder.0.bias: 0.00032923591788858175\n", + "Grad 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"Grad _memory_unit.bias_ih_l1: 5.006504215998575e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.5289355107815936e-05\n", + "Data X Sample: tensor([[2.5004, 3.0164, 3.3163, 3.5181, 3.3534, 3.6021, 3.7084, 3.9451, 3.9935,\n", + " 3.8861, 4.0066, 4.0488, 3.7883, 3.7298, 3.5836, 3.4394, 3.4404, 3.4161,\n", + " 3.3846, 3.4089, 3.2387, 3.2057, 3.1207, 2.9241, 2.7364, 2.6955, 2.7306,\n", + " 2.7857, 2.8066, 2.8526, 2.7961, 2.8545, 3.0405, 2.1535, 3.6449, 3.9548,\n", + " 4.2889, 4.7015, 5.1975, 5.1664, 1.9329, 1.1670, 1.1580, 2.0810, 3.0128,\n", + " 3.3337, 2.2640, 3.0886]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.2767, -0.3667, -0.7561, -0.2223, 0.6917, 0.8540, 0.2424, 0.1349,\n", + " 0.1235, -0.2117, 0.2434, 1.0752, 0.2651, 0.1801, 0.0791, 0.0028,\n", + " 1.0102, -0.4305, -0.7988, -0.7338, 0.3738, -0.5223, 0.9790, 0.2032,\n", + " 0.5415, 0.0531, 0.5352, -1.1379, 0.5281, -0.2163, 0.2682, -0.1209,\n", + " -0.5241, -0.2253, -0.9391, 0.2567, 0.7316, -0.0597, -0.0231, 0.1280,\n", + " 0.1581, -0.2465, 0.3163]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2776, -0.3134, -0.0826, -0.1810, -0.0387, 0.1068, 0.1965, 0.2574,\n", + " 0.2143, 0.2417, 0.2744, -0.1813, -0.1893, -0.2440, -0.2050, -0.2765,\n", + " -0.2069, -0.0758, -0.0904, -0.0539, -0.0729, -0.0199, -0.0785, -0.0144,\n", + " -0.0187, 0.0070, -0.0103, -0.1182, -0.1631, -0.1927, -0.2453, -0.2214,\n", + " -0.2431, -0.3050, -0.1797, -0.1712, -0.0029, 0.0019, 0.0688, 0.3377,\n", + " 0.3671, 0.3714, 0.2602]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00012649204290937632\n", + "Grad encoder.fc1.bias: 0.00011894141789525747\n", + "Grad encoder.encoder.0.weight: 3.9449878386221826e-05\n", + "Grad encoder.encoder.0.bias: 0.0001793416595319286\n", + "Grad encoder.encoder.2.weight: 2.230916652479209e-05\n", + "Grad encoder.encoder.2.bias: 0.0001354356063529849\n", + "Grad encoder.encoder.4.weight: 4.8918649554252625e-05\n", + "Grad encoder.encoder.4.bias: 0.00016860464529599994\n", + "Grad decoder.fc1.0.weight: 1.4193763490766287e-05\n", + "Grad decoder.fc1.0.bias: 5.9856000007130206e-05\n", + "Grad decoder.fc1.2.weight: 1.7344758816761896e-05\n", + "Grad decoder.fc1.2.bias: 8.089047332759947e-05\n", + "Grad decoder.fc1.4.weight: 2.4305656552314758e-05\n", + "Grad decoder.fc1.4.bias: 0.0001326862839050591\n", + "Grad decoder.fc2.weight: 7.98309629317373e-05\n", + "Grad decoder.fc2.bias: 0.0017816202016547322\n", + "Grad _memory_unit.weight_ih_l0: 1.8374181536273682e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 6.601770110137295e-06\n", + "Grad _memory_unit.bias_hh_l0: 3.2952546007436467e-06\n", + "Grad _memory_unit.weight_ih_l1: 5.001442673346901e-07\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 1.4574178749171551e-05\n", + "Grad _memory_unit.bias_hh_l1: 7.222805834317114e-06\n", + "Data X Sample: tensor([[ 0.0064, 0.0131, 0.0045, -0.0022, 0.0068, 0.0192, 0.0277, 0.0085,\n", + " 0.0415, 0.0221, 0.0317, 0.0097, 0.0200, 0.0191, 0.0202, 0.0109,\n", + " -0.1242, -0.1373, -0.1755, -0.1358, -0.1374, -0.1041, -0.1374, -0.1393,\n", + " -0.1182, -0.2534, -0.2105, -0.3712, -0.0763, -0.0557, -0.0840, -0.0387,\n", + " -0.0132, -0.0481, -0.0025, 0.0268, 0.0079, 0.0080, 0.0190, 0.0085,\n", + " -0.0191, -0.0060, 0.0020, -0.0144, -0.0040, 0.0172, 0.0000, 0.0000]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[-0.7599, -0.4215, -0.6867, 0.5391, 0.8111, 1.3969, 0.0700, -0.3263,\n", + " 0.6180, 0.6596, 0.6665, 0.6731, 0.9343, -0.2378, 0.1550, 0.9560,\n", + " 0.6470, 0.2455, 3.1788, 0.0721, -0.1056, -0.1458, -0.0701, -1.0632,\n", + " -0.3899, 0.1917, -0.9739, -0.8763, 0.1475, 0.5521, -0.9064, -1.2679,\n", + " -1.2165, 0.7161, -0.1499, 0.9799, -0.1933, 0.7428, -0.4768, 0.1358,\n", + " 0.7113, 0.0471, -0.6057]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.5875, 0.4964, 0.0773, 0.4359, 0.1511, -0.2558, -0.5402, -0.7615,\n", + " -0.1957, -0.3255, -0.2647, 0.2919, 0.3470, 0.4436, 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"Grad decoder.fc1.4.weight: 3.0883340514265e-05\n", + "Grad decoder.fc1.4.bias: 0.0003518598969094455\n", + "Grad decoder.fc2.weight: 8.633807738078758e-05\n", + "Grad decoder.fc2.bias: 0.0018091212259605527\n", + "Grad _memory_unit.weight_ih_l0: 5.9278490880387835e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.385456511750817e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.760809391271323e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.9796477772615617e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 6.989676330704242e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.52721763192676e-05\n", + "Data X Sample: tensor([[1.5520, 1.6779, 1.8031, 1.8673, 1.9687, 2.1127, 2.1030, 2.0144, 3.2856,\n", + " 4.2558, 4.2319, 4.3702, 3.9680, 3.9343, 3.7576, 3.6609, 3.6055, 3.5670,\n", + " 3.6035, 3.4870, 3.5160, 3.2685, 2.9062, 2.8153, 2.8528, 2.9227, 2.9383,\n", + " 2.9203, 2.9349, 3.1540, 3.2126, 3.3602, 3.7467, 2.7988, 4.5616, 4.9836,\n", 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"Data Y Sample: tensor([[ 0.2692, 0.6672, 0.8417, 0.6311, 0.1524, -0.1937, -0.2836, -0.3274,\n", + " -0.2835, -0.6496, -0.1858, 1.2514, 0.3282, 0.4970, 0.8283, 0.0965,\n", + " -0.1728, 0.3669, 1.3400, -0.6295, -0.1452, -0.4484, -0.2394, 0.0798,\n", + " -0.0941, 0.5937, -0.5714, 0.6609, -0.6488, -0.2664, 1.0549, -0.1648,\n", + " -0.1665, 0.8830, -0.2397, 0.5748, -0.0091, 0.7816, -0.0156, -0.0921,\n", + " -0.5081, -0.5766, 0.3843]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.5142, 0.4596, 0.0792, 0.3721, 0.1702, -0.1918, -0.4206, -0.5469,\n", + " -0.1688, -0.2727, -0.2529, 0.1783, 0.3592, 0.4072, 0.2652, 0.4605,\n", + " 0.3872, 0.1723, 0.1115, 0.2528, 0.0868, 0.0963, 0.0938, 0.0768,\n", + " 0.0961, 0.1139, -0.1400, 0.0288, 0.2670, 0.2504, 0.2522, 0.2192,\n", + " 0.3651, 0.3477, 0.2380, 0.2163, 0.0035, -0.0395, -0.1303, -0.3260,\n", + " -0.3410, -0.3205, -0.3829]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 6.972660776227713e-05\n", + "Grad 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0.8407, 1.3749, 2.1288,\n", + " 2.1729, 1.5570, 2.2207]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.6378, 1.2676, -0.6436, -0.0395, 0.9106, 0.0338, -0.9182, -0.5668,\n", + " -0.6442, -0.7321, -0.7854, 0.6543, -1.1237, -0.0557, 0.4282, -0.6317,\n", + " 0.8238, 0.2843, -0.3167, 0.0114, 0.5713, 0.3160, 0.3173, 0.2124,\n", + " -0.1471, 1.1989, 0.8546, 1.2906, 1.4581, 1.4378, 1.2814, 0.3432,\n", + " 0.5736, 0.1235, -0.1320, 0.7218, -0.9960, -0.3175, 0.8307, -0.7557,\n", + " -0.8920, -1.3739, 0.3111]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.5022, 0.4174, 0.0545, 0.3191, 0.1295, -0.2085, -0.4300, -0.5195,\n", + " -0.2116, -0.2944, -0.2819, 0.1844, 0.3007, 0.3895, 0.2943, 0.4261,\n", + " 0.3245, 0.1652, 0.1132, 0.2312, 0.0893, 0.0940, 0.0929, 0.0736,\n", + " 0.0580, 0.0919, -0.0993, 0.0365, 0.2968, 0.3102, 0.2753, 0.2259,\n", + " 0.3493, 0.3379, 0.2074, 0.2204, -0.0211, -0.0438, -0.0993, -0.3336,\n", + " -0.3326, -0.2934, -0.3359]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00038717000279575586\n", + "Grad encoder.fc1.bias: 0.00047595356591045856\n", + "Grad encoder.encoder.0.weight: 0.00011684214405249804\n", + "Grad encoder.encoder.0.bias: 0.0005752557190135121\n", + "Grad encoder.encoder.2.weight: 6.719087832607329e-05\n", + "Grad encoder.encoder.2.bias: 0.00038758304435759783\n", + "Grad encoder.encoder.4.weight: 0.0001699598942650482\n", + "Grad encoder.encoder.4.bias: 0.0005638540023937821\n", + "Grad decoder.fc1.0.weight: 4.607886876328848e-05\n", + "Grad decoder.fc1.0.bias: 0.0002088773180730641\n", + "Grad decoder.fc1.2.weight: 6.066873902454972e-05\n", + "Grad decoder.fc1.2.bias: 0.0002708743268158287\n", + "Grad decoder.fc1.4.weight: 6.969295645831153e-05\n", + "Grad decoder.fc1.4.bias: 0.00047589457244612277\n", + "Grad decoder.fc2.weight: 0.00013690083869732916\n", + "Grad decoder.fc2.bias: 0.00212717498652637\n", + "Grad _memory_unit.weight_ih_l0: 6.422375463444041e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 2.055548975476995e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.0448045031807851e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.6622169596303138e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 3.387722244951874e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.6894689906621352e-05\n", + "Data X Sample: tensor([[1.5276, 1.6124, 1.8858, 1.8651, 1.9721, 2.0169, 1.8734, 2.3807, 3.8519,\n", + " 4.3347, 4.3778, 4.3449, 4.0945, 4.0406, 3.9468, 3.7744, 3.6951, 3.6501,\n", + " 3.5250, 3.5651, 3.4375, 3.4644, 2.9689, 2.8936, 2.8659, 3.0402, 2.9597,\n", + " 3.0549, 2.9141, 3.1867, 3.1951, 3.3740, 3.7665, 2.8927, 4.6376, 4.9374,\n", + " 5.1187, 5.5523, 6.1229, 5.8899, 1.1597, 0.7090, 0.6548, 1.2945, 1.7442,\n", + " 1.8298, 1.2374, 1.8844]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.2501, -0.4222, -1.0874, -0.1349, -0.1027, 0.1967, 0.3000, 0.2048,\n", + " 0.3884, 0.3434, 0.4672, -0.6353, -0.0398, 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-0.7806, -1.0124, 0.2395,\n", + " 0.6727, 1.0256, -0.1510]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4846, 0.3810, 0.0458, 0.2953, 0.0978, -0.2137, -0.4327, -0.5101,\n", + " -0.2281, -0.2964, -0.2893, 0.1917, 0.2726, 0.3710, 0.2904, 0.3980,\n", + " 0.2926, 0.1633, 0.1084, 0.2140, 0.0939, 0.0921, 0.0943, 0.0814,\n", + " 0.0353, 0.0836, -0.0727, 0.0499, 0.3101, 0.3275, 0.2821, 0.2346,\n", + " 0.3434, 0.3334, 0.2045, 0.2147, -0.0330, -0.0448, -0.0856, -0.3381,\n", + " -0.3249, -0.2706, -0.2978]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 7.425568765029311e-05\n", + "Grad encoder.fc1.bias: 0.00021535289124585688\n", + "Grad encoder.encoder.0.weight: 1.944878022186458e-05\n", + "Grad encoder.encoder.0.bias: 0.0002203015610575676\n", + "Grad encoder.encoder.2.weight: 1.5440426068380475e-05\n", + "Grad encoder.encoder.2.bias: 0.00011788693518610671\n", + "Grad encoder.encoder.4.weight: 4.3809006456285715e-05\n", + "Grad encoder.encoder.4.bias: 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-0.0742, -0.4094,\n", + " -0.4073, -0.3289, -0.3483]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 5.1088962209178135e-05\n", + "Grad encoder.fc1.bias: 0.0001963882241398096\n", + "Grad encoder.encoder.0.weight: 1.552616413391661e-05\n", + "Grad encoder.encoder.0.bias: 0.00031187228159978986\n", + "Grad encoder.encoder.2.weight: 1.637699460843578e-05\n", + "Grad encoder.encoder.2.bias: 0.00024257751647382975\n", + "Grad encoder.encoder.4.weight: 5.8264191466150805e-05\n", + "Grad encoder.encoder.4.bias: 0.0005666174693033099\n", + "Grad decoder.fc1.0.weight: 3.22524610965047e-05\n", + "Grad decoder.fc1.0.bias: 0.00021365846623666584\n", + "Grad decoder.fc1.2.weight: 4.930980139761232e-05\n", + "Grad decoder.fc1.2.bias: 0.00019836598949041218\n", + "Grad decoder.fc1.4.weight: 5.5797063396312296e-05\n", + "Grad decoder.fc1.4.bias: 0.00022351404186338186\n", + "Grad decoder.fc2.weight: 9.56429066718556e-05\n", + "Grad decoder.fc2.bias: 0.0018207360990345478\n", + 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2.7158, 2.8130, 2.8531,\n", + " 2.9121, 2.8933, 2.9476, 2.9466, 3.0811, 3.3265, 2.3938, 4.1596, 4.5361,\n", + " 4.8611, 5.3429, 6.0215, 5.8303, 1.3268, 0.6950, 0.6710, 1.2199, 1.8394,\n", + " 1.8870, 1.2918, 1.8297]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.1908, -0.6133, -0.5963, 2.1083, 0.2185, 0.6193, -0.5929, 2.4050,\n", + " -0.5441, -0.3960, -0.5914, -2.2416, -1.3492, -0.0322, 0.4744, -0.4565,\n", + " -1.3220, -0.1507, 0.1166, -0.3928, -0.3105, -0.5883, -0.0876, -0.6689,\n", + " -0.2264, -0.3768, 0.7900, -0.4535, 0.3636, -0.9257, 1.6541, 0.0498,\n", + " 0.2950, 0.5596, 0.1265, 0.4790, 0.8233, 0.0000, 0.2870, 0.2426,\n", + " 0.6625, 0.6651, 1.1814]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2475, -0.2736, -0.0578, -0.1603, -0.0762, 0.0705, 0.1561, 0.2516,\n", + " 0.1804, 0.2150, 0.2193, -0.1368, -0.1420, -0.2165, -0.1393, -0.2605,\n", + " -0.2102, -0.0730, -0.0653, -0.0531, -0.0402, -0.0250, -0.0543, -0.0518,\n", + " -0.0242, 0.0123, 0.0284, -0.0784, 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" 0.1806, 0.1507, 0.0898, 0.1285, -0.0203, 0.0117, 0.0264, -0.1724,\n", + " -0.1214, -0.1471, -0.1236]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002328821865376085\n", + "Grad encoder.fc1.bias: 0.00010419153841212392\n", + "Grad encoder.encoder.0.weight: 7.247830944834277e-05\n", + "Grad encoder.encoder.0.bias: 0.00013369925727602094\n", + "Grad encoder.encoder.2.weight: 3.930790990125388e-05\n", + "Grad encoder.encoder.2.bias: 0.00010448363900650293\n", + "Grad encoder.encoder.4.weight: 0.00010590351303108037\n", + "Grad encoder.encoder.4.bias: 0.00034877797588706017\n", + "Grad decoder.fc1.0.weight: 2.8677935915766284e-05\n", + "Grad decoder.fc1.0.bias: 6.702099199173972e-05\n", + "Grad decoder.fc1.2.weight: 4.546490526990965e-05\n", + "Grad decoder.fc1.2.bias: 0.00015476200496777892\n", + "Grad decoder.fc1.4.weight: 6.76979761919938e-05\n", + "Grad decoder.fc1.4.bias: 0.0003546166990417987\n", + "Grad decoder.fc2.weight: 7.088331767590716e-05\n", + "Grad decoder.fc2.bias: 0.002610171679407358\n", + "Grad _memory_unit.weight_ih_l0: 7.977663472047425e-07\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 7.148854592742282e-07\n", + "Grad _memory_unit.bias_hh_l0: 5.793901891593123e-07\n", + "Grad _memory_unit.weight_ih_l1: 5.019886089030479e-07\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 1.6728738046367653e-05\n", + "Grad _memory_unit.bias_hh_l1: 8.260034519480541e-06\n", + "Data X Sample: tensor([[1.5138, 1.7245, 1.9384, 2.0860, 2.2931, 2.4088, 2.4928, 2.5383, 2.5777,\n", + " 2.6523, 2.7758, 2.6661, 2.5588, 2.5792, 2.6051, 2.5013, 2.4828, 2.5669,\n", + " 2.4760, 2.4641, 2.4511, 2.4464, 2.4435, 2.3401, 2.4042, 2.4604, 2.4349,\n", + " 2.5450, 2.2064, 2.2729, 2.3272, 2.2880, 2.2440, 1.4578, 1.9487, 1.8096,\n", + " 1.7010, 1.6432, 1.6543, 1.6115, 1.1741, 0.7348, 0.7154, 1.3089, 2.0415,\n", + " 2.0528, 1.3938, 2.0017]], device='cuda:0')\n", + "Data Y Sample: 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-0.2052,\n", + " -0.0544, 1.5941, -0.1195, -0.1583, -0.1182, 0.1377, 0.2726, 0.7398,\n", + " 0.3104, 0.0361, 0.5988, 0.3525, -1.0293, 0.7916, 0.1407, -0.7592,\n", + " -0.2623, -0.5448, 0.3577]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.5697, 0.4400, 0.1513, 0.3002, 0.1152, -0.2869, -0.5937, -0.6963,\n", + " -0.3574, -0.4277, -0.3682, 0.3449, 0.2366, 0.3783, 0.3587, 0.4445,\n", + " 0.3373, 0.2016, 0.1628, 0.1998, 0.1734, 0.1605, 0.0317, 0.0915,\n", + " 0.0859, 0.0490, -0.0578, 0.1224, 0.4453, 0.5386, 0.4926, 0.2851,\n", + " 0.4636, 0.4558, 0.2677, 0.2703, 0.0103, 0.0587, 0.0451, -0.4393,\n", + " -0.4396, -0.4002, -0.3492]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 5.219333979766816e-05\n", + "Grad encoder.fc1.bias: 0.00023746490478515625\n", + "Grad encoder.encoder.0.weight: 1.3546396075980738e-05\n", + "Grad encoder.encoder.0.bias: 0.00019337775302119553\n", + "Grad encoder.encoder.2.weight: 1.2360207620076835e-05\n", + "Grad 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"Grad _memory_unit.bias_hh_l1: 6.6135235101683065e-06\n", + "Data X Sample: tensor([[1.5934, 1.8439, 2.0631, 2.2018, 2.3102, 2.4014, 2.5452, 2.6220, 2.6705,\n", + " 2.6824, 2.7282, 2.7460, 2.7519, 2.6419, 2.5673, 2.5136, 2.4828, 2.5282,\n", + " 2.5131, 2.4920, 2.5297, 2.5520, 2.4869, 2.5863, 2.5900, 2.6197, 2.5894,\n", + " 2.5899, 2.4909, 2.4400, 2.4217, 2.4870, 2.3562, 1.5196, 2.0050, 1.9044,\n", + " 1.7423, 1.6856, 1.6290, 1.5235, 1.2791, 0.8045, 0.7255, 1.3491, 1.9742,\n", + " 2.2415, 1.4346, 2.0486]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.5360, 0.7007, 0.1805, 0.6548, -0.6817, -0.0510, -0.6714, -0.4758,\n", + " -0.7944, -0.6189, -0.7027, 0.5720, 0.4943, 0.3700, -0.5615, -0.6915,\n", + " -0.2453, -0.0217, 0.2589, 0.2625, -1.0607, 0.3345, 0.8236, -0.4053,\n", + " 1.0941, 0.1310, -0.5191, 0.4966, 0.6971, 0.6905, 0.9097, 0.7984,\n", + " 1.3099, 0.0391, 0.4493, -1.6006, 0.1137, 0.5269, 0.4697, -0.3232,\n", + " -0.5065, -0.6218, -0.9440]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2470, 0.2290, 0.0712, 0.0252, 0.0483, -0.1628, -0.2689, -0.2322,\n", + " -0.2105, -0.2461, -0.2368, 0.1282, 0.0837, 0.1905, 0.1954, 0.2067,\n", + " 0.1333, 0.0573, 0.0799, 0.1199, 0.0890, 0.1021, 0.0011, -0.0008,\n", + " 0.0484, 0.0458, -0.0146, -0.0176, 0.1705, 0.2290, 0.2069, 0.1077,\n", + " 0.2000, 0.1706, 0.1070, 0.1381, -0.0131, 0.0209, 0.0661, -0.1898,\n", + " -0.1426, -0.1855, -0.1498]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 2.255549406982027e-05\n", + "Grad encoder.fc1.bias: 7.702364382566884e-05\n", + "Grad encoder.encoder.0.weight: 6.2139811234374065e-06\n", + "Grad encoder.encoder.0.bias: 0.00010737132106442004\n", + "Grad encoder.encoder.2.weight: 5.855448307556799e-06\n", + "Grad encoder.encoder.2.bias: 9.846847387962043e-05\n", + "Grad encoder.encoder.4.weight: 2.0762765416293405e-05\n", + "Grad encoder.encoder.4.bias: 0.0002082762512145564\n", + "Grad decoder.fc1.0.weight: 1.4454490155912936e-05\n", + "Grad 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0.0416, 0.0441, -0.0193, -0.0116, 0.1702, 0.2270, 0.2070, 0.1137,\n", + " 0.2033, 0.1769, 0.1145, 0.1346, -0.0179, 0.0201, 0.0653, -0.1920,\n", + " -0.1438, -0.1834, -0.1506]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 9.594568109605461e-05\n", + "Grad encoder.fc1.bias: 6.45868931314908e-05\n", + "Grad encoder.encoder.0.weight: 3.486750938463956e-05\n", + "Grad encoder.encoder.0.bias: 7.693329825997353e-05\n", + "Grad encoder.encoder.2.weight: 1.8982429537572898e-05\n", + "Grad encoder.encoder.2.bias: 8.31856596050784e-05\n", + "Grad encoder.encoder.4.weight: 7.682891737204045e-05\n", + "Grad encoder.encoder.4.bias: 0.0003223127278033644\n", + "Grad decoder.fc1.0.weight: 2.7910235075978562e-05\n", + "Grad decoder.fc1.0.bias: 8.843040268402547e-05\n", + "Grad decoder.fc1.2.weight: 5.5614334996789694e-05\n", + "Grad decoder.fc1.2.bias: 0.00016783745377324522\n", + "Grad decoder.fc1.4.weight: 8.761607750784606e-05\n", + "Grad decoder.fc1.4.bias: 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"Grad _memory_unit.bias_ih_l0: 2.571068580436986e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.3284903616295196e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.7043205389200011e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 7.053918670862913e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.5126166039844975e-05\n", + "Data X Sample: tensor([[ 0.0180, 0.0175, 0.0195, 0.0284, 0.0120, 0.0280, 0.0200, 0.0185,\n", + " 0.0366, 0.0205, 0.0317, 0.0136, 0.0244, 0.0245, 0.0126, 0.0191,\n", + " -0.1337, -0.1489, -0.1797, -0.1581, -0.1938, -0.0964, -0.1590, -0.1565,\n", + " -0.1520, -0.2795, -0.2691, -0.4079, -0.0763, -0.0753, -0.0700, -0.0359,\n", + " -0.0176, -0.0526, -0.0196, 0.0073, -0.0098, -0.0133, 0.0063, -0.0255,\n", + " 0.0143, 0.0020, 0.0040, 0.0115, 0.0317, 0.0572, -0.0136, 0.0235]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[-0.2073, 0.6737, 0.0384, 0.6988, 1.0051, 0.0947, -0.3660, -0.3578,\n", + " 0.3079, -0.4696, -0.4212, 0.4357, -0.2032, 0.0267, 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_memory_unit.bias_ih_l1: 5.4072937928140163e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.6617204639478587e-05\n", + "Data X Sample: tensor([[2.7020, 2.3683, 1.9414, 1.9876, 2.1258, 2.2953, 3.5990, 4.1510, 4.1253,\n", + " 4.3584, 4.3143, 4.2416, 4.0502, 3.9534, 3.7778, 3.7403, 3.6967, 3.8126,\n", + " 3.6158, 3.6134, 3.4080, 3.3496, 3.1231, 2.9394, 2.8115, 2.9044, 2.9117,\n", + " 2.8224, 2.9210, 2.9476, 3.0621, 3.1170, 3.3639, 2.5311, 4.0616, 4.5215,\n", + " 4.8021, 5.2793, 5.8123, 5.8927, 1.3745, 0.7249, 0.6952, 1.2400, 1.8751,\n", + " 1.8927, 1.3054, 1.7828]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1226, -0.0957, 0.2285, 0.2056, -0.9547, -0.6327, 0.1212, -0.0173,\n", + " -0.1180, 0.0855, -0.1374, -0.2663, 0.3617, 1.4576, 0.6298, 0.7257,\n", + " 0.5147, 0.8942, 0.9379, -0.2747, 0.3883, 0.1561, -0.1299, 0.0160,\n", + " 0.0495, -0.2102, -0.4454, -0.7233, -0.0572, -0.3292, -0.2596, 1.0534,\n", + " -0.1621, 0.1070, 0.0899, 0.0145, -0.5002, 0.0936, -0.2890, 0.2198,\n", + " -0.0111, -0.0602, -0.7667]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2611, -0.2765, -0.0608, -0.1565, -0.0687, 0.0893, 0.1583, 0.2733,\n", + " 0.1801, 0.2183, 0.2256, -0.1430, -0.1470, -0.2330, -0.1454, -0.2768,\n", + " -0.2086, -0.0831, -0.0688, -0.0764, -0.0418, -0.0300, -0.0569, -0.0412,\n", + " -0.0275, 0.0133, 0.0203, -0.0883, -0.1557, -0.1834, -0.2185, -0.1780,\n", + " -0.2252, -0.2806, -0.1608, -0.1696, 0.0111, 0.0494, 0.0253, 0.2995,\n", + " 0.3260, 0.3377, 0.2590]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00010414377902634442\n", + "Grad encoder.fc1.bias: 0.0005183914909139276\n", + "Grad encoder.encoder.0.weight: 3.6537618143483996e-05\n", + "Grad encoder.encoder.0.bias: 0.0005625398480333388\n", + "Grad encoder.encoder.2.weight: 2.608172144391574e-05\n", + "Grad encoder.encoder.2.bias: 0.00036689150147140026\n", + "Grad encoder.encoder.4.weight: 7.480471685994416e-05\n", + "Grad encoder.encoder.4.bias: 0.0006340149557217956\n", + "Grad 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encoder.encoder.2.weight: 3.9483704313170165e-05\n", + "Grad encoder.encoder.2.bias: 0.00024165371723938733\n", + "Grad encoder.encoder.4.weight: 0.000105859617178794\n", + "Grad encoder.encoder.4.bias: 0.0006218778435140848\n", + "Grad decoder.fc1.0.weight: 3.0433029678533785e-05\n", + "Grad decoder.fc1.0.bias: 0.0001921853981912136\n", + "Grad decoder.fc1.2.weight: 3.9325703255599365e-05\n", + "Grad decoder.fc1.2.bias: 0.00019717795657925308\n", + "Grad decoder.fc1.4.weight: 5.195155245019123e-05\n", + "Grad decoder.fc1.4.bias: 0.0003743913839571178\n", + "Grad decoder.fc2.weight: 8.648022048873827e-05\n", + "Grad decoder.fc2.bias: 0.0021788913290947676\n", + "Grad _memory_unit.weight_ih_l0: 2.443862967993482e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 1.3631887668452691e-05\n", + "Grad _memory_unit.bias_hh_l0: 6.948199370526709e-06\n", + "Grad _memory_unit.weight_ih_l1: 1.1963011274929158e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 3.914176704711281e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.9256560335634276e-05\n", + "Data X Sample: tensor([[1.5616, 1.8541, 1.9504, 2.1647, 2.1821, 2.2836, 2.3017, 2.3324, 2.3531,\n", + " 2.3411, 2.4807, 2.4694, 2.3702, 2.2957, 2.2445, 2.3508, 2.3145, 2.3464,\n", + " 2.4140, 2.4716, 2.4904, 2.5321, 2.5038, 2.4813, 2.4173, 2.4709, 2.4588,\n", + " 2.5083, 2.1231, 2.1059, 1.9667, 1.8542, 1.6236, 1.0504, 1.5124, 1.4520,\n", + " 1.4316, 1.3940, 1.3881, 1.3306, 1.3029, 0.7587, 0.7417, 1.3232, 2.0217,\n", + " 2.1958, 1.3394, 2.0174]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.0278e+00, -7.9765e-01, -7.6073e-01, 9.7115e-04, -4.1858e-01,\n", + " 6.6089e-01, 1.6750e+00, 4.1440e-01, -2.1836e-01, 5.3144e-01,\n", + " 8.8377e-01, -5.6114e-01, -3.0130e-01, -4.4728e-01, -1.8252e+00,\n", + " -2.1173e-01, -1.8356e-01, 8.8537e-01, -1.8494e+00, -1.3110e+00,\n", + " -1.0228e-01, -2.9506e-01, -2.5643e-01, -4.7563e-01, 9.5364e+00,\n", + " 7.9722e-01, 9.6610e-01, 3.8899e-01, -6.4186e-01, -2.3759e-01,\n", + " -1.5215e+00, 1.2778e-01, -1.2040e-01, -4.1425e-01, -2.5888e+00,\n", + " -7.9168e-01, 1.3434e-01, -6.5961e-01, -2.2862e+00, 6.6694e-01,\n", + " 7.3233e-01, 3.6885e-01, 5.1410e-02]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3089, 0.2707, 0.1008, 0.1662, 0.0919, -0.1301, -0.3346, -0.3317,\n", + " -0.2232, -0.2498, -0.2402, 0.1697, 0.1245, 0.1914, 0.1745, 0.2223,\n", + " 0.1889, 0.1179, 0.0865, 0.1002, 0.1066, 0.0974, 0.0182, 0.0395,\n", + " 0.0398, 0.0417, -0.0700, 0.0366, 0.1779, 0.2372, 0.2640, 0.1515,\n", + " 0.2736, 0.2572, 0.1880, 0.1476, -0.0325, 0.0119, 0.0554, -0.2539,\n", + " -0.2107, -0.2469, -0.2057]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00014652148820459843\n", + "Grad encoder.fc1.bias: 0.00015925160550978035\n", + "Grad encoder.encoder.0.weight: 3.936046778107993e-05\n", + "Grad encoder.encoder.0.bias: 0.00016034915461204946\n", + "Grad encoder.encoder.2.weight: 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_memory_unit.bias_ih_l1: 2.5169323635054752e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.2531781976576895e-05\n", + "Data X Sample: tensor([[1.4777, 1.6590, 1.8828, 2.0860, 2.1309, 2.2570, 2.2725, 2.4077, 2.4971,\n", + " 2.4865, 2.5601, 2.5220, 2.4390, 2.4047, 2.4135, 2.4835, 2.3963, 2.4064,\n", + " 2.3872, 2.4213, 2.4511, 2.4709, 2.4170, 2.5176, 2.4812, 2.5492, 2.4588,\n", + " 2.5165, 2.0746, 2.2697, 2.3307, 2.3654, 2.2792, 1.4418, 1.9487, 1.7609,\n", + " 1.6145, 1.5159, 1.6036, 1.5661, 1.2934, 0.7448, 0.6993, 1.2170, 1.8632,\n", + " 2.0300, 1.3734, 1.8141]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.5406, -1.4109, 1.3330, -0.5608, 0.9574, 0.0869, 0.5446, -0.6666,\n", + " 0.7893, 0.9100, 1.0467, 1.6432, -0.7137, 0.6521, 0.2274, 0.7501,\n", + " 1.6616, 0.0570, 0.1614, -0.0776, 0.3046, 0.1082, 0.1671, 0.2197,\n", + " -0.0524, -0.0077, 1.3965, 0.0296, 0.1546, 0.8283, -1.1242, 0.5124,\n", + " -0.3318, -0.7190, -0.5834, 0.4626, -0.5754, 0.0940, -0.3730, 1.7204,\n", + " -1.0987, 0.7697, 0.0744]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4167, 0.3667, 0.1313, 0.2608, 0.1338, -0.1620, -0.4237, -0.4550,\n", + " -0.2945, -0.3342, -0.3146, 0.2250, 0.1803, 0.2610, 0.2348, 0.3163,\n", + " 0.2755, 0.1671, 0.1029, 0.1302, 0.1275, 0.1323, 0.0503, 0.0673,\n", + " 0.0519, 0.0356, -0.1013, 0.0520, 0.2417, 0.3289, 0.3446, 0.2017,\n", + " 0.3673, 0.3477, 0.2564, 0.1985, -0.0404, 0.0035, 0.0679, -0.3469,\n", + " -0.3198, -0.3404, -0.2943]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 7.616858056280762e-05\n", + "Grad encoder.fc1.bias: 0.00022287775937002152\n", + "Grad encoder.encoder.0.weight: 1.786928260116838e-05\n", + "Grad encoder.encoder.0.bias: 0.0003699931548908353\n", + "Grad encoder.encoder.2.weight: 1.6962878362392075e-05\n", + "Grad encoder.encoder.2.bias: 0.0002784943499136716\n", + "Grad encoder.encoder.4.weight: 4.843936039833352e-05\n", + "Grad encoder.encoder.4.bias: 0.0003813159419223666\n", + "Grad decoder.fc1.0.weight: 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2.5438, 2.5194, 2.5984, 2.5189, 2.4934,\n", + " 2.5627, 2.4678, 2.5076, 2.6270, 2.6122, 2.5328, 2.5074, 2.6145, 2.5041,\n", + " 2.5287, 2.2064, 2.3745, 2.3867, 2.3267, 1.9822, 1.2312, 1.8040, 1.6393,\n", + " 1.6086, 1.6114, 1.5656, 1.4526, 1.2218, 0.7787, 0.7215, 1.3319, 1.8909,\n", + " 2.2358, 1.4754, 1.8922]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 2.9279e-01, 6.6411e-04, -7.3179e-01, -5.0052e-01, -1.8919e-01,\n", + " -2.4271e-01, -4.1477e-01, -3.3865e-01, -2.8051e-01, -4.1018e-01,\n", + " -4.6720e-01, 6.9119e-01, 5.0525e-01, 7.1732e-01, 1.9896e+00,\n", + " 6.0470e-01, 3.2113e-01, 3.8110e-01, 8.3358e-01, 1.7891e+00,\n", + " 2.4401e-02, -1.2793e+00, 1.8767e-01, 8.3809e-02, 1.3267e+00,\n", + " 1.1382e-01, 1.5625e-01, 7.5458e-01, 6.8320e-01, 6.4396e-01,\n", + " 8.8334e-01, 2.2765e-01, 6.0634e-01, -2.2583e-01, 1.2221e+00,\n", + " -4.0802e-01, 7.3289e-01, -5.0732e-01, 4.4037e-01, -2.9483e-01,\n", + " -3.1629e-01, -7.3887e-01, -2.5250e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3879, 0.3411, 0.1256, 0.2453, 0.1234, -0.1474, -0.4023, -0.4252,\n", + " -0.2726, -0.3087, -0.2935, 0.2091, 0.1734, 0.2411, 0.2135, 0.2899,\n", + " 0.2537, 0.1583, 0.1001, 0.1215, 0.1210, 0.1257, 0.0433, 0.0614,\n", + " 0.0505, 0.0353, -0.0948, 0.0544, 0.2242, 0.3018, 0.3257, 0.1862,\n", + " 0.3453, 0.3259, 0.2404, 0.1862, -0.0375, 0.0039, 0.0590, -0.3221,\n", + " -0.2916, -0.3193, -0.2740]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003777240344788879\n", + "Grad encoder.fc1.bias: 0.0004935472388751805\n", + "Grad encoder.encoder.0.weight: 0.00010208319872617722\n", + "Grad encoder.encoder.0.bias: 0.0005182881141081452\n", + "Grad encoder.encoder.2.weight: 6.687191489618272e-05\n", + "Grad encoder.encoder.2.bias: 0.0006171776331029832\n", + "Grad encoder.encoder.4.weight: 0.00023195594258140773\n", + "Grad encoder.encoder.4.bias: 0.0025158836506307125\n", + "Grad decoder.fc1.0.weight: 8.209893712773919e-05\n", + "Grad decoder.fc1.0.bias: 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3.4832, 3.3811, 3.4583, 3.1472, 2.9890, 2.9185, 2.8600, 2.6746,\n", + " 2.8387, 2.5602, 2.7053, 2.6736, 2.9264, 3.1835, 2.2473, 3.6302, 3.9135,\n", + " 4.3184, 4.7360, 5.1848, 4.9621, 1.9615, 1.1451, 1.1277, 2.0351, 3.0802,\n", + " 3.2537, 2.2504, 3.3075]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.8475, -0.6518, -0.2553, -1.3371, 0.0605, -0.0195, 0.7194, 0.5719,\n", + " 1.0029, 1.4466, 0.3934, -1.3531, 0.6037, -0.9570, -0.3685, -0.1753,\n", + " 0.1233, 0.3426, -0.0793, 0.0000, 0.9915, 0.1578, 6.0159, -0.4785,\n", + " 1.0258, 0.1464, 0.5683, -0.3157, -0.0370, -0.0443, -0.6121, -0.0594,\n", + " 0.0330, 0.0513, 1.0489, 0.8265, -0.5701, 0.8248, -0.8482, 0.1388,\n", + " -0.2469, 0.1992, 0.1700]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2472, -0.2729, -0.0678, -0.1438, -0.0651, 0.0973, 0.1447, 0.2757,\n", + " 0.1791, 0.2167, 0.2199, -0.1529, -0.1358, -0.2360, -0.1508, -0.2794,\n", + " -0.2066, -0.0741, -0.0662, -0.0978, -0.0568, -0.0334, -0.0546, -0.0400,\n", + " 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1.8473,\n", + " 2.1100, 1.3598, 2.0408]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.2055, -0.0056, 0.9858, -0.1393, 0.5610, -0.4667, 1.7127, 0.1848,\n", + " 0.2455, 0.0374, 0.0248, -0.7908, 0.6753, 0.2633, 0.0821, -0.7714,\n", + " -0.6356, -0.1718, -0.2018, -0.4028, 0.5630, -0.4044, -1.2998, -0.2659,\n", + " -0.4111, -0.6734, 0.5246, -0.4664, 1.0723, 0.5287, 0.5212, 1.6300,\n", + " -0.7698, 0.3665, 1.0094, -0.1919, -0.4229, -0.7806, 0.0545, -0.7285,\n", + " 0.9400, 0.6886, 0.1892]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4103, 0.3584, 0.1281, 0.2686, 0.1241, -0.1468, -0.4188, -0.4559,\n", + " -0.2784, -0.3194, -0.3103, 0.2083, 0.2039, 0.2602, 0.2266, 0.3063,\n", + " 0.2683, 0.1739, 0.1052, 0.1257, 0.1171, 0.1358, 0.0633, 0.0693,\n", + " 0.0514, 0.0240, -0.0947, 0.0754, 0.2425, 0.3194, 0.3371, 0.1949,\n", + " 0.3682, 0.3420, 0.2517, 0.1968, -0.0334, -0.0005, 0.0499, -0.3351,\n", + " -0.3140, -0.3365, -0.2999]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad 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-0.8609, 0.2468, 0.2910, -2.2649, 0.9285, 0.0096, -0.2339,\n", + " 0.3517, -0.2833, 0.0387, 2.0627, 0.8153, 0.6663, 1.0216, 0.9237,\n", + " 0.1890, 0.6253, 0.7699, 0.7744, -0.3045, 0.0000, -0.0231, -0.6430,\n", + " -0.6715, -0.7226, -0.6425]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4063, 0.3542, 0.1239, 0.2642, 0.1178, -0.1464, -0.4158, -0.4538,\n", + " -0.2743, -0.3164, -0.3117, 0.2002, 0.2139, 0.2640, 0.2256, 0.3035,\n", + " 0.2630, 0.1742, 0.1048, 0.1249, 0.1124, 0.1367, 0.0684, 0.0672,\n", + " 0.0504, 0.0178, -0.0884, 0.0812, 0.2438, 0.3169, 0.3323, 0.1903,\n", + " 0.3671, 0.3362, 0.2462, 0.1963, -0.0294, 0.0007, 0.0463, -0.3297,\n", + " -0.3103, -0.3336, -0.3036]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 8.324990631081164e-05\n", + "Grad encoder.fc1.bias: 0.0002374609757680446\n", + "Grad encoder.encoder.0.weight: 2.291392593178898e-05\n", + "Grad encoder.encoder.0.bias: 0.00031644769478589296\n", + "Grad encoder.encoder.2.weight: 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device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2166, -0.2507, -0.0692, -0.1444, -0.0747, 0.0797, 0.1153, 0.2441,\n", + " 0.1593, 0.1942, 0.1835, -0.1392, -0.1097, -0.2064, -0.1363, -0.2581,\n", + " -0.1978, -0.0643, -0.0518, -0.0957, -0.0531, -0.0329, -0.0512, -0.0425,\n", + " -0.0257, 0.0134, 0.0336, -0.0450, -0.1284, -0.1858, -0.1844, -0.1508,\n", + " -0.2010, -0.2394, -0.1416, -0.1399, 0.0091, 0.0324, 0.0014, 0.2567,\n", + " 0.2934, 0.2806, 0.2249]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 8.12327562016435e-05\n", + "Grad encoder.fc1.bias: 0.00016460465849377215\n", + "Grad encoder.encoder.0.weight: 2.6966046789311804e-05\n", + "Grad encoder.encoder.0.bias: 0.00012473881361074746\n", + "Grad encoder.encoder.2.weight: 1.5049137800815515e-05\n", + "Grad encoder.encoder.2.bias: 0.00013751606456935406\n", + "Grad encoder.encoder.4.weight: 4.159212039667182e-05\n", + "Grad encoder.encoder.4.bias: 0.0002747859980445355\n", + "Grad decoder.fc1.0.weight: 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device='cuda:0')\n", + "Data Y Sample: tensor([[-0.4448, -0.4697, 0.7536, -2.1454, -1.1862, -0.4132, 0.0348, 0.5409,\n", + " 0.6448, 0.3377, 0.3852, -0.5403, -0.6442, -1.0662, -0.7269, -0.8769,\n", + " -0.5167, -0.4405, 1.1864, -0.8617, -1.6825, -1.1140, -1.4504, 1.4217,\n", + " 2.1209, -1.1525, -0.1624, -0.3284, -0.4119, -0.1176, -0.4484, -0.4897,\n", + " -1.1936, -0.4015, -1.2986, -0.7296, 0.7316, 0.1005, -0.0231, 0.8572,\n", + " 0.5073, 0.8440, 0.0471]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2522, -0.2874, -0.0764, -0.1433, -0.0528, 0.1183, 0.1619, 0.3063,\n", + " 0.2002, 0.2233, 0.2302, -0.1665, -0.1389, -0.2611, -0.1709, -0.2901,\n", + " -0.2003, -0.0659, -0.0706, -0.1066, -0.0929, -0.0338, -0.0271, -0.0399,\n", + " -0.0384, 0.0140, 0.0197, -0.0815, -0.1892, -0.2507, -0.2414, -0.2096,\n", + " -0.2643, -0.3138, -0.1806, -0.1652, -0.0047, 0.0257, -0.0074, 0.3061,\n", + " 0.3311, 0.3299, 0.2686]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 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-1.2363, -2.5410, 0.9480, 0.1832, 0.3165, -0.4167, 0.9596,\n", + " -1.0245, -0.8695, -0.6368, -1.0553, -0.5591, -0.0296, -1.3748, -1.6445,\n", + " -1.0251, -0.6855, 0.4899, 0.9094, 1.0293, 1.5847, 1.0566, 0.6192,\n", + " 0.4663, 0.8452, -0.2314, 0.7002, 1.0300, -0.5083, -0.2912, -2.3529,\n", + " -1.5166, -1.2133, 1.1100]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3800, 0.3778, 0.1506, 0.2818, 0.1115, -0.1092, -0.3906, -0.3942,\n", + " -0.2280, -0.2799, -0.2886, 0.1616, 0.2754, 0.3312, 0.1874, 0.3170,\n", + " 0.2828, 0.1649, 0.1499, 0.1667, 0.1312, 0.0951, 0.1240, 0.0872,\n", + " 0.0258, -0.0072, -0.0656, 0.0860, 0.1787, 0.1884, 0.2296, 0.1351,\n", + " 0.2898, 0.2485, 0.1813, 0.2181, -0.0255, 0.0301, -0.0347, -0.3019,\n", + " -0.2931, -0.2946, -0.3128]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 2.8329513952485286e-05\n", + "Grad encoder.fc1.bias: 0.00017076003132387996\n", + "Grad encoder.encoder.0.weight: 1.1322794307488948e-05\n", + "Grad 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tensor([[1.3356, 1.5206, 1.7536, 1.9001, 2.0472, 1.9447, 1.8488, 1.7575, 3.2050,\n", + " 4.1278, 4.2382, 4.0683, 3.9325, 3.8170, 3.7602, 3.7307, 3.6322, 3.5264,\n", + " 3.3309, 3.3642, 3.3590, 3.3665, 3.0508, 2.7943, 2.7814, 2.8078, 2.7519,\n", + " 2.8101, 2.8343, 2.9804, 2.9396, 3.1557, 3.5883, 2.6821, 4.4194, 4.8158,\n", + " 4.8493, 5.1998, 5.7426, 5.6572, 1.0691, 0.6472, 0.6184, 1.1740, 1.6451,\n", + " 1.8413, 1.2714, 1.7046]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.6052, -0.1055, 0.9637, -0.8262, -0.0523, 1.0302, 0.9212, 0.9099,\n", + " -0.1356, 0.8108, 0.6204, -0.7482, -0.9602, -0.6551, -0.5885, -0.3617,\n", + " -0.7703, -0.4094, -0.5084, 1.4056, -0.7921, -0.4012, -0.9001, -0.4970,\n", + " 0.6545, 0.5174, 0.0063, -0.0951, 0.6109, -0.3337, -0.7283, 0.5521,\n", + " -1.0093, -0.9284, 0.4590, 0.0657, -1.0293, 0.9799, -0.2505, 0.7631,\n", + " 0.7630, 0.4496, 0.3560]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2275, -0.2695, -0.0766, -0.1624, -0.0658, 0.0930, 0.1342, 0.2660,\n", + " 0.1757, 0.1979, 0.1934, -0.1343, -0.1200, -0.2170, -0.1657, -0.2564,\n", + " -0.1893, -0.0847, -0.0554, -0.0999, -0.0908, -0.0318, -0.0312, -0.0379,\n", + " -0.0470, 0.0058, 0.0280, -0.0647, -0.1636, -0.2379, -0.2019, -0.1921,\n", + " -0.2364, -0.2766, -0.1682, -0.1540, -0.0262, 0.0342, -0.0157, 0.2776,\n", + " 0.3054, 0.2943, 0.2485]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00037803928717039526\n", + "Grad encoder.fc1.bias: 0.000570765056181699\n", + "Grad encoder.encoder.0.weight: 0.0001039049748214893\n", + "Grad encoder.encoder.0.bias: 0.0007274262607097626\n", + "Grad encoder.encoder.2.weight: 6.144726648926735e-05\n", + "Grad encoder.encoder.2.bias: 0.0004895571619272232\n", + "Grad encoder.encoder.4.weight: 0.00019609548326116055\n", + "Grad encoder.encoder.4.bias: 0.0011506755836308002\n", + "Grad decoder.fc1.0.weight: 5.533444709726609e-05\n", + "Grad decoder.fc1.0.bias: 0.0002902083215303719\n", + "Grad decoder.fc1.2.weight: 6.741980905644596e-05\n", + "Grad decoder.fc1.2.bias: 0.0003466215857770294\n", + "Grad decoder.fc1.4.weight: 7.88633624324575e-05\n", + "Grad decoder.fc1.4.bias: 0.0005225300556048751\n", + "Grad decoder.fc2.weight: 0.00012108534428989515\n", + "Grad decoder.fc2.bias: 0.0023001020308583975\n", + "Grad _memory_unit.weight_ih_l0: 8.625059308542404e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 2.60503056779271e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.3471842066792306e-05\n", + "Grad _memory_unit.weight_ih_l1: 3.556839601515094e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 8.849647565511987e-05\n", + "Grad _memory_unit.bias_hh_l1: 4.37030648754444e-05\n", + "Data X Sample: tensor([[ 0.0032, 0.0000, 0.0015, 0.0087, -0.0068, 0.0044, 0.0031, -0.0028,\n", + " 0.0146, 0.0158, 0.0095, 0.0117, 0.0111, 0.0055, 0.0050, 0.0082,\n", + " -0.0330, -0.0348, -0.0351, -0.0279, -0.0442, -0.0230, -0.0361, -0.0267,\n", + " -0.0169, -0.0313, -0.0213, -0.0326, -0.0069, 0.0098, -0.0315, 0.0193,\n", + " 0.0176, -0.0160, 0.0147, 0.0195, 0.0118, 0.0027, 0.0444, 0.0085,\n", + " 0.0382, 0.0060, 0.0081, 0.0115, 0.0277, 0.0000, -0.0136, 0.0235]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.1110, 0.8474, 0.8914, 0.3977, -0.6481, -0.9052, -1.0228, -1.2453,\n", + " -1.5036, -0.8970, -0.8167, 0.8644, 0.6201, 0.6974, -0.2019, 0.9472,\n", + " 0.3130, -2.5763, 0.3975, -0.0575, 2.0423, 0.0066, -0.6954, 0.2723,\n", + " -1.3605, 0.3311, 0.4947, -0.2585, 0.8103, 1.9029, 0.6404, 0.5003,\n", + " 1.6653, 0.3892, 1.0064, 0.3866, 0.3232, -0.6228, 0.9794, -0.8960,\n", + " -0.6549, -0.8300, -0.9418]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 9.2572e-01, 7.7004e-01, 9.7422e-02, 4.1945e-01, -7.2032e-01,\n", + " -8.6180e-01, -1.4482e+00, -1.2759e+00, -8.9488e-01, -1.0050e+00,\n", + " -1.0545e+00, 4.0511e-01, 5.8772e-01, 7.7675e-01, 3.8764e-01,\n", + " 6.0333e-01, 6.0415e-01, 1.3765e-03, 1.5993e-01, 1.0646e-01,\n", + " 2.3699e-01, -2.7690e-02, 1.7673e-01, 2.4893e-02, 2.7655e-02,\n", + " -6.8510e-02, 4.3861e-01, 7.8992e-01, 1.1389e+00, 9.4431e-01,\n", + " 9.2619e-01, 4.9549e-01, 7.5711e-01, 6.8418e-01, 3.8163e-01,\n", + " 3.9716e-01, 5.5260e-02, 1.4577e-01, 6.2000e-02, -9.4992e-01,\n", + " -7.9001e-01, -6.8421e-01, -4.7363e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 9.774739010026678e-05\n", + "Grad encoder.fc1.bias: 0.00043269633897580206\n", + "Grad encoder.encoder.0.weight: 2.6826734028873034e-05\n", + "Grad encoder.encoder.0.bias: 0.00042317132465541363\n", + "Grad encoder.encoder.2.weight: 2.0364062947919592e-05\n", + "Grad encoder.encoder.2.bias: 0.00030886445892974734\n", + "Grad encoder.encoder.4.weight: 6.696996570099145e-05\n", + "Grad encoder.encoder.4.bias: 0.0006146759260445833\n", + "Grad decoder.fc1.0.weight: 2.6477768187760375e-05\n", + "Grad decoder.fc1.0.bias: 0.00019213088671676815\n", + "Grad decoder.fc1.2.weight: 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2.3469,\n", + " 2.4349, 2.1856, 2.2762, 2.2712, 2.2217, 2.2352, 1.4669, 1.9462, 1.7877,\n", + " 1.6774, 1.4841, 1.5529, 1.5576, 1.2313, 0.6831, 0.7134, 1.3519, 1.7244,\n", + " 2.0071, 1.3734, 1.8453]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.2958e+00, 1.3605e+00, -1.4252e-01, 1.2439e+00, 1.7919e+00,\n", + " -2.2817e-01, -4.1532e-01, -7.1662e-01, -1.7667e-01, -2.4433e-01,\n", + " -4.2581e-01, 1.0969e+00, 8.4771e-01, 4.0783e-01, -1.0890e-01,\n", + " 1.0786e+00, -1.6426e+00, -2.5415e+00, -1.7910e-02, 3.5063e-01,\n", + " -9.0240e-01, 1.9377e-01, 7.2693e-01, -2.3884e-01, 2.2564e-01,\n", + " 2.9037e-01, 7.7926e-01, 2.4974e-02, 5.1044e-01, 4.1021e-01,\n", + " 5.5339e-02, -6.0155e-01, 3.9036e-01, -2.1445e-01, -6.0088e-01,\n", + " -1.1999e-04, 9.7267e-01, 4.1658e-01, 2.2533e-01, -5.7210e-01,\n", + " -7.3933e-01, -6.4496e-01, -8.5656e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3719, 0.3725, 0.1343, 0.2780, 0.1151, -0.1019, -0.3824, -0.4114,\n", + " -0.2190, -0.2791, 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3.2973, 3.4645, 3.3894, 2.9062, 2.8707, 2.8377, 2.8444, 2.9170,\n", + " 3.0671, 2.9974, 3.0557, 3.1636, 3.2690, 3.5509, 2.5585, 4.1866, 4.6164,\n", + " 4.9634, 5.3031, 5.6412, 5.6828, 1.1263, 0.6452, 0.6164, 1.1568, 1.8156,\n", + " 1.8584, 1.1830, 1.6499]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.0581, -0.7974, 0.0536, 0.0670, 1.2944, -0.0089, 0.3558, 0.8484,\n", + " 1.0247, 0.8906, 0.8358, 0.2462, -0.7957, -0.2114, -0.7733, -0.4198,\n", + " -0.7762, 1.0063, 0.8952, -0.1136, 1.8031, -1.3344, -1.7941, -4.3443,\n", + " -3.3745, 1.3897, 1.4732, 0.0445, -0.3431, -0.3761, -0.0486, -1.4825,\n", + " 0.2222, -0.1654, 0.9534, 0.0678, 0.5193, -0.0140, 1.1137, 0.6659,\n", + " 1.2371, 0.2146, 1.2232]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2988, -0.3415, -0.0889, -0.1712, -0.0286, 0.1555, 0.2156, 0.3472,\n", + " 0.2308, 0.2500, 0.2727, -0.1751, -0.1749, -0.2916, -0.2241, -0.2958,\n", + " -0.2056, -0.1094, -0.0819, -0.1097, -0.1163, -0.0308, -0.0248, -0.0374,\n", + " 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1.5976,\n", + " 1.8527, 1.1558, 1.6499]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.0458, -0.3146, 1.1054, -0.4295, -1.2549, 2.3108, 0.3446, 0.2695,\n", + " 0.1850, 0.2431, 0.2929, 0.1494, -0.1971, -0.5441, -0.0791, 0.8145,\n", + " 0.5324, 0.8843, 1.3103, 0.5720, -0.8430, 1.3970, 0.1470, 0.9298,\n", + " 0.4896, 0.4903, -0.1877, 1.0123, -0.1370, -0.3135, -0.1042, -0.9167,\n", + " -0.0639, -0.3211, -0.4216, -0.0364, -0.4582, -0.7204, 1.1574, 0.3243,\n", + " 0.2868, 0.0143, -0.0398]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.3134, -0.3552, -0.0904, -0.1710, -0.0205, 0.1682, 0.2308, 0.3631,\n", + " 0.2422, 0.2607, 0.2886, -0.1847, -0.1872, -0.3093, -0.2350, -0.3041,\n", + " -0.2076, -0.1107, -0.0882, -0.1124, -0.1198, -0.0282, -0.0223, -0.0374,\n", + " -0.0510, 0.0108, -0.0052, -0.1449, -0.2425, -0.3057, -0.2696, -0.2732,\n", + " -0.3171, -0.3867, -0.2156, -0.2197, -0.0439, 0.0375, 0.0098, 0.3792,\n", + " 0.3726, 0.4027, 0.3302]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.000138989242259413\n", + "Grad encoder.fc1.bias: 0.000723914010450244\n", + "Grad encoder.encoder.0.weight: 4.090392030775547e-05\n", + "Grad encoder.encoder.0.bias: 0.00047973543405532837\n", + "Grad encoder.encoder.2.weight: 2.8749087505275384e-05\n", + "Grad encoder.encoder.2.bias: 0.00038072597817517817\n", + "Grad encoder.encoder.4.weight: 8.059318497544155e-05\n", + "Grad encoder.encoder.4.bias: 0.0009120157919824123\n", + "Grad decoder.fc1.0.weight: 3.221472434233874e-05\n", + "Grad decoder.fc1.0.bias: 0.0003492283867672086\n", + "Grad decoder.fc1.2.weight: 3.47329732903745e-05\n", + "Grad decoder.fc1.2.bias: 0.0002621954772621393\n", + "Grad decoder.fc1.4.weight: 4.35155670857057e-05\n", + "Grad decoder.fc1.4.bias: 0.00038077300996519625\n", + "Grad decoder.fc2.weight: 0.00013328916975297034\n", + "Grad decoder.fc2.bias: 0.0020595293026417494\n", + "Grad _memory_unit.weight_ih_l0: 5.5425734899472445e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.083845876972191e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.5536317732767202e-05\n", + "Grad _memory_unit.weight_ih_l1: 2.661420467120479e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 8.809156133793294e-05\n", + "Grad _memory_unit.bias_hh_l1: 4.350589733803645e-05\n", + "Data X Sample: tensor([[1.5764, 1.7828, 1.9564, 2.0466, 2.1411, 2.1244, 2.2124, 2.3523, 2.3067,\n", + " 2.3617, 2.3253, 2.4344, 2.2770, 2.3120, 2.2521, 2.3344, 2.3114, 2.2884,\n", + " 2.3190, 2.3339, 2.4340, 2.4740, 2.5038, 2.4603, 2.4849, 2.6276, 2.5521,\n", + " 2.5369, 2.3660, 2.4727, 2.5232, 2.4234, 2.1406, 1.3571, 1.8212, 1.7804,\n", + " 1.6204, 1.4550, 1.4832, 1.5519, 1.2552, 0.7548, 0.7579, 1.3433, 1.8592,\n", + " 2.0071, 1.4074, 2.0252]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.4440e+00, 2.3659e-01, 1.9534e-01, 5.5173e-01, -9.5061e-02,\n", + " -1.4852e+00, -4.7485e-01, -3.1885e-01, -1.1247e+00, 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_memory_unit.bias_ih_l0: 6.832287908764556e-05\n", + "Grad _memory_unit.bias_hh_l0: 3.408048360142857e-05\n", + "Grad _memory_unit.weight_ih_l1: 2.8919052965648007e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 6.935191777301952e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.4805372706614435e-05\n", + "Data X Sample: tensor([[2.5237, 2.7295, 3.0428, 3.0459, 3.2441, 3.3163, 3.4218, 3.5292, 3.6639,\n", + " 3.7692, 3.7687, 3.7314, 3.6729, 3.6480, 3.6038, 3.3710, 3.3759, 3.5786,\n", + " 3.5105, 3.4386, 3.1995, 3.1261, 2.9833, 2.8878, 2.7420, 2.6772, 2.6693,\n", + " 2.5695, 2.5117, 2.6529, 2.5861, 2.7412, 2.6247, 1.9246, 3.1473, 3.5900,\n", + " 3.8189, 3.9356, 4.2911, 4.2075, 1.9568, 1.0634, 1.0833, 2.0093, 2.8027,\n", + " 2.7447, 2.0329, 2.8931]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.2028, -0.1461, -0.9671, -0.0146, 0.3525, -0.4305, 0.6736, 0.8460,\n", + " 0.8431, 0.9534, 0.9058, -0.1551, -0.3466, -0.8356, -0.9089, -0.9039,\n", + " 0.1539, 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"Grad _memory_unit.bias_ih_l1: 4.4852971768705174e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.198722904722672e-05\n", + "Data X Sample: tensor([[1.4332, 1.8469, 2.2059, 2.2871, 2.4553, 2.5311, 2.6006, 2.6476, 2.6875,\n", + " 2.7361, 2.7853, 2.8570, 2.7142, 2.6283, 2.7413, 2.6490, 2.6149, 2.7739,\n", + " 2.8105, 2.6910, 2.7039, 2.7526, 2.6966, 2.6855, 2.3798, 2.4735, 2.2351,\n", + " 2.2514, 1.9011, 1.9029, 1.7148, 1.6497, 1.4344, 0.9543, 1.3555, 1.3645,\n", + " 1.4139, 1.5027, 1.7431, 1.7335, 1.2504, 0.8145, 0.7680, 1.3634, 2.1525,\n", + " 2.2072, 1.4958, 2.2363]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.3975, 0.6246, 0.6859, 0.4766, 0.7998, 2.1428, 0.7531, 0.1895,\n", + " -0.6407, -0.5134, -0.2623, -0.4532, -0.0644, 0.2387, 1.1547, -0.1893,\n", + " 0.1553, 2.0131, -0.9624, -1.5705, -0.6891, 1.4378, -0.7334, -1.0932,\n", + " 0.1136, -0.7003, -1.2386, -0.1107, 0.0438, -0.3359, 0.1738, -0.4957,\n", + " 0.2759, 0.2314, -0.4754, -1.2211, -0.3718, -0.0459, 0.2253, -0.2032,\n", + " -0.4053, -0.4248, -0.8473]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.2185, 0.2276, 0.0852, 0.1178, 0.0882, -0.0414, -0.2398, -0.2456,\n", + " -0.1226, -0.1547, -0.1463, 0.1291, 0.1255, 0.2086, 0.0945, 0.1971,\n", + " 0.1584, 0.0595, 0.1013, 0.0920, 0.1042, 0.0604, 0.0353, 0.0156,\n", + " 0.0030, 0.0148, -0.0319, 0.0239, 0.0899, 0.1061, 0.1516, 0.0726,\n", + " 0.1813, 0.1448, 0.1080, 0.1386, -0.0305, 0.0113, -0.0376, -0.1748,\n", + " -0.1513, -0.1734, -0.1629]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00011831634765258059\n", + "Grad encoder.fc1.bias: 0.0011382634984329343\n", + "Grad encoder.encoder.0.weight: 3.560907134669833e-05\n", + "Grad encoder.encoder.0.bias: 0.0007176926592364907\n", + "Grad encoder.encoder.2.weight: 3.416023537283763e-05\n", + "Grad encoder.encoder.2.bias: 0.0005911170737817883\n", + "Grad encoder.encoder.4.weight: 8.598310523666441e-05\n", + "Grad encoder.encoder.4.bias: 0.001263970509171486\n", + "Grad 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device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00016442465130239725\n", + "Grad encoder.fc1.bias: 0.00015929563960526139\n", + "Grad encoder.encoder.0.weight: 4.245563832228072e-05\n", + "Grad encoder.encoder.0.bias: 0.00014197059499565512\n", + "Grad encoder.encoder.2.weight: 1.9692888599820435e-05\n", + "Grad encoder.encoder.2.bias: 0.0001379938912577927\n", + "Grad encoder.encoder.4.weight: 5.830366717418656e-05\n", + "Grad encoder.encoder.4.bias: 0.00033436849480494857\n", + "Grad decoder.fc1.0.weight: 2.6898062060354277e-05\n", + "Grad decoder.fc1.0.bias: 0.00017513222701381892\n", + "Grad decoder.fc1.2.weight: 3.111444675596431e-05\n", + "Grad decoder.fc1.2.bias: 0.00012648297706618905\n", + "Grad decoder.fc1.4.weight: 3.197266778443009e-05\n", + "Grad decoder.fc1.4.bias: 0.0001741068554110825\n", + "Grad decoder.fc2.weight: 9.70002802205272e-05\n", + "Grad decoder.fc2.bias: 0.0017779947957023978\n", + "Grad _memory_unit.weight_ih_l0: 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1.4848e-01,\n", + " -9.1794e-02, -3.5571e-01, -4.5328e-01, -2.2498e-01, -2.6862e-01,\n", + " -2.5607e-01, 2.2068e-01, 2.3648e-01, 3.6339e-01, 2.2926e-01,\n", + " 3.8583e-01, 2.9319e-01, 1.1794e-01, 9.8096e-02, 1.3464e-01,\n", + " 1.4611e-01, 1.1213e-01, 7.9053e-02, 5.0974e-02, 2.0011e-02,\n", + " -5.3576e-05, -6.7183e-02, 3.5631e-02, 1.9924e-01, 2.5651e-01,\n", + " 2.6180e-01, 1.6293e-01, 3.2470e-01, 2.7307e-01, 2.0595e-01,\n", + " 2.2684e-01, -2.8019e-02, -6.7601e-03, -5.9002e-02, -3.2163e-01,\n", + " -3.3439e-01, -3.2388e-01, -3.2664e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002221480681328103\n", + "Grad encoder.fc1.bias: 0.0001574521156726405\n", + "Grad encoder.encoder.0.weight: 6.31592411082238e-05\n", + "Grad encoder.encoder.0.bias: 0.00017011314048431814\n", + "Grad encoder.encoder.2.weight: 3.6228295357432216e-05\n", + "Grad encoder.encoder.2.bias: 0.0001530753361294046\n", + "Grad encoder.encoder.4.weight: 0.00010427524102851748\n", + "Grad 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"Grad encoder.encoder.0.weight: 0.00010777622810564935\n", + "Grad encoder.encoder.0.bias: 0.0003936230787076056\n", + "Grad encoder.encoder.2.weight: 6.410095375031233e-05\n", + "Grad encoder.encoder.2.bias: 0.0003161478671245277\n", + "Grad encoder.encoder.4.weight: 0.0001757332356646657\n", + "Grad encoder.encoder.4.bias: 0.0011058388045057654\n", + "Grad decoder.fc1.0.weight: 5.1837527280440554e-05\n", + "Grad decoder.fc1.0.bias: 0.0003413324011489749\n", + "Grad decoder.fc1.2.weight: 7.056362665025517e-05\n", + "Grad decoder.fc1.2.bias: 0.00042372223106212914\n", + "Grad decoder.fc1.4.weight: 8.616999548394233e-05\n", + "Grad decoder.fc1.4.bias: 0.0008584444876760244\n", + "Grad decoder.fc2.weight: 0.0001293435343541205\n", + "Grad decoder.fc2.bias: 0.002951523521915078\n", + "Grad _memory_unit.weight_ih_l0: 5.048054390499601e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 2.7665269954013638e-05\n", + "Grad _memory_unit.bias_hh_l0: 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"Grad encoder.encoder.4.weight: 8.021695975912735e-05\n", + "Grad encoder.encoder.4.bias: 0.00034971412969753146\n", + "Grad decoder.fc1.0.weight: 3.0426685043494217e-05\n", + "Grad decoder.fc1.0.bias: 0.0001500275102443993\n", + "Grad decoder.fc1.2.weight: 4.4195272494107485e-05\n", + "Grad decoder.fc1.2.bias: 0.0002215211425209418\n", + "Grad decoder.fc1.4.weight: 4.737973358714953e-05\n", + "Grad decoder.fc1.4.bias: 0.0003792145289480686\n", + "Grad decoder.fc2.weight: 6.826828757766634e-05\n", + "Grad decoder.fc2.bias: 0.0015509951626881957\n", + "Grad _memory_unit.weight_ih_l0: 2.5857368655124446e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 9.053468602360226e-06\n", + "Grad _memory_unit.bias_hh_l0: 5.017987859901041e-06\n", + "Grad _memory_unit.weight_ih_l1: 1.5270468338712817e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 3.852525696856901e-05\n", + "Grad _memory_unit.bias_hh_l1: 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_memory_unit.bias_ih_l0: 3.400085915927775e-06\n", + "Grad _memory_unit.bias_hh_l0: 1.8788293800753308e-06\n", + "Grad _memory_unit.weight_ih_l1: 6.120129114606243e-07\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 1.5780435205670074e-05\n", + "Grad _memory_unit.bias_hh_l1: 7.980268492246978e-06\n", + "Data X Sample: tensor([[1.5117, 1.6560, 1.7986, 1.9067, 2.0062, 2.0802, 2.0198, 4.0686, 4.6428,\n", + " 4.5369, 4.4761, 4.4150, 4.2454, 4.0324, 3.9695, 3.8114, 3.7580, 3.7198,\n", + " 3.7026, 3.5576, 3.7246, 3.5578, 3.2002, 2.9069, 2.8528, 2.9097, 2.7785,\n", + " 2.7653, 2.8655, 3.0819, 3.1566, 3.2303, 3.6829, 2.7828, 4.5567, 4.9326,\n", + " 5.0774, 5.7325, 6.2053, 6.0686, 1.0022, 0.6412, 0.6245, 1.1654, 1.5817,\n", + " 1.8012, 1.3122, 1.7750]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.4816, -0.2893, -0.2022, -2.2519, -0.0218, 0.2458, 0.1715, 0.2202,\n", + " 0.3489, 0.4419, 0.1487, -0.4665, 0.3536, -1.0816, -0.5290, -0.5745,\n", + " -0.6518, 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4.5130, 4.4401, 1.9711, 1.1451, 1.0893, 1.9633, 2.7432,\n", + " 2.9277, 2.0465, 2.7993]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1987, -0.6965, 1.0525, -1.8932, -1.5096, 0.4993, -0.2788, 0.0462,\n", + " 0.2449, -0.1666, 0.0836, 0.4305, -1.9282, -0.5817, -0.0456, 0.4265,\n", + " -0.4796, -0.7398, -1.4783, 2.5543, 0.5384, -0.3383, 0.4883, 0.5712,\n", + " 2.2624, 0.8621, -0.8900, -0.5895, 0.2212, 0.0397, 0.0422, 0.0746,\n", + " -0.3010, 0.1676, -1.3201, -0.8663, -0.8471, -0.5073, 0.7565, -0.1049,\n", + " -0.2147, -0.0027, 0.7220]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1632, -0.2085, -0.0690, -0.1734, -0.0735, 0.0435, 0.0742, 0.1724,\n", + " 0.1046, 0.1411, 0.1098, -0.0885, -0.0817, -0.1451, -0.1320, -0.1883,\n", + " -0.1781, -0.0951, -0.0355, -0.0824, -0.0447, -0.0294, -0.0459, -0.0489,\n", + " -0.0276, 0.0268, 0.0362, 0.0017, -0.0842, -0.1671, -0.0970, -0.1092,\n", + " -0.1656, -0.1755, -0.1199, -0.1098, -0.0472, 0.0238, -0.0272, 0.1968,\n", + " 0.2422, 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"Grad _memory_unit.bias_ih_l1: 7.492730946978554e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.750705946004018e-05\n", + "Data X Sample: tensor([[1.3377, 1.3910, 1.6935, 1.7733, 1.9072, 2.0714, 2.1061, 2.1933, 3.7274,\n", + " 4.2968, 4.4825, 4.2533, 4.1345, 4.0543, 3.8837, 3.9604, 3.8618, 3.6714,\n", + " 3.6241, 3.5502, 3.5528, 3.4782, 2.9424, 2.8707, 2.8959, 2.9567, 2.9756,\n", + " 3.0956, 3.0529, 3.1638, 3.3000, 3.3353, 3.8237, 2.8240, 4.6645, 4.9374,\n", + " 5.0696, 5.4171, 5.9708, 5.9495, 1.0357, 0.5915, 0.6326, 1.0620, 1.5103,\n", + " 1.7097, 1.2850, 1.7046]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.1954, -0.1741, -1.1000, -0.8597, -0.3952, 0.1299, -0.1451, 0.2288,\n", + " -0.0758, 0.3654, 0.2018, -1.3118, -1.4657, -0.2142, 0.6282, -1.2022,\n", + " -0.6671, 0.4781, -0.8353, -1.5923, 0.3659, -0.9393, -0.4371, -1.1120,\n", + " 0.0733, 0.5017, 0.2353, 0.5245, -0.0742, -1.1052, -0.2278, -0.0438,\n", + " -0.2585, -0.4062, -0.1842, -0.0293, -0.6348, -0.7204, 0.0073, 0.2503,\n", + " 0.5119, -0.0228, 0.5109]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.3278, -0.3487, -0.0845, -0.1614, 0.0065, 0.1794, 0.2478, 0.3743,\n", + " 0.2425, 0.2731, 0.3037, -0.2024, -0.2079, -0.3456, -0.2600, -0.3023,\n", + " -0.2253, -0.1231, -0.1076, -0.1161, -0.0974, -0.0222, -0.0316, -0.0231,\n", + " -0.0223, 0.0608, -0.0461, -0.1310, -0.2572, -0.3046, -0.2558, -0.2735,\n", + " -0.3375, -0.4002, -0.2031, -0.2434, -0.0753, 0.0076, 0.0197, 0.3896,\n", + " 0.3790, 0.4146, 0.3237]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00015352011541835964\n", + "Grad encoder.fc1.bias: 0.00015678617637604475\n", + "Grad encoder.encoder.0.weight: 4.4554042688105255e-05\n", + "Grad encoder.encoder.0.bias: 0.00012615317245945334\n", + "Grad encoder.encoder.2.weight: 2.296776801813394e-05\n", + "Grad encoder.encoder.2.bias: 0.00014132481010165066\n", + "Grad encoder.encoder.4.weight: 7.023721263976768e-05\n", + "Grad encoder.encoder.4.bias: 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-0.0793, -0.2682,\n", + " -0.2624, -0.2653, -0.2734]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0001312459644395858\n", + "Grad encoder.fc1.bias: 0.000391327659599483\n", + "Grad encoder.encoder.0.weight: 3.0138136935420334e-05\n", + "Grad encoder.encoder.0.bias: 0.00027063232846558094\n", + "Grad encoder.encoder.2.weight: 3.261233723605983e-05\n", + "Grad encoder.encoder.2.bias: 0.000249133154284209\n", + "Grad encoder.encoder.4.weight: 6.91916502546519e-05\n", + "Grad encoder.encoder.4.bias: 0.0005116278189234436\n", + "Grad decoder.fc1.0.weight: 3.602176730055362e-05\n", + "Grad decoder.fc1.0.bias: 0.0002168201026506722\n", + "Grad decoder.fc1.2.weight: 4.960694059263915e-05\n", + "Grad decoder.fc1.2.bias: 0.00027595245046541095\n", + "Grad decoder.fc1.4.weight: 5.673339182976633e-05\n", + "Grad decoder.fc1.4.bias: 0.00043175515020266175\n", + "Grad decoder.fc2.weight: 0.00011280746548436582\n", + "Grad decoder.fc2.bias: 0.001707619521766901\n", + "Grad 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-0.0883, 0.0355, 0.0708, 0.1756,\n", + " 0.1055, 0.1480, 0.1147, -0.0917, -0.0865, -0.1514, -0.1323, -0.1942,\n", + " -0.1967, -0.0841, -0.0360, -0.0823, -0.0457, -0.0316, -0.0468, -0.0416,\n", + " -0.0216, 0.0330, 0.0382, 0.0065, -0.0951, -0.1613, -0.0938, -0.0957,\n", + " -0.1710, -0.1713, -0.1121, -0.1174, -0.0540, 0.0204, -0.0264, 0.1989,\n", + " 0.2468, 0.2126, 0.1841]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0002806201227940619\n", + "Grad encoder.fc1.bias: 8.952127245720476e-05\n", + "Grad encoder.encoder.0.weight: 7.70424521761015e-05\n", + "Grad encoder.encoder.0.bias: 8.89139628270641e-05\n", + "Grad encoder.encoder.2.weight: 3.96118666685652e-05\n", + "Grad encoder.encoder.2.bias: 7.158549851737916e-05\n", + "Grad encoder.encoder.4.weight: 0.00010554897016845644\n", + "Grad encoder.encoder.4.bias: 0.00021182010823395103\n", + "Grad decoder.fc1.0.weight: 2.409137596259825e-05\n", + "Grad decoder.fc1.0.bias: 3.88367916457355e-05\n", + "Grad 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"Data Y Sample: tensor([[-0.4937, -0.5946, -1.2731, 0.4681, -0.4793, 0.4038, -0.0322, 0.4892,\n", + " 0.9805, 0.5995, 0.5493, -0.5377, -0.0180, 0.0122, -0.6211, -0.7552,\n", + " -0.7433, 0.3163, 0.7675, -0.7937, -0.1307, -1.6294, -0.8005, 0.3167,\n", + " 3.7779, 5.7369, -0.3167, -0.6262, 0.3780, -0.4330, -1.8695, -0.5192,\n", + " -1.3130, -0.4214, -0.3315, -1.2673, -0.3045, -0.6429, -0.9853, 0.5291,\n", + " 0.8441, 0.8818, 0.7018]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.3423, -0.3809, -0.1077, -0.1670, -0.0284, 0.1871, 0.2775, 0.4158,\n", + " 0.2932, 0.3186, 0.3581, -0.2248, -0.2267, -0.3865, -0.2927, -0.3517,\n", + " -0.2664, -0.0850, -0.1298, -0.1304, -0.0972, -0.0189, -0.0328, -0.0129,\n", + " -0.0143, 0.0700, -0.0537, -0.1520, -0.2817, -0.3114, -0.2978, -0.2914,\n", + " -0.3839, -0.4518, -0.2252, -0.2783, -0.0796, -0.0271, 0.0566, 0.4323,\n", + " 0.4193, 0.4673, 0.3535]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00023347785463556647\n", 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_memory_unit.bias_ih_l1: 2.1683161321561784e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.0717772056523245e-05\n", + "Data X Sample: tensor([[1.6019, 1.8075, 1.9940, 2.0925, 2.1941, 2.2526, 2.3973, 2.4914, 2.4703,\n", + " 2.5291, 2.5442, 2.6291, 2.5055, 2.4811, 2.4967, 2.2674, 2.3554, 2.4044,\n", + " 2.3500, 2.4158, 2.5370, 2.5735, 2.6387, 2.6149, 2.5656, 2.5362, 2.5334,\n", + " 2.4798, 2.2966, 2.4432, 2.3797, 2.3709, 2.2726, 1.4624, 1.9658, 1.8242,\n", + " 1.6302, 1.5663, 1.5656, 1.6058, 1.2695, 0.6691, 0.7437, 1.2917, 1.7918,\n", + " 2.1615, 1.2714, 2.0408]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.5643, 0.7312, -0.1484, 0.0717, -0.9352, -0.1042, -0.0801, -0.4763,\n", + " -0.3694, -0.8545, -0.5373, 0.0708, -2.6336, 1.3840, 0.4879, 0.6002,\n", + " 1.5612, -0.7731, 0.5468, 0.4551, 0.0744, -0.2147, 0.1273, -0.6889,\n", + " -1.0525, -0.5598, -0.5888, 0.7430, 0.0239, 0.5586, 0.1270, 0.5744,\n", + " -0.3637, 0.0566, -0.3295, 0.2773, 0.8379, -0.5278, 1.1238, -0.1701,\n", + " -0.0611, -0.4090, -0.5369]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.4106, 0.3586, 0.0926, 0.2407, 0.0506, -0.1785, -0.3962, -0.4443,\n", + " -0.2349, -0.3149, -0.2760, 0.1686, 0.1789, 0.2775, 0.1994, 0.3088,\n", + " 0.2060, 0.1339, 0.1108, 0.1720, 0.0673, 0.1037, 0.0503, 0.0190,\n", + " 0.0785, 0.0106, -0.0834, 0.0333, 0.1791, 0.2974, 0.3078, 0.2456,\n", + " 0.2778, 0.2855, 0.1492, 0.1753, -0.0289, -0.0032, 0.0177, -0.3112,\n", + " -0.3459, -0.3288, -0.2916]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 5.130683348397724e-05\n", + "Grad encoder.fc1.bias: 7.302494486793876e-05\n", + "Grad encoder.encoder.0.weight: 1.2042857633787207e-05\n", + "Grad encoder.encoder.0.bias: 8.574012463213876e-05\n", + "Grad encoder.encoder.2.weight: 1.0822659533005208e-05\n", + "Grad encoder.encoder.2.bias: 0.00013269326882436872\n", + "Grad encoder.encoder.4.weight: 3.2330688554793596e-05\n", + "Grad encoder.encoder.4.bias: 0.00041260055149905384\n", + "Grad 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" 4.2653, 4.6141, 5.1088, 4.9451, 2.1334, 1.1072, 1.0428, 1.9232, 2.9929,\n", + " 3.3280, 2.2029, 3.0338]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.1019, 0.0878, -0.1860, -0.1211, -1.0508, -0.8451, -1.1650, -2.3857,\n", + " -0.2040, -0.8047, -0.4691, -0.1337, 1.4374, 0.8297, -0.7747, -1.3776,\n", + " 0.7985, -0.3484, 0.1508, -0.0645, -1.5658, 0.7403, -0.2686, -1.1724,\n", + " -0.2108, -0.4636, 0.2565, 0.2822, 1.4443, 1.1259, 0.6180, 0.3735,\n", + " 1.0813, -0.6354, 1.0106, 1.7736, 0.6586, 0.3924, 0.7106, 0.7896,\n", + " 1.0134, 0.3396, -0.2878]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1836, -0.2390, -0.0891, -0.1795, -0.1119, 0.0462, 0.0989, 0.2127,\n", + " 0.1478, 0.1767, 0.1564, -0.1096, -0.1074, -0.1939, -0.1627, -0.2351,\n", + " -0.2149, -0.0599, -0.0441, -0.0951, -0.0526, -0.0378, -0.0588, -0.0416,\n", + " -0.0190, 0.0397, 0.0277, -0.0139, -0.1110, -0.1797, -0.1320, -0.1286,\n", + " -0.2062, -0.2175, -0.1377, -0.1450, -0.0489, 0.0068, 0.0039, 0.2375,\n", 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_memory_unit.weight_ih_l0: 4.3516761252249125e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.279443990322761e-05\n", + "Grad _memory_unit.bias_hh_l0: 1.633167630643584e-05\n", + "Grad _memory_unit.weight_ih_l1: 1.213724658555293e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 3.9881379052530974e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.9531944417394698e-05\n", + "Data X Sample: tensor([[1.4194, 1.6764, 1.8437, 1.9110, 2.0455, 2.1038, 2.1107, 2.0655, 3.5102,\n", + " 4.1167, 4.1240, 4.0703, 3.7616, 3.7680, 3.6769, 3.5269, 3.5316, 3.4799,\n", + " 3.4796, 3.3717, 3.2706, 3.3267, 3.0195, 2.7695, 2.7871, 2.9697, 2.8531,\n", + " 2.9529, 3.0251, 3.0852, 3.2755, 3.3519, 3.5399, 2.5768, 4.3631, 4.7039,\n", + " 5.0204, 5.1521, 5.7553, 5.6686, 1.1311, 0.6791, 0.6629, 1.1855, 1.6927,\n", + " 1.9099, 1.1898, 1.6733]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.2753, -0.4478, -0.7545, 0.5509, 1.1686, 2.6017, 0.3809, 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_memory_unit.weight_ih_l1: 7.116591405065265e-07\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 1.3900505109631922e-05\n", + "Grad _memory_unit.bias_hh_l1: 6.821406259405194e-06\n", + "Data X Sample: tensor([[1.7536, 2.2081, 2.7032, 2.6829, 3.0375, 3.1572, 3.2107, 3.2864, 3.4125,\n", + " 3.3695, 3.3944, 3.4101, 3.3045, 3.2963, 3.2154, 3.1139, 3.0410, 3.2304,\n", + " 3.1781, 3.1689, 3.2486, 3.0664, 2.9881, 2.7199, 2.6013, 2.5335, 2.4162,\n", + " 2.3452, 2.0850, 2.0437, 1.8617, 1.8348, 1.5752, 1.0664, 1.5197, 1.7317,\n", + " 1.8170, 2.1865, 2.5354, 2.5080, 1.5177, 0.9559, 0.9761, 1.7882, 2.4895,\n", + " 2.5103, 1.8017, 2.5569]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.1436, 0.2041, -0.2584, -1.5286, 0.1500, 0.0964, 0.4318, 0.2363,\n", + " 0.0204, -0.1072, -0.0580, -0.2031, 0.1214, -0.3651, -0.5973, 0.3644,\n", + " -0.7090, 2.6740, -0.4823, 1.0109, -0.8810, 0.4338, -0.4174, -0.3603,\n", + " -0.7436, 3.7600, -0.3101, -0.4537, -0.5344, -0.4093, -0.0498, 0.1288,\n", + " 0.2268, -0.2775, -0.0063, -0.4215, -0.3740, -0.1765, -1.1357, -0.1966,\n", + " -0.2306, 0.2733, -0.9516]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1597, -0.2176, -0.0866, -0.1817, -0.1244, 0.0266, 0.0744, 0.1828,\n", + " 0.1272, 0.1548, 0.1266, -0.0921, -0.0909, -0.1661, -0.1429, -0.2176,\n", + " -0.2063, -0.0558, -0.0305, -0.0897, -0.0451, -0.0407, -0.0640, -0.0467,\n", + " -0.0186, 0.0347, 0.0375, 0.0067, -0.0875, -0.1632, -0.1086, -0.1066,\n", + " -0.1800, -0.1822, -0.1241, -0.1231, -0.0418, 0.0106, -0.0027, 0.2088,\n", + " 0.2562, 0.2212, 0.1937]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0003708635922521353\n", + "Grad encoder.fc1.bias: 0.0011115786619484425\n", + "Grad encoder.encoder.0.weight: 0.00010834542626980692\n", + "Grad encoder.encoder.0.bias: 0.0007930463179945946\n", + "Grad encoder.encoder.2.weight: 5.672245242749341e-05\n", + "Grad encoder.encoder.2.bias: 0.0006406706525012851\n", + "Grad encoder.encoder.4.weight: 0.0001669383782427758\n", + "Grad encoder.encoder.4.bias: 0.0014370180433616042\n", + "Grad decoder.fc1.0.weight: 5.17850712640211e-05\n", + "Grad decoder.fc1.0.bias: 0.00033723871456459165\n", + "Grad decoder.fc1.2.weight: 6.0354483139235526e-05\n", + "Grad decoder.fc1.2.bias: 0.00023463135585188866\n", + "Grad decoder.fc1.4.weight: 7.371536048594862e-05\n", + "Grad decoder.fc1.4.bias: 0.0003613700100686401\n", + "Grad decoder.fc2.weight: 8.465070277452469e-05\n", + "Grad decoder.fc2.bias: 0.002033272059634328\n", + "Grad _memory_unit.weight_ih_l0: 1.233885905094212e-05\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 9.083360055228695e-05\n", + "Grad _memory_unit.bias_hh_l0: 4.469899431569502e-05\n", + "Grad _memory_unit.weight_ih_l1: 2.8672745884250617e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00010021850903285667\n", + "Grad _memory_unit.bias_hh_l1: 4.9262009270023555e-05\n", + "Data X Sample: tensor([[1.6570, 1.8090, 2.1172, 2.2346, 2.3238, 2.4766, 2.4928, 2.6149, 2.6705,\n", + " 2.6855, 2.6425, 2.6895, 2.5632, 2.4865, 2.4614, 2.5683, 2.4922, 2.5321,\n", + " 2.5173, 2.5273, 2.5174, 2.5260, 2.5206, 2.4756, 2.5037, 2.6772, 2.4428,\n", + " 2.4961, 2.3070, 2.4891, 2.3342, 2.2770, 1.9690, 1.2884, 1.7893, 1.7147,\n", + " 1.6656, 1.5345, 1.5846, 1.4725, 1.3411, 0.7448, 0.7599, 1.4180, 1.9068,\n", + " 2.1901, 1.5502, 2.1659]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.0155, 0.3913, 0.5152, -0.4398, 0.5562, 0.5196, -0.3045, -0.2409,\n", + " 0.1019, -0.0223, -0.1280, 0.0195, -1.4288, -0.0318, 1.0436, 0.1274,\n", + " 0.8760, -1.5069, 0.3191, 0.2719, -0.6051, 0.5986, -0.0738, 0.3143,\n", + " 1.0537, -0.3895, 1.1240, 0.1702, -0.2557, 0.3717, -0.6191, 0.3477,\n", + " -1.0984, -0.0663, -0.2192, -0.6423, 0.3306, -0.7806, 0.0545, 0.8287,\n", + " -0.7431, 0.4622, 1.2101]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3096, 0.2868, 0.0721, 0.1535, 0.0259, -0.1289, -0.3059, -0.3174,\n", + " -0.1458, -0.2226, -0.1883, 0.1212, 0.1133, 0.1884, 0.1387, 0.2239,\n", + " 0.1574, 0.1133, 0.1159, 0.1522, 0.0759, 0.0825, 0.0092, -0.0095,\n", + " 0.0789, 0.0349, -0.0818, 0.0215, 0.1020, 0.1824, 0.2316, 0.1673,\n", + " 0.1957, 0.2053, 0.0942, 0.1181, -0.0113, -0.0034, 0.0314, -0.2184,\n", + " -0.2418, -0.2522, -0.2038]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.000284785230178386\n", + "Grad encoder.fc1.bias: 0.00037930600228719413\n", + "Grad encoder.encoder.0.weight: 8.528345642844215e-05\n", + "Grad encoder.encoder.0.bias: 0.00038987206062301993\n", + "Grad encoder.encoder.2.weight: 4.781594543601386e-05\n", + "Grad encoder.encoder.2.bias: 0.00036897259997203946\n", + "Grad encoder.encoder.4.weight: 0.00011918629752472043\n", + "Grad encoder.encoder.4.bias: 0.0008699805475771427\n", + "Grad decoder.fc1.0.weight: 4.614379577105865e-05\n", + "Grad decoder.fc1.0.bias: 0.00029924209229648113\n", + "Grad 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2.9504, 3.0611, 3.0449,\n", + " 3.2588, 3.1396, 3.2719, 3.3175, 3.3823, 3.7951, 2.7714, 4.5077, 4.8085,\n", + " 4.9614, 5.4675, 5.9771, 5.7736, 1.1359, 0.6751, 0.6669, 1.2285, 1.8909,\n", + " 2.0357, 1.3326, 1.9001]], device='cuda:0')\n", + "Data Y Sample: tensor([[-2.1897e-01, -1.6467e+00, -1.1962e+00, -2.7252e-01, 3.6968e-01,\n", + " -2.4490e-01, -7.8943e-01, 8.0959e-01, -7.5139e-01, -5.6647e-01,\n", + " -5.2994e-01, 4.0783e-01, -9.3386e-02, -5.3694e-04, -1.3465e-01,\n", + " 6.0974e-01, -5.0477e-01, -2.2742e-01, 1.0453e+00, 2.6075e-01,\n", + " 1.8884e-01, -2.0895e-01, -3.6852e-01, -4.2983e-01, -4.4752e-01,\n", + " 9.1826e-02, 3.3761e-01, -5.1297e-01, 4.9142e-01, 9.2184e-02,\n", + " 1.7116e-01, 1.0927e+00, -2.7301e-01, -1.0869e+00, -4.8477e-01,\n", + " 4.3177e-01, -3.6154e-01, -6.1947e-01, -7.1302e-01, -6.9728e-01,\n", + " -1.5841e+00, -6.6711e-03, -7.1552e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1760, -0.2334, -0.0911, -0.1808, -0.1172, 0.0418, 0.0950, 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2.4668,\n", + " 2.5491, 2.2966, 2.3155, 2.3307, 2.2770, 2.2374, 1.4440, 1.9242, 1.7317,\n", + " 1.6125, 1.4576, 1.6353, 1.5718, 1.1597, 0.6791, 0.6609, 1.2974, 1.7482,\n", + " 1.8984, 1.2510, 1.9626]], device='cuda:0')\n", + "Data Y Sample: tensor([[-1.9555, -2.0144, 0.1536, 0.3249, 0.6457, 0.4066, 0.9787, -0.5954,\n", + " 0.1592, 0.2165, 0.3413, 0.3299, -1.3523, -0.6704, 0.3678, 0.7329,\n", + " -0.2615, -0.2561, 0.5105, 0.1081, 0.9370, 0.2084, -0.4874, -0.5810,\n", + " 0.2913, -1.1739, -0.1071, 0.3836, -0.9644, 0.5981, -0.0284, 0.2224,\n", + " -0.3763, 0.4547, 0.6554, 1.6841, -0.8287, -0.7806, 0.5660, 1.1520,\n", + " 0.8504, -0.7623, 0.4176]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3454, 0.3267, 0.0834, 0.1918, 0.0472, -0.1276, -0.3329, -0.3624,\n", + " -0.1543, -0.2419, -0.2029, 0.1425, 0.1325, 0.2123, 0.1610, 0.2654,\n", + " 0.1897, 0.1375, 0.1344, 0.1788, 0.0911, 0.0945, 0.0137, -0.0076,\n", + " 0.0914, 0.0281, -0.1018, 0.0220, 0.1110, 0.2008, 0.2528, 0.1820,\n", + " 0.2221, 0.2401, 0.1135, 0.1388, -0.0056, -0.0024, 0.0367, -0.2442,\n", + " -0.2808, -0.2930, -0.2360]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0001291909284191206\n", + "Grad encoder.fc1.bias: 5.729289841838181e-05\n", + "Grad encoder.encoder.0.weight: 2.444301571813412e-05\n", + "Grad encoder.encoder.0.bias: 5.284020517137833e-05\n", + "Grad encoder.encoder.2.weight: 1.9370247173355892e-05\n", + "Grad encoder.encoder.2.bias: 8.161745790857822e-05\n", + "Grad encoder.encoder.4.weight: 4.2565436160657555e-05\n", + "Grad encoder.encoder.4.bias: 0.00019831175450235605\n", + "Grad decoder.fc1.0.weight: 2.0960851543350145e-05\n", + "Grad decoder.fc1.0.bias: 9.415544627700001e-05\n", + "Grad decoder.fc1.2.weight: 3.725740680238232e-05\n", + "Grad decoder.fc1.2.bias: 0.00016226347361225635\n", + "Grad decoder.fc1.4.weight: 4.346245259512216e-05\n", + "Grad decoder.fc1.4.bias: 0.0002616728306747973\n", + "Grad decoder.fc2.weight: 5.766923277406022e-05\n", + "Grad decoder.fc2.bias: 0.0018047059420496225\n", + "Grad _memory_unit.weight_ih_l0: 1.0658957307896344e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 4.267518306733109e-06\n", + "Grad _memory_unit.bias_hh_l0: 2.281491788380663e-06\n", + "Grad _memory_unit.weight_ih_l1: 6.175275757414056e-07\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 1.6233087080763653e-05\n", + "Grad _memory_unit.bias_hh_l1: 8.051807526499033e-06\n", + "Data X Sample: tensor([[1.5372, 1.6240, 1.8152, 1.9504, 1.9089, 2.0861, 2.1677, 4.1609, 4.3279,\n", + " 4.4074, 4.5808, 4.4539, 4.2210, 4.0733, 3.9518, 3.7033, 3.7611, 3.6482,\n", + " 3.5725, 3.4665, 3.4841, 3.5257, 3.0363, 2.8783, 2.8941, 2.8052, 2.7679,\n", + " 2.8509, 2.9245, 3.1572, 3.2301, 3.1972, 3.7687, 2.7965, 4.6082, 4.9909,\n", + " 5.1148, 5.4913, 6.1926, 6.1254, 1.1597, 0.6154, 0.6851, 1.0506, 1.5936,\n", + " 1.8584, 1.1830, 1.7671]], device='cuda:0')\n", + "Data Y Sample: 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_memory_unit.bias_hh_l0: 4.0623599488753825e-06\n", + "Grad _memory_unit.weight_ih_l1: 1.0641184644555324e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 3.3506075851619244e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.6694681107765064e-05\n", + "Data X Sample: tensor([[2.7656, 3.0674, 2.8550, 2.1778, 2.5373, 2.7182, 3.7176, 4.0204, 4.0569,\n", + " 4.1594, 3.9210, 4.0313, 3.9569, 3.7652, 3.5836, 3.5105, 3.5300, 3.5089,\n", + " 3.6406, 3.3940, 3.3884, 3.2869, 2.9496, 2.8478, 2.7552, 2.7686, 2.6746,\n", + " 2.6266, 2.7129, 2.8101, 2.7961, 2.8435, 3.0581, 2.2267, 3.8851, 4.1397,\n", + " 4.5190, 4.9666, 5.4384, 5.5409, 2.0236, 1.0853, 1.0226, 1.6533, 2.2477,\n", + " 2.1500, 1.4278, 2.1034]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.1266, -0.3392, -0.2033, -0.6261, 0.3141, -0.4255, -0.8094, 0.7293,\n", + " -0.5796, -0.4940, -0.6674, 0.9919, -0.0553, 0.2283, 0.8022, -1.0271,\n", + " -0.8205, -1.2152, -1.1245, -0.5724, -0.2397, -0.3927, -2.0070, 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_memory_unit.bias_hh_l1: 5.3427909733727574e-05\n", + "Data X Sample: tensor([[1.3632, 1.4827, 1.6874, 1.7930, 1.9448, 1.9874, 2.0552, 2.3537, 3.9300,\n", + " 4.0899, 4.2604, 4.1521, 3.9925, 3.9234, 3.7425, 3.5679, 3.5441, 3.6134,\n", + " 3.3391, 3.3717, 3.3295, 3.3864, 2.8460, 2.8611, 2.8209, 2.8104, 2.9144,\n", + " 2.9814, 2.8690, 3.0557, 3.1356, 3.2966, 3.5949, 2.6959, 4.2944, 4.7550,\n", + " 4.8139, 5.2554, 5.6792, 5.6572, 1.0500, 0.5974, 0.5922, 1.0419, 1.5579,\n", + " 1.8070, 1.2238, 1.6108]], device='cuda:0')\n", + "Data Y Sample: tensor([[-4.5823e-01, -7.8408e-01, 3.0367e-02, -4.7443e-01, -5.9545e-01,\n", + " -1.3593e+00, 1.6892e-01, 5.2181e-01, 1.3465e+00, 7.0046e-01,\n", + " 7.2783e-01, 8.1149e-02, -2.1002e-01, -1.7254e+00, 4.2771e-01,\n", + " -2.0441e+00, 9.0156e-02, 6.5017e-01, -5.9043e-01, -3.3417e-02,\n", + " 2.6457e+00, -1.9141e-01, 6.1176e-01, 2.5169e-02, -6.0698e-02,\n", + " -9.5185e-01, -3.6988e-02, -1.5179e-01, -8.3270e-01, -2.2973e-04,\n", + " -1.1091e-01, -5.8146e-01, 3.7015e-01, -1.0310e+00, -5.3566e-01,\n", + " -1.0928e+00, 8.4931e-02, 2.3763e-01, 5.6860e-01, 8.7216e-01,\n", + " 8.9061e-01, 7.5306e-01, 6.8892e-01]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2128, -0.2707, -0.1009, -0.1768, -0.0992, 0.0770, 0.1384, 0.2484,\n", + " 0.1847, 0.2014, 0.1937, -0.1223, -0.1370, -0.2341, -0.1820, -0.2594,\n", + " -0.2239, -0.0545, -0.0588, -0.1008, -0.0593, -0.0450, -0.0739, -0.0375,\n", + " -0.0133, 0.0409, 0.0016, -0.0437, -0.1430, -0.2147, -0.1766, -0.1768,\n", + " -0.2369, -0.2599, -0.1463, -0.1567, -0.0348, 0.0032, 0.0177, 0.2759,\n", + " 0.3098, 0.2944, 0.2410]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0006229750579223037\n", + "Grad encoder.fc1.bias: 0.0005078461254015565\n", + "Grad encoder.encoder.0.weight: 0.00016350609075743705\n", + "Grad encoder.encoder.0.bias: 0.00040248659206554294\n", + "Grad encoder.encoder.2.weight: 8.594270911999047e-05\n", + "Grad encoder.encoder.2.bias: 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"Grad decoder.fc2.weight: 0.00015799487300682813\n", + "Grad decoder.fc2.bias: 0.0029625880997627974\n", + "Grad _memory_unit.weight_ih_l0: 8.717103810340632e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.88806001865305e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.0266961655579507e-05\n", + "Grad _memory_unit.weight_ih_l1: 3.91740286431741e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00013032768038101494\n", + "Grad _memory_unit.bias_hh_l1: 6.460816075559705e-05\n", + "Data X Sample: tensor([[1.5700, 1.7536, 1.8572, 1.9810, 2.0848, 2.1907, 2.2555, 2.2771, 2.2994,\n", + " 2.4391, 2.4617, 2.4246, 2.3058, 2.2302, 2.2142, 2.3248, 2.2611, 2.3270,\n", + " 2.2860, 2.3377, 2.5027, 2.4617, 2.5761, 2.4908, 2.5018, 2.5806, 2.6187,\n", + " 2.5532, 2.2203, 2.4400, 2.4252, 2.4483, 2.1604, 1.4418, 1.8898, 1.7853,\n", + " 1.6184, 1.4656, 1.5846, 1.5547, 1.3029, 0.6910, 0.6811, 1.1998, 1.8513,\n", + " 1.9328, 1.2714, 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"Grad _memory_unit.bias_ih_l1: 7.639870455022901e-05\n", + "Grad _memory_unit.bias_hh_l1: 3.802355786319822e-05\n", + "Data X Sample: tensor([[1.3812, 1.5352, 1.7205, 1.7842, 1.9567, 2.1230, 2.1138, 2.2984, 3.5077,\n", + " 4.1357, 4.2985, 4.2144, 4.1167, 3.9561, 3.7526, 3.6596, 3.6196, 3.7101,\n", + " 3.5415, 3.5167, 3.6264, 3.2547, 2.9496, 2.7829, 2.7852, 2.9541, 3.0183,\n", + " 3.0834, 3.0945, 3.2424, 3.2196, 3.3464, 3.8875, 2.8560, 4.5640, 5.0396,\n", + " 5.2249, 5.5311, 5.9898, 5.8956, 1.0261, 0.5656, 0.6204, 1.0592, 1.6253,\n", + " 1.8184, 1.1490, 1.6264]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.3799, -0.1727, 0.0966, -0.5370, 0.7326, 0.5997, 0.4794, 0.5854,\n", + " 0.7457, 0.5812, 0.6233, -0.7638, -0.2060, -0.6351, -0.2989, 0.0601,\n", + " 0.3721, -0.1008, -0.4254, 1.0010, 0.0123, -0.1796, -0.2283, 0.3097,\n", + " -0.8553, -0.2180, 0.9029, -0.5733, -0.4210, 0.1158, -0.1013, -0.0995,\n", + " -0.8863, -0.8464, -0.1791, 0.5037, -0.5002, 0.4164, 0.2630, 0.2586,\n", + " -0.1130, -0.2728, 0.0328]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2480, -0.3059, -0.1085, -0.1719, -0.0751, 0.1118, 0.1767, 0.2942,\n", + " 0.2174, 0.2302, 0.2347, -0.1407, -0.1688, -0.2783, -0.2038, -0.2858,\n", + " -0.2368, -0.0574, -0.0799, -0.1086, -0.0683, -0.0524, -0.0889, -0.0293,\n", + " -0.0117, 0.0518, -0.0229, -0.0811, -0.1752, -0.2410, -0.2235, -0.2200,\n", + " -0.2750, -0.3149, -0.1608, -0.1768, -0.0320, -0.0013, 0.0267, 0.3210,\n", + " 0.3484, 0.3420, 0.2752]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00023511708423029631\n", + "Grad encoder.fc1.bias: 0.0003951573162339628\n", + "Grad encoder.encoder.0.weight: 6.497776485048234e-05\n", + "Grad encoder.encoder.0.bias: 0.00035003124503418803\n", + "Grad encoder.encoder.2.weight: 3.962947812397033e-05\n", + "Grad encoder.encoder.2.bias: 0.00026900123339146376\n", + "Grad encoder.encoder.4.weight: 9.87676321528852e-05\n", + "Grad encoder.encoder.4.bias: 0.0005025416612625122\n", + "Grad decoder.fc1.0.weight: 3.2940915843937546e-05\n", + "Grad decoder.fc1.0.bias: 0.0001961400412255898\n", + "Grad decoder.fc1.2.weight: 3.630554783740081e-05\n", + "Grad decoder.fc1.2.bias: 0.00018151724361814559\n", + "Grad decoder.fc1.4.weight: 4.492334119277075e-05\n", + "Grad decoder.fc1.4.bias: 0.0002050900657195598\n", + "Grad decoder.fc2.weight: 9.538396989228204e-05\n", + "Grad decoder.fc2.bias: 0.002191278152167797\n", + "Grad _memory_unit.weight_ih_l0: 3.6117032777838176e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 1.404399790772004e-05\n", + "Grad _memory_unit.bias_hh_l0: 7.1297490649158135e-06\n", + "Grad _memory_unit.weight_ih_l1: 1.5232469650072744e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 3.861238292302005e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.9081302525592037e-05\n", + "Data X Sample: tensor([[1.4937, 1.7332, 1.8227, 1.9001, 1.9158, 2.0405, 2.0953, 3.4454, 4.2864,\n", + " 4.3711, 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-0.2147,\n", + " -0.2045, -0.0542, -0.0341, -0.0852, -0.0441, -0.0475, -0.0898, -0.0421,\n", + " -0.0128, 0.0371, 0.0267, -0.0018, -0.0815, -0.1632, -0.1198, -0.1138,\n", + " -0.1767, -0.1839, -0.1125, -0.1081, -0.0313, 0.0122, -0.0020, 0.2105,\n", + " 0.2630, 0.2206, 0.1973]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.0001968698634300381\n", + "Grad encoder.fc1.bias: 0.00013416798901744187\n", + "Grad encoder.encoder.0.weight: 4.728247586172074e-05\n", + "Grad encoder.encoder.0.bias: 0.00014698720769956708\n", + "Grad encoder.encoder.2.weight: 2.4949758881120943e-05\n", + "Grad encoder.encoder.2.bias: 0.0001353835396002978\n", + "Grad encoder.encoder.4.weight: 6.530604150611907e-05\n", + "Grad encoder.encoder.4.bias: 0.0005595838883891702\n", + "Grad decoder.fc1.0.weight: 2.5458572054049e-05\n", + "Grad decoder.fc1.0.bias: 0.00018591097614262253\n", + "Grad decoder.fc1.2.weight: 3.172954166075215e-05\n", + "Grad decoder.fc1.2.bias: 0.00022234550851862878\n", 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4.5677,\n", + " 4.5386, 5.0010, 5.3813, 5.4898, 1.0166, 0.5476, 0.5436, 1.0477, 1.4866,\n", + " 1.6183, 1.1150, 1.5638]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.4032, 0.6015, -0.7497, 0.8004, 0.6803, 1.9720, 0.2924, 0.3707,\n", + " 0.0143, 0.1071, 0.7198, 1.0903, -0.2158, 0.7716, -0.8195, 0.4145,\n", + " 0.1281, 0.8005, -1.0187, 0.5505, 0.1868, -1.4798, 0.6787, 0.2877,\n", + " -0.6485, -0.0600, -0.7389, 0.0605, -0.7822, 0.1944, -0.4905, 0.8485,\n", + " -1.0090, -1.1472, 0.0374, 1.1332, -0.4404, -0.7172, 0.0000, -0.5441,\n", + " 0.1402, 0.2809, -0.3472]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.2279, -0.2885, -0.1043, -0.1724, -0.0794, 0.0961, 0.1542, 0.2710,\n", + " 0.1970, 0.2122, 0.2091, -0.1268, -0.1548, -0.2540, -0.1859, -0.2693,\n", + " -0.2292, -0.0572, -0.0698, -0.1032, -0.0643, -0.0533, -0.0917, -0.0300,\n", + " -0.0119, 0.0493, -0.0118, -0.0667, -0.1539, -0.2203, -0.2029, -0.1976,\n", + " -0.2544, -0.2873, -0.1487, -0.1592, -0.0320, 0.0030, 0.0192, 0.2958,\n", + " 0.3302, 0.3145, 0.2588]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00035158038372173905\n", + "Grad encoder.fc1.bias: 0.0006216575857251883\n", + "Grad encoder.encoder.0.weight: 0.0001029130580718629\n", + "Grad encoder.encoder.0.bias: 0.0004802662879228592\n", + "Grad encoder.encoder.2.weight: 5.9806767239933833e-05\n", + "Grad encoder.encoder.2.bias: 0.00029339970205910504\n", + "Grad encoder.encoder.4.weight: 0.00016594944463577121\n", + "Grad encoder.encoder.4.bias: 0.0003372509381733835\n", + "Grad decoder.fc1.0.weight: 4.625641668098979e-05\n", + "Grad decoder.fc1.0.bias: 0.00015700230142101645\n", + "Grad decoder.fc1.2.weight: 4.769052247866057e-05\n", + "Grad decoder.fc1.2.bias: 0.0001625760196475312\n", + "Grad decoder.fc1.4.weight: 6.034346006345004e-05\n", + "Grad decoder.fc1.4.bias: 0.0003847157349810004\n", + "Grad decoder.fc2.weight: 0.00011719556641764939\n", + "Grad decoder.fc2.bias: 0.001752105774357915\n", + "Grad 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"Data X Sample: tensor([[-0.0074, 0.0015, -0.0075, 0.0153, -0.0034, 0.0103, 0.0139, 0.0099,\n", + " 0.0171, 0.0000, 0.0063, 0.0058, 0.0200, 0.0218, 0.0151, 0.0000,\n", + " -0.0299, -0.0348, -0.0516, -0.0353, -0.0589, -0.0383, -0.0120, -0.0172,\n", + " -0.0300, -0.0418, -0.0133, -0.0571, -0.0278, 0.0033, -0.0105, 0.0166,\n", + " 0.0176, -0.0252, 0.0098, 0.0316, 0.0177, 0.0239, 0.0127, 0.0199,\n", + " 0.0382, 0.0219, 0.0182, 0.0057, 0.0000, 0.0400, 0.0204, 0.0000]],\n", + " device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.6976, 1.0125, 0.7825, -0.2446, -2.1382, 0.1285, -2.2015, -1.9106,\n", + " -1.4240, -1.2350, -1.0112, 0.3734, -0.1702, 0.6873, -0.4683, 0.7010,\n", + " 0.9485, -7.0119, 0.0806, 0.3576, -0.4131, 0.0877, -0.1531, 0.3779,\n", + " 0.7827, 0.6756, 0.6052, 1.8949, 1.3632, 1.6123, 1.6157, 1.5315,\n", + " 0.7587, 0.8408, 0.0729, 0.1999, 0.7316, 0.0533, -0.7367, -1.1038,\n", + " -0.6827, -0.9421, 0.0468]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 1.1104, 0.8229, 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-0.0370,\n", + " 1.0286, 0.3638, 0.7329, 0.2156, 0.1749, 0.0419, 0.6296, -0.3068,\n", + " 0.6406, 0.7832, 0.1435, 0.9074, 0.8612, 0.3182, 0.2593, -0.1173,\n", + " 1.8101, 1.1528, 0.2579, -0.4132, 0.4289, -0.5278, -0.7546, -1.0264,\n", + " -1.0288, -1.0436, -1.2338]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 1.0733, 0.7997, 0.0544, 0.5228, -0.7113, -0.8860, -1.5883, -1.4189,\n", + " -1.0151, -1.0528, -1.0937, 0.4559, 0.4842, 0.8834, 0.4853, 0.7024,\n", + " 0.5814, 0.0309, 0.1549, 0.1641, 0.0892, 0.0457, -0.0326, 0.0209,\n", + " 0.0640, -0.0702, 0.4559, 0.7198, 1.2661, 1.3253, 0.9860, 0.6659,\n", + " 0.7641, 0.8294, 0.4951, 0.4637, -0.0212, 0.0629, 0.0657, -1.0155,\n", + " -0.8243, -0.7747, -0.3648]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 9.604100341675803e-05\n", + "Grad encoder.fc1.bias: 0.0004484299279283732\n", + "Grad encoder.encoder.0.weight: 3.161889617331326e-05\n", + "Grad encoder.encoder.0.bias: 0.0005592755042016506\n", + "Grad encoder.encoder.2.weight: 2.8561160434037447e-05\n", + "Grad encoder.encoder.2.bias: 0.0004505949909798801\n", + "Grad encoder.encoder.4.weight: 8.051459008129314e-05\n", + "Grad encoder.encoder.4.bias: 0.001297210925258696\n", + "Grad decoder.fc1.0.weight: 4.728898420580663e-05\n", + "Grad decoder.fc1.0.bias: 0.0004309014184400439\n", + "Grad decoder.fc1.2.weight: 7.310754881473258e-05\n", + "Grad decoder.fc1.2.bias: 0.0003593434812501073\n", + "Grad decoder.fc1.4.weight: 9.60163160925731e-05\n", + "Grad decoder.fc1.4.bias: 0.0005393869942054152\n", + "Grad decoder.fc2.weight: 0.00013385075726546347\n", + "Grad decoder.fc2.bias: 0.0015867603942751884\n", + "Grad _memory_unit.weight_ih_l0: 6.6110401348851155e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.996471059508622e-05\n", + "Grad _memory_unit.bias_hh_l0: 2.017947190324776e-05\n", + "Grad _memory_unit.weight_ih_l1: 3.15995248456602e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 0.00011337925388943404\n", + "Grad _memory_unit.bias_hh_l1: 5.67154711461626e-05\n", + "Data X Sample: tensor([[2.5418, 2.9902, 3.2246, 3.3563, 3.4439, 3.7126, 3.8748, 3.8500, 3.8910,\n", + " 3.7787, 3.9337, 3.7918, 3.7062, 3.6044, 3.6315, 3.5830, 3.4765, 3.4819,\n", + " 3.3391, 3.3177, 3.2289, 3.1812, 2.8460, 2.8516, 2.7326, 2.8208, 2.7519,\n", + " 2.7286, 2.6886, 2.8919, 2.8276, 2.8407, 3.0339, 2.1489, 3.6522, 4.0107,\n", + " 4.3027, 4.6750, 5.1341, 5.0019, 1.8804, 1.0734, 1.1681, 1.9088, 2.9454,\n", + " 3.3337, 2.0873, 3.0338]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 1.1065, -0.4767, 0.3058, 0.1989, 1.2765, -0.1760, -0.6744, 0.5137,\n", + " -0.6513, -0.5967, -0.9709, -0.6318, -0.1803, -0.2045, -0.7549, -0.5768,\n", + " -0.5750, 1.1832, -0.0320, 0.7483, 0.7464, 0.6798, 0.0610, 0.0234,\n", + " 0.3096, 0.9018, 1.2123, 1.6609, 0.3243, 0.5832, -0.2760, 0.4759,\n", + " 0.0047, 0.3279, -1.1440, -1.4317, -0.7582, 0.5417, 0.2870, 0.0996,\n", + " 1.2089, -0.3724, 0.3501]], device='cuda:0')\n", + "Prediction Sample: tensor([[-0.1514, -0.2157, -0.0903, -0.1773, -0.1098, 0.0278, 0.0645, 0.1779,\n", + " 0.1078, 0.1397, 0.1028, -0.0764, -0.0905, -0.1530, -0.1205, -0.2119,\n", + " -0.1978, -0.0553, -0.0280, -0.0825, -0.0400, -0.0522, -0.0917, -0.0343,\n", + " -0.0155, 0.0421, 0.0361, -0.0055, -0.0675, -0.1452, -0.1103, -0.0978,\n", + " -0.1707, -0.1662, -0.0992, -0.0924, -0.0341, 0.0168, -0.0090, 0.1940,\n", + " 0.2568, 0.2066, 0.1917]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00010573035979177803\n", + "Grad encoder.fc1.bias: 0.0005039945826865733\n", + "Grad encoder.encoder.0.weight: 3.513378032948822e-05\n", + "Grad encoder.encoder.0.bias: 0.00047799578169360757\n", + "Grad encoder.encoder.2.weight: 2.9255168556119315e-05\n", + "Grad encoder.encoder.2.bias: 0.00036115513648837805\n", + "Grad encoder.encoder.4.weight: 8.5470310295932e-05\n", + "Grad encoder.encoder.4.bias: 0.0005536341341212392\n", + "Grad decoder.fc1.0.weight: 2.7044539820053615e-05\n", + "Grad decoder.fc1.0.bias: 0.00020005303667858243\n", + "Grad decoder.fc1.2.weight: 3.7856792914681137e-05\n", + "Grad decoder.fc1.2.bias: 0.00021705339895561337\n", + "Grad decoder.fc1.4.weight: 4.414354043547064e-05\n", + "Grad decoder.fc1.4.bias: 0.0001941652735695243\n", + "Grad decoder.fc2.weight: 0.00011179713328601792\n", + "Grad decoder.fc2.bias: 0.001826736843213439\n", + "Grad _memory_unit.weight_ih_l0: 4.388427441881504e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 1.7405056496500038e-05\n", + "Grad _memory_unit.bias_hh_l0: 8.78425817063544e-06\n", + "Grad _memory_unit.weight_ih_l1: 1.7932214859683882e-06\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 4.228629404678941e-05\n", + "Grad _memory_unit.bias_hh_l1: 2.1137171643204056e-05\n", + "Data X Sample: tensor([[1.5414, 1.6968, 1.8572, 2.0947, 2.0353, 2.1127, 1.9289, 2.3807, 3.5273,\n", + " 4.1120, 4.4254, 4.4812, 4.2943, 4.1006, 3.6921, 3.8278, 3.6825, 3.5882,\n", + " 3.6241, 3.6134, 3.5037, 3.2853, 2.9255, 2.8287, 2.7852, 2.9044, 2.8771,\n", + " 2.8428, 2.9800, 3.1998, 3.2475, 3.2552, 3.8061, 2.8149, 4.5837, 4.8620,\n", + " 5.0185, 5.4039, 5.9074, 5.8218, 1.2027, 0.6612, 0.6346, 1.1941, 1.8235,\n", + " 2.0757, 1.3530, 1.9548]], device='cuda:0')\n", + "Data Y Sample: tensor([[ 0.1684, -0.0296, -0.9086, 0.5276, -0.6458, -0.3797, -0.1893, 0.2223,\n", + " 0.6215, 0.1659, -0.0296, -0.0690, -0.3048, 0.7114, -0.5181, 0.1292,\n", + " 0.4042, -0.3237, -0.3862, 2.2650, -1.8772, -1.0829, -0.0780, -0.7893,\n", + " 0.0141, 0.4781, -0.6581, -0.2931, -0.4410, -1.0473, 0.4966, -0.4582,\n", + " -0.7484, -0.6567, -0.5363, -0.1033, 0.0423, 0.1005, -1.2279, 0.1981,\n", + " -0.1013, 0.2631, 0.4324]], device='cuda:0')\n", + "Prediction Sample: tensor([[-2.7940e-01, -3.4138e-01, -1.1089e-01, -1.5514e-01, -3.3909e-02,\n", + " 1.5244e-01, 2.2153e-01, 3.5225e-01, 2.4435e-01, 2.5732e-01,\n", + " 2.7297e-01, -1.6071e-01, -2.0950e-01, -3.2317e-01, -2.1973e-01,\n", + " -3.2248e-01, -2.4582e-01, -6.0298e-02, -9.4058e-02, -1.1358e-01,\n", + " -7.2816e-02, -6.2503e-02, -9.3212e-02, -1.2436e-02, -1.5513e-02,\n", + " 6.7246e-02, -3.8001e-02, -1.3754e-01, -2.1184e-01, -2.5664e-01,\n", + " -2.7223e-01, -2.5791e-01, -3.2248e-01, -3.6360e-01, -1.6723e-01,\n", + " -1.8738e-01, -3.4706e-02, -3.2870e-04, 2.8756e-02, 3.5634e-01,\n", + " 3.8484e-01, 3.8787e-01, 3.0878e-01]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00031723760184831917\n", + "Grad encoder.fc1.bias: 0.0002676474687177688\n", + "Grad encoder.encoder.0.weight: 8.197902207029983e-05\n", + "Grad encoder.encoder.0.bias: 0.0001765932684065774\n", + "Grad encoder.encoder.2.weight: 3.931431638193317e-05\n", + "Grad encoder.encoder.2.bias: 0.00013824060442857444\n", + "Grad encoder.encoder.4.weight: 0.00011344767699483782\n", + "Grad encoder.encoder.4.bias: 0.00040563903166912496\n", + "Grad decoder.fc1.0.weight: 3.162299617542885e-05\n", + "Grad decoder.fc1.0.bias: 0.00010989051224896684\n", + "Grad decoder.fc1.2.weight: 4.576138235279359e-05\n", + "Grad decoder.fc1.2.bias: 0.00015546957729384303\n", + "Grad decoder.fc1.4.weight: 6.736352224834263e-05\n", + "Grad decoder.fc1.4.bias: 0.00038893232704140246\n", + "Grad decoder.fc2.weight: 7.156305218813941e-05\n", + "Grad decoder.fc2.bias: 0.0022494851145893335\n", + "Grad _memory_unit.weight_ih_l0: 1.1945018059122958e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 5.757866347266827e-06\n", + "Grad _memory_unit.bias_hh_l0: 3.155302238155855e-06\n", + "Grad _memory_unit.weight_ih_l1: 9.194045560434461e-07\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 2.3558151951874606e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.1868811270687729e-05\n", + "Data X Sample: tensor([[1.4841, 1.7871, 1.8753, 2.0291, 2.1428, 2.3528, 2.3572, 2.3892, 2.4801,\n", + " 2.4991, 2.5505, 2.5259, 2.4545, 2.4538, 2.3706, 2.3590, 2.3743, 2.3889,\n", + " 2.4078, 2.3786, 2.3726, 2.5168, 2.4773, 2.4641, 2.3761, 2.4395, 2.3976,\n", + " 2.4104, 2.1578, 2.2828, 2.2187, 2.2797, 2.2330, 1.4852, 1.9438, 1.7974,\n", + " 1.6440, 1.5106, 1.5529, 1.5576, 1.2457, 0.6831, 0.6730, 1.2974, 1.9345,\n", + " 2.0586, 1.4278, 1.8922]], device='cuda:0')\n", + "Data Y Sample: tensor([[-0.0571, 0.6855, 0.0378, 0.9752, 0.5068, 0.7126, -0.3710, -0.3954,\n", + " -1.0811, -0.4638, -0.2657, -0.4541, 1.2469, -0.3358, 0.0945, 0.0133,\n", + " -0.2083, 0.3786, 0.6928, -0.0311, 0.7191, 0.0376, 0.1846, 0.0949,\n", + " -0.1512, -0.9076, -0.2300, 0.4163, -1.1473, 0.2972, 0.2634, 0.7229,\n", + " 0.3841, 0.3385, -0.0244, -0.1105, 0.3148, -0.6596, 0.0040, -0.0647,\n", + " -0.0807, -0.3303, 1.1761]], device='cuda:0')\n", + "Prediction Sample: tensor([[ 0.3141, 0.2717, 0.0637, 0.2125, 0.0796, -0.0860, -0.3337, -0.3388,\n", + " -0.1963, -0.2176, -0.2294, 0.1394, 0.1274, 0.2666, 0.1773, 0.2594,\n", + " 0.2027, 0.1346, 0.0994, 0.1529, 0.0572, 0.0516, -0.0011, 0.0471,\n", + " 0.0661, 0.0518, -0.0665, 0.0193, 0.1731, 0.2399, 0.2150, 0.2132,\n", + " 0.1885, 0.2402, 0.1523, 0.1817, -0.0451, 0.0033, -0.0035, -0.2342,\n", + " -0.2147, -0.2508, -0.1980]], device='cuda:0',\n", + " grad_fn=)\n", + "Grad encoder.fc1.weight: 0.00018611193809192628\n", + "Grad encoder.fc1.bias: 0.00018479872960597277\n", + "Grad encoder.encoder.0.weight: 4.963254468748346e-05\n", + "Grad encoder.encoder.0.bias: 0.00017832417506724596\n", + "Grad encoder.encoder.2.weight: 3.199366619810462e-05\n", + "Grad encoder.encoder.2.bias: 0.0001306546328123659\n", + "Grad encoder.encoder.4.weight: 8.335164602613077e-05\n", + "Grad encoder.encoder.4.bias: 0.0002852723700925708\n", + "Grad decoder.fc1.0.weight: 2.5465331418672577e-05\n", + "Grad decoder.fc1.0.bias: 9.226420661434531e-05\n", + "Grad decoder.fc1.2.weight: 3.854696115013212e-05\n", + "Grad decoder.fc1.2.bias: 0.00015648434055037796\n", + "Grad decoder.fc1.4.weight: 5.050345498602837e-05\n", + "Grad decoder.fc1.4.bias: 0.0003858767740894109\n", + "Grad decoder.fc2.weight: 0.00010046646639239043\n", + "Grad decoder.fc2.bias: 0.0017829707358032465\n", + "Grad _memory_unit.weight_ih_l0: 1.8297679389434052e-06\n", + "Grad _memory_unit.weight_hh_l0: 0.0\n", + "Grad _memory_unit.bias_ih_l0: 3.993375685240608e-06\n", + "Grad _memory_unit.bias_hh_l0: 2.4000855773920193e-06\n", + "Grad _memory_unit.weight_ih_l1: 9.978708703783923e-07\n", + "Grad _memory_unit.weight_hh_l1: 0.0\n", + "Grad _memory_unit.bias_ih_l1: 2.310437776031904e-05\n", + "Grad _memory_unit.bias_hh_l1: 1.1327261745464057e-05\n", + "Data X Sample: tensor([[ 0.0011, -0.0087, 0.0060, 0.0000, 0.0000, -0.0029, 0.0031, 0.0028,\n", + " 0.0073, -0.0095, -0.0159, 0.0000, -0.0022, 0.0109, -0.0252, 0.0027,\n", + " 0.0189, 0.0271, 0.0186, 0.0019, 0.0221, 0.0276, 0.0145, 0.0057,\n", + " 0.0188, 0.0104, 0.0240, 0.0449, -0.0347, 0.0197, 0.0000, 0.0193,\n", + " -0.0154, -0.0252, 0.0074, 0.0170, 0.0020, 0.0027, 0.0380, -0.0057,\n", + " 0.0143, -0.0060, 0.0182, 0.0144, 0.0277, 0.0057, -0.0408, -0.0078]],\n", + " device='cuda:0')\n", + "Data Y Sample: " + ] + } + ], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import numpy as np\n", + "import random\n", + "\n", + "class Config:\n", + " def __init__(self, input_dim, embed_dim, hidden_dim, output_dim, device):\n", + " print('device:', device)\n", + " self.IN_DIM = input_dim\n", + " self.EMBED_DIM = embed_dim\n", + " self.OUT_DIM = output_dim\n", + " self.HIDDEN = hidden_dim\n", + " self.DEVICE = device\n", + "\n", + "def build_model(in_dim, out_dim, layers, hidden, activation, normalize=lambda x: x):\n", + " model = [normalize(nn.Linear(in_dim, hidden))]\n", + " model += [activation()]\n", + " for i in range(layers - 1):\n", + " model += [normalize(nn.Linear(hidden, hidden))]\n", + " model += [activation()]\n", + " model += [normalize(nn.Linear(hidden, out_dim))]\n", + " return nn.Sequential(*model)\n", + "\n", + "class Decoder(nn.Module):\n", + " def __init__(self, embed, hidden, out_dim, layers=2):\n", + " super().__init__()\n", + " self.fc1 = build_model(embed + hidden, hidden, layers, hidden, nn.ReLU)\n", + " self.fc2 = nn.Linear(hidden, out_dim)\n", + "\n", + " def forward(self, z):\n", + " x = F.relu(self.fc1(z))\n", + " return self.fc2(x), x\n", + "\n", + "class Encoder(nn.Module):\n", + " def __init__(self, in_dim, hidden, embed, layers=2):\n", + " super().__init__()\n", + " self.fc1 = nn.Linear(in_dim, hidden)\n", + " self.encoder = build_model(hidden, embed, layers, hidden, nn.ReLU)\n", + "\n", + " def forward(self, x):\n", + " embed = F.relu(self.fc1(x))\n", + " return self.encoder(F.relu(embed))\n", + "\n", + "class HackNet(nn.Module):\n", + " def __init__(self, config):\n", + " super().__init__()\n", + " self.encoder = Encoder(in_dim=config.IN_DIM, hidden=config.HIDDEN, embed=config.EMBED_DIM)\n", + " self.decoder = Decoder(embed=config.EMBED_DIM, hidden=config.HIDDEN, out_dim=config.OUT_DIM)\n", + " self._memory_unit = nn.GRU(config.EMBED_DIM, config.HIDDEN, num_layers=2, batch_first=True)\n", + "\n", + " def forward(self, x):\n", + " embed = self.encoder(x)\n", + " mem_out = self._memory_unit(embed)[0]\n", + " pred, _ = self.decoder(torch.cat([embed, mem_out], axis=2))\n", + " return pred\n", + "\n", + " def to(self, device):\n", + " super().to(device)\n", + " self.encoder = self.encoder.to(device)\n", + " self.decoder = self.decoder.to(device)\n", + " self._memory_unit = self._memory_unit.to(device)\n", + " return self\n", + "\n", + "def init_weights(m):\n", + " if isinstance(m, nn.Linear):\n", + " torch.nn.init.xavier_uniform_(m.weight)\n", + " if m.bias is not None:\n", + " torch.nn.init.zeros_(m.bias)\n", + "\n", + "def train_epoch(network, optim, data_x, data_y, device):\n", + " network.train()\n", + " data_x, data_y = data_x.to(device), data_y.to(device)\n", + " \n", + " print(\"Data X Sample:\", data_x[0]) # Debug: Print sample input\n", + " print(\"Data Y Sample:\", data_y[0]) # Debug: Print sample target\n", + " \n", + " pred = network(data_x)\n", + " \n", + " print(\"Prediction Sample:\", pred[0]) # Debug: Print sample prediction\n", + " \n", + " loss = F.smooth_l1_loss(pred, data_y)\n", + " \n", + " optim.zero_grad()\n", + " loss.backward()\n", + " \n", + " # Debug: Check gradients\n", + " for name, param in network.named_parameters():\n", + " if param.grad is not None:\n", + " print(f\"Grad {name}: {param.grad.abs().mean().item()}\")\n", + " else:\n", + " print(f\"Grad {name}: None\")\n", + " \n", + " optim.step()\n", + " return loss.item()\n", + "\n", + "def val_epoch(network, data_x, data_y, device):\n", + " network.eval()\n", + " data_x, data_y = data_x.to(device), data_y.to(device)\n", + " with torch.no_grad():\n", + " pred = network(data_x)\n", + " loss = F.smooth_l1_loss(pred, data_y)\n", + " return loss.item()\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "config = Config(48, 24, 64, 43, device=device)\n", + "net = HackNet(config).to(device)\n", + "net.apply(init_weights)\n", + "opt = torch.optim.Adam(net.parameters(), lr=3e-3)\n", + "scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=30, gamma=0.1)\n", + "\n", + "num_epochs = 100\n", + "\n", + "for epoch in range(num_epochs):\n", + " train_losses = []\n", + " for data_x, data_y in train_loader:\n", + " train_loss = train_epoch(net, opt, data_x, data_y, device)\n", + " train_losses.append(train_loss)\n", + "\n", + " val_losses = []\n", + " for data_x, data_y in test_loader:\n", + " val_loss = val_epoch(net, data_x, data_y, device)\n", + " val_losses.append(val_loss)\n", + " \n", + " scheduler.step()\n", + " print(f\"Epoch {epoch} Train Loss: {np.mean(train_losses)} Val Loss: {np.mean(val_losses)}\")" + ] + }, { "cell_type": "code", "execution_count": null, From 85b5eccb1873cbca4fa995827533effe34f37a8d Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Wed, 9 Jul 2025 20:17:21 -0400 Subject: [PATCH 023/103] Refactor project structure and update dependencies. Renamed package from "fusion_ai_hub" to "fusionaihub". Removed several unused modules and files related to data loading, processing, and visualization. Added new dependencies for enhanced functionality, including ipykernel, ipywidgets, scikit-learn, torch, tables, and pyyaml. --- .gitignore | 4 +- notebooks/accessing_data.ipynb | 192 ++ notebooks/data_preparation.ipynb | 439 +++ pyproject.toml | 34 +- .../__init__.py | 0 .../base/__init__.py | 0 .../base/load.py | 0 .../base/merge.py | 0 .../base/save.py | 0 .../core/__init__.py | 0 .../core/fusion_signal/__init__.py | 0 .../core/fusion_signal/interpolation.py | 0 .../core/fusion_signal/resampling.py | 0 .../core/magnitude_scaling/compute_norms.py | 0 .../core/magnitude_scaling/norm.py | 0 .../core/magnitude_scaling/rescale.py | 0 .../core/scaling.py | 0 .../core/spectral_representation/__init__.py | 0 .../core/spectral_representation/sft.py | 0 .../core/time_domain_filtering/__init__.py | 0 .../core/time_domain_filtering/filtering.py | 0 .../core/time_domain_filtering/preemphasis.py | 0 .../core/time_domain_processing/cut_time.py | 0 .../get_windowed_data.py | 0 .../datasets/__init__.py | 0 .../datasets/fetch/fetch.py | 0 src/fusionaihub/datasets/prepare/README.md | 174 + .../datasets/prepare}/__init__.py | 0 .../datasets/prepare/config/default.yaml | 39 + .../datasets/prepare/core/__init__.py | 30 + .../datasets/prepare/core/data_extraction.py | 107 + .../datasets/prepare/core/dataset_utils.py | 84 + .../prepare/core/sample_processing.py | 160 + .../datasets/prepare/core/shot_processing.py | 122 + .../prepare/core/signal_processing.py | 56 + src/fusionaihub/datasets/prepare/prepare.py | 286 ++ src/fusionaihub/datasets/prepare/prepare2.py | 443 +++ .../datasets/prepare/prepare_dataset.py | 159 + .../datasets/toy_loader/load.py | 0 .../display}/__init__.py | 0 .../display/display.py | 0 .../display/specshow.py | 0 .../display/waveshow.py | 0 .../feature}/__init__.py | 0 .../util => fusionaihub/sampling}/__init__.py | 0 .../sampling/match_times.py | 0 src/fusionaihub/util/__init__.py | 0 src/fusionaihub/util/parmap.py | 170 + .../util/utils.py | 0 uv.lock | 3052 +++++++++++++++++ 50 files changed, 5547 insertions(+), 4 deletions(-) create mode 100644 notebooks/accessing_data.ipynb create mode 100644 notebooks/data_preparation.ipynb rename src/{fusion_ai_hub => fusionaihub}/__init__.py (100%) rename src/{fusion_ai_hub => fusionaihub}/base/__init__.py (100%) rename src/{fusion_ai_hub => fusionaihub}/base/load.py (100%) rename src/{fusion_ai_hub => fusionaihub}/base/merge.py (100%) rename src/{fusion_ai_hub => fusionaihub}/base/save.py (100%) rename src/{fusion_ai_hub => fusionaihub}/core/__init__.py (100%) rename src/{fusion_ai_hub => fusionaihub}/core/fusion_signal/__init__.py (100%) rename src/{fusion_ai_hub => fusionaihub}/core/fusion_signal/interpolation.py (100%) rename src/{fusion_ai_hub => fusionaihub}/core/fusion_signal/resampling.py (100%) rename src/{fusion_ai_hub => fusionaihub}/core/magnitude_scaling/compute_norms.py (100%) rename src/{fusion_ai_hub => fusionaihub}/core/magnitude_scaling/norm.py (100%) rename src/{fusion_ai_hub => fusionaihub}/core/magnitude_scaling/rescale.py (100%) rename src/{fusion_ai_hub => fusionaihub}/core/scaling.py (100%) rename src/{fusion_ai_hub => fusionaihub}/core/spectral_representation/__init__.py (100%) rename src/{fusion_ai_hub => fusionaihub}/core/spectral_representation/sft.py (100%) rename src/{fusion_ai_hub => fusionaihub}/core/time_domain_filtering/__init__.py (100%) rename src/{fusion_ai_hub => fusionaihub}/core/time_domain_filtering/filtering.py (100%) rename src/{fusion_ai_hub => fusionaihub}/core/time_domain_filtering/preemphasis.py (100%) rename src/{fusion_ai_hub => fusionaihub}/core/time_domain_processing/cut_time.py (100%) rename src/{fusion_ai_hub => fusionaihub}/core/time_domain_processing/get_windowed_data.py (100%) rename src/{fusion_ai_hub => fusionaihub}/datasets/__init__.py (100%) rename src/{fusion_ai_hub => fusionaihub}/datasets/fetch/fetch.py (100%) create mode 100644 src/fusionaihub/datasets/prepare/README.md rename src/{fusion_ai_hub/display => fusionaihub/datasets/prepare}/__init__.py (100%) create mode 100644 src/fusionaihub/datasets/prepare/config/default.yaml create mode 100644 src/fusionaihub/datasets/prepare/core/__init__.py create mode 100644 src/fusionaihub/datasets/prepare/core/data_extraction.py create mode 100644 src/fusionaihub/datasets/prepare/core/dataset_utils.py create mode 100644 src/fusionaihub/datasets/prepare/core/sample_processing.py create mode 100644 src/fusionaihub/datasets/prepare/core/shot_processing.py create mode 100644 src/fusionaihub/datasets/prepare/core/signal_processing.py create mode 100644 src/fusionaihub/datasets/prepare/prepare.py create mode 100644 src/fusionaihub/datasets/prepare/prepare2.py create mode 100644 src/fusionaihub/datasets/prepare/prepare_dataset.py rename src/{fusion_ai_hub => fusionaihub}/datasets/toy_loader/load.py (100%) rename src/{fusion_ai_hub/feature => fusionaihub/display}/__init__.py (100%) rename src/{fusion_ai_hub => fusionaihub}/display/display.py (100%) rename src/{fusion_ai_hub => fusionaihub}/display/specshow.py (100%) rename src/{fusion_ai_hub => fusionaihub}/display/waveshow.py (100%) rename src/{fusion_ai_hub/sampling => fusionaihub/feature}/__init__.py (100%) rename src/{fusion_ai_hub/util => fusionaihub/sampling}/__init__.py (100%) rename src/{fusion_ai_hub => fusionaihub}/sampling/match_times.py (100%) create mode 100644 src/fusionaihub/util/__init__.py create mode 100644 src/fusionaihub/util/parmap.py rename src/{fusion_ai_hub => fusionaihub}/util/utils.py (100%) create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore index 6769e21..faa5f5e 100644 --- a/.gitignore +++ b/.gitignore @@ -157,4 +157,6 @@ cython_debug/ # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ \ No newline at end of file +#.idea/ + +*.pkl \ No newline at end of file diff --git a/notebooks/accessing_data.ipynb b/notebooks/accessing_data.ipynb new file mode 100644 index 0000000..301e1b0 --- /dev/null +++ b/notebooks/accessing_data.ipynb @@ -0,0 +1,192 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 65, + "id": "914fa271", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "836b5c67", + "metadata": {}, + "outputs": [], + "source": [ + "import joblib\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "af64eb59", + "metadata": {}, + "outputs": [], + "source": [ + "file = \"/scratch/gpfs/nc1514/FusionAIHub/data/foundation_v2/train/170000_0.pkl\"\n", + "\n", + "with open(file, 'rb') as f:\n", + " data = joblib.load(f)\n", + " data1 = data['co2v1']\n", + " data2 = data['mhrb4']" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "0818d229", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['mhrb1', 'mhrb2', 'mhrb3', 'mhrb4', 'mhrb5', 'mhrb6', 'mhrb7', 'mhrb8', 'ece01', 'ece02', 'ece03', 'ece04', 'ece05', 'ece06', 'ece07', 'ece08', 'ece09', 'ece10', 'ece11', 'ece12', 'ece13', 'ece14', 'ece15', 'ece16', 'ece17', 'ece18', 'ece19', 'ece20', 'ece21', 'ece22', 'ece23', 'ece24', 'ece25', 'ece26', 'ece27', 'ece28', 'ece29', 'ece30', 'ece31', 'ece32', 'ece33', 'ece34', 'ece35', 'ece36', 'ece37', 'ece38', 'ece39', 'ece40', 'ece41', 'ece42', 'ece43', 'ece44', 'ece45', 'ece46', 'ece47', 'ece48', 'co2r0', 'co2v1', 'co2v2', 'co2v3', 'gasgasa', 'gasgasb', 'gasgasc', 'gasgasd', 'gasgase', 'echechpwr', 'echechpwrc', 'echecleifpwrc', 'echecleipolang', 'echecleixmfrac', 'echeclukfpwrc', 'echeclukpolang', 'echeclukxmfrac', 'echecr2dfpwrc', 'echecr2dpolang', 'echecr2dxmfrac', 'pinpinjf_15l', 'pinpinjf_15r', 'pinpinjf_21l', 'pinpinjf_21r', 'pinpinjf_30l', 'pinpinjf_30r', 'pinpinjf_33l', 'pinpinjf_33r', 'tintinj_15l', 'tintinj_15r', 'tintinj_21l', 'tintinj_21r', 'tintinj_30l', 'tintinj_30r', 'tintinj_33l', 'tintinj_33r'])" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "36eb742e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(513, 11066)" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['mhrb4'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "59d841a1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(11066,)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['gasgasa'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "434b288f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.subplot(2, 1, 1)\n", + "plt.imshow(data['mhrb4'], aspect='auto', origin='lower')\n", + "plt.title('mhrb4')\n", + "plt.subplot(2, 1, 2)\n", + "plt.imshow(data['co2r0'], aspect='auto', origin='lower')\n", + "plt.title('co2r0')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "74391de4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(data['gasgasa'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a656412", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/data_preparation.ipynb b/notebooks/data_preparation.ipynb new file mode 100644 index 0000000..4db2cbe --- /dev/null +++ b/notebooks/data_preparation.ipynb @@ -0,0 +1,439 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "e23296a9", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b8f64ca", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from pathlib import Path\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from fusionaihub.datasets.prepare import prepare2 as p2" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "4baa86aa", + "metadata": {}, + "outputs": [], + "source": [ + "cfg = p2.sample_cfg\n", + "shot = 170000" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "id": "742dfd1b", + "metadata": {}, + "outputs": [], + "source": [ + "import h5py" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "id": "dece5ac7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " CLASS: b'GROUP'\n", + " TITLE: Empty(dtype=dtype('S1'))\n", + " VERSION: b'1.0'\n", + " axis0_variety: b'regular'\n", + " axis1_variety: b'regular'\n", + " block0_items_variety: b'regular'\n", + " encoding: b'UTF-8'\n", + " end_time_ms: 13100.0\n", + " errors: b'strict'\n", + " missing_channels: Empty(dtype=dtype('S1'))\n", + " nblocks: 1\n", + " ndim: 2\n", + " pandas_type: b'frame'\n", + " pandas_version: b'0.15.2'\n", + " r_coordinates: b'N.'\n", + " sampling_frequency_kHz: 100.00000223517424\n", + " start_time_ms: 0.0\n", + " z_coordinates: b'N.'\n" + ] + } + ], + "source": [ + "import h5py\n", + "\n", + "def get_h5_attrs(directory, shot):\n", + " with h5py.File(directory + f'/{shot}.h5', 'r') as f:\n", + " mhr_group = f['p_inj']\n", + " for attr_name, attr_value in mhr_group.attrs.items():\n", + " print(f\" {attr_name}: {attr_value}\")\n", + "\n", + "# Get attrs for shot 170000\n", + "get_h5_attrs(cfg[\"raw_data_dir\"], 170000)" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "id": "484333c0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['/bes', '/bes_slow', '/beta', '/cer_amp', '/cer_amp_error', '/cer_coord_phi', '/cer_coord_r', '/cer_coord_z', '/cer_fz', '/cer_nz', '/cer_rot', '/cer_rot_error', '/cer_ti', '/cer_ti_error', '/cer_vb', '/cer_vb_error', '/cer_zeff', '/co2_density', '/co2_density_slow', '/co2_phase', '/coil_field_strength', '/d_alpha', '/divertor_geo', '/e_dens_fit', '/e_temp_fit', '/ece_cali', '/ece_slow', '/ech', '/gas', '/i_dens_fit', '/i_temp_fit', '/ip', '/mag_b0', '/mag_geo_para', '/mag_mode_number', '/mag_others', '/mag_pcb_coil', '/magnetics', '/magnetics_high_resolution', '/mse', '/neutron', '/other_profiles', '/p_inj', '/pressure', '/psi_r_z', '/q_psi', '/rho_qmin', '/rmp_current', '/ssi', '/t_inj', '/t_rot_fit', '/ts_core_density', '/ts_core_density_error', '/ts_core_temperature', '/ts_core_temperature_error', '/ts_divertor_density', '/ts_divertor_density_error', '/ts_divertor_temperature', '/ts_divertor_temperature_error', '/ts_tangential_density', '/ts_tangential_density_error', '/ts_tangential_temperature', '/ts_tangential_temperature_error']\n" + ] + }, + { + "data": { + "text/html": [ + "
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gasagasbgascgasdgase
Time [ms]
-8053.6000980.0074010.0103830.0049320.0116070.008635
-8053.549805-0.0006170.0048860.0067810.0109960.003701
-8053.5000000.0148020.0079400.0110960.0085520.009252
-8053.450195-0.0061680.0024430.002466-0.000611-0.001234
-8053.3999020.0086350.0183230.0049320.0122170.006168
..................
11606.9501950.0037010.007940-0.001849-0.0012220.004318
11607.000000-0.014185-0.000611-0.017877-0.010996-0.012953
11607.0498050.0061680.0183230.0000000.001222-0.001234
11607.099609-0.004317-0.001222-0.007398-0.007330-0.011719
11607.1503910.0000000.004886-0.0024660.000000-0.000617
\n", + "

393216 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " gasa gasb gasc gasd gase\n", + "Time [ms] \n", + "-8053.600098 0.007401 0.010383 0.004932 0.011607 0.008635\n", + "-8053.549805 -0.000617 0.004886 0.006781 0.010996 0.003701\n", + "-8053.500000 0.014802 0.007940 0.011096 0.008552 0.009252\n", + "-8053.450195 -0.006168 0.002443 0.002466 -0.000611 -0.001234\n", + "-8053.399902 0.008635 0.018323 0.004932 0.012217 0.006168\n", + "... ... ... ... ... ...\n", + " 11606.950195 0.003701 0.007940 -0.001849 -0.001222 0.004318\n", + " 11607.000000 -0.014185 -0.000611 -0.017877 -0.010996 -0.012953\n", + " 11607.049805 0.006168 0.018323 0.000000 0.001222 -0.001234\n", + " 11607.099609 -0.004317 -0.001222 -0.007398 -0.007330 -0.011719\n", + " 11607.150391 0.000000 0.004886 -0.002466 0.000000 -0.000617\n", + "\n", + "[393216 rows x 5 columns]" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "with pd.HDFStore(cfg[\"raw_data_dir\"] + '/170000.h5', \"r\") as store:\n", + " print(store.keys())\n", + " signal = store['gas']\n", + "\n", + "signal" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "id": "fdb1b47f", + "metadata": {}, + "outputs": [], + "source": [ + "channel = signal['gasa']" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "id": "f9cb5191", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 172, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "channel.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "id": "91f88685", + "metadata": {}, + "outputs": [], + "source": [ + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "id": "cf665d9e", + "metadata": {}, + "outputs": [], + "source": [ + "# Apply STFT transform to the channel data\n", + "x_tensor = torch.from_numpy(channel.values).float()\n", + "stft_result = torch.stft(\n", + " x_tensor, \n", + " n_fft=1024, \n", + " hop_length=256, \n", + " window=torch.hann_window(1024), \n", + " return_complex=True\n", + ")\n", + "# Take log of absolute values\n", + "log_abs_stft = torch.log(torch.abs(stft_result))" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "id": "1b0ca730", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original signal length: 1310001\n", + "STFT time frames: 5118\n", + "Decimation factor: 255.95955451348183\n", + "Decimated signal length: 5118\n" + ] + } + ], + "source": [ + "# Decimate the original signal to match the STFT time dimension\n", + "stft_time_frames = log_abs_stft.shape[1]\n", + "original_length = len(channel)\n", + "\n", + "print(f\"Original signal length: {original_length}\")\n", + "print(f\"STFT time frames: {stft_time_frames}\")\n", + "print(f\"Decimation factor: {original_length / stft_time_frames}\")\n", + "\n", + "# Create decimated signal by taking every nth sample\n", + "decimation_factor = original_length // stft_time_frames\n", + "decimated_signal = channel.values[::decimation_factor][:stft_time_frames]\n", + "\n", + "print(f\"Decimated signal length: {len(decimated_signal)}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "id": "c9129a21", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the spectrogram\n", + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize=(12, 6))\n", + "plt.subplot(2, 1, 1)\n", + "plt.imshow(log_abs_stft.numpy(), aspect='auto', origin='lower', cmap='viridis')\n", + "plt.xlabel('Time Frame')\n", + "plt.ylabel('Frequency Bin')\n", + "plt.title('STFT Spectrogram of pinjf_15l channel')\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(decimated_signal)\n", + "plt.xlim(0, len(decimated_signal))\n", + "plt.xlabel('Time Frame')\n", + "plt.ylabel('Magnitude')\n", + "plt.title('Original Signal of pinjf_15l channel')\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7912cd5d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index 34eea6b..1f712c4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,5 +1,5 @@ [project] -name = "fusion_ai_hub" +name = "fusionaihub" version = "0.0.1" authors = [ {name="Peter Steiner", email="peter.steiner@princeton.edu"}, @@ -17,10 +17,38 @@ classifiers = [ ] license = {file = "LICENSE"} dependencies = [ - "h5py", "numpy", "pandas", "matplotlib", "scipy", "tqdm", "opencv-python", - "paramiko" + "h5py", + "numpy", + "pandas", + "matplotlib", + "scipy", + "tqdm", + "opencv-python", + "paramiko", + "ipykernel>=6.29.5", + "ipywidgets>=8.1.7", + "scikit-learn>=1.6.1", + "torch>=2.7.1", + "tables>=3.9.2", + "pyyaml>=6.0.2", ] +[tool.uv.sources] +torch = [ + { index = "pytorch-cpu" }, +] +torchvision = [ + { index = "pytorch-cpu" }, +] + +[[tool.uv.index]] +name = "pytorch-cpu" +url = "https://download.pytorch.org/whl/cpu" +explicit = true + +[tool.uv] +package = true + [project.urls] Homepage = "https://github.com" Documentation = "https://readthedocs.org" diff --git a/src/fusion_ai_hub/__init__.py b/src/fusionaihub/__init__.py similarity index 100% rename from src/fusion_ai_hub/__init__.py rename to src/fusionaihub/__init__.py diff --git a/src/fusion_ai_hub/base/__init__.py b/src/fusionaihub/base/__init__.py similarity index 100% rename from src/fusion_ai_hub/base/__init__.py rename to src/fusionaihub/base/__init__.py diff --git a/src/fusion_ai_hub/base/load.py b/src/fusionaihub/base/load.py similarity index 100% rename from src/fusion_ai_hub/base/load.py rename to src/fusionaihub/base/load.py diff --git a/src/fusion_ai_hub/base/merge.py b/src/fusionaihub/base/merge.py similarity index 100% rename from src/fusion_ai_hub/base/merge.py rename to src/fusionaihub/base/merge.py diff --git a/src/fusion_ai_hub/base/save.py b/src/fusionaihub/base/save.py similarity index 100% rename from src/fusion_ai_hub/base/save.py rename to src/fusionaihub/base/save.py diff --git a/src/fusion_ai_hub/core/__init__.py b/src/fusionaihub/core/__init__.py similarity index 100% rename from src/fusion_ai_hub/core/__init__.py rename to src/fusionaihub/core/__init__.py diff --git a/src/fusion_ai_hub/core/fusion_signal/__init__.py b/src/fusionaihub/core/fusion_signal/__init__.py similarity index 100% rename from src/fusion_ai_hub/core/fusion_signal/__init__.py rename to src/fusionaihub/core/fusion_signal/__init__.py diff --git a/src/fusion_ai_hub/core/fusion_signal/interpolation.py b/src/fusionaihub/core/fusion_signal/interpolation.py similarity index 100% rename from src/fusion_ai_hub/core/fusion_signal/interpolation.py rename to src/fusionaihub/core/fusion_signal/interpolation.py diff --git a/src/fusion_ai_hub/core/fusion_signal/resampling.py b/src/fusionaihub/core/fusion_signal/resampling.py similarity index 100% rename from src/fusion_ai_hub/core/fusion_signal/resampling.py rename to src/fusionaihub/core/fusion_signal/resampling.py diff --git a/src/fusion_ai_hub/core/magnitude_scaling/compute_norms.py b/src/fusionaihub/core/magnitude_scaling/compute_norms.py similarity index 100% rename from src/fusion_ai_hub/core/magnitude_scaling/compute_norms.py rename to src/fusionaihub/core/magnitude_scaling/compute_norms.py diff --git a/src/fusion_ai_hub/core/magnitude_scaling/norm.py b/src/fusionaihub/core/magnitude_scaling/norm.py similarity index 100% rename from src/fusion_ai_hub/core/magnitude_scaling/norm.py rename to src/fusionaihub/core/magnitude_scaling/norm.py diff --git a/src/fusion_ai_hub/core/magnitude_scaling/rescale.py b/src/fusionaihub/core/magnitude_scaling/rescale.py similarity index 100% rename from src/fusion_ai_hub/core/magnitude_scaling/rescale.py rename to src/fusionaihub/core/magnitude_scaling/rescale.py diff --git a/src/fusion_ai_hub/core/scaling.py b/src/fusionaihub/core/scaling.py similarity index 100% rename from src/fusion_ai_hub/core/scaling.py rename to src/fusionaihub/core/scaling.py diff --git a/src/fusion_ai_hub/core/spectral_representation/__init__.py b/src/fusionaihub/core/spectral_representation/__init__.py similarity index 100% rename from src/fusion_ai_hub/core/spectral_representation/__init__.py rename to src/fusionaihub/core/spectral_representation/__init__.py diff --git a/src/fusion_ai_hub/core/spectral_representation/sft.py b/src/fusionaihub/core/spectral_representation/sft.py similarity index 100% rename from src/fusion_ai_hub/core/spectral_representation/sft.py rename to src/fusionaihub/core/spectral_representation/sft.py diff --git a/src/fusion_ai_hub/core/time_domain_filtering/__init__.py b/src/fusionaihub/core/time_domain_filtering/__init__.py similarity index 100% rename from src/fusion_ai_hub/core/time_domain_filtering/__init__.py rename to src/fusionaihub/core/time_domain_filtering/__init__.py diff --git a/src/fusion_ai_hub/core/time_domain_filtering/filtering.py b/src/fusionaihub/core/time_domain_filtering/filtering.py similarity index 100% rename from src/fusion_ai_hub/core/time_domain_filtering/filtering.py rename to src/fusionaihub/core/time_domain_filtering/filtering.py diff --git a/src/fusion_ai_hub/core/time_domain_filtering/preemphasis.py b/src/fusionaihub/core/time_domain_filtering/preemphasis.py similarity index 100% rename from src/fusion_ai_hub/core/time_domain_filtering/preemphasis.py rename to src/fusionaihub/core/time_domain_filtering/preemphasis.py diff --git a/src/fusion_ai_hub/core/time_domain_processing/cut_time.py b/src/fusionaihub/core/time_domain_processing/cut_time.py similarity index 100% rename from src/fusion_ai_hub/core/time_domain_processing/cut_time.py rename to src/fusionaihub/core/time_domain_processing/cut_time.py diff --git a/src/fusion_ai_hub/core/time_domain_processing/get_windowed_data.py b/src/fusionaihub/core/time_domain_processing/get_windowed_data.py similarity index 100% rename from src/fusion_ai_hub/core/time_domain_processing/get_windowed_data.py rename to src/fusionaihub/core/time_domain_processing/get_windowed_data.py diff --git a/src/fusion_ai_hub/datasets/__init__.py b/src/fusionaihub/datasets/__init__.py similarity index 100% rename from src/fusion_ai_hub/datasets/__init__.py rename to src/fusionaihub/datasets/__init__.py diff --git a/src/fusion_ai_hub/datasets/fetch/fetch.py b/src/fusionaihub/datasets/fetch/fetch.py similarity index 100% rename from src/fusion_ai_hub/datasets/fetch/fetch.py rename to src/fusionaihub/datasets/fetch/fetch.py diff --git a/src/fusionaihub/datasets/prepare/README.md b/src/fusionaihub/datasets/prepare/README.md new file mode 100644 index 0000000..7394c25 --- /dev/null +++ b/src/fusionaihub/datasets/prepare/README.md @@ -0,0 +1,174 @@ +# Fusion Dataset Preparation Pipeline + +This module provides a modular, configurable pipeline for preparing fusion plasma datasets from HDF5 data files. + +## Structure + +The pipeline has been refactored into a modular structure: + +``` +src/fusionaihub/datasets/prepare/ +├── config/ +│ └── default.yaml # Default configuration file +├── core/ # Core processing modules +│ ├── __init__.py # Package initialization +│ ├── signal_processing.py # Signal resampling and transformation +│ ├── data_extraction.py # Data extraction and alignment +│ ├── sample_processing.py # Sample splitting and transformation +│ ├── dataset_utils.py # Dataset utilities and indexing +│ └── shot_processing.py # Shot-level processing orchestration +├── prepare_dataset.py # Main executable script +├── prepare2.py # Original monolithic script (legacy) +└── README.md # This file +``` + +## Core Modules + +### `signal_processing.py` +- `resample_nearest()`: Resample signals to new length +- `transform_individual_sample()`: Apply STFT transformation + +### `data_extraction.py` +- `extract()`: Extract signal data from HDF5 files +- `running_time()`: Determine plasma running time from current threshold +- `align()`: Align signals to common timebase and sampling frequency + +### `sample_processing.py` +- `split()`: Split signals into overlapping time windows +- `transform_samples()`: Apply transformations and resample to match dimensions +- `save_samples()`: Save processed samples to disk + +### `dataset_utils.py` +- `create_missing_signal_dataframes()`: Create placeholder data for missing signals +- `index_dataset()`: Create dataset index files + +### `shot_processing.py` +- `process_shot()`: Main orchestration function for processing individual shots + +## Configuration + +The pipeline is configured using YAML files. The default configuration is in `config/default.yaml`: + +```yaml +# Signal configuration - list of signals to process +signal: + - ["magnetics_high_resolution", "mhr", true] + - ["ece_cali", "ece", true] + - ["co2_density", "co2", true] + - ["gas", "gas", false] + - ["ech", "ech", false] + - ["p_inj", "pin", false] + - ["t_inj", "tin", false] + +# Processing parameters +randomize_shots: true +random_seed: 42 +num_shots: 50 +fs_khz: 500 +ip_threshold: 0.1 +window_ms: 250 +hop_ms: 50 + +# Directory paths +raw_data_dir: "/scratch/gpfs/EKOLEMEN/d3d_fusion_data" +output_dir: "/scratch/gpfs/nc1514/FusionAIHub/data/foundation_v2" + +# Additional parameters... +``` + +### Signal Configuration Format +Each signal is configured as a list: `[signal_name, abbreviation, should_transform]` +- `signal_name`: Name in the HDF5 file +- `abbreviation`: Short name for column prefixes +- `should_transform`: Whether to apply STFT transformation (boolean) + +## Usage + +### Command Line +```bash +# Use default configuration +python -m src.fusionaihub.datasets.prepare.prepare_dataset + +# Use custom configuration file +python -m src.fusionaihub.datasets.prepare.prepare_dataset --config /path/to/config.yaml +``` + +### Programmatic Usage +```python +from src.fusionaihub.datasets.prepare.prepare_dataset import load_config, prepare_dataset + +# Load configuration +cfg = load_config("path/to/config.yaml") + +# Run dataset preparation +prepare_dataset(cfg) +``` + +### Customizing Configuration +Create a custom YAML file based on `config/default.yaml`: + +```python +import yaml +from src.fusionaihub.datasets.prepare.prepare_dataset import prepare_dataset + +# Load and modify configuration +with open("config/default.yaml", "r") as f: + cfg = yaml.safe_load(f) + +# Customize settings +cfg["num_shots"] = 100 +cfg["raw_data_dir"] = "/path/to/your/data" +cfg["output_dir"] = "/path/to/output" + +# Run with custom configuration +prepare_dataset(cfg) +``` + +## Output Structure + +The pipeline produces the following output structure: + +``` +output_dir/ +├── train/ +│ ├── 170000_0.pkl # Processed samples +│ ├── 170001_0.pkl +│ ├── ... +│ └── index.pkl # Dataset index +└── valid/ + ├── 170010_0.pkl + ├── 170011_0.pkl + ├── ... + └── index.pkl +``` + +Each `.pkl` file contains a dictionary with signal arrays, where transformed signals have STFT representation and non-transformed signals are resampled to match dimensions. + +## Key Features + +1. **Modular Design**: Each processing step is in a separate module for easier testing and modification +2. **YAML Configuration**: All parameters are configurable via YAML files +3. **Parallel Processing**: Uses `ParallelMapper` for efficient multi-shot processing +4. **Missing Signal Handling**: Automatically creates placeholder data for missing signals +5. **Flexible Transformations**: Configurable per-signal transformation (STFT or raw) +6. **Train/Validation Split**: Automatic dataset splitting with indexing + +## Migration from Legacy Code + +If you were using the original `prepare2.py`, the new modular structure provides the same functionality with improved organization: + +- Configuration moved from Python dictionary to YAML file +- Functions split into logical modules +- Same processing pipeline and output format +- Command-line interface added for easier use + +## Dependencies + +- numpy +- pandas +- scipy +- scikit-learn +- torch +- joblib +- PyYAML +- pathlib (built-in) \ No newline at end of file diff --git a/src/fusion_ai_hub/display/__init__.py b/src/fusionaihub/datasets/prepare/__init__.py similarity index 100% rename from src/fusion_ai_hub/display/__init__.py rename to src/fusionaihub/datasets/prepare/__init__.py diff --git a/src/fusionaihub/datasets/prepare/config/default.yaml b/src/fusionaihub/datasets/prepare/config/default.yaml new file mode 100644 index 0000000..bd25673 --- /dev/null +++ b/src/fusionaihub/datasets/prepare/config/default.yaml @@ -0,0 +1,39 @@ +# Dataset preparation configuration +# Configuration for fusion data processing pipeline + +# Signal configuration - list of signals to process +# Each signal has: [signal_name, abbreviation, should_transform] +signal: + - ["magnetics_high_resolution", "mhr", true] + - ["ece_cali", "ece", true] + - ["co2_density", "co2", true] + - ["gas", "gas", false] + - ["ech", "ech", false] + - ["p_inj", "pin", false] + - ["t_inj", "tin", false] + +# Data processing parameters +randomize_shots: true +random_seed: 42 +num_shots: 50 +fs_khz: 500 # Sampling frequency in kHz +ip_threshold: 0.1 # Plasma current threshold +window_ms: 250 # Window size in milliseconds +hop_ms: 50 # Hop size in milliseconds +remove_empty: true + +# Train/test split configuration +train_test_split: 0.2 + +# Directory paths +raw_data_dir: "/scratch/gpfs/EKOLEMEN/d3d_fusion_data" +output_dir: "/scratch/gpfs/EKOLEMEN/nc1514/foundation_v2" + +# Processing parameters +stft: + n_fft: 1024 + hop_length: 256 + window_type: "hann" + +# Output settings +compression: true # Whether to compress saved files \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/core/__init__.py b/src/fusionaihub/datasets/prepare/core/__init__.py new file mode 100644 index 0000000..d608038 --- /dev/null +++ b/src/fusionaihub/datasets/prepare/core/__init__.py @@ -0,0 +1,30 @@ +""" +Core modules for fusion dataset preparation. + +This package contains modular components for processing fusion data: +- signal_processing: Signal resampling and transformation functions +- data_extraction: Data extraction and alignment functions +- sample_processing: Sample splitting, transformation, and saving +- shot_processing: Shot-level processing logic +- dataset_utils: Dataset utilities and indexing +""" + +from .signal_processing import resample_nearest, transform_individual_sample +from .data_extraction import extract, running_time, align +from .sample_processing import split, transform_samples, save_samples +from .dataset_utils import create_missing_signal_dataframes, index_dataset +from .shot_processing import process_shot_stft + +__all__ = [ + 'resample_nearest', + 'transform_individual_sample', + 'extract', + 'running_time', + 'align', + 'split', + 'transform_samples', + 'save_samples', + 'create_missing_signal_dataframes', + 'index_dataset', + 'process_shot_stft' +] \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/core/data_extraction.py b/src/fusionaihub/datasets/prepare/core/data_extraction.py new file mode 100644 index 0000000..6ee2118 --- /dev/null +++ b/src/fusionaihub/datasets/prepare/core/data_extraction.py @@ -0,0 +1,107 @@ +""" +Data extraction utilities for fusion dataset preparation. + +This module contains functions for extracting data from HDF5 files, +determining plasma running time, and aligning signals to a common timebase. +""" + +import numpy as np +import pandas as pd +from pathlib import Path +from scipy.signal import resample + + +def extract(shot: int, directory: Path, signal: str) -> pd.DataFrame: + """ + Extract signal data from HDF5 file for a given shot. + + Args: + shot: Shot number + directory: Directory containing HDF5 files + signal: Signal name to extract + + Returns: + DataFrame containing the signal data + """ + path = (directory / str(shot)).with_suffix(".h5") + df = pd.read_hdf(path, key=signal) + return pd.DataFrame(df) + + +def running_time(directory: Path, shot: int, ip_threshold: float) -> tuple[float, float]: + """ + Determine the plasma running time for a shot based on plasma current threshold. + + Args: + directory: Directory containing HDF5 files + shot: Shot number + ip_threshold: Plasma current threshold + + Returns: + Tuple of (start_time, end_time) in milliseconds + """ + path = (directory / str(shot)).with_suffix(".h5") + with pd.HDFStore(path, 'r') as store: + df = store['ip']['ipsip'] + df = df.loc[df > ip_threshold] + start_time = df.index[0] + end_time = df.index[-1] + return start_time, end_time + + +def align(df: pd.DataFrame, start_time: float, end_time: float, fs: float) -> pd.DataFrame: + """ + Align signal data to a common timebase and sampling frequency. + + Crops the signal to the specified time window, resamples to the target + sampling frequency, and adds padding and state information. + + Args: + df: Input DataFrame with signal data + start_time: Start time for alignment (ms) + end_time: End time for alignment (ms) + fs: Target sampling frequency (kHz) + + Returns: + Aligned DataFrame with data and state columns + """ + # get sampling frequency + fs_raw = len(df) / (df.index[-1] - df.index[0]) + + # crop time + df = df.loc[(df.index >= start_time) & (df.index <= end_time)] + + # resample + num = len(df) + num = int(num * fs / fs_raw) + + df = pd.DataFrame( + {col: resample(df[col].values, num) for col in df.columns}, + index=np.linspace(df.index[0], df.index[-1], num) + ) + + # mark on-off states + start_nan = (df.index[0] - start_time) * fs + end_nan = (end_time - df.index[-1]) * fs + start_pad = pd.DataFrame( + 0, index=pd.RangeIndex(start=int(start_nan)), columns=df.columns) + end_pad = pd.DataFrame( + 0, index=pd.RangeIndex(start=int(len(df) + start_nan), stop=int(len(df) + start_nan + end_nan)), columns=df.columns) + + df_state = pd.DataFrame(True, index=df.index, columns=df.columns) + start_pad_state = pd.DataFrame(False, index=start_pad.index, columns=df.columns) + end_pad_state = pd.DataFrame(False, index=end_pad.index, columns=df.columns) + + df = pd.concat([start_pad, df, end_pad], ignore_index=True) + df_state = pd.concat([start_pad_state, df_state, end_pad_state], ignore_index=True) + df_state.columns = [f"{col}_state" for col in df.columns] + + # combine data with state + df = df.astype(np.float32) + df_state = df_state.astype(np.bool) + df = pd.concat([df, df_state], axis=1) + + # convert time to ms + df = df.rename_axis("time") + + return df \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/core/dataset_utils.py b/src/fusionaihub/datasets/prepare/core/dataset_utils.py new file mode 100644 index 0000000..0d10803 --- /dev/null +++ b/src/fusionaihub/datasets/prepare/core/dataset_utils.py @@ -0,0 +1,84 @@ +""" +Dataset utilities for fusion dataset preparation. + +This module contains utility functions for handling missing signals, +creating placeholder dataframes, and indexing dataset files. +""" + +import pandas as pd +from pathlib import Path +from typing import Set, List, Dict + + +def create_missing_signal_dataframes( + cfg: Dict, + processed_signals: Set, + reference_df: pd.DataFrame +) -> List[pd.DataFrame]: + """ + Create fully off dataframes for missing signals using reference dataframe structure. + + When some signals are missing from a shot, this function creates placeholder + dataframes with the same structure but with all data set to 0 and all states + set to False. + + Args: + cfg: Configuration dictionary containing signal definitions + processed_signals: Set of signal abbreviations that were successfully processed + reference_df: Reference DataFrame to use for structure + + Returns: + List of DataFrames for missing signals + """ + missing_dfs = [] + + for signal in cfg["signal"]: + signal_name, signal_abbr, do_transform = signal + if signal_abbr not in processed_signals: + print(f"Creating fully off dataframe for missing signal {signal_name}") + + # Create off dataframe by copying structure and zeroing values + off_df = reference_df.copy() + + # Get columns that belong to the reference signal (to replace with new signal columns) + ref_signal_cols = [col for col in off_df.columns if not col.endswith('_state')] + + # Create new column names for the missing signal + new_cols = {} + new_state_cols = {} + + for i, col in enumerate(ref_signal_cols): + new_col_name = f"{signal_abbr}col{i}" + new_cols[col] = new_col_name + new_state_cols[f"{col}_state"] = f"{new_col_name}_state" + + # Rename columns to match the missing signal + off_df = off_df.rename(columns={**new_cols, **new_state_cols}) + + # Set all data columns to 0 and all state columns to False + for col in off_df.columns: + if col.endswith('_state'): + off_df[col] = False + else: + off_df[col] = 0.0 + + missing_dfs.append(off_df) + + return missing_dfs + + +def index_dataset(out_dir: Path) -> None: + """ + Create an index file listing all dataset files in the directory. + + Scans the output directory for .pkl files and creates an index.pkl + file containing the list of all dataset files. + + Args: + out_dir: Directory to index + """ + files = list(out_dir.glob("*.pkl")) + df_files = pd.DataFrame({'files': [str(file) for file in files]}) + df_files.to_pickle(out_dir / "index.pkl") + + print(f"Indexed {len(files)} files.") \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/core/sample_processing.py b/src/fusionaihub/datasets/prepare/core/sample_processing.py new file mode 100644 index 0000000..7b7109a --- /dev/null +++ b/src/fusionaihub/datasets/prepare/core/sample_processing.py @@ -0,0 +1,160 @@ +""" +Sample processing utilities for fusion dataset preparation. + +This module contains functions for splitting signals into time windows, +applying transformations to samples, and saving processed data. +""" + +import numpy as np +import pandas as pd +import joblib +from pathlib import Path +from typing import List, Dict +from .signal_processing import resample_nearest, transform_individual_sample + + +def split(df: pd.DataFrame, window_ms: int, hop_ms: int, fs_khz: float) -> List[pd.DataFrame]: + """ + Split signal data into overlapping time windows. + + Args: + df: Input DataFrame with signal data + window_ms: Window size in milliseconds + hop_ms: Hop size in milliseconds + fs_khz: Sampling frequency in kHz + + Returns: + List of DataFrame samples + """ + # Create sample indicies + num_samples = int((window_ms) * fs_khz) + hop_samples = int((hop_ms) * fs_khz) + + # Separate samples + samples = [] + for start in range(0, len(df) - num_samples + 1, hop_samples): + end = start + num_samples + sample = df.iloc[start:end] + if len(sample) == num_samples: + samples.append(sample) + + return samples + + +def transform_samples( + samples: List[pd.DataFrame], + directory: Path, + signal_config: List[tuple], + shot: int, +) -> List[Dict]: + """ + Transform and process samples based on signal configuration. + + Applies transformations (like STFT) to specified signals and resamples + non-transformed signals to match the dimensions of transformed ones. + + Args: + samples: List of sample DataFrames + directory: Output directory + signal_config: List of (signal_name, abbreviation, should_transform) tuples + shot: Shot number for logging + + Returns: + List of dictionaries containing processed sample data + """ + directory.mkdir(parents=True, exist_ok=True) + print(f"Processing {len(samples)} samples for shot {shot}") + samples_dict = [] + + # Create mapping from signal abbreviation to whether it should be transformed + transform_map = {} + for signal_name, signal_abbr, should_transform in signal_config: + transform_map[signal_abbr] = should_transform + + for i, sample in enumerate(samples): + + # Remove columns ending with '_state' + sample_to_save = sample.loc[:, ~sample.columns.str.endswith('_state')] + + # Only save if not fully off (i.e., at least one True in any state col) + state_cols = [col for col in sample.columns if col.endswith('_state')] + if np.any(sample[state_cols].to_numpy()): + + # First pass: apply transformations and collect results + sample_dict = {} + transformed_sample = None + original_time_length = len(sample_to_save) + + for col in sample_to_save.columns: + # Convert each column to float32 numpy array + col_array = sample_to_save[col].values.astype(np.float32) + + # Determine if this column should be transformed based on signal abbreviation + should_transform = False + for signal_abbr in transform_map.keys(): + if col.startswith(signal_abbr): + should_transform = transform_map[signal_abbr] + break + + if should_transform: + transformed_array = transform_individual_sample(col_array) + sample_dict[col] = transformed_array + # Store an example transformed sample to get target dimensions + if transformed_sample is None: + transformed_sample = transformed_array + print(f"Reference transformed sample shape: {transformed_array.shape}") + else: + # Store original array for now, will resample later + sample_dict[col] = col_array + + # Second pass: resample non-transformed samples to match transformed dimensions + if transformed_sample is not None: + target_width = transformed_sample.shape[-1] # Last dimension is time + # Calculate target sample frequency based on transformed sample + target_fs = target_width / original_time_length + print(f"Target frequency ratio: {target_fs:.4f} (target width: {target_width}, original length: {original_time_length})") + + for col in sample_dict.keys(): + # Check if this column was transformed + should_transform = False + for signal_abbr in transform_map.keys(): + if col.startswith(signal_abbr): + should_transform = transform_map[signal_abbr] + break + + if not should_transform: + # Resample non-transformed data to match target width + original_array = sample_dict[col] + resampled_array = resample_nearest(original_array, target_width) + + # Crop end if needed to ensure exact match + if len(resampled_array) > target_width: + resampled_array = resampled_array[:target_width] + elif len(resampled_array) < target_width: + # Pad with zeros if too short (shouldn't happen with resample_nearest) + pad_width = target_width - len(resampled_array) + resampled_array = np.pad(resampled_array, (0, pad_width), mode='constant') + + sample_dict[col] = resampled_array.astype(np.float32) + print(f"Resampled {col} from {len(original_array)} to {len(resampled_array)}") + + samples_dict.append(sample_dict) + print(f"Sample {i} processed with {len(sample_dict)} signals") + + return samples_dict + + +def save_samples(samples: List[Dict], directory: Path, shot: int) -> None: + """ + Save processed samples to disk using joblib compression. + + Args: + samples: List of sample dictionaries to save + directory: Output directory + shot: Shot number for filename generation + """ + directory.mkdir(parents=True, exist_ok=True) + print(f"Saving {len(samples)} samples to {directory}") + for i, sample in enumerate(samples): + # Save using joblib + joblib.dump(sample, directory / f"{shot}_{i}.pkl", compress=True) \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/core/shot_processing.py b/src/fusionaihub/datasets/prepare/core/shot_processing.py new file mode 100644 index 0000000..6cf17c8 --- /dev/null +++ b/src/fusionaihub/datasets/prepare/core/shot_processing.py @@ -0,0 +1,122 @@ +""" +Shot processing utilities for fusion dataset preparation. + +This module contains the main shot processing logic that orchestrates +data extraction, alignment, transformation, and saving for individual shots. +""" + +import numpy as np +import pandas as pd +from pathlib import Path +from typing import Dict + +from .data_extraction import extract, running_time, align +from .sample_processing import transform_samples, save_samples +from .dataset_utils import create_missing_signal_dataframes + + +def process_shot_stft(shot: int, cfg: Dict, out_dir: Path) -> None: + """ + Process a single shot through the complete data preparation pipeline accounting for STFT transformations. + + This function orchestrates the complete processing workflow for a shot: + 1. Determines plasma running time + 2. Extracts and aligns all configured signals to be transformed + 3. Handles missing signals by creating placeholder dataframes + 4. Combines all signals into a unified dataframe + 5. Transforms and saves the processed samples using STFT transformations. + 6. Downsamples shots not transformed to the same length as the transformed shots. + + Args: + shot: Shot number to process + cfg: Configuration dictionary + out_dir: Output directory for processed files + """ + try: + dfs = [] + start_time = None + end_time = None + + try: + start_time, end_time = running_time( + directory=Path(cfg["raw_data_dir"]), + shot=shot, + ip_threshold=cfg["ip_threshold"] + ) + reference_len = int((end_time - start_time) * cfg["fs_khz"]) + print(f"Running time for shot {shot}: {start_time} to {end_time}") + except Exception as e: + print(f"Error: Could not determine running time for shot {shot}: {e}") + return + + # Process each signal and track which ones succeeded + processed_signals = set() + + for signal in cfg["signal"]: + signal_name, signal_abbr, is_transformed = signal + df = None + + if is_transformed: + try: + # Try to extract and process the signal + df = extract(shot=shot, directory=Path(cfg["raw_data_dir"]), signal=signal_name) + df.columns = [f"{signal_abbr}{col}" if col != "time" else col for col in df.columns] + df = align(df, start_time, end_time, cfg["fs_khz"]) + processed_signals.add(signal_abbr) + dfs.append(df) + print(f"Successfully processed signal {signal_name} for shot {shot}") + except Exception as e: + print(f"Error processing signal {signal_name} for shot {shot}: {e}") + + if not dfs: + print(f"Error: No dataframes created for shot {shot}") + return + + df = pd.concat(dfs, axis=1, join='inner') + + # num_samples = len(df) + # new_index = np.linspace(start_time, end_time, num_samples) + # df.index = new_index + # df.index = pd.to_timedelta(df.index, unit='ms') + + samples = [df] # no splitting for this dataset + print(f"Shot {shot} has {len(samples)} samples after splitting.") + samples_dict_list = transform_samples(samples, out_dir, cfg["signal"], shot) + + # Get the first (and only) sample dictionary since we don't split + sample_dict = samples_dict_list[0] + + # Get a sample from sample_dict to determine STFT dimensions + first_key = next(iter(sample_dict.keys())) + stft_width = sample_dict[first_key].shape[-1] + print(f"Using {first_key} as reference for STFT dimensions: {stft_width}") + stft_fs = stft_width / (end_time - start_time) + + for signal in cfg["signal"]: + signal_name, signal_abbr, is_transformed = signal + if not is_transformed: + try: + df = extract(shot=shot, directory=Path(cfg["raw_data_dir"]), signal=signal_name) + df.columns = [f"{signal_abbr}{col}" if col != "time" else col for col in df.columns] + df = align(df, start_time, end_time, stft_fs) + # Ensure signal matches STFT width by truncating if necessary + if len(df) > stft_width: + df = df.iloc[:stft_width] + print(f"Truncated {signal_abbr} from {len(df)} to {stft_width} samples to match STFT width") + elif len(df) < stft_width: + print(f"Warning: {signal_abbr} has {len(df)} samples, less than STFT width {stft_width}") + pad_width = stft_width - len(df) + df = np.pad(df, (0, pad_width), mode='constant', constant_values=0) + print(f"Padded {signal_abbr} from {len(df)} to {stft_width} samples with zeros") + print(f"Successfully processed non-transformed signal {signal_name} for shot {shot}") + except Exception as e: + print(f"Error processing non-transformed signal {signal_name} for shot {shot}: {e}") + + save_samples([sample_dict], out_dir, shot) + print(f"Processed shot {shot} successfully with {len(cfg['signal'])} signals.") + + except Exception as e: + print(f"Error processing shot {shot}: {e}") + return + + return \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/core/signal_processing.py b/src/fusionaihub/datasets/prepare/core/signal_processing.py new file mode 100644 index 0000000..eacb05a --- /dev/null +++ b/src/fusionaihub/datasets/prepare/core/signal_processing.py @@ -0,0 +1,56 @@ +""" +Signal processing utilities for fusion dataset preparation. + +This module contains functions for signal resampling and transformation, +including STFT transformations and nearest-neighbor resampling. +""" + +import numpy as np +import torch +from scipy.signal import resample + + +def resample_nearest(y: np.ndarray, new_len: int) -> np.ndarray: + """ + Resample a signal to a new length using scipy.signal.resample. + + Args: + y: Input signal array + new_len: Target length for resampled signal + + Returns: + Resampled signal as numpy array + """ + orig_len = len(y) + gcd = np.gcd(orig_len, new_len) + up = new_len // gcd + down = orig_len // gcd + # return resample_poly(y, up, down) + resampled = resample(y, new_len) + return np.asarray(resampled) + + +def transform_individual_sample(x: np.ndarray) -> np.ndarray: + """ + Apply STFT transformation to an individual sample. + + Transforms time-domain signal to frequency-domain representation using + Short-Time Fourier Transform with logarithmic magnitude scaling. + + Args: + x: Input time-domain signal + + Returns: + Log-magnitude STFT representation + """ + x_tensor = torch.from_numpy(x).float() + y = torch.stft( + x_tensor, + n_fft=1024, + hop_length=256, + window=torch.hann_window(1024), + return_complex=True + ) + y = torch.log(torch.abs(y)) + # y = torch.clip(y, min=-10, max=5) + return y.numpy() \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/prepare.py b/src/fusionaihub/datasets/prepare/prepare.py new file mode 100644 index 0000000..aa1e9f3 --- /dev/null +++ b/src/fusionaihub/datasets/prepare/prepare.py @@ -0,0 +1,286 @@ +import re +import numpy as np +import pandas as pd +import polars as pl +import h5py +from pathlib import Path +from scipy.interpolate import interp1d +from scipy.signal import resample, resample_poly +from sklearn.model_selection import train_test_split +from datetime import time, timedelta +from ..utils.parmap import ParallelMapper +import logging +from tqdm.auto import tqdm +import pickle +import torch +from concurrent.futures import ProcessPoolExecutor + + +log = logging.getLogger(__name__) + +sample_cfg = { + "signal": [ + ("magnetics_high_resolution", "mhr"), + ("ece_cali", "ece"), + ("bes", "bes"), + ("co2_density", "co2"), + ], + "randomize_shots": True, + "random_seed": 42, + "num_shots": 50, + "fs_khz": 500, + "start_ms": 0, + "end_ms": 5000, + "window_ms": 250, + "hop_ms": 50, + "remove_empty": True, + "train_test_split": 0.2, + "raw_data_dir": "/scratch/gpfs/EKOLEMEN/d3d_fusion_data", + "output_dir": "/scratch/gpfs/nc1514/specseg/data/foundation_v1", +} + + +def resample_nearest(y: np.ndarray, new_len: int) -> np.ndarray: + orig_len = len(y) + gcd = np.gcd(orig_len, new_len) + up = new_len // gcd + down = orig_len // gcd + return resample_poly(y, up, down) + # return resample(y, new_len) + # return interp1d(np.linspace(0, 1, len(y)), y, kind='cubic')(np.linspace(0, 1, new_len)) + + +def extract( + shot: int, + directory: Path, + signal: str, +) -> pd.DataFrame: + + path = (directory / str(shot)).with_suffix(".h5") + df = pd.read_hdf(path, key=signal) + + return pd.DataFrame(df) + + +def align( + df: pd.DataFrame, + start_time: float, + end_time: float, + fs: float, +) -> pd.DataFrame: + + # get sampling frequency + fs_raw = len(df) / (df.index[-1] - df.index[0]) + + # crop time + df = df.loc[(df.index >= start_time) & (df.index <= end_time)] + + # resample + num = len(df) + num = int(num * fs / fs_raw) + + df = pd.DataFrame( + {col: resample(df[col].values, num) for col in df.columns}, + index=np.linspace(df.index[0], df.index[-1], num) + ) + + # mark on-off states + start_nan = (df.index[0] - start_time) * fs + end_nan = (end_time - df.index[-1]) * fs + start_pad = pd.DataFrame( + 0, index=pd.RangeIndex(start=int(start_nan)), columns=df.columns) + end_pad = pd.DataFrame( + 0, index=pd.RangeIndex(start=int(len(df) + start_nan), stop=int(len(df) + start_nan + end_nan)), columns=df.columns) + + df_state = pd.DataFrame(True, index=df.index, columns=df.columns) + start_pad_state = pd.DataFrame(False, index=start_pad.index, columns=df.columns) + end_pad_state = pd.DataFrame(False, index=end_pad.index, columns=df.columns) + + df = pd.concat([start_pad, df, end_pad], ignore_index=True) + df_state = pd.concat([start_pad_state, df_state, end_pad_state], ignore_index=True) + df_state.columns = [f"{col}_state" for col in df.columns] + + # combine data with state + df = df.astype(np.float32) + df_state = df_state.astype(np.bool) + df = pd.concat([df, df_state], axis=1) + + # convert time to ms + df = df.rename_axis("time") + df.index = pd.to_timedelta(df.index, unit='ms') + + return df + + +def split( + df: pd.DataFrame, + window_ms: int, + hop_ms: int, + fs_khz: float, +) -> list[pd.DataFrame]: + + # Create sample indicies + num_samples = int((window_ms) * fs_khz) + hop_samples = int((hop_ms) * fs_khz) + + # Separate samples + samples = [] + for start in range( + 0, len(df) - num_samples + 1, hop_samples + ): + end = start + num_samples + sample = df.iloc[start:end] + if len(sample) == num_samples: + samples.append(sample) + + return samples + +def transform_individual_sample( + x: np.ndarray, + ) -> np.ndarray: + x = torch.from_numpy(x).float() + transformed = torch.stft( + x, + n_fft=1024, + hop_length=256, + window=torch.hann_window(1024), + return_complex=True + ) + transformed = torch.log(torch.abs(transformed).clamp(min=1e-10)) + transformed = torch.clip(transformed, min=-10, max=5).numpy() + return transformed + +def save_samples( + samples: list[pd.DataFrame], + directory: Path, + shot: int +) -> None: + directory.mkdir(parents=True, exist_ok=True) + for i, sample in enumerate(samples): + + # Remove columns ending with '_state' + sample_to_save = sample.loc[:, ~sample.columns.str.endswith('_state')] + + # Only save if not fully off (i.e., at least one True in any state col) + state_cols = [col for col in sample.columns if col.endswith('_state')] + if np.any(sample[state_cols].to_numpy()): + sample_array = sample_to_save.to_numpy().T + sample_array = sample_array.astype(np.float32) + sample_array = transform_individual_sample(sample_array) + with open(directory / f"{shot}_{i}.pkl", 'wb') as f: + pickle.dump(sample_array, f, protocol=pickle.HIGHEST_PROTOCOL) + + +def process_shot( + shot: int, + cfg: dict, + out_dir: Path, +) -> None: + + dfs = [] + for signal in cfg["signal"]: + signal_name, signal_abbr = signal + + try: + df = extract(shot=shot, directory=Path(cfg["raw_data_dir"]), signal=signal_name) + except FileNotFoundError: + print(f"Missing {shot} -- {signal_name}") + return + + try: + df.columns = [f"{signal_abbr}{col}" if col != "time" else col for col in df.columns] + except Exception as e: + print(f"Error renaming columns for shot {shot} and signal {signal_name}: {e}") + return + + try: + df = align(df, cfg["start_ms"], cfg["end_ms"], cfg["fs_khz"]) + except ValueError: + print(f"Error aligning data for shot {shot} and signal {signal_name}.") + return + + dfs.append(df) + + df = pd.concat(dfs, axis=1) + + # Split into windows + samples = split(df, cfg["window_ms"], cfg["hop_ms"], cfg["fs_khz"]) + print(f"Shot {shot} has {len(samples)} samples after splitting.") + + # Save to cache (only non-fully-off windows) + save_samples(samples, out_dir, shot) + print(f"Processed shot {shot} successfully.") + + return + + +def index_dataset(out_dir: Path) -> None: + + files = list(out_dir.glob("*.pkl")) + df_files = pd.DataFrame({'files': [str(file) for file in files]}) + df_files.to_pickle(out_dir / "index.pkl") + + print(f"Indexed {len(files)} files.") + + +def prepare_dataset(cfg: dict) -> None: + + cfg["num_samples"] = int((cfg["end_ms"] - cfg["start_ms"]) * cfg["fs_khz"]) + raw_data_dir = Path(cfg["raw_data_dir"]) + cache_dir = Path(cfg["output_dir"]) / "cache" + cache_dir.mkdir(parents=True, exist_ok=True) + + # Collect and sort all shot numbers + print(f"Collecting shots from {raw_data_dir}...") + all_shots = [ + int(p.stem) + for p in raw_data_dir.iterdir() + if p.suffix == ".h5"] + all_shots.sort() + + # if cfg["randomize_shots"]: + # np.random.seed(cfg["random_seed"]) + # all_shots = np.random.permutation(all_shots) + # all_shots = all_shots[:cfg["num_shots"]] + + # print(f"Processing {len(all_shots)} shots into cache...") + + mapper = ParallelMapper() + mapper(process_shot, [170000], cfg=cfg, out_dir=cache_dir) + + # for shot in tqdm(all_shots): # for debugging + # process_shot(shot, cfg, out_dir=cache_dir) + # break + + # Move cached files into train/test split + print("Splitting dataset into train and valid sets...") + all_files = list(cache_dir.glob("*.pkl")) + all_files.sort() + train_files, valid_files = train_test_split( + all_files, + test_size=cfg.get("train_test_split", 0.2), + random_state=cfg["random_seed"]) + + train_dir = Path(cfg["output_dir"]) / "train" + valid_dir = Path(cfg["output_dir"]) / "valid" + train_dir.mkdir(parents=True, exist_ok=True) + valid_dir.mkdir(parents=True, exist_ok=True) + + for f in train_files: + f.rename(train_dir / f.name) + for f in valid_files: + f.rename(valid_dir / f.name) + + # Index the datasets + index_dataset(train_dir) + index_dataset(valid_dir) + + # Remove cache directory after splitting + for f in cache_dir.glob("*"): f.unlink() + cache_dir.rmdir() + + print("Dataset preparation complete.") + +if __name__ == "__main__": + cfg = sample_cfg.copy() + prepare_dataset(cfg=cfg) \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/prepare2.py b/src/fusionaihub/datasets/prepare/prepare2.py new file mode 100644 index 0000000..67a8186 --- /dev/null +++ b/src/fusionaihub/datasets/prepare/prepare2.py @@ -0,0 +1,443 @@ +import re +import numpy as np +import pandas as pd +from pathlib import Path +from scipy.signal import resample, resample_poly +from sklearn.model_selection import train_test_split +from ...util.parmap import ParallelMapper +import logging +import joblib +import torch +from concurrent.futures import ProcessPoolExecutor +from typing import Optional + + +log = logging.getLogger(__name__) + +sample_cfg = { + "signal": [ # start with signals to be transformed. this is hacked on + ("magnetics_high_resolution", "mhr", True), + ("ece_cali", "ece", True), + ("co2_density", "co2", True), + ("gas", "gas", False), + ("ech", "ech", False), + ("p_inj", "pin", False), + ("t_inj", "tin", False), + ], + "randomize_shots": True, + "random_seed": 42, + "num_shots": 50, + "fs_khz": 500, + "ip_threshold": 1e-1, + "window_ms": 250, + "hop_ms": 50, + "remove_empty": True, + "train_test_split": 0.2, + "raw_data_dir": "/scratch/gpfs/EKOLEMEN/d3d_fusion_data", + "output_dir": "/scratch/gpfs/nc1514/FusionAIHub/data/foundation_v2", +} + + +def resample_nearest(y: np.ndarray, new_len: int) -> np.ndarray: + orig_len = len(y) + gcd = np.gcd(orig_len, new_len) + up = new_len // gcd + down = orig_len // gcd + # return resample_poly(y, up, down) + resampled = resample(y, new_len) + return np.asarray(resampled) + # return interp1d(np.linspace(0, 1, len(y)), y, kind='cubic')(np.linspace(0, 1, new_len)) + + +def extract( + shot: int, + directory: Path, + signal: str, +) -> pd.DataFrame: + + path = (directory / str(shot)).with_suffix(".h5") + df = pd.read_hdf(path, key=signal) + + return pd.DataFrame(df) + +def running_time( + directory: Path, + shot: int, + ip_threshold: float, +) -> tuple[float, float]: + + path = (directory / str(shot)).with_suffix(".h5") + with pd.HDFStore(path, 'r') as store: + df = store['ip']['ipsip'] + df = df.loc[df > ip_threshold] + start_time = df.index[0] + end_time = df.index[-1] + return start_time, end_time + +def align( + df: pd.DataFrame, + start_time: float, + end_time: float, + fs: float, +) -> pd.DataFrame: + + # get sampling frequency + fs_raw = len(df) / (df.index[-1] - df.index[0]) + + # crop time + df = df.loc[(df.index >= start_time) & (df.index <= end_time)] + + # resample + num = len(df) + num = int(num * fs / fs_raw) + + df = pd.DataFrame( + {col: resample(df[col].values, num) for col in df.columns}, + index=np.linspace(df.index[0], df.index[-1], num) + ) + + # mark on-off states + start_nan = (df.index[0] - start_time) * fs + end_nan = (end_time - df.index[-1]) * fs + start_pad = pd.DataFrame( + 0, index=pd.RangeIndex(start=int(start_nan)), columns=df.columns) + end_pad = pd.DataFrame( + 0, index=pd.RangeIndex(start=int(len(df) + start_nan), stop=int(len(df) + start_nan + end_nan)), columns=df.columns) + + df_state = pd.DataFrame(True, index=df.index, columns=df.columns) + start_pad_state = pd.DataFrame(False, index=start_pad.index, columns=df.columns) + end_pad_state = pd.DataFrame(False, index=end_pad.index, columns=df.columns) + + df = pd.concat([start_pad, df, end_pad], ignore_index=True) + df_state = pd.concat([start_pad_state, df_state, end_pad_state], ignore_index=True) + df_state.columns = [f"{col}_state" for col in df.columns] + + # combine data with state + df = df.astype(np.float32) + df_state = df_state.astype(np.bool) + df = pd.concat([df, df_state], axis=1) + + # convert time to ms + df = df.rename_axis("time") + + return df + + +def split( + df: pd.DataFrame, + window_ms: int, + hop_ms: int, + fs_khz: float, +) -> list[pd.DataFrame]: + + # Create sample indicies + num_samples = int((window_ms) * fs_khz) + hop_samples = int((hop_ms) * fs_khz) + + # Separate samples + samples = [] + for start in range( + 0, len(df) - num_samples + 1, hop_samples + ): + end = start + num_samples + sample = df.iloc[start:end] + if len(sample) == num_samples: + samples.append(sample) + + return samples + +def transform_individual_sample( + x: np.ndarray, + ) -> np.ndarray: + x_tensor = torch.from_numpy(x).float() + y = torch.stft( + x_tensor, + n_fft=1024, + hop_length=256, + window=torch.hann_window(1024), + return_complex=True + ) + y = torch.log(torch.abs(y)) + # y = torch.clip(y, min=-10, max=5) + return y.numpy() + + +def create_missing_signal_dataframes( + cfg: dict, + processed_signals: set, + reference_df: pd.DataFrame +) -> list[pd.DataFrame]: + """Create fully off dataframes for missing signals using reference dataframe structure.""" + + missing_dfs = [] + + for signal in cfg["signal"]: + signal_name, signal_abbr, do_transform = signal + if signal_abbr not in processed_signals: + print(f"Creating fully off dataframe for missing signal {signal_name}") + + # Create off dataframe by copying structure and zeroing values + off_df = reference_df.copy() + + # Get columns that belong to the reference signal (to replace with new signal columns) + ref_signal_cols = [col for col in off_df.columns if not col.endswith('_state')] + + # Create new column names for the missing signal + new_cols = {} + new_state_cols = {} + + for i, col in enumerate(ref_signal_cols): + new_col_name = f"{signal_abbr}col{i}" + new_cols[col] = new_col_name + new_state_cols[f"{col}_state"] = f"{new_col_name}_state" + + # Rename columns to match the missing signal + off_df = off_df.rename(columns={**new_cols, **new_state_cols}) + + # Set all data columns to 0 and all state columns to False + for col in off_df.columns: + if col.endswith('_state'): + off_df[col] = False + else: + off_df[col] = 0.0 + + missing_dfs.append(off_df) + + return missing_dfs + + +def transform_samples( + samples: list[pd.DataFrame], + directory: Path, + signal_config: list[tuple], + shot: int, +) -> list[dict]: + directory.mkdir(parents=True, exist_ok=True) + print(f"Processing {len(samples)} samples for shot {shot}") + samples_dict = [] + + # Create mapping from signal abbreviation to whether it should be transformed + transform_map = {} + for signal_name, signal_abbr, should_transform in signal_config: + transform_map[signal_abbr] = should_transform + + for i, sample in enumerate(samples): + + # Remove columns ending with '_state' + sample_to_save = sample.loc[:, ~sample.columns.str.endswith('_state')] + + # Only save if not fully off (i.e., at least one True in any state col) + state_cols = [col for col in sample.columns if col.endswith('_state')] + if np.any(sample[state_cols].to_numpy()): + + # First pass: apply transformations and collect results + sample_dict = {} + transformed_sample = None + original_time_length = len(sample_to_save) + + for col in sample_to_save.columns: + # Convert each column to float32 numpy array + col_array = sample_to_save[col].values.astype(np.float32) + + # Determine if this column should be transformed based on signal abbreviation + should_transform = False + for signal_abbr in transform_map.keys(): + if col.startswith(signal_abbr): + should_transform = transform_map[signal_abbr] + break + + if should_transform: + transformed_array = transform_individual_sample(col_array) + sample_dict[col] = transformed_array + # Store an example transformed sample to get target dimensions + if transformed_sample is None: + transformed_sample = transformed_array + print(f"Reference transformed sample shape: {transformed_array.shape}") + else: + # Store original array for now, will resample later + sample_dict[col] = col_array + + # Second pass: resample non-transformed samples to match transformed dimensions + if transformed_sample is not None: + target_width = transformed_sample.shape[-1] # Last dimension is time + # Calculate target sample frequency based on transformed sample + target_fs = target_width / original_time_length + print(f"Target frequency ratio: {target_fs:.4f} (target width: {target_width}, original length: {original_time_length})") + + for col in sample_dict.keys(): + # Check if this column was transformed + should_transform = False + for signal_abbr in transform_map.keys(): + if col.startswith(signal_abbr): + should_transform = transform_map[signal_abbr] + break + + if not should_transform: + # Resample non-transformed data to match target width + original_array = sample_dict[col] + resampled_array = resample_nearest(original_array, target_width) + + # Crop end if needed to ensure exact match + if len(resampled_array) > target_width: + resampled_array = resampled_array[:target_width] + elif len(resampled_array) < target_width: + # Pad with zeros if too short (shouldn't happen with resample_nearest) + pad_width = target_width - len(resampled_array) + resampled_array = np.pad(resampled_array, (0, pad_width), mode='constant') + + sample_dict[col] = resampled_array.astype(np.float32) + print(f"Resampled {col} from {len(original_array)} to {len(resampled_array)}") + + samples_dict.append(sample_dict) + print(f"Sample {i} processed with {len(sample_dict)} signals") + + return samples_dict + +def save_samples( + samples: list[dict], + directory: Path, + shot: int +) -> None: + directory.mkdir(parents=True, exist_ok=True) + print(f"Saving {len(samples)} samples to {directory}") + for i, sample in enumerate(samples): + # Save using joblib + joblib.dump(sample, directory / f"{shot}_{i}.pkl", compress=True) + +def process_shot( + shot: int, + cfg: dict, + out_dir: Path, +) -> None: + + dfs = [] + start_time = None + end_time = None + + try: + start_time, end_time = running_time( + directory=Path(cfg["raw_data_dir"]), + shot=shot, + ip_threshold=cfg["ip_threshold"] + ) + reference_len = int((end_time - start_time) * cfg["fs_khz"]) + print(f"Running time for shot {shot}: {start_time} to {end_time}") + except Exception as e: + print(f"Error: Could not determine running time for shot {shot}: {e}") + return + + # Process each signal and track which ones succeeded + processed_signals = set() + + for signal in cfg["signal"]: + signal_name, signal_abbr, is_transformed = signal + df = None + + try: + # Try to extract and process the signal + df = extract(shot=shot, directory=Path(cfg["raw_data_dir"]), signal=signal_name) + df.columns = [f"{signal_abbr}{col}" if col != "time" else col for col in df.columns] + df = align(df, start_time, end_time, cfg["fs_khz"]) + processed_signals.add(signal_abbr) + dfs.append(df) + print(f"Successfully processed signal {signal_name} for shot {shot}") + + except Exception as e: + print(f"Error processing signal {signal_name} for shot {shot}: {e}") + + if not dfs: + print(f"Error: No dataframes created for shot {shot}") + return + + # For missing signals, create "fully off" dataframes using the structure of the last dataframe + if len(processed_signals) < len(cfg["signal"]): + reference_df = dfs[-1] # Use the last successfully processed dataframe as reference + missing_dfs = create_missing_signal_dataframes(cfg, processed_signals, reference_df) + dfs.extend(missing_dfs) + + df = pd.concat(dfs, axis=1, join='inner') + + num_samples = len(df) + new_index = np.linspace(start_time, end_time, num_samples) + df.index = new_index + df.index = pd.to_timedelta(df.index, unit='ms') + + samples = [df] # no splitting for this dataset + print(f"Shot {shot} has {len(samples)} samples after splitting.") + samples_dict = transform_samples(samples, out_dir, cfg["signal"], shot) + + save_samples(samples_dict, out_dir, shot) + print(f"Processed shot {shot} successfully with {len(cfg['signal'])} signals.") + + return + + +def index_dataset(out_dir: Path) -> None: + + files = list(out_dir.glob("*.pkl")) + df_files = pd.DataFrame({'files': [str(file) for file in files]}) + df_files.to_pickle(out_dir / "index.pkl") + + print(f"Indexed {len(files)} files.") + + +def prepare_dataset(cfg: dict) -> None: + + raw_data_dir = Path(cfg["raw_data_dir"]) + cache_dir = Path(cfg["output_dir"]) / "cache" + cache_dir.mkdir(parents=True, exist_ok=True) + + # Collect and sort all shot numbers + print(f"Collecting shots from {raw_data_dir}...") + all_shots = [ + int(p.stem) + for p in raw_data_dir.iterdir() + if p.suffix == ".h5"] + all_shots.sort() + + # if cfg["randomize_shots"]: + # np.random.seed(cfg["random_seed"]) + # all_shots = np.random.permutation(all_shots) + # all_shots = all_shots[:cfg["num_shots"]] + + # print(f"Processing {len(all_shots)} shots into cache...") + + mapper = ParallelMapper() + mapper(process_shot, [170000], cfg=cfg, out_dir=cache_dir) + + # for shot in tqdm(all_shots): # for debugging + # process_shot(shot, cfg, out_dir=cache_dir) + # break + + # Move cached files into train/test split + print("Splitting dataset into train and valid sets...") + all_files = list(cache_dir.glob("*.pkl")) + all_files.sort() + train_files, valid_files = train_test_split( + all_files, + test_size=cfg.get("train_test_split", 0.2), + random_state=cfg["random_seed"]) + + train_dir = Path(cfg["output_dir"]) / "train" + valid_dir = Path(cfg["output_dir"]) / "valid" + train_dir.mkdir(parents=True, exist_ok=True) + valid_dir.mkdir(parents=True, exist_ok=True) + + for f in train_files: + f.rename(train_dir / f.name) + for f in valid_files: + f.rename(valid_dir / f.name) + + # Index the datasets + index_dataset(train_dir) + index_dataset(valid_dir) + + # Remove cache directory after splitting + for f in cache_dir.glob("*"): f.unlink() + cache_dir.rmdir() + + print("Dataset preparation complete.") + +if __name__ == "__main__": + cfg = sample_cfg.copy() + prepare_dataset(cfg=cfg) \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/prepare_dataset.py b/src/fusionaihub/datasets/prepare/prepare_dataset.py new file mode 100644 index 0000000..152b0fd --- /dev/null +++ b/src/fusionaihub/datasets/prepare/prepare_dataset.py @@ -0,0 +1,159 @@ +#!/usr/bin/env python3 +""" +Main script for fusion dataset preparation. + +This script orchestrates the complete dataset preparation pipeline using +modular components and YAML configuration. +""" + +import yaml +import numpy as np +from pathlib import Path +from sklearn.model_selection import train_test_split +from typing import Optional +from ...util.parmap import ParallelMapper + +from .core import process_shot_stft, index_dataset + + +def load_config(config_path: Optional[str] = None) -> dict: + """ + Load configuration from YAML file. + + Args: + config_path: Path to YAML configuration file. + If None, uses default.yaml in config directory. + + Returns: + Configuration dictionary + """ + if config_path is None: + config_path = str(Path(__file__).parent / "config" / "default.yaml") + + with open(config_path, 'r') as f: + cfg = yaml.safe_load(f) + + return cfg + + +def prepare_dataset(cfg: dict) -> None: + """ + Prepare the complete fusion dataset using the modular pipeline. + + This function orchestrates the complete dataset preparation workflow: + 1. Collects shot numbers from raw data directory + 2. Processes shots in parallel using the modular pipeline + 3. Splits processed data into train/validation sets + 4. Creates dataset indices for both sets + + Args: + cfg: Configuration dictionary loaded from YAML + """ + raw_data_dir = Path(cfg["raw_data_dir"]) + cache_dir = Path(cfg["output_dir"]) / "cache" + cache_dir.mkdir(parents=True, exist_ok=True) + + # Collect and sort all shot numbers + print(f"Collecting shots from {raw_data_dir}...") + all_shots = [ + int(p.stem) + for p in raw_data_dir.iterdir() + if p.suffix == ".h5" + ] + all_shots.sort() + + # Apply shot selection and randomization if configured + if cfg.get("randomize_shots", False): + np.random.seed(cfg["random_seed"]) + all_shots = np.random.permutation(all_shots) + + if cfg.get("num_shots") is not None: + all_shots = all_shots[:cfg["num_shots"]] + + print(f"Processing {len(all_shots)} shots into cache...") + + # Clean up existing cache directory if it exists + if cache_dir.exists(): + import shutil + print(f"Removing existing cache directory: {cache_dir}") + shutil.rmtree(cache_dir) + cache_dir.mkdir(parents=True, exist_ok=True) + # Process shots using parallel mapping + # For debugging, process single shot + # process_shot_stft(170000, cfg, cache_dir) + mapper = ParallelMapper() + mapper(process_shot_stft, all_shots[:10], cfg=cfg, out_dir=cache_dir) + + # For production, uncomment this line and comment the above + # mapper(process_shot_stft, all_shots, cfg=cfg, out_dir=cache_dir) + + # Move cached files into train/test split + print("Splitting dataset into train and valid sets...") + all_files = list(cache_dir.glob("*.pkl")) + all_files.sort() + + if len(all_files) == 0: + print("Warning: No processed files found. Dataset preparation incomplete.") + return + + # Handle edge case where there are too few files for train-test split + if len(all_files) == 1: + print("Warning: Only 1 file found. Placing in train directory.") + train_files = all_files + valid_files = [] + else: + train_files, valid_files = train_test_split( + all_files, + test_size=cfg.get("train_test_split", 0.2), + random_state=cfg["random_seed"] + ) + + # Create train and validation directories + train_dir = Path(cfg["output_dir"]) / "train" + valid_dir = Path(cfg["output_dir"]) / "valid" + train_dir.mkdir(parents=True, exist_ok=True) + valid_dir.mkdir(parents=True, exist_ok=True) + + # Move files to appropriate directories + for f in train_files: + f.rename(train_dir / f.name) + for f in valid_files: + f.rename(valid_dir / f.name) + + # Index the datasets + index_dataset(train_dir) + index_dataset(valid_dir) + + # Clean up cache directory + for f in cache_dir.glob("*"): + f.unlink() + cache_dir.rmdir() + + print("Dataset preparation complete.") + print(f"Training samples: {len(train_files)}") + print(f"Validation samples: {len(valid_files)}") + + +def main(): + """Main entry point for the dataset preparation script.""" + import argparse + + parser = argparse.ArgumentParser(description="Prepare fusion dataset") + parser.add_argument( + "--config", + type=str, + default=None, + help="Path to configuration YAML file (default: config/default.yaml)" + ) + + args = parser.parse_args() + + # Load configuration + cfg = load_config(args.config) + + # Prepare dataset + prepare_dataset(cfg) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/fusion_ai_hub/datasets/toy_loader/load.py b/src/fusionaihub/datasets/toy_loader/load.py similarity index 100% rename from src/fusion_ai_hub/datasets/toy_loader/load.py rename to src/fusionaihub/datasets/toy_loader/load.py diff --git a/src/fusion_ai_hub/feature/__init__.py b/src/fusionaihub/display/__init__.py similarity index 100% rename from src/fusion_ai_hub/feature/__init__.py rename to src/fusionaihub/display/__init__.py diff --git a/src/fusion_ai_hub/display/display.py b/src/fusionaihub/display/display.py similarity index 100% rename from src/fusion_ai_hub/display/display.py rename to src/fusionaihub/display/display.py diff --git a/src/fusion_ai_hub/display/specshow.py b/src/fusionaihub/display/specshow.py similarity index 100% rename from src/fusion_ai_hub/display/specshow.py rename to src/fusionaihub/display/specshow.py diff --git a/src/fusion_ai_hub/display/waveshow.py b/src/fusionaihub/display/waveshow.py similarity index 100% rename from src/fusion_ai_hub/display/waveshow.py rename to src/fusionaihub/display/waveshow.py diff --git a/src/fusion_ai_hub/sampling/__init__.py b/src/fusionaihub/feature/__init__.py similarity index 100% rename from src/fusion_ai_hub/sampling/__init__.py rename to src/fusionaihub/feature/__init__.py diff --git a/src/fusion_ai_hub/util/__init__.py b/src/fusionaihub/sampling/__init__.py similarity index 100% rename from src/fusion_ai_hub/util/__init__.py rename to src/fusionaihub/sampling/__init__.py diff --git a/src/fusion_ai_hub/sampling/match_times.py b/src/fusionaihub/sampling/match_times.py similarity index 100% rename from src/fusion_ai_hub/sampling/match_times.py rename to src/fusionaihub/sampling/match_times.py diff --git a/src/fusionaihub/util/__init__.py b/src/fusionaihub/util/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fusionaihub/util/parmap.py b/src/fusionaihub/util/parmap.py new file mode 100644 index 0000000..2e98e75 --- /dev/null +++ b/src/fusionaihub/util/parmap.py @@ -0,0 +1,170 @@ +"""Defines function parmap, a parallel version of map.""" +from ctypes import c_ulong +import multiprocessing as mp +from multiprocessing.sharedctypes import Value +import gc +import sys + + +def _identity(x): + """The identity function.""" + return x + + +class ProgressBar: + """ + Usage : update_progress(progress) + An ASCII progression bar. + Values of input should be floats or ints ranging from 0 to ntot (default 1). + """ + def __init__(self, output=None, bar_length=50, ntot=None): + """ + "output" is a function that takes a string as input and prints it (defaults to stdout). + bar_length is the length of the progress bar in characters. + ntot is the maximum value of the progress bar (default 1). + """ + self.output = output + self.bar_length = bar_length + self.ntot = ntot + self._last = -1 + + def set_ntot(self, value): + self.ntot = value + + def _update(self, progress): + status = "" + try: + progress = float(progress) + except (ValueError, TypeError): + progress = 0 + status = "error: progress var must be float\r\n" + if self.ntot is not None: + progress /= self.ntot + if progress < self._last: + status = "Non monotonic" + self._last = progress + if progress < 0: + progress = 0 + status = "Halt...\r\n" + if progress >= 1: + progress = 1 + status = "Done...\r\n" + block = int(round(self.bar_length * progress)) + text = "\rPercent: [{0}] {1:.3f}% {2}" + text = text.format("#" * block + "-" * (self.bar_length - block), + progress * 100, status) + if self.output is None: + sys.stdout.write(text) + sys.stdout.flush() + else: + self.output(text) + + def __call__(self, progress): + """Update the progress bar to specified value.""" + self._update(progress) + + +class ParallelMapper: + """Parallel version of map.""" + def __init__(self, nprocs=None, progress=True, fetcher=_identity): + """ + nprocs is the number of processors to use (default is mp.cpu_count()). + progress is a boolean or a ProgressBar object. If True, a ProgressBar is used. + fetcher is a function that fetches the data from the iterable, defaults to identity function. + """ + if nprocs is None: + nprocs = mp.cpu_count() + self.nprocs = nprocs + self.fetcher = fetcher + self._counter = Value(c_ulong, 0) + self._set_counter(0) + if progress is True: + progress = ProgressBar() + self._progress = progress + + def progress(self, value, ntot=None): + """Update the progress bar to specified value.""" + if self._progress: + if ntot and value == 0: + self._progress.set_ntot(ntot) + self._progress(value) + + def _set_counter(self, value): + """Set the counter to value.""" + with self._counter.get_lock(): + self._counter.value = value + + def _get_counter(self): + """Return the current value of the counter.""" + with self._counter.get_lock(): + out = self._counter.value + return out + + def _inc_counter(self, value=1): + """Increment the counter by value and return the new value.""" + with self._counter.get_lock(): + self._counter.value += value + out = self._counter.value + return out + + def _fun(self, f, q_in, q_out, **kwargs): + """Function to be executed by each process. Wrapper for f and progress bar.""" + while True: + i, x = q_in.get() + if i is None: + break + q_out.put((i, f(self.fetcher(x), **kwargs))) + # Try to free memory + gc.collect() + self.progress(self._inc_counter()) + + def parmap(self, f, x, **kwargs): + """ + Parallelizes the computation of 'map(f, X)' over 'nprocs' processors, + where f is a function, X is an iterable. Returns list of length len(X). + """ + self.progress(0, len(x)) + if self.nprocs == 1: + return list(map(f, map(self.fetcher, x))) + + # Create input/output queues + q_in = mp.Queue(1) + q_out = mp.Queue() + + # Create and start processes + proc = [mp.Process(target=self._fun, args=(f, q_in, q_out), kwargs=kwargs) + for _ in range(self.nprocs)] + for p in proc: + p.daemon = True + p.start() + + # Feed/execute processes + sent = [q_in.put((i, x)) for i, x in enumerate(x)] + [q_in.put((None, None)) for _ in range(self.nprocs)] + res = [q_out.get() for _ in range(len(sent))] + + [p.join() for p in proc] + + # compile and return results + out = [x for i, x in sorted(res)] + for p in proc: + p.close() + del p + del (proc, res, sent, q_in, q_out) + gc.collect() + return out + + def __call__(self, f, x, **kwargs): + """ + Parallelizes the computation of 'map(f, X)' over 'nprocs' processors, + where f is a function, X is an iterable. Returns list of length len(X). + """ + return self.parmap(f, x, **kwargs) + + +def parmap(f, x, nprocs=None, **kwargs): + """ + Parallelizes the computation of 'map(f, x)' over 'nprocs' processors, + where f is a function, x is an iterable. Returns list of length len(x). + """ + return ParallelMapper(nprocs=nprocs, **kwargs)(f, x) \ No newline at end of file diff --git a/src/fusion_ai_hub/util/utils.py b/src/fusionaihub/util/utils.py similarity index 100% rename from src/fusion_ai_hub/util/utils.py rename to src/fusionaihub/util/utils.py diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..b805b42 --- /dev/null +++ b/uv.lock @@ -0,0 +1,3052 @@ +version = 1 +revision = 2 +requires-python = ">=3.9" +resolution-markers = [ + "python_full_version >= '3.12' and sys_platform == 'darwin'", + "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version == '3.11.*' and sys_platform == 'darwin'", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and 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Modify Jupyter notebooks to reset execution counts, streamline data loading, and enhance visualization. Refactor dataset preparation scripts to improve logging, configuration handling, and remove deprecated modules. --- .gitignore | 2 + commands/prepare_data.sh | 16 + notebooks/accessing_data.ipynb | 152 ++---- notebooks/data_preparation.ipynb | 473 +++++++----------- src/fusionaihub/datasets/prepare/__main__.py | 4 + .../datasets/prepare/config/default.yaml | 26 +- .../datasets/prepare/config/raw.yaml | 39 ++ .../datasets/prepare/core/__init__.py | 30 -- .../datasets/prepare/core/dataset_utils.py | 84 ---- .../prepare/core/sample_processing.py | 160 ------ .../datasets/prepare/core/shot_processing.py | 122 ----- .../prepare/{core => }/data_extraction.py | 39 +- .../datasets/prepare/dataset_utils.py | 30 ++ .../datasets/prepare/logging_config.py | 64 +++ src/fusionaihub/datasets/prepare/prepare.py | 286 ----------- src/fusionaihub/datasets/prepare/prepare2.py | 443 ---------------- .../datasets/prepare/prepare_dataset.py | 84 +++- .../datasets/prepare/sample_processing.py | 95 ++++ .../datasets/prepare/shot_processing.py | 193 +++++++ .../prepare/{core => }/signal_processing.py | 58 ++- 20 files changed, 801 insertions(+), 1599 deletions(-) create mode 100644 commands/prepare_data.sh create mode 100644 src/fusionaihub/datasets/prepare/__main__.py create mode 100644 src/fusionaihub/datasets/prepare/config/raw.yaml delete mode 100644 src/fusionaihub/datasets/prepare/core/__init__.py delete mode 100644 src/fusionaihub/datasets/prepare/core/dataset_utils.py delete mode 100644 src/fusionaihub/datasets/prepare/core/sample_processing.py delete mode 100644 src/fusionaihub/datasets/prepare/core/shot_processing.py rename src/fusionaihub/datasets/prepare/{core => }/data_extraction.py (74%) create mode 100644 src/fusionaihub/datasets/prepare/dataset_utils.py create mode 100644 src/fusionaihub/datasets/prepare/logging_config.py delete mode 100644 src/fusionaihub/datasets/prepare/prepare.py delete mode 100644 src/fusionaihub/datasets/prepare/prepare2.py create mode 100644 src/fusionaihub/datasets/prepare/sample_processing.py create mode 100644 src/fusionaihub/datasets/prepare/shot_processing.py rename src/fusionaihub/datasets/prepare/{core => }/signal_processing.py (51%) diff --git a/.gitignore b/.gitignore index faa5f5e..0d9cab5 100644 --- a/.gitignore +++ b/.gitignore @@ -159,4 +159,6 @@ cython_debug/ # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ +data/ +logs/ *.pkl \ No newline at end of file diff --git a/commands/prepare_data.sh b/commands/prepare_data.sh new file mode 100644 index 0000000..28bc22f --- /dev/null +++ b/commands/prepare_data.sh @@ -0,0 +1,16 @@ +#!/bin/bash +#SBATCH --job-name=dataprep # create a short name for your job +#SBATCH --nodes=1 # node count +#SBATCH --ntasks=1 # total number of tasks across all nodes +#SBATCH --cpus-per-task=96 # cpu-cores per task (>1 if multi-threaded tasks) +#SBATCH --mem=100GB # memory per node +#SBATCH --time=01:00:00 # maximum time needed (HH:MM:SS) +#SBATCH --output=logs/%A_%a.out +#SBATCH --error=logs/%A_%a.err + +# Set environment +module purge +source .venv/bin/activate + +# Run pipeline +srun python -m fusionaihub.datasets.prepare \ No newline at end of file diff --git a/notebooks/accessing_data.ipynb b/notebooks/accessing_data.ipynb index 301e1b0..86fbc6f 100644 --- a/notebooks/accessing_data.ipynb +++ b/notebooks/accessing_data.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 65, + "execution_count": 1, "id": "914fa271", "metadata": {}, "outputs": [], @@ -13,104 +13,50 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "id": "836b5c67", "metadata": {}, "outputs": [], "source": [ + "import pandas as pd\n", "import joblib\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", - "execution_count": 69, - "id": "af64eb59", + "execution_count": null, + "id": "d564239f", "metadata": {}, "outputs": [], "source": [ - "file = \"/scratch/gpfs/nc1514/FusionAIHub/data/foundation_v2/train/170000_0.pkl\"\n", - "\n", - "with open(file, 'rb') as f:\n", - " data = joblib.load(f)\n", - " data1 = data['co2v1']\n", - " data2 = data['mhrb4']" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "0818d229", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['mhrb1', 'mhrb2', 'mhrb3', 'mhrb4', 'mhrb5', 'mhrb6', 'mhrb7', 'mhrb8', 'ece01', 'ece02', 'ece03', 'ece04', 'ece05', 'ece06', 'ece07', 'ece08', 'ece09', 'ece10', 'ece11', 'ece12', 'ece13', 'ece14', 'ece15', 'ece16', 'ece17', 'ece18', 'ece19', 'ece20', 'ece21', 'ece22', 'ece23', 'ece24', 'ece25', 'ece26', 'ece27', 'ece28', 'ece29', 'ece30', 'ece31', 'ece32', 'ece33', 'ece34', 'ece35', 'ece36', 'ece37', 'ece38', 'ece39', 'ece40', 'ece41', 'ece42', 'ece43', 'ece44', 'ece45', 'ece46', 'ece47', 'ece48', 'co2r0', 'co2v1', 'co2v2', 'co2v3', 'gasgasa', 'gasgasb', 'gasgasc', 'gasgasd', 'gasgase', 'echechpwr', 'echechpwrc', 'echecleifpwrc', 'echecleipolang', 'echecleixmfrac', 'echeclukfpwrc', 'echeclukpolang', 'echeclukxmfrac', 'echecr2dfpwrc', 'echecr2dpolang', 'echecr2dxmfrac', 'pinpinjf_15l', 'pinpinjf_15r', 'pinpinjf_21l', 'pinpinjf_21r', 'pinpinjf_30l', 'pinpinjf_30r', 'pinpinjf_33l', 'pinpinjf_33r', 'tintinj_15l', 'tintinj_15r', 'tintinj_21l', 'tintinj_21r', 'tintinj_30l', 'tintinj_30r', 'tintinj_33l', 'tintinj_33r'])" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "36eb742e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(513, 11066)" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data['mhrb4'].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "59d841a1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(11066,)" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data['gasgasa'].shape" + "files = pd.read_csv(\"/scratch/gpfs/EKOLEMEN/hackathon/foundation25/train/index.csv\").values[:,0]" ] }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 45, "id": "434b288f", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mhr (8, 513, 13195)\n", + "ece (48, 513, 13195)\n", + "co2 (4, 513, 13195)\n", + "gas (5, 1, 13195)\n", + "ech (11, 1, 13195)\n", + "pin (8, 1, 13195)\n", + "tin (8, 1, 13195)\n" + ] + }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -118,54 +64,22 @@ } ], "source": [ - "plt.subplot(2, 1, 1)\n", - "plt.imshow(data['mhrb4'], aspect='auto', origin='lower')\n", + "file_name = files[-1]\n", + "data = joblib.load(file_name)\n", + "for key, value in data.items():\n", + " print(key, value.shape)\n", + "plt.subplot(3, 1, 1)\n", + "plt.imshow(data['mhr'][4], aspect='auto', origin='lower')\n", "plt.title('mhrb4')\n", - "plt.subplot(2, 1, 2)\n", - "plt.imshow(data['co2r0'], aspect='auto', origin='lower')\n", + "plt.subplot(3, 1, 2)\n", + "plt.imshow(data['co2'][0], aspect='auto', origin='lower')\n", "plt.title('co2r0')\n", + "plt.subplot(3, 1, 3)\n", + "plt.imshow(data['pin'][:,0,:], aspect='auto', origin='lower', interpolation='none')\n", + "plt.title('pin')\n", "plt.tight_layout()\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "74391de4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(data['gasgasa'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4a656412", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/notebooks/data_preparation.ipynb b/notebooks/data_preparation.ipynb index 4db2cbe..534d2fa 100644 --- a/notebooks/data_preparation.ipynb +++ b/notebooks/data_preparation.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "9b8f64ca", "metadata": {}, "outputs": [], @@ -21,366 +21,249 @@ "import pandas as pd\n", "from pathlib import Path\n", "import matplotlib.pyplot as plt\n", + "import yaml\n", + "\n", + "shot_number = 170000\n", + "yaml_path = \"/scratch/gpfs/nc1514/FusionAIHub/src/fusionaihub/datasets/prepare/config/default.yaml\"\n", + "with open(yaml_path, 'r') as f:\n", + " cfg = yaml.safe_load(f)\n", "\n", - "from fusionaihub.datasets.prepare import prepare2 as p2" + "from fusionaihub.datasets.prepare.data_extraction import (\n", + " extract_signal, \n", + " extract_running_time, \n", + " align_signal,\n", + ")\n", + "from fusionaihub.datasets.prepare.sample_processing import (\n", + " split_samples,\n", + " remove_empty_samples,\n", + " save_sample,\n", + ")\n", + "from fusionaihub.datasets.prepare.signal_processing import (\n", + " identity_transform,\n", + " stft_transform,\n", + " resample_transform,\n", + ")" ] }, { "cell_type": "code", - "execution_count": 50, - "id": "4baa86aa", + "execution_count": 3, + "id": "c8e825ce", "metadata": {}, "outputs": [], "source": [ - "cfg = p2.sample_cfg\n", - "shot = 170000" + "start_time, end_time = extract_running_time(\n", + " shot_number=shot_number,\n", + " directory=Path(cfg[\"raw_data_dir\"]),\n", + " ip_threshold=cfg[\"ip_threshold\"],\n", + " start_time=cfg[\"start_time\"],\n", + " end_time=cfg[\"end_time\"],\n", + " )" ] }, { "cell_type": "code", - "execution_count": 141, - "id": "742dfd1b", + "execution_count": 4, + "id": "a68aaa6f", "metadata": {}, "outputs": [], "source": [ - "import h5py" + "dfs = []\n", + "missing_signals = []\n", + "for signal in cfg[\"signal\"]:\n", + " try:\n", + " df = extract_signal(\n", + " shot_number=shot_number,\n", + " directory=Path(cfg[\"raw_data_dir\"]),\n", + " signal=signal['name'], \n", + " start_time=start_time,\n", + " end_time=end_time,\n", + " )\n", + " df.columns = [\n", + " f\"{signal['abbr']}_{col}\" if col != \"time\" else col\n", + " for col in range(len(df.columns))\n", + " ]\n", + "\n", + " # Add a column to the dataframe for this signal indicating if a transform is present.\n", + " # We'll use the signal's abbreviation to name the column, e.g., 'IP_transform'\n", + " df = align_signal(\n", + " df=df,\n", + " start_time=start_time,\n", + " end_time=end_time,\n", + " fs=cfg[\"fs_khz\"],\n", + " )\n", + " dfs.append(df)\n", + " except Exception as e:\n", + " missing_signals.append((signal['name'], signal['abbr']))" ] }, { "cell_type": "code", - "execution_count": 146, - "id": "dece5ac7", + "execution_count": 55, + "id": "fe35d6ca", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " CLASS: b'GROUP'\n", - " TITLE: Empty(dtype=dtype('S1'))\n", - " VERSION: b'1.0'\n", - " axis0_variety: b'regular'\n", - " axis1_variety: b'regular'\n", - " block0_items_variety: b'regular'\n", - " encoding: b'UTF-8'\n", - " end_time_ms: 13100.0\n", - " errors: b'strict'\n", - " missing_channels: Empty(dtype=dtype('S1'))\n", - " nblocks: 1\n", - " ndim: 2\n", - " pandas_type: b'frame'\n", - " pandas_version: b'0.15.2'\n", - " r_coordinates: b'N.'\n", - " sampling_frequency_kHz: 100.00000223517424\n", - " start_time_ms: 0.0\n", - " z_coordinates: b'N.'\n" - ] - } - ], + "outputs": [], "source": [ - "import h5py\n", - "\n", - "def get_h5_attrs(directory, shot):\n", - " with h5py.File(directory + f'/{shot}.h5', 'r') as f:\n", - " mhr_group = f['p_inj']\n", - " for attr_name, attr_value in mhr_group.attrs.items():\n", - " print(f\" {attr_name}: {attr_value}\")\n", - "\n", - "# Get attrs for shot 170000\n", - "get_h5_attrs(cfg[\"raw_data_dir\"], 170000)" + "df = pd.concat(dfs, axis=1, join='inner')\n", + "for signal_name, signal_abbr in missing_signals:\n", + " df[signal_abbr] = 0.0\n", + " df[f\"{signal_abbr}_state\"] = False" ] }, { "cell_type": "code", - "execution_count": 167, - "id": "484333c0", + "execution_count": 56, + "id": "87a1b47e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['/bes', '/bes_slow', '/beta', '/cer_amp', '/cer_amp_error', '/cer_coord_phi', '/cer_coord_r', '/cer_coord_z', '/cer_fz', '/cer_nz', '/cer_rot', '/cer_rot_error', '/cer_ti', '/cer_ti_error', '/cer_vb', '/cer_vb_error', '/cer_zeff', '/co2_density', '/co2_density_slow', '/co2_phase', '/coil_field_strength', '/d_alpha', '/divertor_geo', '/e_dens_fit', '/e_temp_fit', '/ece_cali', '/ece_slow', '/ech', '/gas', '/i_dens_fit', '/i_temp_fit', '/ip', '/mag_b0', '/mag_geo_para', '/mag_mode_number', '/mag_others', '/mag_pcb_coil', '/magnetics', '/magnetics_high_resolution', '/mse', '/neutron', '/other_profiles', '/p_inj', '/pressure', '/psi_r_z', '/q_psi', '/rho_qmin', '/rmp_current', '/ssi', '/t_inj', '/t_rot_fit', '/ts_core_density', '/ts_core_density_error', '/ts_core_temperature', '/ts_core_temperature_error', '/ts_divertor_density', '/ts_divertor_density_error', '/ts_divertor_temperature', '/ts_divertor_temperature_error', '/ts_tangential_density', '/ts_tangential_density_error', '/ts_tangential_temperature', '/ts_tangential_temperature_error']\n" - ] - }, - { - "data": { - "text/html": [ - "
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Time [ms]
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-8053.549805-0.0006170.0048860.0067810.0109960.003701
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-8053.450195-0.0061680.0024430.002466-0.000611-0.001234
-8053.3999020.0086350.0183230.0049320.0122170.006168
..................
11606.9501950.0037010.007940-0.001849-0.0012220.004318
11607.000000-0.014185-0.000611-0.017877-0.010996-0.012953
11607.0498050.0061680.0183230.0000000.001222-0.001234
11607.099609-0.004317-0.001222-0.007398-0.007330-0.011719
11607.1503910.0000000.004886-0.0024660.000000-0.000617
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393216 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " gasa gasb gasc gasd gase\n", - "Time [ms] \n", - "-8053.600098 0.007401 0.010383 0.004932 0.011607 0.008635\n", - "-8053.549805 -0.000617 0.004886 0.006781 0.010996 0.003701\n", - "-8053.500000 0.014802 0.007940 0.011096 0.008552 0.009252\n", - "-8053.450195 -0.006168 0.002443 0.002466 -0.000611 -0.001234\n", - "-8053.399902 0.008635 0.018323 0.004932 0.012217 0.006168\n", - "... ... ... ... ... ...\n", - " 11606.950195 0.003701 0.007940 -0.001849 -0.001222 0.004318\n", - " 11607.000000 -0.014185 -0.000611 -0.017877 -0.010996 -0.012953\n", - " 11607.049805 0.006168 0.018323 0.000000 0.001222 -0.001234\n", - " 11607.099609 -0.004317 -0.001222 -0.007398 -0.007330 -0.011719\n", - " 11607.150391 0.000000 0.004886 -0.002466 0.000000 -0.000617\n", - "\n", - "[393216 rows x 5 columns]" - ] - }, - "execution_count": 167, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "import pandas as pd\n", - "\n", - "with pd.HDFStore(cfg[\"raw_data_dir\"] + '/170000.h5', \"r\") as store:\n", - " print(store.keys())\n", - " signal = store['gas']\n", - "\n", - "signal" + "samples = split_samples(\n", + " df=df,\n", + " shot_number=shot_number,\n", + " window_ms=cfg[\"window_ms\"],\n", + " hop_ms=cfg[\"hop_ms\"],\n", + " fs_khz=cfg[\"fs_khz\"],\n", + ")" ] }, { "cell_type": "code", - "execution_count": 168, - "id": "fdb1b47f", + "execution_count": 57, + "id": "40750cb0", "metadata": {}, "outputs": [], "source": [ - "channel = signal['gasa']" + "samples = remove_empty_samples(samples)" ] }, { "cell_type": "code", - "execution_count": 172, - "id": "f9cb5191", + "execution_count": null, + "id": "de25ce4f", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 172, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "mhr (8, 1, 2832874)\n", + "ece (48, 1, 2832874)\n", + "co2 (4, 1, 2832874)\n", + "gas (5, 1, 2832874)\n", + "ech (11, 1, 2832874)\n", + "pin (8, 1, 2832874)\n", + "tin (8, 1, 2832874)\n" + ] } ], "source": [ - "channel.plot()" + "for sample in samples:\n", + " transformed_samples = {}\n", + " for key, value in sample.items():\n", + " for signal in cfg[\"signal\"]:\n", + " abbr = signal['abbr']\n", + " cols = [col for col in value.columns if abbr in col]\n", + " transformed_samples[abbr] = identity_transform(\n", + " x=value[cols].to_numpy().T)\n", + " print(abbr, transformed_samples[abbr].shape)\n", + " save_sample(transformed_samples, Path('.'), key)" ] }, { "cell_type": "code", - "execution_count": 152, - "id": "91f88685", + "execution_count": 58, + "id": "bc37f8e0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2832874,) (513, 11066)\n" + ] + } + ], "source": [ - "import torch" + "first_arr = list(samples[0].values())[0].iloc[:, 0].values\n", + "transform_shape = stft_transform(x=first_arr).shape\n", + "print(first_arr.shape, transform_shape)" ] }, { "cell_type": "code", - "execution_count": 159, - "id": "cf665d9e", + "execution_count": 83, + "id": "f6cae5ac", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mhr (8, 513, 11066)\n", + "ece (48, 513, 11066)\n", + "co2 (4, 513, 11066)\n", + "gas (5, 1, 11066)\n", + "ech (11, 1, 11066)\n", + "pin (8, 1, 11066)\n", + "tin (8, 1, 11066)\n" + ] + } + ], "source": [ - "# Apply STFT transform to the channel data\n", - "x_tensor = torch.from_numpy(channel.values).float()\n", - "stft_result = torch.stft(\n", - " x_tensor, \n", - " n_fft=1024, \n", - " hop_length=256, \n", - " window=torch.hann_window(1024), \n", - " return_complex=True\n", - ")\n", - "# Take log of absolute values\n", - "log_abs_stft = torch.log(torch.abs(stft_result))" + "for sample in samples:\n", + " transformed_samples = {}\n", + " for key, value in sample.items():\n", + " for signal in cfg[\"signal\"]:\n", + " abbr = signal['abbr']\n", + " cols = [col for col in value.columns if abbr in col]\n", + " if signal[\"make_stft\"]:\n", + " transformed_samples[abbr] = stft_transform(\n", + " x=value[cols].to_numpy().T,\n", + " n_fft=cfg[\"stft\"][\"n_fft\"],\n", + " hop_length=cfg[\"stft\"][\"hop_length\"],\n", + " )\n", + " else:\n", + " transformed_samples[abbr] = resample_transform(\n", + " x=value[cols].to_numpy().T,\n", + " ref_shape=transform_shape,\n", + " )\n", + " print(abbr, transformed_samples[abbr].shape)\n", + " save_sample(transformed_samples, Path('.'), key)\n", + " break\n", + " break" ] }, { "cell_type": "code", - "execution_count": 162, - "id": "1b0ca730", + "execution_count": 85, + "id": "c8797e68", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Original signal length: 1310001\n", - "STFT time frames: 5118\n", - "Decimation factor: 255.95955451348183\n", - "Decimated signal length: 5118\n" + "float32\n" ] } ], "source": [ - "# Decimate the original signal to match the STFT time dimension\n", - "stft_time_frames = log_abs_stft.shape[1]\n", - "original_length = len(channel)\n", - "\n", - "print(f\"Original signal length: {original_length}\")\n", - "print(f\"STFT time frames: {stft_time_frames}\")\n", - "print(f\"Decimation factor: {original_length / stft_time_frames}\")\n", - "\n", - "# Create decimated signal by taking every nth sample\n", - "decimation_factor = original_length // stft_time_frames\n", - "decimated_signal = channel.values[::decimation_factor][:stft_time_frames]\n", - "\n", - "print(f\"Decimated signal length: {len(decimated_signal)}\")\n" + "import joblib\n", + "test_load = joblib.load('/scratch/gpfs/EKOLEMEN/nc1514/foundation_v2/train/170000_0.pkl')\n", + "print(test_load['mhr'].dtype)" ] }, { "cell_type": "code", - "execution_count": 166, - "id": "c9129a21", + "execution_count": 86, + "id": "883637b2", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -388,31 +271,9 @@ } ], "source": [ - "# Plot the spectrogram\n", - "import matplotlib.pyplot as plt\n", - "plt.figure(figsize=(12, 6))\n", - "plt.subplot(2, 1, 1)\n", - "plt.imshow(log_abs_stft.numpy(), aspect='auto', origin='lower', cmap='viridis')\n", - "plt.xlabel('Time Frame')\n", - "plt.ylabel('Frequency Bin')\n", - "plt.title('STFT Spectrogram of pinjf_15l channel')\n", - "plt.subplot(2, 1, 2)\n", - "plt.plot(decimated_signal)\n", - "plt.xlim(0, len(decimated_signal))\n", - "plt.xlabel('Time Frame')\n", - "plt.ylabel('Magnitude')\n", - "plt.title('Original Signal of pinjf_15l channel')\n", - "plt.tight_layout()\n", - "plt.show()\n" + "plt.imshow(test_load['ece'][4], aspect='auto', origin='lower')\n", + "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7912cd5d", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/src/fusionaihub/datasets/prepare/__main__.py b/src/fusionaihub/datasets/prepare/__main__.py new file mode 100644 index 0000000..444fa44 --- /dev/null +++ b/src/fusionaihub/datasets/prepare/__main__.py @@ -0,0 +1,4 @@ +from .prepare_dataset import main + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/config/default.yaml b/src/fusionaihub/datasets/prepare/config/default.yaml index bd25673..28d8ff6 100644 --- a/src/fusionaihub/datasets/prepare/config/default.yaml +++ b/src/fusionaihub/datasets/prepare/config/default.yaml @@ -4,13 +4,13 @@ # Signal configuration - list of signals to process # Each signal has: [signal_name, abbreviation, should_transform] signal: - - ["magnetics_high_resolution", "mhr", true] - - ["ece_cali", "ece", true] - - ["co2_density", "co2", true] - - ["gas", "gas", false] - - ["ech", "ech", false] - - ["p_inj", "pin", false] - - ["t_inj", "tin", false] + - {name: "magnetics_high_resolution", abbr: "mhr", make_stft: true} + - {name: "ece_cali", abbr: "ece", make_stft: true} + - {name: "co2_density", abbr: "co2", make_stft: true} + - {name: "gas", abbr: "gas", make_stft: false} + - {name: "ech", abbr: "ech", make_stft: false} + - {name: "p_inj", abbr: "pin", make_stft: false} + - {name: "t_inj", abbr: "tin", make_stft: false} # Data processing parameters randomize_shots: true @@ -18,22 +18,24 @@ random_seed: 42 num_shots: 50 fs_khz: 500 # Sampling frequency in kHz ip_threshold: 0.1 # Plasma current threshold -window_ms: 250 # Window size in milliseconds -hop_ms: 50 # Hop size in milliseconds +window_ms: null # Window size in milliseconds +hop_ms: null # Hop size in milliseconds remove_empty: true +do_stft: true +start_time: null # Start time for signal extraction (null for auto-detection) +end_time: null # End time for signal extraction (null for auto-detection) # Train/test split configuration train_test_split: 0.2 # Directory paths -raw_data_dir: "/scratch/gpfs/EKOLEMEN/d3d_fusion_data" -output_dir: "/scratch/gpfs/EKOLEMEN/nc1514/foundation_v2" +raw_data_dir: /scratch/gpfs/EKOLEMEN/d3d_fusion_data +output_dir: /scratch/gpfs/EKOLEMEN/hackathon/foundation25/ # Processing parameters stft: n_fft: 1024 hop_length: 256 - window_type: "hann" # Output settings compression: true # Whether to compress saved files \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/config/raw.yaml b/src/fusionaihub/datasets/prepare/config/raw.yaml new file mode 100644 index 0000000..79dc8f4 --- /dev/null +++ b/src/fusionaihub/datasets/prepare/config/raw.yaml @@ -0,0 +1,39 @@ +# Dataset preparation configuration +# Configuration for fusion data processing pipeline + +# Signal configuration - list of signals to process +# Each signal has: [signal_name, abbreviation, should_transform] +signal: + - ["magnetics_high_resolution", "mhr", false] + - ["ece_cali", "ece", false] + - ["co2_density", "co2", false] + - ["gas", "gas", false] + - ["ech", "ech", false] + - ["p_inj", "pin", false] + - ["t_inj", "tin", false] + +# Data processing parameters +randomize_shots: true +random_seed: 42 +num_shots: 50 +fs_khz: 500 # Sampling frequency in kHz +ip_threshold: 0.1 # Plasma current threshold +window_ms: 250 # Window size in milliseconds +hop_ms: 50 # Hop size in milliseconds +remove_empty: true + +# Train/test split configuration +train_test_split: 0.2 + +# Directory paths +raw_data_dir: "/scratch/gpfs/EKOLEMEN/d3d_fusion_data" +output_dir: "/scratch/gpfs/EKOLEMEN/nc1514/foundation_v1" + +# Processing parameters +stft: + n_fft: 1024 + hop_length: 256 + window_type: "hann" + +# Output settings +compression: true # Whether to compress saved files \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/core/__init__.py b/src/fusionaihub/datasets/prepare/core/__init__.py deleted file mode 100644 index d608038..0000000 --- a/src/fusionaihub/datasets/prepare/core/__init__.py +++ /dev/null @@ -1,30 +0,0 @@ -""" -Core modules for fusion dataset preparation. - -This package contains modular components for processing fusion data: -- signal_processing: Signal resampling and transformation functions -- data_extraction: Data extraction and alignment functions -- sample_processing: Sample splitting, transformation, and saving -- shot_processing: Shot-level processing logic -- dataset_utils: Dataset utilities and indexing -""" - -from .signal_processing import resample_nearest, transform_individual_sample -from .data_extraction import extract, running_time, align -from .sample_processing import split, transform_samples, save_samples -from .dataset_utils import create_missing_signal_dataframes, index_dataset -from .shot_processing import process_shot_stft - -__all__ = [ - 'resample_nearest', - 'transform_individual_sample', - 'extract', - 'running_time', - 'align', - 'split', - 'transform_samples', - 'save_samples', - 'create_missing_signal_dataframes', - 'index_dataset', - 'process_shot_stft' -] \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/core/dataset_utils.py b/src/fusionaihub/datasets/prepare/core/dataset_utils.py deleted file mode 100644 index 0d10803..0000000 --- a/src/fusionaihub/datasets/prepare/core/dataset_utils.py +++ /dev/null @@ -1,84 +0,0 @@ -""" -Dataset utilities for fusion dataset preparation. - -This module contains utility functions for handling missing signals, -creating placeholder dataframes, and indexing dataset files. -""" - -import pandas as pd -from pathlib import Path -from typing import Set, List, Dict - - -def create_missing_signal_dataframes( - cfg: Dict, - processed_signals: Set, - reference_df: pd.DataFrame -) -> List[pd.DataFrame]: - """ - Create fully off dataframes for missing signals using reference dataframe structure. - - When some signals are missing from a shot, this function creates placeholder - dataframes with the same structure but with all data set to 0 and all states - set to False. - - Args: - cfg: Configuration dictionary containing signal definitions - processed_signals: Set of signal abbreviations that were successfully processed - reference_df: Reference DataFrame to use for structure - - Returns: - List of DataFrames for missing signals - """ - missing_dfs = [] - - for signal in cfg["signal"]: - signal_name, signal_abbr, do_transform = signal - if signal_abbr not in processed_signals: - print(f"Creating fully off dataframe for missing signal {signal_name}") - - # Create off dataframe by copying structure and zeroing values - off_df = reference_df.copy() - - # Get columns that belong to the reference signal (to replace with new signal columns) - ref_signal_cols = [col for col in off_df.columns if not col.endswith('_state')] - - # Create new column names for the missing signal - new_cols = {} - new_state_cols = {} - - for i, col in enumerate(ref_signal_cols): - new_col_name = f"{signal_abbr}col{i}" - new_cols[col] = new_col_name - new_state_cols[f"{col}_state"] = f"{new_col_name}_state" - - # Rename columns to match the missing signal - off_df = off_df.rename(columns={**new_cols, **new_state_cols}) - - # Set all data columns to 0 and all state columns to False - for col in off_df.columns: - if col.endswith('_state'): - off_df[col] = False - else: - off_df[col] = 0.0 - - missing_dfs.append(off_df) - - return missing_dfs - - -def index_dataset(out_dir: Path) -> None: - """ - Create an index file listing all dataset files in the directory. - - Scans the output directory for .pkl files and creates an index.pkl - file containing the list of all dataset files. - - Args: - out_dir: Directory to index - """ - files = list(out_dir.glob("*.pkl")) - df_files = pd.DataFrame({'files': [str(file) for file in files]}) - df_files.to_pickle(out_dir / "index.pkl") - - print(f"Indexed {len(files)} files.") \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/core/sample_processing.py b/src/fusionaihub/datasets/prepare/core/sample_processing.py deleted file mode 100644 index 7b7109a..0000000 --- a/src/fusionaihub/datasets/prepare/core/sample_processing.py +++ /dev/null @@ -1,160 +0,0 @@ -""" -Sample processing utilities for fusion dataset preparation. - -This module contains functions for splitting signals into time windows, -applying transformations to samples, and saving processed data. -""" - -import numpy as np -import pandas as pd -import joblib -from pathlib import Path -from typing import List, Dict -from .signal_processing import resample_nearest, transform_individual_sample - - -def split(df: pd.DataFrame, window_ms: int, hop_ms: int, fs_khz: float) -> List[pd.DataFrame]: - """ - Split signal data into overlapping time windows. - - Args: - df: Input DataFrame with signal data - window_ms: Window size in milliseconds - hop_ms: Hop size in milliseconds - fs_khz: Sampling frequency in kHz - - Returns: - List of DataFrame samples - """ - # Create sample indicies - num_samples = int((window_ms) * fs_khz) - hop_samples = int((hop_ms) * fs_khz) - - # Separate samples - samples = [] - for start in range(0, len(df) - num_samples + 1, hop_samples): - end = start + num_samples - sample = df.iloc[start:end] - if len(sample) == num_samples: - samples.append(sample) - - return samples - - -def transform_samples( - samples: List[pd.DataFrame], - directory: Path, - signal_config: List[tuple], - shot: int, -) -> List[Dict]: - """ - Transform and process samples based on signal configuration. - - Applies transformations (like STFT) to specified signals and resamples - non-transformed signals to match the dimensions of transformed ones. - - Args: - samples: List of sample DataFrames - directory: Output directory - signal_config: List of (signal_name, abbreviation, should_transform) tuples - shot: Shot number for logging - - Returns: - List of dictionaries containing processed sample data - """ - directory.mkdir(parents=True, exist_ok=True) - print(f"Processing {len(samples)} samples for shot {shot}") - samples_dict = [] - - # Create mapping from signal abbreviation to whether it should be transformed - transform_map = {} - for signal_name, signal_abbr, should_transform in signal_config: - transform_map[signal_abbr] = should_transform - - for i, sample in enumerate(samples): - - # Remove columns ending with '_state' - sample_to_save = sample.loc[:, ~sample.columns.str.endswith('_state')] - - # Only save if not fully off (i.e., at least one True in any state col) - state_cols = [col for col in sample.columns if col.endswith('_state')] - if np.any(sample[state_cols].to_numpy()): - - # First pass: apply transformations and collect results - sample_dict = {} - transformed_sample = None - original_time_length = len(sample_to_save) - - for col in sample_to_save.columns: - # Convert each column to float32 numpy array - col_array = sample_to_save[col].values.astype(np.float32) - - # Determine if this column should be transformed based on signal abbreviation - should_transform = False - for signal_abbr in transform_map.keys(): - if col.startswith(signal_abbr): - should_transform = transform_map[signal_abbr] - break - - if should_transform: - transformed_array = transform_individual_sample(col_array) - sample_dict[col] = transformed_array - # Store an example transformed sample to get target dimensions - if transformed_sample is None: - transformed_sample = transformed_array - print(f"Reference transformed sample shape: {transformed_array.shape}") - else: - # Store original array for now, will resample later - sample_dict[col] = col_array - - # Second pass: resample non-transformed samples to match transformed dimensions - if transformed_sample is not None: - target_width = transformed_sample.shape[-1] # Last dimension is time - # Calculate target sample frequency based on transformed sample - target_fs = target_width / original_time_length - print(f"Target frequency ratio: {target_fs:.4f} (target width: {target_width}, original length: {original_time_length})") - - for col in sample_dict.keys(): - # Check if this column was transformed - should_transform = False - for signal_abbr in transform_map.keys(): - if col.startswith(signal_abbr): - should_transform = transform_map[signal_abbr] - break - - if not should_transform: - # Resample non-transformed data to match target width - original_array = sample_dict[col] - resampled_array = resample_nearest(original_array, target_width) - - # Crop end if needed to ensure exact match - if len(resampled_array) > target_width: - resampled_array = resampled_array[:target_width] - elif len(resampled_array) < target_width: - # Pad with zeros if too short (shouldn't happen with resample_nearest) - pad_width = target_width - len(resampled_array) - resampled_array = np.pad(resampled_array, (0, pad_width), mode='constant') - - sample_dict[col] = resampled_array.astype(np.float32) - print(f"Resampled {col} from {len(original_array)} to {len(resampled_array)}") - - samples_dict.append(sample_dict) - print(f"Sample {i} processed with {len(sample_dict)} signals") - - return samples_dict - - -def save_samples(samples: List[Dict], directory: Path, shot: int) -> None: - """ - Save processed samples to disk using joblib compression. - - Args: - samples: List of sample dictionaries to save - directory: Output directory - shot: Shot number for filename generation - """ - directory.mkdir(parents=True, exist_ok=True) - print(f"Saving {len(samples)} samples to {directory}") - for i, sample in enumerate(samples): - # Save using joblib - joblib.dump(sample, directory / f"{shot}_{i}.pkl", compress=True) \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/core/shot_processing.py b/src/fusionaihub/datasets/prepare/core/shot_processing.py deleted file mode 100644 index 6cf17c8..0000000 --- a/src/fusionaihub/datasets/prepare/core/shot_processing.py +++ /dev/null @@ -1,122 +0,0 @@ -""" -Shot processing utilities for fusion dataset preparation. - -This module contains the main shot processing logic that orchestrates -data extraction, alignment, transformation, and saving for individual shots. -""" - -import numpy as np -import pandas as pd -from pathlib import Path -from typing import Dict - -from .data_extraction import extract, running_time, align -from .sample_processing import transform_samples, save_samples -from .dataset_utils import create_missing_signal_dataframes - - -def process_shot_stft(shot: int, cfg: Dict, out_dir: Path) -> None: - """ - Process a single shot through the complete data preparation pipeline accounting for STFT transformations. - - This function orchestrates the complete processing workflow for a shot: - 1. Determines plasma running time - 2. Extracts and aligns all configured signals to be transformed - 3. Handles missing signals by creating placeholder dataframes - 4. Combines all signals into a unified dataframe - 5. Transforms and saves the processed samples using STFT transformations. - 6. Downsamples shots not transformed to the same length as the transformed shots. - - Args: - shot: Shot number to process - cfg: Configuration dictionary - out_dir: Output directory for processed files - """ - try: - dfs = [] - start_time = None - end_time = None - - try: - start_time, end_time = running_time( - directory=Path(cfg["raw_data_dir"]), - shot=shot, - ip_threshold=cfg["ip_threshold"] - ) - reference_len = int((end_time - start_time) * cfg["fs_khz"]) - print(f"Running time for shot {shot}: {start_time} to {end_time}") - except Exception as e: - print(f"Error: Could not determine running time for shot {shot}: {e}") - return - - # Process each signal and track which ones succeeded - processed_signals = set() - - for signal in cfg["signal"]: - signal_name, signal_abbr, is_transformed = signal - df = None - - if is_transformed: - try: - # Try to extract and process the signal - df = extract(shot=shot, directory=Path(cfg["raw_data_dir"]), signal=signal_name) - df.columns = [f"{signal_abbr}{col}" if col != "time" else col for col in df.columns] - df = align(df, start_time, end_time, cfg["fs_khz"]) - processed_signals.add(signal_abbr) - dfs.append(df) - print(f"Successfully processed signal {signal_name} for shot {shot}") - except Exception as e: - print(f"Error processing signal {signal_name} for shot {shot}: {e}") - - if not dfs: - print(f"Error: No dataframes created for shot {shot}") - return - - df = pd.concat(dfs, axis=1, join='inner') - - # num_samples = len(df) - # new_index = np.linspace(start_time, end_time, num_samples) - # df.index = new_index - # df.index = pd.to_timedelta(df.index, unit='ms') - - samples = [df] # no splitting for this dataset - print(f"Shot {shot} has {len(samples)} samples after splitting.") - samples_dict_list = transform_samples(samples, out_dir, cfg["signal"], shot) - - # Get the first (and only) sample dictionary since we don't split - sample_dict = samples_dict_list[0] - - # Get a sample from sample_dict to determine STFT dimensions - first_key = next(iter(sample_dict.keys())) - stft_width = sample_dict[first_key].shape[-1] - print(f"Using {first_key} as reference for STFT dimensions: {stft_width}") - stft_fs = stft_width / (end_time - start_time) - - for signal in cfg["signal"]: - signal_name, signal_abbr, is_transformed = signal - if not is_transformed: - try: - df = extract(shot=shot, directory=Path(cfg["raw_data_dir"]), signal=signal_name) - df.columns = [f"{signal_abbr}{col}" if col != "time" else col for col in df.columns] - df = align(df, start_time, end_time, stft_fs) - # Ensure signal matches STFT width by truncating if necessary - if len(df) > stft_width: - df = df.iloc[:stft_width] - print(f"Truncated {signal_abbr} from {len(df)} to {stft_width} samples to match STFT width") - elif len(df) < stft_width: - print(f"Warning: {signal_abbr} has {len(df)} samples, less than STFT width {stft_width}") - pad_width = stft_width - len(df) - df = np.pad(df, (0, pad_width), mode='constant', constant_values=0) - print(f"Padded {signal_abbr} from {len(df)} to {stft_width} samples with zeros") - print(f"Successfully processed non-transformed signal {signal_name} for shot {shot}") - except Exception as e: - print(f"Error processing non-transformed signal {signal_name} for shot {shot}: {e}") - - save_samples([sample_dict], out_dir, shot) - print(f"Processed shot {shot} successfully with {len(cfg['signal'])} signals.") - - except Exception as e: - print(f"Error processing shot {shot}: {e}") - return - - return \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/core/data_extraction.py b/src/fusionaihub/datasets/prepare/data_extraction.py similarity index 74% rename from src/fusionaihub/datasets/prepare/core/data_extraction.py rename to src/fusionaihub/datasets/prepare/data_extraction.py index 6ee2118..a6ff154 100644 --- a/src/fusionaihub/datasets/prepare/core/data_extraction.py +++ b/src/fusionaihub/datasets/prepare/data_extraction.py @@ -11,7 +11,13 @@ from scipy.signal import resample -def extract(shot: int, directory: Path, signal: str) -> pd.DataFrame: +def extract_signal( + shot_number: int, + directory: Path, + signal: str, + start_time: float | None = None, + end_time: float | None = None, +) -> pd.DataFrame: """ Extract signal data from HDF5 file for a given shot. @@ -23,33 +29,50 @@ def extract(shot: int, directory: Path, signal: str) -> pd.DataFrame: Returns: DataFrame containing the signal data """ - path = (directory / str(shot)).with_suffix(".h5") + path = (directory / str(shot_number)).with_suffix(".h5") df = pd.read_hdf(path, key=signal) return pd.DataFrame(df) -def running_time(directory: Path, shot: int, ip_threshold: float) -> tuple[float, float]: +def extract_running_time( + shot_number: int, + directory: Path, + ip_threshold: float, + start_time: float | None = None, + end_time: float | None = None, +) -> tuple[float, float]: """ Determine the plasma running time for a shot based on plasma current threshold. Args: directory: Directory containing HDF5 files shot: Shot number - ip_threshold: Plasma current threshold + ip_threshold: Plasma current threshold for a shot to be considered running + start_time: Manually set start time for the shot (ms) (optional) + end_time: Manually set end time for the shot (ms) (optional) Returns: Tuple of (start_time, end_time) in milliseconds """ - path = (directory / str(shot)).with_suffix(".h5") + path = (directory / str(shot_number)).with_suffix(".h5") with pd.HDFStore(path, 'r') as store: df = store['ip']['ipsip'] df = df.loc[df > ip_threshold] - start_time = df.index[0] - end_time = df.index[-1] + if start_time is not None: + df = df.loc[df.index >= start_time] + if end_time is not None: + df = df.loc[df.index <= end_time] + start_time = float(df.index[0]) + end_time = float(df.index[-1]) return start_time, end_time -def align(df: pd.DataFrame, start_time: float, end_time: float, fs: float) -> pd.DataFrame: +def align_signal( + df: pd.DataFrame, + start_time: float, + end_time: float, + fs: float, +) -> pd.DataFrame: """ Align signal data to a common timebase and sampling frequency. diff --git a/src/fusionaihub/datasets/prepare/dataset_utils.py b/src/fusionaihub/datasets/prepare/dataset_utils.py new file mode 100644 index 0000000..26ba018 --- /dev/null +++ b/src/fusionaihub/datasets/prepare/dataset_utils.py @@ -0,0 +1,30 @@ +""" +Dataset utilities for fusion dataset preparation. + +This module contains utility functions for handling missing signals, +creating placeholder dataframes, and indexing dataset files. +""" + +import pandas as pd +import logging +from pathlib import Path +from typing import Set, List, Dict + +# Set up logger for this module +logger = logging.getLogger(__name__) + +def index_dataset(out_dir: Path) -> None: + """ + Create an index file listing all dataset files in the directory. + + Scans the output directory for .joblib files and creates an index.pkl + file containing the list of all dataset files. + + Args: + out_dir: Directory to index + """ + files = list(out_dir.glob("*.joblib")) + df_files = pd.DataFrame({'files': [str(file) for file in files]}) + df_files.to_csv(out_dir / "index.csv", index=False) + + logger.info(f"Indexed {len(files)} files.") \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/logging_config.py b/src/fusionaihub/datasets/prepare/logging_config.py new file mode 100644 index 0000000..20ddf51 --- /dev/null +++ b/src/fusionaihub/datasets/prepare/logging_config.py @@ -0,0 +1,64 @@ +""" +Logging configuration for fusion dataset preparation. + +This module provides centralized logging configuration for the signal processing +and dataset preparation pipeline. +""" + +import logging +import sys +from typing import Optional + + +def setup_logging( + level: str = "INFO", + format_string: Optional[str] = None, + logger_name: str = "fusionaihub.datasets.prepare" +) -> logging.Logger: + """ + Set up logging configuration for the dataset preparation pipeline. + + Args: + level: Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL) + format_string: Custom format string for log messages + logger_name: Name for the logger + + Returns: + Configured logger instance + """ + if format_string is None: + format_string = "%(asctime)s - %(name)s - %(levelname)s - %(message)s" + + # Create logger + logger = logging.getLogger(logger_name) + logger.setLevel(getattr(logging, level.upper())) + + # Remove existing handlers to avoid duplicates + for handler in logger.handlers[:]: + logger.removeHandler(handler) + + # Create console handler + console_handler = logging.StreamHandler(sys.stdout) + console_handler.setLevel(getattr(logging, level.upper())) + + # Create formatter + formatter = logging.Formatter(format_string) + console_handler.setFormatter(formatter) + + # Add handler to logger + logger.addHandler(console_handler) + + return logger + + +def get_logger(name: str) -> logging.Logger: + """ + Get a logger instance for a specific module. + + Args: + name: Module name (typically __name__) + + Returns: + Logger instance + """ + return logging.getLogger(f"fusionaihub.datasets.prepare.{name}") \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/prepare.py b/src/fusionaihub/datasets/prepare/prepare.py deleted file mode 100644 index aa1e9f3..0000000 --- a/src/fusionaihub/datasets/prepare/prepare.py +++ /dev/null @@ -1,286 +0,0 @@ -import re -import numpy as np -import pandas as pd -import polars as pl -import h5py -from pathlib import Path -from scipy.interpolate import interp1d -from scipy.signal import resample, resample_poly -from sklearn.model_selection import train_test_split -from datetime import time, timedelta -from ..utils.parmap import ParallelMapper -import logging -from tqdm.auto import tqdm -import pickle -import torch -from concurrent.futures import ProcessPoolExecutor - - -log = logging.getLogger(__name__) - -sample_cfg = { - "signal": [ - ("magnetics_high_resolution", "mhr"), - ("ece_cali", "ece"), - ("bes", "bes"), - ("co2_density", "co2"), - ], - "randomize_shots": True, - "random_seed": 42, - "num_shots": 50, - "fs_khz": 500, - "start_ms": 0, - "end_ms": 5000, - "window_ms": 250, - "hop_ms": 50, - "remove_empty": True, - "train_test_split": 0.2, - "raw_data_dir": "/scratch/gpfs/EKOLEMEN/d3d_fusion_data", - "output_dir": "/scratch/gpfs/nc1514/specseg/data/foundation_v1", -} - - -def resample_nearest(y: np.ndarray, new_len: int) -> np.ndarray: - orig_len = len(y) - gcd = np.gcd(orig_len, new_len) - up = new_len // gcd - down = orig_len // gcd - return resample_poly(y, up, down) - # return resample(y, new_len) - # return interp1d(np.linspace(0, 1, len(y)), y, kind='cubic')(np.linspace(0, 1, new_len)) - - -def extract( - shot: int, - directory: Path, - signal: str, -) -> pd.DataFrame: - - path = (directory / str(shot)).with_suffix(".h5") - df = pd.read_hdf(path, key=signal) - - return pd.DataFrame(df) - - -def align( - df: pd.DataFrame, - start_time: float, - end_time: float, - fs: float, -) -> pd.DataFrame: - - # get sampling frequency - fs_raw = len(df) / (df.index[-1] - df.index[0]) - - # crop time - df = df.loc[(df.index >= start_time) & (df.index <= end_time)] - - # resample - num = len(df) - num = int(num * fs / fs_raw) - - df = pd.DataFrame( - {col: resample(df[col].values, num) for col in df.columns}, - index=np.linspace(df.index[0], df.index[-1], num) - ) - - # mark on-off states - start_nan = (df.index[0] - start_time) * fs - end_nan = (end_time - df.index[-1]) * fs - start_pad = pd.DataFrame( - 0, index=pd.RangeIndex(start=int(start_nan)), columns=df.columns) - end_pad = pd.DataFrame( - 0, index=pd.RangeIndex(start=int(len(df) + start_nan), stop=int(len(df) + start_nan + end_nan)), columns=df.columns) - - df_state = pd.DataFrame(True, index=df.index, columns=df.columns) - start_pad_state = pd.DataFrame(False, index=start_pad.index, columns=df.columns) - end_pad_state = pd.DataFrame(False, index=end_pad.index, columns=df.columns) - - df = pd.concat([start_pad, df, end_pad], ignore_index=True) - df_state = pd.concat([start_pad_state, df_state, end_pad_state], ignore_index=True) - df_state.columns = [f"{col}_state" for col in df.columns] - - # combine data with state - df = df.astype(np.float32) - df_state = df_state.astype(np.bool) - df = pd.concat([df, df_state], axis=1) - - # convert time to ms - df = df.rename_axis("time") - df.index = pd.to_timedelta(df.index, unit='ms') - - return df - - -def split( - df: pd.DataFrame, - window_ms: int, - hop_ms: int, - fs_khz: float, -) -> list[pd.DataFrame]: - - # Create sample indicies - num_samples = int((window_ms) * fs_khz) - hop_samples = int((hop_ms) * fs_khz) - - # Separate samples - samples = [] - for start in range( - 0, len(df) - num_samples + 1, hop_samples - ): - end = start + num_samples - sample = df.iloc[start:end] - if len(sample) == num_samples: - samples.append(sample) - - return samples - -def transform_individual_sample( - x: np.ndarray, - ) -> np.ndarray: - x = torch.from_numpy(x).float() - transformed = torch.stft( - x, - n_fft=1024, - hop_length=256, - window=torch.hann_window(1024), - return_complex=True - ) - transformed = torch.log(torch.abs(transformed).clamp(min=1e-10)) - transformed = torch.clip(transformed, min=-10, max=5).numpy() - return transformed - -def save_samples( - samples: list[pd.DataFrame], - directory: Path, - shot: int -) -> None: - directory.mkdir(parents=True, exist_ok=True) - for i, sample in enumerate(samples): - - # Remove columns ending with '_state' - sample_to_save = sample.loc[:, ~sample.columns.str.endswith('_state')] - - # Only save if not fully off (i.e., at least one True in any state col) - state_cols = [col for col in sample.columns if col.endswith('_state')] - if np.any(sample[state_cols].to_numpy()): - sample_array = sample_to_save.to_numpy().T - sample_array = sample_array.astype(np.float32) - sample_array = transform_individual_sample(sample_array) - with open(directory / f"{shot}_{i}.pkl", 'wb') as f: - pickle.dump(sample_array, f, protocol=pickle.HIGHEST_PROTOCOL) - - -def process_shot( - shot: int, - cfg: dict, - out_dir: Path, -) -> None: - - dfs = [] - for signal in cfg["signal"]: - signal_name, signal_abbr = signal - - try: - df = extract(shot=shot, directory=Path(cfg["raw_data_dir"]), signal=signal_name) - except FileNotFoundError: - print(f"Missing {shot} -- {signal_name}") - return - - try: - df.columns = [f"{signal_abbr}{col}" if col != "time" else col for col in df.columns] - except Exception as e: - print(f"Error renaming columns for shot {shot} and signal {signal_name}: {e}") - return - - try: - df = align(df, cfg["start_ms"], cfg["end_ms"], cfg["fs_khz"]) - except ValueError: - print(f"Error aligning data for shot {shot} and signal {signal_name}.") - return - - dfs.append(df) - - df = pd.concat(dfs, axis=1) - - # Split into windows - samples = split(df, cfg["window_ms"], cfg["hop_ms"], cfg["fs_khz"]) - print(f"Shot {shot} has {len(samples)} samples after splitting.") - - # Save to cache (only non-fully-off windows) - save_samples(samples, out_dir, shot) - print(f"Processed shot {shot} successfully.") - - return - - -def index_dataset(out_dir: Path) -> None: - - files = list(out_dir.glob("*.pkl")) - df_files = pd.DataFrame({'files': [str(file) for file in files]}) - df_files.to_pickle(out_dir / "index.pkl") - - print(f"Indexed {len(files)} files.") - - -def prepare_dataset(cfg: dict) -> None: - - cfg["num_samples"] = int((cfg["end_ms"] - cfg["start_ms"]) * cfg["fs_khz"]) - raw_data_dir = Path(cfg["raw_data_dir"]) - cache_dir = Path(cfg["output_dir"]) / "cache" - cache_dir.mkdir(parents=True, exist_ok=True) - - # Collect and sort all shot numbers - print(f"Collecting shots from {raw_data_dir}...") - all_shots = [ - int(p.stem) - for p in raw_data_dir.iterdir() - if p.suffix == ".h5"] - all_shots.sort() - - # if cfg["randomize_shots"]: - # np.random.seed(cfg["random_seed"]) - # all_shots = np.random.permutation(all_shots) - # all_shots = all_shots[:cfg["num_shots"]] - - # print(f"Processing {len(all_shots)} shots into cache...") - - mapper = ParallelMapper() - mapper(process_shot, [170000], cfg=cfg, out_dir=cache_dir) - - # for shot in tqdm(all_shots): # for debugging - # process_shot(shot, cfg, out_dir=cache_dir) - # break - - # Move cached files into train/test split - print("Splitting dataset into train and valid sets...") - all_files = list(cache_dir.glob("*.pkl")) - all_files.sort() - train_files, valid_files = train_test_split( - all_files, - test_size=cfg.get("train_test_split", 0.2), - random_state=cfg["random_seed"]) - - train_dir = Path(cfg["output_dir"]) / "train" - valid_dir = Path(cfg["output_dir"]) / "valid" - train_dir.mkdir(parents=True, exist_ok=True) - valid_dir.mkdir(parents=True, exist_ok=True) - - for f in train_files: - f.rename(train_dir / f.name) - for f in valid_files: - f.rename(valid_dir / f.name) - - # Index the datasets - index_dataset(train_dir) - index_dataset(valid_dir) - - # Remove cache directory after splitting - for f in cache_dir.glob("*"): f.unlink() - cache_dir.rmdir() - - print("Dataset preparation complete.") - -if __name__ == "__main__": - cfg = sample_cfg.copy() - prepare_dataset(cfg=cfg) \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/prepare2.py b/src/fusionaihub/datasets/prepare/prepare2.py deleted file mode 100644 index 67a8186..0000000 --- a/src/fusionaihub/datasets/prepare/prepare2.py +++ /dev/null @@ -1,443 +0,0 @@ -import re -import numpy as np -import pandas as pd -from pathlib import Path -from scipy.signal import resample, resample_poly -from sklearn.model_selection import train_test_split -from ...util.parmap import ParallelMapper -import logging -import joblib -import torch -from concurrent.futures import ProcessPoolExecutor -from typing import Optional - - -log = logging.getLogger(__name__) - -sample_cfg = { - "signal": [ # start with signals to be transformed. this is hacked on - ("magnetics_high_resolution", "mhr", True), - ("ece_cali", "ece", True), - ("co2_density", "co2", True), - ("gas", "gas", False), - ("ech", "ech", False), - ("p_inj", "pin", False), - ("t_inj", "tin", False), - ], - "randomize_shots": True, - "random_seed": 42, - "num_shots": 50, - "fs_khz": 500, - "ip_threshold": 1e-1, - "window_ms": 250, - "hop_ms": 50, - "remove_empty": True, - "train_test_split": 0.2, - "raw_data_dir": "/scratch/gpfs/EKOLEMEN/d3d_fusion_data", - "output_dir": "/scratch/gpfs/nc1514/FusionAIHub/data/foundation_v2", -} - - -def resample_nearest(y: np.ndarray, new_len: int) -> np.ndarray: - orig_len = len(y) - gcd = np.gcd(orig_len, new_len) - up = new_len // gcd - down = orig_len // gcd - # return resample_poly(y, up, down) - resampled = resample(y, new_len) - return np.asarray(resampled) - # return interp1d(np.linspace(0, 1, len(y)), y, kind='cubic')(np.linspace(0, 1, new_len)) - - -def extract( - shot: int, - directory: Path, - signal: str, -) -> pd.DataFrame: - - path = (directory / str(shot)).with_suffix(".h5") - df = pd.read_hdf(path, key=signal) - - return pd.DataFrame(df) - -def running_time( - directory: Path, - shot: int, - ip_threshold: float, -) -> tuple[float, float]: - - path = (directory / str(shot)).with_suffix(".h5") - with pd.HDFStore(path, 'r') as store: - df = store['ip']['ipsip'] - df = df.loc[df > ip_threshold] - start_time = df.index[0] - end_time = df.index[-1] - return start_time, end_time - -def align( - df: pd.DataFrame, - start_time: float, - end_time: float, - fs: float, -) -> pd.DataFrame: - - # get sampling frequency - fs_raw = len(df) / (df.index[-1] - df.index[0]) - - # crop time - df = df.loc[(df.index >= start_time) & (df.index <= end_time)] - - # resample - num = len(df) - num = int(num * fs / fs_raw) - - df = pd.DataFrame( - {col: resample(df[col].values, num) for col in df.columns}, - index=np.linspace(df.index[0], df.index[-1], num) - ) - - # mark on-off states - start_nan = (df.index[0] - start_time) * fs - end_nan = (end_time - df.index[-1]) * fs - start_pad = pd.DataFrame( - 0, index=pd.RangeIndex(start=int(start_nan)), columns=df.columns) - end_pad = pd.DataFrame( - 0, index=pd.RangeIndex(start=int(len(df) + start_nan), stop=int(len(df) + start_nan + end_nan)), columns=df.columns) - - df_state = pd.DataFrame(True, index=df.index, columns=df.columns) - start_pad_state = pd.DataFrame(False, index=start_pad.index, columns=df.columns) - end_pad_state = pd.DataFrame(False, index=end_pad.index, columns=df.columns) - - df = pd.concat([start_pad, df, end_pad], ignore_index=True) - df_state = pd.concat([start_pad_state, df_state, end_pad_state], ignore_index=True) - df_state.columns = [f"{col}_state" for col in df.columns] - - # combine data with state - df = df.astype(np.float32) - df_state = df_state.astype(np.bool) - df = pd.concat([df, df_state], axis=1) - - # convert time to ms - df = df.rename_axis("time") - - return df - - -def split( - df: pd.DataFrame, - window_ms: int, - hop_ms: int, - fs_khz: float, -) -> list[pd.DataFrame]: - - # Create sample indicies - num_samples = int((window_ms) * fs_khz) - hop_samples = int((hop_ms) * fs_khz) - - # Separate samples - samples = [] - for start in range( - 0, len(df) - num_samples + 1, hop_samples - ): - end = start + num_samples - sample = df.iloc[start:end] - if len(sample) == num_samples: - samples.append(sample) - - return samples - -def transform_individual_sample( - x: np.ndarray, - ) -> np.ndarray: - x_tensor = torch.from_numpy(x).float() - y = torch.stft( - x_tensor, - n_fft=1024, - hop_length=256, - window=torch.hann_window(1024), - return_complex=True - ) - y = torch.log(torch.abs(y)) - # y = torch.clip(y, min=-10, max=5) - return y.numpy() - - -def create_missing_signal_dataframes( - cfg: dict, - processed_signals: set, - reference_df: pd.DataFrame -) -> list[pd.DataFrame]: - """Create fully off dataframes for missing signals using reference dataframe structure.""" - - missing_dfs = [] - - for signal in cfg["signal"]: - signal_name, signal_abbr, do_transform = signal - if signal_abbr not in processed_signals: - print(f"Creating fully off dataframe for missing signal {signal_name}") - - # Create off dataframe by copying structure and zeroing values - off_df = reference_df.copy() - - # Get columns that belong to the reference signal (to replace with new signal columns) - ref_signal_cols = [col for col in off_df.columns if not col.endswith('_state')] - - # Create new column names for the missing signal - new_cols = {} - new_state_cols = {} - - for i, col in enumerate(ref_signal_cols): - new_col_name = f"{signal_abbr}col{i}" - new_cols[col] = new_col_name - new_state_cols[f"{col}_state"] = f"{new_col_name}_state" - - # Rename columns to match the missing signal - off_df = off_df.rename(columns={**new_cols, **new_state_cols}) - - # Set all data columns to 0 and all state columns to False - for col in off_df.columns: - if col.endswith('_state'): - off_df[col] = False - else: - off_df[col] = 0.0 - - missing_dfs.append(off_df) - - return missing_dfs - - -def transform_samples( - samples: list[pd.DataFrame], - directory: Path, - signal_config: list[tuple], - shot: int, -) -> list[dict]: - directory.mkdir(parents=True, exist_ok=True) - print(f"Processing {len(samples)} samples for shot {shot}") - samples_dict = [] - - # Create mapping from signal abbreviation to whether it should be transformed - transform_map = {} - for signal_name, signal_abbr, should_transform in signal_config: - transform_map[signal_abbr] = should_transform - - for i, sample in enumerate(samples): - - # Remove columns ending with '_state' - sample_to_save = sample.loc[:, ~sample.columns.str.endswith('_state')] - - # Only save if not fully off (i.e., at least one True in any state col) - state_cols = [col for col in sample.columns if col.endswith('_state')] - if np.any(sample[state_cols].to_numpy()): - - # First pass: apply transformations and collect results - sample_dict = {} - transformed_sample = None - original_time_length = len(sample_to_save) - - for col in sample_to_save.columns: - # Convert each column to float32 numpy array - col_array = sample_to_save[col].values.astype(np.float32) - - # Determine if this column should be transformed based on signal abbreviation - should_transform = False - for signal_abbr in transform_map.keys(): - if col.startswith(signal_abbr): - should_transform = transform_map[signal_abbr] - break - - if should_transform: - transformed_array = transform_individual_sample(col_array) - sample_dict[col] = transformed_array - # Store an example transformed sample to get target dimensions - if transformed_sample is None: - transformed_sample = transformed_array - print(f"Reference transformed sample shape: {transformed_array.shape}") - else: - # Store original array for now, will resample later - sample_dict[col] = col_array - - # Second pass: resample non-transformed samples to match transformed dimensions - if transformed_sample is not None: - target_width = transformed_sample.shape[-1] # Last dimension is time - # Calculate target sample frequency based on transformed sample - target_fs = target_width / original_time_length - print(f"Target frequency ratio: {target_fs:.4f} (target width: {target_width}, original length: {original_time_length})") - - for col in sample_dict.keys(): - # Check if this column was transformed - should_transform = False - for signal_abbr in transform_map.keys(): - if col.startswith(signal_abbr): - should_transform = transform_map[signal_abbr] - break - - if not should_transform: - # Resample non-transformed data to match target width - original_array = sample_dict[col] - resampled_array = resample_nearest(original_array, target_width) - - # Crop end if needed to ensure exact match - if len(resampled_array) > target_width: - resampled_array = resampled_array[:target_width] - elif len(resampled_array) < target_width: - # Pad with zeros if too short (shouldn't happen with resample_nearest) - pad_width = target_width - len(resampled_array) - resampled_array = np.pad(resampled_array, (0, pad_width), mode='constant') - - sample_dict[col] = resampled_array.astype(np.float32) - print(f"Resampled {col} from {len(original_array)} to {len(resampled_array)}") - - samples_dict.append(sample_dict) - print(f"Sample {i} processed with {len(sample_dict)} signals") - - return samples_dict - -def save_samples( - samples: list[dict], - directory: Path, - shot: int -) -> None: - directory.mkdir(parents=True, exist_ok=True) - print(f"Saving {len(samples)} samples to {directory}") - for i, sample in enumerate(samples): - # Save using joblib - joblib.dump(sample, directory / f"{shot}_{i}.pkl", compress=True) - -def process_shot( - shot: int, - cfg: dict, - out_dir: Path, -) -> None: - - dfs = [] - start_time = None - end_time = None - - try: - start_time, end_time = running_time( - directory=Path(cfg["raw_data_dir"]), - shot=shot, - ip_threshold=cfg["ip_threshold"] - ) - reference_len = int((end_time - start_time) * cfg["fs_khz"]) - print(f"Running time for shot {shot}: {start_time} to {end_time}") - except Exception as e: - print(f"Error: Could not determine running time for shot {shot}: {e}") - return - - # Process each signal and track which ones succeeded - processed_signals = set() - - for signal in cfg["signal"]: - signal_name, signal_abbr, is_transformed = signal - df = None - - try: - # Try to extract and process the signal - df = extract(shot=shot, directory=Path(cfg["raw_data_dir"]), signal=signal_name) - df.columns = [f"{signal_abbr}{col}" if col != "time" else col for col in df.columns] - df = align(df, start_time, end_time, cfg["fs_khz"]) - processed_signals.add(signal_abbr) - dfs.append(df) - print(f"Successfully processed signal {signal_name} for shot {shot}") - - except Exception as e: - print(f"Error processing signal {signal_name} for shot {shot}: {e}") - - if not dfs: - print(f"Error: No dataframes created for shot {shot}") - return - - # For missing signals, create "fully off" dataframes using the structure of the last dataframe - if len(processed_signals) < len(cfg["signal"]): - reference_df = dfs[-1] # Use the last successfully processed dataframe as reference - missing_dfs = create_missing_signal_dataframes(cfg, processed_signals, reference_df) - dfs.extend(missing_dfs) - - df = pd.concat(dfs, axis=1, join='inner') - - num_samples = len(df) - new_index = np.linspace(start_time, end_time, num_samples) - df.index = new_index - df.index = pd.to_timedelta(df.index, unit='ms') - - samples = [df] # no splitting for this dataset - print(f"Shot {shot} has {len(samples)} samples after splitting.") - samples_dict = transform_samples(samples, out_dir, cfg["signal"], shot) - - save_samples(samples_dict, out_dir, shot) - print(f"Processed shot {shot} successfully with {len(cfg['signal'])} signals.") - - return - - -def index_dataset(out_dir: Path) -> None: - - files = list(out_dir.glob("*.pkl")) - df_files = pd.DataFrame({'files': [str(file) for file in files]}) - df_files.to_pickle(out_dir / "index.pkl") - - print(f"Indexed {len(files)} files.") - - -def prepare_dataset(cfg: dict) -> None: - - raw_data_dir = Path(cfg["raw_data_dir"]) - cache_dir = Path(cfg["output_dir"]) / "cache" - cache_dir.mkdir(parents=True, exist_ok=True) - - # Collect and sort all shot numbers - print(f"Collecting shots from {raw_data_dir}...") - all_shots = [ - int(p.stem) - for p in raw_data_dir.iterdir() - if p.suffix == ".h5"] - all_shots.sort() - - # if cfg["randomize_shots"]: - # np.random.seed(cfg["random_seed"]) - # all_shots = np.random.permutation(all_shots) - # all_shots = all_shots[:cfg["num_shots"]] - - # print(f"Processing {len(all_shots)} shots into cache...") - - mapper = ParallelMapper() - mapper(process_shot, [170000], cfg=cfg, out_dir=cache_dir) - - # for shot in tqdm(all_shots): # for debugging - # process_shot(shot, cfg, out_dir=cache_dir) - # break - - # Move cached files into train/test split - print("Splitting dataset into train and valid sets...") - all_files = list(cache_dir.glob("*.pkl")) - all_files.sort() - train_files, valid_files = train_test_split( - all_files, - test_size=cfg.get("train_test_split", 0.2), - random_state=cfg["random_seed"]) - - train_dir = Path(cfg["output_dir"]) / "train" - valid_dir = Path(cfg["output_dir"]) / "valid" - train_dir.mkdir(parents=True, exist_ok=True) - valid_dir.mkdir(parents=True, exist_ok=True) - - for f in train_files: - f.rename(train_dir / f.name) - for f in valid_files: - f.rename(valid_dir / f.name) - - # Index the datasets - index_dataset(train_dir) - index_dataset(valid_dir) - - # Remove cache directory after splitting - for f in cache_dir.glob("*"): f.unlink() - cache_dir.rmdir() - - print("Dataset preparation complete.") - -if __name__ == "__main__": - cfg = sample_cfg.copy() - prepare_dataset(cfg=cfg) \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/prepare_dataset.py b/src/fusionaihub/datasets/prepare/prepare_dataset.py index 152b0fd..4f623b9 100644 --- a/src/fusionaihub/datasets/prepare/prepare_dataset.py +++ b/src/fusionaihub/datasets/prepare/prepare_dataset.py @@ -8,12 +8,17 @@ import yaml import numpy as np +import logging from pathlib import Path from sklearn.model_selection import train_test_split from typing import Optional from ...util.parmap import ParallelMapper -from .core import process_shot_stft, index_dataset +from .shot_processing import process_shot_stft +from .dataset_utils import index_dataset + +# Set up logger for this module +logger = logging.getLogger(__name__) def load_config(config_path: Optional[str] = None) -> dict: @@ -36,6 +41,22 @@ def load_config(config_path: Optional[str] = None) -> dict: return cfg +def should_use_stft_processing(cfg: dict) -> bool: + """ + Determine whether to use STFT processing based on configuration. + + Args: + cfg: Configuration dictionary + + Returns: + True if any signals should be transformed with STFT, False otherwise + """ + for signal in cfg["signal"]: + if signal.get("make_stft", False): + return True + return False + + def prepare_dataset(cfg: dict) -> None: """ Prepare the complete fusion dataset using the modular pipeline. @@ -53,8 +74,17 @@ def prepare_dataset(cfg: dict) -> None: cache_dir = Path(cfg["output_dir"]) / "cache" cache_dir.mkdir(parents=True, exist_ok=True) + logger.info(f"Target sampling frequency: {cfg['fs_khz']} kHz") + logger.info("Signals configured:") + for signal in cfg["signal"]: + signal_name = signal["name"] + signal_abbr = signal["abbr"] + should_transform = signal.get("make_stft", False) + logger.info(f" - {signal_name} ({signal_abbr}): transform={should_transform}") + logger.info("=" * 40) + # Collect and sort all shot numbers - print(f"Collecting shots from {raw_data_dir}...") + logger.info(f"Collecting shots from {raw_data_dir}...") all_shots = [ int(p.stem) for p in raw_data_dir.iterdir() @@ -70,35 +100,32 @@ def prepare_dataset(cfg: dict) -> None: if cfg.get("num_shots") is not None: all_shots = all_shots[:cfg["num_shots"]] - print(f"Processing {len(all_shots)} shots into cache...") + logger.info(f"Processing {len(all_shots)} shots into cache...") # Clean up existing cache directory if it exists - if cache_dir.exists(): - import shutil - print(f"Removing existing cache directory: {cache_dir}") - shutil.rmtree(cache_dir) - cache_dir.mkdir(parents=True, exist_ok=True) - # Process shots using parallel mapping - # For debugging, process single shot - # process_shot_stft(170000, cfg, cache_dir) - mapper = ParallelMapper() - mapper(process_shot_stft, all_shots[:10], cfg=cfg, out_dir=cache_dir) - - # For production, uncomment this line and comment the above + # if cache_dir.exists(): + # import shutil + # logger.info(f"Removing existing cache directory: {cache_dir}") + # shutil.rmtree(cache_dir) + # cache_dir.mkdir(parents=True, exist_ok=True) + + # Process shots using the appropriate function + # process_shot_stft(170000, cfg, cache_dir) # For debugging + # mapper = ParallelMapper() # mapper(process_shot_stft, all_shots, cfg=cfg, out_dir=cache_dir) # Move cached files into train/test split - print("Splitting dataset into train and valid sets...") - all_files = list(cache_dir.glob("*.pkl")) + logger.info("Splitting dataset into train and valid sets...") + all_files = list(cache_dir.glob("*.joblib")) all_files.sort() if len(all_files) == 0: - print("Warning: No processed files found. Dataset preparation incomplete.") + logger.warning("Warning: No processed files found. Dataset preparation incomplete.") return # Handle edge case where there are too few files for train-test split if len(all_files) == 1: - print("Warning: Only 1 file found. Placing in train directory.") + logger.warning("Warning: Only 1 file found. Placing in train directory.") train_files = all_files valid_files = [] else: @@ -129,9 +156,9 @@ def prepare_dataset(cfg: dict) -> None: f.unlink() cache_dir.rmdir() - print("Dataset preparation complete.") - print(f"Training samples: {len(train_files)}") - print(f"Validation samples: {len(valid_files)}") + logger.info("Dataset preparation complete.") + logger.info(f"Training samples: {len(train_files)}") + logger.info(f"Validation samples: {len(valid_files)}") def main(): @@ -145,9 +172,22 @@ def main(): default=None, help="Path to configuration YAML file (default: config/default.yaml)" ) + parser.add_argument( + "--log-level", + type=str, + default="INFO", + choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"], + help="Set logging level (default: INFO)" + ) args = parser.parse_args() + # Set up logging + logging.basicConfig( + level=getattr(logging, args.log_level.upper()), + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" + ) + # Load configuration cfg = load_config(args.config) diff --git a/src/fusionaihub/datasets/prepare/sample_processing.py b/src/fusionaihub/datasets/prepare/sample_processing.py new file mode 100644 index 0000000..d3e9fb0 --- /dev/null +++ b/src/fusionaihub/datasets/prepare/sample_processing.py @@ -0,0 +1,95 @@ +""" +Sample processing utilities for fusion dataset preparation. + +This module contains functions for splitting signals into time windows, +applying transformations to samples, and saving processed data. +""" + +import numpy as np +import pandas as pd +import joblib +import logging +from pathlib import Path +from typing import Dict + +# Set up logger for this module +logger = logging.getLogger(__name__) + + +def split_samples( + df: pd.DataFrame, + shot_number: int, + window_ms: int | None = None, + hop_ms: int | None = None, + fs_khz: float | None = None, +) -> list[dict[str, pd.DataFrame]]: + """ + Split signal data into overlapping time windows. + + Args: + df: Input DataFrame with signal data + window_ms: Window size in milliseconds + hop_ms: Hop size in milliseconds + fs_khz: Sampling frequency in kHz + + Returns: + List of DataFrame samples + """ + + if window_ms is None or hop_ms is None or fs_khz is None: + return [{f"{shot_number}_0": df}] + + else: + # Create sample indicies + num_samples = int((window_ms) * fs_khz) + hop_samples = int((hop_ms) * fs_khz) + + # Separate samples + samples = [] + for start_index in range(0, len(df) - num_samples + 1, hop_samples): + end_index = start_index + num_samples + sample = df.iloc[start_index:end_index] + if len(sample) == num_samples: + samples.append({ + f"{shot_number}_{start_index}": sample, + }) + + return samples + + +def remove_empty_samples( + samples: list[dict[str, pd.DataFrame]], +) -> list[dict[str, pd.DataFrame]]: + """ + Remove empty samples from a list of samples. + + Args: + samples: List of sample DataFrames + """ + samples = [ + { + key: value.drop(columns=[col for col in value.columns if col.endswith('_state')]) + for key, value in sample.items() + if np.any(value.loc[:, value.columns.str.endswith('_state')].to_numpy()) + } + for sample in samples + ] + + return samples + + +def save_sample( + sample: Dict, + directory: Path, + id_val: str, +) -> None: + """ + Save processed samples to disk using joblib compression. + + Args: + samples: List of sample dictionaries to save + directory: Output directory + shot: Shot number for filename generation + """ + logger.info(f"Saving sample to {directory / f'{id_val}.joblib'}") + joblib.dump(sample, directory / f"{id_val}.joblib") \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/shot_processing.py b/src/fusionaihub/datasets/prepare/shot_processing.py new file mode 100644 index 0000000..448f812 --- /dev/null +++ b/src/fusionaihub/datasets/prepare/shot_processing.py @@ -0,0 +1,193 @@ +""" +Shot processing utilities for fusion dataset preparation. + +This module contains the main shot processing logic that orchestrates +data extraction, alignment, transformation, and saving for individual shots. +""" + +import numpy as np +import pandas as pd +import logging +from pathlib import Path +from typing import Dict + +from .data_extraction import ( + extract_signal, + extract_running_time, + align_signal, +) +from .sample_processing import ( + split_samples, + remove_empty_samples, + save_sample, +) +from .signal_processing import ( + identity_transform, + stft_transform, + resample_transform, +) + +# Set up logger for this module +logger = logging.getLogger(__name__) + + +def process_shot_stft( + shot_number: int, + cfg: Dict, + out_dir: Path, +) -> None: + """ + Process a single shot through the complete data preparation pipeline accounting for STFT transformations. + + This function orchestrates the complete processing workflow for a shot: + 1. Determines plasma running time + 2. Extracts and aligns all configured signals to be transformed + 3. Handles missing signals by creating placeholder dataframes + 4. Combines all signals into a unified dataframe + 5. Splits into samples + 6. Transforms and saves the processed samples using STFT transformations. + + Args: + shot: Shot number to process + cfg: Configuration dictionary + out_dir: Output directory for processed files + """ + + # Extract running time + try: + start_time, end_time = extract_running_time( + shot_number=shot_number, + directory=Path(cfg["raw_data_dir"]), + ip_threshold=cfg["ip_threshold"], + start_time=cfg["start_time"], + end_time=cfg["end_time"], + ) + logger.info(f"Running time for shot {shot_number}: {start_time} to {end_time}") + except Exception as e: + logger.error(f"Error: Could not determine running time for shot {shot_number}: {e}") + return + + # Extract all signals + try: + dfs = [] + missing_signals = [] + for signal in cfg["signal"]: + try: + df = extract_signal( + shot_number=shot_number, + directory=Path(cfg["raw_data_dir"]), + signal=signal['name'], + start_time=start_time, + end_time=end_time, + ) + df.columns = [ + f"{signal['abbr']}{col}" if col != "time" else col + for col in range(len(df.columns)) + ] + + # Add a log to validate our assumption about the config and the transform key: + logger.debug(f"Signal config for {signal['name']}: {signal}") + + # Add a column to the dataframe for this signal indicating if a transform is present. + # We'll use the signal's abbreviation to name the column, e.g., 'IP_transform' + df = align_signal( + df=df, + start_time=start_time, + end_time=end_time, + fs=cfg["fs_khz"], + ) + dfs.append(df) + except Exception as e: + missing_signals.append((signal['name'], signal['abbr'])) + except Exception as e: + logger.error(f"Error: Could not extract signals for shot {shot_number}: {e}") + raise e + + # Create main aligned dataframe (important since interpolated signals + # could have alignment off) + try: + df = pd.concat(dfs, axis=1, join='inner') + except Exception as e: + logger.error(f"Error: Could not concatenate dataframes for shot {shot_number}: {e}") + raise e + + # Add missing signals + try: + for signal_name, signal_abbr in missing_signals: + df[signal_abbr] = 0.0 + df[f"{signal_abbr}_state"] = False + except Exception as e: + logger.error(f"Error: Could not add missing signals for shot {shot_number}: {e}") + raise e + + # Split into samples + try: + samples = split_samples( + df=df, + shot_number=shot_number, + window_ms=cfg["window_ms"], + hop_ms=cfg["hop_ms"], + fs_khz=cfg["fs_khz"], + ) + except Exception as e: + logger.error(f"Error: Could not split samples for shot {shot_number}: {e}") + raise e + + # Remove empty samples + try: + samples = remove_empty_samples(samples) + except Exception as e: + logger.error(f"Error: Could not remove empty samples for shot {shot_number}: {e}") + raise e + + # If no transform function is provided, save the samples as is + try: + if not cfg["do_stft"]: + for sample in samples: + transformed_samples = {} + for key, value in sample.items(): + for signal in cfg["signal"]: + abbr = signal['abbr'] + cols = [col for col in value.columns if abbr in col] + transformed_samples[abbr] = identity_transform( + x=value[cols].to_numpy().T) + save_sample(transformed_samples, out_dir, key) + return + except Exception as e: + logger.error(f"Error: Could not save samples for shot {shot_number}: {e}") + raise e + + # Get the first transformed sample to determine STFT dimensions + try: + first_arr = list(samples[0].values())[0].iloc[:, 0].values + transform_shape = stft_transform(x=first_arr).shape + logger.info(f"Using {getattr(first_arr, 'name', 'unknown')} as reference for STFT dimensions: {transform_shape}") + except Exception as e: + logger.error(f"Error: Could not get first transformed sample for shot {shot_number}: {e}") + raise e + + # Transform and save samples + try: + for sample in samples: + transformed_samples = {} + for key, value in sample.items(): + for signal in cfg["signal"]: + abbr = signal['abbr'] + cols = [col for col in value.columns if abbr in col] + if signal["make_stft"]: + transformed_samples[abbr] = stft_transform( + x=value[cols].to_numpy().T, + n_fft=cfg["stft"]["n_fft"], + hop_length=cfg["stft"]["hop_length"], + ) + else: + transformed_samples[abbr] = resample_transform( + x=value[cols].to_numpy().T, + ref_shape=transform_shape, + ) + save_sample(transformed_samples, out_dir, key) + except Exception as e: + logger.error(f"Error: Could not transform samples for shot {shot_number}: {e}") + raise e + + return \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/core/signal_processing.py b/src/fusionaihub/datasets/prepare/signal_processing.py similarity index 51% rename from src/fusionaihub/datasets/prepare/core/signal_processing.py rename to src/fusionaihub/datasets/prepare/signal_processing.py index eacb05a..2339962 100644 --- a/src/fusionaihub/datasets/prepare/core/signal_processing.py +++ b/src/fusionaihub/datasets/prepare/signal_processing.py @@ -8,9 +8,13 @@ import numpy as np import torch from scipy.signal import resample +import logging +# Set up logger for this module +logger = logging.getLogger(__name__) -def resample_nearest(y: np.ndarray, new_len: int) -> np.ndarray: + +def resample_fn(y: np.ndarray, new_len: int) -> np.ndarray: """ Resample a signal to a new length using scipy.signal.resample. @@ -30,7 +34,11 @@ def resample_nearest(y: np.ndarray, new_len: int) -> np.ndarray: return np.asarray(resampled) -def transform_individual_sample(x: np.ndarray) -> np.ndarray: +def stft_transform( + x: np.ndarray, + n_fft: int = 1024, + hop_length: int = 256, +) -> np.ndarray: """ Apply STFT transformation to an individual sample. @@ -43,14 +51,50 @@ def transform_individual_sample(x: np.ndarray) -> np.ndarray: Returns: Log-magnitude STFT representation """ + x = x.astype(np.float32) x_tensor = torch.from_numpy(x).float() y = torch.stft( x_tensor, - n_fft=1024, - hop_length=256, - window=torch.hann_window(1024), + n_fft=n_fft, + hop_length=hop_length, + window=torch.hann_window(n_fft), return_complex=True ) y = torch.log(torch.abs(y)) - # y = torch.clip(y, min=-10, max=5) - return y.numpy() \ No newline at end of file + return y.numpy() + + +def resample_transform( + x: np.ndarray, + ref_shape: tuple, +) -> np.ndarray: + """ + Resample a signal to match a reference shape. + + Args: + x: Input signal + ref_shape: Reference shape (tuple from STFT result) + + Returns: + Resampled signal to match reference time dimension + """ + x = x.astype(np.float32) + target_length = ref_shape[1] + y = [resample_fn(x_, target_length) for x_ in x] + y = np.expand_dims(y, axis=1) + return np.array(y) + + +def identity_transform(x: np.ndarray) -> np.ndarray: + """ + Identity transform. + + Args: + x: Input signal + + Returns: + Input signal + """ + y = x.astype(np.float32) + y = np.expand_dims(y, axis=1) + return y \ No newline at end of file From 2f2d04688a971bef6bf9c3fa575c9d7f9e99007d Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Thu, 10 Jul 2025 21:46:05 -0400 Subject: [PATCH 025/103] Update README.md to include instructions for activating the virtual environment and installing necessary packages. --- README.md | 12 +++++++- notebooks/data_preparation.ipynb | 48 ++------------------------------ 2 files changed, 14 insertions(+), 46 deletions(-) diff --git a/README.md b/README.md index 7a531b0..c0436b3 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,14 @@ # FusionAIHub -This will be the readme of the FusionAIHub. \ No newline at end of file +This will be the readme of the FusionAIHub. + +When you are in the root directory, you can run the following command to activate the virtual environmnent: +``` +module load anaconda3/2024.10 +python -m venv .venv +source .venv/bin/activate +pip install --upgrade pip +pip install uv +uv sync +``` \ No newline at end of file diff --git a/notebooks/data_preparation.ipynb b/notebooks/data_preparation.ipynb index 534d2fa..c795280 100644 --- a/notebooks/data_preparation.ipynb +++ b/notebooks/data_preparation.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "9b8f64ca", "metadata": {}, "outputs": [], @@ -24,7 +24,7 @@ "import yaml\n", "\n", "shot_number = 170000\n", - "yaml_path = \"/scratch/gpfs/nc1514/FusionAIHub/src/fusionaihub/datasets/prepare/config/default.yaml\"\n", + "yaml_path = \"../src/fusionaihub/datasets/prepare/config/default.yaml\"\n", "with open(yaml_path, 'r') as f:\n", " cfg = yaml.safe_load(f)\n", "\n", @@ -138,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 80, "id": "de25ce4f", "metadata": {}, "outputs": [ @@ -232,48 +232,6 @@ " break\n", " break" ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "c8797e68", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "float32\n" - ] - } - ], - "source": [ - "import joblib\n", - "test_load = joblib.load('/scratch/gpfs/EKOLEMEN/nc1514/foundation_v2/train/170000_0.pkl')\n", - "print(test_load['mhr'].dtype)" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "883637b2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.imshow(test_load['ece'][4], aspect='auto', origin='lower')\n", - "plt.show()" - ] } ], "metadata": { From 9c8a50cca7a7fc679e0e7e70e896e363acd83f4b Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Thu, 10 Jul 2025 21:57:28 -0400 Subject: [PATCH 026/103] Enhance README.md with project purpose, team members, and setup instructions. Remove deprecated logging configuration file and update dataset preparation README for improved clarity and modular structure. --- README.md | 20 +++++- src/fusionaihub/datasets/prepare/README.md | 32 ++++------ .../datasets/prepare/logging_config.py | 64 ------------------- 3 files changed, 29 insertions(+), 87 deletions(-) delete mode 100644 src/fusionaihub/datasets/prepare/logging_config.py diff --git a/README.md b/README.md index c0436b3..88d3d3c 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,24 @@ # FusionAIHub +A general fusion hub for the Princeton cluster designed to standardize fusion machine learning processes for the plasma control group at Princeton University. -This will be the readme of the FusionAIHub. +## Purpose -When you are in the root directory, you can run the following command to activate the virtual environmnent: -``` +This repository serves as a centralized platform for fusion-related machine learning workflows, providing standardized tools, processes, and methodologies for plasma control research at Princeton. + +## Team + +This project is led by: +- **Egemen Kolemen** +- **Azarakash Jalalvand** +- **Peter Steiner** +- **Kouroche Bouichat** +- **Nathaniel Chen** + +## Setup + +When you are in the root directory, you can run the following command to activate the virtual environment: +```bash module load anaconda3/2024.10 python -m venv .venv source .venv/bin/activate diff --git a/src/fusionaihub/datasets/prepare/README.md b/src/fusionaihub/datasets/prepare/README.md index 7394c25..b194a3b 100644 --- a/src/fusionaihub/datasets/prepare/README.md +++ b/src/fusionaihub/datasets/prepare/README.md @@ -9,16 +9,16 @@ The pipeline has been refactored into a modular structure: ``` src/fusionaihub/datasets/prepare/ ├── config/ -│ └── default.yaml # Default configuration file -├── core/ # Core processing modules -│ ├── __init__.py # Package initialization -│ ├── signal_processing.py # Signal resampling and transformation -│ ├── data_extraction.py # Data extraction and alignment -│ ├── sample_processing.py # Sample splitting and transformation -│ ├── dataset_utils.py # Dataset utilities and indexing -│ └── shot_processing.py # Shot-level processing orchestration +│ ├── default.yaml # Default configuration file +│ └── raw.yaml # Raw signal configuration +├── __init__.py # Package initialization +├── signal_processing.py # Signal resampling and transformation +├── data_extraction.py # Data extraction and alignment +├── sample_processing.py # Sample splitting and transformation +├── dataset_utils.py # Dataset utilities and indexing +├── shot_processing.py # Shot-level processing orchestration ├── prepare_dataset.py # Main executable script -├── prepare2.py # Original monolithic script (legacy) +├── __main__.py # Module entry point for direct execution └── README.md # This file ``` @@ -87,10 +87,10 @@ Each signal is configured as a list: `[signal_name, abbreviation, should_transfo ### Command Line ```bash # Use default configuration -python -m src.fusionaihub.datasets.prepare.prepare_dataset +python -m src.fusionaihub.datasets.prepare # Use custom configuration file -python -m src.fusionaihub.datasets.prepare.prepare_dataset --config /path/to/config.yaml +python -m src.fusionaihub.datasets.prepare --config /path/to/config.yaml ``` ### Programmatic Usage @@ -152,15 +152,7 @@ Each `.pkl` file contains a dictionary with signal arrays, where transformed sig 4. **Missing Signal Handling**: Automatically creates placeholder data for missing signals 5. **Flexible Transformations**: Configurable per-signal transformation (STFT or raw) 6. **Train/Validation Split**: Automatic dataset splitting with indexing - -## Migration from Legacy Code - -If you were using the original `prepare2.py`, the new modular structure provides the same functionality with improved organization: - -- Configuration moved from Python dictionary to YAML file -- Functions split into logical modules -- Same processing pipeline and output format -- Command-line interface added for easier use +7. **Multiple Configurations**: Includes default and raw signal configurations ## Dependencies diff --git a/src/fusionaihub/datasets/prepare/logging_config.py b/src/fusionaihub/datasets/prepare/logging_config.py deleted file mode 100644 index 20ddf51..0000000 --- a/src/fusionaihub/datasets/prepare/logging_config.py +++ /dev/null @@ -1,64 +0,0 @@ -""" -Logging configuration for fusion dataset preparation. - -This module provides centralized logging configuration for the signal processing -and dataset preparation pipeline. -""" - -import logging -import sys -from typing import Optional - - -def setup_logging( - level: str = "INFO", - format_string: Optional[str] = None, - logger_name: str = "fusionaihub.datasets.prepare" -) -> logging.Logger: - """ - Set up logging configuration for the dataset preparation pipeline. - - Args: - level: Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL) - format_string: Custom format string for log messages - logger_name: Name for the logger - - Returns: - Configured logger instance - """ - if format_string is None: - format_string = "%(asctime)s - %(name)s - %(levelname)s - %(message)s" - - # Create logger - logger = logging.getLogger(logger_name) - logger.setLevel(getattr(logging, level.upper())) - - # Remove existing handlers to avoid duplicates - for handler in logger.handlers[:]: - logger.removeHandler(handler) - - # Create console handler - console_handler = logging.StreamHandler(sys.stdout) - console_handler.setLevel(getattr(logging, level.upper())) - - # Create formatter - formatter = logging.Formatter(format_string) - console_handler.setFormatter(formatter) - - # Add handler to logger - logger.addHandler(console_handler) - - return logger - - -def get_logger(name: str) -> logging.Logger: - """ - Get a logger instance for a specific module. - - Args: - name: Module name (typically __name__) - - Returns: - Logger instance - """ - return logging.getLogger(f"fusionaihub.datasets.prepare.{name}") \ No newline at end of file From 5aaff62b3787c9ef083d11d0e28e9cebdfb56493 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Thu, 10 Jul 2025 21:57:59 -0400 Subject: [PATCH 027/103] Update dataset preparation README to reflect changes in output file formats from .pkl to .joblib and .csv for dataset indexing, enhancing clarity on the output structure. --- src/fusionaihub/datasets/prepare/README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/fusionaihub/datasets/prepare/README.md b/src/fusionaihub/datasets/prepare/README.md index b194a3b..0903a2d 100644 --- a/src/fusionaihub/datasets/prepare/README.md +++ b/src/fusionaihub/datasets/prepare/README.md @@ -131,18 +131,18 @@ The pipeline produces the following output structure: ``` output_dir/ ├── train/ -│ ├── 170000_0.pkl # Processed samples -│ ├── 170001_0.pkl +│ ├── 170000_0.joblib # Processed samples +│ ├── 170001_0.joblib │ ├── ... -│ └── index.pkl # Dataset index +│ └── index.csv # Dataset index └── valid/ - ├── 170010_0.pkl - ├── 170011_0.pkl + ├── 170010_0.joblib + ├── 170011_0.joblib ├── ... - └── index.pkl + └── index.csv ``` -Each `.pkl` file contains a dictionary with signal arrays, where transformed signals have STFT representation and non-transformed signals are resampled to match dimensions. +Each `.joblib` file contains a dictionary with signal arrays, where transformed signals have STFT representation and non-transformed signals are resampled to match dimensions. ## Key Features From aff09d3296b6a13a831e3b294274c8bc73049301 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Fri, 11 Jul 2025 09:40:29 -0400 Subject: [PATCH 028/103] Update README.md --- README.md | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 88d3d3c..18d92ed 100644 --- a/README.md +++ b/README.md @@ -6,15 +6,6 @@ A general fusion hub for the Princeton cluster designed to standardize fusion ma This repository serves as a centralized platform for fusion-related machine learning workflows, providing standardized tools, processes, and methodologies for plasma control research at Princeton. -## Team - -This project is led by: -- **Egemen Kolemen** -- **Azarakash Jalalvand** -- **Peter Steiner** -- **Kouroche Bouichat** -- **Nathaniel Chen** - ## Setup When you are in the root directory, you can run the following command to activate the virtual environment: @@ -25,4 +16,12 @@ source .venv/bin/activate pip install --upgrade pip pip install uv uv sync -``` \ No newline at end of file +``` + +## Contact + +For more information, please contact +- **Azarakash Jalalvand** +- **Peter Steiner** +- **Kouroche Bouichat** +- **Nathaniel Chen** From c67f69870696282eff410ec28660022e6368fbf5 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Fri, 11 Jul 2025 09:42:16 -0400 Subject: [PATCH 029/103] Update README.md --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 18d92ed..1450255 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,9 @@ This repository serves as a centralized platform for fusion-related machine lear When you are in the root directory, you can run the following command to activate the virtual environment: ```bash +git clone git@github.com:PlasmaControl/FusionAIHub.git +cd FusionAIHub +git switch foundation25 module load anaconda3/2024.10 python -m venv .venv source .venv/bin/activate From 9202eaa5309da64ded8a231064318fec855d1a67 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Fri, 11 Jul 2025 09:42:35 -0400 Subject: [PATCH 030/103] Update README.md --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index 1450255..67bf10b 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,6 @@ This repository serves as a centralized platform for fusion-related machine lear ## Setup -When you are in the root directory, you can run the following command to activate the virtual environment: ```bash git clone git@github.com:PlasmaControl/FusionAIHub.git cd FusionAIHub From f8eac8c1ea30fbf6eec494b5c16905e58f86301b Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Fri, 11 Jul 2025 09:42:49 -0400 Subject: [PATCH 031/103] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 67bf10b..c8b2ba6 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ A general fusion hub for the Princeton cluster designed to standardize fusion ma This repository serves as a centralized platform for fusion-related machine learning workflows, providing standardized tools, processes, and methodologies for plasma control research at Princeton. ## Setup - +In your scratch directory, run ```bash git clone git@github.com:PlasmaControl/FusionAIHub.git cd FusionAIHub From f6e301fcda2cfb0cd38305c0f28f11c5081664f1 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Fri, 11 Jul 2025 12:56:21 -0400 Subject: [PATCH 032/103] Refactor signal configuration in YAML and update related processing scripts to enhance clarity and functionality. Adjusted execution counts in Jupyter notebooks and improved handling of missing signals. Updated README.md to include a to-do for changing YAML loading to hydra. --- notebooks/accessing_data.ipynb | 37 +++--- notebooks/data_preparation.ipynb | 106 +++++++++++------- src/fusionaihub/datasets/prepare/README.md | 5 +- .../datasets/prepare/config/default.yaml | 35 ++++-- .../datasets/prepare/prepare_dataset.py | 22 ++-- .../datasets/prepare/shot_processing.py | 29 ++--- 6 files changed, 150 insertions(+), 84 deletions(-) diff --git a/notebooks/accessing_data.ipynb b/notebooks/accessing_data.ipynb index 86fbc6f..be68705 100644 --- a/notebooks/accessing_data.ipynb +++ b/notebooks/accessing_data.ipynb @@ -13,19 +13,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "836b5c67", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import joblib\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "import numpy as np" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "d564239f", "metadata": {}, "outputs": [], @@ -35,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 20, "id": "434b288f", "metadata": {}, "outputs": [ @@ -43,18 +44,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "mhr (8, 513, 13195)\n", - "ece (48, 513, 13195)\n", - "co2 (4, 513, 13195)\n", - "gas (5, 1, 13195)\n", - "ech (11, 1, 13195)\n", - "pin (8, 1, 13195)\n", - "tin (8, 1, 13195)\n" + "/scratch/gpfs/EKOLEMEN/hackathon/foundation25/train/175210_0.joblib\n", + "mhr (8, 513, 12028)\n", + "ece (48, 513, 12028)\n", + "co2 (4, 513, 12028)\n", + "gas (5, 1, 12028)\n", + "ech (11, 1, 12028)\n", + "pin (8, 1, 12028)\n", + "tin (8, 1, 12028)\n" ] }, { "data": { - "image/png": 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u7GG8B1ZsDswDRGln9nhbA3AQDyswcObtym4dGDICaqi7Nd1FzYPGJ7PiZOh4+3Fss7EFNu4DxI1gy8eKOGDkRohjvW9cOkDiMxVALj3QPhiAGucFAl/RgZXujamLBMDVKoCzq40bwuEr8awTWLNRska/gNc4XjrbNWmwmr3/7ZmoNdt3nYWLkzeBikDqA9cDxFyGNQWAi37nJpDPx2abPcS2AbuH3voiShBBQAH4DtQnZFHpLB0Qk2dM+DGJo4OL+Mc+a4s/gkVJyeJg7DUCGDg7p1g1edybgbpGlxWZNTEwU9WOFXdVEZMkFwhVsQVV+7WKgzxV6dgnrt13+M5IqseOMRZGHZRxtx+smC9SwS6RsVBB09QXaW9+MglJO4No16PBGqmgx+yQ8WM/8R687U5AEBerYNYMCAn7o7SBsbIf5XsBxCxeKS66yQmgDcDZ+LYfw0EG2dW+Z1BglR5r15uivza4sOmuiv9LrNWxANPsNHZ+xvil1Do7wg8ldhZvwvonxqYDqrg2b/N4xcFkfxw03Ke9X73d2d8B9Pq0SOaH/aMETdxEDJsFVIk+JpMUQIXjoSHAFJ/VYJf64+HA+7Q9GruM9BufZ9g8oMN99ANp/+FbHHfcVJVmz8LQ5CeoJ9hKOwKfRSEQ9GOmEwbYx98Dx+Wlw4Fo90LY3KX8+DAedZi7lOArvBEYNi0diElutjnh+zUFwBX4hohzgc8NOjLjjO3MQ7vx3rDZZg+3bcDuEbGYSDFMLJzEqy/Cw640AJ5vuFEc4AWg0lj8g/CgG3QAgGSYKmNU1LgSTtxrzbg4zkACqiYca3FWCJgYHI3TWB/AgroFlhyhzvoob9S/T3ZuXJsJGATu5uWi3xib5ECCIztrX2jJhg3tBHSgIDCAygQMTu4YFvBgG2tnJhDAUSxRZXBNRvsObl8dgR8Xw6zxPS46tkiNq7QzVQ4oyRIqJFgEIZAMNrVfB9Kw8DPRgQMqFm7toDfHi3ZsxvZxleO1j+Mm9zbTEWixI3k5tS/igMSxo7352fH70dcdJahfl/oX0nsDJmRfoo37Z9IDjF88R/6MiOLEFRwuwfUUWCYCxAFwiMfFiigaen8ZuNPBFU7X4wjW+L6eXN6JDe5Lu4ge0jAgvt4/DjLF2cPoK7JXDnp4TcHI+YbP2pIdgR5OQLTjiTPpAeDL2Fgmdpx+10GVaGxSRaKrT8G7A8tgKattWDU2r0P7nQ7dGCcyTiC8n8HzoLXHskYzDp9X4EUbns02e9hsA3YPu3FySwCgManQ5WLsU18k1F0bmvQUPAAdFXFBJVD0SZ2uN2kSMUDKxV6BuqSBXTP3ylITlpYBURzXYi6XYAdsESGwamTkAHftGiDjQlDVkgvCjRMslV177MYD4PS36bFSdSZodFWp2Dn82ydQyd1akpsFVI9vDoumtuElgjmCmfG1aFw/UOq7fhVYu1YHCThlQuJGyBBmumnxYuDk3TKyPCm17iYlgHGQpA46hOcn0AyQNABete8EQGgCLPKiRAhmNZ/YuCim/hkhGOK5ivZjkEmLld7bewQU3n+MX+MCKw7GmiPWsenjXkYXPTNiH2B1W0tDSEJvA2B41siYrpawEzGe3vccQ3F+hWXOPuiG5HkjzhP9OR1ZOu9AusoB9EQQUQR7Kx5zyjHCk5DF4izfxJKZVE4+x0sTPOASJuAcnll4XKz6uAhXLqcMFYRz1NtMigar3k/gzvT0AHhmjK84+KRHwvuPGzkAPWSE901wNj5P2sd1gHKOL/X7iOsZnoHad9HBBrPfNtvsIbcN2D305rOmPrCDzX2SkpHZIdvgLkFxEAEGSw/fUwcGYyKBHUWHfxlIDpuwHSAcVpM9WdeMBltYVRy8tYQaE7zHxjGQHQjXU8oNwbpwoeKtJO1uLa5DPoNrijk7mohMgzi4EviCnmAyCUOcIlQDAAFwF5504MOF1IHIGM8X8TUEA2NPCTqbyUVMYQDWEYA62Imu5SI7uOjIxp3QkwTcZFnZz9ndsAJzZTEOiFmOAQjoAEXP9PNMU15bXBPwIoBibMuAItkf6rGKXCgrgb0GmA93uI89Bu6LA7wT4Mu+ZtNWggQxWQ4MgfRAzwxONpYYwzYmAhBU2C36OHcgwRADujcVAKjI0xEPULQDzRfFxKEDP7JjYHt4XGpp7uJDZJ3HTY84yzdqBsS9rcJlC5fheOCcnBcGNjKGzYCx4nPFOsuYU/P7K8co+uWHu9KPY/MFs8QHwC39d/Z+FwKysY0Evnn0Z5ThA3TpVumMdzwXQ+PwkR3Y8NjcDkC31QRRHdzn0pNw/DoSwyh4K+pjmHGQDozjuk9A82abPdy2AbtHxMQn1c6yDbOMgxkdFn4t6jEmw4IF+ISpHUQAfQLV0wk+nQS3+3GbYF0T1pqxtmQLPvpCu9Qc3hrKoDDjLbtcQ8QYwd5LPvmWUvvEPQRqjxmWjAkDJDS1eHlcTGKeJzBcEqQZ8BG2nd9zLLIOxIQsmcAXmY4sCbaizca4JW9luqFHBgHNwB1xGoYFE2LaeGRXYvWSfooxWDz6H/3eTZamGWMHtjcbQeJ4XNACAHu/UEbCblI6I6J+DGZ5kjriwudtrgPeC3cqkyecXYtMYg94P2VTtMfH6XC5bIcAihLu0Z6E46dz1kpjLA8u29H8FsaFOthJjs2gpYfvEUSNLsrhYw0SjFW8mAdQIcOBAqjIAJoZO4bheRzYuiZo1TdLBK8Dhjxxq4+3y+OeNMXw8IdrFM6SI64jNj8cfyNoZEgB0DtqsFad3SczzC8O84wQTNGGZzmycLkxFbVNADpYjechkGh/HjnPjCEpaOibWX6Ql+7z5biROmngZGOMCWObbfYw2wbsHgUbJlUMSQcxSQq6S1Y6HjpZITkJOsCJHSrX8XGh8++qAuKrGBdnhU/KYlp4L4oD8wlxrSZeKrlFQkQbFsVcGpIDvS5bAc96RLhnaQya1uZCuA5KmDUY7eExMicB6XFfDuAgp4HqDnRCwFZdDoKf0b7oBa7x4zBeiYt3dhYyFkYHWbbQ+YUwY9eufGBv/No8LlCG9o62HdzcgMcZwtqtajrpilbTkPnZx0UQHmT3lOCGi3xf4OTBBe5BrDS498c+JBAyYNrbuTOKGkywZTHroK+HDnh4vz4o4zI4jh0EJN4Uj++MS7Sfn0OSApP20AbeKl2zjZIiCLd8gHC6IgloHTwymYJ7AEi/Z24a6FIfY/DifeBFYEGGMREgNth2MaZtiFML8JX7PZ0yUr0Pea9MookkihcBJfQG8LFA5jfn2u8xwgc8wcOfzTEBYswGZoPysgiopcEZSsR1nMxNis66c1M2Nhu/A0TIRyR9+SUyKawpM6HhMcroMbe8fSZOMC7RQd3mjd3sYbcN2D0CJgPzEH8zBok2gA7l/9sDE7troo3ZoQJAFp+4OXGF9AjM3UjQ5Et/8kVZYQAt5xoxbCKKmVIozv2RTclkRPz6COq4GCQPvCaopKYdd+nqv4UuLxk22HK6k46FbUhISKV1V61nwI1aW9rQGREgYtBSGgL7dTypf8/dZaM0RWf4+mdTabFQReJAMGEEA+6CZkzPCFKHm1O/ZxW4VItG9rL11zAmhu8Adt9NEJIaBCYnMYIKd5X5b+nnBjowzS79EfFQBEPcPCTGCUoPUm8wcVg/HgElk2U4fMmiKjg+O8hlhizBCMf8iXYaQRDbeXDrOaRBaP2xv+miq94Xfm5tYq756tfnDKOMCT4cE96/Y0xYRHhyIxObEz/P4GIN3Tl+FujPKsc5WWwei7GPShyk8beNAQJD7QCGoRcDIBpj2jgO2SEByodnOJ4HtWcnZU8UCeBqx+R/ua/qMiuIi1TOQdE/A8AdAZW3DQd0hEpER6AzgujdG0Az+kg6YObfAYLl9Bny0JD3li2+2WYPm23A7hGw0QOmRU/AmzQTyAWAk6xHoLsRyU7wOFwD6M55sP5IBNcjMtqMXWuYpoppWlGSZaSaHIoNo5ysGkVODbtptVi7avp21X9Sashi5ZW4SLFixLgrNpeXnEzYUdqr1AjIJpBJuUHzAJgIcPibx3AQc1LBA7DFhwvquEjwO84ABftJN6d/N5iJB87XY7g8rmeIwYoFcmCL+N3kbqHmJZAk93uJ2EHpi5p4/NnJvVCk1+P3GDMVY8bZEWZsBjjhgkmWkbfJhW8AlhSvRmWgff9sg5fxGoR/DbBJADp2QwdGCE3D6H4uxv0jnYAa2lLQNwP9+RhAJo/hjGQqrcdandyfAboGsUQcZrE+kLksw73w5shwMiYtmMM6uGRP4usGEEoX69olUwgETxjIE/f6MCa9MYL5BE7iyuK1sdFHN+sYrwc+m3p6Lh6CIR0eOwgCYLY1hs+n0+8yozhYeYFntcvJ83GymXCgNU5vEMTYxnCd3CTGmGE85TjO8nh87d93d79ys/LACnlyX5tt9pDaBuweBfNJ2eKHuGjZzlGzWjydwtL/q/SvDTvbE12oBybOADvjIuVAIBWb5EupKFPFblpxNi/YTQtyaijSEyCy15MVUZRUMU/rSVxd7L5zi9cTQRNMFLaFIv6QBTnusNXi1fokjP7e4HYNIKjoches/xiNg9NFFg42fEGN2qRAZAMScUhWFzf2gHhfUOjuDlkLNcYvkdkK1xsswWFcpfzfkPrw+xFeBV27w7Ul6eOBjIc6KLb4xdbjBmHXFWvnyPg+ON54zwPDMwasKwhe6Jb3jxR1VlhOSjYF+0qA5CtsgB1mYgM9C5XXSHDIj7JTBp+YlYCTE1Ab98C+GxhFAMFI9k5GHy8El2MM58joOMIggFQ4qKTYHJjAM/RhsH4DeAHiek/jZk/b4CQbGQig0vUS0RMPsnbgMnYxAdIDrNgINsX7KOIOHTTFuOTpR4ZwSNBqTPYYxgbgzL8CWRSYWndl+7WLV6ihnA7ZtDgG5zDp85UxdziR2EleujDY+9zHJkuGcVNxAtB4bxGOgVPPBmWTBoZ9s80eVtuA3aNg41wy7O5tsUBnUZK7UWEgbySUOkB48G994PcwoYMAwArET6miO1WssP2UrPZnFsU+rzibjkgwfTsmRgAIBsLKGRmTx8LcpdTumuGkruKuOZvRuyYbOng6iZPrbt4+sevQVoyJ4rWcMkwBDrlOpv5dAqUH3Tg8RSANLhjoCwsXg2DraCH90N1uSD1eL653AOrR/zy+r23jQsZgfcUQQD5kQlevsXnCzKHfZ1TU4BtDGzCuS3j+pJEpG4cjMEDPdpbS+nX4Yt6qxzkRmPs4O3G5De0YAGkAGQTKPXlHA3Tr0BbcOMR1K05q7fbxozEjxvBxMBhtOLY1b1wwaAoObwzMsORmLxftZe6GcaiU+OB98W/RiCW0NpXBfYj+E2DYAMuIP0+TaIZz04X73sYYP14RwFwdAJ5owZ3MS/Z6Lh2ZkwnmBmF0pcfjOYDnk9di0yA+BoZNDbOLybQNK1nvn95ufJ4wnBdACCRzXPXni39rfD69FwZvs80eRtuG6aNgAT6ky5ioOIizifnB0kzKDC/GKel4HMQEL4wtIXtCwU6eD33Ry0lxOBYsNeO4FlQVlFTNRVsqclLMqRpbV1abp7VP5Dn1QtqsMkGB3VgcPTaGJaCiPBGvI64fXfzUFwHqTTG+iAtiTNaA6331YwLoYIa79KQ985JMINDd3AQHBK0Cc3+1fl3BDGRb0NLAHIR7jOBuBFlDnBz1x05Eb9nPA7NBYGaYVyNRYqwU0r+vnTnkb8WJK/NU2qGjP2ZYj+Dmwfix0FlMGu61WITZpu627Zp7GuPxRFePi7vfX1TtIPvmGdzCGr1+HdHObDu+5e0RYAiINlBI1Dq2/jIpjjG+NUBr6nVoeb3J2dEXox3HO01ssg39SR0AKOKaQsxX0JOV/HltNVn/EqyIWkjCwHqOIF8GPbhonkEShfc/illzDJzE97Gt6G6Xvhk4HSV81vs5eI8nLmeel38PLtY0jLdAVGOzCsWoOxCOfmGpNxnGvzOIHEMca7an6g0Rz9HAYpKVjLEa9zuOsM02e/js9wTsvuVbvgUigi//8i+P166urvDss8/iZS97Ga5fv47Xve51eP7550++9/a3vx2vfe1rcX5+jieffBJf/dVfjXVdfy+X8tI2Hf5gTMoQVDxmUY7kBQBQv038bzJS4coFfMeucawxlqwzB5Zl2ZpgWTOWlrDUDIEnRQA4toylZkzSXVyj6G1rdLMSlMmp25ATr7s5ueirX6/CPpuGRYKZlhHYndrAKGivEAFEvFsP4HZA6ILGBmK93QRdpy6wgQ5MG4YJH86ieLuTCfH7jsUZpyDNGDnEojyuF0JwOS7cQ3vqmhyExDe6i1r6dUV2rfbj92xOO2/mYsd7chdsZK6yv3xFl9IiIzIni1OL5ITU+yYAZbgneX70uNEBIIbbd4xDTEOsYABgf9Pdd3EcAiYgNhCRoTm0K4FmnmrEacqwaEtpkZwRwsNAPD8pKbAO42oYBwQx4mxz6PclSg/BmUScyNWEkDVnZIIw15ljHOUYCykcD2yX3JwVS6dgiMzw0HSAbRx6JqwdMAAgQeE0hAvweuuQVEFA6fWWU24Rd9kTaFp8dsx0h7dVxC16GbAAZ9kSgiJjXdGFg7lh9YSwmCtiYzKwn37tD46F5HHCvA3qDFqmeR8/EEVdUozLERdvttnDaO83sPv5n/95fNd3fRf++B//4yevf8VXfAV+4id+Aj/yIz+CN7/5zXjnO9+Jz/3cz433a6147Wtfi+PxiJ/7uZ/D93//9+P7vu/78PVf//Xv/1281G0AG7EgEiENcXGxAR122qOkSSxddNly0mNW2MhuDAHHyRf91d+vzeRMVO23wGQOWFliShaLV1I9ib+i6y07+NLGmBwEIAnWxF8iS0DwJ6kFQ9SDxnEaI0Rm0MEdv98IDtFLKHHhjgD6kTHwBUKBU1kXLhKMAfL+kIaoNRuMHQFxTX0hjguFJUV4H0SReHa3xyGy71NpAQgD+PhrUHcVSe83AoAAuSNzxnv098JFNjJmDmySdKa1xwnatZogtfRNQ2lxD3lqw/12kBYLcfQrImv5pCi8+kU4ZgtGZowrQwdVUpq1gV1dAN9oB5cEylMd2tGAUFNBhUCdJm0EqXzOhvsI96p3vKBvekZBYbgrnUMxvs/+GG5D/PN0cYcwd4wjjexs5XfHMU+h56jMIQFaO2PbXf3hDiaI5rGkuy1TcgkXZ/UiazlRqkZiDEZlD2WcWx/LkjrQ5rjlhkEH0MlmiXq8Du7iOeZ1oL8WoJZjyc/RWvK5bwCK47PE6yn9uGPSVFtTuHzH2Ef202abPcz2fgG7e/fu4fM///PxT//pP8Xjjz8er9++fRvf/d3fjW/7tm/Dp37qp+LjP/7j8b3f+734uZ/7Obz1rW8FALzpTW/Cr/zKr+Cf//N/jle/+tX4zM/8THzTN30T3vjGN+J4PH5g7uqlZvLA38Kp8MW78JOMTs5VDlJOYlmiPqSe1vRUWPyaZ+YRUC1rwbJmMFs1YsBg8XdJLBt2X1acTwdzyXpAe4pJ3rJim5emMlFdjz8jGzSANUEHEpIaIggfDkRaD85Wj62CAMo4MqCXoCJDRdDAJgppEbvfGtoZrklHzTkuuN4ZkRzBJs8DqwDYtXGRcmZJBe7iY18ObiqXUSCIFDiY6mSHXScrTwxtxMzOE6Zy6M+Thd13BcECUoR6aD+IL+J0x/lno/CEepvA3ZfJrpOAOvpMCc4w1FJFXCcBQPP2R+2xTmOCCF3lfAxOGExfkBv70QP5o0JFaOEN940BOEi/Vqx+ga3fVyRhBFvj7cX3ByCccr/3SFIIrbd+jBhfpYaLbxwXitOwiMZEBTJOBDpRcst/+7Of05CoE3PFAxsCvxcagVVU8ih+TEfV8Vn2xcBkcrMwyqG8KPnCr4dtFP0KBBvJZwZsIoJ4GDh8EaDyi5akJ0wfYBnNDIMQzhcYYmUfpN44LjlUSh/sY6gGGrq3ZLPNHlJ7v4Dds88+i9e+9rX49E//9JPX3/a2t2FZlpPXP+ZjPgYf9mEfhre85S0AgLe85S34uI/7ODz11FPxmde85jW4c+cOfvmXf/n9uZyXvtFdAXREwkn7gRqbAkvVP9nUUgKCmYU+QQWz46Av9KDI3AHBrNUmDsgIhlKUcYIAWRqmXHE2HXGej9ilBfuyYs4r5lLjYpqzI5ElKh3AiAOpLruAyEYLVsfBx8mCywzC1kGnEBQJ4v8Re5eas1964soy16QvhFz0MZyP7Y4OrLhGMAi9QboERCwIQ4UM9WN5W4f7cQCdXPDG4P6x2gGZws54GEgis8U+bNoXWvQuHeLyEPfCJANm6tJVGOAIGuC1X4h/R33h5QJOADKUIIsF1eMNOa5G1pAoWYDQXIRf50lNWmoxNglgFOflNSYK2Q7AMNPdzmonBJ56AkgiQaCmDixY25ZsFxuTzxpLmrF9srklI/vSx3P0gz9HJ+OJiU+pM2ABNmBgle7D2AD52KHYc4wrgvEhIzoYOwKy+DxOM4Q5pHmTnGuGzFzWY025RVYvmWu+zzE2ZsQrxn7VHufJZ8uZ25Q7WOzxbQOY9Wc2iVrfkHH26426yT6fxHzg45gajry+NLVgO4WDDuixt/x7EM3ebLOH1R5UMPvf2g/90A/hP//n/4yf//mff9F7zz33HOZ5xmOPPXby+lNPPYXnnnsuPjOCOr7P996bHQ4HHA6H+P+dO3fe18t+tE0FUjveebCkkc1OPvkCEUTGmB4panpeQzB+z3z0CbH6cSp6LI2zKIC7UQe3EABUj/N62e4+mibs8xG3pkuIAmdpwTxX3D3scKgFxStQaBPk3KA1mTZe7hN+xAb5pbQqpv7PCd6ZqdYSRCzO60S6xRfWcIUmGAuTHQzVPmWfaIEBoddlunzZ21NiEWUigsRXJBaXeJGAheCE+oEMwua6yGOHy03DTWoLmYPLNtwXF9YBjI2/o78GoCcKWJN6myvdwc6cebA/Xe3il0s2RgJ8NkgWtKidKycgidUVLMawy65IoF6JRZtxj2RIiC1i8fW2Ru7dOoK/cNOl/jIFf60tJMa3uYp7xQ+pchKbmngvIIhtSBlR+7g1QaaUkCjS1F32Yxm4lBU5awhcG3ATi09rNi5atTg0bTnai8xcjy3kdRiIa2BbeAhC1OF14A10MXEghKTbkuy5EY7dFGNFCAyTJWNEdQUBdGrRhmMyhWgHxa12kKMcn2zS0rxP1NHcsGHwz9WagCpIu9Y3kg5+GwTJwyVyahZfx1ADHwxaAZWueamr9zXFtGN3w3GjAb45zsxLkYJpjOt0xrypQNQAIOcDjrlxzthss4fV3ifG7h3veAe+7Mu+DD/wAz+A/X7/f+uaXmRveMMbcOvWrfh55Stf+ft27ofFDLBJ35WPRsZrHXaZ4VawhVYf1MUSBPCLxAFFj3ejK7E58zNQPgxkV9hnChrO8wE35yu8bL4PVWBtCfu8YM4rRICSKlrrNU3DDQVbfHKxib5RXFWstiyAWMRZmoz6aGQEAHM/ZbrlXMLgpEYl75OAxI8bbFBcC8KVZmBuaOfkAJmHe3DnrpZIAqADiOSxb+jgMD7OC4nrkA7w1PpMHPyOQI4sScTfEYyJl3RKrv4PBCvWk1D8HzIZrC8LnCzAbU3e194AVU5YkVG4tscnYjjHMBZTd53TNSYwtzQzHDEcT1yEW9CzOSNjc+BTgtESiiEjFnyO/ZOgeV+oe5Yk4r1g7wZcEONmYMUDiDmbLHl4LfpTT2INCRxHWY2Utbet9LHAii1BnIt3VeNGwC9qAFVpdHGSYTzxaTo4TeO19M+wIguZV7LpARgHV+rpOdBBb2G4gZxsuEIuhnQe24dJTskAsyWKSLQDAXzSfh0WIzm0sfgzjO7mTXy+yZjzPvwZp3u2u7VP2V7Wgo34S/Z5GpjPzTZ7yO19AnZve9vb8K53vQt/4k/8CZRSUErBm9/8Znznd34nSil46qmncDwe8Z73vOfke88//zyefvppAMDTTz/9oixZ/p+fedC+9mu/Frdv346fd7zjHe/LZT/6Nrjoxhg6UQk3a0x2/IjPo0g6VKagi0n6widDdirQ3UweZwd3lTIonKXEJJn+HIAOthpwqAVzqSipYW3J4+8aisfTNU3hGgSAWgcA4vcQC9zArozuRwBxLeMk3lkhbxKv28pSXpSoiIxH9EXL/h4ANLx9CFrIQAkXlJ6RqYBninZ3L9RA7OiKM4ZKAsAlaE8ijfu1PspDGa9WpQMtX0DpXmZGKuMkWTNVAO8rB/aMRySiIchUQeMlOxCK6g0qscBJbp2J889G2z3AYITrm2CNMhzO2CnEXOFjzFfSiOvi3mIESpmJI1ykh8WZYN5YNAOjCothG5NPVD2onsAB/V4CdA2uu0j8IfAX02ijtAYBwziGTsSH1atXtH78E0JJvbLF8AwF8z7KxgiiXeJaxgSUgBzWronM2dg3dEV2FB8MMfUdXWs8rkdjIkE89xxPUa+ZrssRaJLpi5i73gccGyFtBJyWU/OxHRsB+OaK7lUeewCAPeFH43ZPpEo4f9TUn/tx2Iqzlz5XMElFvA5zj2tE77zNNnuI7X0Cdp/2aZ+GX/zFX8Qv/MIvxM8nfMIn4PM///Pj72ma8DM/8zPxnV/91V/F29/+djzzzDMAgGeeeQa/+Iu/iHe9613xmZ/+6Z/GzZs38apXveq9nne32+HmzZsnP3+YjLpr0IEJ8r9t7pVY9CK+WMYf36HXPpnydRmL24tGQXAIIqYq5xYJAGTDtCEmQwMURmUdW8ZZXnBejiZe7MemwDHlMXosnKvR+0Jrp/bfA0tAYBCB4b6cBeALNNAXu5jk3aXM3bjkhjw1d6M1ZE7mxVcnMhac4Nk+BJLO2MD/5vvQzrJIOhVWBTwOymUkwuVM96SS5ehSJARqZL0S+8Lbg4trsIQEoQ48lddaeyIM9dYicYAr3JAVy+xbxqIBZHMcTHr8GHzsjceJzM1ISHHgzj4VDl6J5InkcX2MpRz7PudqCz3kRJ5Ck0JXljwbSkgpnHU6HUMERBwDTAJi1u/IcEWs3rCKj4BBW+pA2T/SuDHJne1hYhLHSfQR+87Pxd+M+yJrF3JECmAs7zaOd0Wv/ODHVz6jJyBEIj5MeR8Mr1Bujvy6+kMU4zFY8AB8Q6P4WG++YYgkCU9AGLUEx/nJgCWs/zCARKBXD/HJKoAYYyLZWWx3NRe0YAgr4FCDf2V4Npu4JyIN1+f31iufsH1cG1P68Tbb7GG29ynG7saNG/jYj/3Yk9euXbuGl73sZfH661//enzlV34lnnjiCdy8eRNf+qVfimeeeQaf+ImfCAD4jM/4DLzqVa/CF37hF+Jbv/Vb8dxzz+Hrvu7r8Oyzz2K3232AbuulZaZK7wsEwV1HLT15gGCNQA8w5s0/G7Fn4dLtQEDpfmgwtfnZF8bcQcm6CsrgYiKQaU3QNKFBcLlOyNJwlhfk0vDblzes7BhwwnBkGTLq/DcBn0KQpEXsl52/BTORRKNup4q79NRdcTGLowe6K6DO5DTJzpBZ3FhicgY9jhibVkBR3GCKBEhzRV0yZNbIXOSilcRYtXbM0ba89xNNOC5QjNlSCcYmDYA9iWuD1YRcancZ+jFF3EU9Gl1soHaadsAkitayjami7iZvOAQAkgDsPLbFHyEWvuTsSW0U7kPfXMS46m0CeDWCgXGlOHWMC782rdLr4vK4agfuchYSrkmC3A5qNUBW8vs+AVXa74uAuDVxCR50WKDDRqk5T9SY0+Htm7VLfXi7R7A9X3OgU73mb7BKPq4oJp2Z9a2AKoGyy45wYwXf9GgH3VU78ArALxxLCMDdqsWNtZYsfszvo0K63BHHprexJRN1QE4B8FZ9oDsgJhi04axdTxHwBBEHn9ViM2tNFr829m+wu308KBCs3wnzWAlSB5Dlz108ZwqLrxWE+5abOl07n2FMpbPa8DYazj8+T95o/ZnebLOH1N7n5In/nX37t387Ukp43eteh8PhgNe85jX4x//4H8f7OWf85E/+JP7G3/gbeOaZZ3Dt2jX85b/8l/GN3/iNH+hLeYmYAtkm+3ChKBdQQNT5DGdvtHRAF5+VfihOip0xssk45BR8wh6zEhV9QY+C9s7kJFEcdLLkDgBFGy5EcaMcIGLApOTW3S56ynQInJVCPy7P0Sg7IbbwrTXbAptcX8sXxNZSXwyaVw0YGRiCO+7AHcDo0DApmTvGwJkxCcoLZFM5uyZJocVZqzUHeGhNMIkG0Eq5IaeGumYHFJ2phAKlNKyLA0B35fZKGgiQJyLBqPAewlXlICgAMnq75snPLbawqrdx3JgDsWBagJO4I4WxqQp4ZQ2xcQi6NIci8cFP+vkFSAQofIdsmQfWJ1FD0j6eR7CG1V6nGzNJs+SaYdzE4LSmsjEDl7eoApRxY6BR9EHR24tWcoM68B+zZFtNvtHwxJ01RUA/M3MpuMxkBBvjzC7uIKpJB09jIpIMD2lsmhIs6ac5AFNBg6LQna2MMVVvH+8Bb1fhho64KICyP2Oi4XZnDF/QUU0GphFxrxEz6AdkMlSCaVtmfyjVj2fC36ynrOFWjsxryyyxvnQXeHMWldcpLwJSBkTjpaR9LPs9hFg7EAymjYl0skEOFyvH/RDSIA6GNcnp+TmANtvsIbbfM7D72Z/92ZP/7/d7vPGNb8Qb3/jG/+l3PvzDPxw/9VM/9Xs99R8SE8gKm+R1mFfoFnEZBqVLx92kAXQcxEWMiMDlUBzgnTgupP93COhf12zE4BCvlJyFOLaCioR76w43p0s8Nl/i1nSFRQ3MvGy+j4qMe+su3JAMIBegl7gcwCcBVPhQdAgQJ4CQ03YIuQ3pgrp0iSVnnMZSR9mzdJPYokkwUYRuSF+MGyLLGKUzKdTgi5bLGu45AsaczM2rAmA1YJtzM8ydeQxX6W9iEhnSF9AUi3hvG1XL1LQkZltEm7tuoy4vgchQA9TkIxyMp0YSLBhXUfQ4Oo6dKlF7mE0Nd3YRsPVYuz5eJC7WF9Cpx2KeuBJLj30S7e5TjbHJi+9ad9wUJDFZH7qHox181SWYSNoBRWqdrDbQmFDmtQMK0myDm10JYJOiOvBsVaCroKWEMekmiUa8Ih/UDkCc8RpBB8eLJygF+M7ei56hHhsKjjXxBIdh7PV4MAe3DlpEEdm2vSKDbQSZwcv705ZsPyeWBNFBtD17kZU7KdC62z1K5lUgeiDZiSU70HTA5/m5BnzZNgRlfs1p4vj0+MQMj6/DEDrQN7eG67rrNnkfBUZ2ZlOqDmMQ0TlS+saT8a2rZAsFKBYrHFn1I3u32WYPqb3flSc2+30yddbB3Y6dfkPsbGPGHOU/gL6zdMV99feFk1hFyCwQYelQ0zT01oBguETU3UZ2zBUJl+uEq7VYoLgIrpcrAIwNA162v4dbu8uIseOiquguuVZ7TBYneoXEDpogLHl8DK+JweLUrZJsTILAFj84GEzDQiewhW6aasQQKqQvQp7Fmj1rUbMDIL++ZbH9UPW4JxHFbl5R/N54rt1u7SDWm9liyryclfcRwW7yxSzEak8YEj/fGLQu/V5WDwyPuCl/sg0QN3ORkdkgPGYz0h3HrFU/jsJj/RwEsGKIDcshKQAwUMqKIorOzsDHmJ+T7/XYSGFXgkjoJHnDQV9nVuDg6wG2ZaDyIplhYG0ibm5giwmOqBtXh6zzcI3y2fPL63GDEuzWSdIMEBsphzY9nhTwjFG/b495DXDtmxeNfuMNDPc//P9U5qW3BdvHbtJBWjDwEpu7MVY0gKg/swQ1BEARU5gUWKU/T3wNnKc02o7sZBoqUyQ+i0NsY+gdejuxv7jhkwC3iH5VIDYTZD+zP7c8NqlKUXRNR4atJD6X9juX5vGrCPc4mwVAv9Y+WDfb7KG1Ddg97CbGFPFPaTB3qk9YQpkTf12AiANxgsVsjHsRY/LU/w4ZgaQnG9KTYvB0GUlnu7IoqrtBczJAdVUn3F93qC1hlhXHtaCg4cmzuzifrLKIxTd1TTBO9AqbaNs6gEg5BTCNyR9whX0o5nn1bEX0GZluTD8fs/CsHFpCXRPmaQU1z4LhAyJBgcZSSqyRSXCyrrkv7qLBglF/a1kz1jUbM0eXmYPQ/X4Jl7Fy8fISYjm5xMkA5NuaLDO2STBs6n3ERYyxb+oL0whuutBqR7fhihqAmAV89VVNgaH0W9cyrJ7NGYsvgThOXVcEgm01RiqSF8ispR7TxIWY62aMP+8fgLIkBDboLBs/M8Z7+YaBwtUBzJwhIzA3l1+n87R2MW6C/LamIUt7ANAEgrmDXoKvsQIKgQXo4mdIAIypjKxfsq/OnvaG1BhDIc490H9MhujxbQLqJ8IZOBk/y3vwNibbFcCp9eQmjjNJiuL1dfm86snz6O774tmrzK9hBuzJ5NKf6egwbsx8I3DC7Hv9Wbq6o1k4xrx/MxN3HHhzTiAgo3yRqvTwvtQFq0eWlax0B5Vs2uE+NtvsIbQN2D0Kpj1TS7kg+KSkZC+cyUHx99SYJoI/Mj3hhhhi6LiCczGMmS0WQ/uvASxFXXMHeWSpXEduaRnPH27i9nKG3z7eAGAgYJ9WXJ+uUHI92Smfxu1w0euXxYWGbMqUa5xb4aCNn4WG63PMlGX2Y8TjqGBdMmrrLBerBDCMawy6ZzC2nLSLnFROYFwdF7JSKpYlO8vWtfkEPdg8XMVioC77j0KCneO99r7pC1tzYKLwzGJvT3XXYvUgcb4WQHpkzNh2HR/FPQGuZycwt6kzuG1JAfrYD4yRi4WzeTILSzNx3LGdpIPNxP6vYlna4FfE48CcsfVxz6LxvFTKdRAwkQ0e+4ysY1REaP08bJteXaQ3QZKu6db8/sgyjaEJZC+bgya4a5UANsYKWXDGuA4bLrrdtSFiwwj4eIx1tTHbhtJyxm4NwInHl359J+58b2/WSx0lQAh8u1sfIV/CY43gr3OlEuNnrbkLH/tmrHlfNj5zZJ7HKh7RIQA1LanLWBfb2NAlm3LrIFYFrQG1pXDZ92cMlkSxyunxOc7akNWtw/iQfq/x/AB9E7zZZg+xbUP0UbHYUQ6LMRk6B3cQdOkDX1M0JnIDfyFnEosJIKPr4+Q9BzZ+ysiUBIJRq627ZxfNWFrC7eMZjrXgcp3tNWTcWfaYcsOt3VUHJpFQwUWl77CDURAuqgQxEgH7ZOJiNw9E7dmmg+sPMPdKcEo2iS/HjLYmrKsxeAHWhjZIyTJ4B2efLTQ1dealAcer0iUkhoU4gtA9c5faZcdjwbIUW+tzw/nZEdWvY1lyxAnGojy4zugitpJuvOeuU0M2JQ2AhCxZvO5hbyFvESs+PEmgu8W5ELIt05AMEy7GYPkcfPKoA6AkIGjr4NuMhZOL/bCvcGAe7l6OV+9/glMRRSGArF0ag2Or8dKYUCTeN2uKPgr5EQ9NEKBnbg7sFeVdQjpHuiZbsD3Dhuu9WWdjO6iKjYjPyNQtDFf1kB06RJoGGymw5J9W0wlO0vdyCTI8K+KbHVUxQewB0I19x7rBlUCfY9yZ2GjngRVD7sdpzbUYfQ7hc8vrV4YZjG3BPnSwLHnYVEIigai14fujyz1Z+7Ts+NnHDJKFQkSb+jPZfEOkYH/bc964MRt/NtvsIbYN2D0KRhcTXSc+qzIxwnHKyYQe6zxgDATnJvZ4BkJDja/zGL5zBWxSrTV1NkL7DpdZq9mz1A61IIli1YSLNqNCcGfZ4+6yw+9enePdl9extuQu1L5Aj0xSplYZJ2F/r5QaSv2tObiDgajqLAZdpYAvfrXv2mNRHoLtq2fBmqsxdSbHF76qCXmqFtNHFkX758esx9bSILTqLmo46zAck224HIpft+mzrdXYvVpTxJW11hcY4RgYE2Gc7QtmjsyrNUCwmWzr5G6supjchLpL14BbCqZHq7FtPKZkRZ697X2ghX6cM5AEGpGxK9a+BN8AhjqftuiSeOO9BSvS7N7seiTcX6qCDMqSSICsk4QcojoaXYWeACPeJzbgehzX6N7D4uAo4g3RNwmZQrd9UxX1TKN+MYZN1fBghsvwNH4sgKGDvZE5UnTwzfCH8GQrIFOPWR3DB9gf4/fjBcBZUH82MCTaBMHq8HGI1xzbSDEAcQfbBGQMY4PCN0ydHUwO4hkLGxtRj5lkhnuvEduTg2xT4HF4AR59zIttsExjECfAWVcXRSfj689jgkYSRvMQC5ZQ5PMqMDc8WXD1Z3CzzR5m+4DLnWz2f8F8MRUPXBau3s0BWRr+rgggB+B0keOMG/O/BFGDJgH0AjiMCwLj20SBlkwAl5OfaMTYHdqEKVcsTaCr4KoWpGqB+2vLATbIRpXckD0ojKxHQpcMMTkFDRFjm4ydXZCeDZngIG213TtyDRcRY6802aKeco3bihgjLrqC0MXTVbAiWQyjuEuKCzCMeVF3aatKBMPDv2uH60HiETTvbc1Mu1oz1que9twUca3IYouVAwtIT+IgAxZuM/H+YP86MGSGY1sTisf1SRNUl5hInuUIByIBJDG0DeO1oF1SRi1ZhlVJtDwQx+WgLERfm2WSqghqBbR0kM4YQ1mzL9bOm4gxTONY1kFs0FiVDOowysCktZYiYQRONDXtMaKdg+1gmNqHQRqJGgs4NyAJtHXkclIdgmM3adSaT8kydymO3XyMBKMoPa6xP4PoSRSNLH2PTSDgY7k9urz5IHOMiG9EwE1NfNbjCTXHZ8mCn7hrxTJXqU+ZCLR8TKWsWFsH6pS34fNaa/ZMb2o2IuI+o1wYPFyYba/oQpKl91eQkx57F4k9PhajzBj7jH2a1MIBkj1U0lKAtFEMO5I1Wq+dG+UWHfDVmkx65sGNw2abPYS2MXaPgEnHIZa1yoWFk0wbPucLxYtcrpw4ZfgM9GQhJGEwgj/LHl3DbZOpEecAiBp0lSACBjxqS7haC46t4GKdsbaMpSZUBzfZXZxJmgcuS9Q35cSZk4ZmGJMtqjNFca4q4R5uD7jEmu/eLX6qu0KT9FJexg4g4vCytL5DdxePOIOk3v7WRD3TkaCIJcyUUgxtYCYGFpQuxHADUuZi+FEVpKl2lMZFWKOXoq0j1svdzYCDMV/YRlYU1DihR42xUFx0B6kcHicyldGPxWBzBSDFM25Du2bIIiQAGypD+Ct2OeyzsU3cNRZlvwZAEX029GUfrN1h3isoaGdz/B4j2zgNsWPeLOodQBHipmJ6eBx3vqGA9xmB+okuXmIf9EsbH0MyvUyWERgjzSx3S+RAuMtDq84BEdsvQDQcUDrwGYF5Si0yUXt7AMiK7KLPJ/F4gJd768CQjJj4/RpQ10EiBEPjjddor6fcAjCPbULBk7hebl4FvaZ06g0YMZx8jQk/6ICxM/a9waOPyRajP2On7KC1P93Zgg6y+fzJycE32+zhtA3YPQKm2f+IyfW9zC1Jg70TZ6fIVMTkPNRkhLMhCnQXGMHDEJQMFaw1e0khhItxrLXaVHBcC5ZqF5pEUXKN0khrNZCnYnE1lMUoZdDi8sW0tmTK+R4nk+BZoj6Zs+YsBN19pP37KfdaqjxutJkCVN2XqQbYCWZD0SU9agdMIekgdjz1ygyM7SOrxUXp5GcIRA8wgw5YIsuPfeQLSeS2ePwS31O6m3yxTe72zanfe7igBVGFge2sEGiy2EhFd3cFW5cH0eETQNjbOWLLcApYgzFzsBhVSugm9c8wfjBTy8yBAds5mOHeLHHcEPcFInhfwGvqj0Nk+8bz0UEkky2CoQNC082+3MHEGA4Qpcf8OUl8EP2aImkjgB7jD03PkJ+LNvdOUgdtHCKM92J4hbgESlVBVaYydGAF6WXZhK5aoMeXJkodDSLjyqvz00a84jBm2ObenmOcH4WgeR7lb/FECRCgjbIt3qHuWo3veBuwfeDJXqGvKKf9zhhHyh9haDvec2Qp87scnzEFDq5m6df3YIIS4z85IDZMt9mjYBuwexRM++QZtRGB094jUzVOdNRj8jgZHXal8Z2IsxrYrliF7WOhmZY8ns3dcoxVWmqO7LXjWkzQE4i6sNl12Xq8mB139UDoFpPpsBDz3P5nTi2OB8DFUnsgusrAAOTOhoR7GZ0p6WjCF40hG1ISYrFkG0b8mA4gj+3KxdTf12Hxyi5tEhmjQNd6I7MS/dsBWEot3IVcSIPJyH2xItBIDn8CxCZzjcfiCURx8wRFhsvIxLVwjDjIYXmqYdFTb6tIqEkGILL0PokOg90Py8ZB0XXaqIGIHq83ZpeObM/pAm5l1aDoMW4EDAlRxYLVB/i9E1ZVMCzY/j2yZAj8BgwAVYZrifv27zF+jlVLemUNBOvLc/EZ6+BjGOzeT2SlRoZMPekgiZ6wV61J15lsljTB4zX0ZzrAVEVcG3ivqbcfNwmdO4aXCpQAXhwJWbpUUQhzoz8njIXlvNWqV6UY7jtA28h0Sg8zsJAJG29k4gX9+TzJAHYrk7ksTuIlneXn1Xe5HcZeWhtRvofSJ5HQQ0Z86O8N3W32sNsG7B4VG1aeEzerPvAZoLN1fDvcLYhFK9wS46IccXm+AAzghoHWpbQTJosMXkJfvBXSde40BZNTPbuMTIcBNsWcK1iPc2REAHgpJQkAGNIUBB4DTlXeArXLoAFCGUfkS4snCHSXFSdyskWUdon2YPMzKIg7f79esgJ1TZaY4oA2lxYuxnEtZ+WKEzAu6CzN2J9jIJK/Zi5x+xBZmqTqsYfszkEahcHh8AU4deAQwIzMpbdtKjZo1PYFPZvTwYlpmvniRzaNACFpuDN57y3A+CAoHeCrt0UIYLeeSTkC6OTjlgkzNj5TMFu8vrGP+rmG//vfmYk5fm+haUhQPTxkZEoDWAzsmwFd7ZsFf1HVx7HHnUbJLr4WGwIJxnZMzEkehtB0eD4AsCZ0tDEQWZ9kZVcCPpbVY6KLksHSF3/f2cwkQx9qH1fxLACRvStJXcrINwf8K5pOO2AVDSmfLivkcanRBlaKMKIL6Qof2j4Yc39GtCYb8+jXamNCHMS3LocyAlugV3yRzspxw6jePkLgva2amz3ktg3RR8ACYwl6OTC+1+SkUPaYmYnc2ajY6Q5xUJQ96Ium2rEijssmUxXEYtQlAfAiZg/AUDsUkQAR2Z6+EDWYm2kq1YFAi+ORpQlTAaoE4JpKRc7VgMzADNAtIxH4LgitNHcJ0z2jKhFHRBdunMdncoVrwxG4EAQS9Ljm3Amjxvbmms8Ad6AnS2BkjRxUOrvG+yGrJXDVfoISMUA3TRUlN5Spokw1/m/eLI0wOo6d0QVJVrN5QkYvn2UXSqkI6pwxBpI6f92d7cdBdzvazXFsDGAsNhqCzLgyX9zDzTUAlJQUyXXzxqoFOTVz1Q/PhLL9vNpBSpa1KnAwK5THGTc0fi7+SYDU7Icu7KiAUTkmegJBCFWjC9uOsicRLuDnmkq1e/EyaqzWwGzllFsPIxDtY0fIEmqMV/5QM09yG8CI32vuoImgMUDQwApSL48C5ewLux660DXGcyoNmhzP8pnQXoXEpozOhkU8KQEmNzn+zNhmymVQ0lgxIoaTM3X2rE37JZjo3o4IYWMpvpkCbBPmfROJDxy6A8C1ttZ+fSo9gbnJoC8JA8gDwN1ss4fRNmD3iNgobRKxdr4z19IBjvhiIy72ylgiHSZKYNyNirsW/YWs/f/CrNN0uijAWQ0Aq8cDddeXLURry6C+FrgQaQdOXFjWmjA5e5UzGSCc7s4JCsSLoVfqjw1sAVkeP25wBw7qhsqUsegTeBDIEqfGgsyvKHo7MoifLBsXigEL87vVk0UChKicLC50fLWVbjyEm5Ylm0JTzds/54ZruwPO5gXX9wecTQumXFFyNckLv+SSa/QRMy3jvuFjRLQf/4TRAqpn+ObS+sLXiGKtUQLgj8wjEFIwsTcgUPTFkawUs4+jX5J/wWOsuOgTXDRnWmITkh3EiI97X/xZHSF731ufDEkg7DftmwWBWriCdDaYbJUkjb3OyBzZ85BOmKxRcFc9+YYJFsYqSfQnwU/OzWoU8zveT5b80w+euDHoj3u0FfsUcMDaOrjuqK+DuhPjvTBpIA3PHrGev5ZZrznYNgOWzPq157/FcUO70RuF45GMLEt4RYgFOitJGSMm+sSGcRzDQGfxpcslOUYDfF5SDJvYNMgqRRvYr1wqJmbUKwKYnoC7zTZ7yG0Ddo+AMcMSgE1A1JwjU+eLTTeuLP63qGfMDhNhCBn797ICHuAdh1Axdw9398w4UxcXdUsO0Ozv1oPMffJnwkNxmRG66ziZL2oTuMlWdNZGYJUmcq4DO+JAiVmW0UgIlon/pwoEfFInG2Dnt+PVNSOqCDhQtmtEsDvBTAKRoDIuVryOXsPWfq1LDpcTXM0EZAulxzCdxBiObUOMkLorcSormiZMuWJfFpzNxwBHTPyQpJgoXuzw8UTOgsdvCID14IrFGDIBUFIHcXBQm3OPeRzLStni35CzZfSKZwan0oxt9WxHGytefmoEyQRsQ792MNiQMbjv1ECFfU5i8T8J/vcNBZ+RxL73Rb9WY4tyGoCMehY2u929wa2mcOGPmwhtiBhIJhkF+PHNQHUWqEsK6QmbnMSSXhQ9Pi42EEAX6fWLihrAgzvWQKv0kngcP/FID2M7jZsX6dI4NpA7c5X6M0OwzUoq6u1PEL+qNRRZXLrGk7clkzxSMrYZ0Yd9roiNDbO1MQJFxKau78LQXeveVsU9ASrDZgoDuIu5tAPXPv76ZoXPYzD2GkTrZps91LYBu0fBRvYI6K6hmNA1kiBUYCwemSPfGYebAejuCwdDYYP0BCe9E2V8akIREHZywkCHs0bLmq0UExCLBatmTMliZxokYu24tkRFg6EcUZwb6PUceX3oC3wkMjirUTwBgQygDgul7eRbLJYEamTvKHUSDJgDvIiVo24bXapwt1i1hIE0XLf4Imxgw667B+xrLMLRD7xejDGLdg2SFGvNuDxMuDjOuFomXK3lFIwDHfiIadidMJuTlyzT5PeN0Ah0HriX7iIAcTcnmaaULaM559YzW9WZkGL9G/fj95qkYTcvpqPnx2oOeiKZJK4dvX9TB7p0i6l/Dh6UH27ObExgdrdmrL/i7KiKl6Sye8qpoVCPkedzpqvVBIo0w12VKQ9UmRDM9ZrEwehBIiFhjAWNigg+5nKp3VUJxHcCcNFFL4Ob0u+n5Gqg3dnL8dpO41C76xsC31R4f3t2d4DUoQZtc+Yw7tPHRl2yl6W2OcXAfTPmUcxVTrAKj98k2wWI7UkHEDZ0USQa2Xjpz2rEcIrGM3EqPI0uDTNOlOibV5UuycRnUoY2DsmTOIJ1QhtKuDEs4uQUm232ENoG7B4BO3GJOPDqciaKYR1E15Qavv/A58NtRoCUBjZH0WNTxmLwnpBgAfCdXYgC9KK4Ph9xNi0QUSw1m+Yceqwdi9QzOJ7sG2OgsrTIoA3gNEyitSVUTXG8LA0UIK3NMgdZJUPVJvIuwuqLe+ugDcnuS9RBozg49gm+ruaME7JuZK4iVqsvtFqZ9UpmQ4LZGsVwI4NSEFmOSXoReESTe6ZhM9aUICE5y7SsCReHGZfH2eLOHLRUBwlLy37cGgwhS48xy1REraKHNHd3qcdR+rAQtdg9ZxXVAWOGsa9zqZ0dckC2K6tV0li9Rm6weZ1RY01PJggUxu8B0a7m4hsACQHQmOwhQ7a0s0LaJJhEPje6ygBk0PuMLng/jgCmWUeWm33scZO8hg46HCw6C5VKi6zciBX1Nit5oNwVQDM2eppMFqg629WzYRE7s860aQeLAKbJ6i6bu7wDYbooAXQm0q9DYO0AbjSaaTxSEy4lRZ78ORzCD2gEe+yLnFy2KLdwXVPOhEDdxpi3ibToIzLhI3Dvmes2NuP5SF2HUhR93DHMIA9thL6xiUQY8IKsbftYs0suLn80yjhFu/lzS4Z2A3abPey2AbtHwci4AZ2d8UBvskcYBWAV4b5jnIs4Q0RXDlmDYOkAC/4fVNwJ7igtQfcRmSiB687VDrTOpwPmsuJ8OkbGbEy0XKDc9dRgWbY5Nc+os+B/M4VIz2KzhbXFIsAJ3CZbOLPQXTYEGlzQegwPEAmQnKS5sYe1F4FXyF24pIOxit6GDjCG2+rtxjuoXVSXLkcFQtU+wI24C6+hs228WrIpftxlyYAKljVbfCPdY87CNQev1d+zNtJgeNlvI9jqMKCzZqUY6MjZmdfMbENek6DSZReuMbvYUirIPPJ1BXBYC9ZqFQ/GtksDwCWYhf8m2zTlinlaDYTwuKlL5bDJkgOCuayha5imDlwYwwbAwS2CJbLkIb5nAPa0BuxpXKBzf8EMA4CUzm6NYt7RbsTvKii5YS4eH1ntwSN4Z8yqxZyxbX2oOcDIDmiEQD2YXxt3KTZuiGd4ntaYQ5IzWeKuRrKnVr6vx8fx2YjrUgeE/lxNpWKaVws3kO5+Vgdpc6nOjCISOUK6iLGAaRhfblPprCTBIWCl/sYsb3gmeGTJE4BxHHkfR8xpHva93m+7eYXAXb0eZpFyC1Y15a5TuOG6zR5224Ddw246/HDx8IXV/jP8X5xtGl2BVay80OjieS9g5MTFmsY3x0mw74oJGBu66642wdIyqiaszTTq1tarTaiakHEXOe7JFHQpKSQkVZp2bS5Flz3p7Afiu1wECZwUxmClrBF7Rte0ZnQWUodmHlyroh2Y2Od6HFmAReiQdazdVcyPpX5wSo+QyZFYuK3PgkGMvnQ05zIiZErYDwkm8QGPd7Qi7NLHiSBqsZJ2EFamCHDu7J8LSyssxpDnmz22MSV3ufqxV01Ya8JxKcZGOviyWLJeR7WlroMm6DGF4990e/P2IiOxdYaPv7j4ciwpLHmnVgO7CTZ2rw4TjmuJsc64t2ClHKiP7jeCNB36kqCC7W5xcFQNBESdPdNekotMaE4NpQyafb5ZiC7yZ2nKFZNXnRgrJ9jzweGm/fFrAnjM3tre+/QtohbvSSDvz5bw+pICedCty+7OFo2C9yXXGEAi2l243g7m6pZ4HwBW3+yoj31mS1scKaKdMTyrY2JPtLE6E5hal1Xhszm0M4Fr1IjWvlEan4M4tnJzavcZmeB097sLv/lOLw8bWaizukP/bLbZw2obsHvYTdC1yXxF0WEHL7UDL+KgRObNXRUGZMjO2N8Rf1f7cWMy5GyaNCZLshbhooAzaa1P7neOZ7hYZhSxovZzMhmOJDa5syLF6m5TsiUmXyAhg9J4386imKZVDQ2u0KYTBBvhX/EF3ttjcEmz3iOSuQJVjIXKpcfiMY4NzoDk7Iutt6V9hMwJAYh3E8HNCHTINJLlSV1XrweqI9qQbMG6ZNQ1myuZiQCKKFBOPTMG6CdRTKmeJF0kWJYxhYedyAnJF7KbCoS+IJfXVpODOIvpIxCPzEYHkR3+azBva004LAV17dnSIuo6iHYjdGVmBxlR/osbEul/B2ioOcZgB0fW7s03GL2/uz7cWDotjX0kXo9VexykJIUUjYQTfoaJG+amrGC8HmCDtQ6MunAsoAswj1m18Xgl4PI4xQYpwJtfh2UAi5/CDl4hxj5mRW02PvrARwA1C0tow7OKvjlAjzMMUWyyZNI3JtmfW7v+YWPizytZ86VmHNZiJQKTVxNJ/byAuOvdgJSozU8xNjgOm3jIAyKWzjZ+9j2TRPF7TR3oga7w3CCpQZsBeTLDNHHmki7iSOjwh2DM8GbiD8c6Y2HV22izzR5224DdI2IB3oQAC/ZHUV+UOXn1iYfxeBC1mqvxPY1Ei5AS4NaWJax4DBDo+HnUJuoI+HZAgmauuYj3yg1rS1Z7VY1hYA1ZTrDFFx8Q5MBf036PBJXZWcEEjdi9UWMqFjF+zhevoLoSF0gNN01IbRjajd154kLCxT55DVigy1mIDhmyCMAGdJBn9Ukl3H8jKBnFlpsvmHDGCZk1eQkSHPShM3q8B7rhTBC6IQV6HxhRMmJkCMnuDitbKizFRiAiOCwT1jWj1nRS/JyuXvH+p7swwGyTcFlHLq1XRxjlTYJUGWKk6I4lExusWxMcloLijBjjBuFtwmvg+KvV4ZlvWJKPZTJSfVPhTDE3TavEGBoZ0tj3SI9z02EM0e0tMAapqWBdU08A4tjhMwirvHJ5nHE4ljgZgW4k/bQOPAncyAyTqUV8bhj7crqh4HmXkKLpYDL7GGJGrcJc/ZHZ6z0Vz4Qz9hwLjeNB/RnTznwCwHEpfSyP33Mmn5Nammo8swrgail9s+X9IKIei9sBcR/ynqnO9iKDLhptTkCXg73zvnDgScYwEoh87jAw2J+PzTZ7mG0Ddo+KDSn+xAAAgCohF5BY59PfV6C7G1UiGSLWK9/FAhjiofrCw3Mk30G3YSIN5oXExeAaJsgQAGdl8bqg6mBCYlKPqhVibpeQwpCuVVW5sEkLF1FJLVw4rBYx7roZTwP0xaQv4NIXpmY3STeisS8usiUa95+9/QM89nXG+0PihTE2KeIcB4sQRoLQNKwfxcAamSXGtyl63zPOh8kMbD/AGJ7kbQN0HTbGLxmNZAt0VT76PUlDYdecUkP1BXu8+hMGyM+d5RRosMwcAEzTGkxR/Pa2ZRJEeJ3VF11ntySrsVMOCGpLVrOYm5EevmZ97/eZ3U2fU1+4c2qIXhpyGASd5YsEH0rRsP8c8LNfAXSR5dxCCDhiOLX3FRmuTtXZqZlgoQosxxySK/FgOlgrmc/DACadvhXQJdr7o0VSSgdP1j7SQSn8Wc8abn5x4WcmwWgTrGuONiLjCgySR3RjizG8tSbPdPd+GJ6TiGEUDTHltaaQLuluVgRr2lrCupZoD26EACBNLVzeJE7hmwgR7RI6D4zdBj8O51J6G5IGw8vsf1bOCKFq0P3bTtzlm232MNoG7B4REwAS2nNODjV3E6plTurq7FAVA3wEbslchrokcx2Gr5LIQE8qJvQT2nKoBC9iE15rEmCKZgsHYjFdakZFsoXfQUprBjymZP7fAIPorh0yKmQoAIRbhi4qWzA1XLydKRoYH78eLtIKWyiZ/cgA6ZNkEO7Ypd+TXZu7eoBeLYFAmIuWi+UG6na0FgukX6MKXJdLTNw2txOGj67V1gQlVW+L1sGnIJiiiKuLy+m6YdUrS0TG4Xg/ubfBeCvBrtFtrx1gR2ID+3lgWQHtyRWM0VOYVEQb2Tz7bvaqBqy0AMDaIYCQn6gN7daMBSZjRP2/SHTBAA4dRCRvi5HZs3GhXRvN7ycP9/feTOCB9fCMTgyxm9rZP7qbm5eBy6X28ejPGbX34nz+fEV9YyAYMYYWjICZ77dmAtLhFvdnr28cEFnoBLx1ZE19vJ/UyyWDPGwGuusVIVidSnehBoil259I3cHWydjxARef92ttzvgTaCtszsrBMmPQICRLPCRSpX6tzJAncykuI8ON0ZhYxo1ai3AE8wr06i8IgW6GEGzu2M0edtuA3aNgSb3CBGc8X6ss5j0mPiWf1gC6CpmlJhH7hb6Sx7eHPzkpOxVirhEZmD/7aBnkJbhgknkjM6YKXKwTstdoZBYgZJBhgIT8BWCLX5YhCUFsYj3W4rFkLcp5pc6VdeAXiRnov33hpMTCCYNHZsqBQ5FmzAeoVM82M7ayyRCgzdpWEfQ1LobWWAOB093D0tkFgo8oRyX9nCkWo85EwmUwQvXf79MWJomEiqhUwWxJRZcFURnax13rHGoDMNaWkAemSjl+CDIHEADtgBOKHpfmGwbxhvGl/HRRH4BEYA4HsBjY2NaSJd+sBuZSaePoNTZImUXq9+Au7GBWyfRSUDvEcx1c+nNANkj5+RiOHhOYENp5EePpwIa1Xk1WQziMT5JpjEWT7iLkJoauUj83svZqGycMlbVLXXt1iuxxZiGvox2QjoLDCukl5oafkd2mJFBsuDT1DULr1819TGuWvDIyXHQ9r6vHu5HtC4ZucMEnlhLU0+oucnrM6vFzjfG2HCecF0U7uOfA8A1FnFOsdq9tkHu5wuLhCPD5stZkYRh+bs4tJ9qfm232ENoG7B4Fq32BA9C3+nRHOQAYwZC6uwrAANj87wd3nYnHk2D44IDOXC12/s742OLQtKf/F19QOElGVYmWkaCmbyYaIGIsL2agRQLsGatFNs7eW9YcgIRadgr0Cd6PwZgaauZx0Yl4JPhuPzGmrn9P3TUTyRlNIPzssCAyEJ+gRVOXirAGG4AT/xa6eZ1tA12gwIj+Uu4abBHd5E+pCaqSsej9Xpl5HHGQBiKoacfC5Z0NdQkTBwzZBYiDpdWBIfL2gSKAdM+09PFA8DsMt5SsrBsTVk4SSmAs6Aga2S4Y+9ETY0KQu3nw/ggC/PUENQbUX49x5dfLsQIAdc3BLlrQP3X0HLw7MGYcIwEDpI9J69tkmbEgoHCh4OFz7EeyWvEMMfbONxaE1pE04GAmC+PkHKj4eIlya4P71JhNlmAza8O46GO7g0yJ9u7ntS+6W3+Ezv798Ba0oZ3Y99pDLAjI2TbxKR9q1WvExqZumDdA8MnYRd6Un7ud9EtPfmEcXHamNlQChnHM67GNkcazzs+kPAJ5Z7h57m3F3OwRsG2YPgrGDFeCMydkhMRM6+CDcVtoElmUfeuKvtsHOrgjqMvaY5CGYGyb0Nyd0nqJHu7ud9OK/bTgbFrCpZVzV/U/1gJViXg7AgwAJ4s92SwdFwRn/6yOrAm5Cuy3VgY5a0zkAHrSwLC4knkMWQw4ExMuJGdIAjg01/ay18y1RhDUQbSkoaaodACQksf6eBtTv2tcKJk1CSBkKOi+ZEJISKB4W9mHtS/IQMjAEFcSwEesHwGsX2MHGEMwO6+RVzi4K9nPSXzM+HkUBrKZVRulnAjGCLh9Ie5g0MBOZP0CwTKLAEvriQ+s/9qvYQiKr4I8CNOqGoMVYMOfCcbJCWVOgmWUYLUI7oLZ4XcIZloHP2xmZsNyc0ApFwTI6i7t5ElEoDtxiAGrtTNBHD/CMRHPwDByBjDZO2z47lA7emSuRoHllC2OEskScxrj/HwcDPjKgSMik3V8HegAc23Jxv2Y+BNzTwdTQJegkbiufrx4H76JFA65Prf0jVv/0gkT6CtbEsRGK4CjV4ixDyBAN/uP4RrBYMLHb7hnB7C72WYPoW3A7lExn6AAWN1XTtoPTMD98wrlzpOTpqJn/BFovAj02Y8Uxjb14xmj5CxRNZ26khv204LDWpCkYc6r1XeFVUGozvQdV6tEYfF1nXUad/EsF8Q4IWjf1VNKo0GCGWSZoZNbEK9NKwbKODnn3CIon66ZvnMPJ/ZJjFHX4nKGJRglP1n8NgB5wk40i/8aNf6SmO/WFq3OAI1NzJuxrNPOsIQ5U8KX6poDQFRNHVjpEHsI9ne/7MTYvmHs9OQSP9XAvETs1AkzaHGAdBmrSnclJrqS1ZkgdxFLb2fwOjAEx4sJAwOnwJRsbpTR4jh2UWdAgkXtcZgOZJ39GRNqIoEBXPB7G1GeZCyr1fz7HGsNnqmt3GR1UWEOXrKJBIU9dIAAszNNiL7u4CTcs0Nfkjlm9ucIZtj2Y5xoIPAYx65HqBJ1T8dwii603Hpb+W/e7wi046ehCyLH8yunx5T+rCXXzgvgCOkyRLGhkRiPHFtxVwRv3pcKxg/a+2WYZzKTrUSHOWBk6dCZWo5FINqphxicuvk32+xhtQ3YPQpG9CYdkzGRQocFLlg22KQnwyINDJtb1SiTFIAwgB+CtbOFn4BgAIHOwJTUMOWKZc2etdiXrpwUu1yDYRFqTwljeNJJFYnkNWTJ/Jzvjh6PN5QF4jU6uGOSQGuCVXtZLeGCpXLCwkT2XTrVTuvCvs4ODPGEoTHGNZeTP1lHhVWmACJx5b1lzREsaCyMPDZCAHlkzQJooscfWYC9BjtnbiNjRdeaOrDM7RQEjQwl49YIMQZAFVI4AfCGWEel+HCPAyPISKwXCgNqrRl4pfwKXVuK7uKN2E1ltjFOGJyIQXQwwfYgGOXfkL4QW7+48LK7JJX97u1FxrE528axRYAtnmADSI+pGmNTeRwFGIgwgvp41iJe0ttE+/9jGKO7Ppml3LF9fzD5WaglpPQEIMSmh+EFPPK493AMexJTB9Ge2EHGWq3fIt6T49FBFduMUipkAJsDqiQmykx2LUIf1DYgnD8i9EF97LO9BiDOOSg17S54v6vkIJhMZ9QjJlPoLRdAjdcNxDwU+xyx6jcqPYFL3Q1NtQFuQjmOgrXcbLOH1N4nYPeGN7wBf/JP/kncuHEDTz75JD7ncz4Hv/qrv3rymaurKzz77LN42ctehuvXr+N1r3sdnn/++ZPPvP3tb8drX/tanJ+f48knn8RXf/VXY13X3/vdvFSticXL+WQIhSVS5OEzviiHy2/YpfM97kCh4oXNFawHeRJzR6AoAIZFLQKrIdjvFtzaX+L6dMBhmVBbwrEVHGrGsVppB2rYWbyQxdsRhDBImQswY+UWF8St6hp1yZaJiOHTHrPGyV7R2RDVB8VqO8MV7Jdg2IX761w4XeWZgsqRZOEVPAJgOGskgh7LiFNZBi4SrYnVKdVY2yLhIDLt+NrwocjaRY9djIxkgQ8E+52StXfEbpEB8QU6eRF2lX7uMcYwMhl9AETcpi/k6G/10zaEqzzuPwLNrU9SfLiDrmh3H1NsKwJwSm8w61kdyMsIwlpncGwoDSu69/+JliEZR/8Cxwr7uverg3aXwgnQz9tIDA3wDZQzw9rESm0pTnT5ZEjY8Y7pbVU5Tgg6WowhHrdXdrFjSjBZ/V45xluVeJbiVtmuTLSq/ny0wa3qz4DKAFqkbzIY99fBl1VgCPDn420EhnBAP8rEsN509GPjxgTRbrHh8gNzXPIcfO6sCkg/nl0nk0QQ426UiImt0jAGx8zr5vqcHHMRa8um9uthv2222cNq7xOwe/Ob34xnn30Wb33rW/HTP/3TWJYFn/EZn4H79+/HZ77iK74CP/ETP4Ef+ZEfwZvf/Ga8853vxOd+7ufG+7VWvPa1r8XxeMTP/dzP4fu///vxfd/3ffj6r//6D9xdvdTMF1gWVI96rnwPGLf6scCpAyoVRCwJYMcJADHInIT7kSK8w4QLIBZ7KHDr7BI35yvUNeGwFtSaQmIDEFwuM5aacV4WQBByJ4daDMC1DBZRp+DwuiYktTip41Kw1IymwG5eMJc1At1ZD5WLTfNYO2Z8Nmc/8lQ7w1a7nAGrR4xuyx6439uJgIpteiI2SyaMn/d2og6aQqKOZ6spkgUkKVifVyEBnFNkZAraaoyY0mUbIMwXGSEo6tdJN6Fol46QGB8O7gbgEPFtatIxWpNlPjrrk3yRlbhfdLYqxtBwTGcw25pO+qMyto6uxIEVGYEJgT7dgpHwsLp7LfH8iIV5jHkD1APh2SZ+eIL51sdBqynqAJN5hlDvju3McdTPEQwe2MbMrLU+VJF+b3G+7OypbwoGYM3GpTRHiIC3zgwl1ZOxHi50Mpf+eWOn+jiOLciwuUnS/BoR90OcSX2308zdzmKxXZraJgV16AsfR2OpwFoT2ipxbkkEpYhnUriBcIAdju42ZAvzPsWAMI9NHronfaRgAWvNMWcGCOa5uFliO6fheWa5NQxjKFzcZLRxMm432+xhtPK+fPjf/tt/e/L/7/u+78OTTz6Jt73tbfikT/ok3L59G9/93d+NH/zBH8SnfuqnAgC+93u/F3/sj/0xvPWtb8UnfuIn4k1vehN+5Vd+Bf/u3/07PPXUU3j1q1+Nb/qmb8LXfM3X4Bu+4Rswz/MH7u5eKtbg2/pB7sAXQsY8PbCBNxuAGFbp36NsChCTKhdtSQpdxYqZi4GQ0OTynfN+t0AVuHvc485hHy4NqC8WYu6YRTPOpyPuLzOqJky5BliYcsWxZlN8968fagnWaNQpUwGWkjHliiIN9Sp1FsRBowAhk8DFALEgC6Zczd3VAE0S985mEJccsXgcCRcdY+sioYPtPDa2qIkLR81KB421MwzUFNMqkbQRbJYaS8UYpihBBUCcoRsDueO07oYj46SC+IyqQBNO2MqT4HKnIFpLyNLlMdrg3jqR4nClfggg4oBUu7vLYjclgEtKfRGF2DGqMpNaI7M6AOvgqsyiWFXQakaZ6gmgFBi415Sgqggxau1ARlVC5mUEwJ0B4lgVLEtBmheL60sKrHLiestgJmgKOR2ZusYZn8dg2Ea2Vo1Pttgsp8AHZjx0GD0zk+511R7z2TRF5nRrQPEEkAZBdp3D6INhQ0Zmu7OPBE6d1W9D3GRKNn7JzhETRhUMn0aYOa9ici/MBm/o2dHcYFUMYDgptJJBs1CNrubHx0jR0DNliwNYXU43NHDWv7isiW3AvDNmGyPHxTab4qLfkroTAn6sCDshg8zx496NyEYfNra19Ti+zTZ7WO33FGN3+/ZtAMATTzwBAHjb296GZVnw6Z/+6fGZj/mYj8GHfdiH4S1veQsA4C1veQs+7uM+Dk899VR85jWveQ3u3LmDX/7lX/69XM5L15iwxlmpwQSIBzeRUA4hZmA4OPNJjEAtMmyH4xN8xLE6aJSsIVfRfGd+YzqgasK9ZYcmgrmsANTdqBILOQBcrhN2ZUVODfuyBEBpKiaB4vFCEIKbduL2sUnbCtUf14Kq3d1Ed88o0xAgMxZ13ltnMkKdX3rwPAFJkXaii8VjChkjMgFk3Ij0HBQL+qJOlq8UC+RuIXfiByXzIp0NGQV+6ZodkyHQLyvamDqBBDh8j5ieFQmiWgWvbaiDGrFHQLS/uZETRsZwXfKJPAUgUVIO4CIoA7Do7CSvv9ZT7bvQR3PGJUSRXVSXgs3AKNWiPaaKLltudKyVQ5pGoAHKOO6Z2FFy9RJk2aRCZNjzeFvwHDyvgTrvi8ElGQwqNwFiLCLbs6452kl9HIx6gHRBm0aijAmfA5Pc3cPcMIQIubtCRS3ejdndQYSRoeWmkEzgMKj4f8awNWf3U279u5w/BqY7+kk7UyxpiG9Tsm4pWHaGKbQIzZDoc2tQjefEOzXG3Zh8E/MdN3TS24tVNujmNt2/fmwb0+kklpDSTgx7IHi1LclwIZtt9pDa+w3sWmv48i//cvyZP/Nn8LEf+7EAgOeeew7zPOOxxx47+exTTz2F5557Lj4zgjq+z/femx0OB9y5c+fk5w+VKSxJImgcGHARDAwAM2B94SBIQ1+oeKw4xvAZ+A42FpDUj0s2RyEo0woV4Gqd0NSYMIhlou6mFefzcagoobhaTVj4bFqsbJh0d9eyZtMygy2U02QAKJd6UhmgB3Iz/qqF7AgcrxK4ECAKxWFVXIKluwCDp3PgRTFcCTA1AGkiOWiA0vTAQjO2Y2Rr+qIdsTx+mFHQlwkEXPwIRCnnwLbKU7VYJC6SAcYY2C2RkTlmw4bmmi9gvGS6Q2M8+X1zzPS2t8ZtBCH8LpyZdZcvmWADX911l8iepoFZg9dzHaRqkhiTebJYA+G+HNdRsoRkc5nowDFFYDGK6Y6AYGRIA6h5hwtMbmZsl5O0c++bpoK6pqgMEnF4o2C1j1cJTUgHJOwPZ8EtcUEj2ztch87MsewXr2dMdjnZANWeeVzXbLfc+n1Y8kOvLSvZ2p1hAclB6GhkwltNBkrhs4HX4x0//uCmw1zE6KDYGU2CQm6K1BvEzpFCBoX3ygzz2DAN7u+RHaXF5kD78au3S4x9B8Hh/tdhjFBmKD6PGEccWxtjt9nDbu83sHv22WfxS7/0S/ihH/qhD+T1vFd7wxvegFu3bsXPK1/5yv/r53yoTIET0WDfqQs8IYK9yIlzCN5mVheZJcnaY+wiA3aIr4lFEL7LlbgEEcWN+YCrOqGkhrmsuD4fsCsr5lKxyytmz4Sdc41Yt6UlNAVulkucT0swMGNJMQgiTm/MuhQxPThmAmaYxMo0rZZtyCQQrmHcZYMZj6eAJLkUCgSxKI7ZhKPwaSwYI6jWzvaxfaNtubgNAeCtuiur9esATgElQV6ZarRDMCHO3ggYjE5Aa99jjcsAiv416v51uRp0VyqMfWDMki3WgKhEvNhJ9QbvqzS0h/rinMDFVOJ7IZ2BzmwBfREnCxtB+fysdHezNOmJB36c7BVHCFqSVx8ZwaR7jU/AXCTByFAVAgaGhIObbcTSd+hAWf0ek2jv2wGcBNvFDULqz1yAEa/jHG0Y7evAvvY+DOYpGg6n36FrdpXQWQx2DuYiPREhF/RM7+jUYV9SJc47JjJkeXHNVQKnsU+jPjKoVWjnJsttpxs2Fn6eYIYJ6EuzTVi2DjxhPMdjeBhA39N29zEBnY0Tl6DhxpQbqMZnXoI151iJuTYZyxcAET0L98E22Wyzh83eL2D3JV/yJfjJn/xJ/If/8B/woR/6ofH6008/jePxiPe85z0nn3/++efx9NNPx2cezJLl//mZB+1rv/Zrcfv27fh5xzve8f5c9qNrAsu8HCpE2IIksbMEhvcftJE1ilUUQ6LEA5ObDNOofz7nhrPdEceWkVPD42f3I4B8ytXYOGfkki/epnHma5FaTMwuryi5BsMUlSPU4qK6Dhgi8J3xRlOpkd045dp32oqohQkx8EaBX9aJ7Lt1ArxxdSKQtZsNbTsdmpMAg26gcIP7385sKrxdSSUmZjXiREuNGXgjMAzXFxyA5s6CxBqmroyvAGPUsvR4uGBdCJh8we/Cyp1FTF5knmCOTBllKgjucnYdMKAzgJ4ZSGFoccB4kq05XBfvw1ZfZ+1c8kapX+b9IkkhxQL9U7Ct6BmXymQTjRnMhKsRTGa4xhknBQzjzRMWMmVROtgbr7VrGA5JFUO7QoeEi2CVh5jGOrBEYydK/36vXOFD3l2TxGDixxR0bUNuwFLx6iepbxiIduyZk7jGNFU/zrBJYR/l3k6jtmUvodXBM9s4O4MsvVdjYzFqQ7JtFOibzWGIxPU66K0cnxEni6HtEax9YFR/jaAsNgvukiY7SRdyZOyKRlxeCEmDT/FpP6nCpZz6BmazzR5me5+AnariS77kS/BjP/Zj+Pf//t/jIz/yI0/e//iP/3hM04Sf+Zmfidd+9Vd/FW9/+9vxzDPPAACeeeYZ/OIv/iLe9a53xWd++qd/Gjdv3sSrXvWq93re3W6Hmzdvnvz8oTIdFpri084qnTkhwBiYoth5Ck527hGDNK5iAaQQQOXEheWLdWvGqF2fDi4/Ytlpa8tYfUFbW47FuEhDTgb6Sqq4s5yhNsH1csAur11WAJ1JG90qklzEFAix41qzx0R5hYZi2lmsA3vCtHAxQAfCERiuI8vUXawm/OruRgzyHKLhFgspEGc2WB8UGCZ9X+Aiy1Q0XGEBogbgCQd92XXpxiy/zmwgFkzFaVxh8mxhyR1MUbCXxwSkB/vn7sqzOCztPwTbI3sJWIKJ9wvBSiR0COMU0V3b6XSgEVz19m4RRzgCJFZaQOtDkhsAEDzzfPB+HEB4SJ1Id9uG+3p8rgjcZNDrg0TsXo5r7UxTgBve18A4jck4AY6o06a97ZMnF1FPMPsmwCqVII4F2HiOhANWXGApu7hhOyEBIDUFCRhFO2AeN3dsjTHbewS0Y2tR4zCqR/gxIv7Tx13Er2JI+OEmiXGDft0pQOXwIyNIRZTvY+8Ha+2gSxJiA8R2brW3NTfFPevd54LWE32CsVdKHD1AkjormRMZ13Hy3Gyzh8/ep6zYZ599Fj/4gz+If/2v/zVu3LgRMXG3bt3C2dkZbt26hde//vX4yq/8SjzxxBO4efMmvvRLvxTPPPMMPvETPxEA8Bmf8Rl41atehS/8wi/Et37rt+K5557D133d1+HZZ5/Fbrf7wN/hS8HEJhccpceouDsiWBEFoiZk7L75Yx8QUWiPH49jE7mxKkMAOmecuG7l1HC+O+L+OmM5mN7c4okNSRQXKki7A+ZcIU0w5xXNqyGclQUJDa0lTKliV1as7boBQQyq7x7gzoD4OTUsvoC0lszFqANo8l16c6GulBQsTzVsw8EYqjQszqZ3Z8AvZUMRvP2UGlB7fVphhYMmllWrtqDU1f5mNYlwTRJEi7FCpldn4sl0H2ugw76YWRZwi9JskgBpOrBgCMaB32OixMSyVRGTNWTUaoP6dRRpDpQSWuosoNS+gAuZFyUgaxB02Qe24eritOLntQSE7kqs3p9kSC2GEAFabT31ZTt5ZukDyRMsZZWkIWvPmrXkA8RGomkyL1pi9uIAKsWqSyx+j+pjTaY14rXCjT/EbiW/R8kWArAixTjS5s9hIrPTs6bD3T+cX0Aw5gxRsiobZarhZky5BUitLSFPK5JXdSip9ni+4ZhRji3Z2Erc/BHglwZZc1xjztU3OxqJAfZ8EKAigNS40YJvKewWbL5Qz6QtzoaWyWM/m8XNncgsAaGfZxEg2jOB2efu/rXnoGdV27PfEyW44YG0U9c7EBs7vxVrRwf1khuo2JxSQ6sZw53786+uAGCzRJ4q5rJiXXKwsZtt9jDb+8TY/ZN/8k9w+/ZtfPInfzI++IM/OH5++Id/OD7z7d/+7fisz/osvO51r8MnfdIn4emnn8aP/uiPxvs5Z/zkT/4kcs545pln8AVf8AX4oi/6InzjN37jB+6uXopGto7sAdD1nkJkGH0HPDIlWUOb7kFQJ3lgXlJnp8QZJsBATU4Nu7KitoSrZcLaTGPusEwngfqHdQo9uyk1FI/1yqlh1YQ5dSHqko3Jg9hE2gPfG+ZpdXfrwPKwVuPAFoU2Gl2fZMSkZ+QxTkbE4spOmEFfKJkFGO5gdDcbs4PJjo56X9b0EswFgTTrCATjMrrMwT4cKiuEoK0zhzWhOWJntYmcGqayWsWPUrvGF+habFF5Y6zGEOwbBCtj2wQRrA9f4BmXNAIzjoU4T+ruViOJ6fI0VjBYmtRjnAg+QxTXmZ+xJFxIifh4jXAD/ssNjChWTRHTxf6VbIwXWVZ+Z9SmGxlOGd5v6Akswdw2r5yRWzB9wfL6QXLSHgLgbTXlGsBIh9e5OepklJ6Mo6h64PI+fI2bDds8pGBH7f4s0UjoYvTmUrLA0sEzBOGOFxgTOOVqEjtMHBr6XdLg4vef8AwMGyUyZ2SvmfAzuj85roGe9BAbB34f9lwVD7EIFtGvn/OT9RGviaEPiE0c75vJKGg9tCLl/mxH5ZchJKTRhev9E3QxhvsHfg+R6Ztt9vtj7xNjN+6A/2e23+/xxje+EW984xv/p5/58A//cPzUT/3U+3LqP9zGiSyruWAx/L+JswY20U47ZyBWL/HFmBnPg+huC/u93y9IqWFZC5YlO3iSznC5zEHxWqtXy9RBw7BjZvwO3UxVEy7WCVdrcb0pY4gOrSCp4nY1/bu5rDjLK47ragAkActxcuZOI2avLzh6ogVHcBfSyA5IGUtlC7LLKrggMqs9iHYXUAAoBZpmgABB+jmhQPZsQuq9mRgtIBNdpL64uAu0eBZlgiJnYzLYf6VUq7k7usnqoElHok7Ri6sPwJsJJYCGyDOZsZwbplTRIDEOlHGaCYADuepZoEpQ4yzMgOwCOPc2Q7T9lCuWmgc/KLp7GR2ER7twOAtLngHQwb2a7PVwSzq4idJ0ositYa05FlpzkzUcl2zVTmBZ2nEyOHhO1gd0F4qzMgIPK1A4i4qIF22arZ2qMcLZAatWgcZGosvbIFGYmWJvGi6+7s5vcZ+1CVIVTGlgghw8masa8RwWKFKprgfo5/XNnQrCrc6uS16JRKCQ3KwkXJOoAw0YAFPGrCWY1pyDJBVnEGtQhKd9lDxOzS/ZYuRs8yZQ1DnhyGxabo6AcJNHDJ63D8uUAR3whSeimVSJDOOAIDU2tQ7yp1TRREJPD8M1qyZIMwmiFaaVyDEUc0lqNoj99D2W0JntDtk32+yhtPcJ2G32B2eSGnS1slRK148vwJIUZVdxtj/i1rULQIA7d85Rm+B4nLCuGUi2WM67xcVNzd23rglXV/MwsVE0D8NkCVybD0iqUQ+WcTyATYS7ssZiUVzst6oFyF/UEu6NY5swZ2OdjrUAAkxizMFOFlweTaD6JAvTbc4VWRQHIGKWcmHZKHcTkYGiW4yxVb5oU7oDvhBpFSu3xAmd8TgNaKkL9wY7iGFRJPuRG6Z5xXos5uqamku22HczjBVYqrmd7fPVAUNCzsOxS4/10wYDF83EWpmVl0SxtGRB7NKCEQOMZcle77SUinVNWBdT4pds2cpHzS4H0uVQugabuaXpItcH3vNhgaZWsWHOFZdQlGJgprbOsIFgIw+xeXR2aR9DTAABPEYyNWgtaA2eGetMIIGpECRKB2LCChhywlihoYsuO9gnIKRGHxnXnBUVDdCMkhsSGqQlaE12Td7pogpkBwNNgdrDB5JvEKoDM8qxpKQGkLQTPsEOqicO5Qap3iZJo4xe5rOO1vuAwFQR41l882DJHtqTjxKvFVEZpZRqmm4O3KONRjaMfwPBXDUVNBfwfm9yIyEO7n1J0+ZAjnqQ/vzkUlGPE+Bg3hvZxJd5PQF03eUeDKMBbTUZTSAhyhCmpljc5etDBSU1LIcCuqFB4OoajvHag+BRDMRG1mzf9Wy22UNpG7B7FEyBMjWsAqs5mjhpG3jJpeHGjQs8dnaJ2hLe9Z6bPmkjgN98tmDeLSdFz9cl4Xh0Bi58NojdNH+XrJin1VgKIGqSqvZFmorzEwueKyIRQZvgYpkx5Yo5VVzWCQAD2xN2ecXSEtaWOxPlYCWnhnXtzGNtfVFjFYulZpxPCyplOR7IEgyZA+1uukT2ZLL7nNIa7kqFX3v4xNSqcfhCmpx1Sbkha4vFwyRGbKFtLUHEi6z7YbLHVE1TRa3JZGLmFa0mXC0Fu3mBtoQ89+B3Y/rMxdqaBOOUczVA2CzjOImB7pQrBN5fzCD1UlLGRFUkX7HF28diK7uLMZgysbi0aMukEMYcuhstOVCjkG0hI6x0rbXIdIw4O4I79KxXE1jmRsXP7+egSzt5fBT7MWcrWZa8nYxRtTi4ngFqi39dUyQDUDaFmdgCALmalppveoJbJPEn7t4f2OyOW7q7PaUGXbzDhayaAcOkJn5LF3hzho1QiCW5KBtDtjNq3sLqvLaakEvrLLkzZ4wdzLkhw4CkIh7rALScF1bGwKEDPvXM7OSvM/u5CzsiQDCZLCYxsfKGNsGylhO3OJngnC3b2+JMRzbMNh423qu51tGztB9MfvGIxQCiPE/JDTlXf5HzV+3sn9pmoWoKsMi/1Td9UbUk4v78+gJgb7bZw21btMCjYEmxuiREBA070EAxkFVrxtIy7l7ssRyzF0q3ZXd/7YDdbsFyLBEgfjhMWNYcum2WSTZUN9C+2GRffO6uO1Alnwv1VCxmblktsHhtCcdaoP5dzzNw5s8W1+pSH3OuXrPUbnNfFmNufNc8l3WIxeEPMJcau+qmPZ6u5IZ5quG6SsUFfslGwhaY4lINBBpsAxPJ7cAxXD3OSlK2RBwMWcau65SRWRDrr+Yxe7HgiLvw/F6bCo41Y1kz1jU7qyU9bs6BZ5mqXS/j4fzaJmc3CIys+oRpAHLNi3glIRPWY+X8tjzWrZ24nBjnB3SAMcpMSLLFMicDbREb1vq4oHxGuMPg7s2kEZvWEzV4Lg33Ys/wNOaKBeUlsqdhiRoA5mlx+Q3TP5xKxW5eHogVtH6JOsh82ZkngrZcWhDWnQlDbDKS2PixsdFdoXbd7Fv7HuP2gA4O0sRNEVCmGuM7KlgINyG+OSBT1gTiIQDEk1HuDkPso8IY+hAH9g9r728Rq8tM/pTSQHwOuJFIoGRQZ9qaCyGv1bQpJXtllYHJqtoryIjAstY9vm11cWPGAo/VVlbftNYl+3V0xoyMuVYJAOn7xtNMa1FnfzvwlqRY1mxhB77BW5ux2HV1FtzbIHmYAdu/urAzN6sKOWEiN9vsYbSNsXsETFTQKjwexne4zGBtXej3/uUO9y/2SFOL2K95by7Sdc1YropNznpaKkcBc8E2jQw/7qKTuzuWNUfQ8jytATSSACWZi3RK1Ri7JlEDdnVgSNeqZcl210dODQc198hczCW7tIQPufEePLG/wAtX57i77JBFcbFMACxmbJfWCHyGx/WtNdmOne4vX83CRZYaKktiiXqBenS3HdzNbXRjqPE7kWesmaZwjZJxjNJDBMh+MBNotkxGsheS1MsaSS+nNZRrqtUXzSZAE9RsjCXBVva4K8BcqTlZlivgzE5ukNUWoHXJ3d0H187zDEtzdMkJ+GHMWcQ8SU8ACNLOgV/2WKNVc4+7SilkZwADCHUhA6SROEHAYZmsXXYCYlptxTcLPdB9WLizAqtGe5Vi4ChEt+HxUzVHRjZgC/e6FIsvA8IFbe1mY3edEg7HYuOCGyjh7xaafUygiZrD7ra0gH2EW1/FqqsgIeR5on6wA1Dxa8sgS82qI4MEi1JnTTCVGlnCFiOZTc8Olm0c1xdsnoHBtRqQyaUhqWd8poYKB1Zw8qt08DhKuuTcsEoKt6WNbXOnT66Rp1U8GcXGljrwm+cVWu26Vo/HpNs1pGIAkyWp0kG8kLEVlFJN9zAPz0EgN2sjHsrqPffybD3D1weyu+AZKlK8HyyKQ1EhsWminl7yXR4B5WabPcy2MXaPgNGdUtzNFjUmfSIn63Dv/h5VjblKSbE7W8L9cLyyOqvHY7HAc19g6HrjJGguNAzMizEUV8uEOmStcZecpGFyNouSB3OusfAqbJCV1CKxwpIs6NoRXNUJu7zisBSsLeGx80ssreDtdx+HAvjQ6+/BR9z8Hfw/t34HH7S/F8kPdJ027Zpb1QFT84lblayRz8eCYBJqNVDC5I79fkGZGmbPNIylI2KJ+o/WDqhDMNYBT8g3KMJdzIxPauml1CswkKnr4MdAUXOQNYpHl0TAbYt/Tg1TPpXBAIxBZdkn3oPFYBkbE3U6lWxE7y+CyMmBdvbsZgpPc9PAscUM1jERhWODMVwQFyUmKGFCjUuwNLrbyYyMDBPjm5wFE+9v25CIM201+oISK8WzqgM8DMwvRawprJ1EQ3MuFc+w9e/5HUUSzH6ycdITVdQxpQxgFFYiT2ExeWwrBxoUPc65msC3x2SybeAMFYP+uYlJHlNp2eIuLaKITF4CUvY12V4mPJVSI5M3RLr5rPs9lFIxT/YMEzDThZqTAedx7CvERccty13Q2UxVLx04uL/ZqApE1mq/lvgnhnP2soWTu1RHRh3eluobL/ZViH2rbYwZL0odOvZTxAUPbbK6ePaYoDVlvy8dNQ832+zhtI2xewSMC24pFUspsBXO3/QF43A1Ya0J+/2C8/0RhzUjCXBcSggLCyw+JyWrJKAqSMUXX5+tRlmC0A5LDcsQuC/S44FGZkfV3WB5sdi4mtA0YfXFG7Cd79JyfKep7cJffnYPv3N1zXbRKrh7NE3D314nvHA4t1JkUOyzudiKNLQkaIu5WiepuPAamWMywSgn0VSs7qo01MXi/CL2qwEHb6smCNC0MrvW2TRUQEXQHAhP0xosRPIqHmjG0uRkwOFsXnB1nMx1uZBHtM6zuChP6Gi2qMIZRq4f7AtxljHYUk+AyalhFg82bxIxdTk1iObI/o02AV13p2AQzuCm5Fmnzgy2lpC0RduOsYpTqjjmbDp/MDdgIos4ZnliODfdftxIxBsKdc0++1wLALTWDMkWHyhZMcvqiSfdJT3Pq53XyGeUZACtoEFb9nhICbDKa1lcpy4lhaw4CUew+Ct3UTsbttRs7kDXWSPzfHSAS7aO4QEtGXNtyS5+2yrOKgpEVqS8uh6j7RyuzQsW5N7W6m5AMTd48Ri77HIsx9X02FKukB5i5gOoA9BlydhlyzaweNsMEavownEewtW5Adqf98gSB7D6WEETOO8IQAI4NbqUqycsZdtEqMcGG/tWI3YuS4/9BfAiUM0RROCbcwMo/twk2NSoVMKNkxJPKxp6qT4eqzpIrtrrLWdRqL8+ELuYUkXduJDNHgHbRukjYBZaZRNimarJa4yLImwivH79Co/fuEBKDY+dX3ZXFxTXzg7GBM01XBgitvCNrAhdMsY+VWeWHKREVYQenwaQPenMEiAoUnF9OmCfF2Sx7E+oBPujzjTOZUWShv/n7LfxR248jyQNS82YnMmYPDvuUAvuHHe4v8yYS8W1+Yh9Nq27s+mIx88uMDlrIEzq8KWNYrHUUiPDIWJsZHNXE9skaswOwdN0TlM77GxecH0+BCtxfXdAVq+AkRU3zq7wsuv3cevsCtfnA87KEkwhHADfOruyPpCG82nBrfML7MuC83lxxqk5S+VB9ejuwZHZWRwgNE0W+5YV5/MRu2k1F53fJ9DjDc2zREBuF0VpG8bpmfu4u0KDpRlcqEx0MXYyBZvUF8sWCyaAAGrGUrbuKvU4TmYspjLUheW1OiJuQzwfx2NU6FDX6vMNBVmv1ujis/FXm7knqyeeMGmHsVZCAEFpEgcsHFNrS6HRSBc2CAS8k8kYUdbFsmtHAMUxidh4QYHzeYn2LaVan7oLNYkxkoelGNtajYUPmQ7vz4Ze29Re82fVY3Uj8YryRj7WWaeYoRTvZSKK50boqiQVDmBxeZje371yBBQxZlnnl7F9o0Zgyp3t42aD9V3JgFNoOZhi6XGQzd2yZPYp4F1riuNTcy+09DgqxdzyEV5QPNkDNkeY63+j7DZ7uG1j7B4FcwB3eTFj2q9gKTGTgLDFcD8d8ZFP/A7OpgX3lxn3jjtcnw9Yl4yb51e4PEzIxWU5arIAZfFMTamnGWwyZpL5lKcJ1TWuyMZlMa20ko0VQQLmvOLWfIljLTg0A2iX6xQuRsAWuurgqjZL2vilux+CJ3b3sUsr7i87y6D17LhVkycfFCQoHju7gChw97jHjfmAqVTcX2bL/s0VtZYAAwIPDvcFoq0ZbbKFdzetePLaPeTUcLUU3G87k2lJvqikLtzbas9AnvcL5lJx57jH+f7oiQvGtpXStebec3kGCHA2HTFJw5xXLJoxTxXXdga0z+YlZC0ulhnX5mN0+zytwOqVKgoXM8sEFWhote2nBdfLFd5zdR4g62qZ0ERCUiWJxR3VmoA8VHNwXT5Rr0OK08WQ44EZpJos8J7vZQfLTGYBenKKxWHZ+A19RDfWC+bvkF2BAzQHGJQ1CRbNmT4TG3ZwxHGq8GoVgn1ZsLTc2adE6kYiFpPlxrK4VEtNPr69bZMYOKXrX40NNQAmEbdmWoQVyBTaBVKBZ8KqJSVBgzkz5hMRFpDEQHApxlxdLcWrgxi7W12Qed4f434UAvHKLvCsZm62iosk9ywQnJYh83ale5aAPYnFkxKYp9RjP0cwY5eu3fXsn2d8qyiBv57Uh23K8mmsJNGTLgLEFctAD4bXXdGrcxAGFD35aVJI7WyewBMexCSGmNyT4rfd31gMQ2DsbvY+qf68c4MhcBe9t+eYxbvZZg+rbcDuUTBqPzXLKBX/P9xVBlE8/fgdPLG/wEWdcG064N5xh9uXZ1hqxu/eP8d6NPfgcizhNrUi6M7CpO5+UhmDly3Yf3H9upJrfC4VjTiw/bzg6C7Wyzo5eLMMWbIdDKpPAuzLikPNnnTRcPu4x635Eq+8/gJ2ecUrzm8jieLQCu4tO8y54t66w2EtuH15FvIXF8uMulhc362zK+zygve0c2MHxJiDl5/fw72LHQ7HM1BA9uZ0ZS7SWnA8FlybDpHlm6eG49E09s71iPPpiGPL4Wa8WGZcHHbWFgLcP5gO4Nm04HKdIGKJLLmY7ERrCfBg8XlakYri4jgboyaKy9Xc6Dkp7lztMWfLBj7WjL2sxhg4OLEYptTlOnLDoRY8PjfsyxIK+tbOirV2hiMJ45EM2KxiwCEHg4cBfLtGmCAC9rEiXJJRLQCdPSbrVchyqZr7Uyw7WpIBqOIxbYwtDEDJRTTDkkDo+lO+7zFiqAF6uuwNZT8SiCet/7t0jLHE3d1YirnlS+qZsACiCklzFy3d0ZHUkbrw73rMyJ7dSimMUfevuqwHdfqYYalNTkDPlBpaA9ZWME2+qbKka3vODoLjUiwTW9HjBZMiQbw8nrFYyWP6OP6zhy5UDEy7IDJ64dOIhVUA2lJn4YHOMsLbLVeoCJK2qJ5BtnTO5lFI2vr84cxhcQZe3ceZc0VpOTKrp/3Sk0h80xKSQq3HzMFjKlNSpKkBa4n5cHVh8yl53KJIMIPGCtv7KojX57Liyev3UJtgaRnLWoLFteu0uWyQ7txss4faNmD3KNhQCaCtPdaF1RB204qb+0vcW2c8Pl/gf9x7DO++cx1XrlEnLn0AEhs+odvCk2IHLhiqNui46Drgg0uYJGO7xAO5Dy1jPZx7bViBFkSM2JQqFnfrjJlsyQHdnFdIajjWjN+6uIlZKs6mBf/fvZfhap3w+O4ixI8/aHcP65Rw9/ByvyaLdyLYvH+ccV9nnE3m/t3BFuL7yw7zVPHk7i7uH3a4tbvERZ1x+2ofLpzLdTLplia4OMy2yKnisNojwnq4tcIqHDiAoWAzoDguBataPGOmEKzX5azNYw2huHfY9e+s2SVl3BXoWa/rapnMmFZQsw2KyPRcvd8aBPu0+vXbotragqk0TKjhpnXiK/p9FAsG4MH7LVzydM+z9u2cV2P/HNwztqyOq5y700y82BZSaQR/nfUDhjhIB1qRHEDZifF9z9SmS98LZwTzxXgqOzDBp90bC7fzuyImxrvfLXb9zTQQgx10UcIpV2gWSAKOfm9k+QzstBD+TQ5iS2nB8qoYUwwRV8EZZDicrRN3S/KHOnTwZ6Vle/pYBm8uqxOTJuCbksWC8Z6yu2mLrBF6wJiz5H015YpdWXDZZgNnDsb384Krq8kAeKk4m484zwvuXVqsK+Mxw60+bP7Yr4zDy0lxXEtkQZPhJKhcawZj3shaCkx6ZC6rf84TjPwZJyO4zyuKBxGqSyWl3DX9dmXFflqsgga80oRvIjzdxq/Hxs2taxc4mxbcvtr7xqnh+u4KV2uxGsbuPm41YcorRimfzTZ7WG0Ddo+AMYYkJAX2Li+w2Oo2lYoXrs7x1Pld/PrvPonnbt/CcSkW/KswV40MjBknQl9Q4QwC3A3TJy6biBcHH2VaXd6kx/JceRJCczfcnIGl5c5QQMLVKDCm7mqdQm2fx2Fcz9Iy3nVxA7vJJvC7yx53V+D6fMBvH66htoTHzy6wSysu1hmHVnB9PmBOFb99/zqaCq7WgjlVXCwzDmvB2f6IPQT364ySGu4edri77NFUcLZj5rBgaQVtFaySkFWBbMCtJANlmT4nB0glVQO7tceW5aRo1X6vHg80lYrUumuT8YhjzJpJr0gAqeTggv0P2MKWpIVkjC1mBo5XTTjWjBvzAXVNLtzL2qwtzjeV1Vja1l2v07RiXSaLIaqtawT69fv6a7FpWkAFNMZdZgIeB4uM52Ow/zRVZ+jsnnbZgEdGA3SCCUozblAHEGQuV44TAj3GOzF+NLIqnTVMyTK1bez75kKzZ2w3VOmARNBwXo7mVuXzJs78oMdtjdHIAgQrWSYCkQ6Ex+SkLvat7tLvz2BJzUvnITY+2WP3FKeyMwRFXXfQYMo8rx7q0KIdJs9cNzBUkfaK63Kw2DW15/nG2VUwvsWzWXd5taQHNddwhWCeVhyXjCk37KcFZ2XBcc04LiWAV8kV13cH7Mpqm4/m36uWtn99d8AHnd3D85c3rO60NJzvjnhsf4l33bsBiOL67oCDs+TMQn3i7D7amrAsGedlwdnuiMMy4SwvuJQF5/mIdclWhUUUN/YHnM9Hcyknwf3DLhjmkNXx/jOJFuvr5+/dCOax5AbZa9RinvNqYNHv7Xc9TnKzzR5m24Ddo2AN0NQrBVBo0ykbrGraZ//9PU/ghXvnFvjsKv2MP0no8UlaJcR7x8zIgB7qcTfMYhNjCjUnpGm1DFmxEl+Uyih5jUX5LC1YdKgR6TEvR5cXqU2wKxYfdKgT9tNiNUebZXBSgiJ7HNDaEg5rQRbF+XxAQsOqGasmnE9HCICLtR/n4jCHoDMXTdZSPS4lJvqUFPuyYM4VV8sEbcBUXPpitdirnjRhbkJm70WQPXoAuoEaSmzAF3kTUQZsMTdBVO2sVK5YkW1hFmb12fmq2LFT7VU4TGy4BWtCtyaDvu8ddriqJUDSflpw8CoAI4MzlxVLM8DeKuUguq6dsTAVlGojuTd59i1jrrIfq0nCAltgq28Uru2OmPOK+8scDGAGA93NTX2+O2InK65aQVtTsK+WjW1MW6+Ja5uOWVa7n4HBFGf7uoyOF6TPlmzRGkulGaOamrVl8SoF+7IgJ0VVDdFbuu1z7vGjS8soYn1cYeXgRPy+ptUzNjXkN0zJUDDn1bJ1MWOaKs5kwZWPRTKou3mxesaeaTp5oH6ShqJ1SHrpMWXr4np9J0kxriHomopzWXF1nHD7Yo+mCdemg2WSZxsvh7Vgnw20LZJxdZxQcsUsFTfPrjDN1idLzXjh6hyP7y9M2mgV7PNqx18n/M79a3ji7AI3dgccmzHNu2nFYS24WCY8dnaJOVVc1YLDUnD3uMMHXb9nkig14ezagn1aMC3GA9857PHE7gLX0wGXy4SLi2vQJtidLdhnY/FvnF1hpyvWlnB/mdEgOJsWFFRjYpMd89CKM5eWpX4+H6ENePe96ydZtZxvz6YFCsH94w73jzs8dn7ZtS1DkmCzzR5O24DdQ28KTL7YVo+JqQIUp+Mmk5h4/oWbVtCak7svpBD0GpueWZYy4+mIDelmaT2eS05/S5S1MrmU7LvWnAzFZGk4z0fs8oondvfxO4frKKlaFQp30c3ZmJs5V4spUgNWCbYzvqoTinhGmoOkLBqxLYfqbJFYCa65GLNx+3CGs3JEcXfQfrIEkaXZ4jWVitwskB2pIaeK3Eq4ve4fZxRp5j6Eu7NcR05gDEOVFOodAsW6ZKjUcEEpAIiVQVLNgBiQqCrIEJRUcWglWBUmHDQvZ2SuqupsUA15FiYumCtcvMi9gUiCXoFiThU1rzgAWB1gZXhWrX+XbsqcFMsxmavJLjtAOTXs2MeqLA1nzGQVa5PdtFq8H91cnoBhAespMl7pwqbbP+XV3O+w67tcJqwpWaJPNvYF1TOvHVivaw520OrKNiSIs5wt3KRTbrb45hZl1oqYAHZTc/Mdm6A2xDjeRdyXM20tBzBC7Xpwqycw8F6ofVjVKy34WEipYTdb4obAwHaCZYRXvydK+mTpSRlCN3ajblqLjQWt+U6FoFzVqjosdagZm+z3lYcFGMtsUj7C+AVYksBxnVG92L0k4FAnlpeN5I77hxkXqwGm4s/v1VqwzyvO5yPuH3e4c7WPUIGrtQToTaL43YtrWGvCE/v7uDgWpKa4v86oNWM/LbhapxiDc16xFsHdww61ZuRUcVf3uFfNHZx8DKwt4fbVGSaxercXOmNx9+61+WDxhC3hbD7i7rpD1YTHr993Rhe4e7XDcc04rNYmY3k5yjkd14xFs4HqbIwmnKW/vuvs7mabPYy2RQs8ChZb9C75QFQiUBwPxXbuLix80qv++eSLDiUdEIABoQuVkomPTsUrSThjUEqL2pQAWTiXQZHWEyoguL/ucNVmnHl5sLNytMBj0VgUzbVm368qkWRhC/7pbWcXNabbzdT9DSQKjDV8bHcJqJXoqmqM2FyqgY/cIlHEJFRcKNXbLmLQxEDd1WJ7nV1ZQUkO9XOOIqa5WE3KkrpcCsWJkwfxsPTa7G6uLD2QfCrV451Wj8U66a6QXJjL6sr47ppUAprOsBqwUhy91i4zinfTihvzFc6mJRalW/srRFksv5f9vFjSiDNe1MpLHkeYxGKX5rKGBAjPkZPJVtA1KWILNDUHKVeTnFHc5RVzqieZiRRzXmuKOK15qgEoFX1TsCsLzifbQAgGmRAAU14jmYftl93NWFL1Pu3u2pIs4US9vZO7S7k5MrkVu3dB10gjyFFFtAlEY6yQzbRYMsSzQvcvxDYyqsaU7srqjKs/n6nXS2Z7U2qopGoC2u6etQQcVvvoMXOMf5uKxayOrlzAgLAkeLKCPYc5V3f3d3mSEN8W9T5T35QJLo4zDu6SDUkd7wyrQNEzaskoz6VGFRj4811rCldpXY2BpGv51pnLNrnweK3JYmi9fUrxUn3uhp8SRZJNL/OwTKg14f5hh/dcneE9l2e4WicDxqDkDrOpvY/8HiavqCOi2OUVu7SehJ9sttnDahuwewTMduJOryWEdpRkDxoXkxUYM9xaSydATFlD1N+HuxPJ3tEFB9ikTw9txEupRKkrxpMBpgZ/bTri1nyJs3LEzekSL5/vokiNmDlorzMKUIjU49aG2DTWiWVcjKrVWE2ufrsvFnd3tU6Ax0FdLBOOLUPFwNjsLuE2gAXAFjB1seFGfa+BgeHisjoTwvtLDvh49Vww+Z61Xddwy77gMxaRBdyNQWrhTiVbyExD/obfc7h86YL1SxZnFCh3sivrCbtHlipHYkvBLq/OFgH3jjusLm1CAEBmNEszNtUXeBqTGiwppScjiBpTWLLp+iWCAdjCvsurJ5ykiBs8K4v3kUvlSMPOa70WB4x0ezOUKfF1mBA1NxY5W3whYHWG99nKzBWCInHQ6fdy77CL8Z3cjX2tHEM02zY07qqbjjifDzifF8wOTqt2nb3srBpB31Qq9mXFnCtu7q8MGPp1UzvO8X60IcH0lIw1tJjN3ucE0/tptQ2OCs6nI/bTEs/kLi++mUHcM7OY6cpO0jCXNeJsRRRn89GSMQAPK0BUV2Cf76cFN3YHuy5mz4vi2mSvxZhPDaWsMaZKabgxX+F8OkacJsdtcXkkjruzaQkgacBvDX26TKDtcyCfvWvT0e7TE0GqGsOeAAvNEDWNS9Rg0uO1AL2I8UbPRYQX+HOQCcgBT9qw0m9r2xi7zR5u24Ddw26CKJ8VAsCedWcoBeAWM0+1IwbY+6wHS7ZP0YP9Q5jVAV5TAz2VZZ48HsmA4KnQb+LCllY8Nl/ig8/v4LwseHy+xFWd8MH723j57i7Osum0kd1LvvtNohaQDOB8WrwEmLkWmY07M3kAwOzuMoAZeJ3NEXfvXpuOsQu/ub8MlqQ4ezB7ADTvvRQDCVOu2M0mK3LmOnKHWkzbzGvdsowSBYIJPg04OsND15l0IV4AwepFBQNnIGpLOK75JFaPpbySg7O6JgtUl85MTB7QXRwYAcYuUOpE3fULGMN3f9lF1m2DoEiLOqesg7FqioWTyQDGYjUPpu/1Q602sLlqDRQy9dWrZYizmg7Ib+6venbqwDNm1/bL0GCjJgeJs8erjYkUczYW71gzjjUjS8O+rJEscLHO5uL2aw/WSLs7m5p1cJfuWV5wXo4Brgj0UzImrTbBVS2WJMJxQ7YoVWcf7foISteaQ8z2bD6iOHtK0MGyfPybzO3qbnkC82vz0dyL0wGTixSrCi6XCcWFxd9zdR7nZ5yYZZvXYBDvHXZRl1abuZHPyuIlujpDfDYtfePgc0NtEvct6IkcU25dcgQaAsesUnG5TF5fWiNZa1kzzvLiz21/NviQn88WTtFqr/F777gLIMZ4y9uHvWXKwjZzyZ8nwNruLC+mXQdjc0tquDYdcX0+IOcajHIRAm/Plx363e6rxeZqzivmtEK9osxmmz3MtsXYPexGl2uyyVOSWpYi2SJXURd4uTAP/g+XLXrSBJrH8LgMAT9C4VAGyrfQt/JjqwLIXivSFpw5r6Yxt+5wf53xrssbuDYfTDutJezLavFuXnnCSAVDo1WTgwNf3JMxHWvT2A2LIEBLFsV5OUaMXa0pyjmtNePYspUag8XHtSa4Vo64sTvgUG2I061ytUzBtBnjteD6ZEzjneMeF5gBEaxLsnJZyRMMXCQ5MlmlV6QoXlOVbmbWQQ0AmasBo3DNsWtdJBgGWKbEKgcNxVkDHYFobnh8f4EsDTemAw6rxQkWB8wU3E1Z4zysm0sRZUl0f9vq2iAnrOpSs7vgEeLT5/mIpWXc9zJvrJebYCCmQUy/D93tmEVxrAWtGeAqDtImX4hLEouL8r4gK13dnX6sJbKCExRzsk0AXa+1JTQAyZki3g+THiCCycWEq1fkoM4chbXJaBnY9XN5XCEUuHM4w5RXSzaShls72yzMqeLuYY85VdxbZgNQarFhZ8UEwgnyL5YZ8M0Dg+/JpHsYmz0jqeFYC0K7T9S/q7h/3OFynXrMq9/rtelgST8CPH1+By/b38MkFb91ecuSL8TckkdkXB5msMIIANw+nHUXp4+xq3WyuEbt18kY2eJu97Ul3DnsjbnyTOLkgFLFxt+xZiQkXNUChWn5tWxj7v4y4+o4gQLJFBZPUNw77nBruvSYXpt7KM5M0NrUxivj4e4e9gEujdlnKUBL2Jl983hVC7RKCB1frZPF9a4FpRg7ys1K1RSZtnzeizTUZIz+KNa82WYPo23A7qE3m8SleB1Nxq0wOQIWP7afjzibLV5ocUZjXUoIydKVa5lz0o/D3Tk0wMrqmlgn0hNqCywrJtxbdljXjN20hlu0asLaYJIIPunfPp5FDllVwewL/Mg0EZiIAxAufKwKALGF/VhLZ1X8eM0Xq+QLYU4Nx6XgPZfn9jrgrFyDouHG2RVmVOxlxXk5RszeC8dzPDZf4OZ8hecvb6ImA4izrrGrz2hYYXVRWYz9fHcMN9raMpaag32hZEUwAbKiJWCxvFYDF3mMXUR8HslivAoaZjWAJVDcPe4BAGvLuDVd4iPO340npvt44XiO5y9uYPL6qjemK1ugavYyYsbS7NKCi8MUrNXN6QrXpiNeOJyDshy35ks8eXYXe68pemwFdw57MKHlvBzx9NkdzLnisBb87tW5ZfsWc4U9Nl+hiCXOVNf125cVDYgKHHtdcfQMzJythi7UgvyLu1uhxoIVMZcpszjvr7PzfjY2z3aHAH4lVXObpYqCikVnB3EZTZ1FKwteNt/HzfkST+wuLIljnfBb9255xrYxcffWBHGZlKlUPLW/E3Gpdy73mMsKETv+E7v7eP7+jUg2euzsEk+f38H/uPsYNFlc1j4v2E8r9nnB2iyh4NbuMtjWjIYpC/bTgpfv7+Hdl9fsLv05SFksOWl/gSfP72CShnvrDmfliLvLHm+//wQUpvf4inwb5+WIa+WAX7v9FJY144mz+7g5X+HGfIW333kiKoNcm4948tpdvPviOnJquLG7wq39FSZZ8dsXN6zusLNlu7JiXROguTP/nnCzK+YKX6uJobOSx5QazucFy2IsOGM5z8qCi+McrNpaE+7pDpLs/+flOCSTVNyYTVIlYlrlVPz4vCy4Vo7YpRXHXKLeNMurrdVCNppaNm9TK5/38uv38NjuEouLR+ekuH+YMU8Vk1bLmJaGM9/YRqb4Zps9pLYBu4fcBIqzsyMu1gkQwW4+AmJaVAyyphvhYpkiYDtiZ2BeEnOvWayWqk2cxnK4FphPlEAPLqf8xrImlMliqS4OMwDEbpa719ndq4tmzxI0VeQqysIZHojfs/3WZswdXVAEdwxMD4HaRsatniRTNHdCUXal1hQuSFErQ8QYu4vjDIXt1NN0hTvLHrdlby4uv4e7hz2SOnjzuDoCDFvcFWjdZUo247JNJrPgbiYyZ1CBJnWWSUMLz5IxrE0OawmWcS5raP4RKBd4fJfrqjW1tqtF8PzlDVzVCWfliA/e3cYff+x/oIgBr0MrqJpxODNW9SybvtfFOuODzu7jhcM5zj2xZdWED5t/FwUVB73A2hLefu8JE9l19vBGPuAV129Hqa6LOuPeau39imu3cftqj1tnl5aF2ibcXfYoUnFzvsKUKh7fX4QuWHMm6Ngyzoq5Qu8ueysY78knCuDx/QVednYfrZkcyb11h11Z8bJ0P5izx3cXaCq4u+zx8vO7uDldYV8Wj9FquH08w/XpgGPNuLfscHO+QkXCvXWH37p8zKpwSI3SYQkG2q9PB9zJlu05acOTZ/fw3+8/YX0iDR98/Q4A4In9BYo0XKwTPuLW72KfF1ybDrg2HfHf7z6Bp87u4nw6Rrzo3WWPlBoe213i3rLDO+/fwpQrbk5XePm1e9hl23BcLhMe318Aamziflpwa74EAFzWCe+6uolDLchoOLRbuFonVHeznqcjbu2u8MLxHHeWPT7k2ntQzxLurjvcPpzh/jrjg2/cweUy4TFcYGkZdw573NpfYldW3D/OuFwnLEi4tbvEVZtwc74CYFmvBxSTHZmsXvKt3ZUlIzQgF4UWyz9Za8Jj5zYmprSanmFTzHONbPicujv99tUZrmXT27uxO9jzsRTs53s250Bx97jD9emAJ84usM8L7l7u8bL9PWeEfWMIphrB6lXLgqUWHGHj6KyYBh4EqPuERRP++53H7f8t4anzu7g2H3FjPuCJ/QVqE5znI44oEXKx2WYPs23A7mE3AZJHQpbd2t1XyQDE5JlolojAWCePgwuXoJxIlChgiQPorJ+fymLuSo0yTYCL7VbBYS1RBxSpx4oZmEke9+QB7hDcKJd4z/EMV23qYEdcHsIDm9eWTW7C49JELX6uZI+RUV6jRNB+SooJDasqDjVjbQmzB16fFK13poWuaf4+rjni86JMVwMOKKD/WtDdURTBtXhDa5N1zUiTYllzSFesLNXlrlsAKA4a12afQ/NsSbpzlBnLxqKuLSNBcagFl8uMMlUcV2MBu4sXkaRQ3dX5W3oLazX30QvLubGvje4p4GKdIYIhi7Hhty9vmNs8LxaDKCvuHM96HVcH90vLePfxmmWcJnEGzoSdb0wH3F72OM9HvOvyJhZNIV1xY6poMEB/f51xWWdcnw44y0skqqTUcLWayHSBuaJvTFeWkALBO+49bgxLWnBUAzKM0zzWgv/v3geh1oTzYgH1z1/cRBPBLq0RA/dbFzexaMYurfiNuy93LTrbYDx1fifEi19+fg9PtPu2UWiCp67fwVlZcFknPHdx0yqCOBP7jnuPWTxZTSYx0hBABQq8cHWOqzrhuYsbxpiqxYHeOe6NqXJ2OaeG2SuE3Ft2eI+emXsfFns3Mb4Tgucub0JVcOewx6IJ+7yitsmSYUQhIjgsBe+W63b8bGP7PVfnqE1w53gGVeDm7grvvHsT5/OCF67OcTgWnO+OuDjOOJ+PuLcYgK4qWEWRiuKFK9PHPK4F13dXuD4fsJtWXNWCd19es0oTUMyThVXs84LHzy9w53iG+8fZ6ke3gpvTJe4ed/jdi2s4mwxc3b/coXliCARYNOOdd25hLhbXdv+wi7COqglLWnFv2aGkHaZUcedwhqVlJDTcnC9D4272etdJrL70Dd9wHqrNR4dacFgnvHB1Zp4HD0e4t+xi03pYJwgUT5xd4O5hZ8B76vWcN9vsYbQN2D3kZixTtgxYz2RVFaBafN1aM3IxjSUKnZ7EW3msXNOEujK+y9GfoheVF4EOjJ2qoAIRywL0pFomYyRRd/VZ/EmB5XIcW3G3a7GF8egsn7NVqsChZjQkW8w9nu7YCqBiavzJyoxdnw62oLQc8Xc5NQ8CN/kSCh83TVAHgrzmYP2gsYuHmLBuSpZsslRb9COgUY3h3OUVFw6ctTHWDS5zUcNtKgkm5osu6cIAecYHHWqJpAHGoTEOr7m70iQ6zJ3YYFm0bHfLKnb3pINVlm/i5567vIlr5WgaYjD5k7WaexgOjmsz0HP3ah+1SqdUcbnOaOJZz8WBrbvvmxedX1uO0l50na0t4bBMuDzMkNzlRdZmOmFXteBimXF9PkTmbniy1N3naLhYZ2QoLlYTir63Gpu6nxaPBfTMTxjruCCDlQOWZjFRLxzOLZYtWdyaQvDuq2uYXU/xXt2himVqp6KRdVudMf6di3Mbg2LhBPevdthPC+4sexxbxvXdwWv6WgLGLq+4v844m5eI/RJR3Lk6w7XZxq22ySRJmuIAMrl0uyMScmpLuHfcYXEWM3sd5JQVdRW8sJwji8WMWWylYEXXGRRnge05o0QPwr19VV0vzsfvumZcNInkHSaVZE+gsWtLWF1mpOspAsfV4tXurTt/tlw022N9L5cJrRoAVd9oUabnuBbcP+6cKW+4f5zRYIDr/uUOZW4G2vx6WMoQHtfZWs9gB+z/azNmfs7mJaDkzd2rPXJpWMVAMzPEFYLrBThqwWWdME+rJ33YMdfVQhh2nnFNWZoiprt4+2r/fzx/b7bZH4RtwO4RsJQVoHhmAuoqsdC1atMqVCDFAp9XV6JvNXm11CG2jmyPT+DrmkysNbdgxuwLXs4JCBaiQaBrCk2tBstonXPFnFbcmC1Z4bH5AneWPS7r7KWZFCUxyy5FwD8X6X0y1X94TVsmG0yoDsgsM3HRjAnVFiCxupGXdUKWzmI1z+JNWaMYem228FGM1Mp9+UIlPVuXMiR09S6tM3vZmb1Ve7m0nBpWIeBw97fHKyp6gfXsbt1IeAFlaoDqOnqMYTQpDqA1i0frcNruY8od7NliLGBqoy3mKZjN5gLEFpNkcXbm2muY8ooqKeRcGGfJhXptCak6LQiJrMP4rLcXWafWcrQhq2OYgG/qZa6qXc/ilVJWZBe49kzevKIed1hqNhccQZ0ne5DxnVx+Y23JWBkHx5ZQo5G1aEk6JrvCmr/RnIoo+0XxYSsllj2RIZvIdUpY1oypdLaUsajR70pwY/IvY2x91EFFgjjgKg6M2Z628epyNbUmK4Umxv5qZtu3nkzlmyvKGJFhErFrMDHrnuk75QptOe4hFy8jJmuMZdbJZVxfySuW1bJPa5PI2MYD919bwlRcSqRvn2IjanWN7buWamOf4cZTPClJRSIsQ30eyFDoIv5sNczFNQi1RYWQnJptMCP2NoVc09ISSvJx4e23toSakj9/CbvJZGXW2qvZXC0Ewr2c26LZdRax2WYPtW3A7iE3EeD6/og1mdsvp4aduwAPV5MtTEsB3C1KViclNZam9ZJYwkKkQEzeALxerIEMAsBRnJfMDSfnm/srZLFA6qf2d/Hk7g5ePt3DRZsxScXj5RJ7OeL55Sb+2+UH4V1XN3HVCiaXxzisJUhDZiPa4mhJEhfrHFInx1pwsc62e1YDL+C1q4aLkxpaTV2zD6cSHRnm2iWTaYuVxUsl9ESNpWUraUaZj5pQk7ENkxCMCZAE2ReW2hxUQNCaadit1VyrJdU4tmFAha6IoG/1fyirIOqyEg6YQFZG3T3tQLnBQNNVnXBeTBbDqmYIjlpwbToMrvUuOrufTLssi20WGP/HsdCBKYunOxuTEcxNdY3AknrVhimbO702wd6vk5IvZOkIIq0axf+fvTePtu2qysS/tc+59773kryEBJIYektKQFARBKL+LIemTGnKFrEZFIXN0JKKCsYCpFQcaikMdKilhdiMEqghikLZFNgiIHYRFAVpJGqhQhESUExeIHnv3nP2/P2x1jfXN+fe9yVBQvLCnmPce87Ze+215pxrdmuuZrcgYDCYxK+7O/W8sCO7B55l5dtKTm4HxpkY23qyep/no1Ver1tsUc/yq+slV6u6K3jTsk41+K1Zzd1hiwPUDR6ntnWX6Wqoz6wbnv6WFqs7ZMtQ2pR7y4q3QNoPOQY8g4vWD+t2GO+wOwodQ8jCWgtq6pFDtX1m2oahHzvCAH5YbwFbYf+gvnVm1ZYweHxl7bBrdLzqmXiD78AdtwXD2lDWjdYmH4X9OtRdvYPx8N421b0epX82bbPLWN8e0ugbVoZTmzWO755EGYA9bGDr4m9X2d9u2rKJ+trAC45+ELubrZ9TV0bDsSP7OGhnXt5ysIO9nS2O7N6CnVI3UhwpBzjYqQec77cBxtHVAc47dkvdeIEtjmzra8dQql04utrHiAE3n9rFLQc7OLVZ18FMMZy35sHP/YgoQ+XjWbuncNbu4jYXuGvDIqF3cTADPnjLLspeP20eBTjryCnsrrf44Af32g7HmqHjUSYjgMJ3YIEZO/iUBq+NcmadB3Wln1DPgLHAcN6xkzj/6AdxydEb8IHNEbz/1DG85+bjeM/Nx7HbXpl1bH2Ag3HAsdU+zt7Zxz12Poi9Y+/HtafO8/e18t2fB2360V92jn4wcYFVB1W26BmNmsE42K7a8QPdsVrLsPmO2rEFZe31TeNY6lqqbR3Nb8a6Nm+Aoaz7blQGvH7emzFD06bPNm16dV13Ae6stvjgqT3w7Qw83uTgYFcySTXbOXjmEr7L9djufs1QtaNLNuOAo+t+APS6jO6QuWuVQW1dbwkcQV2Izixn6zYMGHGw3atZC2uH9tY5ZWxs8LP0NmMNCgbUAQPP/dvf1qCH068nx4Kd0o+gsLZOqQDYbgbYugbem5blWTW5OnlQg8+xTTnW/qmbNsq2LifYtszTZrvCzmrreHGKjFOXWxtw82an9n+pGUxmJPmO1YMtcHS97+8YHoYRB5s6jV8DJg50zKfqDQX744pJK+wMW2xWA+5x9Gbs7mzxjyfPkows/ADsodTAlO+sHa0tZWhTgjy02cZSs7ulnrE3toHUsBp9MFbPpIQfGl7rHL2fTo1r7Oye8sFNQQ3KajYTrgeD1eCur70z7O/XaUezguO7t1R9Adqh45VPxWrQd2p/jbIGVqs6pT4O9Qy6+k7XVT0mZLWuG5HagPPoer8e+YOaaeRRJljVNspYByNnD6fw3lvOAd8icrDth/2uYLgRR3HD5qifMXds2Mc/njobQD3njrJ1y2YXu6iDipvHXYxjwbHd/TrwHbbYbAvef/NZ2Fsf4KxhH7eM9TgeZvfWWGOLyjsrDcfSbd4HTu3h6M5B5b1VWdzftjdZMHBdYIG7KCyB3RkA223BjrWDUQ3tPKi9ujNtb4OT+3XNzVDG5liqI6lr5to6LgZ9xsF8O++sMNPXNk+s2jSeHNcxFMPRvfrGgH+6+Sz80811IT0DC2Yzdnbq2XbjWHBTOYL3nTK8AxfUw1CHrb+eCm0qeMendOs5aKu2oP1I2x3KKdLddq4ap5eOrA+wahm6nTbtxAX/BfW4lr3VFvs4gB3s+HEja9QMw85qxEkzP9Gfr8EabQdltfWND5v2flEU+KuVdna3HmhwRL/bDjSt+LXDa1db75MeuI6e6dx4QN0zkJw65GubtsPgmQ3Cznrr5+qd2q6xHmqWcTsMGHR94cAM24gVagB00I6xKcMI2Lq2j5aRaNk4AM6rofRXmxEDZnQMddNHnfaDT+Ftx3qW4qYtZt8ZtjiJdc2qGnzt4aZlqkYb6nqr7ejB0rgdYGNd+7hugf+qZdiKtU/UQHeF0adgieSmbeDoGWmutTQMNvqaNhj8bQKrUoP/U+1cuFUZcXJ/B/9sx/BPp86qa7XaGs/KtyqXp1pws9kMWBlw0N6ViiLTm023xoY1A8LValPXCqK90WKwetaecTnAgGFs78Bter1tmbpt6wRmAlsOsGfZ0d74MZR61mA7vHpnVY/Y2W5ru3XzUxswWuUV3wwxtE0fo1W5GVZtBztqIH9qs/bXm9XdwhvfXMXDjEsxP9R3tFXLPNZXpPG1gGjB+2DWlmhUOeESj3V7e8fYDmbfW21wYCvsDlV/Dw54np75a+5sqGcXbscBq3Vbr9cC//3tqmW54YPJkS/mabrDV80Z+hl+pU0P74+L21zgrg2LhJ4BsD1Y1fVeqBm1oa33sbG9Fiztdt1u+iG1dMi++7Vd9AN2mxPiFJKNBcNQ172YVSN6dH2Af/zgWTi5v9NGtujTeYXrwtrBwe3VVXUx86aeY7atL+QuI7A3bHDLZgdH1wc9kEA16HVhc3XIJzfrdiTCCNiqbrawapjr2jhz58yMYw02GCTVA1pvMZ6F19YoYQhBGZMl2/aarQNbYWcYYVsAPJAUW9jQ1mxhbIGfYbstLXtVF4wDwAVHPoj1MGJ/RN0p2XbT+hEz7ZDojbzybWyv3LJSg9zNOOCs3X3ccrDrXXYw9kzWqmWEODXIQrtt+og7R0+1xfCjWT1oF+saKA11GrOsDeOmv1lgtRo9k7gZ65sdxlWd2t4p25btrU58RN2oMraAZI26e3Lc1AEB14yh1On7EZzGrTiti8HKFptSj8cp7SiU1WbEznqDNdY4ulvPDyOdXD+3g21fk7YasT2owRRlfAXzs9d2Vxs/nmJVrB3YXPw8tTpNLusKS30l1ljqmq/6MvvKYK6RA9CCnYM2oBgx1HUO9Yy2lp2ylmVerdohyG1jCcDXtBn2Nyuf7lsN9XVgdW3ZWAcvLbO4Xm09+N5db2rgXOrU6FgA2xZ/tzNfOVeGypPVsMXeTvGsFIM57jAvgOtIxaUfF7IaRhxYzSxy9LdajRisZn+HgSFllY2Tmx3sli0GjP7eXx7nw7MTC9p7gdvSCE5Jl3Zm5ZbZ4M0Ku6tK56mWKTTABzZ1/V+BjcDGVsAInzpHC+y50WX/YNXsTWlv1KnrlFcrQ2UHjyWCB/676y2sFJ9B2GnLKlznFljgLgp3Wk75ec97Hh7wgAfgyJEjeMxjHoPXv/71dxYqd3nwDByzHPsrP0V/PBhwcHKN7f6qnSLf1su0k9v5aqP1ujqYI7sHOHpkH0eP7NfDWvcO2uLhLY7t7uOsI6dwzpGTOPfILTj/2M24+OybqpG9ZRcnT+1gu11h/9QaB/trHGyG+gqyFtDdsr+Dk/s7zVnVjN62ncd2alODwls2u37UxS2bHd9pN1qdCrvlYBe37O9gf7uuo/dSHe5OGTHySBdUB9SnSerf2Aw4pwmtZTdbnsQXrPPNAlwYPo7VSRxs1vUQ1XZsCAB/RyZaVrIedFrp3mz6myS22zat2+rdHKywf7DGuKm48aBWWHVq+wdrnGptbdpr3DbbOm150IJYs+aE2maD/c2q4jkO7b2lbfMCmCGrOJP/fFVWKW0tYntv57itawbpIOFr2VBfKzfWIGDbnCcHA+sWCPHMQPJ43Q6v3d+sfUdhXrt3sF239/AWD9C5ycF53YLEtv+39mkLMk6Oa1+zttN23W6s4s8ptk3Lyowo+MDBrk/zcfp60wJo7qrcjkNdEI/aDje5bMea/WS2dWzTwHUZwOAbgtg3xmzkMIKHTBcuH2hz6nwjC9/UMaIeWjyOgy8h4DuLydtV24ywLq1/22CL5xyi8WPbgl7unOa7Y5n1q5sa+juf1y1I4+Bwf9P0dEvO8200dZPHwWaF/bb5hAOH9dDfKzuO9Siks9b7dSlAabQP/U0tnmX1AVnxg8uZqWW2emiDBx4LVIO/Nj29aZsrrK99G23AZtP0yIoHjAVWN8G0wRDfFMOspo21be60rRvJitfNJRyGgoNx7YNULpNYYIG7Ktwpgd0v/uIv4qqrrsJ3f/d348///M/xSZ/0Sbj88svx3ve+985A564NBRjWI3b2NtjZ22C1M9bXhq3raHlYGdZ7Gww79fuwGlvmpZ3hNNTfXJ+32daA4+T+Dg5a8FEDi+poTh2scdPJPdx06ghOnDqCf/jn8/HPNx+rxm4sbRcufMps24KUk/s7dRTd3npxsK2vhTpouwvrlGNzki1rUlADr51hdEPORcycmrEWBK7bAvDdYYv9zRonD3Zq5qjVVZ24eZ2+qWFbz9djhrEAdR1hczi37O/i1Gblr1rbGeqaPt/B2oICvhkAqD6GOY2hmK/7Wfu0ZfEsHTOQXAPn026FOxt7V3MtI+/TiQM107TZrHzBPtcIYoTzB423nOoczNxxbVp/bLfFF5FzI0RpGZN6XEZfB8fDnfkWkW0LQJkd5bQgj8sY27S6oU47eiZ35GaW4kENebo/1ozVpq3tqzyvZU8drLG/Wdf2xsoH1newqUHwvgeR8HfOeh0Y2vt4+6u6eOA0IMfGwFqgJNO3sHrQcdOb0WrwQrxJz3bkzt/RM0KlZdZXZcTeTs1a8+Dc/U07tqf0Y3s2bcMO9ZVT+vCBSZNLXzNbPEjhtC3v+FsqrL4+b7+ds8jgnIHLMFibbuw7yvl6Ne7O5RS2vgmmbu5pR8SA5zyiZdXWbdd436xDHeJyj7EFVmUYa2a8TSfXo4RG1x3iuhrqwek8gJg72619p7zt7NTB68aqPK2GGoDW99WivYGnTfW3tbXMUPJVelyysdteP7htQd22BePrwlePLZHdAndtuFOmYn/4h38YX//1X4+v+ZqvAQD85E/+JH79138dP/uzP4tv//ZvvzNQustCgeHso6cw+hqYOO26acePcJrM2ueAtm6ldMM5SkDD9SSlGIzHnZT+vlOgOp7ttjrV1Vmjt8upnuoUzOujc6NJ5zlt9aDV6kTXZWxHrVT66job8wNl6/ljdDx1fdveauPvnt0Z6ku8d4Y6Nbi73vhUUl0YvcaRYVMPZ7URR9YHbd2NtfV7B9gdtthsVv4eSWZ81qsR46Y6wRooFeztbOrUX5tKW5UR41DX9B0p9aXg5xw5iZ3NTj1Qdb3F3rjx6cPd9QZ76/oaqS0G7K03OGb72N1ucXY762wohl1s/OiGo6sDAFanIjHiGA7qGxEaf+nozt7dx97qwNcf7qxrcDeM7TVwba0QSp/mPbK7AcaC9WofB1bPTBsGw5G9A6zL6C9iH7bm6xuButbuSDmAlfrGgWGwtmOzYD20V77Zpp1jV/92h62fRbez2npQB6tTy+esTuIW260L6Xfq7sjd9RZ76w3OsnpY7eCOvR3bUbZY2Yiz9/bbGsj25pACHN09wBpbnBx3sNeWAZR21MoGNdDdDgdY79YAhucLfvCgHrlzbHe/Lqgvddqb/b4pQz2KY1vP8Tu6PsCmbeA5e28fVuDrwbZjnWLEUA/rPVIOcMvYdnlvC87e28eR9QFObtq5hk2+V6saNDDg322BemlrQXe3mzqIW43YGTc4a6++ag2jeVYQqM8eX5+sA4Bt3VSyXm2wshFlXWXy7LaxZG+9wbHNPkoxnL1zCuthxC3jTl02gbZ2FHWDz3o1+g7oI8MBxjJgO+yHsynPO3Izhv02HT7Uacz9sfbdsdU+sK3B9Nm7pzAMhmOrOmvA9w7vDFusxhFn79VXh223A47sbLA3brCz3mKnbDG0NbhHxk2b8jXYqvg6PDPg1LjG0WEfZ+3tw1YFO+sNjg19A9POWF/RtyojzsYpP6C6oOJ3bGcf+1hhO64wtONgTrW3zJy1ewrr9QYLLHBXho94YLe/v483vOENeOYzn+nXhmHAZZddhquvvnr2mVOnTuHUqVP++8YbbwQAnDhx4o5F9i4Ap265CQ858rfVfK7ayfftWAVOW+2WEds2r1EPK20jbamHo3LT3w0MPXVr/g8t4kmZHU4BcoqkGXZuBBiGliVCDyC5CLsAvvOOAebOZouyNdyvnMBgNXA5f1Xf8XhkWx3dPbDGsf1TOD4O2MGIc8raz7K7oLRsWVuLdVAGrIe6/udgWOFgpzl3ttk24R0Mfbpq1d6ysFNGbFd1x2hdkD5gs2rHanC9zmrA/mYAUPxF8gfbAdipDroYcK/1Cgc8364YdophbXXqaiwFB+sBm1KnyFarEac2PHi5Mn5dRuxaPWcOKLjHagdHdzY4tV21NUo9MC4GrDHiyHYf5zVeHOzUNzSUsa232qlrvsbVUDcbrOprqk6NO/W8tGGDjdVA5eLdmo3bDFxNXuFI2aDstOm+ofiOy6M4wHZVHeapdT15EC1jcmw8hXOG92PcGVq2pmYYrWUGj2AfFwztgOsy1LPvdtq04m6dWj5Azd7uoG4cWpctbF2AVc1T1TcRDDiGfYyrmkU6GNYe6Pt7eJulO9l28Tr/TqJtnhhxVim4cFV3Y69QX4O1HQds10Pd5DDUjPMOanZ8Z9j6K9nqu4+tzmfv1jPPuEb1oKxwdLWPg7LGZlVfK3dBWzu2HesZiTvbDe5Zaltcm7fTNpqsbItz1+/3fi8r8/Peytp8+pUHFJ817KOsgP2hrkHjcSenxh2YoQbHOzXXdeGRqmt8ddymVNkcARwtNbC7aG/taxrN6kajAuBg3frIqvzuHmxw/tCO+BkHbMvgdmCn1HWq6zLiwt26Q3VAPYqH06Y8X++isuPHu6zGLS5Yv89fHziWgp1xxEXrqgfcPGQoGGuaFmUDnL0+ifPW/4T9soO94QDbJvsHtm7rb2vQjj20g9GBTZuOP2b7uNe6r8MbrWA4VQcVu8MGZlucOHEC2+0WCyzwkQLGO3Yb1gJ8xAO7f/zHf8R2u8VFF10Url900UV4+9vfPvvMs5/9bHzP93zP5Pp973vfOwTHBRZYYIEFFjgM/gd+9s5GYYGPUrjppptw7rnnnrbMGbEr9pnPfCauuuoq/33DDTfg/ve/P975znfeKoEL3D44ceIE7nvf++Jd73oXjh8/fmejc7eChbd3HCy8veNg4e0dBwtv7zi4u/HWzHDTTTfhkksuudWyH/HA7p73vCdWqxWuv/76cP3666/HxRdfPPvM3t4e9vb2JtfPPffcu0WH3RXh+PHjC2/vIFh4e8fBwts7Dhbe3nGw8PaOg7sTb29rIusjvit2d3cXj3zkI/GqV73Kr43jiFe96lW49NJLP9LoLLDAAgsssMACC9xt4E6Zir3qqqvwpCc9CY961KPw6Ec/Gj/6oz+KD37wg75LdoEFFlhggQUWWGCB2w93SmD3FV/xFXjf+96HZz3rWbjuuuvwyZ/8yfit3/qtyYaKw2Bvbw/f/d3fPTs9u8C/DBbe3nGw8PaOg4W3dxwsvL3jYOHtHQcfzbwtdlv2zi6wwAILLLDAAgsscJeHO+2VYgsssMACCyywwAILfHhhCewWWGCBBRZYYIEF7iawBHYLLLDAAgsssMACdxNYArsFFlhggQUWWGCBuwmccYHd8573PDzgAQ/AkSNH8JjHPAavf/3r72yU7nLw7Gc/G5/6qZ+Kc845BxdeeCG++Iu/GNdcc00oc/LkSVx55ZW44IILcPbZZ+Nxj3vc5NDod77znbjiiitw7NgxXHjhhXja056GzSa+APv3fu/38Cmf8inY29vDx33cx+GFL3zhHU3eXQae85znoJSCpz71qX5t4euHDu9+97vxH/7Df8AFF1yAo0eP4uEPfzj+7M/+zO+bGZ71rGfhYz7mY3D06FFcdtll+Ju/+ZtQx/vf/3484QlPwPHjx3Heeefh677u6/CBD3wglPnLv/xL/H//3/+HI0eO4L73vS+e+9znfkTou7Ngu93iu77ru/DABz4QR48exb/6V/8K3/d93xfeObnw9rbD7//+7+MLvuALcMkll6CUgl/91V8N9z+SvHzpS1+KBz/4wThy5Age/vCH4zd+4zc+7PR+JOF0vD04OMAznvEMPPzhD8dZZ52FSy65BP/xP/5HXHvttaGOhbcA7AyCl7zkJba7u2s/+7M/a29961vt67/+6+28886z66+//s5G7S4Fl19+ub3gBS+wt7zlLfbGN77RPv/zP9/ud7/72Qc+8AEv843f+I123/ve1171qlfZn/3Zn9ljH/tY+7RP+zS/v9ls7GEPe5hddtll9hd/8Rf2G7/xG3bPe97TnvnMZ3qZd7zjHXbs2DG76qqr7G1ve5v9+I//uK1WK/ut3/qtjyi9dwa8/vWvtwc84AH2iZ/4ifaUpzzFry98/dDg/e9/v93//ve3r/7qr7bXve519o53vMN++7d/2/72b//WyzznOc+xc8891371V3/V3vSmN9kXfuEX2gMf+EC75ZZbvMy/+3f/zj7pkz7J/uRP/sT+4A/+wD7u4z7Ovuqrvsrv33jjjXbRRRfZE57wBHvLW95iv/ALv2BHjx61n/qpn/qI0vuRhO///u+3Cy64wF7xilfY3/3d39lLX/pSO/vss+2///f/7mUW3t52+I3f+A37ju/4DvvlX/5lA2C/8iu/Eu5/pHj5R3/0R7Zarey5z32uve1tb7Pv/M7vtJ2dHXvzm998h/PgjoLT8faGG26wyy67zH7xF3/R3v72t9vVV19tj370o+2Rj3xkqGPhrdkZFdg9+tGPtiuvvNJ/b7dbu+SSS+zZz372nYjVXR/e+973GgB77Wtfa2ZVQXZ2duylL32pl/mrv/orA2BXX321mVUFG4bBrrvuOi/z/Oc/344fP26nTp0yM7OnP/3p9gmf8Amhra/4iq+wyy+//I4m6U6Fm266yR70oAfZK1/5Svs3/+bfeGC38PVDh2c84xn2GZ/xGYfeH8fRLr74YvvBH/xBv3bDDTfY3t6e/cIv/IKZmb3tbW8zAPanf/qnXuY3f/M3rZRi7373u83M7Cd+4ifsHve4h/OabX/8x3/8h5ukuwxcccUV9rVf+7Xh2pd+6ZfaE57wBDNbePsvgRx8fCR5+eVf/uV2xRVXBHwe85jH2H/6T//pw0rjnQVzQXOG17/+9QbA/uEf/sHMFt4Szpip2P39fbzhDW/AZZdd5teGYcBll12Gq6+++k7E7K4PN954IwDg/PPPBwC84Q1vwMHBQeDlgx/8YNzvfvdzXl599dV4+MMfHg6Nvvzyy3HixAm89a1v9TJaB8vc3fvjyiuvxBVXXDGhfeHrhw7/5//8HzzqUY/C4x//eFx44YV4xCMegZ/5mZ/x+3/3d3+H6667LvDl3HPPxWMe85jA2/POOw+PetSjvMxll12GYRjwute9zst85md+JnZ3d73M5ZdfjmuuuQb//M//fEeTeafAp33ap+FVr3oV/vqv/xoA8KY3vQl/+Id/iM/7vM8DsPD2wwkfSV5+NNqJDDfeeCNKKTjvvPMALLwlnDGB3T/+4z9iu91O3k5x0UUX4brrrruTsLrrwziOeOpTn4pP//RPx8Me9jAAwHXXXYfd3V1XBoLy8rrrrpvlNe+drsyJEydwyy233BHk3Onwkpe8BH/+53+OZz/72ZN7C18/dHjHO96B5z//+XjQgx6E3/7t38aTn/xkfMu3fAte9KIXAei8OZ3+X3fddbjwwgvD/fV6jfPPP/928f/uBt/+7d+Or/zKr8SDH/xg7Ozs4BGPeASe+tSn4glPeAKAhbcfTvhI8vKwMh8tvD558iSe8Yxn4Ku+6qtw/PhxAAtvCXfKK8UW+MjBlVdeibe85S34wz/8wzsblTMe3vWud+EpT3kKXvnKV+LIkSN3Njp3KxjHEY961KPwAz/wAwCARzziEXjLW96Cn/zJn8STnvSkOxm7Mxt+6Zd+CS9+8Yvx8z//8/iET/gEvPGNb8RTn/pUXHLJJQtvFzgj4eDgAF/+5V8OM8Pzn//8OxuduxycMRm7e97znlitVpMdhtdffz0uvvjiOwmruzZ80zd9E17xilfgNa95De5zn/v49Ysvvhj7+/u44YYbQnnl5cUXXzzLa947XZnjx4/j6NGjH25y7nR4wxvegPe+9734lE/5FKzXa6zXa7z2ta/Fj/3Yj2G9XuOiiy5a+Pohwsd8zMfgoQ99aLj2kIc8BO985zsBdN6cTv8vvvhivPe97w33N5sN3v/+998u/t/d4GlPe5pn7R7+8IfjiU98Ir71W7/Vs84Lbz988JHk5WFl7u68ZlD3D//wD3jlK1/p2Tpg4S3hjAnsdnd38chHPhKvetWr/No4jnjVq16FSy+99E7E7K4HZoZv+qZvwq/8yq/g1a9+NR74wAeG+4985COxs7MTeHnNNdfgne98p/Py0ksvxZvf/OagJFQiOuBLL7001MEyd9f++JzP+Ry8+c1vxhvf+Eb/e9SjHoUnPOEJ/n3h64cGn/7pnz45kuev//qvcf/73x8A8MAHPhAXX3xx4MuJEyfwute9LvD2hhtuwBve8AYv8+pXvxrjOOIxj3mMl/n93/99HBwceJlXvvKV+PiP/3jc4x73uMPouzPh5ptvxjBEU79arTCOI4CFtx9O+Ejy8qPRTjCo+5u/+Rv87u/+Li644IJwf+Ftgzt798btgZe85CW2t7dnL3zhC+1tb3ubfcM3fIOdd955YYfhAmZPfvKT7dxzz7Xf+73fs/e85z3+d/PNN3uZb/zGb7T73e9+9upXv9r+7M/+zC699FK79NJL/T6P5fjcz/1ce+Mb32i/9Vu/Zfe6171mj+V42tOeZn/1V39lz3ve8+72x3Jk0F2xZgtfP1R4/etfb+v12r7/+7/f/uZv/sZe/OIX27Fjx+znfu7nvMxznvMcO++88+zXfu3X7C//8i/ti77oi2aPkXjEIx5hr3vd6+wP//AP7UEPelA46uCGG26wiy66yJ74xCfaW97yFnvJS15ix44du9sdyaHwpCc9ye5973v7cSe//Mu/bPe85z3t6U9/updZeHvb4aabbrK/+Iu/sL/4i78wAPbDP/zD9hd/8Re+M/Mjxcs/+qM/svV6bT/0Qz9kf/VXf2Xf/d3ffUYdyTEHp+Pt/v6+feEXfqHd5z73sTe+8Y3Bt+kO14W3Z9hxJ2ZmP/7jP273u9/9bHd31x796Efbn/zJn9zZKN3lAMDs3wte8AIvc8stt9h//s//2e5xj3vYsWPH7Eu+5EvsPe95T6jn7//+7+3zPu/z7OjRo3bPe97Tvu3bvs0ODg5Cmde85jX2yZ/8yba7u2sf+7EfG9r4aIAc2C18/dDh5S9/uT3sYQ+zvb09e/CDH2w//dM/He6P42jf9V3fZRdddJHt7e3Z53zO59g111wTyvzTP/2TfdVXfZWdffbZdvz4cfuar/kau+mmm0KZN73pTfYZn/EZtre3Z/e+973tOc95zh1O250JJ06csKc85Sl2v/vdz44cOWIf+7Efa9/xHd8RnOHC29sOr3nNa2bt65Oe9CQz+8jy8pd+6ZfsX//rf227u7v2CZ/wCfbrv/7rdxjdHwk4HW//7u/+7lDf9prXvMbrWHhrVszk+PEFFlhggQUWWGCBBc5YOGPW2C2wwAILLLDAAgsscHpYArsFFlhggQUWWGCBuwksgd0CCyywwAILLLDA3QSWwG6BBRZYYIEFFljgbgJLYLfAAgsssMACCyxwN4ElsFtggQUWWGCBBRa4m8AS2C2wwAILLLDAAgvcTWAJ7BZYYIEFFlhggQXuJrAEdgsssMACCyywwAJ3E1gCuwUWWGCBBRZYYIG7CSyB3QILLLDAAgsssMDdBJbAboEFFlhggQUWWOBuAktgt8ACCyywwAILLHA3gSWwW2CBBRZYYIEFFribwBLYLbDAAgsssMACC9xNYAnsFlhggQUWWGCBBe4msAR2CyywwAILLLDAAncTWAK7BRZYYIEFFlhggbsJLIHdAgsssMBp4F3vehe+53u+B49+9KNxj3vcA/e85z3xWZ/1Wfjd3/3d213Xu9/9bnz5l385zjvvPBw/fhxf9EVfhHe84x13ANYLLLDARysUM7M7G4kFFlhggbsq/I//8T/w9Kc/HV/8xV+MT//0T8dms8H/+l//C3/+53+On/3Zn8XXfM3X3KZ6PvCBD+BTPuVTcOONN+Lbvu3bsLOzgx/5kR+BmeGNb3wjLrjggjuYkgUWWOCjAZbAboEFFljgNPDWt74VF110Ee55z3v6tVOnTuGTP/mT8YEPfADvete7Tvv8Bz/4QZx11ll47nOfi2c84xl4/etfj0/91E8FALz97W/Hwx72MDz96U/HD/zAD9yhdCywwAIfHbBMxS6wwAJ3W3j3u9+Nr/u6r8Mll1yCvb09PPCBD8STn/xk7O/vAwDe8Y534PGPfzzOP/98HDt2DI997GPx67/+66GOT/iETwhBHQDs7e3h8z//8/H//t//w0033eTXv/qrvxpnn302/u///b/4/M//fJxzzjl4whOeAAB42ctehk/91E/1oA4AHvzgB+NzPudz8Eu/9Et3FAsWWGCBjzJY39kILLDAAgvcEXDttdfi0Y9+NG644QZ8wzd8Ax784Afj3e9+N172spfh5ptvxj//8z/j0z7t03DzzTfjW77lW3DBBRfgRS96Eb7wC78QL3vZy/AlX/Ilp63/uuuuw7Fjx3Ds2LFwfbPZ4PLLL8dnfMZn4Id+6Idw7NgxjOOIv/zLv8TXfu3XTup59KMfjd/5nd/BTTfdhHPOOefDyoMFFljgow+WwG6BBRa4W8Izn/lMXHfddXjd616HRz3qUX79e7/3e2FmuOqqq3D99dfjD/7gD/AZn/EZAICv//qvxyd+4ifiqquuwhd90RdhGOYnNf72b/8Wv/zLv4zHP/7xWK1W4d6pU6fw+Mc/Hs9+9rP92j/+4z/i1KlT+JiP+ZhJXbx27bXX4uM//uP/xXQvsMACH92wTMUusMACdzsYxxG/+qu/ii/4gi8IQR2hlILf+I3fwKMf/WgP6gDg7LPPxjd8wzfg7//+7/G2t71ttu6bb74Zj3/843H06FE85znPmS3z5Cc/Ofy+5ZZbANQp3AxHjhwJZRZYYIEF/iWwBHYLLLDA3Q7e97734cSJE3jYwx52aJl/+Id/mM2QPeQhD/H7GbbbLb7yK78Sb3vb2/Cyl70Ml1xyyaTMer3Gfe5zn3Dt6NGjAGo2L8PJkydDmQUWWGCBfwksU7ELLLDAArcRvv7rvx6veMUr8OIXvxif/dmfPVtmb29vMoV7/vnnY29vD+95z3sm5XltLkhcYIEFFri9sAR2CyywwN0O7nWve+H48eN4y1vecmiZ+9///rjmmmsm19/+9rf7fYWnPe1peMELXoAf/dEfxVd91VfdLnyGYcDDH/5w/Nmf/dnk3ute9zp87Md+7LJxYoEFFviwwDIVu8ACC9ztYBgGfPEXfzFe/vKXzwZTZobP//zPx+tf/3pcffXVfv2DH/wgfvqnfxoPeMAD8NCHPtSv/+AP/iB+6Id+CP/1v/5XPOUpT/mQcPqyL/sy/Omf/mnA55prrsGrX/1qPP7xj/+Q6lxggQUWyLAcULzAAgvcLeHd7343HvWoR+HEiRP4hm/4BjzkIQ/Be97zHrz0pS/FH/7hH+LUqVP4pE/6JJw8eRLf8i3fgvPPPx8vetGL8KY3vQn/+3//bz/u5Fd+5VfwpV/6pXjQgx6EZz3rWZN2/u2//be46KKLANRz7F72spfhAx/4wKTcTTfdhEc84hG46aab8F/+y3/Bzs4OfviHfxjb7RZvfOMbca973euOZcgCCyzwUQHLVOwCCyxwt4R73/veeN3rXofv+q7vwotf/GKcOHEC9773vfF5n/d5OHbsGM477zz88R//MZ7xjGfgx3/8x3Hy5El84id+Il7+8pfjiiuu8Hre9KY3AQD+5m/+Bk984hMn7bzmNa/xwO50cM455+D3fu/38K3f+q34b//tv2EcR3zWZ30WfuRHfmQJ6hZYYIEPGywZuwUWWGCBBRZYYIG7CSxr7BZYYIEFFlhggQXuJrAEdgsssMACCyywwAJ3E1gCuwUWWGCBBRZYYIG7CSyB3QILLLDAAgsssMDdBJbAboEFFlhggQUWWOBuAktgt8ACCyywwAILLHA3gTPyHLtxHHHttdfinHPOQSnlzkZngQUWWGCBBRZY4A4DM8NNN92ESy65ZPIu6gxnZGB37bXX4r73ve+djcYCCyywwAILLLDARwze9a534T73uc9py5yRgR1flv3Aq74L5ciRcK8YYAMAa981oWcASvuzVJ7XWpkytkeGWykL+d3qLtvULr+PqJPf7VlHQ+vMz5jcB4BNam+uPEFwR2nt89mV3LP0vUj5AbAdoBykeobKi2JS7xZ9cl/bG3A4v5Cu62/Sznr0Of3EIfdz+4I7RvkOoWnV+l7b5P3GM6db68z9p7xv5ay1VVgecj/zIPMlf9f6c5n8/IDYNwr63Fyf8Dvby3xRWcl9Mdf/yvM5udA2k04G/IaqY9TTcP+w/lC+s17ho/d9pj3ZjKzvGDEF0ph5SjnKz2SeCT+s2ZQJHsCUd9rn2r7NlJ87mn5OtrR+/b7F1MZomxnmZC3jRVvHflAZGdJzc3iqzCmPMz5apz6HdJ1tkdZBfvN+xkdlQ9sjTrmstpPp4jPUYV7TehWUp1nf1fYpHOa7Tqef2p7ilm32YbhlHmk7bFtsVhm7/Qy45H7Uumd0acLXLLNq3+bkU2VbdT/7isPuK79OJ8sz9I0nT+Lvf+j7PP45HZyRgR2nX8uRI1gdPTJv+AFgbD/pWEv/ZEBSDLB142lzFG5Ih6lA8T6A2PEEA7A7vedBZhI4G2ac00ww48HEzkx7Ut5p0yIDYpCrDqMZGmcbedIMrKE6vGE14xOUXxD65hwj27Ber+IyEe4S8QP6/Rywl23tQ34fd4Bhv9dBpSd+Zdt5zvver+yfHJjlAH8Gb8qUrWb4UKSvreNhq873yFxMjEt4XvgB1HpKM67eJ8prqatYugZMcC3bKJs+wMlBzZzBQ28jyL06r/y83CvkIXHVQCg95/p5iC4yAD/MKRS9l+1H6iPyZI7/1Lk5vupAoIzAuAaGLYKumTwTbEw2+Jl32YERde23xJMccLiclI6nPzvHV20z8yI782RPvI+F70EPpA6XgxSgzdlg2p9At8r7nG3KZKV2tI1gHxqewYZk/jS7MOsv5viQfUW2SUnOZttTmSudbrVXaue1r8JglXUM0/Y0aZJ9rctQxkHrmRnQuF1mHe262yDRYdczHSRD6Mu6ogPAbL/mQO0RpnwMZVguyZWWnfQ/MLGlk4F+tqvo9hDAbVp+dkZvntBOLGJoikbDEMMJdKFEve+COvbOL82xTww5kpNCbANF+ueQ+2yfijFRkKE1pwomgukCnA03Bd16OT5XxsYOCveIENy4nWn8GFNgogrPujzgnHHwykteg/RBNpjZKKnRY78WuefdoYaI+DcHYWvEftJ+V+Wlk5I63cmkgET7gH2oDpv883oGBByyczDlMfmcdVYdXZNrryLzSfHXNpLRCLqguiK0qvFxuoZUVzZwykPAs7qBHOVJDmYpJyu5nOV7lOcs8jA77cJ/SS+LZFvmAjV1QiZ4Ki4+MEyy4w0pbQkp6hB1LfDWpr8jQUKDyr/qD5IzERz8K58t4THHK9OUM5nBzuXMjn4f8gOIwTQQHZ3IRwhuMz3ZebKulTzXyk+C+mSbiZM/zzo0EGh0qby5XpRQVddjsden6yf6m4luKY1N74MNndNFKZ8H+f6d2d+kL6pXXj7RoPLu3ZzsFq95UEdZBUL/5kAw2NmEM+25DgY8u0h7vEUA91uZ9xMC2s/W93MDthAwql1L+jk7OJK2c4CtsjLRXRZM928LnNGBnc0YgTBCKMILFWQGC3MOQYy9ZiyKjLA7AulZdeaWPtv9SYZgJsgozcFNaNV2koPOzpj0j2t0Ry8GKGRjEIUxK72PnFDxckVUZyS4uPIRF28kORwkIZbruS7HUQwgHbdnd2R0xGA+BBJU/iE6ACdXjWJ2bsnh2tD/XFZSn2uAS4dQtO+UZ8DUwYl8albXIM+XXjfxI90hQBU6tG98JEgc04haM5o+fX2Ic9SgqAjNED55oL5FxEvlBLEvJ/gQKOdiPH1wZsIHHdiR5hnjOcnoK41CG9DrcPkUWfPPFARoG8R9kt3l97mpLM5A5OyR9FtJ/TvXnyrHIZsl/Mg2Kjsy2o+wFEOdGNuWfmSfhyl0LS/1A4hyJ3UGXlovF8oK7jnwCH4gO2ANKnLZxi9NDIT6iBcvM6hptEzwUbpH4SframUndlpvS3866/ic9pXJrXWX8eBDknzm4CZPdyovQpDL66LvmbduD2V2QenRpQcm/ev9TB2SgZ7bO+G/f1/JtRK/h5kapcVSWQLpGOQ54bn7BcWFOpASLSHwEzvJNid6fhvhjA7s3AASZhxmoJDGp6CvCeBzonieARFlcUMujtsDDFV2Km4eybdPDzwgBpaQRvmedUsOM6xvU9rU8QgPNDunRsidLSIEhdfghXhnI470W5xqNjIBkkFE40kwqBDDlh203PO+ECWj4mswEBx95p/2qcpHCg4ddzVulvBREnOAME7xnkxlJWfEQG0yKJgxwD561Sx04n8wcMyQ5cxBzhqpvHKKTfk29HrdyGqmLTBFaGjfQ9/OOWdg0g9zAUjB1Fhnfk70QZzQJNhh3ckRzxlaHbH71LhF+j0Y1OlH1kn91jWwjnTUyyBDKTAjLkHm1Pk0/WSwMFkOgi5zk2Ck4ZodqTo4RyMP/qzL8ewgOenYJOMFsSvimB1XoTfIGRB0PgwSVU6y7Ils5yA92C305/2rpX4sovtCbwgwIDJUEOgL/Bp7vwVdR3yO9WmwEXyItBcCS/KZtl98nz9f5HqJz09mJnKWHXA518yr65/odcjSl5asEL65zYY8p/4P/fmcwfQ6xsgDl132W9Z/yjKD6pwpdibGtuh7itxX3uZgmL4w4HMb4MwO7IAYUDSGWGI+gKnRoFDKtI+uxQpCXITZ1qtjgGNalyo8HQ2NvOJFRU3K7XhRWTLe6Hi5g6CwiFJ5wCE0QQTZp/VE4PhMSOEnRXCgcVK+qAHjbwqz9kH+rgaXgdiQfkMMhxqGZGC1z7RPnO6cKUisDVNcOQAh/1SGknEiH4MM0vhC6sh8YFDFZ1T+JFDwAYdmnZT/gZgu385/oTsYOzVKOdDQ33wGQmMOQNhMxkfamfBA9EADksAH0ZFJ3SIH4XnydKbf3GGnZ91pIN4PzkYDGHVE7b6tUDdJCH/Kpn3mqSbSzhXPkr3xoKukP5E3bUNtgvMl4Rccy5CeEVon+ibPu8Mnj1R+Gy5jzhhTjxt/dJZlsqZVnxtiHTqYmtMB1780yFCHOa7keZUt8pt8akHTbJAIeTbhHabjVlGugi1S+4oEcl+nHcOgmf5HEw+l97HNtENbYCWZ5ISA8riQfzrYE7sb1sc1YjSDx35gMDlZHgSRwxJtqH/KgDwMRqQss2AhAQMpm2ccCNIvbueSTS1kniYsJKhW3xnkk/0xpnqAGEzP2XP0fro9cOYHdkmBNGgBpoZJlTukyhmkpDJqGEPAw3LN2XqKVQVGBDQYsdYeldXkehbITOvsqBMIPe9tAn2doBqDRvtc6jfgKO0Gx5Yd4pxjEwVWPk6UhIokPNPgXDM/Ob3tj6jhEr558KMGIGfG6OSysWIAIfwLCoup4ZlT4skIEsCwibyY9Lc6e8VH7vlInc9mh651AcE5hjpnMlol8SEYHvb3jDyHQH8ORHZzUFaAbiBzJkrkwcnS7KHgk+nRkXAe5EwCYXSHEaaeea+t68lBnve3fs70b1gSkbI2IePCwZCM8Of45lkcpVntC9vIcgpM6tMBaAjoFEfVXX4ZOk9ClnsmM6UyPJfBBJKjU1zzwFr1AZg4Urcvyof2XFg20ehw56m6rDYn6fjAIF37Q6b71Obqch7liQfy/K28yLRQD4RPLjfCT83KzvpAycIF/gofiHe26QFPkXH3e4bo3xqtViqtZRv7ybswDVRVviY4NHpC9kxx50/qKTf9sY8ZCAuOQU7V18kzKoP01zqDMtGtpHMuPnmQoP4qfZ+zu7cVzshdsQGyQ1LhQh2Z+UjDkvK27yH1rfVaFIrZyLp0QfdbWlcyaqbGV+sZEYIsFV4KEhRPltugO6wVPCvgZLRnnCVUQOKScAiOO9EZDHzioX8lHcITKO4QZUm8Cpm5meB64pwBFFHSSHhUolBfxnPdfusOPGv3ZbopBIVqwHhPr5PPo1xu+FtW2EaPj3olwM6DCeXFYTsJgxMVfoT7afSdR8hh1KzZHg0mWA4IOqS0TJ4DpsGfOKyJAcsBBoslGWXWjP0aHN3QafOs9AAM206rl80ynvqT00o6iFQnl6fMFBfyFgXT44ZSm4F/Q29H5d/XJ7FODfCA6BR5XXjkdB32zIye58Gf6wj1ZabPvRrpo4mNbPWPWU5E55jlCfa6lfXnso1PNIRAWGQfq+kzWaaVL5OpN6GraKDaaFZezAbiijOS/aXdS58GBDnKWWzPHo2I+pz0Jpef66OyrVOgCu4bJQgKMoaub5oBU5vhNI5JHJIvDGtCdVCVeV3SdR1gZzup19i+yJoHn+QJdT0lgDxbKnLLxIbi6TQ3vgW7m3zsRO6yXbwVOLMzdjlAaCNsjaQnU4piAAc5HmISnbNsYmioV405U7GQurJyQgRdnGJwWPJ8uDj0+nJK3oMGMTgFCGtQwkhKnHgQIMVZM3JAV8zMEwpo42VYd6M0ANMpA+GxTjtN1p+l+qhceYphYrCIlxgdfS5nBoKsiAKz7qCA4khYr/dHXjs1Z8CzUcu4CI4TY6V9pXya65vsPIU+zQ5bfk544s5JppzV0LKc68aY6jShJ5//lfiq2Q4vNyN3dJCaFVBcAt1z/FV8WecMPmEgIX9FaCn+T2SGdCdcctAXdA3ybJKRsFvTiUjtCc+K3NdMYAjw+ZO8OETvVO5CJpr0ZIeT7LLzas6uJlrngn/PXCLZF3Qe5oyd3xOaPfupNJb0XclQulLQ7O3ntZBio1S+1OZ6nzMIsv6n5dle4TW2rzolQYYhyofb9saHHGyo7gS602yDDTWoy4mNwB/Fv+GiflAHACW3qfontlcHus4O0aVgsxz5xEPt39wepG61PaSdekH7S/tHX1siPlk3qIe6TnWSPUXqV9KodQNxKddtgDM+sCvy3a9tEJkgnRGuUTFK/8yZIv4e0hEqAQd5JmcN8zSKpoP1mTw9FAwyuoAZy0vgFRyakBcONQXCjs7gVNu0XphOngt6ZnD0qYDMG/K3GS/yKjwrMOZpjAHTPiO/lC4JCLzpRlcx9OkOIPCCgUqYppZyOfswG5xInXkRsGbOvC3SWHpdAOJawTHyci77pRDkBjM8E+MWAmtmnMRYq+MLU0Z0PjnTIAbag88ZXrphlMyFP6NyaJhknJ0WCTpcPtRRS1ldI0UnOilnXUYCz5OuOhkmdKmzUvoh34VuhyTTWQdygBF4mur0gVTWrRJ1OOOpbXmwgBn6hc6SnvPyOasiOIfgpMCPUNJAcJJV0YaU902fdPBH4HmA9QcCD+dmcLycBD2TzOYMXb6OkHhbx8lpaLLmzyXZzVPCkw1oajOEDgZsHijKbxajfJQkLxpgBT2jfYbIC2IfFG+s46VBl6V7Xgd6vQrExWVY7SQDqdPZkTlfoDKhtizhGnSuVL+gsuTJgtTvHqQKDbaqz+ddwvws0s6sLeb9OblHv6aB6WHLkA6DMzuwI9OpRGQCzzAD/HgLdcA5wFFnFoIuMeZqHDXoCgEFn9WRWnZIIpChbgEN3MIUnxgFz27JtaC0QBjdMoA7dITDOrKwal3JaLphyXgCfS1HNgypXWSjkh1iEnoPTjRwFHzndgCr8vJ5W9kkY6oZWw2uwsiPzkCdbaNDy+pILgRQiP3reOcgTwIqNYRB1pWXalh1ZCn05WDRs4xFiqixGdLzxFVkWjOjxdI1xVMMlfNYFqZ72UHoLOkvtYnEU97XgclsJpLZR9I/kwljJiUMwFIwNZv5Fp7ZzO8J/pbuCw7OTwY1mlGTYNWzba2tUTIEk7WVWdaFL5oVDNmInK3Se4pDok2DiTJ3TZ5zHs45deGJf5IOYLpmLD8jdjFknXJAIM/o1Js3lG0YkqyS9pxtZ9DC4FaDDaE3ZHa0r0t/bqL3SVdmgwRgsu7cCgAZ2DLID4E3v6ocZFuSd0YnO+nAGS3JcLruyO7xUI0mN7SP+JzadQboYh+Juw8E0WUxzCqkOMBB/TjxpCyrLc7+AQiZQPfTKfOo9pJZbZ3mDbORM7J3OjizAzthToCkNA6tQ619h3Zye26WsejXWOfYAsQwxZA/G45qWAIu245LyNKIEAZFUsfUBHaUQxV9PVfmR3ZsrFfrz0ZvzrhKMBumIFhXnspJRjT/1lEVr89Nwyo/wihZ8AsB1YwDwADYjrnzKmMJTijUm/FFxzc4Z96icWA5zQKkNtQIZYOkmcbDFDlMcenIeY5nh/Uj4DoymWYTGpWXIQuQBzNiONXRzcoxeh8XIBjcAOq85wY3c5kWlevsAIHOYwg+pTuWiRzrs8Q1O4UMci3bpkngInQxkEDi+SSAFUc3iEMMJ9MzoEtZkCxXOajRrJjXA+E/ecQyM7rqfEq2jNdD1oq4a1+Kjusge7JQnXxbx9+sI2RQRT4nNkl5Lc9OBl8l3vP2Ee8zgPMlMcnOWWlr1bKdEpw8G5eCBw7OwwA4B08quyZVJ9+Us5g6uPYAtJXLh5hr0K+ZqTxV6/gpj4m/yj7XaApdhtjnIbhtv30QohlU0sZASnRIkzEhgaMZ38YjDxIFbx9ACI9dzEQWPBNeOn4a6AUbVvpzYVYn27bUX7cGZ3Zgp6CGR5VWHCiyMEIEWpW/RD5qepj3XV7ac3m6w7Ih0e9zTkcvJYcQRnBSZjLFABEevaDOQkdMSu/QFUkVN0+XAEm5Sn8mKKsaWqVHrudMWFAaMWqeHZxzNFkBBzG+EMM6AmVbuqGSNgIfVRGzsUP/7bTNBFchazgjG2VE2CEWRuLCf82oBdD+1MxSDjaVzyblyDdxos7XVj5kFUqqV/t8TuZVzxCvsZz+TYJH7Q/NICpNORBXHddgJTuy0nXHjfcMf3Ng4LiRR7J0IazrlbpDHzKrJryY8DA5Oh9cqg0Tx684BpshwYDSFPSkYNJ//iyirPB7GHxqUMG2lFapL8hnibrsuCfceD1Micl15WOY4dD2s52zhFuycTqo5/VMT8iEJ55Myhxm24lOxiXpiy4V8fa0vAaWpHXVcfKgQWnIgYYJHWxD7Q5tnwxAPchKtHl91M2mG7osBUN7bs6Wqy9QVqi/zoEsbZV1mtyXZx1Ig+I5f+xJC/0uOgi5r9OpGmRnuzKZ3m3/gh9gfeSN/s3Rfitw5u+KBeIOP3SDSgEtBTBDcA5u3HOHEMQQhcBDAoke3bWf6jhZRo0M76kjFwGfLL6UNkLgoEaOwQkQR+1A36VDEIGejK7E+YfRjvKBPJPnaKT9jQ5UXpka0vpDm+16yADpkRBi7DO+Yco0lcmZp9C+9ntBXMzPcpBrwpNJhhGYBJKhn8UhZly8v9VRAtEQCh4qHyynGzgKeZJ5xbaGXi7wRgy7G0XyUByEVBdGyG780zOQcoG/kPb0PqQvV7Gc44FKB/U78K4gGmupLwRLaTNNDjKCDiDqWuhPCYI9aMv3VDeTQ3AaEHnmDj71ucugys1M4KB4HcZXP8KHbcuzTqrKR8MvnEuXbUur3+0DeVMi/zD2PnR8RSfnMkn5nn+nrswEiib09hupbg3YpJ9zwOyZJj3ztH3RbJPKZLYRwc5RZ3OmOOu5SVuqizloKLE7crA0yzvV+2QLbIV+FFe2iyJ33qbSPdNOHpiGLi5JHrWA1OVtm7RX+nOBb2o/Sf8ofEm2x/2FojVzrJF/sk3pa5Vnb0t8jm9eSWW9PtoPfVZ8fwGiTN0K3I4Y8C4I7Pg1+ghWnb1E7kHxIeW186ROIDqKIOA0Knou0YAgBO5okyDmHXoTI6FGUwUAvWPnMjhG3JKzyyN7VV4t60GhKHkRw+ntJD7MjZ7VMIXMUa6Dj8oUQn7Prxo07189awupLMsPHRenQY1OmrZQI5anLeaMkxsWuR4yKUoDYpnQBubLaVkPyEt3BvnQU6AbjeCg1KjrYcXqgOiMU8Cm00kA4tlkh0DIblmSB2nPaWI/ZSeQnGwY9CiUKU7BqGrRZPDRDGY+v9KS4YY+krKzQWccAeGFju4hjpRF1VHmrHCWJwh+ykMNBEhj8PSxDi4cn5sy9UyjOH6lrczw1XkBRNmbe3ZOz1q7PH5G7dwkmyf2y7MpjUZfxyX4lCSzIYMK9KBQea06074He0kZl/6aZO5YXwoesy9iP+Z2nadi07J9DnxRfFLQGLJnstkkyyJ1IQdBc/jmoMiDEN4i/dpHQMxKssrkf+eyqgGUB/roKvG89CLBbyW+zma/k8/1vsmD0RHR7yt9KhNzsxcz/ep8n5Gb09ndDP+iwO45z3kOSil46lOf6tdOnjyJK6+8EhdccAHOPvtsPO5xj8P1118fnnvnO9+JK664AseOHcOFF16Ipz3tadhs8na42wCpg3VLsBsEOi5x9JNAb9Mes268vf72u0gnuMCWLkz5GIfgFMjlsVVRpk5Ycc2BykSxkvML39PzbtjUkamhnwnOvKk5BVPjJn8TmjXTADEiLTjRjEnIfqSsxqxxycouCqr3PNCS4F6n4fK0j1ctOE+Cg7Sg2qcBUgDiI8VsPDXLNSA40jncPOghv+aO8hFas0ZPHIvwbJL5sfjJANr51NoIQZYGJhqkCV5qSB1H8kczA9qvqb+cXtY9JD4JD4KjZhaTWQGh0YMiyRKHoFRkOAR05CWDHw0erOMZpuSV/xI4TrK5EJqTrQoBUUl/CgbfDa5y6QMdrs3LR88QR8qSOi1IfYNFXpVpOQ1Ag1ynDJ3yKweXs7tuNZATHChXgd4xyoDikmVd+3bIbwgRv5HtRbb3ahPdHwmuwYdI4DMb0KHjngf3fj+vrdQ6VDaIp+xODvaa+sjqFAfyYMY3hazoIH+aFEkZRH1+cr0ku5T62fkvJxOEwH07Xz60yTq5aUrkIQ8wXb4hZbXKrHuZFpMd4dmGaDyS+7VgekxTavt08CEHdn/6p3+Kn/qpn8InfuInhuvf+q3fipe//OV46Utfite+9rW49tpr8aVf+qV+f7vd4oorrsD+/j7++I//GC960Yvwwhe+EM961rNuPxJZ8dvvMCIr8TqQlL2V4TSEdyA/xXlNjBfqA0W+h+uaYVNjhoSffA1pWCA6NSAYpXA0hApdzqpk4QZCAMLralgDqFETp1Jm6nLeDrG+cRWfD84JXRHngu9ghBItQXmT43bcVFmzQUuKrPiFwFX7a8Yg6u8wBTjjNEnvXBA0CQIlCAgn1bOMBDpQYyBBhzs/zcClUXKgZxW/B2NHXdEAaTutZzKlpvomAdBsBjPxIxhf4i8Z89pH1uvRsmmqfVb3xdkGXKQvNGAMn8I3lRnPhEow0ytrnxrAZByTw/AAVWzKRF9VL9XWyNmRLgczp+ZnJxL6ONihEngYsnMSgISMOK8LnacLMCjL2e4WQ1zfyGuS8VeZ11cEuh0y0b3W/rCNvycBrQZWan/HRnq2IUC3R7T/7Ds9YUDb0OeH+IwHtp1Nvd5pVR6MhYeknknhPBtkYuokuMn1hTbEN4XBgh4PJfcKnxO5Bqb6SbzDRkXeE10Pn2nQ6bKp9FGOWKT1pSYqJvxinWw3+evAl1THJPGgm0P4jA6G9Dpwu6K1Dymw+8AHPoAnPOEJ+Jmf+Rnc4x738Os33ngj/uf//J/44R/+YXz2Z382HvnIR+IFL3gB/viP/xh/8id/AgD4nd/5HbztbW/Dz/3cz+GTP/mT8Xmf93n4vu/7Pjzvec/D/v7+7UNEHQS6QQjToxrwACFdDYgB2PRrKsye8cOMs9KgREeScl+nNQDpXDHABd0YBfxt2obSM7szlY5ZnLAGDJPjXoSHmT46qrCmoEQcJp90kmK0iwYbzuCZ5/Ty3GGslmjJmUJxznwmj9o0KHFHvBYcWU6MzWRK3hIvBD+FoJiN3xNeaL18LhlONQghs5f5mI0eOp4a7E1Gmdo23yGsI0jKRtKl/Kw64RwsaNYpyHJ6blKn3Ke86sDBg6hEo+vRjJPW7EvAYSbA8AwT9dO6Dcm0BP0F/EgVzUAEviQZCNngGQjyrzrQcNGjg/J6HGZjwkwGpjgTnyCDQ2pHnazKjkwLT4JF4g5Ex3ga0EEL+9n7ZKa/ZvFP+lmk39U2afbXj8KZyUIGvIjLIQP+8D3J0tx6qZzRmdhGtXPye6I7eRCVeTZjA0r+wb6aCdTcx+SH1TaeDpJt8mZoa7LsysDFg2+d2dFBBvtD/OekH9XOq2znt4+wzbwOe64u7e9x5howTVqoLReclY/ez3N0nAY+pMDuyiuvxBVXXIHLLrssXH/DG96Ag4ODcP3BD34w7ne/++Hqq68GAFx99dV4+MMfjosuusjLXH755Thx4gTe+ta33m5c5jIB/n2MjNSRXXBYQDceluoRZaaQBKNxiEPy9tgWYscYYl06IrekROF+MloaxPkIjo5CgpW5kVrOSoWArUx/h2mj5GAA+PqNPCL1IE0UeqL8GuCwXW0/B22JDgidcyO+SYAqn6z7MJz8KJls5JIyh6AzO3FWKbyYm7pUHozCR39PaXKcuQ9y28HYquHUrG4yZrpOq+R+URjg03WT4Ekd6DDlx2QtSmpjshyi1eeoqnyV+s/bypms1G85uFecVf/diWgWOh3NENY1WbwX6G880MyDTo8GyIEJf0vGF4KnlxO6PVsF+M47FbPJDIDQGvjbnnPnNvSHQyAN+S58ngxYVC6Ts3L+tGwss4sZNw1U+Jy3lQMY+Z0zf+zPiT2yjgv5Sbxyf3p5KcP6FR8NjCZ+JPetI5l+N5ydB9lPWC+Wn1OfoPx0uc5t6aAo+0pgejQP8coZKP3O8nkGSO9p+zM88H6Z0ZlQf3v2sIzbZNCefZ/IRogpZmKDCQ9ynyRbEHxrrhP9e0nPzmZbD4HbvSv2JS95Cf78z/8cf/qnfzq5d91112F3dxfnnXdeuH7RRRfhuuuu8zIa1PE+783BqVOncOrUKf994sQJ/+6Rbw4GGiNG7tQqiIZIHSuiIZibRgrrjSD1UEBaHV5Xe8ZtWStb2rNeDpgYCL+2RujkENXnzlectC4T3SC+2XiLoAchVWWm8cpZEOu/uwOxJsBlIrTZIavzDGXUOdO5bXv/hH5AehYdH/KoAJPNKZPM2Crp1+C3nC85U+ayNYML+BxlFOjv5xWnoTh4cD9InSKXTsMoMpQMY8g2JIcV2lQDw99sR/o/9IHK4dhpKtqGyqGygs6E8pb6cLK5gfc0mxKiE6Bs0DMsSqrolgeFKj/SxtxgahLI0pmkYDGvqXQkJEs3NyXpJEhGTwPfEBzO9aXWQR1SGVA7l49Pyfqodk+dliVcnO7itCjfNasaBp/6CcGDcky5Zl1tV2Zw/Er/IY4VA2CSvTV0PDzbOqMPQaadqfIzfXe+rNDfcjSHJzAfoGR7m2Qv93PZog8gJMM48Wvku9rIJJN6HIpmgN0vqb0HAi+NPBtivRObrkFX7jM+L3bf9Z7PSf1WEp1AGEzl6U3VS74GrSQ8C8S2i9xPBnxie4LuiM1Veic2jfTQbqh8UO9aWe8vsZ0h+Ms7vG8FblfG7l3vehee8pSn4MUvfjGOHDly+1r6F8Czn/1snHvuuf533/ve1++F0RqBjFnBgypfTNsM/GzWwHoZDaDU8Gpw5x2KaNwKEHfCArFTtVkRzDCaF0MRRlvNUB/6zkQdWaPjHXCYC0BYByJf9NnMh9y2byTxYMRC8DjJYGVaskFM05ZhbdCcExR8ch8Hpyu05UMus7MIWZU0StcAINSlx3Vsp2VdHreRF/7X5ECdWyENQ2zT+TgIrarVmqEuydBl7W/1z9Hn14AeIDVHHAJkpV/rxXwfOEh7OlLW0Xk+HiJMcQ4Wj39RGeOfDmiyQ8iZnSwHdEjqkFWfslHmoEyDKA42UmA1riOfZ0f+6qRTFogBVdCVJBN+bqE6EW0DvY6QqVRZI/9WFtrW4FTXLLH+MKAWmzbRTXXe8vagwG/hXV5wHl6RpQPFw7zcTGCScaMOK180kNLsXciADUg2WDs90uDySTwkmx6y4dR/CRLCtG6J98IADui7oTOt0j8hAC6xnNuH1A9OVpFyLKN2TGlXvSpyKfFiks1W/5NwdGAwll79qLgFX6d+nniP8ql4SxuUhTlbo4ONSVCvdYlPzTIzCfoPs53zLLjt8IY3vAHvfe978Smf8ilYr9dYr9d47Wtfix/7sR/Der3GRRddhP39fdxwww3hueuvvx4XX3wxAODiiy+e7JLlb5bJ8MxnPhM33nij/73rXe+qNyjcQMwsYCrALvT8PsewbEj0OemQSUZPy5vYewpgKjvJ0kg7efro1qZpTme0rCl0CFaFbhphTvFpWlpfEQVgOsXBz1U1Wsa0GOlT5Z/ja2ZKFmTlt/BAF0MHkOeGjTinfMgnizdFzgt782gzPG/yXMJ5bnqgtBGyZkzcWNBIk2fCC3cWuomB/E+0unO1xBLtO/TvYVo89Y/K7USPStcxn47konwNNDTY0e+IZeb6JMhymYpK1tngRIEQdAXDrUZT+3JuhK00oMvbXKYnBF5KK9vWQEUyBDq9mG1QyXzTJQDaJmnLkPrcpG7NZk8e00BiwvjcbuscHUho3by+tpiFYVA5CPnZlmrwO+PI1CZRHmYHFqRHH5YAeMLTXC+Srst1HQiGNVOsS4MFDxqEgRoMalsqkynwyX5hogfZl1m0baw7zxKFQERlVdCdG+zM+dqSfAhtHAcuIXD1yrtv8qUnBdH3OBGdp2GQmGWFzw5SLgVvecf65OifYebaITKp7eZAWfEOx7GwTBrEhb4wBJpvD9yuwO5zPudz8OY3vxlvfOMb/e9Rj3oUnvCEJ/j3nZ0dvOpVr/JnrrnmGrzzne/EpZdeCgC49NJL8eY3vxnvfe97vcwrX/lKHD9+HA996ENn293b28Px48fDn4MIZhixAv24AFmUXHhTlNVHGqN8V+FsTlNHrUG4acipZOjP5ug9jAazYU+dp2udgnORjp5MJ0qPavDBjMPkVG3yQa9nIwG5loS1CnJBQQnX+T0ovfYF+aN1JqPm2ZthWm8ONJ0vMgJGEV4L3prSD+eMST8FKMKf7OC13xLf1Kh4WyIXk/7MMqBBSevLgk6TBy9ytl1AW9bB6AaQ4MgGuZwNktKgNCf+B0NFfcmDlyJ1sc+0b1KWkVmXkF1T2ROaSlNOzUTxXuBRcrjByTHgEAeq66UCzyz9YcZR0GYkfSqp3pBRQ3IqM3Kkf9m5zQ7yRLcmAyUJJDxDyswb2839OONgdPouHkNRIv5aT3JkGhDkDW5KSwgwhl7HJHvHepLseGCpdt7Ss+hlNCOnwYoOdCZ4IdHHusWRawCnOIX68vNaDz9ETiA4Bnw0g1ki7WHW4DDI9kOzhIJbCFqEFwz4JrYYmPjLEGAqD3N/nCahoTqaEz4AwuZBr0vs8dwg3etMg0+dCfD2U+zAsjm7l59xe0A9UN4conuHwe1aY3fOOefgYQ97WLh21lln4YILLvDrX/d1X4errroK559/Po4fP45v/uZvxqWXXorHPvaxAIDP/dzPxUMf+lA88YlPxHOf+1xcd911+M7v/E5ceeWV2Nvbuz3odJhzipoN0bVVSegw/dmvUxiTEKkA0Ki4sRhFMNj+cEiHEfdJw/2ejwxVSNEFREeOnrkpSW6kTFgn2GgpJZYLeIgzCEqrhoaOkUgNAMYCXRClgWFBv1UA2Mr6iFaN/koM4Bx/EB2084vPz/V3mqbJQRkzBmHHseClWVDlkc3hrMGLGgtV+hw0zOCkjlP71s9s4u5L1qFTXKRZ11kJ3qF+pS052xHoTkT6pBT5rddzoGGNbAYlmoEo0+/O+hwUSLmoVwYbyqw+Oc/EgHrdor8YVJgi/mEJBu9pILBCDHBUFsmbAcA2PqPOkuty/Z7ySIJnpYdvCZiABj9qHwuCTWH95KXas1nQfh76s7n/nab0rLOeMil0MBgeRH6t1N8mz0x2D4tTntj4ZK90fV+gdc7hsgrV1/Y5WZc541eCbEl7AV9+tyjqYXoVSWZJo2RHAUzWcvlME8ROGqKPSHwMIPVNsszUBfE7oR7Vc65FBPo0aauHuHhflF6XfpoOspKOqx3Qc/rqzYQbhIZ0L5A/Z3ckpgiDPnnYUttzg5NwTeIL9omfuZll5Hak4T7srxT7kR/5EQzDgMc97nE4deoULr/8cvzET/yE31+tVnjFK16BJz/5ybj00ktx1lln4UlPehK+93u/9/Y3NvSOJlPCaITCD/grxVzABwCb6ph0NOYKK6lYFbphC9gOutBq56mDzIFB6lDiGkZcVEgxJDm7F6aW1FgHq4CerdTRTTIgOi1SRhw6sgvCKrggXYqBiXXnm9ZYhLJAD+rUaKEr/6F8VGMtwXsIXiW4cUOiBlH7Svk/Z+gNMZDhNcrUDE+K8DyceTSTSZwLYNw4b+rzRcuqkQm8R3TOA3yTgRtRdD7pVK6TcBqdyllLH2zIdefHIQHwYYGsZ3AOmSL3jNZcwA9xFKJXeXory18PMosHyCHwTCN+76bk7A2Y6jOfmzHKmmnSIzZCdkR1oOFSFK+ESwgoxcGHVyS1OscVooNqfPX+BNzxuI3ItPM5kY3ZgWUxlG3pOMzQGOxziXV6PZjqa+alozgzM6Kym3UkT8VPZoD4ZYBvtJpMnc/Y5TwYn9jPgtj3QNQ5DQaTTQ5B1SF6EWjO+qt0JRqLIQ6O1Z+29se12FvtA6E7JDnEh83a+WTb1L54kJuCybnBvyY5vA30eljGLynejbZioiPqj1M2VO16GPCzj6RN0hP8OOryIdYbNn1Yl/fZfjsEitnkjaJ3eThx4gTOPfdcfOwzvx/D2W0ThzBCoYydUc5UCosJv5MxwYi62JQOdQuMO5hMJcLQMzwaWND4iAEcWiTugaIcHDobQGFeEbyzgShMp4GJ06aD5E0NZLIizwQc6vx8tO8KaL0uAOVALUtGZgZJNQLky9xZQhoQUNE12zSH2xxDaDSBntVMx1qELCyNe74uOBUgBgdFyqUAKKxLAUImLo8QnSZx1Jo5PjTQzEEUr6VRfsAzs0yDDnEiHgAiGtswoMiBrDNJPr2hzicPKOaeUygGjKU7u+RsJlkXSP0eAEnm+DQQApfDnJLyofVlzvIG/h/Ccw1yDQjrxIKTYHENzMZobya8a+2GgVGW5Swb1q8HW5h0dxK4a10qD6Q9BwFzfaw4Sbmgh3P2Kj8vOhUGgEpXtheH6UWW32y/qCMslnSc/TsXgISZodTPbNsz8eSx9oPY6YkdkSA+LME4RI4nWTlFg750i+ozDzn3TftpsmxkJnA5LMh3ezAXRPJSsj+euJhbegOhF2I3uEmMdM3wP/iORENe2hIGLgmCXaBJIEsAALRJSURBVLUaswxpcDvechLv+L7vwI033hiXo83A7Uju3QVBDwBVICMHhADIhtiXLrepkz1jY/13yOIMsVxw4LzeRv6K36jCrMbfpp0edpgmunSdxtzUapiapNCLcHHayIhzU5owQlQcTQRP8UAXfCp2sXg/8IfPpufDGhagZxsFtxyE+jEDoJJawC+vkQntzzgdn8pEN1RoDnwSvEmGQh24P1vEt0wCkHS92NQADoJD6yTlUZhmGBGDH/a3Zm30T/FSw6RGLeHCz8kamaHTm+Vmsq7ldN8t4qA4hnUpxDuNdh1xuab1jbrDsvFVsyjc5Vlc+Tpv5oKtoDsQuhOv3f7MDS6U/40PVmb4wHp0IKa4zOh/YIlmIGdoAcRG3loWWe0f2xUZUD7Qkfpzc0ED+0LX1CVbOgnySvocEXaQB9s3J4O8Nsdr8Rv5mu64DfZDyyXd1mCr5DpEN3MAp7/D9HjuZwl4FAfd6T/JFrb72q9elvqTZBhoQUb2h8Qj96f0ofbHZOCtOGj2LPE3ZDHT4BqJV2qDeC37becVcVRbyVuKwwy+oQ7aZBPcZ/yw0ud/o9CkumSdz4wnTjtgSXBmB3ZCfOhsIAZfEgCoIBpo2DE15lqPCI8yV6cxTDrFO0k6pminE680upwoC6/nhfsiqLMjS0RBCriLAfZXqiTBC4euSvZI2w7GPBmUXlFDRA1qch7cwRrQlT4Lwq1GmcdJlB4wB+fXyoVpNUj7pE+dsLZdZvjN+8oDRMeZR2la1o02jcAWdXoqBxH6bDEPONzgaNAg+IbgcxR6hlRO21GcITIqOIYyydHPDjySw5oYVovlvF11EFLnZMlDQdw5SUXOejPY7HIJHcTUa6UbzuSgJ7ZU8dVLIgMuC1upg//UEZLHozyT+DKRQ+qc6J1mHzxjqoGOVcccgpIB0/7J13lN+zLJTmhL5Ft1LnxP/AW6PQogPJi0nfDVMtmxT7Jsyts5R6+/Gy0+/cZ2hoSTtp11P8uEyn6mQQaMOsBXsNQ/YekB+qcPVIWHQTaU5pKeEZx87VqzN7kvi/X65jLDOlhWG65Bt/ItzNDI88RLl1hoHwTfy3tSJ2nI3az8dR7K/UmWUv3RjB0L9CDirwGq2gEPqhv/JrgDIQ65LXBmB3bJmAUjpEY635f0taX7k8xUMw65jeDgKeBJkOpNHOqcdIQ0m+YWAZqMRNEFIdcfMk/oz+uLwSlcQeEAH0HkdUl5YXwW5pBN0jVzxSYjSucLMOG/0kx8APRRudYTDEPpeKbgMWQs1BiIoQoBTasjO1AH4QkzoHMOTwNHwIDBYlZL2tcsoeJUrHSFRy/r6HhgMBWg7EQmo+d8HSIb5FcLeCcZIw2U9Gy2rJMSvCDpVhgcJFzdoSg/NRPp/MF0gOOsaGvmgOgQdABCJ0KHUzqtHJMoPrMWU/RHAzbdPVk/SyyTZRj9ejD2M3oW2lFblI73YcZQWOLlgxNXh9j4lbNHXV5sog/dFlm47nY2Z46y/Rgs2mqtnoGVBm3KLw028hIYwOXON3eVXpf3tdCOIuKo9ijxPWTnRW8dr/T8JGDOdVOWZ+7NvsLLOv6a0dMAIiQcLF4LAQYS/oKj9rX7B/VZh2XCxN+wfAhKWTT5qZAooY1VXLKNkWejTMW/2ayn9Wd9KUsaaGgdMeoT/SwdT9XZcT2VC5+Ozj5X6Hd7o3xW+bkVOLMDO6ArVO7YZCBUMDwYkk7hMybPan2Ts3g0iNP2Wh0hkBAjGjqTb7PI6w3Gfh9ANLy6RkBpbsoQ1iVkUIHNDlMdrihSEGalISs8AJh1nqaUYbikRj0ZlxzchP5K/Tw7skGqD1HRQqCqBkTvawAxAHrQsiX+5EDVy9Doc5fWWOLicZGlMNIVGqrxrRV7MKd+02I9XB+Ug6wggxIABB0QXoeeSyP/iQMjjeI8ev3WAya2t6KC9HbDlFlBX3M0YBL0aeChARD56H0yCF2kKTmF7MjUMXpQd8goW+vqOJg/X3GTQCJBwFuWXbhz0cLangZzqV4fBI2d324P8nmOiaeU2TBFJP2j03ohABY+2oAq42IfJtO0bJYBpzv1Mt9/+ucPi8znPvHgSHg/0G4WNjUZpJRR1jQJ/3NQPZcRcz6JTmlfhayT6qYGEdIfIWhSGyu8oPyGKW+Wb39zU7Qhe6/1pkycBlu8pstN3AaOgofIkA4UcqYq+AGl0e2t4E7bLImLYD/12WSb8vTsYbNbzoMMqmvW7ULQu5K+5nuH9KXS6OfbJXpyJjy51NPCmR/YsfMQjVIwDNkRyegxj4gBhAxMPiqlSD1AEkJRYFV6dchqMBWXnPXT4EDrVTomqfNkeBzP7BiQfmuQo0aY6yGUOSUKmY4s1HBq5i6PJN3QivKrYFeabPKsjk4Vr5x1c0UhWql/iIPLiwSHQXnckJbOZ+KDbjQmhkppTXWFLmS7pGFGcUvbEBD70wJd5LVnD5PchHOnSr9GPNWQhxGt1OPOSafuRQ6qTFg3nppFVeM3ljitnwIsK21NXMJlYvCTHDofpL2QgaGMSTv+Q+RH6QTEWa9Qg1L0dieOgvLfBgNlK/Lv9qIPfjR4yFl2zRYGfdPm1FYUoclcbEUXO72uquKA+3xVv09boDoCwOk6LMMxBxoI+rMQfluvj0h6ZpV8FruRZSgE7EmGJwM70pj0SrPmxNmfmbOfrH/o9c0FVM73IT4bAqzkh/yr2Ba17RPZE58TEhOsTGz6XIY8PCO0BVylTtq/vN48BN3Jz2X/lQeIKkfBHs0c6D2X5euIRVyzH/UmZQOLDio8i9vOHj2dfAecSYPqq+ic6hXxIt/cF8lGGmvZPn815m3QM8KZH9ixMxA/XcCSsQPk91yHl7ruKyiAxQ7KwjUZyYoyjPrKKkyNThjNKRqGFDRJOXVUqkT840g4BwupjUm9rFufY/ZCDIA695Ej75VOuXZml20Jh2Ba6c12XvTyNlgPCuj8xOhOpmzEIIT+bvfCFBvvaeYP/RkDYqAW2q1te1t8rtRfmvmbOGT21Yr0WFfoNsUa+GuAT3clx11Hx71TPRiWDp2MjCUzQofva0DzIKejEWRCdcblITnl0vosOAe2J88HWZixQDowC3w1PmMdwRknEvB3HkThD2uQWK8a+OYMPXhxQtDl2Sw4GKDLJ6ddTaK2Kl+x7yYZx0aD94cE4OqA9LOQVsnGaNbN+zsfwSRt1R3FonNW/+l0owb0OogCILqPrs7ONziS1H9f4I9ev+ub2j2V4yRzLj8MyDSjos+1h4KMi15OvpMUCaBCBlA+84DHwdDPNuwM7dkXrYtyrks6hC/BoavOqD2jbWCVWo/QO9EXLZv4rO1qZlB577o5zrTB6iTbFgJJi2WtIC7XSIGf81wHKZolZb2lPzuZDRgj/tqW5XoU3yRrOeMfZssE35CgyX3STUMoNzc4UV27LXBmB3birBzy+pk54Wn3wkgqd2DpnRUEQ5pSpQ6LMImDOj4tq840CcfctEd28K7MCTRTGBgjuBz2243/gDhSVgVWJ6RBjME3AdQsVqmvE1JFTwbLaaaV9dFo+176TtAQvGhkKHU5XokXYcqE5Tl9IA8wWHHHoA5dggIbzOOJ3laR+1JHoBHJ8pO31h0br7U6NYhyPlusigbXirVnbLKWJPNLM9U5QGP5AsQjJQ4zTpgazz6gMO8Dz7Zmg64O8xDDl0fBDi2bmQOAKrvmzlgDGu3z0G5jRs4qzOIlMomhIZkGiUZDPFQ8bWW1bIuQ5gZ1amtisGfRThQEPOcCH8dzqBc14HFnspLMrzohsFzp9dOpWpW1iSNN/NHgONxoehwyZax3EEXVgIF1KrMou8xeZ3knraM04vj3ejWbGRyq6hc6L3ywIfKm8ukDD6WxMzT89PLc/NX+qYMv8ttBZaGdp0e/EAYnSZbYiPq1ic8o8dP9EftE61Mbl3ye+o9gQzJLNIGi91V2klzP+fDAGy5jKtNnSZMORMKyJ6FJgy7/LTjRrk0SPMn/hmUNSVcyT3Ji6NB+vBU4swM79QnjIQLH7x449PthlKGp3SExUQUod1CZdmxJSjKuutIiC8EMPf5zRhH8b2Lw0Ed9SPf4XUenWXj4k1MvgkPIXJL25swmfOJ9Gl018nOCKYbUR1IFNcATQ2iDtWMpehYhBOZME9DZqoMt3alODFTCrbCqdc8uBE9qRYyndSPeAsCidKtzYJAh5XXDA9tVluQRnfLKC7r8NcfDAJt9tRJ+6fQiEGQoBMalPaevXOO9FEiRtomzbxVZy+aiWN+p29rOvjrIP/sky43Xa/F+kqOaGesbT2ClGWKLzibgXOLAJTkGN+Rr7RjFtyLTxbYfyhtpi68/y5lLD3DI+Eac5Xq0SgnIQjbPNzJRd2aCMvKoo+e6ooPS6HSSbqd+8gwiHV+IHuRrsW5TWoW2skiDynsbvPQMuvBG7G/ZlMlmq0672JUsW0bZt8jTNGCq/LJAUh9sxT73fjORjSLPtiAhoFJC1ZND3hVnvqlj4s1Zr2bBpG89iMRMOdp7bizSTL3cz205zPk3TVYonYf5OSnruCWZnEzfpszpJIsq/jcMjjQoU9utZZIPDfY9dFbDkxv+Zny1x/n5OdanesW++KiaikVnuGdmEkMY/eeESUgRA0GggjHJmQu9PyNguuA8GMZhYt/D5ySdrQI+p6D6p7QqtGdy+j4ay5nPOQOE7hj7MR19GtJaQJCdHkexOZOlEEbLDMyaM668Ke13d4p5jYYavhCcq7MezLNudE557Ysakfpn0UHy+eZ5glNuBE8Nj3R26XzUy5oJmB2dqdNumxCIhmcVmy409jnK1WEpkvKbQae2IfLkwTF5RUdHuXK57QI/0ZPWnpPVHDORnMhGEdqS8fNp34aTZ/Vcz1q2biZD6ri4PLbs2dhlzZ9SfYTIyZg6iZ8SwPO6DR2Pnvm22Dcgn82XITh9gPRtrUvtSZXNhF9DqS/Mr43VZRGUP3ifhYxc5FgI5Fwmgt71vg5BIzqLiutymeJqumki9lemJwQWqH3mSxYaelzaAKAOztqgz2V6oGJLfXP2VnBggBzwWVmfvlt1pPK0KVnXWnQcy7a+XVsX94fgWIMI65v31MEfuilB+2A1vQZI3UDoM8XdB3biDybTxa1s4KP4XOKVbax/ziVDtP5Wbx5oueoJfj7w4kYhS/W174FvaMuJyO8EVjoPNRDTrGgz6dHvEO+8oUZiDpWnkA1M+PK5bM5OB2d2YKeM1vR4EobQ2eL82Rn87YojdnQuI6CC5fWr4A7pealD7aSnwyFKxKm1TGcrM4eTCk1Q7rZWyUcnGrBgqmw5oPBRtI68PTBpj45FeGYRn9OBMpnOxQM6KkQvo9mK7izQHaczzHzTRhytUYuLP+eOJgQEkJGZuVMNxs/7xxoPotyxLgw96GXWyHEdC8qmxOnERotnA0pqWwMwK9PArAj+DADUcXaUJ2xxWks+nsZCQc8+tnrdoGtbqSEG5THoCah7Ex4smqC8qrR5OZXzZBB7+1EGNNsyORrG4IcUAzWDBGYsS5MLx8XSg8VlpFjxTSzaz8qHoL/ofUVG2jrpUGbQ0PH388PmMh7CwOIVUrbZp9PrrDtnNLwizTLzeQZ3tA1qh4lfgWfBbIid39sp8EGUDxJ6PR3vEp7xbFe2+RI8evVbYUpBGMzE7F3MrrHfKf8+0DS0oK/xTV7b5wGv1k1ftLKeeVzN4yDN9wBQgoTJ8giSphk8w3Stdbs+qUdBgwqFcea++M7JYJu4a+YKvV3XK+IKlX3hRZEySm+ybfRzYZAmOjdXjwZVxTA57DqYQNIjNOey4X7DczZzJ0CfM7Ft+nc74MwO7FQgc0Zu7Mqgf65saSSjBiGA9U+PzHmJnblNZTFnKOIzptcavhqd87oLoqSQ/Uwn4kSBaQauj04TLWlKLmQdW7tuPIZ4fyJs6M7S2wyehPQw6Cq02z3IQaqTeNAxsQ4a2WIeMPGeG2rijuIBSnCwGimgeEDqwTbLFet+ceyOujuxaT85Ge6IhWESuDII6plDdGdaUIO8LTMD3UmyHmZbitQLrtET51mDsxLlz59p3xkQ67WWVemZNOv89Cynd0oLwhjcNBw9WCg9UPbgu0BuV1qJGqd02XcrmebeIjp7l6HeD5TXAvTAfmztaSCXnKA6/RAAt+A6B7nDhtZd+CbTgc5yK8Cql8mDiGgAWuMhHWW9LJKuMhibZEU1Y9aXDISAlg64Ba51o4pN2zssEG7Bq7JuOgpV2bKu29smSx7BdwH1gSHlpcliI8WrNtFt2r7p0hlzB93rNafFm9YprxzF5t901kOXK3Xqus5WA2e3V2HTV5opQMSf7OmDV7jN4XN9AEd8Ey/47NxbRWg+5dNPEkinQLiuaT8ozqKDwZcC7ksA9MRFrnti90U0WC9FSfpAfaDjJsG8l5EgLvhKwVWXqtgAjDudxpxJI25+Pp3qpfgGt3GKr/Devw+9XD4vNwegE3t+GjizAzsVOu0gEbRJCpiMokBBnkE3aHmrvRoxr1M+g+ElpCDAd9jNTZ3Iu0kNQhdpU4eUBVXp4IcYdadJn03TCrX+PjL1wIhVj1o3jSSCM6bxClMLIphu0FZSnrWWxmRZHwfM+AA3bs3AD+hZOM2mlI5n7weOkmW3n9clRkEdWsvsTTODjbah0x8MEohX96yaxYgRvtTndIrVDZk6ecTCBzzQlaDOfasykUGi49eez1OEBYDFRvqOVNE3ueYwpI0IzFaJkfLAFrXv8tokpyM4bfR+Z/CiAWTrE8+ysJ+5Ls7gAbuufaxBs7kOEpdR9KHztaR+6oQG4zsW14lxLRmaQfgrAZDyJEOf0i1d57xN1qE4wWVSwW+5k4x67rKqujSyE6Q+N1QW+yxEyF32Q4ynwV1raxJ8Cw1+j4Ge7ixe9Q063pA1fR2kQhmBetM6WKHcSP1up6wApWdix5XVoH0wJSMEVIHZif8M8sO6Och3lWnVF2FrEdRM2s5nn4bACXD57gw8/acHiup/2v2JvVPbofzIzwY6Iw88W99mrhwPZnvz6+eCvW3PpSBqdvo406/456VX0q+cthWzHOtI6+C8/3Ttu8oIbam0HWifmQm8LXBmB3aIDAnb5LNOqZ9kZmumw00CsEnKduwdXQz1faWx+jBqUPxYiH4B/EyCX1QYJ5ULXuJbwm7VRCtpUkXlvW4QxGAGS9HxCkGPKlPKeIT1dur4PbNj/fkQvCbD7t9NPoobFC+nDq1xYJKyZ1n3oWVq4NiGWNIaACQ6M3BKZitBCB2eO2OlIdl78l+VWKnR6SSdKhVHFUeC1tsOGTPhFa+lYJXBvTtGNJxyhkaCRNN+NPSAyQ2TtE0clQ7KB3odxVNfzfEP3DiD2K8SuLusry0a42JxCYI+KzLRA4vS+W4SjCZ7orIePKjTZaFtj8lG9PVaM/JXRtTMlhh7x4d8hHxXOVC6gD4t7DxHlNG2VnHY9MY8w6VpI5Yf0IMdkSXyHdz0wb7TQYkMpqL8W++bJj/ZmXs/NMY7G8JgRTqmcKqU9IqcNxwZdHCg12dEZOAX7KB5ll1EU842FL1rdHkbGmSoLGc+Nb0MdkJsr3ev0yVy5bqEsAtcM3zZL6BABvLtI8/mAHGGKE21JtPrbXqdpfEG8d7ER2p7bifazxWiDLCMpfIl/W7fw9hLdYDPzNRjpC9NPc8Gc7m+knigNGpZxbll9aygvrFDeUt8kg06HZzxgV1gXImdOBUw+QNmdjnBFcm3KDfHVZphqxVLvVQySftOFmGj1uHb2lOK2A16GykUwBXdDVigWY4C8cyi+bM0SsGo6QhfRwFsJ6WBFXk/4oO8VSmj4RY+qhNzRrghbrgpHlzDlKd8pH3PxvB5VY7BepCu1y3+7kFSdzjMyCndHM3r7jsNSJmp7HX3DRmF54GxPv9MfTgIDs1pVLq6ABt5KP3E+jSz6vzeKrHqk4rTX+WsKoEH4ToY0UBSgwL9bO36h04VrczXiUU54hRzo8NkOsuDKfPPSktwG8JLYDZgBNp0NqKs5AORk82IdsLiH5+jjrEol3d4F3Z8bRX7SiEERuEGunFSORS8/TsE59A3pZcpFumxUvtd+6TJth7F4oMA129ABwCBZ8XCwMYdv+tqZy51ptsYPQOwtMFB6QPAZqRVFxVHD7508OJ9046XaUGer//Tbmr8CoHKWALLNFimve2yJTuutZ9Fnkvj8SQgUXC7WYLv6LrcM5Iqjk6L1KGxPeS7Lq/J7ZIOB/ErXhfLC73htXzUq5CJRh/ce7JA7JmaEqWl4QCg+0zSLvrr+LE/Wlu+flH02hLtRa9ZojEUQg+Ssyli/UOqn7orPOv+T/UWPQZg+0wc5QSVLtO6jXDmB3bZPoqwH7YIEtIhuu4uBAFiI/nOuxjsdAHL25CzXwfgnajKkH1SWDsRUnCKfDcCHH0Ww0xWJT4DjyD7dTdeozgjzQhMvAjv0wF1giYBtSh4ADU4zRiILewjdo54G4+D0UikuYFta/dCn3qhKQ7urGRNDHQKyiqvnRXdV8Q6JPtig2GcLIDv91ivGh4NijRICmsRxRiOa+sBmvBDp8/CKHjoTpSZBT12pQAxCG8DAd/5bAh96sY6TZuD9RjxL1FWyStOUeqUnFtbWeeXO5n4kYdDuudIEL9Y1g2wyH/uIw9Icr0MjORB3RnuGdK8bELBgx1IXe2WDxas8wPCb5UXddZodSUb5P3jpFjvg+Z1guNjW/zTs9X4pckkd3S6Ykjw6JmyFWRt2XRKmHrKIKu21/TGj5MpUY4Lgn6G6VegT8FzetmzZ71ZLlVgv7nt5FpDnbUg/kBfrzcgyBCApu/ynAzMglPXqUQNBhRK7atuK5pd980WKTheWeTDmER4qAMtDwo9C9pFV9uWcVewOUFHKAc5m6XLgljcdV/pmyXbBxUmvPK18aIHvZ/bWjcJQilTIRFDOtXuim2cDMLGKS05KHY/oO154el356mKSul8DnaIQR/7kDFG1vHTwJkf2DXQM7fc2QGhk4JAWSwb5vZnBM9KEi4+a/KJ1EG8lqJ5DVhUibxaURpfSLwtQVldqIB4xAioCHSaiV4aHV9XB3ewbCuu2el1+5QFg4OCXs6mbJs4uGQolJfdyVrnX4GshUKYNlBHrRsoOJKfGIOibSSepGyGt9No7Q61TwdYrkPo4G93/K3uPlot/REJVKaL69GdDTrNHqjwyJNsLK1goPMlOmoU2L2CXza27hxyH7KuEHTJp5Qf14bJukd6EzbQsj7e95DNJKQ/A8WgjdadD8HhWsyAKw1NLsa19aDW+Q2RQTX65vh1vSANQr4U08FWCCrFo2YZrf2Q+rvhqEeVhGwD9TL0QxTUieNRe8F+K4Du3HXSkk1U25GXs3gmje2oI2NA4VO6CEHlxOk3nfD6GAyHgJv8KaEt6rrOCvQ+7cRZQVsvJ3wpCTeg35cjW0JWz9Q+Kj/kd4niGQbkPKNTHIKtre7OVvtDW0Jb5pud0Neo6n0GhlKHD/QKoq5lW8R+SUEOr1thf8cdvtlGxpMTerV9Q6PYA+tdAEyPyLLC+vpFTZA4T8TXqN3P2c/q75Qe9OB9kOd02ZXU7XZdM5Eq/6X/DibEqv1y0ZAlG07a2O9lPtwanNGBnadeMyP5szHVg74hMQ69k9SYAehKz/v+gCCQ6lHmu/EtqWOG3lYXDjnOg8bJU/jdyE9w1xGJHPHhL7wuPSgA0Eej/K7alrWO0LIzOdtU2+w4cN1QMPRF9FWmnrsTxvwCYqXTFzQn2+IGvTkaiBKOkffhQWau5J2qIdNFZZ4bscl0sLYBaZu+oQcqcKcyrluVc8EKjTDrCQ7Bus9pZd0/takCf8dgQZ+6cmS8muhAZRqP9caRYz/mI2b0iK/0rdKhho/ynJ515+6IsU3BQ+v0KTdpryFNulQ/qPN9rZQ+IzQZ+sJszNDD5yTDRjkf9aBiANyJ3HE3p5c42gDYjuoNwmCyZH7Qhq27nPcAWdCkvAUjlGiXQCIMsMg7CWq9G6U6z3ZJUy5ehqAfgNhn6mG7plkY7ngum15vCP6kLddzn7IXUhmgcPA1pvqIoztvDZg6L7yPst0P2UHp5GIhCAqDWbEN5IHjQd6miMnbI2PHuLZMg4phQ8HqZPiaP1nrh3B8jAm91utXGSFvCUO4BT8nUqbNGVT6kUvyvJt92qXSAzZOmevbOroONd+3ijyirBfat1Z0kkiwWN6zuZ4hFpbIDEbopNZ+WOqT7RgzbOi/5wLS3ieh+n4tRWP6fmqk+m4NzujADpB+HSEjAERGNEccUpki5yiIhxSKkTURak31TpgsTljxCk7aEObPfbSCjocbU8CVQAXYSSroI5dRjpagsZCALawhY3TANTmScfD7JBiYODQfVQhNarjU4XCTCvnmZzytaNB7nVEJpX31VaJIiiqA3m+CWzQS6LtnxeA6PToyZn1J+apTiRfUQZOFhkpfnFoXeUPHLSi/4BAGGnPy1gJp7gLzwD8bFZE/NXzuU5QX6OW0L7PBDJkk6bdJ0GX5d+k64DSW3rfqL2X9K/mg/JsEp/5crc93B6LzJoxdXA9L72cdICq9BbAd2ZSR6O6yazHjL/SEUb719ieDQSQ+bOpfPnKB/adZmUqDrD8joiUWCQMw5YvIA3FWekbZCQpmEJVXIuNeZ8uO5IGrB2CevbMJb1120WwJMxyNZ8HeOH/NjYNm1txm0RYFm9fl18vrVHfzC65HygfwugR4zsD530FPdAAXltjAkQxBGgOUMrN2sGWug80nmWEdsQilB1uUi94P7DPnjfuA/l5heICHLkvF4Lvp0fmrpwSEMUjjL2DQ2SeVg2jsKw4cvNI2TEDWYfaKvAIwwDThjx943TrfM2yjk+hLoDp9aZ2n2kTS3hy8eR+hL3dINjgkcKzMJnVuDc7owC6PZPIUQDCmgAtmeC5zQIMMz2igv4N2QA/OfIrwMMGS9ltnumHy6ZTa89baC+ceOY7dOGvnhhFLoKF0BS7dILvC69RXSQGNGjwaGS0jxi84gpJGwJJN9b4RIxwCEDoaQT8Gm0jTH6I0DG6kPwDMO1hdV7YVY99Z4Y4zZx/1nhsj4iVyEHC2jrcGqyb3PSCS59SQqvMPzpkGgvzW+zQUM4dym8ok0AP3rAepD3I/Fun7YIDZVOtXBu/d4KdyDGbFiPpAAYJ/MmrOL0MfMIij1RG2J1kEt56xFZloQUjRRqizyuO0Lpe88b4ep/KDRKM7SR3YuIMTOtcINmoShNGhELce1wSd47uHA4EGZ1oYUCX7qLa1BzeIazdn+sllRHSnAJ5Bcv2lvulACIlfyUY4zqRDcHV8BimXZLnT35935y140YySntxmsAmngzLlUdD/gn7uoSt8550TYqX5m6Rsvt61Xy+GuPZaBzE+om33PaAS5IpsrNFEgTWZc0ODLlu3wgPXcfq3tFwlDJRSoORBJK8V9OlnJ1hocyYLzdmYDlanu8U/mvRpP1Eg2Qa1fRqZsUCelRHeV1k3v6wzIyEQdDxahvRW2KtwRgd2hLyjdXJYIg0RoXXayEWJzah7nzPjs5WjGzRQUGcJNEPeFSMbDp/TT4ocpqKKYTgoIfDS0bj3qQhsOMYCkMXR1o1CS7W74AxyX41UqWWDbZhxKHOGE0JGGPmh08/+CY5uSNfFCRBCxkeCkrLtI/4caDmuq/isJZnIDsEyP5TeMmOIeZ+B2VauS3aShs9/JwPmmY4xfSfuwuOg2zryVyc1pmtuLBD6gP1kQFhDos9OnFa2Vxo8ioy7vOv5jAPqRiSKoGTS3MeIzGnGwDOgDMSJi7Sbgx7NigXnLtkHD0yK4MjffMG66nvD0ftn6Nc8CCMNOYPD4EUHiYaJPKoD1GsBBB/lhcqmt5kclEJ4l3DrrzxAmbTJZ0u8HngotlgDGto9D6wR8fXgOsntrB0vsV7VWzeBBdH+qUNOdsP5qXIETGxfThpk+QoDFWDqZcWWhACBz8pUJ99Z3fuCAYFJMCd9K9FHlHnr91Sph5j5c9Df3Pkc1n2ncinLWBhsOs36PX+V7KreL/E5HwDzniGur3UcizzPsgU9ULPAp04fcUeMgVH57XrUBK4f4WNCIvsn4u/NqSPxyhFtilfQNsv4OZK3Hda3XuSuCzmD4EEJnZtSpw6qPbc6Vdc9+VSWLmBsTi+MCNCFi1NgXreVyUimr+FB/00oQN/9V5/33VVA68xq2T3j4eumOv1elzhxHc27YHEnIrLANjraifrUAdeNw3biyDRIdsrF0M/i4oGOefpDnBb7zQO1dtFtJ52VTLNb2xlKh+88WUlbGoyrgxSjqg5Ug7DJq3NUvhSK0KWOQA23G1cpl5xUyIRBHBCzgbme7PTpTEi7ykMqo/U7DWngk6ecc9AdeDQDin+Qp/ZScQ2o2NcBVwgvmK0Rel2Ghc4w7SK2IQRNwreS+sj1VZ/TZ0nzIM+l5R2hHxLOHoByh6UMBPx5BnuJF2zLcaO8zOihrn21ofHcZBpoDpTWXEblm20PFf+JLCJdG7tf9aA8nVagvAtBdGtb/GXgReCV9EcOrJwn8km+hIyj4gJMg8ikczlBNavDWi/bV5ulU/JCW1GZdH6IwWz8KzIdCVAe4mYo9nvRfvR0JO1VJ8ZytqkJFoOnunmvdQzX2eVol9CmrH3D1Vi6fBSDoU7rTjZZ0eCK3TltQrDRY26glGX94TpobhsEJ/SRsfVaGQtM03ektTRmHrZZpchrIZnF0z4Gui8X58FBr/PAIHjdPjjjM3ZlCwxylg3XbgWDnJyxpv/7RSluqBmMpDBhtyrf6yo850JQBnhuzMTJeECRMzITQev3+3ogQIcSmuWpCIiwUenU4YkTLHN1FHiGL2RR0OWZ371e8lNQ0BS671zU/pjQLp8ml1Q5s2K3/gsZK37NU2BIhjRd18yaQsiapLZn8dZMkD5f0h/ryHVrNofFNAOTccj6LgGH9skEb6WP+Kep6/A3k3Fge8HhiUwprSXhGs6gAvoyh2CMhfbs1IXeCU8nRGMyOHHZluyaj9JTn4ascAvIfMOPtDfZTT/j47y+HNCwfH5eR/Fzz1EH8ovGEZ/TRdj5pe6Op679TU5IB7zedqK35PrEH1rqoyDTRT7m+k9tgnW6NADkzArX9AKY6BIzhDyJgHwBprgQ98D7JMMMnp02mVnIyx/CGxRYNvunTHfWTaGLguoBvMpJ0AVFWHCQDYchKC6YKoIv1bC+nIe/81mBsitY32ii+ufTkG5D0ho/xx1ddoBoA3W2irhQZunnJJOuh3P7sgmLlbtenq5PoDy1wF/NBAbdcYLah05Bez2trbFnDSPRvdhthTM7sJOO8qh41TswOG/NdmWHSyVmHXT0KMFgxZGgHdL51o5YKWHKls4gTA9xi3g2DjqC8RQy8TW/HJSSiqzTsHw80esZSeWfTuse4kg1uAvtouMymbaYk8Y8KkfnCfngxkf7UY1iy55o0OuBN/tyzjnNoHPaAEzLYMZoA/FAXCmfpyj7zdQGR7CK+zAtxyBDz9Qi3kG+lZ+sl/iqPOu7GzOvsmyJ7gSHB3lWZGvOidCpe7B/mIOb64c53eWjc/jJYCXIUdObcY3ADy/DrMvcgCRnd2QAkR1kWEKA/l2TG5PslNKieGX6556BlG91T3RVMo45GNF6sp5PBnda3jo9h24eUB+W8dc+0muq27yceOnZOdpRyWZapjXRp7oDSLkV2g7khBPb1P5VHurgW8vrp9KX7Q3Qg8mE7+R5TsOqbLp8tE7yPq8FQua/VcEDmUPgjbk+6soWAiz3ObGY20XNgBMG3WRSuh7Qfge71YPXENATx/Y7DMiybZI+oXusz0UedtkSHxqYUQv5hkVm4shAZyz9Sent8LOIeOhu10YrgLCEyfsZiMtubgOc2YEdjUI+BZsQhLDEIGrOobA4jaIESS7bbih4cr9IDeDOoW87RxC2YPDV6aaRUR41d6dQIq6ClwdqxAEzzzc8VMg10At1YQbkuQnvGq/4wvbw6hw1pslghwzhgOCUg4Mr8TmnqdUdaBe8QpCf6+D9GTq1rzxwzeVEx50HuU41NiHDinnnaqkMosELTmfo5YOBLvKR5Si9icHraBm0YCQ1qCGftQ3l5VyGDf05b39I91QfE92TetonAy+nTxzspH6lCVHe/J71551HOTBPuJuUD7I9Q3ewO1qvO4JEc0nPFPTlH3N8Ik1pGtahBbvOhtb+ZK1gETqVL0oHn0d6RrOBnIouiBmbPD2tPMw2mfTN6KcOPnTqXIMEr1udu15PdPghwDnTdYiMHirrTRdDoJv1XG2TAGMEz7zP6LTEWWE5gU93FsT1bSxM+8xn9ESG8DYa4cHKwnmZuY8mAXm2m0Pvq27be+dOgtnAmxkayIACD/4ltvSH/XWQEid5fzHDyBu6OUSYHI74WvE5C8XckORAG4h9p+hZidm5gm4EuIkk658pA24dzuzADpgaS/0OBF6EUajuOqVzzkELunHyttDlkvP1fdiDaNjYYep4WI+1L6qQKhRADxDFUPkuQuI7xmp9Kpq0qCMXevNnGVHPkpozrmq8C4Jxn0xh6n1Z7zI7+tVP6ssWU1BjpDRIhiXXpdN/YSpQ+0IMlfMqO4nkYMMmAzFsNsT6gvNWeUwOKwTv7T7xVYM3yf6lQKAgZpfZN5ba8+/aLu/lUSHlLztgjprVIc9lqFrdQWYkSPJpTcU7BRJKj9I/lSfrzatBzYGW9LE65rx2LeNa+8pi3bDDp6lnaJ04QjEZgc6UlfCDyFm3ypo8OydLDnnTzmnwcfQz350PiUbFXf9YRG3EXCCpAw3BgybSZTlNIU6WYeTgLfEnOEwCgw6gTum2v/r2i2jLVE/gg3+pT/tnELrVhihQLoj+XF+ozaVdIK0qJ9b55TGABGPhFWHi53oQKYGJ6bt5Oz9ZjLgGP8LHE01Ki/PLs1iCI3FvfJ1ba0l8HAYhmHjJUTS+REFkrU/FotuRrIeAZ4GJb7CtetyJ8CkkVjTaFLp97R2Ebp1+pR1rU7XBr2T5OQ2c0YHdrADNGX1EoSsb+CnuADywsmIxCFJHo8pp6GsDxAH3TpVyTMkqMk2xPJBUQ9v+8kYO4jkZWMwpgBoxRJ5w7dXsNBAPBNW6gKB42anM9oEEs8W6UQmOgqgNaRSkiqhOVh+zzp/u9FLB0AfycKZZeOx8T4FLML7ppP0wLanGX3FJxguABHs2yZqE0brzaYqLt1sS/Qrsw9UMDjMBXlH8RX6Jn6891SCQ9cyMvGenBLXJGWfnNjPLuKEGAZo1Y51zr9US/Q47+rIzLojZFdV7xTvh6gvIU3uTAMINeOo/tqN156BS+qj2ocVnSqqLz69m6hI+zDqJEstP+s35YhN+hAA906d15IBHMq0THuSBRq5e+SO8HNexvVn+5v5J+hPe3ar13Np0s16Tvqs2V/imywVSPWHakX5C+43JBF3nJ7YjJBwCUa3MSrqR/dx4UBpj1VZ7G0kvld5AfpKjkDlj1g697oLu9zQ4J16TgXkxOV81TsP3gM267yRPtr0KZ4nUXaQjQjZW4zixz0ScR6RkCPKZ9L4fI8OCETFdoxh4ehvhdgV2z372s/Gpn/qpOOecc3DhhRfii7/4i3HNNdeEMidPnsSVV16JCy64AGeffTYe97jH4frrrw9l3vnOd+KKK67AsWPHcOGFF+JpT3saNpt83sJtBE21a0eoIqhjpmBL0NCNRwnlkC535eJ7NPWkeZuMrF2+qXRzu2OBPsLTa+rcSxeo7sgQhEZfwj0RviKKos9JWzaIHyciTHPT0G8F97SOyfF1BvZ6whEk+WwiBr7NadlK3msofMh8cafQAgd/gVYyEMrHyTRQKzcJmjRG1INug3OL5UiDOvFQ91aeEa0rKN0wq8wSH3c+1aH7wCNlpUszZgF3yaj5+pWcrVTDTdq07ywaGb43WdcVGrNZ5C+zGhDZkTWsmZ8auFl+lnzIjlDlIk/lpQBTHY0eRJoDA7UJOcjhFFLVLYtlQdwt0OjTWrqRivgonjO2JgYd7etY8eeUoeOu2caMF/t3QF0/ptOjM7qrtirrqq55y9nLMKBVID6iMxM9s47XnFwj2A/A36msfEK0r2EaL5UJOkD9lIHBXN/6X85Yqs5A6rZ0f+yy1wPcKC+Zl/pWnqgrM4EADrGzIVhAH7yqvdb+Sc+ESyKTeaZIv2tfOY8VnOdSLlcmfJ1LQoQd3upvtXyTn9njtjKOyWd6cKxT1Fk3QdtoQaZ9fa7Spg+73SpT+g2+9jHoyu2E2xXYvfa1r8WVV16JP/mTP8ErX/lKHBwc4HM/93PxwQ9+0Mt867d+K17+8pfjpS99KV772tfi2muvxZd+6Zf6/e12iyuuuAL7+/v44z/+Y7zoRS/CC1/4QjzrWc+63cjPzkOTaUrZzMLyKMkCGnRpvTScLdrwAx1XkuVLAu9QrGfg2vSk/1FgJB2rDj07n7n1ONUA1Ash+6O4zwQ0E37RqA+9KICQXq84W3DeORDwgoEHxL24ce6vkamCzJfG+1RzQCLSPEmnQ3ZhbdMzhjACnNAv/eH1J144bcRBRomhr5R0pSFncvlBIzuTMfLR7SBOQe57vTPZoUmgVsLtEFd7RivX6ZlLmZrRrGQ2gtZ5CYsyPnEWlq5ZryvIrAanrj/W+qad80VdZDZI+Ucnqs56BPzAXgk8anY52YUcbKA+G6ZWndeiw1t4IFbGEgdFZGYOTtTB8LdFh8P+ID8syc7c4MMHZ8puod0Tj61Q5cH07QUcgPggKjtH7Tf9rTQiXlP7FgZk2bbzkSZrhw68RcDnpoyL4ib3dJdlmeF5aAeY4OX0aLBONg1pTVfWBbWlQLQzDZdAyjDFr0BsFf0K4BuJQoCTaRQ+8NNZFGws3JY6norTkL6X/hvo/aH94ruZRU51SjMkYITm3NdF+9v6/UnWK9ulhI8T7u8fN59C9Xh6m2jMOFFPhFfdrljFb1v6dC/b1U/lhdqB2wjFTN/9cfvgfe97Hy688EK89rWvxWd+5mfixhtvxL3udS/8/M//PL7sy74MAPD2t78dD3nIQ3D11VfjsY99LH7zN38T//7f/3tce+21uOiiiwAAP/mTP4lnPOMZeN/73ofd3d1bbffEiRM499xz8cDv+X4MR44AaEJsCEcR2Bo1Xb3mGxfa901dQzHsl7oDio+0zuH5dVbqocGcTuKBxj193DRgLD6NaUVxKI3JVbl9DRvQlY9r6KTjfPqSjqyVJ5TcyclbMzPDIDIHnH5UizzrZQ0TI6JG2tvWkXh2ROQfj39pI+jJ1JXj0/pEXrdCorsBanXR0E0cVb+gDklHqOG5mcCC+JIffWQFH6n7OWzJCbjDyNORxvI1y+trSLIhkHrqCHCGV0KDDYKrpTq03jQqDo7NZurNRjMwuZfv/G24Il0b+7lQHjTmgcCQeDfndIn0KPxwB0K5Sf1KR7Dq7fM1diFQF7x4EGigX6blO21TPloLKmvb/d2bHHDUbKwI34CKt/BccaJ9Ae1ISe0CMdtH+vis8K9sEabh47rNLmdVp5stkvfJdluY5I6zFuSL6K4G+bVR6ROluckJeeUDMnXmajdKxLPyo0jdVoPOGRsxsRlSXwikMr4rm9gTiMw7mTPLAKgHcbDV65vYRLEhlFfP5It+6G7TQrbLb28rg9r6nFlkEX+HuOCjbZX4GfgyRB1zn6KQ7ECoc5BrqUzof/GxOemg9Pjgi2SUWJeyfKIf3AAodAc+Cl5Kbwhm6TMFt9BP+ixpaN/dRwu/7ZaT+Pvv/E7ceOONOH78OE4Htytjl+HGG28EAJx//vkAgDe84Q04ODjAZZdd5mUe/OAH4373ux+uvvpqAMDVV1+Nhz/84R7UAcDll1+OEydO4K1vfevtQ0AUUg27Cl8NkJLBpgCN8PcPThJMFBxmAUyflzUI1reMG1qZJnH+2qpm7Dwz4gJI5PvXEEyw4yX6dyGjRBYRIqjRTHwijnRYjn+7J5LgIxo9DkPLad0qQWIwbKAzrg8yUPa61Sm6Uhd3DNwM4KNVK8GQBGPIT3Uk7Vo2dCEzpwrLdrWf+ZwblH4SfBj9jr2+HLz4NCWnHwzTZQKUQ/KjpLqazKphKhooVAZVR6H1qpEDenkN8lP/abbYyxZA1wdp/wXn7l9bAIvSq8qOpHS6QT+tcpb7i3or/dentsQqtrZsHQNOzTLoBqM+fVc6/ZQLHbwYwHU7lW+yc5DNMJDTa/xeuh6UTXGD7+iLcLvz0axonklg1juxIAe4o7zpY3LkRVq0XTY8zoH6b708p5w0e5t1hxn3lkml3HiGtWVWub7Iy1JuJXPlswLa/6RPdxTSeQ89SEQR9vs9dJua6yPLWoDf+wwTx1DGfs27LU31dXtc+vX2bNnE+manjEW1J+sym65wMMNm3O4i+oNAp/rLLE+0lSUdwaXF1OSI7QxLKdT2CA5BZnRJhdQddH/SL4J3slFqw11fgPAmmBD0Nvp8VqbRw6Cr8te63qn+KO9spt6h419opxGfUx8d7M3YZwJ8rbfQdnsydh/ymyfGccRTn/pUfPqnfzoe9rCHAQCuu+467O7u4rzzzgtlL7roIlx33XVeRoM63ue9OTh16hROnTrlv0+cOFG/JGHIi90nmRplYuvYuFnCgNINGy8Zr4szC4s12WFoHb2tCOiaK0OZdJRntfhwCiZcOSiYchCzTlVQCIqeZM/Rko6iLLaTeZgVWY1ASBkTr5Ku6eim9Ud30NXRGiuWrJcwuqWoG88luAPEeJPPGYcxBX98joG5O2gpktcazfGB8jO3INniJ51g2ZQ2bWX19HIGXi0QCiPHFmDUjKDICbPHbdTAfvaRPhu20t+awawF5HuT0VL44uwSLTP5tO5EhFGy8MEmKSaWsakjtFwGjEGnMuj1V3ktJd6bzbjoFJayY5jBkfUP1GXxijPLLyZZRHHoZUQ4JT68XzLTzeycrr8bDFY67wttyrqdxJ/slgGuW0qP6xODfm2bRZOD9cBnC4zrLiueWVIb2ir1N1lSFhoiVtxc9upLr8tYrzurxoc2Fa6867JmLQ5u/bKOdFtvvg8qDNAg3nmBzjtknrb+8Dr88ajgOZvntmNEzRx6FCFtG3HS55o94+zFpg57uP5SB/xub5WIbHfQ7Rrx8eJpmhIDevZXVUNcj3c3AxzlVTr5YJIEUf8AIA8g9BlNTLi5oL/QjLroUg6+/TPJRUdA2tBBd5niTll32hn8r6d1Km9CVpFFrMunPyv9yxlEjt2laqfTUFqWuONtgv9thQ85Y3fllVfiLW95C17ykpd8qFXcZnj2s5+Nc8891//ue9/71huZWE3hA/DIt0Fw8O2+M05H/UPvuDAPn5xNdxSICtOU3U+3PiTbpC/2VWWbjEo0e6cORIVWhCk7gLCI1RAdGWJZ5c9kcWs2coc4Z51q6BJW5FHRREmhlcZ7OgQ3AtxQYWVqLITvHoQn56aOaG5Nw+QEeqnfFZgGUuktwlsaJxPHVqwGm1v0xfcMRizVxY+2aL1n7oRXysvAB0v8RDOccgAoDfXIqdHm0gvddmVkdXRyBqE30lpu7+jVjDbbmmH5vIFW+VQ6Bkc8yjqdnfdZX7MK8pueovFsksEijxn8W+nfC8D3l/oIX5y1816zan4cgbXf6M4j6akHmqMuwq4MUfz83dRUiWKeJXRdVKcsjsavJZ523IUHYkvU/tgQTwWwJsM+XWht1mHbeWewnnHkjES7ns9So4PyYK6VHTYdj4ISp91V/pMN7euwLKyv5GBbM3XMQIagbizBHtBx12at29WUOc3rqOfsRh4UaKYzHF5vJQYfUp/LgvOtt60bWWjbfXOEIlNinQAma0aN1yBtZb8kdUX/1/9c/jn1n4+xETrCwF7tePKzHmQOiS9Ce9aJbhf690nWT/AKU6OqJ6OYdt7L/rFE8rxaun5mVnVwMeTCxNGibeN94jN3FNgh8CEFdt/0Td+EV7ziFXjNa16D+9znPn794osvxv7+Pm644YZQ/vrrr8fFF1/sZfIuWf5mmQzPfOYzceONN/rfu971rnqDBnlotKvxC0GNdearIPh90IpNpjUBOg54BsxfwCz1lNS7cUThVqs7EkgHWxcslvOgtMQqJkZchMxvNTw7cq2cTBlMzg+TOmdHRZA6qSCidK5Q3FIuI2GfbqDgYgo09hasa7vpTr3do/FqbccdWgyq+p9vztB+VbpkCKXBYg+w0y7IkV+tPwPygsfmmONSBwol0l4APzInGZoadHUuOS3KR5ajsCcnzd8eHzbee3HhgZNfrOOagwR1VMBkalj5OXE6md+ZZ2I5nWfovOS1MHIXp+G7TtGNXx4A+JS/Gm0ZuPiGCAhdQqfuOM5ZBXdiyfCHdZ90NC6/WU6ttzdoxT04D/xu8qhZlslgxe2j6FPqRwY8lSd8HWLHuePf9Tnak5atZKa50VBQJtP0Fc/i/HeHLvw0dNyD0pIdja81ALe2hKF2vm+sIe/dnrcHZdMFTxFwx99JbVPDPKwXIRBw21o67c5bAOGMNuGVD9aSHoT+TLpp0lca2GQfxjJRnuEBLnJAIFOCFWfEZRjq10SXJwGHyJO3l/We35Ns6mYEHaT47nexw0oj62B7uvmCTZEXwY+h80f98CSAUn8GlbnevvZLb1Tq0llACcQpzsonD4LZx+v+e7IRR/v8VuB2BXZmhm/6pm/Cr/zKr+DVr341HvjAB4b7j3zkI7Gzs4NXvepVfu2aa67BO9/5Tlx66aUAgEsvvRRvfvOb8d73vtfLvPKVr8Tx48fx0Ic+dLbdvb09HD9+PPxVhOCc12g/CI12po60Joqhnm6qOKFO72EposrUou6wbq0ZIT0BXg2yoStOzhJ6JmFG2IvJfWJHBVFFS0o2OZojrRMJwqeZPjXGc8rcAl/vjzZaBdCzHHRuY/FdR8GZCk8dPJCWxfjuLBOekP5iUM/rs8aLwUQlSA1Cz0jJM15c1k1Shlqw5Zt1KEvWnUnvv+Kjd8/QOfEd3wK2IXwM2Z6IYBgENNlCc6rTkXibDuLjM9ncSbYogWY9VA97FsjibtPBWTWhlwQEXVNZVuPqxYvTpnrovJcHvN3sLFsRHVcEvFoqJARXqisj5d6mdFmfivUAKfQdPTO6nDiynR8BL5+uS4Ebq+WU6laYkHhYRJ6Ck1X+MshQksg7BoTaLy0AzusPA+4lXVcHqX2fBqdF+6Lh3ZcPIPBOD+xW3hXlr7Qf9ML6S+LdZ5CfwiOvT+jka7B6XfDBUrHex3kXvDXe+UB5Mjsg57IZYl/SJtOmZz+X62o0hOwUaVQ5sKgTOmAMPqKk+pId14BQA6XZAVK6VoDpsiOpRwdSBJujV3WXMHYaNIh3n6x2akw2S9QqqLD6JpW1IF/yvPjYAoRNISDtThhuM9yuNXZXXnklfv7nfx6/9mu/hnPOOcfXxJ177rk4evQozj33XHzd130drrrqKpx//vk4fvw4vvmbvxmXXnopHvvYxwIAPvdzPxcPfehD8cQnPhHPfe5zcd111+E7v/M7ceWVV2Jvb+/2oAMbQvJi1jHyu45EVXjr9S5FZQTX+1edHOsC5CLTBQiGtHi7Guz4mriSdlXpVBwNfjZugruuK/ORHXpZrqEPU84zgs0A0OlTQVdj0drR4CaMbnjPR8TWDRabpBNsNHr7yQHljEag2y0oIwQ6RsGlGKBrleSYE9/NVgBYX9MUMkzuXFlf/VIS/0lLSTyS2MiRLp1kD96DAVYZ1bJWAs1qNAwIwZAbDJZpG1PADSushr+b5wnTuykAKSgw69lvny7TxeQUHOJduCIK3aGUyus8XYXW/752SvnWvpcNmkGN8jSRQX5lZoV8Tu1xgb4Ht2yK7VOOW+bJ+av3+YwBJsGC7vgmjl7HYMC2Z11rnQ2PvHtb+jTwYqx8KLQToa/6M4qT2yHlbZMp4xphBiy+BrRBmx5TR5l3PPdYjbqPrqKNr8x0AX09p+qAFVcz55vLGYMfTtWh2RLRV8dG7bn2FeVQeeRyYB641b7ujNMAU/nqs0Gi90E2vQ3pmwFRTmRZia4drXptfXlESbTSNTTbFDJF7Iu009t5oTuMCa6jqbwmP0DdVl6gZ4iZwcz8F9wKZzQoj9zhqfdXDcf85iLFEwj88LpcPrtNBLre6ppyDxbH3k9hE4PUN1nnCKlb+1h01sWCbfB5yjuXEQ2xDdd5Pz1C+GCpPc5Y5X47DdyuwO75z38+AOCzPuuzwvUXvOAF+Oqv/moAwI/8yI9gGAY87nGPw6lTp3D55ZfjJ37iJ7zsarXCK17xCjz5yU/GpZdeirPOOgtPetKT8L3f+723B5UK7LikdIEJ4mmDomzEQUHK8qv4wKj1iJa4GXGgC0M/6qGWdeVWvAdALGWnRWFAD+qUTn43JMciQsCmEtpKZxF+ufCpU2M26pARnwenqa7OGwBjvZH8VsRFja+3oRpr3lYeaVbazZUIY3HjGdrQOvyatOsOrbWRj9BoQUwPUKnBCcexoAzSVHOOvnEiOSc6NF5XwxlGbh5EpTOxGO1qpQUtkO1yT6bXnY2QqUx2QOeTWakOx/u2+JqlvnlCNsOUlBUdBZekewZDGTq/NYnBPukyXUix9wuIj3oOANxs4/wUISOOaMGGy1kLRCh/KhsW6A9NTYGsNmlbA5Z2qzqs2l+6zsu7AXJN7A5xmASAAkGOO+n+IKvnYEw3fwCoxzUVgBtLAl6TdovLdbBJ2n90oobgvOMawv47bLigapFnMg2tZ1+qV8+7YokS5cTb23a6fGMRpK8aj4rWCcgzkbceIAsfehkecdQ3xfi6Y8FTeRwCJ7k2sf1z16SPQnCZ8AvPSBCtPkWfKSbyq/ioToy9XJjKXKW2i7Slz6utleTHJGgtCHTmezmr6HWWXpe6ACiNGpwBgcc5qHNZncm8km5vV/urm2W3/1knHK2C+UHwbYB/0Tl2dxbwHLsHfM/3Y7V3JEbhsp5g3OnGoIwF5QAYj3Rl83PsRKkZlJWxYNxp59gVtPOgupMG0B1cK1sRQDv7Th1FM67EjULWUu+TgAqoQr2ysFbNlXeuk1UIWM+McIcRW75HpS6ikGP/Hs76Elw8YUcBXXUe96AAkkESkVMc0L971k/XMlo1zBMniMZL75PO84kDmoNDbgeDpEaTDqlNofZDlU2e7RmyjrPgJU7EL0swARiGdkZbQIjTT6zDU7atTHJ4Mc0H35EYAyF0XqpDKZ3H052mM3QXxKkof89xkuGSnKYEATzvry+uFhy8+X5UhgfN4egOlqqN1R1pJksJSlufVTwr5bpGI9yaph5qVMVF9z1bknAUfnjfh2ct0lM6PSxDPBh3dyedzn47zCaoTHMnqm/qiSKi58KpPIbAMtgN7vRjFIOe3XPnL7SQl3RcYus4UCjCa5UXlwelr9WRz7jLcqR9JWLRZVx+w6qMeH/Rxo89C69yrVNsPjuz6tWFwUCeTSndzmJlvmPVZUOmitVmu7zptTBImz7jvxP/GcgE35kzcYf5GgU9E5XPqD8THMqmBzwaIKl9zdkp/8061RZneVK+Ipo+r8N6+0GH5Ny68OwoPLeZ65m/Al5G+BMCdZ0py/JtvTxQ+WYf/AidY3dnQ3e86nXRHRXtVTNmXKgYBCpBiPj511sMxx5wu3vuYE5FKG6FwpQFVhQNgK9380W/Exzm8JL2SyojBtyFe4ZXfN6VQoJQKoFnnOaEUX9rd3Chs6S/wkJsZJxkAb32gRsdm/Tb3DWog0U19Fa6wbdC3LgDtTakj1miR88NC9dTW2yvOuDmsOXsrgm/uKlEgro+NdtxjR3JOpLsAyjmk4DtN3nYj1iAOq/GLxNehsNrWb50/njwofxuxXxHbTOI+uqk7tiKO2eXMU7lOZ8rkzxgKH3zST7cleuTnGwDdBqxO39xyKUhLoGCt02ZywbXdcx8fagHkvztnSaPSmbJ9az0fidOHuB5FsU6felVWpWchDNlwsTmcB0np3zIK7bPdXjNYfmmHlCO0W2o2Ai2WZ1nk5WJHrQvni0x3/CmQV3PWunAGWJPpR9Jf5Mh7aMcMHTbLK9hk7rcKq06n4Fq63yXcuOp05Kzjk22jBWWblfof7TPQpDcBlqu78HAkLfoAZzYeA8S2Pdq/9WmMwOW6gVxkXKz06vEnd+1Pm6WG4Uu4qKBi2Gy47p+ibgH/4v0e5ipOy35cPsicjDx6RBaqWeDRdpnILzRSHmU7YTixXvBdqBvsCAtQF9LKEG705Rm4m4NzujAzp2G/KbQhRf+zghn2ZY+768O7rCmrFU0URwxor62Q8+haZIliypdMfV4A0k/A6IoqriQ36oYZeZewLPhqKOo0pUgGGx3xkK8Tct3xnSeeh1zGTIJ5nqg0Oqe2cYdHASV2n83h78p4oAQ+wSQ6STr66m8fQ2yBDf0d/9x5O9Bg3VaA59lLVfvv74mRg+lVfxg6G8sgSzYbhk1d9gSjPZ1UWxI6AZ8ukfX0/nUcbEpXymX20ZHMMQ2ObKgL/Dup7IHQyw64gfxch2JZqPdOBev16f/wcO9C2vs4sTT+FWOMltdlnsgWF/rpd4JgdaJvgAtM9pxBKrOGvsICHpes9PFdUKDlLAjkvc3FVHdbOSZsGZvSmRu/7oyl+uSdMtae31HrPkRJdVxRD4EXkA/2TeCM1me7KDqtAd6KrtaxqMV6T7RiXC2nTfQysnaKRswPR6IdRn7pNcd1qUa2+pt9qAhHT5t6BtMnMbKDA4OI1MiY/2F7ypvaktaf6UugW7Y8/XbM8s03Pa5XmglyWblqUNmqPJAvqQ/dDkICtnqZOIkPJOCrjBYyqxyXxzpV181SUxk26cnPViig20rLlKNq7jSSn4wqcADj2XjYV6XV2boc72x/jvg2frZ+0CCPl8rOGDKs9PAGR3YVdsnp5DL2gTvIGVGZszQGafGvRsVXu8OV1ruCqoIeUDQnwOq0Ic3OWRlEYVl9iIokQpnVgx2POtAfD6cFs97EDzb92Ej90ZMFLMbwY6TGtQuvNNz0MRfxnUJDGiJK/0f+V7Q1/8MqbLm1PrZbJAK0LNcdLjW+7A7j7j2kceHBIOd/GAPIMRYEy81EOIcfPrNnZv0ResfHssSAl91WLDo0EeRfZbQKYuAQ/IaTozwvxlP91E6GBEeNeKn15pD53SWZ8QAD4iUHma4+tEUsnuWstWyvSFLzMAhEC6ZHnTZnmRzlSeqVx3VQEvvA+uGls8PgIXopH3lAK/9K3P1G/o7brWfQ+BJnsnz1uXOs2ToOtVlokhwM/UI1SbU68o378MxZZCo72sL9jGIhhgUrdNtoab7SpeJ8O5fPpcDP2nDB0ABgdKzGuzXQeyQFdcdLzP0P6d1RJsKLY6TnvtGn5N5qf2cdyIzc0+Z7hvTLNbhRCIQrk49TOMxKBN503cSzwbrTc89sOWbX7Q9JU+zZOr+VHfUNue2WEdJz+h1yHX6skHwaAGT00PfRBqUTxII5WlQrKTNImXVBqpAU4VyUCX8MqnP8xnCc4rVRAVJy9BxHTU+gMiy2pDbCGd0YKf2uV7ojGaE3xkSsy6jvKPVvZnh9ByUW8Fx0WbpESJU2rB+JdYRowUE5woVhiE9p9O4ecSV1nMEo239frCx1ssH5ZUMS/iDllOLJ8QQP2bNeCun+5X+NvruUxutbln75UEkijs4z6ixXi5cpQBwKpTBgnH9W/3rJAlDxAEFw2f9tx9rYiXS1DTcD87lM1yrpQZOlZg/+TwvjvxOOS0eJE6CS7R2219fCyRBoBqwIn8tSxaC9ZFHiYhlCgMWsayQvmhWOG/m6TR2OuFdIc5PXwnWpu0mU/QC6sRoTNnYnCPtzkWCyaDfTS64Ns1xNr9fxlLlKux675UM3OUbEGI5pAFN8fZ5PAwzyyl66p9cFtJknFmsvpEq4uU6T72RFKe+9o7VB19GvuuaX01DuHJaaxtNb2XgVKSzG51eGYMfb7DRgl6kd2zc1VqcB6w62Z+x1+mDp3WfmqU6FuF/dRKNN5C2+Jc2VcTARHhS5Bp5WnoAO3kPbqLfG1UoiIN4snUL6OHOk/piFzWblNowxPYsfc/31WekgAtA3xHL2adcV7ZDEPOSdKT2n9yf85NIz2Sfpm3qAFhoCyHADC/0DEziFGgo/beVjjN10+MStqt0C52hviwDtwJndGAHZVAWCv1OYyBrI0p7fnLmDxBHPGXmvmla3joewTFMnwtARVOHxHPuIL+zEmkgl5XChK6CNnprzlKVRIRtzvEGnEsXSo543HmwPe2DdqGoQQXEavZ6Q2Y0BKTWn8m4NePs65AKkuPtD/T+l0+Dr43sQUtvN6z5YnavIRpfjwQYJMMgnV/UuLszZRGJPvyv88YTN0qzj15tOr1RkKbjbLo8gddDJlB4ZekPkHVEOiBidrQx6JBBUAjSVrRUnc5i8Ld6KY7+pg5moorQW7pzBBJPC9q0a+m4K+2ogWI3+PQIkdE981JcTvycNIgOSdnaVuWF49twtTU82Jh1FmovcmYxGPzSg/VV52UPSEsvzoFMqEiabnId1121IJrnLLaCHliylMqq25HS6/Xr/VzK4hVGejQACfxRffEgsB9AHRhpfflC/d6qYHAxtmoG1iU2Z+yZZXIpBz560oC+TcaUJsGpB9WV+M7r1nYz+mF9qqGvfSS9oW8Q+Zd9EtAzUQPikof0rD9GPgxyXWZh1MYF28nHc0AU+kzKN3L43mCnYebT/ZnrVIMxlh3XiDNfSGXbXzzf1Hp5i3SZ0qm2xeSa6uvMtPNkXaL68OY7Q/bRZnyf+ybBgfVpguU2wpkd2AFufEI2Rxk1Z+OkA323ZrsfUuJUwNYROlgGxEjKPa9fAoNwXT4LO1KMi99XxZHRwSSISp09EbJcZozl6hsSmmLNpaOBPqrjVKwYPp0acVwz/YxWDlsnkAyKO0PhP6vpwQJ8CnMydZkduyrbAOiRE74mjM6m1ctRO3dOev18J2uYRqCANHKaw+vrdNQqYkqTyIkxKCBfPPsnBJHnsoxAA4PKJ/M1hZp+qWvNBO92DS1w9wXmYcqiTwHH6TJ4Rqs7+DblpfQ2OQlZw6CXMo2q8sTBBDNTY8/SOjBDT6fLw5Al41L7VNpkhmvXQkYuZHOdtzwipoQgxUp3WB5QpsGTZ4RkujnQPuc4KIciu2HRf9ajlQF0nIEv/QHTYJA64nXKW05yMOdyQDkhjpGfLubM0rX6uA7TbTOz2+hBj/a3qlOtT5ig0R9loMQjd9D6p9PbUNT1r6mtMNgIa7Ci7apq2OW+b24r7gN8HFp0g5gFfQ+Zuva7nzQgNqT98yys2jL2C0FmYXTqVBMTYamOZM/mzkgrrUxoE/1770+5rANznfLUjLfU4YGj8jP41UQf7829MUmf0/pcTkvQM1v39if0C89CZo7taJY932f7fOvLEOvuJylIfZbo3cR7c2vzbwuc8YEdgMkaMmdmEEjrr+9Qg5SNJuReSp90wTFpp6//8U7mmin15/IuyW7YSxRgFS4FdbLyZ1o2f7K+uboguDI7MgDY9gAhTOOKkdBsUOEUYXKs8egJ63Rqqn7sdbJendZjgONGLPdR658QZCTa+85UeS4b71bOO8x6ZtfrLVXGqi+yPhhwh0oGdV7ZyoAdm67rMEcurmkbJCBpNIepw1Ym8CCvgSMvh25Mww5DSP9JHRqY1x21JfJZ6p4EIqgOT3fTBr0Lz+tuS4RgLgS8E90V2v2S0KW3ZvWpTBddG+qL2OWYkblMR0WtZXZUrnUNGb+M6UIyyC7vDJ6ys2Uh6gEDEuodg1VZTwcDyr7IuOga5drLaaAktohZWV1/OkllJVsIIB7LQb6StoaLbzqgPW0F+1srZK1f++cykvtcy4bp817XhP9tHWf9XfzD+s8ex654WHDcdMENKIHO1kAoVxBlIDjw0lSgn6nntdB8cPCmvkMCT9arB2h7EOW+p5fTQb4GecGekrQwIBG8C/p6a5VnrU95orRkPXaCEQNHrVODIeKoA5mcvVKRzokD9q8O6pRmMdv+iKG/llBnpkbhRYJJ8MevaVNglrce7B9Cn/L/dsKZHdgFY9UVKwR5dMKtc3JH6gjUgOkoUwx8P7pDhBLinL1zxMnx4f7ROzItWp7gxeuW/vQRVbKCyXq8MOUiyt7fDymVlW6sJxkz/ytBIawxwDNKybiG1xVlRacBUCsL1J2POsqaCSZmU6iTvtWzwoRhDDbztAerkbUaDOT8yJKC/tl22dnOGLI1PrWyKZWWsOMOPShkGxps+Ui3IcMdraQvG1U+o6/tsuT4NDPHhd3Sl5XnxWV2sm4vfQ+vyXFZ7mwvG7XyqU8YJLkAJdryNMt2ps0CGIqflQipbgLChz59Wlw2rAgOySn4eXFA1FUdkDVaSgoefRfqEB+LG5ykQdLdnHTZlMi6FVxm8lSeDhpCtouOGuncvPCsiUqY951n1FgmD/RIG8WJaytZr9s66zy2GDCh9B28LioyKNEp8a4CFm1FQR2I8NqMPvffFnGQoDcPODRL75tRWKfipDaf/qHRpBk+wDxrVqQPQraYzKP/aVnBfMyGFfiyHa+roA9ID5sZUZ5QFtP3uRfNh0RJ7uPkkyZ2g5ADMsrO6XDVenQAVGau6zNFys0lRWhDBlFjyXpO1h0SzyR6+j0E4+Ln+6Am3qu7/mfsluozLx0yk3Y6OOMDO0CNByZCHQIXeSZ8bwLg/ciOFwYz9S6zAC3Y6/f7lGUyAlb6/SQMEzzYfpjqS/TMKQ668IW6ZP1APJ5iZocmHcHKpgqjDgrC87khD9AdYUpHh8CM9XK9Hw+ILQhGsk+jKr4mxtUQlMeVuXWSvEOxsG3udgPicSuacWh1FRiwlmk+Q3eA24JyIMeZCH+7PFrfwj6gTuHpmpI5I6mZTlg0IiWVbe1MjIiuHWkOiwMU0mortKyi9bq8jRINsspHKxOOeXFdadteUr/47mV+Zz+wTwicvnFZQJ8aFyUx5XPD0dejCS0ueppN1M1NDPD1Ottg/9F5BssOH4QY+S1t6fl9rM8HF+13P4KkyT+zBaU5e9a5RR0olNiGs66ka+3A9L7GtuoLg5rp9BgPahY+SRY6y1udbrdO1iiE6sCVn+Qnea260WjVqWuwW2VKz9D7STPSRiSIQVpuAl2r7FnjTr+tDditOo61DNKa/vjgwXmFblP19yjB+BCvqx+h/ewIw2c+6lrHrsth+q70LtFDiSczVGrP6MdYnL/VdjAAiqIPBoslsqvfT7zw34UyQfoxXR7DZT1pPdrsGmKpN+DMv6D/6LLFdkbEGTOlZRAc+JzbcLmuKNGGGTwJ5LFAoinQIWaJ73e3Of6l9iYB7W2AMzuwI1Obw3A735gzSekCXTlUoax4VoWLlENnWwkRvToOnkUXAq6QzurthoWduv5HhN2FBaL8xHtEVEppw3eTBoPScXa6RaEmuySbEZscoiuCpaMMjuo59egG3g2erJearHuzXm+eJgNqH2zkeuJXUS1hfRQA4W+R37ZuzoEZNZ5lqECag1Eq9d2f7c9ly+WoiJOv9fZp+fa8/B72hx6Q8H7GoaHR+yi+t1NKeJvBwA+oU8HOO5F70YEyQrJiiH2ugZnwRM//C+srpX7TvhHZciMo/ezUCH1hA8i29GlTz36XxpPehp//d9j0fOJxAXpmTYPgdp+7sotkOWuwITQzAAqBOLpN0fPBlNYW2DKY6Wfe9SDA2+Du0ub0eyAnZdV+aVCi7YV1n3DbRnT1nu2MVX6SnLv+az+j3w9ykByjZ74aP2s2P0cenR5vZytllf9OSKfDbSAQN6MZ5PyxSlSxAtsbgb2x66GVbh9K55s6buVb16P6XFj+ofgV+NreQlvWBvzqjzxTp3xjoAdhlciqi14ODrQPlGPUez4v69Ys+xl9Xqd59dmCGEzSh2kiIbUTBigl4d/qCIEecRnS71Q+8GcuGErPYJR+LfDjYgJ/bNoHms8IGxBT/Zb6wgPmxIfJdG7uv9PZshm4Xe+KvcsBCWcnjJFBIYDL28D5Z4C/XxKtrpWhvzRb2pprG+gjQ0sdJDAZ9aR681lEWKGP0Gm70toHF3QGNEAfnYqSh10/GQ8lqWUls4MLglZIZ+kCOkKmTdp9zPDbHSEC/w5b2wQUmFl/Rl5sbWsDNuIQWwDn8p+VQ6oxBn0jgCKveJLPiQFhAJucir+SaiCz1cIwijGXTyeRyAwARnN8PEM0ACaZhkkWihkL8m9sPN8Zq1M8GGrWJr002zMbyvP2fAi21HKNJWRLnWZY6Nc6jdOudxa1/uxHzJjwDsbgxHpwYgX+okMNNjRjO6JnQbVHdM3cKM/NlCNPgs7O6e8gKuYZ6XZPHLsNfJUeb9D5yyHEVu/18ysrr/sRMyZkd0X141cGAFt59RVx0iCBGQUhwXZHCf4AW1NZmqx7n4usbiP+zGAW0EkXn7oEUL2JZqJJH9e5bSWgam+fQNu5T9rqbvNazt9na6gbXdQWeUeKXOYpWPKwvbarbFg3ulyMQNlf1aqZHQsqbPUZ1QfykAGktdf8US+OjJXeTamL4d02dtnRIESDew9+dNel0iz23OWhsddlqgUhHb/eHwxQJlk7scXGBINJ+2Tr2Nkagt1s13OfAP39qcLD7BeUrxrocYwZ6udv2q/8ejOh0bI9F96E2TaLZYs876ZI+35E0DtNLKkbCEFmEVrYVJnWp35zdhnZaeDMztgBfQDWnGRwWCo4BEMwfrVcM2CybqQbrxklaVNYfIdqyGC1Njz4ycojfzr6tYKaUeLp1hQ+CsU4U4fSNjTjxaxKnupDeoY8kPoLU9bkU24zBYZBzjwo6MJtre6OS3MsPAHeA5temU/RGQ1ekbeElI5DC3rc2XMKirhQITVDIcGLL5AN3k/koGWyfO2UrhVC7HeiFUbvEq+EINsXnFv/MDk+RxnrQWLCtTnxkJnivVMDcNAc8bbMT7HqM6RV+YD4Jo2iz1t3xJ7dZtZnhX494eWZNmbIeF83IbQAQ8lX4+pZMC4Y1wwZ+cFMNK2ie0/Si/bOTXPcctbPn2nTtJ4F1OxBGCw1L6dTxrxWVAa1kztvJ9OiHAD4VGu06NSPasdkg4LUqwGB8ay9EfVtLSiehcZmQDk11CAEqNmxUwPKfjekYXPNpgbuHuTKDlQckG+og4oBNaAkHmuD7cgRMlvUJQ5r62+Q2LE6OGlBn/fDiPC2oCz7LiubEqf+fKqWomK1vhVawNduFL/r9phGzP3AKPRJ9s5l+2Cozx2Uqoe0b+zj5ju6rSPeoiiqy+Q5aZOsmB7G66D6Ks97GZ0dEdnXoEUDEjcVzRfYXP3aRm6L90q854EppyLZzra3EXiRfFhoX+7726ZyckfaBBD4phk13SARdvlqc42eYPsZ7Fqfig8b8KgiluhjfUJvyArnxqMZOC2c0YGdvh0hRMmM+PMIQRkjQlWAasBX4rzV4aILNwCfqvUObfW74AZp7X85/ep4g0Y6CtTEGWeQ4C0vtlZbMTEWjf7QRvvO4G6S1p751NGM85f+bNvrqcpSxIh1JPOJ61y47IE04NMbrgTNyPoiazpzao/aSXX+Yqxnp39Dfp3l0aZsewf6gutS13wx9vKpNHU+Wg/EiDLDuO1TziEjRNwBhJ51J2Qd5bF9oTEp6Gv+PPgSPlFvRmlP1gFpkA6Ttx+oc2P7EMc/wgO1wrK6c7J5CxuAvCDcf8haMJ+WSuo0nT4vqR6SlfqTxVeRrpDB7w8Hm1Fk+lDRhb+mjI671xN2o9LBkT8MiLO+Wx/YeHZEEdUDggPBjeebKPNW4FPZqnv5vc9lRA3ufO0axCm3qfCWHS9WWiDW7M6m+OvaGHz56+QO+v2yKRgO2ppUBqOb4ksiykENMMv+4AMGXTJiBaLz7XMLpy3cY39y3Rt3qevyBC6rYMZf+EFeWYHvPDVA1spaC1wNWCG+MtDga2+pYyZ6pkcGMf73vlJ7QbEq/bv3DcUgLw3pqMcsD32LoWendH2Y67e0TXyLlOPJEhqU6Xo2ypeqnixHCHS2cu5bS8RX69JpXVV1D7qFbqXDlGcJ8iHDYZBf5JpMl+dA1OONgnDmpdLpj/iMhJRprPCkytjpdP5s4VPbtxXO6KlYTgvk0YwHFsx+pRSTUeFD8FL8WR20+1TDWKcPyqagtHR9DRAsGFtVwn52l9wTx5YVlh2YN3yoomqQQJosjY40VTwZHfAZII74mMYu0agTp7l2iSP7QI3DXPv5WSffZO0Up59HwFbN4A+iHRM8mrMhAs0h0rGrseSZdCVbByBO17VpPs+MtOyXraI35ZqnEBSY/A4WyB/qzoblfPq60eORYivEqTU+1M4c7AvOk1PR4bbzosDfgTmW3q/AZMNID17EulB3eCQEHR8zEAx8RaecDQxuWr+SDA+Q150nk12PVq8VZurE6eSMnmdXXadKNMi6dnaie/EIDmsNGEqgiXiHDQa6cWBE5A3gAxM+z0w2HaJmM/28sdLlD0Vw8Pa074rzxTO5nn2Gb4go2bG0+t2OCk+dPxw452lvE11l2ZDm6Y4xO0xfsD4IjXyWmcWh06MDCq+LfGYmVW1Z01/SYyNQxqHzWdYBU7TDmqrcZyOa/UfPHHrBSJvLOW9p9pftUMeVV8L3viRD6qfMsA5IX5d4TQMhzbLZCjEZIYFCyCYZbU29ETJgm+KzQ+7niJ/yhJVp5lV8g/pE11FJEOhGQA0+Z32gsDlnIIOPJ4oql80vQAJt7ZM+cO7XrKCf+yr1uT0SPmi/+jp4bTP5C0fV12x2Xbg9x56c0Rk7F4ZyyPXGjGzMPZNEwWEHqQDyrykiMxAa+IUNCPJchzI1bqxTbIM/XuT5EoXM8WyCZu13aC7jNjNSClk4cYw++kDHLwRoglug0TDJqqiBCHxFx6u/TD2WzYc6Ao1eLrynQTetLzFrED4I0cyo0RDlhct+JIQ6NTFceZNM2KUn9DlfPevQKmGWsWhfMUBIBsiAMAWnOLVPn3JkOe2HYCTrFwPxgo9Cwwg9jXJJaFj0H4asnRdSvP9sbZKv3t/ygBtJxy1NK7IhH7E78xs+iV41uLzHzRc0ovJ8IIdZTNd9yXSq0SVP2A8mpHfR9Kwyzy5zJ8sCjk+Jy0hU7xU/Qw9e9b7yq3Rc++69OP3d37BinU+Kk9NceruSyfdBUsrUFOGd87YNIhQfbSdPM/vg1x118YEj29CBCzMqgQe5/9H1bGJvNHBpwROD+WDDPKNC757wHzFLDyAbfHbiW1x80CU2P/QDLws/JzyUYEN5UlowOrs2S4MQkhLse6XfpA2gyaccazMJ2sWPeCCriQGWsfi98iQ6RNpa11nxD+rnvKzaMsRP9W9OEnVk2xncdb3RL3o2MUkpcZGD0xDUiR71AaV1OsjM4DfzxjDh0W2AMzuwQzS2gAi4dLremyxCpzMA5NUucAMYjEG7nqcHXHBV0LclBFYqWGy/ZAEQ4x8FI9FShG43gIKbC0SntdLZd3XSWIYRXKtHj3BxBcvrF9RJ6oiO35meVp7Q+OnUbeJ7yXzxehmcJeOpisL2xaH14LIiUqRe2hMdgbszzMFMCgi8AsVhrpwam2J9zWFrXNdhalCv/epZHBqPVbyvmUpmMFmRO8pUPxfz+9sTaHx8alH4ocG0IdCku3Z1sOIDI7KlyVGcTug6pJf6YvJavzp2LewbEqRAN/zmxXn0yLQd6YtiYQG1gwzCsrPnsRg83oX6FfvRXM8Og2LwwDMEI51FXcYUP8o6dUwDc8UB6GsEaQd06r2VK9qe4x/5UbbdQE3kNQgZvI0weNpKgM17rrPd8/apNAtyFwYiKajwZ1sAFgbTVnyQ7lOQSqfUUbZavt4fmQEsFjKmead6z/aiZ7ZJAwcdKj9sl3zQ/hbawsBC7GPZRjmJMiTn6il9wjsp6lOFDLS83FgkqSBHfkH8g2TitWJNMkx0QOyzH8Ujl/0oEVnrDlBvS7DbYYBG/mRfpXxUPZ4AD6pG7Bcuawk2nXU1/6IDHo83usx4ECvyX9imvNebeIe1etMR76Fwxgd2uoi5GOqLtwkaWKjCiEKpwHvQoZ0ldYWRQpFjFXj+kvhEPydNFVQzdSpYra0+2pHn3MEJHnTUTaGGtmB4NqAPzxr8wFsqCmVM1g6psqpSBHzVkMjoKu/Mq7xoz0gArHiF6Qve16yE9a/BOITRa1fyaUYE8hqy6AQLIGu6uuKEnZVKh/YLp418cXVqt/RnPGtC+WlOMGQI3WCXxlvpJ0idimcRB7uiwy2hr8q2QI+H4ZSAxEMhc5jXMfkGF9WjxnId/GgWq+uKzdsjOmrRXb+uZQr6tI/0g6/1knJ6blxfnwgUpLPtKCotc+meUWSdWS1OY1qxuhFgbXXBPwM2Py4lvVFAM7suA4hZdMph6a9sC0GU8kN1mG2srGd+ViNw9hY4a9sPvxY8Ap/Zb1r3VppW55zskDum3F/BaXOHt3X6WVYyGmq36/1ptjasT4TYB7ch1JWOuw1AodwZ+kw1aWnrf3XaHKtmG9u7Vq0gBPohI+S63eVNP4ONIr+AGrwfdFsXPoXPmoly2dZ7Nq2DGTqyGYDsMg5qG3WpdNvN0yHyAENJcX7wqwaq6oBM6h21A0pcSqABtKxdd7z43Uq8D3RdEPl2+kJGQdrP9kXL501MvFfQp05dgaR+fazIcTZcsjHAGVqavoe+HDv7ekKh9H4m3jPoHQZn9Bq7HkQhZoMIh42Sm0GnYe9bl1PHozthHf1R+cumeManBnXmAm1D7wUP1vScMPCTFgG9L2UUXqQz+wLPUkeOrV7ueisc9eozcwoq9PQgA30dIXeKqeECPIMXpjDTKJMZEI5USQ/b8e8FbZ2isEIdtfattiXvas2npHtAmdZVelaroE/ptcxfUW/MIIXTB0Hx0QM78qOgrn/Yyn3NTGk5a+1JAOGJsS36Vn3ySd+0wM/EawPq9M6qCfqmLZai8VDe8TH2M/tqpQS1D9kx6sHmIEaHWVNxsL0/SgjK+25A9IwFq86vn0sOEkDdWOKy2vHK2e/QlvNIBb3+ruuMSj2nDYh0Uu6dWW6DK992x15mWwkxa0fk7I3VaaPxdsfq+Wj7pb7ya9XW661r8GAjUNp37/NTK8fFdjhFWLodGNHOlmsBJuXHADsYUG5e1bJ7Y6dvZXXXK9qRHC17WfbbDk46nVXj1QbdptCukk+0fSFa6rfHHe6mbnaQTqtoX1uXkXakk+NBOWGFpHEsMlBu0/v7g7xdpQgdaLxGfwOKtX+i6jpYsR1DWY21f0ZUw6TLO7hRghkk0k2dFBkMvGk3fHE9+4MbmNbWnbbymNVsq9zoGrDqs5JRVxeiJw64vejnRzpQlXQzVOOzteu+NIXtZp1rLK/HEzWGuEyQ9xb1UHmVr6PppqxFY326Btrx0tS0TpFzIDWiroum/fcTAtTImPOIy3a6r09GMC0TsVAG8862tc21rrRDpMNnZGYeDfJAGm8jnNGBnYOPOBANUftzB9pkty8M7UFGiPgL2oLPdi5RMzy2smq8raAc9PLmlTchT2u3wpRPgy6TXRlCpqW13YO5qgT+dUiaqkrSnHfIbRPTsU/VubK7gM1Mr2ylzT5kF+WU9tR4MshVJ83NIVRW8kDPEQT8OQYf1IUwRZAMaxkB85GsKKA6C58CbW3oWYUG39lW+VJ6f6mRgXwqEJeCGKQ5L7sB6Ya/9O5hQLhC7zduGGjTHD3zUZ+vxrZd3G3OfI2+sYcf2xbQ0NG1+icbXPx26dNjnMppQQF1w50T9cLgU+qG5mB4IDT5sGbTxdcIelw9tudROq8Hw8hDcmd2loJrxaygmFX6tc9WqIFKC3J9I8GmBja6kNt2+iufcrd6YHLzCmW/TbWc1RaO7aLTcnQUxwbg5qGW2xu7jWndjx0A+wNwSzsjZqfhz2fHUo/+8AP94BnJYgBuXtUyu2OtRx3vpgUEBwXYH+q5amNzXG0Xq+1Z2xA0dtnfNdgufKepMfBEtQ1G2Ri4qcSqBxmsojmg1sdM2AY14OVzOy14akwugBwajL4JbRe+Ua1vaGl0bUrl57FtD3bZNulo9KOgHp2yQn+f7mD94GUYjOte91euntgx2K7BtiI3bjcq3WoeUAAc2baAvfKcwsODi7sNFDmmXZBNLWFjAdezSebTF+qP2niyny64cL/ifNzKeYuS4efZgfkMS/oFoPJYzy+kcQ/xTx55NZ55ER69I6jTD5eRZwYKrzWznOnTo5yKvDaPs0cycHTfQTujA76mVJb9EOtsOHVfDM9qev80G+A0GsuV1DVyVqoPCkrnNaTeVoknilIXnw7O6MCuptzbd8Q4RiN+BlY6xWguTI19nrVD39KtsUbbGevC3aAHKcV7JmyO4HdZV2byHAp3aopMDb0eF7ahOUkV/CaQrqztmoXISL5zVNuUN68/dEbyi69ro/NsNDEzo05bLkH6wYsxi5XaK+q7LPYNR7mqUJNRnjpjdapqPBmgqM42HtgKk52/PiOmtJH1qtziTL3/LMohUJcH2CoeR+P4Qewo+ccMBTBdQxXtrqwnBGxb/MiKmo0zl+thA8/I+vRHmmK1FowrH503LdDj2hPdyOOGOWRIENfiMZMg0yvMYJqhHhshzKgBQqlHYEgWtAaYjblbYLhlqPq9M9ZPBjlEZc0MdOkDs2b4fb0Y+3vVj1b2QJWZkv0hytSpoashHdXaaoZxhX6m2y0rH0xxEGW7I3AwdD7TdPjUfml2S5yAAeXU0Gdl2po8m5ll8CkrZsMOmu5uJUOwX/vf6QKAg942+8xfodfKFQPsLADePzWQcbXkq/cKUPa2NQupr0JbSYA51DPsyv5QWXGkBXybgnJLE9y9sePYbK+VwYNAz5Ruy+SFFHZkW7OVxYBjHB2hBl4GFGa45Q00BYBtZD0efUZp8nfWtplti/au9XPZG32Xd7BHDUJAuCk1CxyCj26L+ia/ag+IUxmlUjNZBiNLO2SjHwbDsOlyBMCDHlZP6qtudkvVM2MAoKcMWA+gyDh/Xn0Z6sHQQ32eO6k9kHSfKYZNkeL3YhNe6iDfjwOhjvr17ru4tMLrLY64dH4rj4aj1KdoMHYorR99018wIJGPLlMFIeDUJJC7uda+V5MzprcCZ3Rgl9c8iK+cOCcVNnfizJAgCqKVJtsSbQ8HfVGkG0dHJOLlQmxyfIGk8V34+V5GkkG5ZtDRBMCdhxLIIAPyxgQeuKlpaSqpj7jbPwYVPo2sPOgn25fWdlBwFWDnf1I8FpVRSRltnldWQj+GkcuAnsJmWaLAqc/W16ogYRCrBtjx67iY3PdAnA63GaWQMhebpDwIR4jI9WnQ3K6Tz0Oqj0Yys1QDlix/dNwMDtoor4zWBzF5qkADe1ad6NLd5Y53MGBTPmgwH9fTyDVOo20BPX6Ga1LKRgI0Qz9qYkR1vAclnFNpQAtqBa8R/eDdgnh2G9A3VFDmeN6bDgRYn+gwo3hO5bt9GVtDss63OwBx9LRLUnc/E464wXXZdXELFGY1maEau30jnSo01BOTZKYG3p4hkj50/ljrK74XmTrCM/HG0t9rSmAGrBhss+rBvrZNp2alHqbNe9xwplNeB8P0zEOlcVvSWYnyebJlWEd5FwoHNa0IdZuJgK6rpduWJgcFgB3UwYa1aeKQgTPUgJ862oLcviYU4PFYBvTlFiurgS6DEbdHYmhSwOD2mgOh0mkLfZr8m2ar6Kc671gIXVia3aWvqe0SHznjkn0+kI5uyD2ILCU0wwyb4zqUZnNK118rPThLQaO6OOLF2YbafIlvz0hZzZAIStOcLkY6TVripwbpYfnUth/f5X6rwF9AlHnsblr7rrVjuT9uIwy3XuQuDJqpQQ9OJrvvdN2GMkiEt47g0R1ViyxKEnKuh5ocFtga18zZxNE1/BjdB2MjQuaBgonwtKyeLhxVxabBdCMlSkpn6tfZuE+F9Y0gPGA0H5EBts322rRccPJI7ULa8++CE9Azqer0xLlkpwP5qs9Rb/1PgvBgeJNyquwAM/2qDVpXPLUTc0fCzO4UZrstm+LvGd4grhdkW4pnu+600WAcwiMabWbS8q5gvtKpL07udFjmkV+LgZEH9K3dvA7HCvo6qKYHzLo573aanK16xUUCNB6VkTdvsLz2B8tr+4H3ysPD5EH7rU05+xtpxN54OKL1QfjBrxavq26qnKI5Js88NGfju9FNabGeKVXSSuoDzkZQF4ljlletBJ03k3uUOclGhWAECOuYpu82Rh94JH0rhnAuXhhE8ZpWp8sI5vo523rPlMh9dPkPxyzJrmy302yPsx1pl2c9GBrhkFoe0q5yVqygnLWpGeY2EK/HiPT6dTMaeVblPPFTHYTIccl95huHOhNrv0nHqQ8aetHCOlidn5upu5stbMoL/cQNY5SLjF/hW5zMA23iLZysl1acLsXED+bgLCXMur8Rc8RMmPpepVs3bxBv7ZcwyLZue0xvostBnNLveDkuLYHcN9z1tgPNtwHO6MCOxz6ooJAZ7vA9eudD089iCNmBWlG73TqNrxCrL5Lv9VpS+ChN7UNG5opfVkCjUdFRWBEnOJgIvig0jRZPqC6JD+SLjsiyR2h/tX4xBFmRNEhiQEW+rOCvDRr3uD7K3NhHHGS7uvDL0Up9GoJtZinSg+4DkhIHvPtHUK6eiZG2kyzop5bnmXxU2kld4mjpALSfHXFugEmOtv/uo3rKbDYyOVgpdMaZ1ZxSDfWjr6kTy+DBIEfgiR82wN9EQByK/zU5HRHWQ/pzoi88eiVszCCPgX5eYUE4MJfyTqNY2Jb1cmWm/aDvwkf2T8i0pswUMwK94VaOOpNlxgdTqEEhnQQHoeyjQWTd11ZZ79dRsySkoXS6RPl9PSv1KsmAyybb9uBDHBzvE+jIJQgITgiI9Ke6bbAqK3yll9o6nUok3nT8M07a3wIh9ftvsf0M1jTj7QEcelkPxBKUVkZxqN1ZvM9CYKjyzeUvtA3bgvGDawxrQ9kb6/T9QenThLTx3STHoMUK/JV4LYul0+fB/oifKbSdrsvoz48l6KQHME2Hoh3V5EErGzaaIAQgvE9br9k2r2/EZBDgeELqo89Qu8CEBf3kKH0hYqjyob4sb5LzoDodPB/402wBT+Dg2Zma/Z0EgEEH0O2s+xC4fQiBn9R1aNJhBs7owI5AQZ5kj0pndhhVqxHi88oJCnVTXBuigOXgLBgjDYRaB/XdMIKjO3tZu2bwEQ4/J0dPePvWp7UME5pIp9IdgsngjOKzxaQ9NVyyCDgI/RY1YNkUDPsFZX/AcHII09fOZzXCPHRY8HE/NczwqvGrPiv4U4NVKfibh6MG44Tu0HC4wkwybVRM0FjxmnVDyOdofyhH7SMMBCzyJwTAEjB4IOiNd7n2kW3L7mgA06NNBHkLAbJO26tsqKPQDJLKEDPB+agcky5jn9CAqcFqjtQAfzWVZmD6ujri0fjDLC8znuKoXN4L+ttUlAYAtoO6WaK9z7T3i0W540ClyT0zYr1rUvAXv/TzBoeOu+/CZ5sSyPuGFLUfEFVtgsypJ6ddz9/MQaU44DBYAtyZlk2X2XBQMusizW3qdWJrXZ/aBa5ZU0EIqRLBg7i1JRd9ANf5VHfDW10DubZuT1sfaZBpK2DcMc8AeSOqh9JPYfc6B+/KB7URGqQW9B3C2ZGzv1luQLN1xTeKjbessNrZ1ilYBmdtNqiwvWITk14jim6fc9Absu6l238r8AEoA4kgupQTsR+euEj9FYJHspXdqxvkAF/uMLe+mo3rICK/zrLyomdIvW5JxFirNASURE9kP/g+E9lj+7ynfMlgcPkb18JzLSzxgN/LOAg/LcuVxhMyGMnPng7O7MCOPitNtXI6YzITUNDXPPAay6oiikDXNT/mO/D6Fvv+UQWifgtBpuCoOGj07crFdK47Y3Ohd2PXfnB3qk5XuHBIfblpP9B1kGMmmsGkdpdGs7FR2fLuwHV8unBblManbDkSHEUyfRjWDa8lIfZR3oj4KiPt71Zes0ReZoi/naV5RNyKh6BMHNEk4LOOH4aeSbA1K0LvC+hvC47PmqHvI/kqMJLMDG3aAODI2B2nXLc16vqMZvAjlE6QGFriQOfhgwk6cQYXbUd07QcLNDOwsFV2otK8Z6Es4ueylx7RPqPIyBl2rmcDYoDvckg+oxtG+W5r9mnbULHtwXAZUY+u8cFBiefnrYT3gqs6maL9x/bZNzupf+hEqaeUE4gu0zZlvhZgZIAD8rc1nNFcsW6hhc5Zdsd7kMm+9+f7D7dtg/DDDWD72TL2tls3EfjvpnvYsUCn7Uhmv+HL59zu8v2u1mzOIPfXdUrTdjqePqW7tp5J3jNYOwbG1gbb63UUwAcoPTtizq+eBDVgb9uPgilWdX+wOlhofLF1f30bhaTocR3kuRXsHN/v6/WAOF1HOaGuAT2g45IfuT/p/LHjHdZtsU9HvxQDf+lPzcQ6bn5D9DTVYXIzDOaIq2ZTge5f/TWgnf6epY/0MQDPuGsApIHuXECJfF2C2tpPFkxomMlIg9aAs5bJwZr5R4B+cL3cpF4WzGaTD4MzevOEOtG6Ubr4fD0DJCuoW94PBvAAyrHU3TmjMNvWBmyq4lg2WisR4pWhvnewGiXfXj8A3KEEoAcQzSoUlJA9UKF2xR3677CwdVt6mr4A/nYAA/ykc9+qzSNaegBTBanhOPbdUAD6mhEDrMS3FkSo9buTF8MQok8GCUJHOEaFVYuz9kzMpshbFaqS192drczQFK0AdjC0Z8hv9OCcxh8lZrvoMFqfWTtHy3m0Y316pxlE22113jL4dI4NpW8cW1dnNcqUCNeQFUM/+mBdDVc5KP4OQDrSuri40l2PuCi+QxBAld12MG7ZF0u6rpbSrGAowDgYMBQfNVfHY2IQZGcuj33YDv18Nsr6QZ/eAETHVs1BjVV3yqbVt7Z2XFVxcQBaXSPc6ZaTxR1RMGomOqtBQkG1UGLwyLbCIzZQ5WMY2lETzdiDu4LTAMYKKg/laAMZR9XNCeqtAGDX6nl0TZ4LB0O+9kmepzi14zrQ1jJy8KSjc938AUNz/qUT2RxdxdHqO5PRjijZGeuRJ0DYSOF4U+EGdNmzAbbRwAjBcfrxMMKTftwJUMhw8oh886+l2sBirl9lhGfVeKRK4Y7hFfqGFdrWTatUgv+y6cc+FWu0so5hwLBf+k3ylnRtgVLaTuSVodzSGMqpQQZiIos1SK/8Kdt67Iw1HMvaYKc8unG6/v/27j26iuruH/9775lzThIgCYIkRu5KpVxUhJLirf0u0yJSr11CKbVo/Wq1aKG0iDxWqe2y8NM+PlYexNrfo3atWlGfehexGG5CIxQEBLmIBYGKARVIgpDknNmf7x97Zs6cEBEpySHH92utLDhn9tmz92dun5k5s0/wxfjo2JzhmHDB8ncNJAi6AN4hF2iXRKxTA7xDLswhJ/0giH9AD48DwbYRuXOTcbXY768KivvrV3gcDE7iIut2ZmLVJGmRJnX7ZdN3JYKvBNj9jwraEz2pRbq9wdU4G3OECVhwoSE4eznsQaCMtqQfGlHhutukDNJxUwLo6FXQoCuRhDZ8Hb6IxEP5+7TIPKJJd3jMlki/kZ53kIxlJKoqPS1scvA6nCfCuwPpBDOyvI9C1hK7WbNm4b777kN1dTXOOusszJw5E0OHDv1CddikzR64xI18sRL++iT2zNzkGbsiBANeumKHhjD+RqkB5HuQBm0ThoTxbxHZgUxVsAPTgILABIlc3ACe458BmvAJLZMw4RmaatB2WoPOvKoWPMEq6ZUjPAMPzmb8ZAZAZuISfMYRm4z6Ox8Dm7QZJeGBRYX1CoInsiCZwzoA9gpAOCSLicwL6Q0hGHtH/CQjfeUuclbj12PHvZL0ga25p+OClTtmYyeibEwF9v8xY5dTsKa7Biruzzc44Pi3F3SQsGkASX95QgEN9mCClK1PxU041pQK5puyQ1cEX2i2iaN/i9EfjkBS9uCgBPbzrv+UcZBwQWw9AqhgCAOBP4aYTQYUkE6egr75MRR/6APln3yohElfFfWH8kCBBxHXrpPBQLfhE8w2oCIIx/ES5X8mGDIkpewVE6OAPC89f+W30z8QSb0D5QHG8Q+gMfGTQH89jNv7p9Lg2OXeLmW3owZ7+Ulcf8BXBSitoAo8W7e4NtFV6S9MA8h8Wje4AqcAuMbOt0Hb9SFIEoKEMdicHP+qjRKbCGh/T6v8bT3ur8B5xvYhFbP1uAIJfgkgYaAKbExUnZse60wAVZCCuBo4YO8Jhw+eBFfijD8mnr9OQPnr9AHHH4bFrr+SMPaKi44kE0kVJlDBmG9I2sF3Jc+kB0Q2yg5RAn/9ShhIMFyH9tdv6PQVI4Fd740CEh60K/apXUfZwZP9JF3yADT4R5R8z95fChI6Dah8z46FF84DUHkexNN+UubvizxltwEoiGvCwXfDfbQWCBxIvmf7HJw8xw1E+RlIwkAaHLudH/Lf85dp9KCePqmBvy75mVwwTfv7J8dAjAOlxW7rDmyMg3HugidR/Su5wfqolX8VMant/iPP2AGgHZO+w2H81UP7JxpJld5nA+G4fiq44+MYiNaZQ+oYBa/BgVfvwol7cDs2AJ6CdyAG8XS6P8GKHl7Vl3B/bE820hmQiEqP3xbcoYkmvf58o2O2hUlIJO+z6wDSJ1X++2HiEWyvYVIXVIJ0RRI2+/DkB5GikaFOwuWrJPw+nUSqjcw2faIVuV0ZTULhL59gH5leP9L1ZCSwKlJvtJ8ah41DGx6vw2XTpM7gblPkqeT0nbd0qDLaEF0WwXzDnVwkPzgKWUnsnnrqKUyaNAkPP/wwysvL8cADD2D48OHYvHkzunTpctT1SMLYgSGNv9OMGXum6A+nEDyurxMeJOakx17yE7xwMMnggB9c2UuY8LPKlcyFH/xfI31ZXuAf+JQ9WEYGc5WYsTsv1wl3JOH3dlKRtdbAJgiefyXR/7xyDUyjzrjdZoIBSGMGctC1TcqzX5owSR0eWCRl2xO237EJbbAxCPwNKfKbe3D9nWvQL4XIrUQ/DP6o9gqwOzzjxzJIRIMvRfsbktbpZRHGMdgYjIJy/WQraROe8AvHftng6piKmeBCGkxchYmPCg5SDdr+G9NQeXYAU3G0PRAGMQt2rMFgtkrs0BpBvP1bM8ZR6XXFT3SVn9xov306Ztutgq3Pj7mK+fMI9qWiwuQlHPRV+Y/m+wkhPGXbCbvsVbAOiE3W3JiB5/oH0zwTHpi1soma4xgoUTAx7f8Chdhfi3CNn5wC4tmDk4rcgrUHIJtIqmB+MWMTX0fsZ2J+YpDS9qeaYiZMOOEpqJgH0RpSmArni6BtjY79vCPw2sFuB/7teZ3vZa6f/vhfSkl4hV3Fjb2tl9R+smKg/YFgVdIuH3EFShuouL+CRp72VjED7W/PyhHAVTBxk06i/V9lQL6BjhuIAUzKrkdKC5DSUK6xsQiuvuZ5dj3z/JODmG1fdLmpuIExCsox0DEDpDQkz8ZXOQaS9NeHWLAOG+g8z54Q1ds6nPwUJKnDbUvyFJDU0HEDHffgFcC2L+6fxCjX9sux6ynits3a9fdvsMsDrrEnQmLX9+BqqUp4MCl7kqGCK1yugbRL2c3Vs0OPaNc/CTAAYpEkzf+FBO0am4j7J1sq+MULV+AkPJgCFcZK53k2d9Cw/Qjm6W+jKnhyNKXtvj04qWnU4Xhxou2yFA17NTZu7PoceYpS59tl5qUUkG+vwCnRkJjfXvgnaf6+WCfsscAoBzrh2WTTFaiYB5MP2x6jbJJb59qkMSbQBR7EwMY55q9jrsCNp5A0yiahKX/cPgMkCpJoPBCHd8iFd9CFihvEihuR+tSFaXT8K56RAYH9+tQhnU5s/PEn7X49vQ4G+8r0HSz7hrj+Ycf4n4tmRUECGVyJUkhvS8YftNogTLTtfP1MKHL7Op3bSXjBILzd7x88gn0kkK4v7CfSiXaY0IaZlp+0Iv1krn3gKvJAGNJJor0LF9nnpfPIjH5mtAuZSV70+5/h8TP4SpN/t0BFYqF0tA1+f6LZXCCYvx+0YFzV9OKInDAIjpoSkS9Q/PgoLy/H1772Nfz3f/83AMAYg27duuHWW2/F7bff/rmfr62tRVFREXr/8ZfQ7fIgBnCCjUgA42lox26s4mk4MQ/GS6fpwW0cBYTj3Gj/bMcYBe1/J0j8A3Jwe8X4VxI8/wqPdo1NsvyEQDxt69USHuxFAKUEXsoJkxQVqT+8EmEUdNxLn4UD9iDsl4N/y1VrA2NsX7RjYJJOpKxto/IPrmIAY7R/pci2SYxd6cIrYQrhAV5E2bgFiU4kXgDS8QiutPjxEz/BCnaQUAgTGaXsdK0EJvg8ABMUlCAeQZvgt9/f6QRJUaROQMH4O5egzYBd7kobGE/Djdn7Y6mUA+1/Tjt+DARhPOH3QQVnc8HBzti6XdeDl9J+2xE525JwOWk/Pp6/cWsnve4EcU3Hwp8OhBuqiILnaWgt0DqdNMOPHfzlnvI0vEZ7dh/EMdhpaCXwjE0CjJ+MCBQcnb6lG8YyjCKQStmThiCGUIAJhkaB2PXHse0KEh7tGH8Z2pMoN+bBGAVjdHh7VGt7FTOVcuDGbHs9T0OM9usPEv5gL2uXozHKxj/lr+OufS+MX7ANRbZPKIHWYrdx/8Bs/O9ZBO1Nr7CAF2wzWsK+Oq4Hx19/U43p812lBG7c9i/VYG+VaT8JDvYhRhSQUjCw24D2199UgwvHtUmx8RSMpyLL3sZOIreB3IQfx5S9ghbLS9qYiV2ORhTEs4ma63pIJR14Kbt/AxS8RsdP3P3b8zqd9Duuh2TSgRi7bwzmqbVdLhAb61SDa/cRjk2gXcdDMulGBhFXiMVT8IyCSTmR/V1636P9E0jlJ+DaT7w8T9sEvzG4Ggfbj0bX/5zJKGP7ZeuV4ApQMC9PZWynKlieKRtjHTPQ2vjrnN2OlRY0HorZ/VewnQQJvJZwfQzaa4yGl9R2+fvLTgF2O/MUpFHDyU8hWe+GJ6hu3LY51ejAiXt2O1CA63hoTLrw/PEZRSmYBidj/6cM7MlScLU/2Id49ha6UrAJZszA1MbtVcO8yMmRn7wo1yb0csjxf3nDJsLBHSvdPmX3tYA9ifdPWMS1SW54ccRvh/IvGkBgT1hSOj0At/+VmPAkOWh/vYYU+MevpL1SqfO9dHKVUjb59ZS94OL5J6OOhHeDpFGn601FkjPxLwZEL0gopK9+B9uuv86IK+m4BhdTggsX/nEwTND8k1J7d0vbEyT/ok1QNuPrOv7dF9Q79u6G9pPMIIkNbq8G27kO82v/wobfAX97EDfyVQWVng0UIJ82YPvUX6KmpgaFhYU4kla/YtfY2IhVq1Zh6tSp4Xtaa1RUVKCqqqrZzzQ0NKChoSF8XVNTAwDo3uEDdGgHuNpDTBtoP9U1oqD926YQOz28vQfACbJf/wDtJ/sZByURu0NxdPrAGST2Kf8A5moT/oyV8esODkwBI7aOxpRjV3IoaGVgoNKJJRDuiO0LpNuJYDrCdphgRxYkfZH2AYD2W2oPzLZ9jr8m+d9E9L+XZROdIFG0/bUHEB0eOG1SEZQPYuBE4+FpaACxWAqplH/Q9FfKmOPBMxquNkh6Dhxt/H7b/iY9m3g5Yb8iCaG/YzOi4PhtVQA80ZGEF+HGLk3i42hjd8J+fa6yVzbSaalNxhw/2TF+m7T/bVUv0ucgCY/GImMZK4EXWRbhcvPnFdymMgLYOydB9Gx7RdLrmoP0sgxKAQIDjZTRcJTAUSZclkY0HGXgiX8lQNLx08rGwHVMeBKDsJ3GX5eVP11lrCO2b/5VOj9xdJRA+/fmU8aBgsDRti2eUWHSZfdrKlynFASe/yUSzz95CvLkYF3T/vIKyiol4TYW+RYURPxEVhRcP3HVADx/ffFM+nRZaZMRK9tOe5rsaLt9BHXHtQdPbIyjiXNMezCikfIcuy/X6RMuHZlfeNqh7NXKpOcg5nhhmz3RYRuC9VT7y08pP46i4ImGht93f/0K+iUAHGWTRwOFlOfAdbww4QjWv2BRB9uyqz2kRKf3gSrYztP7rmAewX4v2BaSxkk/GGKU3abFxlHrdJIfnIxoSLgsJLKMUkYfdoKnlCDl+ScXfruDmAbxCNoXxDuoI4i5MbYf0WQ/OKER/+TaVTaWje1dP3aAGJtMBNurG9n3BfML+mjjAziw25nxp8XdFBpTbhhv10kfg4L+aX97bTAOPGO3MLvsNMJfHQra6q9Dnr9PCO4gpPw+BtOTRQ6MAI7/G7fiH3fsLW8FL2VPAu3VZmOvtvpXk7Tr7wfFnmyIf5KhHZtci1HhCQVgL5pET6hMJJkOTgrgHyeCzdRL2cRcAHiePfbFYqn09/HEftb4JyrGP7EFxN59UEAyab8cF7QvuGBijD2Z8Tx71TLYJwdJvN30I8c5beDG7HFIkvb78dqx65CXcuBou8x0sD9w0ifWXkrDdewJF4yCBw3H8cKLNNqxx/Jk0rXJqrbrr6NN+J1fT3R4az7Y9rTycxBRSPnrk9Z232r8fWCwzhn/KmPDpylsB3A01+JaPbH7+OOP4XkeSkpKMt4vKSnBpk2bmv3M9OnTcffddx/2/qLvPdYibSQiIiI60dTV1aGoqOiIZdrEU7FTp07FpEmTwtf79+9Hjx49sGPHjs/tIH0xtbW16NatG3bu3Pm5l3vpi2FsWw5j23IY25bD2LacXIutiKCurg5lZWWfW7bVE7vOnTvDcRzs3r074/3du3ejtLS02c8kEgkkEonD3i8qKsqJBXYiKiwsZGxbCGPbchjblsPYthzGtuXkUmyP9kKW/vwix1c8HsfgwYNRWVkZvmeMQWVlJYYNG9bazSEiIiLKGVm5FTtp0iSMGzcOQ4YMwdChQ/HAAw/g008/xXXXXZeN5hARERHlhKwkdqNHj8ZHH32Eu+66C9XV1Tj77LMxb968wx6o+CyJRALTpk1r9vYs/XsY25bD2LYcxrblMLYth7FtOV/m2GZlHDsiIiIiOv5a/Tt2RERERNQymNgRERER5QgmdkREREQ5gokdERERUY5oc4ndrFmz0LNnT+Tl5aG8vBwrVqzIdpNOONOnT8fXvvY1dOjQAV26dMEVV1yBzZs3Z5Spr6/H+PHj0alTJ7Rv3x7f/e53Dxs0eseOHRg5ciQKCgrQpUsXTJ48GalUKqPMokWLcM455yCRSOD000/H448/3tLdO2HMmDEDSilMnDgxfI9xPXYffPABfvCDH6BTp07Iz8/HwIEDsXLlynC6iOCuu+7CKaecgvz8fFRUVGDLli0Zdezduxdjx45FYWEhiouLcf311+PAgQMZZd5++21ccMEFyMvLQ7du3XDvvfe2Sv+yxfM83HnnnejVqxfy8/Nx2mmn4Te/+U3Gb04ytkdvyZIluPTSS1FWVgalFJ5//vmM6a0Zy2eeeQZ9+/ZFXl4eBg4ciLlz5x73/ramI8U2mUxiypQpGDhwINq1a4eysjL88Ic/xK5duzLqYGwBSBsyZ84cicfj8uijj8o777wjN9xwgxQXF8vu3buz3bQTyvDhw+Wxxx6T9evXy5o1a+SSSy6R7t27y4EDB8IyN910k3Tr1k0qKytl5cqV8vWvf13OPffccHoqlZIBAwZIRUWFrF69WubOnSudO3eWqVOnhmW2bt0qBQUFMmnSJNmwYYPMnDlTHMeRefPmtWp/s2HFihXSs2dPOfPMM2XChAnh+4zrsdm7d6/06NFDrr32Wlm+fLls3bpVXnvtNXnvvffCMjNmzJCioiJ5/vnnZe3atXLZZZdJr1695NChQ2GZiy++WM466yx588035Y033pDTTz9dxowZE06vqamRkpISGTt2rKxfv16efPJJyc/Plz/84Q+t2t/WdM8990inTp3k5Zdflm3btskzzzwj7du3l9///vdhGcb26M2dO1fuuOMOefbZZwWAPPfccxnTWyuWy5YtE8dx5N5775UNGzbIL3/5S4nFYrJu3boWj0FLOVJs9+/fLxUVFfLUU0/Jpk2bpKqqSoYOHSqDBw/OqIOxFWlTid3QoUNl/Pjx4WvP86SsrEymT5+exVad+Pbs2SMAZPHixSJiN5BYLCbPPPNMWGbjxo0CQKqqqkTEbmBaa6murg7LzJ49WwoLC6WhoUFERG677Tbp379/xrxGjx4tw4cPb+kuZVVdXZ306dNH5s+fL9/4xjfCxI5xPXZTpkyR888//zOnG2OktLRU7rvvvvC9/fv3SyKRkCeffFJERDZs2CAA5B//+EdY5tVXXxWllHzwwQciIvLQQw9Jx44dw1gH8z7jjDOOd5dOGCNHjpQf/ehHGe9dddVVMnbsWBFhbP8dTZOP1ozlqFGjZOTIkRntKS8vlx//+MfHtY/Z0lzS3NSKFSsEgGzfvl1EGNtAm7kV29jYiFWrVqGioiJ8T2uNiooKVFVVZbFlJ76amhoAwEknnQQAWLVqFZLJZEYs+/bti+7du4exrKqqwsCBAzMGjR4+fDhqa2vxzjvvhGWidQRlcn15jB8/HiNHjjys74zrsXvxxRcxZMgQXH311ejSpQsGDRqEP/7xj+H0bdu2obq6OiMuRUVFKC8vz4htcXExhgwZEpapqKiA1hrLly8Py1x44YWIx+NhmeHDh2Pz5s3Yt29fS3czK84991xUVlbi3XffBQCsXbsWS5cuxYgRIwAwtsdTa8byy7ifaKqmpgZKKRQXFwNgbANtJrH7+OOP4XneYb9OUVJSgurq6iy16sRnjMHEiRNx3nnnYcCAAQCA6upqxOPxcGMIRGNZXV3dbKyDaUcqU1tbi0OHDrVEd7Juzpw5eOuttzB9+vTDpjGux27r1q2YPXs2+vTpg9deew0333wzfvrTn+JPf/oTgHRsjrT9V1dXo0uXLhnTXdfFSSed9IXin2tuv/12fO9730Pfvn0Ri8UwaNAgTJw4EWPHjgXA2B5PrRnLzyrzZYl1fX09pkyZgjFjxqCwsBAAYxvIyk+KUesZP3481q9fj6VLl2a7KW3ezp07MWHCBMyfPx95eXnZbk5OMcZgyJAh+O1vfwsAGDRoENavX4+HH34Y48aNy3Lr2rann34aTzzxBP7yl7+gf//+WLNmDSZOnIiysjLGltqkZDKJUaNGQUQwe/bsbDfnhNNmrth17twZjuMc9oTh7t27UVpamqVWndhuueUWvPzyy1i4cCG6du0avl9aWorGxkbs378/o3w0lqWlpc3GOph2pDKFhYXIz88/3t3JulWrVmHPnj0455xz4LouXNfF4sWL8eCDD8J1XZSUlDCux+iUU05Bv379Mt776le/ih07dgBIx+ZI239paSn27NmTMT2VSmHv3r1fKP65ZvLkyeFVu4EDB+Kaa67Bz372s/CqM2N7/LRmLD+rTK7HOkjqtm/fjvnz54dX6wDGNtBmErt4PI7BgwejsrIyfM8Yg8rKSgwbNiyLLTvxiAhuueUWPPfcc1iwYAF69eqVMX3w4MGIxWIZsdy8eTN27NgRxnLYsGFYt25dxkYSbETBAXjYsGEZdQRlcnV5XHTRRVi3bh3WrFkT/g0ZMgRjx44N/8+4HpvzzjvvsCF53n33XfTo0QMA0KtXL5SWlmbEpba2FsuXL8+I7f79+7Fq1aqwzIIFC2CMQXl5eVhmyZIlSCaTYZn58+fjjDPOQMeOHVusf9l08OBBaJ25q3ccB8YYAIzt8dSasfwy7ieCpG7Lli14/fXX0alTp4zpjK0v209vfBFz5syRRCIhjz/+uGzYsEFuvPFGKS4uznjCkERuvvlmKSoqkkWLFsmHH34Y/h08eDAsc9NNN0n37t1lwYIFsnLlShk2bJgMGzYsnB4My/Htb39b1qxZI/PmzZOTTz652WE5Jk+eLBs3bpRZs2bl/LAcTUWfihVhXI/VihUrxHVdueeee2TLli3yxBNPSEFBgfz5z38Oy8yYMUOKi4vlhRdekLffflsuv/zyZoeRGDRokCxfvlyWLl0qffr0yRjqYP/+/VJSUiLXXHONrF+/XubMmSMFBQU5NyRH1Lhx4+TUU08Nhzt59tlnpXPnznLbbbeFZRjbo1dXVyerV6+W1atXCwC5//77ZfXq1eGTma0Vy2XLlonruvK73/1ONm7cKNOmTWtTQ3I050ixbWxslMsuu0y6du0qa9asyTi2RZ9wZWzb2HAnIiIzZ86U7t27Szwel6FDh8qbb76Z7SadcAA0+/fYY4+FZQ4dOiQ/+clPpGPHjlJQUCBXXnmlfPjhhxn1vP/++zJixAjJz8+Xzp07y89//nNJJpMZZRYuXChnn322xONx6d27d8Y8vgyaJnaM67F76aWXZMCAAZJIJKRv377yyCOPZEw3xsidd94pJSUlkkgk5KKLLpLNmzdnlPnkk09kzJgx0r59eyksLJTrrrtO6urqMsqsXbtWzj//fEkkEnLqqafKjBkzWrxv2VRbWysTJkyQ7t27S15envTu3VvuuOOOjIMhY3v0Fi5c2Oz+ddy4cSLSurF8+umn5Stf+YrE43Hp37+/vPLKKy3W79ZwpNhu27btM49tCxcuDOtgbEWUSGT4cSIiIiJqs9rMd+yIiIiI6MiY2BERERHlCCZ2RERERDmCiR0RERFRjmBiR0RERJQjmNgRERER5QgmdkREREQ5gokdERERUY5gYkdERESUI5jYEREREeUIJnZEREREOYKJHREREVGOYGJHRERElCOY2BERERHlCCZ2RERERDmCiR0RERFRjmBiR0RERJQjmNgRER2lnj174tprr812M4iIPhMTOyIiIqIcoUREst0IIqK2oKGhAVprxGKxbDeFiKhZTOyIiIiIcgRvxRLRl96vfvUrKKWwadMmjBo1CoWFhejUqRMmTJiA+vr6sFzT79g9/vjjUEph2bJlmDRpEk4++WS0a9cOV155JT766KMs9ISIvuyY2BER+UaNGoX6+npMnz4dl1xyCR588EHceOONn/u5W2+9FWvXrsW0adNw880346WXXsItt9zSCi0mIsrkZrsBREQnil69euGFF14AAIwfPx6FhYV46KGH8Itf/AJnnnnmZ36uU6dO+Nvf/galFADAGIMHH3wQNTU1KCoqapW2ExEBvGJHRBQaP358xutbb70VADB37twjfu7GG28MkzoAuOCCC+B5HrZv3378G0lEdARM7IiIfH369Ml4fdppp0Frjffff/+In+vevXvG644dOwIA9u3bd1zbR0T0eZjYERF9huhVuCNxHKfZ9znoABG1NiZ2RES+LVu2ZLx+7733YIxBz549s9MgIqIviIkdEZFv1qxZGa9nzpwJABgxYkQ2mkNE9IXxqVgiIt+2bdtw2WWX4eKLL0ZVVRX+/Oc/4/vf/z7OOuusbDeNiOio8IodEZHvqaeeQiKRwO23345XXnkFt9xyC/7nf/4n280iIjpq/EkxIvrS+9WvfoW7774bH330ETp37pzt5hARHTNesSMiIiLKEUzsiIiIiHIEEzsiIiKiHMHv2BERERHlCF6xIyIiIsoRTOyIiIiIckSbHKDYGINdu3ahQ4cOR/1bjkRERERtkYigrq4OZWVl0PrI1+TaZGK3a9cudOvWLdvNICIiImo1O3fuRNeuXY9Ypk0mdh06dAAAfLPLtXB1PMutoVaXiMF9uBEFbiNqk3kocBthRCFlHOS5SaSMPZs5lIqhXawRAFDXmEC7WCO0EhhRqPdiyHOS0ErwaTKOdjFbBwBolX6e6GAqjgK3Ea4y4byaq+NAMoHCWD0MFFJGw9UGGoKU6LBcXHvY35iPwlg9UmLb2Oi5yHOT4bzauw0AgJRouMoAAAwU6lMxxJ0U4tpDbTIP7d0GGCi/3xp5TgoGKqMOAwVXmci8HMQdDxqCAym/vaKQEh3WAQAHUgm0dxvgag+Nxv3MOmqTeSiOH2q2joOpOPLcJOI61Wwd0XkVxurDPtenYihwGzNir5WNoxEVLptoHY3GQVx7YXujbQzqCOIRTIvGrbl5Reuo99ywXLSftpwb1h8ss6Z1AMjoV7SOYNkG05outyPV0ei5aB9rCOOW5ybhKgMjCgYKGpLR56CO9rGGsEz0dbDMNDKfpzuaOoJ21XtuGLdgW2q63Jt+JtqOpn0G8JnLPVhPm26rQTuCeQVtDrazI/U5eB2Nd9M6mtt+ovuMRs+Bqw0+HQOoRBwwfD6R/n0p04hFex4P858jaZOJXXD71dVxJnZfRjqOWDsg5gKxZBwx1+78lXEQcxWUn9glU3HEYvYjbiyBWAxhUuZ5McQce8Bwk3Zac4ldLGXrd5UJ59VcHbFkHLGYgYGdf5DYKdFhuZj2EIvZcso/2InnIuaqyLzsvFWTxM5LxRBztK0jacsFiZ0yGjFHw0Bl1BEkCOl5OYj5B91Yym+vKCjRYR3Rdrjag/hJWbN1JOOIxb0j1KEQ0/oz6oiUi5mwz14qhpibGXutbByjB/hoHWIcxPzETox7eD/9+gxUOC0at+bmFa3D89ywXLSftpwb1q8iiV20DgAZ/YrWES7byJ44utyOVId4LmIxicRNNZvYHVZHTMIy0dfBMmsusfvcOvx22VhlJnZNl3vTz0Tb0bTPAD5zuUfX9egyTLdDh0lZEJ+m7W/a5+B1NN5N62hu+4nuM8RP7FwNKJ0AYEB0vBzN18/48AQRERFRjmBiR0RERJQjmNgRERER5QgmdkREREQ5gokdERERUY5gYkdERESUI5jYEREREeUIJnZEREREOYKJHREREVGOaJO/PJFLTnvpE2z5P3nQhR0Az8OAV3dj/YgSwHUx5JX3sfKSHoDnoeivjTglrwabvtMFkvJwbuVOVI3oDRiDsufrUBw7CCMK7d0G7EsWAABiyoOr7ejoDcZFvpNEg3HhwMDVBiljR0yPKQ8e7Mj+SXHQ0T2IA14CSXFQoBvDUdWT4sCJjKKe0CkcNHE7X8f+xFXSOEiKg86xA6hJ5cODRoFuDKcldApJccI6ityD2Jdsh6Q4aO80IKa8sM4CpxFJcTI+F8xrX6oAG0aWoMtzB7Dnyna2fb1L0fjuB1CuA2iNgS/vwvpLSgERdH72U+wb1Q7wPIgxOHveB1g74hQAwKkv1GDXd4sBz4PZtx+qQweoeAwwBkPnbceK4d0Ax0HsLx4+vToGiABK4ay5u7D2O/Y3+7r8tQ57rrBxRywGyYtDHTgIiGDIazux8pIe+NQYFP1vPfZfFYPSGnAcDH1lm61fKXR69iA+uTIfcBzAdSA1tVB5eYDjoN9LH2LDyBJ8Ki7UHIWa0QVhX0Ys2Ix53+4PACj9ay0+vLwASmtIMgk0GqgOBYAxtr0jTgEcB3v//3x4/1cDnpfZF89D2Qt12HVVke2L5yG152O4JScDIuj7ykfYdFkpYAzUk8Dey2NhvIfM3Y6Vw7sCSqHofxtQMyrfttEz8HqdCveDT9LruL9cdj7cEd1urQNSKUAE33z9PSz6Vh9A64y+wHWx7b+K0euWjwCl0OPF/dh+xUmAMfjo271w8uvbm+1L6fOfovq7hWFfbl26CDMv+D+A1ih7tga7Lm8PKIVNU3qi7wO7gIZGQGsMnft+uFycOUDN6DiQSkFE8I3Xt2HJxX0AY9Dn5Y+x5TudAa3hlXaE3l4NFYsBWuPCeVuw5NunAVojf05jxnLv9+IubLikC6AUur5Qg39ddVJ6/SsqhHJdwBhc+Ld/Ysm3egMAOvw1aWPq9+WSBRsw96L+h/UF8RgkHgvXv2F/2x7uKzr+td5uByn7s1zRaX1e/hhbRnay65/WMHv3QXdoDzgO+r5YjU3f6QKIwHlKYd/o9jYexqD/q3tsXwB0fbE27Iskk4DS4bY06NUPsPriMsBxUPdoAslrVbgtDZhbbdcJf7lXX9HO9sVx8M8bu+O02dsAEbvufKcMMAbtnkli/xWurR/IWP9Knztg63DsvuZbf9uA+RedATgOejy/F9svKwZEsGN2J3SfWAc0JtPr3/AzbF+e24edl3aw21LKwxVL1uGFirMzY6UU9l3YEx2XvH9YX1QM6PPiHmy5vMSum0SthFfsiIiIiHIEEzsiIiKiHMHEjoiIiChHMLEjIiIiyhFM7IiIiIhyBBM7IiIiohzBxI6IiIgoRzCxIyIiIsoRTOyIiIiIcgQTOyIiIqIcwcSOiIiIKEcoEZFsN+KLqq2tRVFRESpKb4Sr49luDhER0ZEl4vjkoThO+uE+qETCvmfMkT9D5EuZRrxe/QhqampQWFh4xLJZuWL3q1/9CkqpjL++fftmoylEREREOcPN1oz79++P119/Pd0QN2tNISIiIsoJWcumXNdFaWlptmZPRERElHOy9vDEli1bUFZWht69e2Ps2LHYsWNHtppCRERElBOycsWuvLwcjz/+OM444wx8+OGHuPvuu3HBBRdg/fr16NChw2HlGxoa0NDQEL6ura1tzeYSERERtQlZSexGjBgR/v/MM89EeXk5evTogaeffhrXX3/9YeWnT5+Ou+++uzWbSERERNTmnBDj2BUXF+MrX/kK3nvvvWanT506FTU1NeHfzp07W7mFRERERCe+EyKxO3DgAP75z3/ilFNOaXZ6IpFAYWFhxh8RERERZcpKYveLX/wCixcvxvvvv4+///3vuPLKK+E4DsaMGZON5hARERHlhKx8x+5f//oXxowZg08++QQnn3wyzj//fLz55ps4+eSTs9EcIiIiopyQlSt2c+bMwa5du9DQ0IB//etfmDNnDk477bRsNKXt0xpXLViD7y/6B771+iaMW1yFi+a/i6sWrMGlletwaeU6jF64Et+cvwVXL3wLly94G5dWrsNVC9bgW69vwuiFK3FJ5Tu4asEaXL3wLVz4t3/i+4v+gUsq3wnLXlq5DtcsWo5vvb4JVy98C6MXrgzrDeq4asEaXLVgTTivHy3+O6SxEee9tg0AMG5xFeC6mF71PAa9+gHgOLh8wduA1viPZXPR6dmDQCKOSyrfAUQw4Y1KSMqDtMvHsNfehxiDaxYtB0TwH8vmostzn6ZjIIIbliwNX1698C1A+6t2fh5Suz8Kp41euBLwf0Wv9LkDkJQXTvvR4r+H0y6a/25Yh6Q8SMfCMN4/Wvz38DOdnj0IBINra407l70SThs6bwfE/8kgMQZb7i8Jp31/0T/C+gfMrQ7nCxHc+sbCsNw3529J98V18Yfl/xtOC2IFAL1e3Ack0j+vN2XZvHDakHk7AccJ6//131/MbIfvvfG90vFoEtPTXvok7AtE7LKIxjRoY14iXYfWmPBGZViu78t7wv/DcXBv1XPN9sXU1kHaF2T2xa+/3yu70+3Q2sbKnzZ64cp0/XkJyKFD4cvoMhs6b0c6Hlrjv978azjt6oVvZUwztXXhtJ+8sShj3QmJYNLS+eHL6LKVlIf3r+sdTpu89G/htLLn6zLWv2gdl1S+k1FHdFkc1pfI+je96vnwc9+cvyVjmVU/kv4Kyw1LloZ92fyfpenl5297gasWrMlY/x5bkY7VuMVVYR0NZ5RB6tMjF9z6xsLmtyVjMvr5kzcWhf9//9reQF4i7Mudy14JPxfsA4I2Xr3wrcx46PShTCI/0/XjJUvC/19auS5dznEw7e8vhdMuX/B2ev078GnG+jd56d/CacG+K2hHdFuNbkvIz0uvOyI2VvUN6HT9AahYzP6UGH9OjFrICfEdOyIiIiL69zGxIyIiIsoRTOyIiIiIcgQTOyIiIqIcwcSOiIiIKEcwsSMiIiLKEUzsiIiIiHIEEzsiIiKiHMHEjoiIiChHMLEjIiIiyhFM7IiIiIhyhBIJfqSy7aitrUVRUREqSm+Eq+Of/wEiIiKiNiplGvF69SOoqalBYWHhEcvyih0RERFRjmBiR0RERJQjmNgRERER5QgmdkREREQ5gokdERERUY5gYkdERESUI5jYEREREeUIJnZEREREOYKJHREREVGOYGJHRERElCOY2BERERHlCCZ2RERERDmCiR0RERFRjmBiR0RERJQjmNgRERER5QgmdkREREQ5gokdERERUY5gYkdERESUI5jYEREREeUIJnZEREREOYKJHREREVGOYGJHRERElCOY2BERERHlCCZ2RJTh3f8sxZb/6gIxBtBHuYvQOv2Z40FrbP7dKcenLp8Yg/ce6HL0ffJt+FU3/PP3J2PL/SXYcn/JF/78CUlrQAT//P3JLdYfSXktUu/x9M+be2HbzM7YdN/xXdcCG357KrbcX4L3HuiCd/+z1L73627Y+mDLxZ2IaxYRERFRjmBiR0RERJQjmNgRERER5QgmdkREREQ5gokdERERUY5gYkdERESUI5jYEREREeUIJnZEREREOYKJHREREVGOYGJHRERElCOUiEi2G/FF1dbWoqioCBWlN8LV8RaZh6Q8bJzRA/3u2gV4Hjbc0y38/8Y7ekIKPPS7axcklcLGe7qj37RdAIAN006F0y6FM37xIZCIY8MdJeh35w5AKWz4TVf0u7saSKWw6baeMIUp9Ltjp532627od/cuIJXChl93h8rz8NWp1ZDGJDb+fz3Q7z92Ao6DDb8uQ79f/gsQwabbeqHvfe/bBmuNDb8uAwSAgv3XKDtNAVBiXwsARwBR6TIxg36/3o0Nd5TasgY25Y9+RvmfM8pOV/4fkH6tm9SrJfM9AeBK820TlTnf6Gst6fJNywRrr/b/E5SLvo7OVyLtd5q854jf/2bmHUwPGBXps/+epzLj9Fl9DtomzdQbvNe0vdG2BcWdSL1BHKMxaO69I/U5WM7Gn64k3bZoXKLt9VQ6Tkmd7tvntS2Ig/LraG4ZwX+vaZ+jy0fJ0bUtmG9SAQlz+PII2hGIbjPBco1yTfPLI9o2Qea6EJ1XSh2+Dnp2Wzxs+9BN4hL0KYhb0OegDtekXze3Dka35+gy+6xtqLl6o+2QyLyC9kbnEyx7r0mfo9tmNEbNtTdYT4+2z03b39z2rJFe14M6j7RtNt2XNLcPa9rnRp0x33537wDRsUiZRrxe/QhqampQWFh4xLK8YkdERESUI7KW2M2aNQs9e/ZEXl4eysvLsWLFimw1hYiIiCgnZCWxe+qppzBp0iRMmzYNb731Fs466ywMHz4ce/bsyUZziIiIiHJCVhK7+++/HzfccAOuu+469OvXDw8//DAKCgrw6KOPZqM5RERERDmh1RO7xsZGrFq1ChUVFelGaI2KigpUVVU1+5mGhgbU1tZm/BERERFRplZP7D7++GN4noeSkpKM90tKSlBdXd3sZ6ZPn46ioqLwr1u3bq3RVCIiIqI2pU08FTt16lTU1NSEfzt37sx2k4iIiIhOOG5rz7Bz585wHAe7d+/OeH/37t0oLS1t9jOJRAKJRKI1mkdERETUZrX6Fbt4PI7BgwejsrIyfM8Yg8rKSgwbNqy1m0NERESUM1r9ih0ATJo0CePGjcOQIUMwdOhQPPDAA/j0009x3XXXZaM5RERERDkhK4nd6NGj8dFHH+Guu+5CdXU1zj77bMybN++wByo+S/AraCnT2GJtFOPBHKpHyjQAxhz2f1EeUqYhsxwAc6geSqVs24z40xoBpSJ1eDD19TCxVPPTDtVDSVB/KlKHTv9fBKa+PhIDW8cx/aRYyiBlGuzn+ZNi/EmxXP9JMXMC/6RYij8plss/KdaSxyzKbcG6czS/Atsmfyt269atOO2007LdDCIiIqJWs3PnTnTt2vWIZbJyxe7fddJJJwEAduzYgaKioiy3JrfU1taiW7du2Llz5+f+0DB9MYxty2FsWw5j23IY25aTa7EVEdTV1aGsrOxzy7bJxE5r+8xHUVFRTiywE1FhYSFj20IY25bD2LYcxrblMLYtJ5die7QXstrEOHZERERE9PmY2BERERHliDaZ2CUSCUybNo2DFrcAxrblMLYth7FtOYxty2FsW86XObZt8qlYIiIiIjpcm7xiR0RERESHY2JHRERElCOY2BERERHlCCZ2RERERDmizSV2s2bNQs+ePZGXl4fy8nKsWLEi20064UyfPh1f+9rX0KFDB3Tp0gVXXHEFNm/enFGmvr4e48ePR6dOndC+fXt897vfxe7duzPK7NixAyNHjkRBQQG6dOmCyZMnI5VKZZRZtGgRzjnnHCQSCZx++ul4/PHHW7p7J4wZM2ZAKYWJEyeG7zGux+6DDz7AD37wA3Tq1An5+fkYOHAgVq5cGU4XEdx111045ZRTkJ+fj4qKCmzZsiWjjr1792Ls2LEoLCxEcXExrr/+ehw4cCCjzNtvv40LLrgAeXl56NatG+69995W6V+2eJ6HO++8E7169UJ+fj5OO+00/OY3v8n4zUnG9ugtWbIEl156KcrKyqCUwvPPP58xvTVj+cwzz6Bv377Iy8vDwIEDMXfu3OPe39Z0pNgmk0lMmTIFAwcORLt27VBWVoYf/vCH2LVrV0YdjC0AaUPmzJkj8XhcHn30UXnnnXfkhhtukOLiYtm9e3e2m3ZCGT58uDz22GOyfv16WbNmjVxyySXSvXt3OXDgQFjmpptukm7dukllZaWsXLlSvv71r8u5554bTk+lUjJgwACpqKiQ1atXy9y5c6Vz584yderUsMzWrVuloKBAJk2aJBs2bJCZM2eK4zgyb968Vu1vNqxYsUJ69uwpZ555pkyYMCF8n3E9Nnv37pUePXrItddeK8uXL5etW7fKa6+9Ju+9915YZsaMGVJUVCTPP/+8rF27Vi677DLp1auXHDp0KCxz8cUXy1lnnSVvvvmmvPHGG3L66afLmDFjwuk1NTVSUlIiY8eOlfXr18uTTz4p+fn58oc//KFV+9ua7rnnHunUqZO8/PLLsm3bNnnmmWekffv28vvf/z4sw9gevblz58odd9whzz77rACQ5557LmN6a8Vy2bJl4jiO3HvvvbJhwwb55S9/KbFYTNatW9fiMWgpR4rt/v37paKiQp566inZtGmTVFVVydChQ2Xw4MEZdTC2Im0qsRs6dKiMHz8+fO15npSVlcn06dOz2KoT3549ewSALF68WETsBhKLxeSZZ54Jy2zcuFEASFVVlYjYDUxrLdXV1WGZ2bNnS2FhoTQ0NIiIyG233Sb9+/fPmNfo0aNl+PDhLd2lrKqrq5M+ffrI/Pnz5Rvf+EaY2DGux27KlCly/vnnf+Z0Y4yUlpbKfffdF763f/9+SSQS8uSTT4qIyIYNGwSA/OMf/wjLvPrqq6KUkg8++EBERB566CHp2LFjGOtg3mecccbx7tIJY+TIkfKjH/0o472rrrpKxo4dKyKM7b+jafLRmrEcNWqUjBw5MqM95eXl8uMf//i49jFbmkuam1qxYoUAkO3bt4sIYxtoM7diGxsbsWrVKlRUVITvaa1RUVGBqqqqLLbsxFdTUwMAOOmkkwAAq1atQjKZzIhl37590b179zCWVVVVGDhwIEpKSsIyw4cPR21tLd55552wTLSOoEyuL4/x48dj5MiRh/WdcT12L774IoYMGYKrr74aXbp0waBBg/DHP/4xnL5t2zZUV1dnxKWoqAjl5eUZsS0uLsaQIUPCMhUVFdBaY/ny5WGZCy+8EPF4PCwzfPhwbN68Gfv27WvpbmbFueeei8rKSrz77rsAgLVr12Lp0qUYMWIEAMb2eGrNWH4Z9xNN1dTUQCmF4uJiAIxtoM0kdh9//DE8z8s4IAJASUkJqqurs9SqE58xBhMnTsR5552HAQMGAACqq6sRj8fDjSEQjWV1dXWzsQ6mHalMbW0tDh061BLdybo5c+bgrbfewvTp0w+bxrgeu61bt2L27Nno06cPXnvtNdx888346U9/ij/96U8A0rE50vZfXV2NLl26ZEx3XRcnnXTSF4p/rrn99tvxve99D3379kUsFsOgQYMwceJEjB07FgBjezy1Ziw/q8yXJdb19fWYMmUKxowZg8LCQgCMbcDNdgOoZY0fPx7r16/H0qVLs92UNm/nzp2YMGEC5s+fj7y8vGw3J6cYYzBkyBD89re/BQAMGjQI69evx8MPP4xx48ZluXVt29NPP40nnngCf/nLX9C/f3+sWbMGEydORFlZGWNLbVIymcSoUaMgIpg9e3a2m3PCaTNX7Dp37gzHcQ57wnD37t0oLS3NUqtObLfccgtefvllLFy4EF27dg3fLy0tRWNjI/bv359RPhrL0tLSZmMdTDtSmcLCQuTn5x/v7mTdqlWrsGfPHpxzzjlwXReu62Lx4sV48MEH4bouSkpKGNdjdMopp6Bfv34Z7331q1/Fjh07AKRjc6Ttv7S0FHv27MmYnkqlsHfv3i8U/1wzefLk8KrdwIEDcc011+BnP/tZeNWZsT1+WjOWn1Um12MdJHXbt2/H/Pnzw6t1AGMbaDOJXTwex+DBg1FZWRm+Z4xBZWUlhg0blsWWnXhEBLfccguee+45LFiwAL169cqYPnjwYMRisYxYbt68GTt27AhjOWzYMKxbty5jIwk2ouAAPGzYsIw6gjK5ujwuuugirFu3DmvWrAn/hgwZgrFjx4b/Z1yPzXnnnXfYkDzvvvsuevToAQDo1asXSktLM+JSW1uL5cuXZ8R2//79WLVqVVhmwYIFMMagvLw8LLNkyRIkk8mwzPz583HGGWegY8eOLda/bDp48CC0ztzVO44DYwwAxvZ4as1Yfhn3E0FSt2XLFrz++uvo1KlTxnTG1pftpze+iDlz5kgikZDHH39cNmzYIDfeeKMUFxdnPGFIIjfffLMUFRXJokWL5MMPPwz/Dh48GJa56aabpHv37rJgwQJZuXKlDBs2TIYNGxZOD4bl+Pa3vy1r1qyRefPmycknn9zssByTJ0+WjRs3yqxZs3J+WI6mok/FijCux2rFihXiuq7cc889smXLFnniiSekoKBA/vznP4dlZsyYIcXFxfLCCy/I22+/LZdffnmzw0gMGjRIli9fLkuXLpU+ffpkDHWwf/9+KSkpkWuuuUbWr18vc+bMkYKCgpwbkiNq3Lhxcuqpp4bDnTz77LPSuXNnue2228IyjO3Rq6urk9WrV8vq1asFgNx///2yevXq8MnM1orlsmXLxHVd+d3vficbN26UadOmtakhOZpzpNg2NjbKZZddJl27dpU1a9ZkHNuiT7gytm1suBMRkZkzZ0r37t0lHo/L0KFD5c0338x2k044AJr9e+yxx8Iyhw4dkp/85CfSsWNHKSgokCuvvFI+/PDDjHref/99GTFihOTn50vnzp3l5z//uSSTyYwyCxculLPPPlvi8bj07t07Yx5fBk0TO8b12L300ksyYMAASSQS0rdvX3nkkUcyphtj5M4775SSkhJJJBJy0UUXyebNmzPKfPLJJzJmzBhp3769FBYWynXXXSd1dXUZZdauXSvnn3++JBIJOfXUU2XGjBkt3rdsqq2tlQkTJkj37t0lLy9PevfuLXfccUfGwZCxPXoLFy5sdv86btw4EWndWD799NPyla98ReLxuPTv319eeeWVFut3azhSbLdt2/aZx7aFCxeGdTC2IkokMvw4EREREbVZbeY7dkRERER0ZEzsiIiIiHIEEzsiIiKiHMHEjoiIiChHMLEjIiIiyhFM7IiIiIhyBBM7IiIiohzBxI6IiIgoRzCxIyIiIsoRTOyIiIiIcgQTOyIiIqIcwcSOiIiIKEf8P3QEE9d8J9BjAAAAAElFTkSuQmCC", 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", 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" ] @@ -64,7 +66,8 @@ } ], "source": [ - "file_name = files[-1]\n", + "file_name = files[4]\n", + "print(file_name)\n", "data = joblib.load(file_name)\n", "for key, value in data.items():\n", " print(key, value.shape)\n", @@ -80,6 +83,14 @@ "plt.tight_layout()\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7fbe52bc", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/notebooks/data_preparation.ipynb b/notebooks/data_preparation.ipynb index c795280..ffea9fe 100644 --- a/notebooks/data_preparation.ipynb +++ b/notebooks/data_preparation.ipynb @@ -21,9 +21,10 @@ "import pandas as pd\n", "from pathlib import Path\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "import yaml\n", "\n", - "shot_number = 170000\n", + "shot_number = 171348\n", "yaml_path = \"../src/fusionaihub/datasets/prepare/config/default.yaml\"\n", "with open(yaml_path, 'r') as f:\n", " cfg = yaml.safe_load(f)\n", @@ -47,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 18, "id": "c8e825ce", "metadata": {}, "outputs": [], @@ -63,27 +64,53 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 60, "id": "a68aaa6f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "magnetics_high_resolution\n", + "{'abbr': 'mhr', 'make_stft': True, 'expected_channels': 8}\n", + "ece_cali\n", + "{'abbr': 'ece', 'make_stft': True, 'expected_channels': 48}\n", + "co2_density\n", + "{'abbr': 'co2', 'make_stft': True, 'expected_channels': 4}\n", + "gas\n", + "{'abbr': 'gas', 'make_stft': False, 'expected_channels': 5}\n", + "ech\n", + "{'abbr': 'ech', 'make_stft': False, 'expected_channels': 11}\n", + "p_inj\n", + "{'abbr': 'pin', 'make_stft': False, 'expected_channels': 8}\n", + "t_inj\n", + "{'abbr': 'tin', 'make_stft': False, 'expected_channels': 8}\n" + ] + } + ], "source": [ "dfs = []\n", "missing_signals = []\n", - "for signal in cfg[\"signal\"]:\n", + "for signal in cfg['signal'].items():\n", + " print(signal[0])\n", + " print(signal[1])\n", " try:\n", " df = extract_signal(\n", " shot_number=shot_number,\n", " directory=Path(cfg[\"raw_data_dir\"]),\n", - " signal=signal['name'], \n", + " signal=signal[0], \n", " start_time=start_time,\n", " end_time=end_time,\n", " )\n", " df.columns = [\n", - " f\"{signal['abbr']}_{col}\" if col != \"time\" else col\n", + " f\"{signal[1]['abbr']}_{col}\" if col != \"time\" else col\n", " for col in range(len(df.columns))\n", " ]\n", "\n", + " # # Add a log to validate our assumption about the config and the transform key:\n", + " # logger.debug(f\"Signal config for {signal['name']}: {signal}\")\n", + "\n", " # Add a column to the dataframe for this signal indicating if a transform is present.\n", " # We'll use the signal's abbreviation to name the column, e.g., 'IP_transform'\n", " df = align_signal(\n", @@ -94,25 +121,26 @@ " )\n", " dfs.append(df)\n", " except Exception as e:\n", - " missing_signals.append((signal['name'], signal['abbr']))" + " for channel in range(int(signal[1]['expected_channels'])):\n", + " missing_signals.append((signal[1]['abbr'], channel))" ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 61, "id": "fe35d6ca", "metadata": {}, "outputs": [], "source": [ "df = pd.concat(dfs, axis=1, join='inner')\n", - "for signal_name, signal_abbr in missing_signals:\n", - " df[signal_abbr] = 0.0\n", - " df[f\"{signal_abbr}_state\"] = False" + "for signal_abbr, channel in missing_signals:\n", + " df[f\"{signal_abbr}_{channel}\"] = np.nan\n", + " df[f\"{signal_abbr}_{channel}_state\"] = False" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 62, "id": "87a1b47e", "metadata": {}, "outputs": [], @@ -128,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 64, "id": "40750cb0", "metadata": {}, "outputs": [], @@ -138,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": null, "id": "de25ce4f", "metadata": {}, "outputs": [ @@ -146,13 +174,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "mhr (8, 1, 2832874)\n", - "ece (48, 1, 2832874)\n", - "co2 (4, 1, 2832874)\n", - "gas (5, 1, 2832874)\n", - "ech (11, 1, 2832874)\n", - "pin (8, 1, 2832874)\n", - "tin (8, 1, 2832874)\n" + "mhr (8, 1, 653000)\n", + "ece (48, 1, 653000)\n", + "co2 (4, 1, 653000)\n", + "gas (5, 1, 653000)\n", + "ech (11, 1, 653000)\n", + "pin (8, 1, 653000)\n", + "tin (8, 1, 653000)\n" ] } ], @@ -160,18 +188,18 @@ "for sample in samples:\n", " transformed_samples = {}\n", " for key, value in sample.items():\n", - " for signal in cfg[\"signal\"]:\n", - " abbr = signal['abbr']\n", + " for signal in cfg['signal'].items():\n", + " abbr = signal[1]['abbr']\n", " cols = [col for col in value.columns if abbr in col]\n", " transformed_samples[abbr] = identity_transform(\n", " x=value[cols].to_numpy().T)\n", " print(abbr, transformed_samples[abbr].shape)\n", - " save_sample(transformed_samples, Path('.'), key)" + " save_sample(transformed_samples, Path('../data'), key)" ] }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 67, "id": "bc37f8e0", "metadata": {}, "outputs": [ @@ -179,7 +207,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "(2832874,) (513, 11066)\n" + "(653000,) (513, 2551)\n" ] } ], @@ -191,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "id": "f6cae5ac", "metadata": {}, "outputs": [ @@ -199,13 +227,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "mhr (8, 513, 11066)\n", - "ece (48, 513, 11066)\n", - "co2 (4, 513, 11066)\n", - "gas (5, 1, 11066)\n", - "ech (11, 1, 11066)\n", - "pin (8, 1, 11066)\n", - "tin (8, 1, 11066)\n" + "mhr (8, 513, 2551)\n", + "ece (48, 513, 2551)\n", + "co2 (4, 513, 2551)\n", + "gas (5, 1, 2551)\n", + "ech (11, 1, 2551)\n", + "pin (8, 1, 2551)\n", + "tin (8, 1, 2551)\n" ] } ], @@ -213,10 +241,10 @@ "for sample in samples:\n", " transformed_samples = {}\n", " for key, value in sample.items():\n", - " for signal in cfg[\"signal\"]:\n", - " abbr = signal['abbr']\n", + " for signal in cfg['signal'].items():\n", + " abbr = signal[1]['abbr']\n", " cols = [col for col in value.columns if abbr in col]\n", - " if signal[\"make_stft\"]:\n", + " if signal[1]['make_stft']:\n", " transformed_samples[abbr] = stft_transform(\n", " x=value[cols].to_numpy().T,\n", " n_fft=cfg[\"stft\"][\"n_fft\"],\n", @@ -228,7 +256,7 @@ " ref_shape=transform_shape,\n", " )\n", " print(abbr, transformed_samples[abbr].shape)\n", - " save_sample(transformed_samples, Path('.'), key)\n", + " save_sample(transformed_samples, Path('../data'), key)\n", " break\n", " break" ] diff --git a/src/fusionaihub/datasets/prepare/README.md b/src/fusionaihub/datasets/prepare/README.md index 0903a2d..55787d7 100644 --- a/src/fusionaihub/datasets/prepare/README.md +++ b/src/fusionaihub/datasets/prepare/README.md @@ -163,4 +163,7 @@ Each `.joblib` file contains a dictionary with signal arrays, where transformed - torch - joblib - PyYAML -- pathlib (built-in) \ No newline at end of file +- pathlib (built-in) + +# To-Do +Change YAML loading to hydra \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/config/default.yaml b/src/fusionaihub/datasets/prepare/config/default.yaml index 28d8ff6..bf84ecc 100644 --- a/src/fusionaihub/datasets/prepare/config/default.yaml +++ b/src/fusionaihub/datasets/prepare/config/default.yaml @@ -4,13 +4,34 @@ # Signal configuration - list of signals to process # Each signal has: [signal_name, abbreviation, should_transform] signal: - - {name: "magnetics_high_resolution", abbr: "mhr", make_stft: true} - - {name: "ece_cali", abbr: "ece", make_stft: true} - - {name: "co2_density", abbr: "co2", make_stft: true} - - {name: "gas", abbr: "gas", make_stft: false} - - {name: "ech", abbr: "ech", make_stft: false} - - {name: "p_inj", abbr: "pin", make_stft: false} - - {name: "t_inj", abbr: "tin", make_stft: false} + magnetics_high_resolution: + abbr: "mhr" + make_stft: true + expected_channels: 8 + ece_cali: + abbr: "ece" + make_stft: true + expected_channels: 48 + co2_density: + abbr: "co2" + make_stft: true + expected_channels: 4 + gas: + abbr: "gas" + make_stft: false + expected_channels: 5 + ech: + abbr: "ech" + make_stft: false + expected_channels: 11 + p_inj: + abbr: "pin" + make_stft: false + expected_channels: 8 + t_inj: + abbr: "tin" + make_stft: false + expected_channels: 8 # Data processing parameters randomize_shots: true diff --git a/src/fusionaihub/datasets/prepare/prepare_dataset.py b/src/fusionaihub/datasets/prepare/prepare_dataset.py index 4f623b9..91f4e54 100644 --- a/src/fusionaihub/datasets/prepare/prepare_dataset.py +++ b/src/fusionaihub/datasets/prepare/prepare_dataset.py @@ -76,10 +76,10 @@ def prepare_dataset(cfg: dict) -> None: logger.info(f"Target sampling frequency: {cfg['fs_khz']} kHz") logger.info("Signals configured:") - for signal in cfg["signal"]: - signal_name = signal["name"] - signal_abbr = signal["abbr"] - should_transform = signal.get("make_stft", False) + for signal in cfg['signal'].items(): + signal_name = signal[0] + signal_abbr = signal[1]['abbr'] + should_transform = signal[1].get("make_stft", False) logger.info(f" - {signal_name} ({signal_abbr}): transform={should_transform}") logger.info("=" * 40) @@ -103,16 +103,16 @@ def prepare_dataset(cfg: dict) -> None: logger.info(f"Processing {len(all_shots)} shots into cache...") # Clean up existing cache directory if it exists - # if cache_dir.exists(): - # import shutil - # logger.info(f"Removing existing cache directory: {cache_dir}") - # shutil.rmtree(cache_dir) - # cache_dir.mkdir(parents=True, exist_ok=True) + if cache_dir.exists(): + import shutil + logger.info(f"Removing existing cache directory: {cache_dir}") + shutil.rmtree(cache_dir) + cache_dir.mkdir(parents=True, exist_ok=True) # Process shots using the appropriate function # process_shot_stft(170000, cfg, cache_dir) # For debugging - # mapper = ParallelMapper() - # mapper(process_shot_stft, all_shots, cfg=cfg, out_dir=cache_dir) + mapper = ParallelMapper() + mapper(process_shot_stft, all_shots, cfg=cfg, out_dir=cache_dir) # Move cached files into train/test split logger.info("Splitting dataset into train and valid sets...") diff --git a/src/fusionaihub/datasets/prepare/shot_processing.py b/src/fusionaihub/datasets/prepare/shot_processing.py index 448f812..1a72158 100644 --- a/src/fusionaihub/datasets/prepare/shot_processing.py +++ b/src/fusionaihub/datasets/prepare/shot_processing.py @@ -71,22 +71,23 @@ def process_shot_stft( try: dfs = [] missing_signals = [] - for signal in cfg["signal"]: + for signal in cfg['signal'].items(): + print(signal[1]) try: df = extract_signal( shot_number=shot_number, directory=Path(cfg["raw_data_dir"]), - signal=signal['name'], + signal=signal[0], start_time=start_time, end_time=end_time, ) df.columns = [ - f"{signal['abbr']}{col}" if col != "time" else col + f"{signal[1]['abbr']}_{col}" if col != "time" else col for col in range(len(df.columns)) ] # Add a log to validate our assumption about the config and the transform key: - logger.debug(f"Signal config for {signal['name']}: {signal}") + logger.debug(f"Signal config for {signal[0]}: {signal[1]}") # Add a column to the dataframe for this signal indicating if a transform is present. # We'll use the signal's abbreviation to name the column, e.g., 'IP_transform' @@ -98,7 +99,8 @@ def process_shot_stft( ) dfs.append(df) except Exception as e: - missing_signals.append((signal['name'], signal['abbr'])) + for channel in range(int(signal[1]['expected_channels'])): + missing_signals.append((signal[1]['abbr'], channel)) except Exception as e: logger.error(f"Error: Could not extract signals for shot {shot_number}: {e}") raise e @@ -113,9 +115,10 @@ def process_shot_stft( # Add missing signals try: - for signal_name, signal_abbr in missing_signals: - df[signal_abbr] = 0.0 - df[f"{signal_abbr}_state"] = False + dfs = [] + for signal_abbr, channel in missing_signals: + df[f"{signal_abbr}_{channel}"] = np.nan + df[f"{signal_abbr}_{channel}_state"] = False except Exception as e: logger.error(f"Error: Could not add missing signals for shot {shot_number}: {e}") raise e @@ -146,8 +149,8 @@ def process_shot_stft( for sample in samples: transformed_samples = {} for key, value in sample.items(): - for signal in cfg["signal"]: - abbr = signal['abbr'] + for signal in cfg['signal'].items(): + abbr = signal[1]['abbr'] cols = [col for col in value.columns if abbr in col] transformed_samples[abbr] = identity_transform( x=value[cols].to_numpy().T) @@ -171,10 +174,10 @@ def process_shot_stft( for sample in samples: transformed_samples = {} for key, value in sample.items(): - for signal in cfg["signal"]: - abbr = signal['abbr'] + for signal in cfg['signal'].items(): + abbr = signal[1]['abbr'] cols = [col for col in value.columns if abbr in col] - if signal["make_stft"]: + if signal[1]['make_stft']: transformed_samples[abbr] = stft_transform( x=value[cols].to_numpy().T, n_fft=cfg["stft"]["n_fft"], From fb18479af196b9860d7b3c6c4c45abc788cbb9ff Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Sun, 13 Jul 2025 10:19:07 -0400 Subject: [PATCH 033/103] Update memory allocation in prepare_data.sh, reset execution counts in Jupyter notebooks, and enhance signal processing configurations in YAML. Adjusted data loading and visualization methods for improved clarity and functionality. --- commands/prepare_data.sh | 2 +- notebooks/accessing_data.ipynb | 36 ++++++++++--------- notebooks/data_preparation.ipynb | 34 +++++++++--------- .../datasets/prepare/config/default.yaml | 3 +- .../datasets/prepare/config/raw.yaml | 23 ++++++------ .../datasets/prepare/prepare_dataset.py | 8 +++-- .../datasets/prepare/shot_processing.py | 32 ++++++++--------- .../datasets/prepare/signal_processing.py | 5 +-- 8 files changed, 73 insertions(+), 70 deletions(-) diff --git a/commands/prepare_data.sh b/commands/prepare_data.sh index 28bc22f..967d552 100644 --- a/commands/prepare_data.sh +++ b/commands/prepare_data.sh @@ -3,7 +3,7 @@ #SBATCH --nodes=1 # node count #SBATCH --ntasks=1 # total number of tasks across all nodes #SBATCH --cpus-per-task=96 # cpu-cores per task (>1 if multi-threaded tasks) -#SBATCH --mem=100GB # memory per node +#SBATCH --mem=500GB # memory per node #SBATCH --time=01:00:00 # maximum time needed (HH:MM:SS) #SBATCH --output=logs/%A_%a.out #SBATCH --error=logs/%A_%a.err diff --git a/notebooks/accessing_data.ipynb b/notebooks/accessing_data.ipynb index be68705..2f0abfc 100644 --- a/notebooks/accessing_data.ipynb +++ b/notebooks/accessing_data.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "id": "836b5c67", "metadata": {}, "outputs": [], @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "id": "d564239f", "metadata": {}, "outputs": [], @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "id": "434b288f", "metadata": {}, "outputs": [ @@ -44,19 +44,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "/scratch/gpfs/EKOLEMEN/hackathon/foundation25/train/175210_0.joblib\n", - "mhr (8, 513, 12028)\n", - "ece (48, 513, 12028)\n", - "co2 (4, 513, 12028)\n", - "gas (5, 1, 12028)\n", - "ech (11, 1, 12028)\n", - "pin (8, 1, 12028)\n", - "tin (8, 1, 12028)\n" + "/scratch/gpfs/EKOLEMEN/hackathon/foundation25/train/170000_0.joblib\n", + "mhr (8, 11066, 513)\n", + "ece (48, 11066, 513)\n", + "co2 (4, 11066, 513)\n", + "gas (5, 11066, 1)\n", + "ech (11, 11066, 1)\n", + "pin (8, 11066, 1)\n", + "tin (8, 11066, 1)\n" ] }, { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -66,19 +66,19 @@ } ], "source": [ - "file_name = files[4]\n", + "file_name = files[0]\n", "print(file_name)\n", "data = joblib.load(file_name)\n", "for key, value in data.items():\n", " print(key, value.shape)\n", "plt.subplot(3, 1, 1)\n", - "plt.imshow(data['mhr'][4], aspect='auto', origin='lower')\n", + "plt.imshow(data['mhr'][4].T, aspect='auto', origin='lower')\n", "plt.title('mhrb4')\n", "plt.subplot(3, 1, 2)\n", - "plt.imshow(data['co2'][0], aspect='auto', origin='lower')\n", + "plt.imshow(data['co2'][0].T, aspect='auto', origin='lower')\n", "plt.title('co2r0')\n", "plt.subplot(3, 1, 3)\n", - "plt.imshow(data['pin'][:,0,:], aspect='auto', origin='lower', interpolation='none')\n", + "plt.imshow(data['pin'][:,:,0], aspect='auto', origin='lower', interpolation='none')\n", "plt.title('pin')\n", "plt.tight_layout()\n", "plt.show()" @@ -90,7 +90,9 @@ "id": "7fbe52bc", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "data = joblib.load('../data/170000/170000.pkl')" + ] } ], "metadata": { diff --git a/notebooks/data_preparation.ipynb b/notebooks/data_preparation.ipynb index ffea9fe..a9b4cfd 100644 --- a/notebooks/data_preparation.ipynb +++ b/notebooks/data_preparation.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "9b8f64ca", "metadata": {}, "outputs": [], @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "id": "c8e825ce", "metadata": {}, "outputs": [], @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 4, "id": "a68aaa6f", "metadata": {}, "outputs": [ @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "id": "fe35d6ca", "metadata": {}, "outputs": [], @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 12, "id": "87a1b47e", "metadata": {}, "outputs": [], @@ -156,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 13, "id": "40750cb0", "metadata": {}, "outputs": [], @@ -199,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 17, "id": "bc37f8e0", "metadata": {}, "outputs": [ @@ -207,19 +207,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "(653000,) (513, 2551)\n" + "(1, 653000) (1, 2551, 513)\n" ] } ], "source": [ - "first_arr = list(samples[0].values())[0].iloc[:, 0].values\n", + "first_arr = np.array([list(samples[0].values())[0].iloc[:, 0].values])\n", "transform_shape = stft_transform(x=first_arr).shape\n", "print(first_arr.shape, transform_shape)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "f6cae5ac", "metadata": {}, "outputs": [ @@ -227,13 +227,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "mhr (8, 513, 2551)\n", - "ece (48, 513, 2551)\n", - "co2 (4, 513, 2551)\n", - "gas (5, 1, 2551)\n", - "ech (11, 1, 2551)\n", - "pin (8, 1, 2551)\n", - "tin (8, 1, 2551)\n" + "mhr (8, 2551, 513)\n", + "ece (48, 2551, 513)\n", + "co2 (4, 2551, 513)\n", + "gas (5, 2551, 1)\n", + "ech (11, 2551, 1)\n", + "pin (8, 2551, 1)\n", + "tin (8, 2551, 1)\n" ] } ], diff --git a/src/fusionaihub/datasets/prepare/config/default.yaml b/src/fusionaihub/datasets/prepare/config/default.yaml index bf84ecc..406ce79 100644 --- a/src/fusionaihub/datasets/prepare/config/default.yaml +++ b/src/fusionaihub/datasets/prepare/config/default.yaml @@ -36,7 +36,7 @@ signal: # Data processing parameters randomize_shots: true random_seed: 42 -num_shots: 50 +num_shots: 15000 fs_khz: 500 # Sampling frequency in kHz ip_threshold: 0.1 # Plasma current threshold window_ms: null # Window size in milliseconds @@ -47,6 +47,7 @@ start_time: null # Start time for signal extraction (null for auto-detection) end_time: null # End time for signal extraction (null for auto-detection) # Train/test split configuration +debug: false train_test_split: 0.2 # Directory paths diff --git a/src/fusionaihub/datasets/prepare/config/raw.yaml b/src/fusionaihub/datasets/prepare/config/raw.yaml index 79dc8f4..f3951a3 100644 --- a/src/fusionaihub/datasets/prepare/config/raw.yaml +++ b/src/fusionaihub/datasets/prepare/config/raw.yaml @@ -4,36 +4,35 @@ # Signal configuration - list of signals to process # Each signal has: [signal_name, abbreviation, should_transform] signal: - - ["magnetics_high_resolution", "mhr", false] - - ["ece_cali", "ece", false] - - ["co2_density", "co2", false] - - ["gas", "gas", false] - - ["ech", "ech", false] - - ["p_inj", "pin", false] - - ["t_inj", "tin", false] + magnetics_high_resolution: + abbr: "mhr" + make_stft: true + expected_channels: 8 # Data processing parameters randomize_shots: true random_seed: 42 num_shots: 50 -fs_khz: 500 # Sampling frequency in kHz +fs_khz: 440 # Sampling frequency in kHz ip_threshold: 0.1 # Plasma current threshold window_ms: 250 # Window size in milliseconds hop_ms: 50 # Hop size in milliseconds remove_empty: true +do_stft: false +start_time: 0 # Start time for signal extraction (null for auto-detection) +end_time: 5000 # End time for signal extraction (null for auto-detection) # Train/test split configuration train_test_split: 0.2 # Directory paths -raw_data_dir: "/scratch/gpfs/EKOLEMEN/d3d_fusion_data" -output_dir: "/scratch/gpfs/EKOLEMEN/nc1514/foundation_v1" +raw_data_dir: /scratch/gpfs/EKOLEMEN/d3d_fusion_data +output_dir: /scratch/gpfs/nc1514/encoding/data/magnetics # Processing parameters stft: n_fft: 1024 hop_length: 256 - window_type: "hann" # Output settings -compression: true # Whether to compress saved files \ No newline at end of file +compression: false # Whether to compress saved files \ No newline at end of file diff --git a/src/fusionaihub/datasets/prepare/prepare_dataset.py b/src/fusionaihub/datasets/prepare/prepare_dataset.py index 91f4e54..22e413f 100644 --- a/src/fusionaihub/datasets/prepare/prepare_dataset.py +++ b/src/fusionaihub/datasets/prepare/prepare_dataset.py @@ -110,9 +110,11 @@ def prepare_dataset(cfg: dict) -> None: cache_dir.mkdir(parents=True, exist_ok=True) # Process shots using the appropriate function - # process_shot_stft(170000, cfg, cache_dir) # For debugging - mapper = ParallelMapper() - mapper(process_shot_stft, all_shots, cfg=cfg, out_dir=cache_dir) + if cfg.get("debug", False): + process_shot_stft(170000, cfg, cache_dir) # For debugging + else: + mapper = ParallelMapper() + mapper(process_shot_stft, all_shots, cfg=cfg, out_dir=cache_dir) # Move cached files into train/test split logger.info("Splitting dataset into train and valid sets...") diff --git a/src/fusionaihub/datasets/prepare/shot_processing.py b/src/fusionaihub/datasets/prepare/shot_processing.py index 1a72158..5d76899 100644 --- a/src/fusionaihub/datasets/prepare/shot_processing.py +++ b/src/fusionaihub/datasets/prepare/shot_processing.py @@ -10,6 +10,8 @@ import logging from pathlib import Path from typing import Dict +from warnings import simplefilter +simplefilter(action="ignore", category=pd.errors.PerformanceWarning) from .data_extraction import ( extract_signal, @@ -72,7 +74,6 @@ def process_shot_stft( dfs = [] missing_signals = [] for signal in cfg['signal'].items(): - print(signal[1]) try: df = extract_signal( shot_number=shot_number, @@ -85,12 +86,6 @@ def process_shot_stft( f"{signal[1]['abbr']}_{col}" if col != "time" else col for col in range(len(df.columns)) ] - - # Add a log to validate our assumption about the config and the transform key: - logger.debug(f"Signal config for {signal[0]}: {signal[1]}") - - # Add a column to the dataframe for this signal indicating if a transform is present. - # We'll use the signal's abbreviation to name the column, e.g., 'IP_transform' df = align_signal( df=df, start_time=start_time, @@ -114,14 +109,14 @@ def process_shot_stft( raise e # Add missing signals - try: - dfs = [] - for signal_abbr, channel in missing_signals: - df[f"{signal_abbr}_{channel}"] = np.nan - df[f"{signal_abbr}_{channel}_state"] = False - except Exception as e: - logger.error(f"Error: Could not add missing signals for shot {shot_number}: {e}") - raise e + if len(missing_signals) > 0: + try: + for signal_abbr, channel in missing_signals: + df[f"{signal_abbr}_{channel}"] = np.nan + df[f"{signal_abbr}_{channel}_state"] = False + except Exception as e: + logger.error(f"Error: Could not add missing signals for shot {shot_number}: {e}") + raise e # Split into samples try: @@ -162,9 +157,9 @@ def process_shot_stft( # Get the first transformed sample to determine STFT dimensions try: - first_arr = list(samples[0].values())[0].iloc[:, 0].values + first_arr = np.array([list(samples[0].values())[0].iloc[:, 0].values]) transform_shape = stft_transform(x=first_arr).shape - logger.info(f"Using {getattr(first_arr, 'name', 'unknown')} as reference for STFT dimensions: {transform_shape}") + logger.info(f"Using {first_arr.shape} as reference for STFT dimensions: {transform_shape}") except Exception as e: logger.error(f"Error: Could not get first transformed sample for shot {shot_number}: {e}") raise e @@ -188,6 +183,9 @@ def process_shot_stft( x=value[cols].to_numpy().T, ref_shape=transform_shape, ) + # logger.info( + # f"Transformed {abbr} with shape {transformed_samples[abbr].shape}" + # ) save_sample(transformed_samples, out_dir, key) except Exception as e: logger.error(f"Error: Could not transform samples for shot {shot_number}: {e}") diff --git a/src/fusionaihub/datasets/prepare/signal_processing.py b/src/fusionaihub/datasets/prepare/signal_processing.py index 2339962..838eaf5 100644 --- a/src/fusionaihub/datasets/prepare/signal_processing.py +++ b/src/fusionaihub/datasets/prepare/signal_processing.py @@ -61,6 +61,7 @@ def stft_transform( return_complex=True ) y = torch.log(torch.abs(y)) + y = y.permute(0, 2, 1) return y.numpy() @@ -79,9 +80,9 @@ def resample_transform( Resampled signal to match reference time dimension """ x = x.astype(np.float32) - target_length = ref_shape[1] + target_length = ref_shape[1] # assuming time dimension is second dimension y = [resample_fn(x_, target_length) for x_ in x] - y = np.expand_dims(y, axis=1) + y = np.expand_dims(y, axis=2) return np.array(y) From 6fc8109bbea07f1e630358c165e7e575ad705c4e Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Sun, 13 Jul 2025 15:26:07 -0400 Subject: [PATCH 034/103] Update time allocation in prepare_data.sh, modify YAML configuration for signal processing, and adjust sample indexing in sample_processing.py. Reset execution counts in Jupyter notebooks for consistency and clarity. --- commands/prepare_data.sh | 2 +- notebooks/data_preparation.ipynb | 111 ++++++++++++------ .../datasets/prepare/config/raw.yaml | 2 +- .../datasets/prepare/sample_processing.py | 4 +- 4 files changed, 83 insertions(+), 36 deletions(-) diff --git a/commands/prepare_data.sh b/commands/prepare_data.sh index 967d552..9ebb115 100644 --- a/commands/prepare_data.sh +++ b/commands/prepare_data.sh @@ -4,7 +4,7 @@ #SBATCH --ntasks=1 # total number of tasks across all nodes #SBATCH --cpus-per-task=96 # cpu-cores per task (>1 if multi-threaded tasks) #SBATCH --mem=500GB # memory per node -#SBATCH --time=01:00:00 # maximum time needed (HH:MM:SS) +#SBATCH --time=010:00:00 # maximum time needed (HH:MM:SS) #SBATCH --output=logs/%A_%a.out #SBATCH --error=logs/%A_%a.err diff --git a/notebooks/data_preparation.ipynb b/notebooks/data_preparation.ipynb index a9b4cfd..b67b55f 100644 --- a/notebooks/data_preparation.ipynb +++ b/notebooks/data_preparation.ipynb @@ -25,7 +25,7 @@ "import yaml\n", "\n", "shot_number = 171348\n", - "yaml_path = \"../src/fusionaihub/datasets/prepare/config/default.yaml\"\n", + "yaml_path = \"../src/fusionaihub/datasets/prepare/config/raw.yaml\"\n", "with open(yaml_path, 'r') as f:\n", " cfg = yaml.safe_load(f)\n", "\n", @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 20, "id": "c8e825ce", "metadata": {}, "outputs": [], @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 21, "id": "a68aaa6f", "metadata": {}, "outputs": [ @@ -73,19 +73,7 @@ "output_type": "stream", "text": [ "magnetics_high_resolution\n", - "{'abbr': 'mhr', 'make_stft': True, 'expected_channels': 8}\n", - "ece_cali\n", - "{'abbr': 'ece', 'make_stft': True, 'expected_channels': 48}\n", - "co2_density\n", - "{'abbr': 'co2', 'make_stft': True, 'expected_channels': 4}\n", - "gas\n", - "{'abbr': 'gas', 'make_stft': False, 'expected_channels': 5}\n", - "ech\n", - "{'abbr': 'ech', 'make_stft': False, 'expected_channels': 11}\n", - "p_inj\n", - "{'abbr': 'pin', 'make_stft': False, 'expected_channels': 8}\n", - "t_inj\n", - "{'abbr': 'tin', 'make_stft': False, 'expected_channels': 8}\n" + "{'abbr': 'mhr', 'make_stft': False, 'expected_channels': 8}\n" ] } ], @@ -127,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "fe35d6ca", "metadata": {}, "outputs": [], @@ -140,10 +128,21 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 23, "id": "87a1b47e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "22" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "samples = split_samples(\n", " df=df,\n", @@ -151,22 +150,53 @@ " window_ms=cfg[\"window_ms\"],\n", " hop_ms=cfg[\"hop_ms\"],\n", " fs_khz=cfg[\"fs_khz\"],\n", - ")" + ")\n", + "len(samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "95af9251", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[dict_keys(['171348_0']), dict_keys(['171348_1']), dict_keys(['171348_2']), dict_keys(['171348_3']), dict_keys(['171348_4']), dict_keys(['171348_5']), dict_keys(['171348_6']), dict_keys(['171348_7']), dict_keys(['171348_8']), dict_keys(['171348_9']), dict_keys(['171348_10']), dict_keys(['171348_11']), dict_keys(['171348_12']), dict_keys(['171348_13']), dict_keys(['171348_14']), dict_keys(['171348_15']), dict_keys(['171348_16']), dict_keys(['171348_17']), dict_keys(['171348_18']), dict_keys(['171348_19']), dict_keys(['171348_20']), dict_keys(['171348_21'])]\n" + ] + } + ], + "source": [ + "print([sample.keys() for sample in samples])" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 25, "id": "40750cb0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "22" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "samples = remove_empty_samples(samples)" + "samples = remove_empty_samples(samples)\n", + "len(samples)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "de25ce4f", "metadata": {}, "outputs": [ @@ -174,13 +204,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "mhr (8, 1, 653000)\n", - "ece (48, 1, 653000)\n", - "co2 (4, 1, 653000)\n", - "gas (5, 1, 653000)\n", - "ech (11, 1, 653000)\n", - "pin (8, 1, 653000)\n", - "tin (8, 1, 653000)\n" + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n", + "mhr (8, 1, 110000)\n" ] } ], @@ -199,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 12, "id": "bc37f8e0", "metadata": {}, "outputs": [ @@ -207,7 +252,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "(1, 653000) (1, 2551, 513)\n" + "(1, 110000) (1, 430, 513)\n" ] } ], diff --git a/src/fusionaihub/datasets/prepare/config/raw.yaml b/src/fusionaihub/datasets/prepare/config/raw.yaml index f3951a3..d243396 100644 --- a/src/fusionaihub/datasets/prepare/config/raw.yaml +++ b/src/fusionaihub/datasets/prepare/config/raw.yaml @@ -6,7 +6,7 @@ signal: magnetics_high_resolution: abbr: "mhr" - make_stft: true + make_stft: false expected_channels: 8 # Data processing parameters diff --git a/src/fusionaihub/datasets/prepare/sample_processing.py b/src/fusionaihub/datasets/prepare/sample_processing.py index d3e9fb0..6e96434 100644 --- a/src/fusionaihub/datasets/prepare/sample_processing.py +++ b/src/fusionaihub/datasets/prepare/sample_processing.py @@ -46,13 +46,15 @@ def split_samples( # Separate samples samples = [] + start_window_idx = 0 for start_index in range(0, len(df) - num_samples + 1, hop_samples): end_index = start_index + num_samples sample = df.iloc[start_index:end_index] if len(sample) == num_samples: samples.append({ - f"{shot_number}_{start_index}": sample, + f"{shot_number}_{start_window_idx}": sample, }) + start_window_idx += 1 return samples From 20a2aadf98e14645da57cae81ce86b0fe3b0629c Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Mon, 14 Jul 2025 14:44:24 -0400 Subject: [PATCH 035/103] Update README.md --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index c8b2ba6..a44725a 100644 --- a/README.md +++ b/README.md @@ -7,6 +7,10 @@ A general fusion hub for the Princeton cluster designed to standardize fusion ma This repository serves as a centralized platform for fusion-related machine learning workflows, providing standardized tools, processes, and methodologies for plasma control research at Princeton. ## Setup + +Go to your scratch directory +/scratch/gpfs/ + In your scratch directory, run ```bash git clone git@github.com:PlasmaControl/FusionAIHub.git From 886fba2f2a644cfebb4efb3b93da5668cbc0bef5 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Mon, 14 Jul 2025 14:44:48 -0400 Subject: [PATCH 036/103] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a44725a..4041ae2 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ This repository serves as a centralized platform for fusion-related machine lear ## Setup Go to your scratch directory -/scratch/gpfs/ +/scratch/gpfs/[username] In your scratch directory, run ```bash From 1ceaaf64d07863f7ac966f4e7b3b2f0653d77d11 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Mon, 14 Jul 2025 14:46:26 -0400 Subject: [PATCH 037/103] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 4041ae2..eb7cae7 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,8 @@ This repository serves as a centralized platform for fusion-related machine lear ## Setup -Go to your scratch directory +Go to your scratch directory while you are on the HEAD node (so you need internet access, which computing nodes do not have). + /scratch/gpfs/[username] In your scratch directory, run From 544440f3e7989d883254c2ace15e160cb9573e4e Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Mon, 14 Jul 2025 14:51:07 -0400 Subject: [PATCH 038/103] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index eb7cae7..59f405f 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,8 @@ This repository serves as a centralized platform for fusion-related machine lear Go to your scratch directory while you are on the HEAD node (so you need internet access, which computing nodes do not have). +We will be using Python 3.12. + /scratch/gpfs/[username] In your scratch directory, run From 6a841b8cfd6ed95a1747c57121b8e6cdf6ba618f Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Mon, 14 Jul 2025 14:51:36 -0400 Subject: [PATCH 039/103] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 59f405f..d692c07 100644 --- a/README.md +++ b/README.md @@ -21,6 +21,7 @@ cd FusionAIHub git switch foundation25 module load anaconda3/2024.10 python -m venv .venv +conda deactivate source .venv/bin/activate pip install --upgrade pip pip install uv From 57eaf9b2cdbfb1b9c84448f7d3b7ed8f4b7c02ee Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Mon, 14 Jul 2025 14:54:10 -0400 Subject: [PATCH 040/103] Update README.md --- README.md | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index d692c07..f810b36 100644 --- a/README.md +++ b/README.md @@ -28,10 +28,15 @@ pip install uv uv sync ``` +From now on, whenever you go into the repo, all you need to do is to run +```bash +source .venv/bin/activate +``` + ## Contact For more information, please contact - **Azarakash Jalalvand** - **Peter Steiner** -- **Kouroche Bouichat** +- **Kouroche Bouchiat** - **Nathaniel Chen** From c6381560dd222988afb94d04362d0a68424c5355 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Tue, 15 Jul 2025 10:02:17 -0400 Subject: [PATCH 041/103] Enhance data preparation script with additional logging options, update YAML configuration for increased shot count, and refine signal processing methods with new TODOs for future improvements. Adjust Jupyter notebook kernel display name and Python version for consistency. --- commands/prepare_data.sh | 2 +- notebooks/accessing_data.ipynb | 4 ++-- src/fusionaihub/datasets/prepare/config/raw.yaml | 4 ++-- src/fusionaihub/datasets/prepare/data_extraction.py | 8 ++++++++ src/fusionaihub/datasets/prepare/prepare_dataset.py | 2 ++ src/fusionaihub/datasets/prepare/shot_processing.py | 6 ++++++ src/fusionaihub/datasets/prepare/signal_processing.py | 9 ++++++--- 7 files changed, 27 insertions(+), 8 deletions(-) diff --git a/commands/prepare_data.sh b/commands/prepare_data.sh index 9ebb115..7e69625 100644 --- a/commands/prepare_data.sh +++ b/commands/prepare_data.sh @@ -13,4 +13,4 @@ module purge source .venv/bin/activate # Run pipeline -srun python -m fusionaihub.datasets.prepare \ No newline at end of file +srun python -m fusionaihub.datasets.prepare --config config/raw.yaml --log-level DEBUG \ No newline at end of file diff --git a/notebooks/accessing_data.ipynb b/notebooks/accessing_data.ipynb index 2f0abfc..4cd7659 100644 --- a/notebooks/accessing_data.ipynb +++ b/notebooks/accessing_data.ipynb @@ -97,7 +97,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -111,7 +111,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/src/fusionaihub/datasets/prepare/config/raw.yaml b/src/fusionaihub/datasets/prepare/config/raw.yaml index d243396..6813c80 100644 --- a/src/fusionaihub/datasets/prepare/config/raw.yaml +++ b/src/fusionaihub/datasets/prepare/config/raw.yaml @@ -12,7 +12,7 @@ signal: # Data processing parameters randomize_shots: true random_seed: 42 -num_shots: 50 +num_shots: 5000 fs_khz: 440 # Sampling frequency in kHz ip_threshold: 0.1 # Plasma current threshold window_ms: 250 # Window size in milliseconds @@ -23,7 +23,7 @@ start_time: 0 # Start time for signal extraction (null for auto-detection) end_time: 5000 # End time for signal extraction (null for auto-detection) # Train/test split configuration -train_test_split: 0.2 +train_test_split: 0.1 # Directory paths raw_data_dir: /scratch/gpfs/EKOLEMEN/d3d_fusion_data diff --git a/src/fusionaihub/datasets/prepare/data_extraction.py b/src/fusionaihub/datasets/prepare/data_extraction.py index a6ff154..79416a8 100644 --- a/src/fusionaihub/datasets/prepare/data_extraction.py +++ b/src/fusionaihub/datasets/prepare/data_extraction.py @@ -89,12 +89,16 @@ def align_signal( Aligned DataFrame with data and state columns """ # get sampling frequency + # TODO: can change to getting mean or individual fs_raw = len(df) / (df.index[-1] - df.index[0]) # crop time df = df.loc[(df.index >= start_time) & (df.index <= end_time)] # resample + # TODO: can this be precomputed? + # TODO: merge dataframe at closest time with a tolerance? + # TODO: have 2 modules based on "make_stft" function? num = len(df) num = int(num * fs / fs_raw) @@ -104,6 +108,9 @@ def align_signal( ) # mark on-off states + # TODO: add intermtermittent detection, not just beginning and end + # TODO: or just want the model to be robust + # TODO: shot defined as plasma current > 0.5 MA, or 0.5s. start_nan = (df.index[0] - start_time) * fs end_nan = (end_time - df.index[-1]) * fs start_pad = pd.DataFrame( @@ -120,6 +127,7 @@ def align_signal( df_state.columns = [f"{col}_state" for col in df.columns] # combine data with state + # TODO: change this to a variable df = df.astype(np.float32) df_state = df_state.astype(np.bool) df = pd.concat([df, df_state], axis=1) diff --git a/src/fusionaihub/datasets/prepare/prepare_dataset.py b/src/fusionaihub/datasets/prepare/prepare_dataset.py index 22e413f..e61c14d 100644 --- a/src/fusionaihub/datasets/prepare/prepare_dataset.py +++ b/src/fusionaihub/datasets/prepare/prepare_dataset.py @@ -97,6 +97,8 @@ def prepare_dataset(cfg: dict) -> None: np.random.seed(cfg["random_seed"]) all_shots = np.random.permutation(all_shots) + # Set to -1 to use all shots, or just don't include as argument + # However, keep argument to stay consistent with other scripts if cfg.get("num_shots") is not None: all_shots = all_shots[:cfg["num_shots"]] diff --git a/src/fusionaihub/datasets/prepare/shot_processing.py b/src/fusionaihub/datasets/prepare/shot_processing.py index 5d76899..cd1c814 100644 --- a/src/fusionaihub/datasets/prepare/shot_processing.py +++ b/src/fusionaihub/datasets/prepare/shot_processing.py @@ -56,7 +56,10 @@ def process_shot_stft( """ # Extract running time + # TODO: Change to call this running_time from ip_threshold try: + # TODO: shot defined as plasma current > 0.5 MA, or 0.5s + # TODO: George Sips, (find reference slide and cite it) start_time, end_time = extract_running_time( shot_number=shot_number, directory=Path(cfg["raw_data_dir"]), @@ -103,6 +106,7 @@ def process_shot_stft( # Create main aligned dataframe (important since interpolated signals # could have alignment off) try: + # TODO: if df is fixed to same length, then join without inner df = pd.concat(dfs, axis=1, join='inner') except Exception as e: logger.error(f"Error: Could not concatenate dataframes for shot {shot_number}: {e}") @@ -119,6 +123,7 @@ def process_shot_stft( raise e # Split into samples + # TODO: rename this to slice_windows try: samples = split_samples( df=df, @@ -132,6 +137,7 @@ def process_shot_stft( raise e # Remove empty samples + # TODO: Add warning if samples change even if no windows and using ip criterion try: samples = remove_empty_samples(samples) except Exception as e: diff --git a/src/fusionaihub/datasets/prepare/signal_processing.py b/src/fusionaihub/datasets/prepare/signal_processing.py index 838eaf5..25abb31 100644 --- a/src/fusionaihub/datasets/prepare/signal_processing.py +++ b/src/fusionaihub/datasets/prepare/signal_processing.py @@ -51,16 +51,19 @@ def stft_transform( Returns: Log-magnitude STFT representation """ + # TODO: make this modular pipeline-ish + # TODO: parameterize window type + # TODO: parameterize window size (check if gives warning or allowed) x = x.astype(np.float32) x_tensor = torch.from_numpy(x).float() y = torch.stft( - x_tensor, + x_tensor, n_fft=n_fft, hop_length=hop_length, window=torch.hann_window(n_fft), - return_complex=True + return_complex=True, ) - y = torch.log(torch.abs(y)) + y = torch.abs(y) y = y.permute(0, 2, 1) return y.numpy() From c6ae9286ac019b2b0dce9c91a2c74ed683ec2809 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Tue, 15 Jul 2025 10:02:17 -0400 Subject: [PATCH 042/103] Enhance data preparation script with additional logging options, update YAML configuration for increased shot count, and refine signal processing methods with new TODOs for future improvements. Adjust Jupyter notebook kernel display name and Python version for consistency. --- commands/prepare_data.sh | 2 +- notebooks/accessing_data.ipynb | 4 ++-- src/fusionaihub/datasets/prepare/config/raw.yaml | 4 ++-- src/fusionaihub/datasets/prepare/data_extraction.py | 8 ++++++++ src/fusionaihub/datasets/prepare/prepare_dataset.py | 2 ++ src/fusionaihub/datasets/prepare/shot_processing.py | 6 ++++++ src/fusionaihub/datasets/prepare/signal_processing.py | 9 ++++++--- 7 files changed, 27 insertions(+), 8 deletions(-) diff --git a/commands/prepare_data.sh b/commands/prepare_data.sh index 9ebb115..7e69625 100644 --- a/commands/prepare_data.sh +++ b/commands/prepare_data.sh @@ -13,4 +13,4 @@ module purge source .venv/bin/activate # Run pipeline -srun python -m fusionaihub.datasets.prepare \ No newline at end of file +srun python -m fusionaihub.datasets.prepare --config config/raw.yaml --log-level DEBUG \ No newline at end of file diff --git a/notebooks/accessing_data.ipynb b/notebooks/accessing_data.ipynb index 2f0abfc..4cd7659 100644 --- a/notebooks/accessing_data.ipynb +++ b/notebooks/accessing_data.ipynb @@ -97,7 +97,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -111,7 +111,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/src/fusionaihub/datasets/prepare/config/raw.yaml b/src/fusionaihub/datasets/prepare/config/raw.yaml index d243396..6813c80 100644 --- a/src/fusionaihub/datasets/prepare/config/raw.yaml +++ b/src/fusionaihub/datasets/prepare/config/raw.yaml @@ -12,7 +12,7 @@ signal: # Data processing parameters randomize_shots: true random_seed: 42 -num_shots: 50 +num_shots: 5000 fs_khz: 440 # Sampling frequency in kHz ip_threshold: 0.1 # Plasma current threshold window_ms: 250 # Window size in milliseconds @@ -23,7 +23,7 @@ start_time: 0 # Start time for signal extraction (null for auto-detection) end_time: 5000 # End time for signal extraction (null for auto-detection) # Train/test split configuration -train_test_split: 0.2 +train_test_split: 0.1 # Directory paths raw_data_dir: /scratch/gpfs/EKOLEMEN/d3d_fusion_data diff --git a/src/fusionaihub/datasets/prepare/data_extraction.py b/src/fusionaihub/datasets/prepare/data_extraction.py index a6ff154..79416a8 100644 --- a/src/fusionaihub/datasets/prepare/data_extraction.py +++ b/src/fusionaihub/datasets/prepare/data_extraction.py @@ -89,12 +89,16 @@ def align_signal( Aligned DataFrame with data and state columns """ # get sampling frequency + # TODO: can change to getting mean or individual fs_raw = len(df) / (df.index[-1] - df.index[0]) # crop time df = df.loc[(df.index >= start_time) & (df.index <= end_time)] # resample + # TODO: can this be precomputed? + # TODO: merge dataframe at closest time with a tolerance? + # TODO: have 2 modules based on "make_stft" function? num = len(df) num = int(num * fs / fs_raw) @@ -104,6 +108,9 @@ def align_signal( ) # mark on-off states + # TODO: add intermtermittent detection, not just beginning and end + # TODO: or just want the model to be robust + # TODO: shot defined as plasma current > 0.5 MA, or 0.5s. start_nan = (df.index[0] - start_time) * fs end_nan = (end_time - df.index[-1]) * fs start_pad = pd.DataFrame( @@ -120,6 +127,7 @@ def align_signal( df_state.columns = [f"{col}_state" for col in df.columns] # combine data with state + # TODO: change this to a variable df = df.astype(np.float32) df_state = df_state.astype(np.bool) df = pd.concat([df, df_state], axis=1) diff --git a/src/fusionaihub/datasets/prepare/prepare_dataset.py b/src/fusionaihub/datasets/prepare/prepare_dataset.py index 22e413f..e61c14d 100644 --- a/src/fusionaihub/datasets/prepare/prepare_dataset.py +++ b/src/fusionaihub/datasets/prepare/prepare_dataset.py @@ -97,6 +97,8 @@ def prepare_dataset(cfg: dict) -> None: np.random.seed(cfg["random_seed"]) all_shots = np.random.permutation(all_shots) + # Set to -1 to use all shots, or just don't include as argument + # However, keep argument to stay consistent with other scripts if cfg.get("num_shots") is not None: all_shots = all_shots[:cfg["num_shots"]] diff --git a/src/fusionaihub/datasets/prepare/shot_processing.py b/src/fusionaihub/datasets/prepare/shot_processing.py index 5d76899..cd1c814 100644 --- a/src/fusionaihub/datasets/prepare/shot_processing.py +++ b/src/fusionaihub/datasets/prepare/shot_processing.py @@ -56,7 +56,10 @@ def process_shot_stft( """ # Extract running time + # TODO: Change to call this running_time from ip_threshold try: + # TODO: shot defined as plasma current > 0.5 MA, or 0.5s + # TODO: George Sips, (find reference slide and cite it) start_time, end_time = extract_running_time( shot_number=shot_number, directory=Path(cfg["raw_data_dir"]), @@ -103,6 +106,7 @@ def process_shot_stft( # Create main aligned dataframe (important since interpolated signals # could have alignment off) try: + # TODO: if df is fixed to same length, then join without inner df = pd.concat(dfs, axis=1, join='inner') except Exception as e: logger.error(f"Error: Could not concatenate dataframes for shot {shot_number}: {e}") @@ -119,6 +123,7 @@ def process_shot_stft( raise e # Split into samples + # TODO: rename this to slice_windows try: samples = split_samples( df=df, @@ -132,6 +137,7 @@ def process_shot_stft( raise e # Remove empty samples + # TODO: Add warning if samples change even if no windows and using ip criterion try: samples = remove_empty_samples(samples) except Exception as e: diff --git a/src/fusionaihub/datasets/prepare/signal_processing.py b/src/fusionaihub/datasets/prepare/signal_processing.py index 838eaf5..25abb31 100644 --- a/src/fusionaihub/datasets/prepare/signal_processing.py +++ b/src/fusionaihub/datasets/prepare/signal_processing.py @@ -51,16 +51,19 @@ def stft_transform( Returns: Log-magnitude STFT representation """ + # TODO: make this modular pipeline-ish + # TODO: parameterize window type + # TODO: parameterize window size (check if gives warning or allowed) x = x.astype(np.float32) x_tensor = torch.from_numpy(x).float() y = torch.stft( - x_tensor, + x_tensor, n_fft=n_fft, hop_length=hop_length, window=torch.hann_window(n_fft), - return_complex=True + return_complex=True, ) - y = torch.log(torch.abs(y)) + y = torch.abs(y) y = y.permute(0, 2, 1) return y.numpy() From ea0ebb2ba0b1dff5af0dd814cd251bd61a78c684 Mon Sep 17 00:00:00 2001 From: Peter Steiner <61472983+renierts@users.noreply.github.com> Date: Tue, 15 Jul 2025 11:49:03 -0400 Subject: [PATCH 043/103] Changed name of Python package to faith. Moved signal processing utilities to preprocess Added empty subpackage for model training --- notebooks/data_preparation.ipynb | 2 +- pyproject.toml | 14 ++++++++------ src/{fusionaihub => faith}/__init__.py | 0 src/{fusionaihub => faith}/base/__init__.py | 0 src/{fusionaihub => faith}/base/load.py | 0 src/{fusionaihub => faith}/base/merge.py | 0 src/{fusionaihub => faith}/base/save.py | 0 src/{fusionaihub => faith}/core/__init__.py | 0 .../core/fusion_signal/__init__.py | 0 .../core/fusion_signal/interpolation.py | 0 .../core/fusion_signal/resampling.py | 0 .../core/magnitude_scaling/compute_norms.py | 0 .../core/magnitude_scaling/norm.py | 0 .../core/magnitude_scaling/rescale.py | 0 src/{fusionaihub => faith}/core/scaling.py | 0 .../core/spectral_representation/__init__.py | 0 .../core/spectral_representation/sft.py | 0 .../core/time_domain_filtering/__init__.py | 0 .../core/time_domain_filtering/filtering.py | 0 .../core/time_domain_filtering/preemphasis.py | 0 .../core/time_domain_processing/cut_time.py | 0 .../time_domain_processing/get_windowed_data.py | 0 src/{fusionaihub => faith}/datasets/__init__.py | 0 src/{fusionaihub => faith}/datasets/fetch/fetch.py | 0 .../datasets/prepare/README.md | 12 ++++++++---- .../datasets/prepare/__init__.py | 0 .../datasets/prepare/__main__.py | 0 .../datasets/prepare/config/default.yaml | 0 .../datasets/prepare/config/raw.yaml | 0 .../datasets/prepare/data_extraction.py | 0 .../datasets/prepare/dataset_utils.py | 0 .../datasets/prepare/prepare_dataset.py | 0 .../datasets/prepare/sample_processing.py | 0 .../datasets/prepare/shot_processing.py | 0 .../datasets/prepare/signal_processing.py | 0 .../datasets/toy_loader/load.py | 0 src/{fusionaihub => faith}/display/__init__.py | 0 src/{fusionaihub => faith}/display/display.py | 0 src/{fusionaihub => faith}/display/specshow.py | 0 src/{fusionaihub => faith}/display/waveshow.py | 0 src/{fusionaihub => faith}/feature/__init__.py | 0 src/{fusionaihub => faith}/sampling/__init__.py | 0 src/{fusionaihub => faith}/sampling/match_times.py | 0 src/{fusionaihub/util => faith/train}/__init__.py | 0 src/faith/util/__init__.py | 0 src/{fusionaihub => faith}/util/parmap.py | 0 src/{fusionaihub => faith}/util/utils.py | 0 47 files changed, 17 insertions(+), 11 deletions(-) rename src/{fusionaihub => faith}/__init__.py (100%) rename src/{fusionaihub => faith}/base/__init__.py (100%) rename src/{fusionaihub => faith}/base/load.py (100%) rename src/{fusionaihub => faith}/base/merge.py (100%) rename src/{fusionaihub => faith}/base/save.py (100%) rename src/{fusionaihub => faith}/core/__init__.py (100%) rename src/{fusionaihub => faith}/core/fusion_signal/__init__.py (100%) rename src/{fusionaihub => faith}/core/fusion_signal/interpolation.py (100%) rename src/{fusionaihub => faith}/core/fusion_signal/resampling.py (100%) rename src/{fusionaihub => faith}/core/magnitude_scaling/compute_norms.py (100%) rename src/{fusionaihub => faith}/core/magnitude_scaling/norm.py (100%) rename src/{fusionaihub => faith}/core/magnitude_scaling/rescale.py (100%) rename src/{fusionaihub => faith}/core/scaling.py (100%) rename src/{fusionaihub => faith}/core/spectral_representation/__init__.py (100%) rename src/{fusionaihub => faith}/core/spectral_representation/sft.py (100%) rename src/{fusionaihub => faith}/core/time_domain_filtering/__init__.py (100%) rename src/{fusionaihub => faith}/core/time_domain_filtering/filtering.py (100%) rename src/{fusionaihub => faith}/core/time_domain_filtering/preemphasis.py (100%) rename src/{fusionaihub => faith}/core/time_domain_processing/cut_time.py (100%) rename src/{fusionaihub => faith}/core/time_domain_processing/get_windowed_data.py (100%) rename src/{fusionaihub => faith}/datasets/__init__.py (100%) rename src/{fusionaihub => faith}/datasets/fetch/fetch.py (100%) rename src/{fusionaihub => faith}/datasets/prepare/README.md (94%) rename src/{fusionaihub => faith}/datasets/prepare/__init__.py (100%) rename src/{fusionaihub => faith}/datasets/prepare/__main__.py (100%) rename src/{fusionaihub => faith}/datasets/prepare/config/default.yaml (100%) rename src/{fusionaihub => faith}/datasets/prepare/config/raw.yaml (100%) rename src/{fusionaihub => faith}/datasets/prepare/data_extraction.py (100%) rename src/{fusionaihub => faith}/datasets/prepare/dataset_utils.py (100%) rename src/{fusionaihub => faith}/datasets/prepare/prepare_dataset.py (100%) rename src/{fusionaihub => faith}/datasets/prepare/sample_processing.py (100%) rename src/{fusionaihub => faith}/datasets/prepare/shot_processing.py (100%) rename src/{fusionaihub => faith}/datasets/prepare/signal_processing.py (100%) rename src/{fusionaihub => faith}/datasets/toy_loader/load.py (100%) rename src/{fusionaihub => faith}/display/__init__.py (100%) rename src/{fusionaihub => faith}/display/display.py (100%) rename src/{fusionaihub => faith}/display/specshow.py (100%) rename src/{fusionaihub => faith}/display/waveshow.py (100%) rename src/{fusionaihub => faith}/feature/__init__.py (100%) rename src/{fusionaihub => faith}/sampling/__init__.py (100%) rename src/{fusionaihub => faith}/sampling/match_times.py (100%) rename src/{fusionaihub/util => faith/train}/__init__.py (100%) create mode 100644 src/faith/util/__init__.py rename src/{fusionaihub => faith}/util/parmap.py (100%) rename src/{fusionaihub => faith}/util/utils.py (100%) diff --git a/notebooks/data_preparation.ipynb b/notebooks/data_preparation.ipynb index b67b55f..f096a22 100644 --- a/notebooks/data_preparation.ipynb +++ b/notebooks/data_preparation.ipynb @@ -25,7 +25,7 @@ "import yaml\n", "\n", "shot_number = 171348\n", - "yaml_path = \"../src/fusionaihub/datasets/prepare/config/raw.yaml\"\n", + "yaml_path = \"../src/faith/datasets/prepare/config/raw.yaml\"\n", "with open(yaml_path, 'r') as f:\n", " cfg = yaml.safe_load(f)\n", "\n", diff --git a/pyproject.toml b/pyproject.toml index 1f712c4..f9658bb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -2,12 +2,12 @@ name = "fusionaihub" version = "0.0.1" authors = [ - {name="Peter Steiner", email="peter.steiner@princeton.edu"}, - {name="Max Tian Curie", email="max.curie@princeton.edu"}, - {name="Nathaniel Chen", email="nathaniel@princeton.edu"}, - {name="Azarakhsh Jalalvand", email="azarakhsh.jalalvand@princeton.edu"} + { name = "Peter Steiner", email = "peter.steiner@princeton.edu" }, + { name = "Nathaniel Chen", email = "nathaniel@princeton.edu" }, + { name = "Kouroche Bouchiat", email = "bouchiat@princeton.edu" }, + { name = "Azarakhsh Jalalvand", email = "azarakhsh.jalalvand@princeton.edu" } ] -description = "FusionAIHub - Fetch nuclear fusion data, preprocess it, and use it for training machine learning models." +description = "FusionAIHub - Fetch nuclear fusion data, preprocess it, train machine learning models." readme = "README.md" requires-python = ">=3.9" classifiers = [ @@ -15,12 +15,14 @@ classifiers = [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ] -license = {file = "LICENSE"} +license = { file = "LICENSE" } dependencies = [ "h5py", + "scikit-learn", "numpy", "pandas", "matplotlib", + "seaborn", "scipy", "tqdm", "opencv-python", diff --git a/src/fusionaihub/__init__.py b/src/faith/__init__.py similarity index 100% rename from src/fusionaihub/__init__.py rename to src/faith/__init__.py diff --git a/src/fusionaihub/base/__init__.py b/src/faith/base/__init__.py similarity index 100% rename from src/fusionaihub/base/__init__.py rename to src/faith/base/__init__.py diff --git a/src/fusionaihub/base/load.py b/src/faith/base/load.py similarity index 100% rename from src/fusionaihub/base/load.py rename to src/faith/base/load.py diff --git a/src/fusionaihub/base/merge.py b/src/faith/base/merge.py similarity index 100% rename from src/fusionaihub/base/merge.py rename to src/faith/base/merge.py diff --git a/src/fusionaihub/base/save.py b/src/faith/base/save.py similarity index 100% rename from src/fusionaihub/base/save.py rename to src/faith/base/save.py diff --git a/src/fusionaihub/core/__init__.py b/src/faith/core/__init__.py similarity index 100% rename from src/fusionaihub/core/__init__.py rename to src/faith/core/__init__.py diff --git a/src/fusionaihub/core/fusion_signal/__init__.py b/src/faith/core/fusion_signal/__init__.py similarity index 100% rename from src/fusionaihub/core/fusion_signal/__init__.py rename to src/faith/core/fusion_signal/__init__.py diff --git a/src/fusionaihub/core/fusion_signal/interpolation.py b/src/faith/core/fusion_signal/interpolation.py similarity index 100% rename from src/fusionaihub/core/fusion_signal/interpolation.py rename to src/faith/core/fusion_signal/interpolation.py diff --git a/src/fusionaihub/core/fusion_signal/resampling.py b/src/faith/core/fusion_signal/resampling.py similarity index 100% rename from src/fusionaihub/core/fusion_signal/resampling.py rename to src/faith/core/fusion_signal/resampling.py diff --git a/src/fusionaihub/core/magnitude_scaling/compute_norms.py b/src/faith/core/magnitude_scaling/compute_norms.py similarity index 100% rename from src/fusionaihub/core/magnitude_scaling/compute_norms.py rename to src/faith/core/magnitude_scaling/compute_norms.py diff --git a/src/fusionaihub/core/magnitude_scaling/norm.py b/src/faith/core/magnitude_scaling/norm.py similarity index 100% rename from src/fusionaihub/core/magnitude_scaling/norm.py rename to src/faith/core/magnitude_scaling/norm.py diff --git a/src/fusionaihub/core/magnitude_scaling/rescale.py b/src/faith/core/magnitude_scaling/rescale.py similarity index 100% rename from src/fusionaihub/core/magnitude_scaling/rescale.py rename to src/faith/core/magnitude_scaling/rescale.py diff --git a/src/fusionaihub/core/scaling.py b/src/faith/core/scaling.py similarity index 100% rename from src/fusionaihub/core/scaling.py rename to src/faith/core/scaling.py diff --git a/src/fusionaihub/core/spectral_representation/__init__.py b/src/faith/core/spectral_representation/__init__.py similarity index 100% rename from src/fusionaihub/core/spectral_representation/__init__.py rename to src/faith/core/spectral_representation/__init__.py diff --git a/src/fusionaihub/core/spectral_representation/sft.py b/src/faith/core/spectral_representation/sft.py similarity index 100% rename from src/fusionaihub/core/spectral_representation/sft.py rename to src/faith/core/spectral_representation/sft.py diff --git a/src/fusionaihub/core/time_domain_filtering/__init__.py b/src/faith/core/time_domain_filtering/__init__.py similarity index 100% rename from src/fusionaihub/core/time_domain_filtering/__init__.py rename to src/faith/core/time_domain_filtering/__init__.py diff --git a/src/fusionaihub/core/time_domain_filtering/filtering.py b/src/faith/core/time_domain_filtering/filtering.py similarity index 100% rename from src/fusionaihub/core/time_domain_filtering/filtering.py rename to src/faith/core/time_domain_filtering/filtering.py diff --git a/src/fusionaihub/core/time_domain_filtering/preemphasis.py b/src/faith/core/time_domain_filtering/preemphasis.py similarity index 100% rename from src/fusionaihub/core/time_domain_filtering/preemphasis.py rename to src/faith/core/time_domain_filtering/preemphasis.py diff --git a/src/fusionaihub/core/time_domain_processing/cut_time.py b/src/faith/core/time_domain_processing/cut_time.py similarity index 100% rename from src/fusionaihub/core/time_domain_processing/cut_time.py rename to src/faith/core/time_domain_processing/cut_time.py diff --git a/src/fusionaihub/core/time_domain_processing/get_windowed_data.py b/src/faith/core/time_domain_processing/get_windowed_data.py similarity index 100% rename from src/fusionaihub/core/time_domain_processing/get_windowed_data.py rename to src/faith/core/time_domain_processing/get_windowed_data.py diff --git a/src/fusionaihub/datasets/__init__.py b/src/faith/datasets/__init__.py similarity index 100% rename from src/fusionaihub/datasets/__init__.py rename to src/faith/datasets/__init__.py diff --git a/src/fusionaihub/datasets/fetch/fetch.py b/src/faith/datasets/fetch/fetch.py similarity index 100% rename from src/fusionaihub/datasets/fetch/fetch.py rename to src/faith/datasets/fetch/fetch.py diff --git a/src/fusionaihub/datasets/prepare/README.md b/src/faith/datasets/prepare/README.md similarity index 94% rename from src/fusionaihub/datasets/prepare/README.md rename to src/faith/datasets/prepare/README.md index 55787d7..70a6020 100644 --- a/src/fusionaihub/datasets/prepare/README.md +++ b/src/faith/datasets/prepare/README.md @@ -87,15 +87,18 @@ Each signal is configured as a list: `[signal_name, abbreviation, should_transfo ### Command Line ```bash # Use default configuration -python -m src.fusionaihub.datasets.prepare +python -m src.faith.datasets.prepare # Use custom configuration file -python -m src.fusionaihub.datasets.prepare --config /path/to/config.yaml +python -m src.faith.datasets.prepare --config /path/to/config.yaml ``` ### Programmatic Usage + ```python -from src.fusionaihub.datasets.prepare.prepare_dataset import load_config, prepare_dataset +from src.faith.datasets.prepare.prepare_dataset import load_config, + prepare_dataset + # Load configuration cfg = load_config("path/to/config.yaml") @@ -109,7 +112,8 @@ Create a custom YAML file based on `config/default.yaml`: ```python import yaml -from src.fusionaihub.datasets.prepare.prepare_dataset import prepare_dataset +from src.faith.datasets.prepare.prepare_dataset import prepare_dataset + # Load and modify configuration with open("config/default.yaml", "r") as f: diff --git a/src/fusionaihub/datasets/prepare/__init__.py b/src/faith/datasets/prepare/__init__.py similarity index 100% rename from src/fusionaihub/datasets/prepare/__init__.py rename to src/faith/datasets/prepare/__init__.py diff --git a/src/fusionaihub/datasets/prepare/__main__.py b/src/faith/datasets/prepare/__main__.py similarity index 100% rename from src/fusionaihub/datasets/prepare/__main__.py rename to src/faith/datasets/prepare/__main__.py diff --git a/src/fusionaihub/datasets/prepare/config/default.yaml b/src/faith/datasets/prepare/config/default.yaml similarity index 100% rename from src/fusionaihub/datasets/prepare/config/default.yaml rename to src/faith/datasets/prepare/config/default.yaml diff --git a/src/fusionaihub/datasets/prepare/config/raw.yaml b/src/faith/datasets/prepare/config/raw.yaml similarity index 100% rename from src/fusionaihub/datasets/prepare/config/raw.yaml rename to src/faith/datasets/prepare/config/raw.yaml diff --git a/src/fusionaihub/datasets/prepare/data_extraction.py b/src/faith/datasets/prepare/data_extraction.py similarity index 100% rename from src/fusionaihub/datasets/prepare/data_extraction.py rename to src/faith/datasets/prepare/data_extraction.py diff --git a/src/fusionaihub/datasets/prepare/dataset_utils.py b/src/faith/datasets/prepare/dataset_utils.py similarity index 100% rename from src/fusionaihub/datasets/prepare/dataset_utils.py rename to src/faith/datasets/prepare/dataset_utils.py diff --git a/src/fusionaihub/datasets/prepare/prepare_dataset.py b/src/faith/datasets/prepare/prepare_dataset.py similarity index 100% rename from src/fusionaihub/datasets/prepare/prepare_dataset.py rename to src/faith/datasets/prepare/prepare_dataset.py diff --git a/src/fusionaihub/datasets/prepare/sample_processing.py b/src/faith/datasets/prepare/sample_processing.py similarity index 100% rename from src/fusionaihub/datasets/prepare/sample_processing.py rename to src/faith/datasets/prepare/sample_processing.py diff --git a/src/fusionaihub/datasets/prepare/shot_processing.py b/src/faith/datasets/prepare/shot_processing.py similarity index 100% rename from src/fusionaihub/datasets/prepare/shot_processing.py rename to src/faith/datasets/prepare/shot_processing.py diff --git a/src/fusionaihub/datasets/prepare/signal_processing.py b/src/faith/datasets/prepare/signal_processing.py similarity index 100% rename from src/fusionaihub/datasets/prepare/signal_processing.py rename to src/faith/datasets/prepare/signal_processing.py diff --git a/src/fusionaihub/datasets/toy_loader/load.py b/src/faith/datasets/toy_loader/load.py similarity index 100% rename from src/fusionaihub/datasets/toy_loader/load.py rename to src/faith/datasets/toy_loader/load.py diff --git a/src/fusionaihub/display/__init__.py b/src/faith/display/__init__.py similarity index 100% rename from src/fusionaihub/display/__init__.py rename to src/faith/display/__init__.py diff --git a/src/fusionaihub/display/display.py b/src/faith/display/display.py similarity index 100% rename from src/fusionaihub/display/display.py rename to src/faith/display/display.py diff --git a/src/fusionaihub/display/specshow.py b/src/faith/display/specshow.py similarity index 100% rename from src/fusionaihub/display/specshow.py rename to src/faith/display/specshow.py diff --git a/src/fusionaihub/display/waveshow.py b/src/faith/display/waveshow.py similarity index 100% rename from src/fusionaihub/display/waveshow.py rename to src/faith/display/waveshow.py diff --git a/src/fusionaihub/feature/__init__.py b/src/faith/feature/__init__.py similarity index 100% rename from src/fusionaihub/feature/__init__.py rename to src/faith/feature/__init__.py diff --git a/src/fusionaihub/sampling/__init__.py b/src/faith/sampling/__init__.py similarity index 100% rename from src/fusionaihub/sampling/__init__.py rename to src/faith/sampling/__init__.py diff --git a/src/fusionaihub/sampling/match_times.py b/src/faith/sampling/match_times.py similarity index 100% rename from src/fusionaihub/sampling/match_times.py rename to src/faith/sampling/match_times.py diff --git a/src/fusionaihub/util/__init__.py b/src/faith/train/__init__.py similarity index 100% rename from src/fusionaihub/util/__init__.py rename to src/faith/train/__init__.py diff --git a/src/faith/util/__init__.py b/src/faith/util/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/fusionaihub/util/parmap.py b/src/faith/util/parmap.py similarity index 100% rename from src/fusionaihub/util/parmap.py rename to src/faith/util/parmap.py diff --git a/src/fusionaihub/util/utils.py b/src/faith/util/utils.py similarity index 100% rename from src/fusionaihub/util/utils.py rename to src/faith/util/utils.py From 4013b095fec670e22a700149156dac03cd73b2a5 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Tue, 15 Jul 2025 11:49:08 -0400 Subject: [PATCH 044/103] Refactor data processing scripts for improved performance, enhance logging in data preparation, and update YAML configuration for better signal handling. Adjust Jupyter notebook settings for consistency and clarity. --- .github/workflows/python-publish.yml | 70 ++++++++++++++++++++++++++++ 1 file changed, 70 insertions(+) create mode 100644 .github/workflows/python-publish.yml diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml new file mode 100644 index 0000000..82f8dbd --- /dev/null +++ b/.github/workflows/python-publish.yml @@ -0,0 +1,70 @@ +# This workflow will upload a Python Package to PyPI when a release is created +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries + +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. + +name: Upload Python Package + +on: + release: + types: [published] + +permissions: + contents: read + +jobs: + release-build: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-python@v5 + with: + python-version: "3.x" + + - name: Build release distributions + run: | + # NOTE: put your own distribution build steps here. + python -m pip install build + python -m build + + - name: Upload distributions + uses: actions/upload-artifact@v4 + with: + name: release-dists + path: dist/ + + pypi-publish: + runs-on: ubuntu-latest + needs: + - release-build + permissions: + # IMPORTANT: this permission is mandatory for trusted publishing + id-token: write + + # Dedicated environments with protections for publishing are strongly recommended. + # For more information, see: https://docs.github.com/en/actions/deployment/targeting-different-environments/using-environments-for-deployment#deployment-protection-rules + environment: + name: pypi + # OPTIONAL: uncomment and update to include your PyPI project URL in the deployment status: + # url: https://pypi.org/p/YOURPROJECT + # + # ALTERNATIVE: if your GitHub Release name is the PyPI project version string + # ALTERNATIVE: exactly, uncomment the following line instead: + # url: https://pypi.org/project/YOURPROJECT/${{ github.event.release.name }} + + steps: + - name: Retrieve release distributions + uses: actions/download-artifact@v4 + with: + name: release-dists + path: dist/ + + - name: Publish release distributions to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + packages-dir: dist/ From 99241a5e12a2c96d996bbfdce086c11b698a8fef Mon Sep 17 00:00:00 2001 From: Peter Steiner <61472983+renierts@users.noreply.github.com> Date: Tue, 15 Jul 2025 12:13:28 -0400 Subject: [PATCH 045/103] Added first blocks for model training. Added one empty unit test in order to having the pytest directory set up. --- pyproject.toml | 18 +- requirements.txt | 10 - src/faith/train/blocks/__init__.py | 11 + src/faith/train/blocks/base.py | 384 +++++++++++++++ src/faith/train/blocks/decoder.py | 667 ++++++++++++++++++++++++++ src/faith/train/blocks/encoder.py | 578 ++++++++++++++++++++++ src/faith/train/blocks/residual.py | 389 +++++++++++++++ src/faith/train/models/__init__.py | 92 ++++ src/faith/train/models/autoencoder.py | 452 +++++++++++++++++ src/faith/train/models/configs.py | 634 ++++++++++++++++++++++++ src/faith/train/models/mae.py | 618 ++++++++++++++++++++++++ src/faith/train/models/utils.py | 571 ++++++++++++++++++++++ tests/test_residual_block.py | 0 13 files changed, 4407 insertions(+), 17 deletions(-) delete mode 100644 requirements.txt create mode 100644 src/faith/train/blocks/__init__.py create mode 100644 src/faith/train/blocks/base.py create mode 100644 src/faith/train/blocks/decoder.py create mode 100644 src/faith/train/blocks/encoder.py create mode 100644 src/faith/train/blocks/residual.py create mode 100644 src/faith/train/models/__init__.py create mode 100644 src/faith/train/models/autoencoder.py create mode 100644 src/faith/train/models/configs.py create mode 100644 src/faith/train/models/mae.py create mode 100644 src/faith/train/models/utils.py create mode 100644 tests/test_residual_block.py diff --git a/pyproject.toml b/pyproject.toml index f9658bb..b69c43b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,14 +25,18 @@ dependencies = [ "seaborn", "scipy", "tqdm", - "opencv-python", "paramiko", - "ipykernel>=6.29.5", - "ipywidgets>=8.1.7", - "scikit-learn>=1.6.1", - "torch>=2.7.1", - "tables>=3.9.2", - "pyyaml>=6.0.2", + "ipykernel", + "ipywidgets", + "torch", + "torchvision", + "torchaudio", + "torchmetrics", + "lightning", + "ray[data,train,tune,serve]", + "tables", + "pyyaml", + "jupyter", ] [tool.uv.sources] diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index a2c1898..0000000 --- a/requirements.txt +++ /dev/null @@ -1,10 +0,0 @@ -torch==1.12.1+cu116 -torchvision==0.13.1+cu116 -torchaudio==0.12.1+cu116 -numpy -pandas -scikit-learn -matplotlib -seaborn -jupyter -h5py \ No newline at end of file diff --git a/src/faith/train/blocks/__init__.py b/src/faith/train/blocks/__init__.py new file mode 100644 index 0000000..352d19b --- /dev/null +++ b/src/faith/train/blocks/__init__.py @@ -0,0 +1,11 @@ +"""Neural network blocks for building autoencoders.""" + +from .residual import ResidualBlock +from .encoder import EncoderBlock, BlockBasedEncoder +from .decoder import DecoderBlock, BlockBasedDecoder +from .base import BaseBlock + +__all__ = ["ResidualBlock", + "EncoderBlock", "BlockBasedEncoder", + "DecoderBlock", "BlockBasedDecoder", + "BaseBlock"] diff --git a/src/faith/train/blocks/base.py b/src/faith/train/blocks/base.py new file mode 100644 index 0000000..f20c23e --- /dev/null +++ b/src/faith/train/blocks/base.py @@ -0,0 +1,384 @@ +"""Base classes and utilities for neural network blocks. + +This module provides abstract base classes, common utilities, and shared +functionality that can be inherited by specific block implementations. +It ensures consistency across different block types and provides common +patterns for initialization, forward passes, and configuration. +""" + +import torch +import torch.nn as nn +from abc import ABC, abstractmethod +from typing import Union, Any, Optional +import math + + +class BaseBlock(nn.Module, ABC): + """Abstract base class for all neural network blocks. + + This class defines the common interface that all blocks should implement, + ensuring consistency across different block types in the autoencoder + architecture. + + Parameters + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + kernel_size : int or tuple of int, default=3 + Kernel size for convolutions. + bias : bool, default=True + Whether to use bias in convolution layers. + + Attributes + ---------- + in_channels : int + Stored input channel count. + out_channels : int + Stored output channel count. + kernel_size : int or tuple + Stored kernel size. + bias : bool + Stored bias setting. + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: Union[int, tuple[int, int]] = 3, + bias: bool = True, + **kwargs + ) -> None: + super().__init__() + + if in_channels <= 0: + raise ValueError( + f"in_channels must be positive, got {in_channels}") + if out_channels <= 0: + raise ValueError( + f"out_channels must be positive, got {out_channels}") + + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = self._normalize_kernel_size(kernel_size) + self.bias = bias + + @staticmethod + def _normalize_kernel_size(kernel_size: Union[int, tuple[int, int]]) \ + -> tuple[int, int]: + """Normalize kernel size to tuple format.""" + if isinstance(kernel_size, int): + return (kernel_size, kernel_size) + return kernel_size + + @staticmethod + def _calculate_padding( + kernel_size: Union[int, tuple[int, int]], + padding: Union[int, tuple[int, int], str]) \ + -> tuple[int, ...]: + """Calculate padding based on kernel size and padding specification.""" + if padding == 'auto': + if isinstance(kernel_size, int): + return (kernel_size // 2, kernel_size // 2) + else: + return tuple(k // 2 for k in kernel_size) + elif isinstance(padding, int): + return (padding, padding) + else: + return padding + + @abstractmethod + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass through the block. + + Parameters + ---------- + x : torch.Tensor + Input tensor. + + Returns + ------- + torch.Tensor + Output tensor. + """ + pass + + def get_config(self) -> dict[str, Any]: + """ + Get configuration dictionary for this block. + + Returns + ------- + dict + Configuration dictionary containing all parameters needed + to reconstruct this block. + """ + return { + 'in_channels': self.in_channels, + 'out_channels': self.out_channels, + 'kernel_size': self.kernel_size, + 'bias': self.bias, + } + + @property + def parameter_count(self) -> int: + """Get total number of trainable parameters in this block.""" + return sum(p.numel() for p in self.parameters() if p.requires_grad) + + def __repr__(self) -> str: + return (f"{self.__class__.__name__}(" + f"in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}, " + f"bias={self.bias})") + + +class SequentialBlock(BaseBlock): + """Base class for blocks that apply operations sequentially. + + This class provides common functionality for blocks that consist of + multiple sequential operations (like EncoderBlock and DecoderBlock). + + Parameters + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + operations : list of nn.Module + List of operations to apply sequentially. + **kwargs + Additional arguments passed to BaseBlock. + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + operations: Optional[list[nn.Module]] = None, + **kwargs + ) -> None: + super().__init__(in_channels, out_channels, **kwargs) + + if operations is None: + operations = [] + + self.operations = nn.Sequential(*operations) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass through sequential operations.""" + return self.operations(x) + + def add_operation(self, operation: nn.Module) -> None: + """Add an operation to the sequential block.""" + self.operations.add_module(str(len(self.operations)), operation) + + +class ConfigurableBlock(BaseBlock): + """ + Base class for blocks with extensive configuration options. + + This class provides utilities for blocks that need to handle complex + configuration dictionaries and parameter validation. + """ + + def __init__(self, **kwargs) -> None: + # Extract base parameters + in_channels = kwargs.pop('in_channels') + out_channels = kwargs.pop('out_channels') + kernel_size = kwargs.pop('kernel_size', 3) + bias = kwargs.pop('bias', True) + + super().__init__(in_channels, out_channels, kernel_size, bias) + + # Store additional configuration + self._config = kwargs + + def get_config(self) -> dict[str, Any]: + """Get full configuration including additional parameters.""" + config = super().get_config() + config.update(self._config) + return config + + @classmethod + def from_config(cls, config: dict[str, Any]) -> 'ConfigurableBlock': + """Create block instance from configuration dictionary.""" + return cls(**config) + + +class BlockRegistry: + """Registry for different block types. + + This class provides a way to register and retrieve different block + implementations, making it easy to create blocks from string names + or configuration files. + """ + + _registry: dict[str, type] = {} + + @classmethod + def register(cls, name: str, block_class: type) -> None: + """Register a block class with a given name.""" + if not issubclass(block_class, BaseBlock): + raise ValueError( + f"Block class must inherit from BaseBlock, got {block_class}") + cls._registry[name] = block_class + + @classmethod + def get(cls, name: str) -> type: + """Get a block class by name.""" + if name not in cls._registry: + raise KeyError(f"Block '{name}' not found in registry. " + f"Available blocks: {list(cls._registry.keys())}") + return cls._registry[name] + + @classmethod + def create(cls, name: str, **kwargs) -> BaseBlock: + """Create a block instance by name.""" + block_class = cls.get(name) + return block_class(**kwargs) + + @classmethod + def list_blocks(cls) -> list[str]: + """List all registered block names.""" + return list(cls._registry.keys()) + + +def register_block(name: str): + """Decorator to register a block class.""" + + def decorator(block_class: type): + BlockRegistry.register(name, block_class) + return block_class + + return decorator + + +class WeightInitializer: + """Utilities for weight initialization in blocks.""" + + @staticmethod + def xavier_uniform_(module: nn.Module) -> None: + """Apply Xavier uniform initialization to conv and linear layers.""" + if isinstance(module, (nn.Conv2d, nn.Linear)): + nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.zeros_(module.bias) + + @staticmethod + def kaiming_normal_(module: nn.Module) -> None: + """Apply Kaiming normal initialization to conv and linear layers.""" + if isinstance(module, (nn.Conv2d, nn.Linear)): + nn.init.kaiming_normal_(module.weight, mode='fan_out', + nonlinearity='relu') + if module.bias is not None: + nn.init.zeros_(module.bias) + + @staticmethod + def init_batch_norm_(module: nn.Module) -> None: + """Initialize batch normalization layers.""" + if isinstance(module, nn.BatchNorm2d): + nn.init.ones_(module.weight) + nn.init.zeros_(module.bias) + + +class BlockUtils: + """Utility functions for working with blocks.""" + + @staticmethod + def calculate_output_shape( + input_shape: tuple[int, ...], + kernel_size: Union[int, tuple[int, int]], + stride: Union[int, tuple[int, int]] = 1, + padding: Union[int, tuple[int, int]] = 0, + dilation: Union[int, tuple[int, int]] = 1) \ + -> tuple[int, ...]: + """Calculate output shape after convolution operation.""" + if isinstance(kernel_size, int): + kernel_size = (kernel_size, kernel_size) + if isinstance(stride, int): + stride = (stride, stride) + if isinstance(padding, int): + padding = (padding, padding) + if isinstance(dilation, int): + dilation = (dilation, dilation) + + batch_size, channels = input_shape[:2] + height, width = input_shape[2:] + + out_height = math.floor( + (height + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1) + / stride[0] + 1 + ) + out_width = math.floor( + (width + 2 * padding[1] - dilation[1] * (kernel_size[1] - 1) - 1) / + stride[1] + 1 + ) + + return (batch_size, channels, out_height, out_width) + + @staticmethod + def count_parameters(block: nn.Module, trainable_only: bool = True) -> int: + """Count parameters in a block.""" + if trainable_only: + return sum( + p.numel() for p in block.parameters() if p.requires_grad) + else: + return sum(p.numel() for p in block.parameters()) + + @staticmethod + def get_memory_usage(block: nn.Module, input_shape: tuple[int, ...]) \ + -> dict[str, float]: + """Estimate memory usage of a block.""" + # This is a simplified estimation + param_memory = BlockUtils.count_parameters( + block) * 4 # 4 bytes per float32 + + # Rough estimation of activation memory + output_elements = math.prod(input_shape) + activation_memory = output_elements * 4 # 4 bytes per float32 + + return { + 'parameters_mb': param_memory / (1024 * 1024), + 'activations_mb': activation_memory / (1024 * 1024), + 'total_mb': (param_memory + activation_memory) / (1024 * 1024) + } + + +# Example usage and testing +if __name__ == "__main__": + # Example of how the base classes would be used + + class ExampleBlock(BaseBlock): + """Example implementation of BaseBlock.""" + + def __init__(self, in_channels: int, out_channels: int, **kwargs): + super().__init__(in_channels, out_channels, **kwargs) + self.conv = nn.Conv2d(in_channels, out_channels, + kernel_size=self.kernel_size) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.conv(x) + + + # Register the block + BlockRegistry.register('example', ExampleBlock) + + # Create block from registry + block = BlockRegistry.create('example', in_channels=64, out_channels=128) + print(f"Created block: {block}") + print(f"Parameter count: {block.parameter_count}") + print(f"Config: {block.get_config()}") + + # Test utility functions + input_shape = (1, 64, 32, 32) + memory_info = BlockUtils.get_memory_usage(block, input_shape) + print(f"Memory usage: {memory_info}") + + output_shape = BlockUtils.calculate_output_shape( + input_shape, kernel_size=3, stride=1, padding=1 + ) + print(f"Output shape: {output_shape}") diff --git a/src/faith/train/blocks/decoder.py b/src/faith/train/blocks/decoder.py new file mode 100644 index 0000000..dae1c76 --- /dev/null +++ b/src/faith/train/blocks/decoder.py @@ -0,0 +1,667 @@ +"""Decoder block implementations derived from base classes. + +This module implements the DecoderBlock and BlockBasedDecoder classes that +inherit from the base classes, following established patterns and interfaces. +The decoder creates a symmetric reconstruction path to the encoder. +""" + +import torch +import torch.nn as nn +from typing import Union, Any, Optional +from .base import (SequentialBlock, ConfigurableBlock, register_block, + WeightInitializer) +from torch_training.blocks.residual import ResidualBlock +from . import EncoderBlock + + +@register_block('decoder') +class DecoderBlock(SequentialBlock): + # TODO: ConvTranspose2d + """Single decoder block: Upsample + ResidualBlock + Dropout. + + This block represents the fundamental building unit of the decoder, + combining spatial upsampling, feature refinement through ResidualBlock, + and regularization through Dropout. + + Parameters + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels from the ResidualBlock. + upsample_factor : tuple of int, default=(1, 2) + Scale factor for upsampling. Format: (height_factor, width_factor). + kernel_size : int or tuple of int, default=3 + Kernel size for convolutions in ResidualBlock. + stride : int or tuple of int, default=1 + Stride for convolutions in ResidualBlock. The DecoderBlock uses + stride=1 and relies on Upsample for dimension changes. + padding : int, tuple of int, or str, default='auto' + Padding for convolutions in ResidualBlock. 'auto' calculates + padding to maintain spatial dimensions. + dropout : float, default=0.3 + Dropout probability. Must be between 0.0 and 1.0. + bias : bool, default=True + Whether to use bias in convolution layers. + use_batch_norm : bool, default=True + Whether to use batch normalization in ResidualBlock. + activation : str, default='relu' + Activation function for ResidualBlock. + upsampling_mode : str, default='nearest' + Upsampling algorithm. Options: 'nearest', 'linear', 'bilinear', + 'bicubic', 'trilinear', 'area'. + residual_init_method : str, default='kaiming' + Weight initialization method for ResidualBlock. + + Attributes + ---------- + upsample : nn.Upsample + Upsampling layer for spatial dimension restoration. + residual_block : ResidualBlock + The residual convolutional block for feature refinement. + dropout : nn.Dropout + Dropout layer for regularization. + upsample_factor : tuple of int + Stored upsampling factor. + dropout_prob : float + Stored dropout probability. + upsampling_mode : str + Stored upsampling mode. + + Examples + -------- + >>> block = DecoderBlock(in_channels=128, out_channels=64) + >>> x = torch.randn(1, 128, 16, 8) + >>> out = block(x) + >>> print(out.shape) + torch.Size([1, 64, 16, 16]) + + >>> # Custom configuration + >>> block = DecoderBlock( + ... in_channels=128, out_channels=64, + ... upsample_factor=(2, 2), upsampling_mode='bilinear', + ... activation='gelu' + ... ) + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + upsample_factor: tuple[int, int] = (1, 2), + kernel_size: Union[int, tuple[int, int]] = 3, + stride: Union[int, tuple[int, int]] = 1, + padding: Union[int, tuple[int, int], str] = 'auto', + dropout: float = 0.3, + bias: bool = True, + use_batch_norm: bool = True, + activation: str = 'relu', + upsampling_mode: str = 'nearest', + residual_init_method: str = 'kaiming' + ) -> None: + """Initialize DecoderBlock.""" + + # Validate parameters + if not 0.0 <= dropout <= 1.0: + raise ValueError( + f"Dropout must be between 0.0 and 1.0, got {dropout}") + + if len(upsample_factor) != 2: + raise ValueError(f"upsample_factor must be a tuple of length 2, " + f"got {upsample_factor}") + + valid_modes = { + 'nearest', 'linear', 'bilinear', 'bicubic', 'trilinear', 'area'} + if upsampling_mode not in valid_modes: + raise ValueError(f"upsampling_mode must be one of {valid_modes}, " + f"got {upsampling_mode}") + + # Store configuration + self.upsample_factor = upsample_factor + self.dropout_prob = dropout + self.upsampling_mode = upsampling_mode + self.use_batch_norm = use_batch_norm + self.activation_name = activation + self.residual_init_method = residual_init_method + + # Build the sequential operations + operations = self._build_operations( + in_channels, out_channels, kernel_size, stride, padding, + bias, use_batch_norm, activation, residual_init_method + ) + + # Initialize SequentialBlock with operations + super().__init__( + in_channels=in_channels, + out_channels=out_channels, + operations=operations, + kernel_size=kernel_size, + bias=bias + ) + + # Store individual components for introspection + self.upsample = self.operations[0] + self.residual_block = self.operations[1] + self.dropout = self.operations[2] + + def _build_operations( + self, + in_channels: int, + out_channels: int, + kernel_size: Union[int, tuple[int, int]], + stride: Union[int, tuple[int, int]], + padding: Union[int, tuple[int, int], str], + bias: bool, + use_batch_norm: bool, + activation: str, + init_method: str + ) -> list[nn.Module]: + """Build the list of operations for this decoder block.""" + + operations = [] + + # 1. Upsample for spatial dimension restoration + upsample_layer = nn.Upsample( + scale_factor=self.upsample_factor, + mode=self.upsampling_mode + ) + operations.append(upsample_layer) + + # 2. ResidualBlock for feature refinement + residual_block = ResidualBlock( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + bias=bias, + use_batch_norm=use_batch_norm, + activation=activation, + init_method=init_method + ) + operations.append(residual_block) + + # 3. Dropout for regularization + dropout_layer = nn.Dropout(p=self.dropout_prob) + operations.append(dropout_layer) + + return operations + + def get_config(self) -> dict[str, Any]: + """Get configuration dictionary for this block.""" + config = super().get_config() + config.update({ + 'upsample_factor': self.upsample_factor, + 'dropout': self.dropout_prob, + 'upsampling_mode': self.upsampling_mode, + 'use_batch_norm': self.use_batch_norm, + 'activation': self.activation_name, + 'residual_init_method': self.residual_init_method, + 'stride': getattr(self.residual_block, 'stride', 1), + 'padding': getattr(self.residual_block, 'padding', 'auto'), + }) + return config + + @classmethod + def from_config(cls, config: dict[str, Any]) -> 'DecoderBlock': + """Create DecoderBlock instance from configuration dictionary.""" + return cls(**config) + + def get_output_shape( + self, + input_shape: tuple[int, ...] + ) -> tuple[int, ...]: + """Calculate output shape given input shape.""" + batch_size, channels, height, width = input_shape + + # Apply upsampling + upsampled_height = height * self.upsample_factor[0] + upsampled_width = width * self.upsample_factor[1] + + # ResidualBlock changes channels but maintains spatial dimensions + return (batch_size, self.out_channels, upsampled_height, + upsampled_width) + + def __repr__(self) -> str: + """String representation of the DecoderBlock.""" + return (f"DecoderBlock(" + f"in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"upsample_factor={self.upsample_factor}, " + f"dropout={self.dropout_prob}, " + f"upsampling_mode='{self.upsampling_mode}', " + f"activation='{self.activation_name}')") + + +class BlockBasedDecoder(ConfigurableBlock): + """Decoder architecture built from a sequence of DecoderBlocks. + + This decoder mirrors the encoder architecture by using the encoder's + block configuration to create a symmetric upsampling path. Each decoding + stage consists of a DecoderBlock (Upsample + ResidualBlock + Dropout). + + Parameters + ---------- + output_channels : int + Number of channels in the final output (should match encoder input). + encoder_blocks : list of EncoderBlock + List of encoder blocks to create symmetric decoder from. + bottleneck_channels : int + Number of channels from the encoder's bottleneck. + kernel_size : int or tuple of int, default=3 + Default kernel size for convolutions. + bias : bool, default=True + Default bias setting for convolutions. + upsampling_mode : str, default='nearest' + Upsampling algorithm for all decoder blocks. + use_batch_norm : bool, default=True + Whether to use batch normalization in blocks. + activation : str, default='relu' + Default activation function for blocks. + init_method : str, default='kaiming' + Weight initialization method. + reconstruction_kernel_size : int or tuple of int, optional + Kernel size for final reconstruction layer. If None, uses kernel_size. + + Attributes + ---------- + decoder_start : nn.Sequential + Initial layers to process bottleneck output. + blocks : nn.ModuleList + List of DecoderBlock modules. + reconstruction : nn.Conv2d + Final reconstruction convolution layer. + output_channels : int + Number of output channels. + upsampling_mode : str + Upsampling mode used throughout decoder. + """ + + def __init__( + self, + output_channels: int, + encoder_blocks: list[EncoderBlock], + bottleneck_channels: int, + kernel_size: Union[int, tuple[int, int]] = 3, + bias: bool = True, + upsampling_mode: str = 'nearest', + use_batch_norm: bool = True, + activation: str = 'relu', + init_method: str = 'kaiming', + reconstruction_kernel_size: + Optional[Union[int, tuple[int, int]]] = None, + **kwargs + ) -> None: + """Initialize BlockBasedDecoder.""" + + # Initialize ConfigurableBlock + super().__init__( + in_channels=bottleneck_channels, + out_channels=output_channels, + kernel_size=kernel_size, + bias=bias, + encoder_blocks=encoder_blocks, + bottleneck_channels=bottleneck_channels, + upsampling_mode=upsampling_mode, + use_batch_norm=use_batch_norm, + activation=activation, + init_method=init_method, + reconstruction_kernel_size=reconstruction_kernel_size, + **kwargs + ) + + # Validate inputs + if output_channels <= 0: + raise ValueError( + f"output_channels must be positive, got {output_channels}") + + if bottleneck_channels <= 0: + raise ValueError(f"bottleneck_channels must be positive, " + f"got {bottleneck_channels}") + + self.output_channels = output_channels + self.encoder_blocks = encoder_blocks + self.bottleneck_channels = bottleneck_channels + self.upsampling_mode = upsampling_mode + self.use_batch_norm = use_batch_norm + self.activation_name = activation + self.init_method = init_method + + if reconstruction_kernel_size is None: + reconstruction_kernel_size = kernel_size + self.reconstruction_kernel_size = reconstruction_kernel_size + + # Build decoder components + self.decoder_start = self._build_decoder_start(kernel_size, bias) + self.blocks = self._build_decoder_blocks() + self.reconstruction = self._build_reconstruction_layer() + + # Initialize weights + self._initialize_weights() + + def _build_decoder_start( + self, + kernel_size: Union[int, tuple[int, int]], + bias: bool + ) -> nn.Sequential: + """Build the initial decoder layers to process bottleneck output.""" + # Calculate padding for convolution + if isinstance(kernel_size, int): + padding = kernel_size // 2 + else: + padding = tuple(k // 2 for k in kernel_size) + + # Determine first block channels + first_block_channels = ( + self.encoder_blocks[-1].out_channels + if self.encoder_blocks + else self.bottleneck_channels + ) + + layers = [ + nn.Conv2d( + self.bottleneck_channels, + first_block_channels, + kernel_size=kernel_size, + padding=padding, + bias=bias and not self.use_batch_norm + ) + ] + + if self.use_batch_norm: + layers.append(nn.BatchNorm2d(first_block_channels)) + + layers.append(self._create_activation(self.activation_name)) + + return nn.Sequential(*layers) + + def _build_decoder_blocks(self) -> nn.ModuleList: + """Build decoder blocks by mirroring encoder blocks.""" + blocks = [] + + # Get the output channels from decoder_start + current_channels = ( + self.encoder_blocks[-1].out_channels + if self.encoder_blocks + else self.bottleneck_channels + ) + + # Create symmetric decoder by reversing encoder blocks + for i, encoder_block in enumerate(reversed(self.encoder_blocks)): + # Determine output channels for this decoder block + if i == len(self.encoder_blocks) - 1: + # Last block outputs to final channels + out_channels = self.output_channels + else: + # Use the input channels of the corresponding encoder block + corresponding_encoder_idx = len(self.encoder_blocks) - 2 - i + out_channels = ( + self.encoder_blocks[corresponding_encoder_idx].in_channels) + + # Create decoder block configuration + block_config = self._create_decoder_block_config( + encoder_block, current_channels, out_channels + ) + + # Create decoder block + decoder_block = DecoderBlock(**block_config) + blocks.append(decoder_block) + current_channels = out_channels + + return nn.ModuleList(blocks) + + def _create_decoder_block_config( + self, + encoder_block: EncoderBlock, + in_channels: int, + out_channels: int + ) -> dict[str, Any]: + """Create configuration for a decoder block based on encoder block.""" + return { + 'in_channels': in_channels, + 'out_channels': out_channels, + 'upsample_factor': encoder_block.pool_size, # Mirror the pooling + 'kernel_size': self.kernel_size, + 'stride': 1, # Always use stride=1 in decoder + 'padding': 'auto', + 'dropout': encoder_block.dropout_prob, # Match encoder dropout + 'bias': self.bias, + 'use_batch_norm': self.use_batch_norm, + 'activation': self.activation_name, + 'upsampling_mode': self.upsampling_mode, + 'residual_init_method': self.init_method, + } + + def _build_reconstruction_layer(self) -> nn.Conv2d: + """Build the final reconstruction convolution layer.""" + # Calculate padding for reconstruction layer + if isinstance(self.reconstruction_kernel_size, int): + padding = self.reconstruction_kernel_size // 2 + else: + padding = tuple(k // 2 for k in self.reconstruction_kernel_size) + + return nn.Conv2d( + self.output_channels, + self.output_channels, + kernel_size=self.reconstruction_kernel_size, + padding=padding, + bias=self.bias + ) + + def _create_activation(self, activation: str) -> nn.Module: + """Create activation function based on name.""" + activations = { + 'relu': nn.ReLU(inplace=True), + 'leaky_relu': nn.LeakyReLU(0.1, inplace=True), + 'gelu': nn.GELU(), + 'swish': nn.SiLU(), + 'mish': nn.Mish(), + } + if activation not in activations: + raise ValueError(f"Unknown activation: {activation}") + return activations[activation] + + def _initialize_weights(self) -> None: + """Initialize weights according to the specified method.""" + if self.init_method == 'kaiming': + self.apply(WeightInitializer.kaiming_normal_) + elif self.init_method == 'xavier': + self.apply(WeightInitializer.xavier_uniform_) + + # Always properly initialize batch norm + if self.use_batch_norm: + self.apply(WeightInitializer.init_batch_norm_) + + def forward(self, z: torch.Tensor) -> torch.Tensor: + """Forward pass through the decoder. + + Parameters + ---------- + z : torch.Tensor + Latent representation with shape + (batch_size, bottleneck_channels, height, width). + + Returns + ------- + torch.Tensor + Reconstructed output with shape + (batch_size, output_channels, height', width') where height' + and width' are restored to approximate original input dimensions. + """ + # Process through decoder start + x = self.decoder_start(z) + + # Process through decoder blocks + for block in self.blocks: + x = block(x) + + # Final reconstruction + x = self.reconstruction(x) + + return x + + def get_feature_maps(self, z: torch.Tensor) -> list[torch.Tensor]: + """Get intermediate feature maps from each decoder block. + + Useful for visualization and debugging. + + Parameters + ---------- + z : torch.Tensor + Latent representation. + + Returns + ------- + list of torch.Tensor + Feature maps after each decoder block. + """ + feature_maps = [] + + # Process through decoder start + x = self.decoder_start(z) + feature_maps.append(x.clone()) + + # Process through decoder blocks + for block in self.blocks: + x = block(x) + feature_maps.append(x.clone()) + + return feature_maps + + def get_output_shape( + self, + input_shape: tuple[int, ...] + ) -> tuple[int, ...]: + """Calculate output shape given input shape.""" + current_shape = input_shape + + # Apply decoder start (changes channels but not spatial dims) + batch_size, _, height, width = current_shape + first_channels = ( + self.encoder_blocks[-1].out_channels + if self.encoder_blocks + else self.bottleneck_channels + ) + current_shape = (batch_size, first_channels, height, width) + + # Apply each decoder block + for block in self.blocks: + current_shape = block.get_output_shape(current_shape) + + # Final reconstruction doesn't change shape + return current_shape + + @classmethod + def from_encoder( + cls, + encoder_blocks: list[EncoderBlock], + bottleneck_channels: int, + output_channels: int, + **kwargs + ) -> 'BlockBasedDecoder': + """Create decoder that mirrors the given encoder blocks. + + Parameters + ---------- + encoder_blocks : list of EncoderBlock + Encoder blocks to mirror. + bottleneck_channels : int + Number of channels from encoder bottleneck. + output_channels : int + Number of output channels. + **kwargs + Additional arguments for decoder configuration. + + Returns + ------- + BlockBasedDecoder + Configured decoder instance. + """ + return cls( + output_channels=output_channels, + encoder_blocks=encoder_blocks, + bottleneck_channels=bottleneck_channels, + **kwargs + ) + + def __repr__(self) -> str: + """String representation of the BlockBasedDecoder.""" + return (f"BlockBasedDecoder(" + f"output_channels={self.output_channels}, " + f"num_blocks={len(self.blocks)}, " + f"bottleneck_channels={self.bottleneck_channels}, " + f"upsampling_mode='{self.upsampling_mode}')") + + +# Example usage and testing +if __name__ == "__main__": + # Test DecoderBlock + print("Testing DecoderBlock...") + decoder_block = DecoderBlock( + in_channels=128, + out_channels=64, + upsample_factor=(1, 2), + dropout=0.3, + upsampling_mode='nearest', + activation='relu' + ) + + x = torch.randn(1, 128, 16, 8) + output = decoder_block(x) + print(f"DecoderBlock - Input: {x.shape}, Output: {output.shape}") + + # Test configuration + config = decoder_block.get_config() + new_block = DecoderBlock.from_config(config) + print(f"Config serialization successful: {new_block}") + + # Test BlockBasedDecoder with mock encoder blocks + print("\nTesting BlockBasedDecoder...") + + # Create mock encoder blocks for testing + from src import EncoderBlock + + + mock_encoder_blocks = [ + EncoderBlock(80, 128, pool_size=(1, 2)), + EncoderBlock(128, 256, pool_size=(1, 4)), + EncoderBlock(256, 128, pool_size=(1, 2)), + ] + + decoder = BlockBasedDecoder( + output_channels=80, + encoder_blocks=mock_encoder_blocks, + bottleneck_channels=64, + upsampling_mode='nearest' + ) + + # Test forward pass + latent = torch.randn(2, 64, 25, 4) + reconstructed = decoder(latent) + print(f"Decoder - Input: {latent.shape}, Output: {reconstructed.shape}") + + # Test feature map extraction + feature_maps = decoder.get_feature_maps(latent) + print(f"Feature maps shapes: {[fm.shape for fm in feature_maps]}") + + # Test from_encoder class method + decoder2 = BlockBasedDecoder.from_encoder( + encoder_blocks=mock_encoder_blocks, + bottleneck_channels=64, + output_channels=80, + upsampling_mode='bilinear' + ) + print(f"Decoder from encoder: {decoder2}") + + # Test registry + from src import BlockRegistry + + + registry_block = BlockRegistry.create( + 'decoder', + in_channels=64, + out_channels=32, + upsample_factor=(2, 2), + upsampling_mode='bilinear' + ) + print(f"Registry block: {registry_block}") diff --git a/src/faith/train/blocks/encoder.py b/src/faith/train/blocks/encoder.py new file mode 100644 index 0000000..d1d6735 --- /dev/null +++ b/src/faith/train/blocks/encoder.py @@ -0,0 +1,578 @@ +"""Encoder block implementations derived from base classes. + +This module implements the EncoderBlock and BlockBasedEncoder classes that +inherit from the base classes, following established patterns and interfaces. +""" + +import torch +import torch.nn as nn +from typing import Union, Any, Optional +from .base import (SequentialBlock, ConfigurableBlock, register_block, + WeightInitializer) +from .residual import ResidualBlock + + +@register_block('encoder') +class EncoderBlock(SequentialBlock): + """Single encoder block: ResidualBlock + Dropout + MaxPool. + + This block represents the fundamental building unit of the encoder, + combining feature extraction through ResidualBlock, regularization + through Dropout, and spatial downsampling through MaxPooling. + + Parameters + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels from the ResidualBlock. + pool_size : tuple of int, default=(1, 2) + Kernel size for MaxPool2d operation. Format: (height, width). + kernel_size : int or tuple of int, default=3 + Kernel size for convolutions in ResidualBlock. + stride : int or tuple of int, default=1 + Stride for convolutions in ResidualBlock. The EncoderBlock uses + stride=1 and relies on MaxPool for downsampling. + padding : int, tuple of int, or str, default='auto' + Padding for convolutions in ResidualBlock. 'auto' calculates + padding to maintain spatial dimensions. + dropout : float, default=0.3 + Dropout probability. Must be between 0.0 and 1.0. + bias : bool, default=True + Whether to use bias in convolution layers. + use_batch_norm : bool, default=True + Whether to use batch normalization in ResidualBlock. + activation : str, default='relu' + Activation function for ResidualBlock. + residual_init_method : str, default='kaiming' + Weight initialization method for ResidualBlock. + + Attributes + ---------- + residual_block : ResidualBlock + The residual convolutional block for feature extraction. + dropout : nn.Dropout + Dropout layer for regularization. + pool : nn.MaxPool2d + Max pooling layer for spatial downsampling. + pool_size : tuple of int + Stored pooling size for decoder symmetry. + dropout_prob : float + Stored dropout probability. + + Examples + -------- + >>> block = EncoderBlock(in_channels=64, out_channels=128) + >>> x = torch.randn(1, 64, 32, 32) + >>> out = block(x) + >>> print(out.shape) + torch.Size([1, 128, 32, 16]) + + >>> # Custom configuration + >>> block = EncoderBlock( + ... in_channels=64, out_channels=128, + ... pool_size=(2, 2), dropout=0.5, activation='gelu' + ... ) + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + pool_size: tuple[int, int] = (1, 2), + kernel_size: Union[int, tuple[int, int]] = 3, + stride: Union[int, tuple[int, int]] = 1, + padding: Union[int, tuple[int, int], str] = 'auto', + dropout: float = 0.3, + bias: bool = True, + use_batch_norm: bool = True, + activation: str = 'relu', + residual_init_method: str = 'kaiming' + ) -> None: + """Initialize EncoderBlock.""" + + # Validate parameters + if not 0.0 <= dropout <= 1.0: + raise ValueError( + f"Dropout must be between 0.0 and 1.0, got {dropout}") + + if len(pool_size) != 2: + raise ValueError( + f"pool_size must be a tuple of length 2, got {pool_size}") + + # Store configuration + self.pool_size = pool_size + self.dropout_prob = dropout + self.use_batch_norm = use_batch_norm + self.activation_name = activation + self.residual_init_method = residual_init_method + + # Build the sequential operations + operations = self._build_operations( + in_channels, out_channels, kernel_size, stride, padding, + bias, use_batch_norm, activation, residual_init_method + ) + + # Initialize SequentialBlock with operations + super().__init__( + in_channels=in_channels, + out_channels=out_channels, + operations=operations, + kernel_size=kernel_size, + bias=bias + ) + + # Store individual components for introspection + self.residual_block = self.operations[0] + self.dropout = self.operations[1] + self.pool = self.operations[2] + + def _build_operations( + self, + in_channels: int, + out_channels: int, + kernel_size: Union[int, tuple[int, int]], + stride: Union[int, tuple[int, int]], + padding: Union[int, tuple[int, int], str], + bias: bool, + use_batch_norm: bool, + activation: str, + init_method: str + ) -> list[nn.Module]: + """Build the list of operations for this encoder block.""" + + operations = [] + + # 1. ResidualBlock for feature extraction + residual_block = ResidualBlock( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + bias=bias, + use_batch_norm=use_batch_norm, + activation=activation, + init_method=init_method + ) + operations.append(residual_block) + + # 2. Dropout for regularization + dropout_layer = nn.Dropout(p=self.dropout_prob) + operations.append(dropout_layer) + + # 3. MaxPool for downsampling + pool_layer = nn.MaxPool2d(kernel_size=self.pool_size) + operations.append(pool_layer) + + return operations + + def get_config(self) -> dict[str, Any]: + """Get configuration dictionary for this block.""" + config = super().get_config() + config.update({ + 'pool_size': self.pool_size, + 'dropout': self.dropout_prob, + 'use_batch_norm': self.use_batch_norm, + 'activation': self.activation_name, + 'residual_init_method': self.residual_init_method, + 'stride': getattr(self.residual_block, 'stride', 1), + 'padding': getattr(self.residual_block, 'padding', 'auto'), + }) + return config + + @classmethod + def from_config(cls, config: dict[str, Any]) -> 'EncoderBlock': + """Create EncoderBlock instance from configuration dictionary.""" + return cls(**config) + + def get_output_shape(self, input_shape: tuple[int, ...]) \ + -> tuple[int, ...]: + """Calculate output shape given input shape.""" + + # Get shape after residual block + residual_output_shape = ( + self.residual_block.get_output_shape(input_shape)) + + # Apply pooling + batch_size, channels, height, width = residual_output_shape + + # Calculate pooled dimensions + pooled_height = height // self.pool_size[0] + pooled_width = width // self.pool_size[1] + + return (batch_size, channels, pooled_height, pooled_width) + + def __repr__(self) -> str: + """String representation of the EncoderBlock.""" + return (f"EncoderBlock(" + f"in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"pool_size={self.pool_size}, " + f"dropout={self.dropout_prob}, " + f"activation='{self.activation_name}')") + + +class BlockBasedEncoder(ConfigurableBlock): + """Encoder architecture built from a sequence of EncoderBlocks. + + This encoder provides a flexible architecture where each encoding stage + consists of an EncoderBlock (ResidualBlock + Dropout + MaxPool) followed + by an optional bottleneck compression layer. + + Parameters + ---------- + input_channels : int + Number of input channels in the data. + block_configs : list of dict + Configuration for each encoder block. Each dict should contain: + - 'out_channels' (int): Output channels for the block + - 'pool_size' (tuple, optional): MaxPool kernel size, default (1, 2) + - 'dropout' (float, optional): Dropout probability, default 0.3 + - 'kernel_size' (int/tuple, optional): Conv kernel size, default 3 + - 'bias' (bool, optional): Use bias in convolutions, default True + - Other ResidualBlock parameters (activation, use_batch_norm, etc.) + bottleneck_channels : int, optional + Number of channels in the bottleneck layer. If None, defaults to + max(16, last_block_channels // 2). + hidden_dim : int, optional + Target frequency dimension after adaptive pooling. If None, no + adaptive pooling is applied. + kernel_size : int or tuple of int, default=3 + Default kernel size for blocks that don't specify one. + bias : bool, default=True + Default bias setting for blocks that don't specify one. + bottleneck_activation : str, default='relu' + Activation function for bottleneck layer. + bottleneck_init_method : str, default='kaiming' + Weight initialization method for bottleneck. + + Attributes + ---------- + blocks : nn.ModuleList + List of EncoderBlock modules. + bottleneck : nn.Sequential + Bottleneck compression layers. + bottleneck_channels : int + Number of channels in the bottleneck output. + block_configs : list of dict + Stored block configurations. + hidden_dim : int or None + Stored target frequency dimension. + """ + + def __init__( + self, + input_channels: int, + block_configs: list[dict[str, Any]], + bottleneck_channels: Optional[int] = None, + hidden_dim: Optional[int] = None, + kernel_size: Union[int, tuple[int, int]] = 3, + bias: bool = True, + bottleneck_activation: str = 'relu', + bottleneck_init_method: str = 'kaiming', + **kwargs + ) -> None: + """Initialize BlockBasedEncoder.""" + + # Initialize ConfigurableBlock + super().__init__( + in_channels=input_channels, + out_channels=input_channels, # Will be updated after building + kernel_size=kernel_size, + bias=bias, + block_configs=block_configs, + bottleneck_channels=bottleneck_channels, + hidden_dim=hidden_dim, + bottleneck_activation=bottleneck_activation, + bottleneck_init_method=bottleneck_init_method, + **kwargs + ) + + # Validate inputs + if not block_configs: + raise ValueError("block_configs cannot be empty") + + if input_channels <= 0: + raise ValueError( + f"input_channels must be positive, got {input_channels}") + + self.input_channels = input_channels + self.block_configs = block_configs + self.hidden_dim = hidden_dim + self.bottleneck_activation = bottleneck_activation + self.bottleneck_init_method = bottleneck_init_method + + # Build encoder blocks + self.blocks = self._build_encoder_blocks() + + # Build bottleneck + self.bottleneck, self.bottleneck_channels = self._build_bottleneck( + bottleneck_channels, kernel_size, bias + ) + + # Update out_channels after building + self.out_channels = self.bottleneck_channels + + def _build_encoder_blocks(self) -> nn.ModuleList: + """Build the sequence of encoder blocks.""" + blocks = [] + current_channels = self.input_channels + + for i, config in enumerate(self.block_configs): + if 'out_channels' not in config: + raise ValueError( + f"Block {i} missing required 'out_channels' key") + + # Extract config with defaults + block_config = self._prepare_block_config(config, current_channels) + + # Validate channels + out_channels = block_config['out_channels'] + if out_channels <= 0: + raise ValueError(f"out_channels must be positive, " + f"got {out_channels} in block {i}") + + # Create encoder block + block = EncoderBlock(**block_config) + blocks.append(block) + current_channels = out_channels + + return nn.ModuleList(blocks) + + def _prepare_block_config( + self, + config: dict[str, Any], + current_channels: int + ) -> dict[str, Any]: + """Prepare block configuration with defaults.""" + block_config = { + 'in_channels': current_channels, + 'out_channels': config['out_channels'], + 'pool_size': config.get('pool_size', (1, 2)), + 'kernel_size': config.get('kernel_size', self.kernel_size), + 'stride': config.get('stride', 1), + 'padding': config.get('padding', 'auto'), + 'dropout': config.get('dropout', 0.3), + 'bias': config.get('bias', self.bias), + 'use_batch_norm': config.get('use_batch_norm', True), + 'activation': config.get('activation', 'relu'), + 'residual_init_method': config.get('residual_init_method', + 'kaiming'), + } + return block_config + + def _build_bottleneck( + self, + bottleneck_channels: Optional[int], + kernel_size: Union[int, tuple[int, int]], + bias: bool + ) -> tuple[nn.Sequential, int]: + """Build the bottleneck compression layers.""" + bottleneck_layers = [] + + # Get input channels from last block + if self.blocks: + current_channels = self.blocks[-1].out_channels + else: + current_channels = self.input_channels + + # Optional adaptive pooling + if self.hidden_dim is not None: + if self.hidden_dim <= 0: + raise ValueError(f"hidden_dim must be positive, " + f"got {self.hidden_dim}") + bottleneck_layers.append( + nn.AdaptiveAvgPool2d((None, self.hidden_dim))) + + # Channel compression + if bottleneck_channels is None: + bottleneck_channels = max(16, current_channels // 2) + + if bottleneck_channels <= 0: + raise ValueError(f"bottleneck_channels must be positive, " + f"got {bottleneck_channels}") + + # Calculate padding for bottleneck convolution + if isinstance(kernel_size, int): + padding = kernel_size // 2 + else: + padding = tuple(k // 2 for k in kernel_size) + + # Add compression layers + bottleneck_layers.extend([ + nn.Conv2d( + current_channels, + bottleneck_channels, + kernel_size=kernel_size, + padding=padding, + bias=bias + ), + nn.BatchNorm2d(bottleneck_channels), + self._create_activation(self.bottleneck_activation), + ]) + + bottleneck = nn.Sequential(*bottleneck_layers) + + # Initialize bottleneck weights + self._initialize_bottleneck_weights(bottleneck) + + return bottleneck, bottleneck_channels + + def _create_activation(self, activation: str) -> nn.Module: + """Create activation function based on name.""" + activations = { + 'relu': nn.ReLU(inplace=True), + 'leaky_relu': nn.LeakyReLU(0.1, inplace=True), + 'gelu': nn.GELU(), + 'swish': nn.SiLU(), + 'mish': nn.Mish(), + } + if activation not in activations: + raise ValueError(f"Unknown activation: {activation}") + return activations[activation] + + def _initialize_bottleneck_weights( + self, + bottleneck: nn.Sequential + ) -> None: + """Initialize bottleneck weights.""" + if self.bottleneck_init_method == 'kaiming': + bottleneck.apply(WeightInitializer.kaiming_normal_) + elif self.bottleneck_init_method == 'xavier': + bottleneck.apply(WeightInitializer.xavier_uniform_) + + # Always properly initialize batch norm + bottleneck.apply(WeightInitializer.init_batch_norm_) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass through the encoder. + + Parameters + ---------- + x : torch.Tensor + Input tensor with shape (batch_size, input_channels, height, width) + + Returns + ------- + torch.Tensor + Encoded latent representation with shape + (batch_size, bottleneck_channels, height', width') where height' + and width' depend on the pooling operations and hidden_dim. + """ + # Pass through encoder blocks + for block in self.blocks: + x = block(x) + + # Pass through bottleneck + x = self.bottleneck(x) + + return x + + def get_feature_maps(self, x: torch.Tensor) -> list[torch.Tensor]: + """Get intermediate feature maps from each encoder block. + + Useful for visualization and debugging. + + Parameters + ---------- + x : torch.Tensor + Input tensor. + + Returns + ------- + list of torch.Tensor + Feature maps after each encoder block. + """ + feature_maps = [] + + for block in self.blocks: + x = block(x) + feature_maps.append(x.clone()) + + return feature_maps + + def get_output_shape( + self, + input_shape: tuple[int, ...] + ) -> tuple[int, ...]: + """Calculate output shape given input shape.""" + current_shape = input_shape + + # Apply each encoder block + for block in self.blocks: + current_shape = block.get_output_shape(current_shape) + + # Apply adaptive pooling if present + if self.hidden_dim is not None: + batch_size, channels, height, _ = current_shape + current_shape = (batch_size, channels, height, self.hidden_dim) + + # Apply bottleneck channel reduction + batch_size, _, height, width = current_shape + return (batch_size, self.bottleneck_channels, height, width) + + def __repr__(self) -> str: + """String representation of the BlockBasedEncoder.""" + return (f"BlockBasedEncoder(" + f"input_channels={self.input_channels}, " + f"num_blocks={len(self.blocks)}, " + f"bottleneck_channels={self.bottleneck_channels}, " + f"hidden_dim={self.hidden_dim})") + + +# Example usage and testing +if __name__ == "__main__": + # Test EncoderBlock + print("Testing EncoderBlock...") + encoder_block = EncoderBlock( + in_channels=64, + out_channels=128, + pool_size=(1, 2), + dropout=0.3, + activation='relu' + ) + + x = torch.randn(1, 64, 32, 32) + output = encoder_block(x) + print(f"EncoderBlock - Input: {x.shape}, Output: {output.shape}") + + # Test configuration + config = encoder_block.get_config() + new_block = EncoderBlock.from_config(config) + print(f"Config serialization successful: {new_block}") + + # Test BlockBasedEncoder + print("\nTesting BlockBasedEncoder...") + block_configs = [ + {'out_channels': 128, 'pool_size': (1, 2), 'dropout': 0.2}, + {'out_channels': 256, 'pool_size': (1, 4), 'dropout': 0.3}, + {'out_channels': 128, 'pool_size': (1, 2), 'dropout': 0.4}, + ] + + encoder = BlockBasedEncoder( + input_channels=80, + block_configs=block_configs, + hidden_dim=16, + bottleneck_channels=64 + ) + + x = torch.randn(2, 80, 100, 128) + latent = encoder(x) + print(f"Encoder - Input: {x.shape}, Output: {latent.shape}") + + # Test feature map extraction + feature_maps = encoder.get_feature_maps(x) + print(f"Feature maps shapes: {[fm.shape for fm in feature_maps]}") + + # Test registry + from src import BlockRegistry + + + registry_block = BlockRegistry.create( + 'encoder', + in_channels=32, + out_channels=64, + activation='gelu' + ) + print(f"Registry block: {registry_block}") diff --git a/src/faith/train/blocks/residual.py b/src/faith/train/blocks/residual.py new file mode 100644 index 0000000..1c84372 --- /dev/null +++ b/src/faith/train/blocks/residual.py @@ -0,0 +1,389 @@ +"""Residual block implementation derived from base classes. + +This module implements the ResidualBlock class that inherits from BaseBlock, +following the established patterns and interfaces defined in the base module. +""" + +import torch +import torch.nn as nn +from typing import Union, Any +from .base import BaseBlock, register_block, WeightInitializer + + +@register_block('residual') +class ResidualBlock(BaseBlock): + """Residual convolutional block with batch normalization and ReLU. + + This block implements a standard residual connection with two convolutional + layers, batch normalization, and ReLU activation. It includes an optional + projection layer for dimension matching in the skip connection. + + Parameters + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + kernel_size : int or tuple of int, default=3 + Size of the convolving kernel. + stride : int or tuple of int, default=1 + Stride of the convolution. + padding : int, tuple of int, or str, default='auto' + Padding added to all four sides of the input. If 'auto', padding is + calculated to maintain spatial dimensions when stride=1. + bias : bool, default=True + If True, adds a learnable bias to the output. + use_batch_norm : bool, default=True + Whether to use batch normalization layers. + activation : str, default='relu' + Activation function to use ('relu', 'leaky_relu', 'gelu', etc.). + init_method : str, default='kaiming' + Weight initialization method ('kaiming', 'xavier', 'default'). + + Attributes + ---------- + conv1 : torch.nn.Conv2d + First convolutional layer. + batch_norm_1 : torch.nn.BatchNorm2d or None + First batch normalization layer. + activation_fn : torch.nn.Module + Activation function. + conv2 : torch.nn.Conv2d + Second convolutional layer. + batch_norm_2 : torch.nn.BatchNorm2d or None + Second batch normalization layer. + skip_conv : torch.nn.Conv2d or None + Optional 1x1 convolution for dimension matching. + stride : tuple of int + Stored stride values. + padding : tuple of int + Stored padding values. + + Examples + -------- + >>> block = ResidualBlock(64, 128) + >>> x = torch.randn(1, 64, 32, 32) + >>> out = block(x) + >>> print(out.shape) + torch.Size([1, 128, 32, 32]) + + >>> # Custom configuration + >>> block = ResidualBlock(64, 128, stride=2, activation='gelu') + >>> config = block.get_config() + >>> new_block = ResidualBlock.from_config(config) + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: Union[int, tuple[int, int]] = 3, + stride: Union[int, tuple[int, int]] = 1, + padding: Union[int, tuple[int, int], str] = 'auto', + bias: bool = True, + use_batch_norm: bool = True, + activation: str = 'relu', + init_method: str = 'kaiming' + ) -> None: + """Initialize ResidualBlock. + + Parameters + ---------- + in_channels : int + Number of input channels. + out_channels : int + Number of output channels. + kernel_size : int or tuple of int, default=3 + Size of the convolving kernel. + stride : int or tuple of int, default=1 + Stride of the convolution. + padding : int, tuple of int, or str, default='auto' + Padding specification. + bias : bool, default=True + Whether to use bias in convolutions. + use_batch_norm : bool, default=True + Whether to use batch normalization. + activation : str, default='relu' + Activation function name. + init_method : str, default='kaiming' + Weight initialization method. + """ + # Initialize base class + super().__init__(in_channels, out_channels, kernel_size, bias) + + # Normalize stride and padding + self.stride = self._normalize_stride(stride) + self.padding = self._calculate_padding(self.kernel_size, padding) + self.use_batch_norm = use_batch_norm + self.activation_name = activation + self.init_method = init_method + + # Validate parameters + self._validate_parameters() + + # Build the block layers + self._build_layers() + + # Initialize weights + self._initialize_weights() + + def _normalize_stride(self, stride: Union[int, tuple[int, int]]) \ + -> tuple[int, int]: + """Normalize stride to tuple format.""" + if isinstance(stride, int): + return (stride, stride) + return stride + + def _validate_parameters(self) -> None: + """Validate input parameters.""" + valid_activations = {'relu', 'leaky_relu', 'gelu', 'swish', 'mish'} + if self.activation_name not in valid_activations: + raise ValueError(f"activation must be one of {valid_activations}, " + f"got {self.activation_name}") + + valid_init_methods = {'kaiming', 'xavier', 'default'} + if self.init_method not in valid_init_methods: + raise ValueError(f"init_method must be one of {valid_init_methods}" + f", got {self.init_method}") + + def _build_layers(self) -> None: + """Build the convolutional layers and other components.""" + # First convolutional layer + self.conv1 = nn.Conv2d( + self.in_channels, + self.out_channels, + kernel_size=self.kernel_size, + stride=self.stride, + padding=self.padding, + bias=self.bias and not self.use_batch_norm + # No bias if using batch norm + ) + + # First batch normalization (optional) + if self.use_batch_norm: + self.batch_norm_1 = nn.BatchNorm2d(self.out_channels) + else: + self.batch_norm_1 = None + + # Activation function + self.activation_fn = self._create_activation() + + # Second convolutional layer (always stride=1 to maintain dimensions) + self.conv2 = nn.Conv2d( + self.out_channels, + self.out_channels, + kernel_size=self.kernel_size, + stride=1, + padding=self.padding, + bias=self.bias and not self.use_batch_norm + ) + + # Second batch normalization (optional) + if self.use_batch_norm: + self.batch_norm_2 = nn.BatchNorm2d(self.out_channels) + else: + self.batch_norm_2 = None + + # Projection for skip connection if dimensions don't match + if self.in_channels != self.out_channels or self.stride != (1, 1): + self.skip_conv = nn.Conv2d( + self.in_channels, + self.out_channels, + kernel_size=1, + stride=self.stride, + padding=0, + bias=self.bias and not self.use_batch_norm + ) + if self.use_batch_norm: + self.skip_batch_norm = nn.BatchNorm2d(self.out_channels) + else: + self.skip_batch_norm = None + else: + self.skip_conv = None + self.skip_batch_norm = None + + def _create_activation(self) -> nn.Module: + """Create activation function based on name.""" + activations = { + 'relu': nn.ReLU(inplace=True), + 'leaky_relu': nn.LeakyReLU(0.1, inplace=True), + 'gelu': nn.GELU(), + 'swish': nn.SiLU(), # SiLU is the same as Swish + 'mish': nn.Mish(), + } + return activations[self.activation_name] + + def _initialize_weights(self) -> None: + """Initialize weights according to the specified method.""" + if self.init_method == 'kaiming': + self.apply(WeightInitializer.kaiming_normal_) + elif self.init_method == 'xavier': + self.apply(WeightInitializer.xavier_uniform_) + elif self.init_method == 'default': + pass # Use PyTorch's default initialization + + # Always properly initialize batch norm + if self.use_batch_norm: + self.apply(WeightInitializer.init_batch_norm_) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass through the residual block. + + TODO: What does ResNet do for initializing the residual connections? + + Parameters + ---------- + x : torch.Tensor + Input tensor with shape (batch_size, in_channels, height, width). + + Returns + ------- + torch.Tensor + Output tensor with shape (batch_size, out_channels, height', width') + where height' and width' depend on stride. + """ + # Store input for residual connection + residual = x + + # First conv block + out = self.conv1(x) + if self.batch_norm_1 is not None: + out = self.batch_norm_1(out) + out = self.activation_fn(out) + + # Second conv block + out = self.conv2(out) + if self.batch_norm_2 is not None: + out = self.batch_norm_2(out) + + # Apply skip connection with optional projection + if self.skip_conv is not None: + residual = self.skip_conv(residual) + if self.skip_batch_norm is not None: + residual = self.skip_batch_norm(residual) + + # Add residual connection + out += residual + + # Final activation + out = self.activation_fn(out) + + return out + + def get_config(self) -> dict[str, Any]: + """Get configuration dictionary for this block. + + Returns + ------- + dict + Configuration dictionary containing all parameters needed + to reconstruct this block. + """ + config = super().get_config() + config.update({ + 'stride': self.stride, + 'padding': self.padding, + 'use_batch_norm': self.use_batch_norm, + 'activation': self.activation_name, + 'init_method': self.init_method, + }) + return config + + @classmethod + def from_config(cls, config: dict[str, Any]) -> 'ResidualBlock': + """Create ResidualBlock instance from configuration dictionary. + + Parameters + ---------- + config : dict + Configuration dictionary. + + Returns + ------- + ResidualBlock + New ResidualBlock instance. + """ + return cls(**config) + + def __repr__(self) -> str: + """String representation of the ResidualBlock.""" + return (f"ResidualBlock(" + f"in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}, " + f"stride={self.stride}, " + f"padding={self.padding}, " + f"bias={self.bias}, " + f"use_batch_norm={self.use_batch_norm}, " + f"activation='{self.activation_name}')") + + @property + def has_skip_connection(self) -> bool: + """Check if this block has a skip connection projection.""" + return self.skip_conv is not None + + def get_output_shape(self, input_shape: tuple[int, ...]) \ + -> tuple[int, ...]: + """Calculate output shape given input shape. + + Parameters + ---------- + input_shape : tuple + Input tensor shape (batch, channels, height, width). + + Returns + ------- + tuple + Output tensor shape. + """ + from src import BlockUtils + + # Account for stride in the first convolution + temp_shape = BlockUtils.calculate_output_shape( + input_shape, + kernel_size=self.kernel_size, + stride=self.stride, + padding=self.padding + ) + + # Update channels + batch_size, _, height, width = temp_shape + return (batch_size, self.out_channels, height, width) + + +# Example usage and testing +if __name__ == "__main__": + # Test basic functionality + block = ResidualBlock(64, 128, stride=2) + x = torch.randn(1, 64, 32, 32) + output = block(x) + print(f"Input shape: {x.shape}") + print(f"Output shape: {output.shape}") + print(f"Block: {block}") + + # Test configuration serialization + config = block.get_config() + print(f"Config: {config}") + + # Create from config + new_block = ResidualBlock.from_config(config) + print(f"Recreated block: {new_block}") + + # Test registry functionality + from src import BlockRegistry + + + registry_block = BlockRegistry.create( + 'residual', + in_channels=32, + out_channels=64, + activation='gelu' + ) + print(f"Registry block: {registry_block}") + + # Test parameter counting + print(f"Parameter count: {block.parameter_count}") + + # Test output shape calculation + output_shape = block.get_output_shape((1, 64, 32, 32)) + print(f"Calculated output shape: {output_shape}") diff --git a/src/faith/train/models/__init__.py b/src/faith/train/models/__init__.py new file mode 100644 index 0000000..f1bffd0 --- /dev/null +++ b/src/faith/train/models/__init__.py @@ -0,0 +1,92 @@ +"""Model implementations for block-based autoencoders. + +This package provides complete autoencoder models built from the modular +blocks, including standard autoencoders, masked autoencoders (MAE), and +various configuration utilities. + +The main components are: +- BlockBasedAutoencoder: Complete autoencoder with configurable architecture +- MaskedAutoencoder: MAE implementation for self-supervised learning +- MaskGenerator: Various masking strategies for MAE training +- Configuration utilities for creating models from presets or config files + +Examples +-------- +Basic usage: +>>> from faith.train.models import BlockBasedAutoencoder, \ +create_block_autoencoder +>>> autoencoder = create_block_autoencoder('default', input_channels=80) +>>> x = torch.randn(1, 80, 100, 128) +>>> reconstructed, latent = autoencoder(x) + +Masked autoencoder: +>>> from faith.train.models import MaskedAutoencoder, MaskGenerator +>>> mask_gen = MaskGenerator(mask_ratio=0.75) +>>> mae = MaskedAutoencoder(autoencoder, mask_gen) +>>> reconstructed, mask, masked_input = mae(x, mask_type='frequency') +""" + +# TODO masked loss functions + +# Core model implementations +from .autoencoder import BlockBasedAutoencoder + +# Masked autoencoder components +from .mae import ( + MaskedAutoencoder, + MaskGenerator, + mae_loss +) + +# Configuration and factory functions +from .configs import ( + create_block_autoencoder, + get_preset_config, + save_model_config, + load_model_config, + ModelConfig, + PRESET_CONFIGS +) + +# Utility functions +from .utils import ( + create_mae_model, + get_model_info, + get_memory_estimate, + validate_input_shape +) + +# Package metadata +__version__ = "0.1.0" +__author__ = "Peter Steiner" +__email__ = "peter.steiner@princeton.edu" + +# Public API - only these should be imported by users +__all__ = [ + # Core models + "BlockBasedAutoencoder", + + # Masked autoencoder components + "MaskedAutoencoder", + "MaskGenerator", + "mae_loss", + + # Configuration utilities + "create_block_autoencoder", + "get_preset_config", + "save_model_config", + "load_model_config", + "ModelConfig", + "PRESET_CONFIGS", + + # Utility functions + "create_mae_model", + "get_model_info", + "get_memory_estimate", + "validate_input_shape", + + # Metadata + "__version__", + "__author__", + "__email__", +] diff --git a/src/faith/train/models/autoencoder.py b/src/faith/train/models/autoencoder.py new file mode 100644 index 0000000..ad8cea8 --- /dev/null +++ b/src/faith/train/models/autoencoder.py @@ -0,0 +1,452 @@ +"""Complete autoencoder model implementation. + +This module provides the BlockBasedAutoencoder class that combines the +encoder and decoder blocks into a complete autoencoder architecture. +The autoencoder uses the modular blocks from the blocks package to create +flexible and configurable models for audio and spectral data. +""" + +import torch +import torch.nn as nn +from typing import Union, Optional, Any + +# Import the building blocks +from ..blocks import BlockBasedEncoder +from ..blocks.decoder import BlockBasedDecoder + + +class BlockBasedAutoencoder(nn.Module): + """Complete autoencoder built from modular encoder and decoder blocks. + + This autoencoder provides a flexible, block-based architecture where + both encoder and decoder are constructed from configurable blocks. + The decoder automatically mirrors the encoder's architecture for + symmetric reconstruction. + + Parameters + ---------- + input_channels : int + Number of input channels in the data. + block_configs : list of dict, optional + Configuration for encoder blocks. If None, uses default configuration. + Each dict should contain 'out_channels' and optionally 'pool_size', + 'dropout', 'kernel_size', 'bias', etc. + bottleneck_channels : int, optional + Number of channels in the bottleneck. If None, auto-calculated. + kernel_size : int or tuple of int, default=3 + Default kernel size for convolutions. + bias : bool, default=True + Whether to use bias in convolution layers. + upsampling_mode : str, default='nearest' + Upsampling algorithm for decoder ('nearest', 'bilinear', etc.). + use_batch_norm : bool, default=True + Whether to use batch normalization in blocks. + activation : str, default='relu' + Default activation function for blocks. + init_method : str, default='kaiming' + Weight initialization method. + + Attributes + ---------- + encoder : BlockBasedEncoder + The encoder module. + decoder : BlockBasedDecoder + The decoder module. + input_channels : int + Number of input channels. + block_configs : list of dict + Encoder block configurations. + + Examples + -------- + >>> # Basic usage with default configuration + >>> autoencoder = BlockBasedAutoencoder(input_channels=80) + >>> x = torch.randn(1, 80, 100, 128) + >>> reconstructed, latent = autoencoder(x) + + >>> # Custom configuration + >>> configs = [ + ... {'out_channels': 64, 'pool_size': (1, 2)}, + ... {'out_channels': 128, 'pool_size': (1, 4)}, + ... ] + >>> autoencoder = BlockBasedAutoencoder( + ... input_channels=80, + ... block_configs=configs, + ... ) + """ + + def __init__( + self, + input_channels: int, + block_configs: Optional[list[dict[str, Any]]] = None, + bottleneck_channels: Optional[int] = None, + kernel_size: Union[int, tuple[int, int]] = 3, + bias: bool = True, + upsampling_mode: str = 'nearest', + use_batch_norm: bool = True, + activation: str = 'relu', + init_method: str = 'kaiming', + **kwargs + ) -> None: + """Initialize BlockBasedAutoencoder. + + Parameters + ---------- + input_channels : int + Number of input channels in the data. + block_configs : list of dict, optional + Configuration for encoder blocks. + bottleneck_channels : int, optional + Number of channels in the bottleneck. + kernel_size : int or tuple of int, default=3 + Default kernel size for convolutions. + bias : bool, default=True + Whether to use bias in convolution layers. + upsampling_mode : str, default='nearest' + Upsampling algorithm for decoder. + use_batch_norm : bool, default=True + Whether to use batch normalization. + activation : str, default='relu' + Default activation function. + init_method : str, default='kaiming' + Weight initialization method. + **kwargs + Additional arguments (for future extensibility). + """ + super().__init__() + + if input_channels <= 0: + raise ValueError( + f"input_channels must be positive, got {input_channels}") + + # Store configuration + self.input_channels = input_channels + self.block_configs = block_configs + self.upsampling_mode = upsampling_mode + self.use_batch_norm = use_batch_norm + self.activation = activation + self.init_method = init_method + + # Use default block configuration if none provided + if block_configs is None: + block_configs = self._get_default_block_configs() + + # Create encoder + self.encoder = BlockBasedEncoder( + input_channels=input_channels, + block_configs=block_configs, + bottleneck_channels=bottleneck_channels, + kernel_size=kernel_size, + bias=bias, + bottleneck_activation=activation, + bottleneck_init_method=init_method + ) + + # Create decoder that mirrors the encoder + self.decoder = BlockBasedDecoder( + output_channels=input_channels, + encoder_blocks=self.encoder.blocks, + bottleneck_channels=self.encoder.bottleneck_channels, + kernel_size=kernel_size, + bias=bias, + upsampling_mode=upsampling_mode, + use_batch_norm=use_batch_norm, + activation=activation, + init_method=init_method + ) + + def _get_default_block_configs(self) -> list[dict[str, Any]]: + """Get default block configuration.""" + return [ + {'out_channels': 32, 'pool_size': (1, 2)}, + {'out_channels': 16, 'pool_size': (1, 2)}, + ] + + def encode(self, x: torch.Tensor) -> torch.Tensor: + """Encode input to latent representation. + + Parameters + ---------- + x : torch.Tensor + Input with shape (batch_size, input_channels, height, width). + + Returns + ------- + torch.Tensor + Latent representation with shape + (batch_size, bottleneck_channels, height', width'). + """ + return self.encoder(x) + + def decode(self, z: torch.Tensor) -> torch.Tensor: + """ + Decode latent representation to reconstructed output. + + Parameters + ---------- + z : torch.Tensor + Latent representation with shape + (batch_size, bottleneck_channels, height, width). + + Returns + ------- + torch.Tensor + Reconstructed output with shape approximately matching + the original input dimensions. + """ + return self.decoder(z) + + def forward(self, inputs: torch.Tensor) -> torch.Tensor: + """Forward pass through the complete autoencoder. + + Parameters + ---------- + inputs : torch.Tensor + Input with shape (batch_size, input_channels, height, width). + + Returns + ------- + torch.Tensor + Reconstructed input with the same shape as input. + """ + latent = self.encode(inputs) + reconstructed = self.decode(latent) + return reconstructed + + def latent_with_reconstruction(self, inputs: torch.Tensor) \ + -> tuple[torch.Tensor, torch.Tensor]: + """Forward pass through the complete autoencoder. + + Parameters + ---------- + inputs : torch.Tensor + Input with shape (batch_size, input_channels, height, width). + + Returns + ------- + torch.Tensor + Latent representation with shape + (batch_size, bottleneck_channels, height', width'). + torch.Tensor + Reconstructed output with shape approximately matching the original + input dimensions. + """ + latent = self.encode(inputs) + reconstructed = self.decode(latent) + return latent, reconstructed + + def get_config(self) -> dict[str, Any]: + """Get configuration dictionary for this autoencoder. + + Returns + ------- + dict + Configuration dictionary containing all parameters needed + to reconstruct this autoencoder. + """ + return { + 'input_channels': self.input_channels, + 'block_configs': self.block_configs, + 'bottleneck_channels': self.encoder.bottleneck_channels, + 'kernel_size': self.encoder.kernel_size, + 'bias': self.encoder.bias, + 'upsampling_mode': self.upsampling_mode, + 'use_batch_norm': self.use_batch_norm, + 'activation': self.activation, + 'init_method': self.init_method, + } + + @classmethod + def from_config(cls, config: dict[str, Any]) -> 'BlockBasedAutoencoder': + """Create BlockBasedAutoencoder instance from configuration dictionary. + + Parameters + ---------- + config : dict + Configuration dictionary from get_config(). + + Returns + ------- + BlockBasedAutoencoder + New autoencoder instance. + """ + return cls(**config) + + def get_output_shape(self, input_shape: tuple[int, ...]) -> tuple[ + int, ...]: + """Calculate output shape given input shape. + + Parameters + ---------- + input_shape : tuple of int + Input tensor shape (batch, channels, height, width). + + Returns + ------- + tuple of int + Output tensor shape (should match input for autoencoder). + """ + # For autoencoder, output shape should match input shape + latent_shape = self.encoder.get_output_shape(input_shape) + output_shape = self.decoder.get_output_shape(latent_shape) + return output_shape + + def get_latent_shape(self, input_shape: tuple[int, ...]) -> tuple[ + int, ...]: + """Calculate latent representation shape given input shape. + + Parameters + ---------- + input_shape : tuple of int + Input tensor shape (batch, channels, height, width). + + Returns + ------- + tuple of int + Latent tensor shape. + """ + return self.encoder.get_output_shape(input_shape) + + def get_feature_maps(self, inputs: torch.Tensor) -> dict[ + str, list[torch.Tensor]]: + """Get intermediate feature maps from encoder and decoder. + + Useful for visualization and debugging. + + Parameters + ---------- + inputs : torch.Tensor + Input tensor. + + Returns + ------- + dict + Dictionary containing: + - 'encoder': List of feature maps from encoder blocks + - 'decoder': List of feature maps from decoder blocks + """ + # Get encoder feature maps + encoder_features = self.encoder.get_feature_maps(inputs) + + # Get latent representation + latent = self.encoder(inputs) + + # Get decoder feature maps + decoder_features = self.decoder.get_feature_maps(latent) + + return { + 'encoder': encoder_features, + 'decoder': decoder_features, + 'latent': latent + } + + @property + def parameter_count(self) -> int: + """Get total number of trainable parameters.""" + return sum(p.numel() for p in self.parameters() if p.requires_grad) + + @property + def encoder_parameter_count(self) -> int: + """Get number of trainable parameters in encoder.""" + return sum( + p.numel() for p in self.encoder.parameters() if p.requires_grad) + + @property + def decoder_parameter_count(self) -> int: + """Get number of trainable parameters in decoder.""" + return sum( + p.numel() for p in self.decoder.parameters() if p.requires_grad) + + def freeze_encoder(self) -> None: + """Freeze encoder parameters (useful for fine-tuning decoder only).""" + for param in self.encoder.parameters(): + param.requires_grad = False + + def freeze_decoder(self) -> None: + """Freeze decoder parameters (useful for feature extraction).""" + for param in self.decoder.parameters(): + param.requires_grad = False + + def unfreeze_all(self) -> None: + """Unfreeze all parameters.""" + for param in self.parameters(): + param.requires_grad = True + + def __repr__(self) -> str: + """String representation of the BlockBasedAutoencoder.""" + return (f"BlockBasedAutoencoder(" + f"input_channels={self.input_channels}, " + f"encoder_blocks={len(self.encoder.blocks)}, " + f"decoder_blocks={len(self.decoder.blocks)}, " + f"bottleneck_channels={self.encoder.bottleneck_channels}, " + f"parameters={self.parameter_count:,})") + + +# Example usage and testing +if __name__ == "__main__": + # Test basic functionality + print("Testing BlockBasedAutoencoder...") + + # Create autoencoder with default config + autoencoder = BlockBasedAutoencoder(input_channels=80) + + # Test forward pass + x = torch.randn(2, 80, 100, 128) + reconstructed = autoencoder(x) + latent = autoencoder.encode(x) + + print(f"Input shape: {x.shape}") + print(f"Latent shape: {latent.shape}") + print(f"Reconstructed shape: {reconstructed.shape}") + print(f"Autoencoder: {autoencoder}") + + # Test individual methods + latent_only = autoencoder.get_latent_representation(x) + reconstructed_only = autoencoder.reconstruct(x) + + print(f"Latent only shape: {latent_only.shape}") + print(f"Reconstructed only shape: {reconstructed_only.shape}") + + # Test configuration serialization + config = autoencoder.get_config() + print(f"Config keys: {list(config.keys())}") + + new_autoencoder = BlockBasedAutoencoder.from_config(config) + print(f"Recreated autoencoder: {new_autoencoder}") + + # Test shape calculation + output_shape = autoencoder.get_output_shape((1, 80, 100, 128)) + latent_shape = autoencoder.get_latent_shape((1, 80, 100, 128)) + print(f"Calculated output shape: {output_shape}") + print(f"Calculated latent shape: {latent_shape}") + + # Test parameter counting + print(f"Total parameters: {autoencoder.parameter_count:,}") + print(f"Encoder parameters: {autoencoder.encoder_parameter_count:,}") + print(f"Decoder parameters: {autoencoder.decoder_parameter_count:,}") + + # Test feature map extraction + feature_maps = autoencoder.get_feature_maps(x) + print(f"Encoder feature maps: {len(feature_maps['encoder'])}") + print(f"Decoder feature maps: {len(feature_maps['decoder'])}") + + # Test custom configuration + custom_configs = [ + {'out_channels': 64, 'pool_size': (1, 2), 'dropout': 0.2}, + {'out_channels': 128, 'pool_size': (1, 4), 'dropout': 0.3}, + ] + + custom_autoencoder = BlockBasedAutoencoder( + input_channels=80, + block_configs=custom_configs, + activation='gelu' + ) + + x_custom = torch.randn(1, 80, 100, 128) + reconstructed_custom, latent_custom = custom_autoencoder(x_custom) + + print(f"\nCustom autoencoder:") + print(f"Input shape: {x_custom.shape}") + print(f"Latent shape: {latent_custom.shape}") + print(f"Reconstructed shape: {reconstructed_custom.shape}") + print(f"Custom autoencoder: {custom_autoencoder}") diff --git a/src/faith/train/models/configs.py b/src/faith/train/models/configs.py new file mode 100644 index 0000000..4e9adba --- /dev/null +++ b/src/faith/train/models/configs.py @@ -0,0 +1,634 @@ +"""Configuration management for autoencoder models. + +This module provides utilities for creating, saving, loading, and managing +configurations for autoencoder models. It includes preset configurations +for common use cases and tools for custom configuration management. +""" + +import json +import yaml +from pathlib import Path +from typing import Union, Optional, Any +from dataclasses import dataclass, asdict, field +from copy import deepcopy + +from .autoencoder import BlockBasedAutoencoder +from .mae import MaskedAutoencoder, MaskGenerator + + +@dataclass +class ModelConfig: + """Configuration dataclass for autoencoder models. + + This dataclass provides a structured way to define and manage + autoencoder configurations with validation and serialization support. + + Parameters + ---------- + input_channels : int + Number of input channels. + block_configs : list of dict + Configuration for each encoder block. + bottleneck_channels : int, optional + Number of channels in bottleneck. + hidden_dim : int, optional + Target frequency dimension after adaptive pooling. + kernel_size : int or tuple of int, default=3 + Default kernel size for convolutions. + bias : bool, default=True + Whether to use bias in convolutions. + upsampling_mode : str, default='nearest' + Upsampling mode for decoder. + use_batch_norm : bool, default=True + Whether to use batch normalization. + activation : str, default='relu' + Default activation function. + init_method : str, default='kaiming' + Weight initialization method. + model_type : str, default='autoencoder' + Type of model ('autoencoder' or 'mae'). + mae_config : dict, optional + Configuration for MAE-specific parameters. + metadata : dict, optional + Additional metadata (description, version, etc.). + + Examples + -------- + >>> config = ModelConfig( + ... input_channels=80, + ... block_configs=[ + ... {'out_channels': 128, 'pool_size': (1, 2)}, + ... {'out_channels': 256, 'pool_size': (1, 4)}, + ... ], + ... hidden_dim=16 + ... ) + >>> model = create_model_from_config(config) + """ + + input_channels: int + block_configs: list[dict[str, Any]] + bottleneck_channels: Optional[int] = None + hidden_dim: Optional[int] = None + kernel_size: Union[int, tuple[int, int]] = 3 + bias: bool = True + upsampling_mode: str = 'nearest' + use_batch_norm: bool = True + activation: str = 'relu' + init_method: str = 'kaiming' + model_type: str = 'autoencoder' + mae_config: Optional[dict[str, Any]] = None + metadata: dict[str, Any] = field(default_factory=dict) + + def __post_init__(self): + """Validate configuration after initialization.""" + self._validate() + + def _validate(self): + """Validate configuration parameters.""" + if self.input_channels <= 0: + raise ValueError( + f"input_channels must be positive, got {self.input_channels}") + + if not self.block_configs: + raise ValueError("block_configs cannot be empty") + + for i, config in enumerate(self.block_configs): + if 'out_channels' not in config: + raise ValueError( + f"Block {i} missing required 'out_channels' key") + if config['out_channels'] <= 0: + raise ValueError(f"Block {i} out_channels must be positive") + + if (self.bottleneck_channels is not None + and self.bottleneck_channels <= 0): + raise ValueError( + "bottleneck_channels must be positive if specified") + + if self.hidden_dim is not None and self.hidden_dim <= 0: + raise ValueError("hidden_dim must be positive if specified") + + valid_model_types = ['autoencoder', 'mae'] + if self.model_type not in valid_model_types: + raise ValueError(f"model_type must be one of {valid_model_types}") + + valid_activations = ['relu', 'leaky_relu', 'gelu', 'swish', 'mish'] + if self.activation not in valid_activations: + raise ValueError(f"activation must be one of {valid_activations}") + + valid_init_methods = ['kaiming', 'xavier', 'default'] + if self.init_method not in valid_init_methods: + raise ValueError( + f"init_method must be one of {valid_init_methods}") + + valid_upsampling_modes = ['nearest', 'bilinear', 'bicubic', 'area'] + if self.upsampling_mode not in valid_upsampling_modes: + raise ValueError( + f"upsampling_mode must be one of {valid_upsampling_modes}") + + def to_dict(self) -> dict[str, Any]: + """Convert configuration to dictionary.""" + return asdict(self) + + @classmethod + def from_dict(cls, config_dict: dict[str, Any]) -> 'ModelConfig': + """Create configuration from dictionary.""" + return cls(**config_dict) + + def save(self, filepath: Union[str, Path], format: str = 'yaml') -> None: + """Save configuration to file. + + Parameters + ---------- + filepath : str or Path + Path to save configuration. + format : str, default='yaml' + File format ('yaml' or 'json'). + """ + filepath = Path(filepath) + config_dict = self.to_dict() + + if format.lower() == 'yaml': + with open(filepath, 'w') as f: + yaml.dump(config_dict, f, default_flow_style=False, indent=2) + elif format.lower() == 'json': + with open(filepath, 'w') as f: + json.dump(config_dict, f, indent=2) + else: + raise ValueError( + f"Unsupported format: {format}. Use 'yaml' or 'json'.") + + @classmethod + def load(cls, filepath: Union[str, Path]) -> 'ModelConfig': + """Load configuration from file. + + Parameters + ---------- + filepath : str or Path + Path to configuration file. + + Returns + ------- + ModelConfig + Loaded configuration. + """ + filepath = Path(filepath) + + if not filepath.exists(): + raise FileNotFoundError( + f"Configuration file not found: {filepath}") + + suffix = filepath.suffix.lower() + + with open(filepath, 'r') as f: + if suffix in ['.yaml', '.yml']: + config_dict = yaml.safe_load(f) + elif suffix == '.json': + config_dict = json.load(f) + else: + raise ValueError(f"Unsupported file format: {suffix}. " + f"Use .yaml, .yml, or .json") + + return cls.from_dict(config_dict) + + def copy(self) -> 'ModelConfig': + """Create a deep copy of the configuration.""" + return ModelConfig.from_dict(deepcopy(self.to_dict())) + + def update(self, **kwargs) -> 'ModelConfig': + """Create a new configuration with updated parameters. + + Parameters + ---------- + **kwargs + Parameters to update. + + Returns + ------- + ModelConfig + New configuration with updated parameters. + """ + config_dict = self.to_dict() + config_dict.update(kwargs) + return self.from_dict(config_dict) + + +# Preset configurations for common use cases +PRESET_CONFIGS = { + 'default': ModelConfig( + input_channels=80, # Will be overridden by user + block_configs=[ + {'out_channels': 128, 'pool_size': (1, 2)}, + {'out_channels': 256, 'pool_size': (1, 2)}, + {'out_channels': 256, 'pool_size': (1, 2)}, + {'out_channels': 128, 'pool_size': (1, 2)}, + {'out_channels': 64, 'pool_size': (1, 2)}, + ], + metadata={ + 'description': 'Default balanced configuration', + 'use_case': 'General purpose autoencoder' + } + ), + + 'light': ModelConfig( + input_channels=80, + block_configs=[ + {'out_channels': 64, 'pool_size': (1, 2)}, + {'out_channels': 128, 'pool_size': (1, 2)}, + {'out_channels': 64, 'pool_size': (1, 2)}, + ], + metadata={ + 'description': 'Lightweight configuration for fast training', + 'use_case': 'Resource-constrained environments' + } + ), + + 'heavy': ModelConfig( + input_channels=80, + block_configs=[ + {'out_channels': 128, 'pool_size': (1, 2)}, + {'out_channels': 256, 'pool_size': (1, 2)}, + {'out_channels': 512, 'pool_size': (1, 2)}, + {'out_channels': 512, 'pool_size': (1, 2)}, + {'out_channels': 256, 'pool_size': (1, 2)}, + {'out_channels': 128, 'pool_size': (1, 2)}, + ], + metadata={ + 'description': 'Heavy configuration for maximum capacity', + 'use_case': 'Large datasets, high-quality reconstruction' + } + ), + + 'asymmetric': ModelConfig( + input_channels=80, + block_configs=[ + {'out_channels': 64, 'pool_size': (1, 4)}, + {'out_channels': 128, 'pool_size': (1, 2)}, + {'out_channels': 256, 'pool_size': (1, 2)}, + ], + metadata={ + 'description': 'Asymmetric pooling for different compression ' + 'ratios', + 'use_case': 'Audio with varying frequency resolution needs' + } + ), + + 'variable_dropout': ModelConfig( + input_channels=80, + block_configs=[ + {'out_channels': 128, 'pool_size': (1, 2), 'dropout': 0.1}, + {'out_channels': 256, 'pool_size': (1, 2), 'dropout': 0.2}, + {'out_channels': 256, 'pool_size': (1, 2), 'dropout': 0.3}, + {'out_channels': 128, 'pool_size': (1, 2), 'dropout': 0.4}, + ], + metadata={ + 'description': 'Progressive dropout for regularization', + 'use_case': 'Preventing overfitting in deep networks' + } + ), + + 'mae_default': ModelConfig( + input_channels=80, + block_configs=[ + {'out_channels': 128, 'pool_size': (1, 2)}, + {'out_channels': 256, 'pool_size': (1, 2)}, + {'out_channels': 128, 'pool_size': (1, 2)}, + ], + model_type='mae', + mae_config={ + 'mask_ratio': 0.75, + 'patch_size': (8, 8), + 'min_mask_size': 1, + 'max_mask_size': None, + 'mask_token_value': 0.0 + }, + metadata={ + 'description': 'Default configuration for Masked Autoencoder', + 'use_case': 'Self-supervised pre-training' + } + ), + + 'mae_aggressive': ModelConfig( + input_channels=80, + block_configs=[ + {'out_channels': 64, 'pool_size': (1, 2)}, + {'out_channels': 128, 'pool_size': (1, 4)}, + {'out_channels': 256, 'pool_size': (1, 2)}, + ], + model_type='mae', + mae_config={ + 'mask_ratio': 0.85, + 'patch_size': (4, 4), + 'min_mask_size': 2, + 'max_mask_size': 16, + 'mask_token_value': 0.0 + }, + metadata={ + 'description': 'Aggressive masking for challenging pre-training', + 'use_case': 'Learning robust representations' + } + ), +} + + +def get_preset_config(name: str) -> ModelConfig: + """Get a preset configuration by name. + + Parameters + ---------- + name : str + Name of the preset configuration. + + Returns + ------- + ModelConfig + Copy of the preset configuration. + + Raises + ------ + KeyError + If preset name is not found. + """ + if name not in PRESET_CONFIGS: + available = list(PRESET_CONFIGS.keys()) + raise KeyError( + f"Unknown preset: {name}. Available presets: {available}") + + return PRESET_CONFIGS[name].copy() + + +def list_preset_configs() -> list[str]: + """List available preset configuration names. + + Returns + ------- + list of str + Available preset names. + """ + return list(PRESET_CONFIGS.keys()) + + +def create_block_autoencoder( + config_name: str = 'default', + input_channels: Optional[int] = None, + **kwargs +) -> BlockBasedAutoencoder: + """Create autoencoder with predefined or custom configuration. + + Parameters + ---------- + config_name : str, default='default' + Name of the preset configuration. + input_channels : int, optional + Override input channels in preset. + **kwargs + Additional parameters to override in preset. + + Returns + ------- + BlockBasedAutoencoder + Configured autoencoder instance. + + Examples + -------- + >>> # Use preset with custom input channels + >>> autoencoder = create_block_autoencoder('light', input_channels=80) + + >>> # Override multiple parameters + >>> autoencoder = create_block_autoencoder( + ... 'default', + ... input_channels=80, + ... hidden_dim=16, + ... activation='gelu' + ... ) + """ + # Get base configuration + config = get_preset_config(config_name) + + # Override input channels if provided + if input_channels is not None: + config = config.update(input_channels=input_channels) + + # Override any additional parameters + if kwargs: + config = config.update(**kwargs) + + # Create model based on type + if config.model_type == 'autoencoder': + return create_autoencoder_from_config(config) + elif config.model_type == 'mae': + return create_mae_from_config(config) + else: + raise ValueError(f"Unknown model_type: {config.model_type}") + + +def create_autoencoder_from_config( + config: ModelConfig) -> BlockBasedAutoencoder: + """Create BlockBasedAutoencoder from configuration. + + Parameters + ---------- + config : ModelConfig + Model configuration. + + Returns + ------- + BlockBasedAutoencoder + Configured autoencoder. + """ + # Extract autoencoder parameters + autoencoder_params = { + 'input_channels': config.input_channels, + 'block_configs': config.block_configs, + 'bottleneck_channels': config.bottleneck_channels, + 'hidden_dim': config.hidden_dim, + 'kernel_size': config.kernel_size, + 'bias': config.bias, + 'upsampling_mode': config.upsampling_mode, + 'use_batch_norm': config.use_batch_norm, + 'activation': config.activation, + 'init_method': config.init_method, + } + + return BlockBasedAutoencoder(**autoencoder_params) + + +def create_mae_from_config(config: ModelConfig) -> MaskedAutoencoder: + """Create MaskedAutoencoder from configuration. + + Parameters + ---------- + config : ModelConfig + Model configuration with MAE parameters. + + Returns + ------- + MaskedAutoencoder + Configured MAE model. + """ + # Create base autoencoder + autoencoder = create_autoencoder_from_config(config) + + # Create mask generator + mae_config = config.mae_config or {} + mask_generator = MaskGenerator( + mask_ratio=mae_config.get('mask_ratio', 0.75), + patch_size=mae_config.get('patch_size', (8, 8)), + min_mask_size=mae_config.get('min_mask_size', 1), + max_mask_size=mae_config.get('max_mask_size', None) + ) + + # Create MAE + mae = MaskedAutoencoder( + autoencoder=autoencoder, + mask_generator=mask_generator, + mask_token_value=mae_config.get('mask_token_value', 0.0) + ) + + return mae + + +def save_model_config( + model: Union[BlockBasedAutoencoder, MaskedAutoencoder], + filepath: Union[str, Path], + format: str = 'yaml', + include_metadata: bool = True +) -> None: + """Save model configuration to file. + + Parameters + ---------- + model : BlockBasedAutoencoder or MaskedAutoencoder + Model to save configuration for. + filepath : str or Path + Path to save configuration. + format : str, default='yaml' + File format ('yaml' or 'json'). + include_metadata : bool, default=True + Whether to include metadata in saved config. + """ + if isinstance(model, MaskedAutoencoder): + config_dict = model.get_config() + model_type = 'mae' + + # Restructure for ModelConfig format + autoencoder_config = config_dict['autoencoder_config'] + mae_params = {k: v for k, v in config_dict.items() + if k != 'autoencoder_config'} + + config = ModelConfig( + model_type=model_type, + mae_config=mae_params, + **autoencoder_config + ) + + elif isinstance(model, BlockBasedAutoencoder): + config_dict = model.get_config() + config = ModelConfig( + model_type='autoencoder', + **config_dict + ) + + else: + raise TypeError(f"Unsupported model type: {type(model)}") + + # Add metadata if requested + if include_metadata: + import datetime + config.metadata.update({ + 'saved_at': datetime.datetime.now().isoformat(), + 'model_class': model.__class__.__name__, + 'parameter_count': getattr(model, 'parameter_count', None) + }) + + config.save(filepath, format) + + +def load_model_config(filepath: Union[str, Path]) -> ModelConfig: + """Load model configuration from file. + + Parameters + ---------- + filepath : str or Path + Path to configuration file. + + Returns + ------- + ModelConfig + Loaded configuration. + """ + return ModelConfig.load(filepath) + + +def create_model_from_config_file(filepath: Union[str, Path]) -> Union[ + BlockBasedAutoencoder, MaskedAutoencoder]: + """Create model from configuration file. + + Parameters + ---------- + filepath : str or Path + Path to configuration file. + + Returns + ------- + BlockBasedAutoencoder or MaskedAutoencoder + Model created from configuration. + """ + config = load_model_config(filepath) + + if config.model_type == 'autoencoder': + return create_autoencoder_from_config(config) + elif config.model_type == 'mae': + return create_mae_from_config(config) + else: + raise ValueError(f"Unknown model_type: {config.model_type}") + + +# Example usage and testing +if __name__ == "__main__": + # Test preset configurations + print("Available presets:", list_preset_configs()) + + # Test creating models from presets + for preset_name in ['default', 'light', 'mae_default']: + print(f"\nTesting {preset_name} preset:") + + try: + model = create_block_autoencoder(preset_name, input_channels=80) + print(f"Created model: {type(model).__name__}") + + if hasattr(model, 'parameter_count'): + print(f"Parameters: {model.parameter_count:,}") + + except Exception as e: + print(f"Error creating {preset_name}: {e}") + + # Test configuration serialization + print("\nTesting configuration serialization:") + + config = get_preset_config('default') + config = config.update(input_channels=80, hidden_dim=16) + + # Save and load + config.save('test_config.yaml') + loaded_config = ModelConfig.load('test_config.yaml') + + print(f"Original: {config.input_channels}, {config.hidden_dim}") + print( + f"Loaded: {loaded_config.input_channels}, {loaded_config.hidden_dim}") + + # Test model config saving + autoencoder = create_autoencoder_from_config(config) + save_model_config(autoencoder, 'model_config.yaml') + + # Load and recreate model + recreated_model = create_model_from_config_file('model_config.yaml') + print(f"Recreated model: {type(recreated_model).__name__}") + + # Cleanup + import os + + + os.remove('test_config.yaml') + os.remove('model_config.yaml') + + print("Configuration tests completed successfully!") diff --git a/src/faith/train/models/mae.py b/src/faith/train/models/mae.py new file mode 100644 index 0000000..3561e62 --- /dev/null +++ b/src/faith/train/models/mae.py @@ -0,0 +1,618 @@ +"""Masked Autoencoder (MAE) implementation for self-supervised learning. + +This module provides the MaskedAutoencoder class and associated utilities +for training autoencoders with various masking strategies. The MAE approach +is particularly effective for learning robust representations from partially +observed data, such as audio spectrograms with missing frequency bands or +time frames. +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F +import random +from typing import Optional, Any +from enum import Enum + +from .autoencoder import BlockBasedAutoencoder + + +class MaskType(Enum): + """Enumeration of available masking strategies.""" + RANDOM = "random" + FREQUENCY = "frequency" + TIME = "time" + PATCH = "patch" + MIXED = "mixed" + + +# Export mask types for convenience +MASK_TYPES = [mask_type.value for mask_type in MaskType] + + +class MaskGenerator: + """Generates various types of masks for audio spectrograms. + + This class provides different masking strategies for Masked Autoencoder + training, including random masking, frequency band masking, temporal + masking, and patch-based masking. + + Parameters + ---------- + mask_ratio : float, default=0.75 + Ratio of input to mask (0.0 to 1.0). Higher values mean more masking. + patch_size : tuple of int, default=(8, 8) + Size of patches for patch-based masking (height, width). + min_mask_size : int, default=1 + Minimum size for contiguous masked regions. + max_mask_size : int, optional + Maximum size for contiguous masked regions. If None, no upper limit. + + Attributes + ---------- + mask_ratio : float + Stored mask ratio. + patch_size : tuple of int + Stored patch size. + min_mask_size : int + Stored minimum mask size. + max_mask_size : int or None + Stored maximum mask size. + + Examples + -------- + >>> mask_gen = MaskGenerator(mask_ratio=0.75) + >>> shape = (2, 80, 100, 128) # batch, channels, time, freq + >>> mask = mask_gen.random_mask(shape) + >>> print(f"Mask shape: {mask.shape}, Masked ratio: {(1 - mask.mean()).item():.2f}") + """ + + def __init__( + self, + mask_ratio: float = 0.75, + patch_size: tuple[int, int] = (8, 8), + min_mask_size: int = 1, + max_mask_size: Optional[int] = None + ) -> None: + """Initialize MaskGenerator. + + Parameters + ---------- + mask_ratio : float, default=0.75 + Ratio of input to mask. + patch_size : tuple of int, default=(8, 8) + Size of patches for patch-based masking. + min_mask_size : int, default=1 + Minimum size for contiguous masked regions. + max_mask_size : int, optional + Maximum size for contiguous masked regions. + """ + if not 0.0 <= mask_ratio <= 1.0: + raise ValueError( + f"mask_ratio must be between 0.0 and 1.0, got {mask_ratio}") + + if len(patch_size) != 2 or any(s <= 0 for s in patch_size): + raise ValueError(f"patch_size must be a tuple of two positive " + f"integers, got {patch_size}") + + if min_mask_size <= 0: + raise ValueError( + f"min_mask_size must be positive, got {min_mask_size}") + + if max_mask_size is not None and max_mask_size < min_mask_size: + raise ValueError( + f"max_mask_size must be >= min_mask_size, got {max_mask_size}") + + self.mask_ratio = mask_ratio + self.patch_size = patch_size + self.min_mask_size = min_mask_size + self.max_mask_size = max_mask_size + + def random_mask(self, shape: tuple[int, ...]) -> torch.Tensor: + """Generate random masking of individual time-frequency bins. + + Parameters + ---------- + shape : tuple of int + Input tensor shape (batch_size, channels, time_steps, freq_bins). + + Returns + ------- + torch.Tensor + Binary mask tensor where 1 = keep, 0 = mask. + """ + batch_size, channels, time_steps, freq_bins = shape + mask = torch.rand(batch_size, 1, time_steps, + freq_bins) > self.mask_ratio + return mask.float() + + def frequency_band_mask(self, shape: tuple[int, ...]) -> torch.Tensor: + """Generate frequency band masking. + + Masks contiguous frequency bands, which is common in audio augmentation + and simulates frequency-selective interference. + + Parameters + ---------- + shape : tuple of int + Input tensor shape (batch_size, channels, time_steps, freq_bins). + + Returns + ------- + torch.Tensor + Binary mask tensor where 1 = keep, 0 = mask. + """ + batch_size, channels, time_steps, freq_bins = shape + mask = torch.ones(batch_size, 1, time_steps, freq_bins) + + for b in range(batch_size): + # Calculate number of frequency bins to mask + num_bins_to_mask = int(freq_bins * self.mask_ratio) + + if num_bins_to_mask > 0: + # Choose number of bands to create + max_bands = min(num_bins_to_mask, + freq_bins // self.min_mask_size) + num_bands = random.randint(1, max(1, max_bands)) + + bins_per_band = num_bins_to_mask // num_bands + remaining_bins = num_bins_to_mask % num_bands + + masked_bins = 0 + for _ in range(num_bands): + if masked_bins >= num_bins_to_mask: + break + + # Size of this band + band_size = bins_per_band + if remaining_bins > 0: + band_size += 1 + remaining_bins -= 1 + + # Ensure we don't exceed limits + if self.max_mask_size is not None: + band_size = min(band_size, self.max_mask_size) + band_size = max(band_size, self.min_mask_size) + band_size = min(band_size, freq_bins - masked_bins) + + if band_size <= 0: + break + + # Choose random start position + max_start = freq_bins - band_size + if max_start >= 0: + start_freq = random.randint(0, max_start) + mask[b, :, :, start_freq:start_freq + band_size] = 0 + masked_bins += band_size + + return mask + + def time_frame_mask(self, shape: tuple[int, ...]) -> torch.Tensor: + """Generate temporal frame masking. + + Masks contiguous time frames, simulating temporal dropouts or + transmission errors in audio signals. + + Parameters + ---------- + shape : tuple of int + Input tensor shape (batch_size, channels, time_steps, freq_bins). + + Returns + ------- + torch.Tensor + Binary mask tensor where 1 = keep, 0 = mask. + """ + batch_size, channels, time_steps, freq_bins = shape + mask = torch.ones(batch_size, 1, time_steps, freq_bins) + + for b in range(batch_size): + # Calculate number of time frames to mask + num_frames_to_mask = int(time_steps * self.mask_ratio) + + if num_frames_to_mask > 0: + # Choose number of segments to create + max_segments = min(num_frames_to_mask, + time_steps // self.min_mask_size) + num_segments = random.randint(1, max(1, max_segments)) + + frames_per_segment = num_frames_to_mask // num_segments + remaining_frames = num_frames_to_mask % num_segments + + masked_frames = 0 + for _ in range(num_segments): + if masked_frames >= num_frames_to_mask: + break + + # Size of this segment + segment_size = frames_per_segment + if remaining_frames > 0: + segment_size += 1 + remaining_frames -= 1 + + # Ensure we don't exceed limits + if self.max_mask_size is not None: + segment_size = min(segment_size, self.max_mask_size) + segment_size = max(segment_size, self.min_mask_size) + segment_size = min(segment_size, + time_steps - masked_frames) + + if segment_size <= 0: + break + + # Choose random start position + max_start = time_steps - segment_size + if max_start >= 0: + start_time = random.randint(0, max_start) + mask[b, :, start_time:start_time + segment_size, :] = 0 + masked_frames += segment_size + + return mask + + def patch_mask(self, shape: tuple[int, ...]) -> torch.Tensor: + """Generate patch-based masking. + + Masks rectangular patches, similar to Vision Transformer masking + but adapted for spectrograms. + + Parameters + ---------- + shape : tuple of int + Input tensor shape (batch_size, channels, time_steps, freq_bins). + + Returns + ------- + torch.Tensor + Binary mask tensor where 1 = keep, 0 = mask. + """ + batch_size, channels, time_steps, freq_bins = shape + mask = torch.ones(batch_size, 1, time_steps, freq_bins) + + patch_time, patch_freq = self.patch_size + + # Calculate number of patches in each dimension + num_patches_time = time_steps // patch_time + num_patches_freq = freq_bins // patch_freq + total_patches = num_patches_time * num_patches_freq + + if total_patches == 0: + return mask + + for b in range(batch_size): + # Number of patches to mask + num_masked_patches = int(total_patches * self.mask_ratio) + + if num_masked_patches > 0: + # Randomly select patches to mask + masked_patches = random.sample(range(total_patches), + min(num_masked_patches, + total_patches)) + + for patch_idx in masked_patches: + # Convert patch index to 2D coordinates + patch_t = (patch_idx // num_patches_freq) * patch_time + patch_f = (patch_idx % num_patches_freq) * patch_freq + + # Apply mask to patch + end_t = min(patch_t + patch_time, time_steps) + end_f = min(patch_f + patch_freq, freq_bins) + + mask[b, :, patch_t:end_t, patch_f:end_f] = 0 + + return mask + + def mixed_mask(self, shape: tuple[int, ...]) -> torch.Tensor: + """Generate mixed masking strategy. + + Combines multiple masking strategies for more diverse augmentation. + + Parameters + ---------- + shape : tuple of int + Input tensor shape (batch_size, channels, time_steps, freq_bins). + + Returns + ------- + torch.Tensor + Binary mask tensor where 1 = keep, 0 = mask. + """ + batch_size = shape[0] + + # For mixed masking, use different strategies for different samples + mask_strategies = [ + self.random_mask, + self.frequency_band_mask, + self.time_frame_mask, + self.patch_mask + ] + + # Generate masks using different strategies + masks = [] + for b in range(batch_size): + strategy = random.choice(mask_strategies) + single_batch_shape = (1,) + shape[1:] + single_mask = strategy(single_batch_shape) + masks.append(single_mask) + + return torch.cat(masks, dim=0) + + def generate_mask(self, shape: tuple[int, ...], + mask_type: str) -> torch.Tensor: + """Generate mask using specified strategy. + + Parameters + ---------- + shape : tuple of int + Input tensor shape. + mask_type : str + Type of masking strategy to use. + + Returns + ------- + torch.Tensor + Generated mask tensor. + """ + if mask_type == MaskType.RANDOM.value: + return self.random_mask(shape) + elif mask_type == MaskType.FREQUENCY.value: + return self.frequency_band_mask(shape) + elif mask_type == MaskType.TIME.value: + return self.time_frame_mask(shape) + elif mask_type == MaskType.PATCH.value: + return self.patch_mask(shape) + elif mask_type == MaskType.MIXED.value: + return self.mixed_mask(shape) + else: + raise ValueError(f"Unknown mask_type: {mask_type}. " + f"Available types: {MASK_TYPES}") + + +class MaskedAutoencoder(nn.Module): + """Masked Autoencoder using block-based architecture. + + This class wraps a BlockBasedAutoencoder and adds masking functionality + for self-supervised learning. The key insight is that the model learns + to reconstruct masked regions based on visible context, leading to + robust representations. + + Parameters + ---------- + autoencoder : BlockBasedAutoencoder + The base autoencoder model to wrap. + mask_generator : MaskGenerator + Generator for creating masks. + mask_token_value : float, default=0.0 + Value to use for masked regions in input. + + Attributes + ---------- + autoencoder : BlockBasedAutoencoder + The wrapped autoencoder. + mask_generator : MaskGenerator + The mask generator. + mask_token_value : float + Value used for masked tokens. + + Examples + -------- + >>> autoencoder = BlockBasedAutoencoder(input_channels=80) + >>> mask_gen = MaskGenerator(mask_ratio=0.75) + >>> mae = MaskedAutoencoder(autoencoder, mask_gen) + >>> + >>> x = torch.randn(2, 80, 100, 128) + >>> reconstructed, mask, masked_input = mae(x, mask_type='frequency') + >>> loss = mae_loss(reconstructed, x, mask) + """ + + def __init__( + self, + autoencoder: BlockBasedAutoencoder, + mask_generator: MaskGenerator, + mask_token_value: float = 0.0 + ) -> None: + """Initialize MaskedAutoencoder. + + Parameters + ---------- + autoencoder : BlockBasedAutoencoder + The base autoencoder model. + mask_generator : MaskGenerator + Generator for creating masks. + mask_token_value : float, default=0.0 + Value to use for masked regions. + """ + super().__init__() + + self.autoencoder = autoencoder + self.mask_generator = mask_generator + self.mask_token_value = mask_token_value + + def apply_mask(self, x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor: + """Apply mask to input tensor. + + Parameters + ---------- + x : torch.Tensor + Input tensor. + mask : torch.Tensor + Binary mask (1 = keep, 0 = mask). + + Returns + ------- + torch.Tensor + Masked input tensor. + """ + # Apply mask: keep visible regions, masked regions to mask_token_value + return x * mask + self.mask_token_value * (1 - mask) + + def forward( + self, + x: torch.Tensor, + mask_type: str = 'random', + mask: Optional[torch.Tensor] = None + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Forward pass with masking. + + Parameters + ---------- + x : torch.Tensor + Input tensor with shape (batch_size, channels, height, width). + mask_type : str, default='random' + Type of masking to apply. Options: 'random', 'frequency', + 'time', 'patch', 'mixed'. + mask : torch.Tensor, optional + Pre-generated mask. If provided, mask_type is ignored. + + Returns + ------- + tuple of torch.Tensor + - reconstructed : torch.Tensor + Full reconstruction of the input. + - mask : torch.Tensor + Applied mask (1 = keep, 0 = mask). + - masked_input : torch.Tensor + Input with mask applied. + """ + # Generate mask if not provided + if mask is None: + mask = self.mask_generator.generate_mask(x.shape, mask_type) + mask = mask.to(x.device) + + # Apply mask to input + masked_input = self.apply_mask(x, mask) + + # Forward through autoencoder + reconstructed, latent = self.autoencoder(masked_input) + + return reconstructed, mask, masked_input + + def encode(self, x: torch.Tensor, + mask_type: str = 'random') -> torch.Tensor: + """Encode masked input to latent representation. + + Parameters + ---------- + x : torch.Tensor + Input tensor. + mask_type : str, default='random' + Type of masking to apply. + + Returns + ------- + torch.Tensor + Latent representation of masked input. + """ + mask = self.mask_generator.generate_mask(x.shape, mask_type) + mask = mask.to(x.device) + masked_input = self.apply_mask(x, mask) + return self.autoencoder.encode(masked_input) + + def get_config(self) -> dict[str, Any]: + """Get configuration dictionary.""" + return { + 'autoencoder_config': self.autoencoder.get_config(), + 'mask_ratio': self.mask_generator.mask_ratio, + 'patch_size': self.mask_generator.patch_size, + 'min_mask_size': self.mask_generator.min_mask_size, + 'max_mask_size': self.mask_generator.max_mask_size, + 'mask_token_value': self.mask_token_value, + } + + @classmethod + def from_config(cls, config: dict[str, Any]) -> 'MaskedAutoencoder': + """Create MaskedAutoencoder from configuration.""" + # Recreate autoencoder + autoencoder = BlockBasedAutoencoder.from_config( + config['autoencoder_config']) + + # Recreate mask generator + mask_generator = MaskGenerator( + mask_ratio=config['mask_ratio'], + patch_size=config['patch_size'], + min_mask_size=config['min_mask_size'], + max_mask_size=config['max_mask_size'] + ) + + return cls( + autoencoder=autoencoder, + mask_generator=mask_generator, + mask_token_value=config['mask_token_value'] + ) + + def __repr__(self) -> str: + """String representation.""" + return (f"MaskedAutoencoder(" + f"mask_ratio={self.mask_generator.mask_ratio}, " + f"autoencoder={self.autoencoder})") + + +def mae_loss( + reconstructed: torch.Tensor, + target: torch.Tensor, + mask: torch.Tensor, + loss_type: str = 'mse', + reduction: str = 'mean' +) -> torch.Tensor: + """Compute MAE loss only on masked regions. + + This is the key insight of MAE: only compute reconstruction loss + on the masked regions, not on the visible regions. + + Parameters + ---------- + reconstructed : torch.Tensor + Model output/reconstruction. + target : torch.Tensor + Original unmasked input. + mask : torch.Tensor + Binary mask tensor (1 = keep, 0 = mask). + loss_type : str, default='mse' + Type of loss function ('mse', 'l1', 'smooth_l1'). + reduction : str, default='mean' + Reduction method ('mean', 'sum', 'none'). + + Returns + ------- + torch.Tensor + Computed loss value. + + Examples + -------- + >>> reconstructed = torch.randn(2, 80, 100, 128) + >>> target = torch.randn(2, 80, 100, 128) + >>> mask = torch.rand(2, 1, 100, 128) > 0.5 + >>> loss = mae_loss(reconstructed, target, mask, loss_type='mse') + """ + # Invert mask: we want loss only on masked regions (where mask=0) + masked_regions = (1 - mask) + + # Compute loss per pixel + if loss_type == 'mse': + loss_per_pixel = F.mse_loss(reconstructed, target, reduction='none') + elif loss_type == 'l1': + loss_per_pixel = F.l1_loss(reconstructed, target, reduction='none') + elif loss_type == 'smooth_l1': + loss_per_pixel = F.smooth_l1_loss(reconstructed, target, + reduction='none') + else: + raise ValueError(f"Unknown loss_type: {loss_type}. " + f"Available types: ['mse', 'l1', 'smooth_l1']") + + # Only compute loss on masked regions + masked_loss = loss_per_pixel * masked_regions + + # Apply reduction + if reduction == 'none': + return masked_loss + elif reduction == 'sum': + return masked_loss.sum() + elif reduction == 'mean': + # Average over masked pixels only + num_masked_pixels = masked_regions.sum() + if num_masked_pixels > 0: + return masked_loss.sum() / num_masked_pixels + else: + return torch.tensor(0.0, device=reconstructed.device) + else: + raise ValueError(f"Unknown reduction: {reduction}. " + f"Available reductions: ['none', 'sum', 'mean']") diff --git a/src/faith/train/models/utils.py b/src/faith/train/models/utils.py new file mode 100644 index 0000000..7677232 --- /dev/null +++ b/src/faith/train/models/utils.py @@ -0,0 +1,571 @@ +"""Utility functions for autoencoder models. + +This module provides convenience functions, model analysis tools, validation +utilities, and helper functions for working with autoencoder models. +""" + +import torch +from typing import Union, Optional, Any + +from torch_training.models.autoencoder import BlockBasedAutoencoder +from torch_training.models.mae import MaskedAutoencoder, MASK_TYPES +from . import create_block_autoencoder, PRESET_CONFIGS + + +def create_mae_model( + config_name: str = 'mae_default', + input_channels: Optional[int] = None, + mask_ratio: float = 0.75, + mask_type: str = 'random', + **kwargs +) -> MaskedAutoencoder: + """Create a masked autoencoder with sensible defaults. + + This convenience function creates a complete MAE model with a single + function call, using preset configurations and sensible defaults. + + Parameters + ---------- + config_name : str, default='mae_default' + Preset configuration name for the base autoencoder. + Use 'mae_default' or 'mae_aggressive' for MAE-specific presets, + or any autoencoder preset which will be converted to MAE. + input_channels : int, optional + Number of input channels. If None, must be specified in kwargs. + mask_ratio : float, default=0.75 + Ratio of input to mask (0.0 to 1.0). + mask_type : str, default='random' + Default masking strategy to use during training. + **kwargs + Additional arguments passed to create_block_autoencoder. + + Returns + ------- + MaskedAutoencoder + Configured MAE model ready for training. + + Examples + -------- + >>> # Simple MAE creation + >>> mae = create_mae_model('mae_default', input_channels=80) + >>> x = torch.randn(1, 80, 100, 128) + >>> reconstructed, mask, masked_input = mae(x) + + >>> # Custom configuration + >>> mae = create_mae_model( + ... 'light', + ... input_channels=80, + ... mask_ratio=0.8, + ... hidden_dim=16 + ... ) + """ + # Handle input_channels + if input_channels is not None: + kwargs['input_channels'] = input_channels + elif 'input_channels' not in kwargs: + raise ValueError("input_channels must be specified either as " + "parameter or in kwargs") + + # Override MAE config if using non-MAE preset + if not config_name.startswith('mae_'): + kwargs.setdefault('model_type', 'mae') + kwargs.setdefault('mae_config', { + 'mask_ratio': mask_ratio, + 'patch_size': (8, 8), + 'min_mask_size': 1, + 'max_mask_size': None, + 'mask_token_value': 0.0 + }) + + # Create the model + model = create_block_autoencoder(config_name, **kwargs) + + if not isinstance(model, MaskedAutoencoder): + raise RuntimeError( + f"Expected MaskedAutoencoder, got {type(model).__name__}") + + return model + + +def get_model_info() -> dict[str, Any]: + """Get information about available models and configurations. + + Returns comprehensive information about the models package including + available configurations, model types, masking strategies, and versions. + + Returns + ------- + dict + Dictionary containing model information including: + - available_configs: List of preset configuration names + - model_types: List of available model types + - mask_types: List of available masking strategies + - preset_descriptions: Descriptions of each preset + - version: Package version (if available) + + Examples + -------- + >>> info = get_model_info() + >>> print(f"Available configs: {info['available_configs']}") + >>> print(f"MAE presets: {info['mae_presets']}") + >>> for name, desc in info['preset_descriptions'].items(): + ... print(f"{name}: {desc}") + """ + # Get preset information + preset_info = {} + mae_presets = [] + autoencoder_presets = [] + + for name, config in PRESET_CONFIGS.items(): + preset_info[name] = { + 'description': config.metadata.get('description', + 'No description'), + 'use_case': config.metadata.get('use_case', 'General'), + 'model_type': config.model_type, + 'num_blocks': len(config.block_configs), + 'has_mae_config': config.mae_config is not None + } + + if config.model_type == 'mae': + mae_presets.append(name) + else: + autoencoder_presets.append(name) + + # Package version + try: + from src import __version__ + + version = __version__ + except ImportError: + version = "unknown" + + return { + 'available_configs': list(PRESET_CONFIGS.keys()), + 'autoencoder_presets': autoencoder_presets, + 'mae_presets': mae_presets, + 'model_types': ['BlockBasedAutoencoder', 'MaskedAutoencoder'], + 'mask_types': MASK_TYPES, + 'preset_descriptions': { + name: info['description'] for name, info in preset_info.items()}, + 'preset_details': preset_info, + 'version': version, + 'description': 'Block-based autoencoders for audio and spectral data' + } + + +def validate_input_shape(shape: tuple[int, ...]) -> bool: + """Validate input tensor shape for autoencoder models. + + Checks if the input shape is compatible with autoencoder models, + which expect 4D tensors (batch, channels, height, width). + + Parameters + ---------- + shape : tuple of int + Input tensor shape to validate. + + Returns + ------- + bool + True if shape is valid, False otherwise. + + Examples + -------- + >>> validate_input_shape((32, 80, 100, 128)) # Valid + True + >>> validate_input_shape((80, 100, 128)) # Missing batch dimension + False + >>> validate_input_shape((32, 80, 100, 128, 1)) # Too many dimensions + False + """ + if len(shape) != 4: + return False + + batch, channels, height, width = shape + return all(dim > 0 for dim in shape) + + +def get_memory_estimate( + model: Union[BlockBasedAutoencoder, MaskedAutoencoder], + input_shape: tuple[int, ...], + batch_size: int = 1, + dtype: torch.dtype = torch.float32 +) -> dict[str, float]: + """Estimate memory usage for a model with given input shape. + + Provides estimates for parameter memory, activation memory, and total + memory usage. Useful for planning training on resource-constrained systems. + + Parameters + ---------- + model : BlockBasedAutoencoder or MaskedAutoencoder + The autoencoder model to analyze. + input_shape : tuple of int + Input tensor shape (channels, height, width) + or (batch, channels, height, width). + batch_size : int, default=1 + Batch size for estimation (ignored if input_shape includes batch). + dtype : torch.dtype, default=torch.float32 + Data type for memory calculation. + + Returns + ------- + dict + Memory estimates in MB including: + - parameters_mb: Model parameter memory + - activations_mb: Estimated activation memory + - total_mb: Total estimated memory + - encoder_mb: Encoder-specific memory + - decoder_mb: Decoder-specific memory + + Examples + -------- + >>> model = create_mae_model('light', input_channels=80) + >>> memory = get_memory_estimate(model, (80, 100, 128), batch_size=32) + >>> print(f"Total memory: {memory['total_mb']:.1f} MB") + >>> print(f"Parameters: {memory['parameters_mb']:.1f} MB") + """ + + # Handle different input shapes + if len(input_shape) == 3: + full_shape = (batch_size,) + input_shape + elif len(input_shape) == 4: + full_shape = input_shape + else: + raise ValueError( + f"Input shape must be 3D or 4D, got {len(input_shape)}D") + + # Validate shape + if not validate_input_shape(full_shape): + raise ValueError(f"Invalid input shape: {full_shape}") + + # Get base autoencoder for analysis + if isinstance(model, MaskedAutoencoder): + base_model = model.autoencoder + else: + base_model = model + + # Bytes per element based on dtype + dtype_bytes = { + torch.float32: 4, + torch.float16: 2, + torch.float64: 8, + torch.int32: 4, + torch.int64: 8, + }.get(dtype, 4) + + # Parameter memory + total_params = sum(p.numel() for p in base_model.parameters()) + param_memory_mb = (total_params * dtype_bytes) / (1024 * 1024) + + # Encoder memory + encoder_params = sum(p.numel() for p in base_model.encoder.parameters()) + encoder_param_mb = (encoder_params * dtype_bytes) / (1024 * 1024) + + # Decoder memory + decoder_params = sum(p.numel() for p in base_model.decoder.parameters()) + decoder_param_mb = (decoder_params * dtype_bytes) / (1024 * 1024) + + # Estimate activation memory (rough approximation) + # This is a simplified estimation - actual memory depends on implementation + # details + + # Input activation memory + input_elements = 1 + for dim in full_shape: + input_elements *= dim + input_memory_mb = (input_elements * dtype_bytes) / (1024 * 1024) + + # Estimate latent representation size + try: + latent_shape = base_model.get_latent_shape(full_shape) + latent_elements = 1 + for dim in latent_shape: + latent_elements *= dim + latent_memory_mb = (latent_elements * dtype_bytes) / (1024 * 1024) + except: + # Fallback estimation + latent_memory_mb = input_memory_mb * 0.1 # Assume 10x compression + + # Rough estimate of intermediate activations (2-3x input + latent) + activation_memory_mb = input_memory_mb * 2.5 + latent_memory_mb * 1.5 + + # Total memory (parameters + activations + gradients during training) + # Gradients roughly equal parameter memory during training + total_memory_mb = param_memory_mb * 2 + activation_memory_mb + + return { + 'parameters_mb': param_memory_mb, + 'encoder_params_mb': encoder_param_mb, + 'decoder_params_mb': decoder_param_mb, + 'activations_mb': activation_memory_mb, + 'input_mb': input_memory_mb, + 'latent_mb': latent_memory_mb, + 'total_mb': total_memory_mb, + 'total_params': total_params, + 'dtype': str(dtype), + 'batch_size': full_shape[0], + } + + +def analyze_model_architecture( + model: Union[BlockBasedAutoencoder, MaskedAutoencoder], + input_shape: tuple[int, ...] = (1, 80, 100, 128) +) -> dict[str, Any]: + """Analyze model architecture and provide detailed information. + + Parameters + ---------- + model : BlockBasedAutoencoder or MaskedAutoencoder + Model to analyze. + input_shape : tuple of int, default=(1, 80, 100, 128) + Input shape for analysis. + + Returns + ------- + dict + Detailed architecture analysis. + """ + # Get base autoencoder + if isinstance(model, MaskedAutoencoder): + base_model = model.autoencoder + model_type = "MaskedAutoencoder" + has_masking = True + mask_info = { + 'mask_ratio': model.mask_generator.mask_ratio, + 'patch_size': model.mask_generator.patch_size, + 'available_mask_types': MASK_TYPES + } + else: + base_model = model + model_type = "BlockBasedAutoencoder" + has_masking = False + mask_info = None + + # Basic model info + total_params = sum(p.numel() for p in base_model.parameters()) + trainable_params = sum( + p.numel() for p in base_model.parameters() if p.requires_grad) + + # Encoder analysis + encoder_blocks = [] + for i, block in enumerate(base_model.encoder.blocks): + encoder_blocks.append({ + 'block_id': i, + 'in_channels': block.in_channels, + 'out_channels': block.out_channels, + 'pool_size': block.pool_size, + 'dropout': block.dropout_prob, + 'parameters': sum(p.numel() for p in block.parameters()) + }) + + # Decoder analysis + decoder_blocks = [] + for i, block in enumerate(base_model.decoder.blocks): + decoder_blocks.append({ + 'block_id': i, + 'in_channels': block.in_channels, + 'out_channels': block.out_channels, + 'upsample_factor': block.upsample_factor, + 'dropout': block.dropout_prob, + 'parameters': sum(p.numel() for p in block.parameters()) + }) + + # Shape analysis + try: + latent_shape = base_model.get_latent_shape(input_shape) + output_shape = base_model.get_output_shape(input_shape) + compression_ratio = (input_shape[2] * input_shape[3]) / ( + latent_shape[2] * latent_shape[3]) + except Exception as e: + latent_shape = None + output_shape = None + compression_ratio = None + + analysis = { + 'model_type': model_type, + 'has_masking': has_masking, + 'mask_info': mask_info, + 'parameters': { + 'total': total_params, + 'trainable': trainable_params, + 'encoder': sum(p.numel() for p in base_model.encoder.parameters()), + 'decoder': sum(p.numel() for p in base_model.decoder.parameters()), + }, + 'architecture': { + 'input_channels': base_model.input_channels, + 'bottleneck_channels': base_model.encoder.bottleneck_channels, + 'hidden_dim': base_model.encoder.hidden_dim, + 'num_encoder_blocks': len(base_model.encoder.blocks), + 'num_decoder_blocks': len(base_model.decoder.blocks), + 'upsampling_mode': base_model.decoder.upsampling_mode, + }, + 'encoder_blocks': encoder_blocks, + 'decoder_blocks': decoder_blocks, + 'shapes': { + 'input': input_shape, + 'latent': latent_shape, + 'output': output_shape, + 'compression_ratio': compression_ratio, + }, + 'config': base_model.get_config() if hasattr(base_model, + 'get_config') else None, + } + + return analysis + + +def compare_models( + models: dict[str, Union[BlockBasedAutoencoder, MaskedAutoencoder]], + input_shape: tuple[int, ...] = (1, 80, 100, 128) +) -> dict[str, Any]: + """Compare multiple models side by side. + + Parameters + ---------- + models : dict + Dictionary of model_name -> model pairs. + input_shape : tuple of int + Input shape for comparison. + + Returns + ------- + dict + Comparison results. + """ + comparison = { + 'input_shape': input_shape, + 'models': {}, + 'summary': {} + } + + # Analyze each model + for name, model in models.items(): + try: + analysis = analyze_model_architecture(model, input_shape) + memory = get_memory_estimate(model, input_shape) + + comparison['models'][name] = { + 'analysis': analysis, + 'memory': memory, + 'error': None + } + except Exception as e: + comparison['models'][name] = { + 'analysis': None, + 'memory': None, + 'error': str(e) + } + + # Create summary comparison + successful_models = {name: data for name, data in + comparison['models'].items() + if data['error'] is None} + + if successful_models: + comparison['summary'] = { + 'parameter_counts': {name: data['analysis']['parameters']['total'] + for name, data in successful_models.items()}, + 'memory_usage': {name: data['memory']['total_mb'] + for name, data in successful_models.items()}, + 'compression_ratios': { + name: data['analysis']['shapes']['compression_ratio'] + for name, data in successful_models.items() + if + data['analysis']['shapes']['compression_ratio'] is not None}, + } + + return comparison + + +def print_model_summary( + model: Union[BlockBasedAutoencoder, MaskedAutoencoder], + input_shape: tuple[int, ...] = (1, 80, 100, 128) +) -> None: + """Print a formatted summary of model architecture. + + Parameters + ---------- + model : BlockBasedAutoencoder or MaskedAutoencoder + Model to summarize. + input_shape : tuple of int + Input shape for analysis. + """ + analysis = analyze_model_architecture(model, input_shape) + memory = get_memory_estimate(model, input_shape) + + print(f"=== {analysis['model_type']} Summary ===") + print(f"Total Parameters: {analysis['parameters']['total']:,}") + print(f"Trainable Parameters: {analysis['parameters']['trainable']:,}") + print(f"Memory Usage: {memory['total_mb']:.1f} MB") + + if analysis['has_masking']: + print(f"Mask Ratio: {analysis['mask_info']['mask_ratio']:.2f}") + print(f"Patch Size: {analysis['mask_info']['patch_size']}") + + print(f"\nArchitecture:") + print(f" Input Channels: {analysis['architecture']['input_channels']}") + print(f" Bottleneck Channels: " + f"{analysis['architecture']['bottleneck_channels']}") + print(f" Encoder Blocks: " + f"{analysis['architecture']['num_encoder_blocks']}") + print(f" Decoder Blocks: " + f"{analysis['architecture']['num_decoder_blocks']}") + + if analysis['shapes']['compression_ratio']: + print(f" Compression Ratio: " + f"{analysis['shapes']['compression_ratio']:.1f}x") + + print(f"\nShapes:") + print(f" Input: {analysis['shapes']['input']}") + print(f" Latent: {analysis['shapes']['latent']}") + print(f" Output: {analysis['shapes']['output']}") + + +# Example usage and testing +if __name__ == "__main__": + # Test utility functions + print("Testing model utilities...") + + # Test model creation + mae = create_mae_model('mae_default', input_channels=80) + autoencoder = create_block_autoencoder('light', input_channels=80) + + print(f"Created MAE: {type(mae).__name__}") + print(f"Created Autoencoder: {type(autoencoder).__name__}") + + # Test model info + info = get_model_info() + print(f"\nAvailable configs: {info['available_configs']}") + print(f"MAE presets: {info['mae_presets']}") + + # Test validation + valid_shape = (32, 80, 100, 128) + invalid_shape = (80, 100, 128) + print(f"\nShape validation:") + print(f" {valid_shape}: {validate_input_shape(valid_shape)}") + print(f" {invalid_shape}: {validate_input_shape(invalid_shape)}") + + # Test memory estimation + memory = get_memory_estimate(mae, (80, 100, 128), batch_size=32) + print(f"\nMemory estimate for MAE:") + print(f" Total: {memory['total_mb']:.1f} MB") + print(f" Parameters: {memory['parameters_mb']:.1f} MB") + print(f" Activations: {memory['activations_mb']:.1f} MB") + + # Test architecture analysis + print(f"\nModel summary:") + print_model_summary(mae, (1, 80, 100, 128)) + + # Test model comparison + models = { + 'mae': mae, + 'autoencoder': autoencoder + } + comparison = compare_models(models) + print(f"\nModel comparison:") + for name, params in comparison['summary']['parameter_counts'].items(): + memory = comparison['summary']['memory_usage'][name] + print(f" {name}: {params:,} params, {memory:.1f} MB") + + print("Utility tests completed successfully!") diff --git a/tests/test_residual_block.py b/tests/test_residual_block.py new file mode 100644 index 0000000..e69de29 From a6fbab5231cc0769980802fec11cc83189c804d7 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Tue, 15 Jul 2025 13:00:01 -0400 Subject: [PATCH 046/103] Refactor fusion dataset preparation pipeline - Introduced modular structure for dataset preparation with dedicated modules for signal processing, data extraction, sample processing, and utilities. - Implemented YAML configuration files for flexible parameter management. - Added functionality for windowed data extraction with `get_window` function. - Enhanced signal extraction and alignment processes, including handling of missing signals. - Integrated STFT transformations and resampling methods for signal processing. - Developed a parallel processing mechanism for efficient shot processing. - Created comprehensive README documentation outlining the pipeline structure, usage, and configuration. - Established logging for better traceability and debugging. - Added utility functions for dataset indexing and sample saving. --- .gitignore | 4 ++- pyproject.toml | 6 ++-- .../fusion_signal/__init__.py => __main__.py} | 0 src/faith/datasets/prepare/__main__.py | 4 --- .../.archive}/base/__init__.py | 0 .../{ => preprocess/.archive}/base/load.py | 0 .../{ => preprocess/.archive}/base/merge.py | 0 .../{ => preprocess/.archive}/base/save.py | 3 ++ .../.archive}/dataset_utils.py | 0 .../.archive}/datasets/__init__.py | 0 .../.archive}/datasets/fetch/fetch.py | 0 .../.archive}/datasets/toy_loader/load.py | 0 .../.archive/sampling}/__init__.py | 0 .../.archive}/sampling/match_times.py | 0 .../.archive/signal}/__init__.py | 0 .../signal/fusion_signal}/__init__.py | 0 .../signal}/fusion_signal/interpolation.py | 0 .../signal}/fusion_signal/resampling.py | 0 .../magnitude_scaling/compute_norms.py | 0 .../signal}/magnitude_scaling/norm.py | 0 .../signal}/magnitude_scaling/rescale.py | 0 .../.archive/signal}/scaling.py | 0 .../spectral_representation/__init__.py | 0 .../signal}/spectral_representation/sft.py | 0 .../signal}/time_domain_filtering/__init__.py | 0 .../time_domain_filtering/filtering.py | 0 .../time_domain_filtering/preemphasis.py | 0 .../time_domain_processing/cut_time.py | 0 .../get_windowed_data.py | 0 .../prepare => preprocess}/README.md | 0 src/faith/preprocess/__init__.py | 1 + src/faith/preprocess/__main__.py | 14 +++++++++ .../config/default.yaml | 2 +- .../prepare => preprocess}/config/raw.yaml | 0 .../extract}/data_extraction.py | 0 src/faith/preprocess/pipelines/__init__.py | 1 + .../pipelines/processing_v0.py} | 10 +++---- .../preprocess.py} | 14 ++++----- .../transform}/__init__.py | 0 .../transform}/sample_processing.py | 0 .../transform}/signal_processing.py | 0 src/faith/preprocess/util/__init__.py | 3 ++ src/faith/{ => preprocess}/util/parmap.py | 0 src/faith/preprocess/util/utils.py | 30 +++++++++++++++++++ src/faith/util/__init__.py | 0 src/faith/util/utils.py | 5 ---- 46 files changed, 71 insertions(+), 26 deletions(-) rename src/faith/{core/fusion_signal/__init__.py => __main__.py} (100%) delete mode 100644 src/faith/datasets/prepare/__main__.py rename src/faith/{ => preprocess/.archive}/base/__init__.py (100%) rename src/faith/{ => preprocess/.archive}/base/load.py (100%) rename src/faith/{ => preprocess/.archive}/base/merge.py (100%) rename src/faith/{ => preprocess/.archive}/base/save.py (95%) rename src/faith/{datasets/prepare => preprocess/.archive}/dataset_utils.py (100%) rename src/faith/{ => preprocess/.archive}/datasets/__init__.py (100%) rename src/faith/{ => preprocess/.archive}/datasets/fetch/fetch.py (100%) rename src/faith/{ => preprocess/.archive}/datasets/toy_loader/load.py (100%) rename src/faith/{datasets/prepare => preprocess/.archive/sampling}/__init__.py (100%) rename src/faith/{ => preprocess/.archive}/sampling/match_times.py (100%) rename src/faith/{core => preprocess/.archive/signal}/__init__.py (100%) rename src/faith/{feature => preprocess/.archive/signal/fusion_signal}/__init__.py (100%) rename src/faith/{core => preprocess/.archive/signal}/fusion_signal/interpolation.py (100%) rename src/faith/{core => preprocess/.archive/signal}/fusion_signal/resampling.py (100%) rename src/faith/{core => preprocess/.archive/signal}/magnitude_scaling/compute_norms.py (100%) rename src/faith/{core => preprocess/.archive/signal}/magnitude_scaling/norm.py (100%) rename src/faith/{core => preprocess/.archive/signal}/magnitude_scaling/rescale.py (100%) rename src/faith/{core => preprocess/.archive/signal}/scaling.py (100%) rename src/faith/{core => preprocess/.archive/signal}/spectral_representation/__init__.py (100%) rename src/faith/{core => preprocess/.archive/signal}/spectral_representation/sft.py (100%) rename src/faith/{core => preprocess/.archive/signal}/time_domain_filtering/__init__.py (100%) rename src/faith/{core => preprocess/.archive/signal}/time_domain_filtering/filtering.py (100%) rename src/faith/{core => preprocess/.archive/signal}/time_domain_filtering/preemphasis.py (100%) rename src/faith/{core => preprocess/.archive/signal}/time_domain_processing/cut_time.py (100%) rename src/faith/{core => preprocess/.archive/signal}/time_domain_processing/get_windowed_data.py (100%) rename src/faith/{datasets/prepare => preprocess}/README.md (100%) create mode 100644 src/faith/preprocess/__init__.py create mode 100644 src/faith/preprocess/__main__.py rename src/faith/{datasets/prepare => preprocess}/config/default.yaml (98%) rename src/faith/{datasets/prepare => preprocess}/config/raw.yaml (100%) rename src/faith/{datasets/prepare => preprocess/extract}/data_extraction.py (100%) create mode 100644 src/faith/preprocess/pipelines/__init__.py rename src/faith/{datasets/prepare/shot_processing.py => preprocess/pipelines/processing_v0.py} (97%) rename src/faith/{datasets/prepare/prepare_dataset.py => preprocess/preprocess.py} (95%) rename src/faith/{sampling => preprocess/transform}/__init__.py (100%) rename src/faith/{datasets/prepare => preprocess/transform}/sample_processing.py (100%) rename src/faith/{datasets/prepare => preprocess/transform}/signal_processing.py (100%) create mode 100644 src/faith/preprocess/util/__init__.py rename src/faith/{ => preprocess}/util/parmap.py (100%) create mode 100644 src/faith/preprocess/util/utils.py delete mode 100644 src/faith/util/__init__.py delete mode 100644 src/faith/util/utils.py diff --git a/.gitignore b/.gitignore index 0d9cab5..4b8399c 100644 --- a/.gitignore +++ b/.gitignore @@ -161,4 +161,6 @@ cython_debug/ data/ logs/ -*.pkl \ No newline at end of file +outputs/ +*.pkl +*.joblib \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index f9658bb..aa1c270 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,13 +1,13 @@ [project] -name = "fusionaihub" -version = "0.0.1" +name = "Fusion Artificial InTelligence Hub" +version = "0.0.1-alpha" authors = [ { name = "Peter Steiner", email = "peter.steiner@princeton.edu" }, { name = "Nathaniel Chen", email = "nathaniel@princeton.edu" }, { name = "Kouroche Bouchiat", email = "bouchiat@princeton.edu" }, { name = "Azarakhsh Jalalvand", email = "azarakhsh.jalalvand@princeton.edu" } ] -description = "FusionAIHub - Fetch nuclear fusion data, preprocess it, train machine learning models." +description = "Fusion Artificial InTelligence Hub - Fetch nuclear fusion data, preprocess it, train machine learning models." readme = "README.md" requires-python = ">=3.9" classifiers = [ diff --git a/src/faith/core/fusion_signal/__init__.py b/src/faith/__main__.py similarity index 100% rename from src/faith/core/fusion_signal/__init__.py rename to src/faith/__main__.py diff --git a/src/faith/datasets/prepare/__main__.py b/src/faith/datasets/prepare/__main__.py deleted file mode 100644 index 444fa44..0000000 --- a/src/faith/datasets/prepare/__main__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .prepare_dataset import main - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/src/faith/base/__init__.py b/src/faith/preprocess/.archive/base/__init__.py similarity index 100% rename from src/faith/base/__init__.py rename to src/faith/preprocess/.archive/base/__init__.py diff --git a/src/faith/base/load.py b/src/faith/preprocess/.archive/base/load.py similarity index 100% rename from src/faith/base/load.py rename to src/faith/preprocess/.archive/base/load.py diff --git a/src/faith/base/merge.py b/src/faith/preprocess/.archive/base/merge.py similarity index 100% rename from src/faith/base/merge.py rename to src/faith/preprocess/.archive/base/merge.py diff --git a/src/faith/base/save.py b/src/faith/preprocess/.archive/base/save.py similarity index 95% rename from src/faith/base/save.py rename to src/faith/preprocess/.archive/base/save.py index d9e38b3..b77b5cf 100644 --- a/src/faith/base/save.py +++ b/src/faith/preprocess/.archive/base/save.py @@ -8,6 +8,9 @@ # also do other file formats +import warnings + +@warnings._deprecated('Use the new function save instead') def dict_to_hdf5( dictionary: dict, h5file: h5py.File, diff --git a/src/faith/datasets/prepare/dataset_utils.py b/src/faith/preprocess/.archive/dataset_utils.py similarity index 100% rename from src/faith/datasets/prepare/dataset_utils.py rename to src/faith/preprocess/.archive/dataset_utils.py diff --git a/src/faith/datasets/__init__.py b/src/faith/preprocess/.archive/datasets/__init__.py similarity index 100% rename from src/faith/datasets/__init__.py rename to src/faith/preprocess/.archive/datasets/__init__.py diff --git a/src/faith/datasets/fetch/fetch.py b/src/faith/preprocess/.archive/datasets/fetch/fetch.py similarity index 100% rename from src/faith/datasets/fetch/fetch.py rename to src/faith/preprocess/.archive/datasets/fetch/fetch.py diff --git a/src/faith/datasets/toy_loader/load.py b/src/faith/preprocess/.archive/datasets/toy_loader/load.py similarity index 100% rename from src/faith/datasets/toy_loader/load.py rename to src/faith/preprocess/.archive/datasets/toy_loader/load.py diff --git a/src/faith/datasets/prepare/__init__.py b/src/faith/preprocess/.archive/sampling/__init__.py similarity index 100% rename from src/faith/datasets/prepare/__init__.py rename to src/faith/preprocess/.archive/sampling/__init__.py diff --git a/src/faith/sampling/match_times.py b/src/faith/preprocess/.archive/sampling/match_times.py similarity index 100% rename from src/faith/sampling/match_times.py rename to src/faith/preprocess/.archive/sampling/match_times.py diff --git a/src/faith/core/__init__.py b/src/faith/preprocess/.archive/signal/__init__.py similarity index 100% rename from src/faith/core/__init__.py rename to src/faith/preprocess/.archive/signal/__init__.py diff --git a/src/faith/feature/__init__.py b/src/faith/preprocess/.archive/signal/fusion_signal/__init__.py similarity index 100% rename from src/faith/feature/__init__.py rename to src/faith/preprocess/.archive/signal/fusion_signal/__init__.py diff --git a/src/faith/core/fusion_signal/interpolation.py b/src/faith/preprocess/.archive/signal/fusion_signal/interpolation.py similarity index 100% rename from src/faith/core/fusion_signal/interpolation.py rename to src/faith/preprocess/.archive/signal/fusion_signal/interpolation.py diff --git a/src/faith/core/fusion_signal/resampling.py b/src/faith/preprocess/.archive/signal/fusion_signal/resampling.py similarity index 100% rename from src/faith/core/fusion_signal/resampling.py rename to src/faith/preprocess/.archive/signal/fusion_signal/resampling.py diff --git a/src/faith/core/magnitude_scaling/compute_norms.py b/src/faith/preprocess/.archive/signal/magnitude_scaling/compute_norms.py similarity index 100% rename from src/faith/core/magnitude_scaling/compute_norms.py rename to src/faith/preprocess/.archive/signal/magnitude_scaling/compute_norms.py diff --git a/src/faith/core/magnitude_scaling/norm.py b/src/faith/preprocess/.archive/signal/magnitude_scaling/norm.py similarity index 100% rename from src/faith/core/magnitude_scaling/norm.py rename to src/faith/preprocess/.archive/signal/magnitude_scaling/norm.py diff --git a/src/faith/core/magnitude_scaling/rescale.py b/src/faith/preprocess/.archive/signal/magnitude_scaling/rescale.py similarity index 100% rename from src/faith/core/magnitude_scaling/rescale.py rename to src/faith/preprocess/.archive/signal/magnitude_scaling/rescale.py diff --git a/src/faith/core/scaling.py b/src/faith/preprocess/.archive/signal/scaling.py similarity index 100% rename from src/faith/core/scaling.py rename to src/faith/preprocess/.archive/signal/scaling.py diff --git a/src/faith/core/spectral_representation/__init__.py b/src/faith/preprocess/.archive/signal/spectral_representation/__init__.py similarity index 100% rename from src/faith/core/spectral_representation/__init__.py rename to src/faith/preprocess/.archive/signal/spectral_representation/__init__.py diff --git a/src/faith/core/spectral_representation/sft.py b/src/faith/preprocess/.archive/signal/spectral_representation/sft.py similarity index 100% rename from src/faith/core/spectral_representation/sft.py rename to src/faith/preprocess/.archive/signal/spectral_representation/sft.py diff --git a/src/faith/core/time_domain_filtering/__init__.py b/src/faith/preprocess/.archive/signal/time_domain_filtering/__init__.py similarity index 100% rename from src/faith/core/time_domain_filtering/__init__.py rename to src/faith/preprocess/.archive/signal/time_domain_filtering/__init__.py diff --git a/src/faith/core/time_domain_filtering/filtering.py b/src/faith/preprocess/.archive/signal/time_domain_filtering/filtering.py similarity index 100% rename from src/faith/core/time_domain_filtering/filtering.py rename to src/faith/preprocess/.archive/signal/time_domain_filtering/filtering.py diff --git a/src/faith/core/time_domain_filtering/preemphasis.py b/src/faith/preprocess/.archive/signal/time_domain_filtering/preemphasis.py similarity index 100% rename from src/faith/core/time_domain_filtering/preemphasis.py rename to src/faith/preprocess/.archive/signal/time_domain_filtering/preemphasis.py diff --git a/src/faith/core/time_domain_processing/cut_time.py b/src/faith/preprocess/.archive/signal/time_domain_processing/cut_time.py similarity index 100% rename from src/faith/core/time_domain_processing/cut_time.py rename to src/faith/preprocess/.archive/signal/time_domain_processing/cut_time.py diff --git a/src/faith/core/time_domain_processing/get_windowed_data.py b/src/faith/preprocess/.archive/signal/time_domain_processing/get_windowed_data.py similarity index 100% rename from src/faith/core/time_domain_processing/get_windowed_data.py rename to src/faith/preprocess/.archive/signal/time_domain_processing/get_windowed_data.py diff --git a/src/faith/datasets/prepare/README.md b/src/faith/preprocess/README.md similarity index 100% rename from src/faith/datasets/prepare/README.md rename to src/faith/preprocess/README.md diff --git a/src/faith/preprocess/__init__.py b/src/faith/preprocess/__init__.py new file mode 100644 index 0000000..5fd6075 --- /dev/null +++ b/src/faith/preprocess/__init__.py @@ -0,0 +1 @@ +from .preprocess import preprocess \ No newline at end of file diff --git a/src/faith/preprocess/__main__.py b/src/faith/preprocess/__main__.py new file mode 100644 index 0000000..70e8be8 --- /dev/null +++ b/src/faith/preprocess/__main__.py @@ -0,0 +1,14 @@ +import hydra +from omegaconf import DictConfig + +from . import preprocess + +@hydra.main( + config_path="config", + config_name="config", + version_base=None, + ) +def main(cfg: DictConfig): + + # TODO: Add hydra config to preprocess + preprocess() \ No newline at end of file diff --git a/src/faith/datasets/prepare/config/default.yaml b/src/faith/preprocess/config/default.yaml similarity index 98% rename from src/faith/datasets/prepare/config/default.yaml rename to src/faith/preprocess/config/default.yaml index 406ce79..bad59d2 100644 --- a/src/faith/datasets/prepare/config/default.yaml +++ b/src/faith/preprocess/config/default.yaml @@ -36,7 +36,7 @@ signal: # Data processing parameters randomize_shots: true random_seed: 42 -num_shots: 15000 +num_shots: 100 fs_khz: 500 # Sampling frequency in kHz ip_threshold: 0.1 # Plasma current threshold window_ms: null # Window size in milliseconds diff --git a/src/faith/datasets/prepare/config/raw.yaml b/src/faith/preprocess/config/raw.yaml similarity index 100% rename from src/faith/datasets/prepare/config/raw.yaml rename to src/faith/preprocess/config/raw.yaml diff --git a/src/faith/datasets/prepare/data_extraction.py b/src/faith/preprocess/extract/data_extraction.py similarity index 100% rename from src/faith/datasets/prepare/data_extraction.py rename to src/faith/preprocess/extract/data_extraction.py diff --git a/src/faith/preprocess/pipelines/__init__.py b/src/faith/preprocess/pipelines/__init__.py new file mode 100644 index 0000000..00c5cd8 --- /dev/null +++ b/src/faith/preprocess/pipelines/__init__.py @@ -0,0 +1 @@ +from .processing_v0 import pipeline as pipeline_v0_stable \ No newline at end of file diff --git a/src/faith/datasets/prepare/shot_processing.py b/src/faith/preprocess/pipelines/processing_v0.py similarity index 97% rename from src/faith/datasets/prepare/shot_processing.py rename to src/faith/preprocess/pipelines/processing_v0.py index cd1c814..32272e1 100644 --- a/src/faith/datasets/prepare/shot_processing.py +++ b/src/faith/preprocess/pipelines/processing_v0.py @@ -13,17 +13,17 @@ from warnings import simplefilter simplefilter(action="ignore", category=pd.errors.PerformanceWarning) -from .data_extraction import ( +from ..extract.data_extraction import ( extract_signal, extract_running_time, align_signal, ) -from .sample_processing import ( +from ..transform.sample_processing import ( split_samples, remove_empty_samples, save_sample, ) -from .signal_processing import ( +from ..transform.signal_processing import ( identity_transform, stft_transform, resample_transform, @@ -33,13 +33,13 @@ logger = logging.getLogger(__name__) -def process_shot_stft( +def pipeline( shot_number: int, cfg: Dict, out_dir: Path, ) -> None: """ - Process a single shot through the complete data preparation pipeline accounting for STFT transformations. + Process a single shot through the complete data preparation pipeline accounting transformations. This function orchestrates the complete processing workflow for a shot: 1. Determines plasma running time diff --git a/src/faith/datasets/prepare/prepare_dataset.py b/src/faith/preprocess/preprocess.py similarity index 95% rename from src/faith/datasets/prepare/prepare_dataset.py rename to src/faith/preprocess/preprocess.py index e61c14d..1317d61 100644 --- a/src/faith/datasets/prepare/prepare_dataset.py +++ b/src/faith/preprocess/preprocess.py @@ -12,10 +12,10 @@ from pathlib import Path from sklearn.model_selection import train_test_split from typing import Optional -from ...util.parmap import ParallelMapper -from .shot_processing import process_shot_stft -from .dataset_utils import index_dataset +from .util import ParallelMapper +from .pipelines import pipeline_v0_stable as pipeline +from .util import index_dataset # Set up logger for this module logger = logging.getLogger(__name__) @@ -113,10 +113,10 @@ def prepare_dataset(cfg: dict) -> None: # Process shots using the appropriate function if cfg.get("debug", False): - process_shot_stft(170000, cfg, cache_dir) # For debugging + pipeline(170000, cfg, cache_dir) # For debugging else: mapper = ParallelMapper() - mapper(process_shot_stft, all_shots, cfg=cfg, out_dir=cache_dir) + mapper(pipeline, all_shots, cfg=cfg, out_dir=cache_dir) # Move cached files into train/test split logger.info("Splitting dataset into train and valid sets...") @@ -165,7 +165,7 @@ def prepare_dataset(cfg: dict) -> None: logger.info(f"Validation samples: {len(valid_files)}") -def main(): +def preprocess(): """Main entry point for the dataset preparation script.""" import argparse @@ -200,4 +200,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + preprocess() \ No newline at end of file diff --git a/src/faith/sampling/__init__.py b/src/faith/preprocess/transform/__init__.py similarity index 100% rename from src/faith/sampling/__init__.py rename to src/faith/preprocess/transform/__init__.py diff --git a/src/faith/datasets/prepare/sample_processing.py b/src/faith/preprocess/transform/sample_processing.py similarity index 100% rename from src/faith/datasets/prepare/sample_processing.py rename to src/faith/preprocess/transform/sample_processing.py diff --git a/src/faith/datasets/prepare/signal_processing.py b/src/faith/preprocess/transform/signal_processing.py similarity index 100% rename from src/faith/datasets/prepare/signal_processing.py rename to src/faith/preprocess/transform/signal_processing.py diff --git a/src/faith/preprocess/util/__init__.py b/src/faith/preprocess/util/__init__.py new file mode 100644 index 0000000..e3ac639 --- /dev/null +++ b/src/faith/preprocess/util/__init__.py @@ -0,0 +1,3 @@ +from .parmap import ParallelMapper + +from .utils import index_dataset \ No newline at end of file diff --git a/src/faith/util/parmap.py b/src/faith/preprocess/util/parmap.py similarity index 100% rename from src/faith/util/parmap.py rename to src/faith/preprocess/util/parmap.py diff --git a/src/faith/preprocess/util/utils.py b/src/faith/preprocess/util/utils.py new file mode 100644 index 0000000..26ba018 --- /dev/null +++ b/src/faith/preprocess/util/utils.py @@ -0,0 +1,30 @@ +""" +Dataset utilities for fusion dataset preparation. + +This module contains utility functions for handling missing signals, +creating placeholder dataframes, and indexing dataset files. +""" + +import pandas as pd +import logging +from pathlib import Path +from typing import Set, List, Dict + +# Set up logger for this module +logger = logging.getLogger(__name__) + +def index_dataset(out_dir: Path) -> None: + """ + Create an index file listing all dataset files in the directory. + + Scans the output directory for .joblib files and creates an index.pkl + file containing the list of all dataset files. + + Args: + out_dir: Directory to index + """ + files = list(out_dir.glob("*.joblib")) + df_files = pd.DataFrame({'files': [str(file) for file in files]}) + df_files.to_csv(out_dir / "index.csv", index=False) + + logger.info(f"Indexed {len(files)} files.") \ No newline at end of file diff --git a/src/faith/util/__init__.py b/src/faith/util/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/faith/util/utils.py b/src/faith/util/utils.py deleted file mode 100644 index 6cd4b51..0000000 --- a/src/faith/util/utils.py +++ /dev/null @@ -1,5 +0,0 @@ -import numpy as np -from typing import Any - - - From e15379f0d23b58dfb55f09d9d2c0c570a8f56978 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Tue, 15 Jul 2025 13:26:52 -0400 Subject: [PATCH 047/103] Add hydra --- pyproject.toml | 4 +- uv.lock | 3239 +++++++++++++++++++++++++++++++++++++++++++++--- 2 files changed, 3073 insertions(+), 170 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 69ff4d7..c0c311d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,5 +1,5 @@ [project] -name = "Fusion Artificial InTelligence Hub" +name = "faith" version = "0.0.1-alpha" authors = [ { name = "Peter Steiner", email = "peter.steiner@princeton.edu" 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+++ b/src/faith/util/logging.py @@ -0,0 +1,38 @@ +import logging +from threading import Lock + +class Logger: + _instance = None + _lock = Lock() + + def __new__(cls, *args, **kwargs): + if not cls._instance: + with cls._lock: + if not cls._instance: + cls._instance = super(Logger, cls).__new__(cls, *args, **kwargs) + cls._instance._initialize() + return cls._instance + + def _initialize(self): + self.logger = logging.getLogger("FusionAIHubLogger") + self.logger.setLevel(logging.DEBUG) + + # Create console handler + console_handler = logging.StreamHandler() + console_handler.setLevel(logging.DEBUG) + + # Create formatter and add it to the handler + formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + console_handler.setFormatter(formatter) + + # Add the handler to the logger + self.logger.addHandler(console_handler) + + def get_logger(self): + return self.logger + +logger = Logger() + +# Usage example: +# logger = Logger().get_logger() +# logger.info("This is a singleton logger.") \ No newline at end of file From cff14969b89bfead4298f8d5c8002cc805e0b478 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Tue, 15 Jul 2025 14:28:42 -0400 Subject: [PATCH 049/103] Revert "Dev nathan" --- pyproject.toml | 4 +- src/faith/util/__init__.py | 0 src/faith/util/logging.py | 38 - uv.lock | 3241 ++---------------------------------- 4 files changed, 171 insertions(+), 3112 deletions(-) delete mode 100644 src/faith/util/__init__.py delete mode 100644 src/faith/util/logging.py diff --git a/pyproject.toml b/pyproject.toml index c0c311d..69ff4d7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,5 +1,5 @@ [project] -name = "faith" +name = "Fusion Artificial InTelligence Hub" version = "0.0.1-alpha" authors = [ { name = "Peter Steiner", email = "peter.steiner@princeton.edu" }, @@ -37,8 +37,6 @@ dependencies = [ "tables", "pyyaml", "jupyter", - "hydra-core", - "omegaconf", ] 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"https://files.pythonhosted.org/packages/b4/2d/2345fce04cfd4bee161bf1e7d9cdc702e3e16109021035dbb24db654a622/yarl-1.20.1-py3-none-any.whl", hash = "sha256:83b8eb083fe4683c6115795d9fc1cfaf2cbbefb19b3a1cb68f6527460f483a77", size = 46542, upload-time = "2025-06-10T00:46:07.521Z" }, -] - [[package]] name = "zipp" version = "3.23.0" From d62ce6bc21c0c954621d6b91bae4d857ac8cb60e Mon Sep 17 00:00:00 2001 From: Peter Steiner <61472983+renierts@users.noreply.github.com> Date: Tue, 15 Jul 2025 15:48:39 -0400 Subject: [PATCH 050/103] Merged the entire model training processes into FusionAIHub. --- development_guide.md | 261 +++ .../joblib_dataset_examples.py | 657 ++++++++ .../lightning_trainer_example.py | 569 +++++++ examples/Machine_Learning/mae_example.py | 43 + .../Machine_Learning/ray_tune_examples.py | 436 +++++ src/faith/train/__init__.py | 12 + src/faith/train/blocks/__init__.py | 15 +- src/faith/train/blocks/base.py | 137 +- src/faith/train/blocks/decoder.py | 86 +- src/faith/train/blocks/encoder.py | 68 +- src/faith/train/blocks/residual.py | 54 +- .../faith/train/data/__init__.py | 0 src/faith/train/data/datasets/__init__.py | 4 + src/faith/train/data/datasets/base.py | 1411 +++++++++++++++++ src/faith/train/data/datasets/file_based.py | 1163 ++++++++++++++ src/faith/train/data/loaders/__init__.py | 0 src/faith/train/data/loaders/factory.py | 265 ++++ src/faith/train/models/__init__.py | 19 +- src/faith/train/models/autoencoder.py | 136 +- src/faith/train/models/configs.py | 51 - src/faith/train/models/utils.py | 18 +- src/faith/train/training/__init__.py | 13 + src/faith/train/training/lightning_trainer.py | 538 +++++++ src/faith/train/tuning/__init__.py | 33 + src/faith/train/tuning/ray_tuner.py | 666 ++++++++ src/faith/train/tuning/schedulers.py | 0 src/faith/train/tuning/search_spaces.py | 360 +++++ tests/test_autoencoder.py | 69 + tests/test_train_blocks_base.py | 26 + tests/test_train_blocks_decoder.py | 60 + tests/test_train_blocks_encoder.py | 46 + tests/test_train_blocks_residual.py | 20 + tests/test_train_configuration.py | 58 + 33 files changed, 6832 insertions(+), 462 deletions(-) create mode 100644 development_guide.md create mode 100644 examples/Machine_Learning/joblib_dataset_examples.py create mode 100644 examples/Machine_Learning/lightning_trainer_example.py create mode 100644 examples/Machine_Learning/mae_example.py create mode 100644 examples/Machine_Learning/ray_tune_examples.py rename tests/test_residual_block.py => src/faith/train/data/__init__.py (100%) create mode 100644 src/faith/train/data/datasets/__init__.py create mode 100644 src/faith/train/data/datasets/base.py create mode 100644 src/faith/train/data/datasets/file_based.py create mode 100644 src/faith/train/data/loaders/__init__.py create mode 100644 src/faith/train/data/loaders/factory.py create mode 100644 src/faith/train/training/__init__.py create mode 100644 src/faith/train/training/lightning_trainer.py create mode 100644 src/faith/train/tuning/__init__.py create mode 100644 src/faith/train/tuning/ray_tuner.py create mode 100644 src/faith/train/tuning/schedulers.py create mode 100644 src/faith/train/tuning/search_spaces.py create mode 100644 tests/test_autoencoder.py create mode 100644 tests/test_train_blocks_base.py create mode 100644 tests/test_train_blocks_decoder.py create mode 100644 tests/test_train_blocks_encoder.py create mode 100644 tests/test_train_blocks_residual.py create mode 100644 tests/test_train_configuration.py diff --git a/development_guide.md b/development_guide.md new file mode 100644 index 0000000..a284219 --- /dev/null +++ b/development_guide.md @@ -0,0 +1,261 @@ +# Development Guide + +This guide covers the development setup and coding standards for this Python package. + +## Code Standards + +- **Line length**: 79 characters maximum +- **Docstring format**: NumPy style +- **Linting**: Ruff +- **Function arguments**: Each argument on a new line for multi-argument functions + +## IDE Setup + +### PyCharm Setup + +#### 1. Install Ruff Plugin + +1. Go to **File → Settings** +2. Navigate to **Plugins** +3. Search for "Ruff" and install the official Ruff plugin +4. Restart PyCharm + +#### 2. Configure Code Style + +1. Go to **File → Settings → Editor → Code Style → Python** +2. Set **Hard wrap at: ** to `79` +3. In the **Wrapping and Braces** tab: + - Set **Method declaration parameters** to "Chop down if long" + - Check "New line after '('" and "')' on new line" + - Set **Function call arguments** to "Chop down if long" + +#### 3. Configure Docstring Format + +1. Go to **File → Settings → Tools → Python Integrated Tools** +2. Set **Docstring format** to "NumPy" + +#### 4. Configure Ruff + +1. Go to **File → Settings → Tools → Ruff** +2. Enable **Use Ruff** +3. Set the **Ruff executable** path (if not auto-detected) +4. Enable **Run Ruff when files are saved** + +### VSCode Setup + +#### 1. Install Extensions + +Install these extensions from the VSCode marketplace: + +- **Ruff** (charliermarsh.ruff) +- **Python** (ms-python.python) +- **Python Docstring Generator** (njpwerner.autodocstring) + +#### 2. Configure Settings + +Create or update `.vscode/settings.json` in your project root: + +```json +{ + "python.defaultInterpreterPath": "./venv/bin/python", + "editor.rulers": [79], + "editor.wordWrap": "wordWrapColumn", + "editor.wordWrapColumn": 79, + + // Ruff configuration + "ruff.enable": true, + "ruff.organizeImports": true, + "ruff.fixAll": true, + "ruff.codeAction.fixViolation": { + "enable": true + }, + + // Python formatting + "[python]": { + "editor.defaultFormatter": "charliermarsh.ruff", + "editor.formatOnSave": true, + "editor.codeActionsOnSave": { + "source.organizeImports": "explicit", + "source.fixAll": "explicit" + } + }, + + // Docstring configuration + "autoDocstring.docstringFormat": "numpy", + "autoDocstring.startOnNewLine": true, + "autoDocstring.includeExtendedSummary": true, + "autoDocstring.includeName": false, + + // Python specific settings + "python.formatting.provider": "none", + "python.linting.enabled": false, + "python.analysis.typeCheckingMode": "basic" +} +``` + +#### 3. Configure Ruff + +Create `ruff.toml` in your project root: + +```toml +line-length = 79 +target-version = "py38" + +[lint] +select = [ + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # pyflakes + "I", # isort + "B", # flake8-bugbear + "C4", # flake8-comprehensions + "UP", # pyupgrade +] +ignore = [ + "E501", # line too long (handled by formatter) +] + +[format] +quote-style = "double" +indent-style = "space" +skip-magic-trailing-comma = false +line-ending = "auto" +``` + +--- + +## Code Examples + +### Function Definition Style + +```python +def my_function( + param1: str, + param2: int, + param3: float = 0.0, + param4: bool = True, +) -> dict: + """ + Brief description of the function. + + Longer description if needed. This can span multiple lines and + should explain what the function does in more detail. + + Parameters + ---------- + param1 : str + Description of param1. + param2 : int + Description of param2. + param3 : float, optional + Description of param3, by default 0.0. + param4 : bool, optional + Description of param4, by default True. + + Returns + ------- + dict + Description of the return value. + + Examples + -------- + >>> result = my_function("hello", 42) + >>> print(result) + {'message': 'hello', 'number': 42} + """ + return {"message": param1, "number": param2, "value": param3} +``` + +### Class Definition Style + +```python +class MyClass: + """ + Brief description of the class. + + Longer description explaining the purpose and usage of the class. + + Parameters + ---------- + name : str + The name of the instance. + value : int + The initial value. + + Attributes + ---------- + name : str + The name of the instance. + value : int + The current value. + + Examples + -------- + >>> obj = MyClass("test", 100) + >>> obj.increment() + >>> print(obj.value) + 101 + """ + + def __init__( + self, + name: str, + value: int, + ) -> None: + self.name = name + self.value = value + + def increment(self) -> None: + """Increment the value by 1.""" + self.value += 1 +``` + +--- + +## Development Workflow + +### 1. Setup Development Environment + +```bash +# Create virtual environment +python -m venv .venv +source venv/bin/activate # On Windows: venv\Scripts\activate + +# Install development dependencies +pip install -e "." +``` + +### 2. Running Tests + +```bash +# Run tests with coverage +pytest --cov=src --cov-report=html + +# Run specific test file +pytest tests/test_example.py + +# Run with verbose output +pytest -v +``` + +### 3. Code Quality Checks + +```bash +# Run ruff linting +ruff check . + +# Run ruff formatting +ruff format . + +# Fix auto-fixable issues +ruff check --fix . +``` + +--- + +## Additional Resources + +- [NumPy Docstring Guide](https://numpydoc.readthedocs.io/en/latest/format.html) +- [Ruff Documentation](https://docs.astral.sh/ruff/) +- [Python Type Hints](https://docs.python.org/3/library/typing.html) +- [pytest Documentation](https://docs.pytest.org/) \ No newline at end of file diff --git a/examples/Machine_Learning/joblib_dataset_examples.py b/examples/Machine_Learning/joblib_dataset_examples.py new file mode 100644 index 0000000..35f70ea --- /dev/null +++ b/examples/Machine_Learning/joblib_dataset_examples.py @@ -0,0 +1,657 @@ +"""Example usage of JoblibDataset with real file inspection and training.""" + +from torch.utils.data import DataLoader +import torch +from pathlib import Path + +# Assuming your package structure +from src.faith.train.data.datasets.file_based import JoblibDataset +from src.faith.train.data.loaders.factory import worker_init_fn +from src.faith.train.models.autoencoder import BlockBasedAutoencoder +from src.faith.train.training import train_model + + +def collate_fn(data: list[tuple[torch.Tensor, ...]]) \ + -> tuple[torch.Tensor, ...]: + """ + Custom collate function to remove the highest frequency bin of + spectrograms. # TODO list of dicts to dict of tensors + + Parameters + ---------- + data : list of tuples + List of tuples containing input and target tensors. + + Returns + ------- + Tuples + Processed list of tuples with the last frequency bin removed. + """ + batch_size = len(data) + if isinstance(data[0], dict): + collated = {} + for key in data[0].keys(): + values = [item[key] for item in data] + + if isinstance(values[0], torch.Tensor): + collated[key] = torch.stack(values) + else: + collated[key] = values + else: + processed_inputs = torch.stack([d[0] for d in data]) + processed_targets = torch.stack([d[1] for d in data]) + + processed_inputs = processed_inputs[:, :, :, :-1] + processed_targets = processed_targets[:, :, :, :-1] + + return processed_inputs, processed_targets + + +def inspect_joblib_file(file_path: str): + """Inspect a joblib file to understand its structure.""" + from joblib import load + + print(f"Inspecting file: {file_path}") + print("=" * 50) + + # Load file to see structure + data_dict = load(file_path, mmap_mode='r') + + print("Available keys:", list(data_dict.keys())) + print() + + # Inspect each key + for key, value in data_dict.items(): + print(f"Key: '{key}'") + if hasattr(value, 'shape'): + print(f" Type: {type(value)}") + print(f" Shape: {value.shape}") + print(f" Dtype: {value.dtype}") + else: + print(f" Type: {type(value)}") + print(f" Value: {value}") + print() + + # Store keys before cleaning up + available_keys = list(data_dict.keys()) + + # Clean up + del data_dict + + return available_keys + + +def example_basic_usage(): + """Basic usage example with a single file.""" + print("EXAMPLE 1: Basic Usage") + print("=" * 40) + + file_path = "171348_0.joblib" # Replace with your actual file + + # First, inspect the file to understand its structure + print("Step 1: Inspect file structure") + available_keys = inspect_joblib_file(file_path) + + # Create dataset with auto-detection + print("Step 2: Create dataset with auto-detection") + dataset = JoblibDataset( + file_paths=[file_path], + subseq_len=128, # Extract 128-sample subsequences + auto_detect_keys=True, # Let it figure out the keys + validate_on_init=True + ) + + print("Dataset created successfully!") + print(f"Total subsequences: {len(dataset)}") + print(f"Is autoencoder mode: {dataset.is_autoencoder_mode}") + print() + + # Test data loading without worker_init() + print("Step 3: Inspect data shapes") + input_shape, target_shape = dataset.get_sample_shape() + print(f"Input shape: {input_shape}") + print(f"Target shape: {target_shape}") + + # Get a sample for inspection + sample_input, sample_target = dataset.peek_sample() + print(f"Sample input dtype: {sample_input.dtype}") + print(f"Sample target dtype: {sample_target.dtype}") + print() + + # Create DataLoader with proper worker init function + print("Step 4: Test DataLoader") + + loader = DataLoader( + dataset, + batch_size=4, + shuffle=True, + num_workers=2, + worker_init_fn=worker_init_fn + ) + + # Test loading a batch + for batch_idx, (inputs, targets) in enumerate(loader): + print(f"Batch {batch_idx}:") + print(f" Input shape: {inputs.shape}") + print(f" Target shape: {targets.shape}") + print(f" Input dtype: {inputs.dtype}") + print(f" Target dtype: {targets.dtype}") + + if batch_idx == 0: # Only show first batch + break + + print() + return dataset + + +def example_custom_configuration(): + """Example with custom key configuration.""" + print("EXAMPLE 2: Custom Configuration") + print("=" * 40) + + file_path = "170797_0.joblib" # Replace with your actual file + + # Create dataset with specific keys (adjust based on your file) + dataset = JoblibDataset( + file_paths=[file_path], + subseq_len=256, # Longer subsequences + input_key=['co2', 'ece'], # Specify your input keys as list + target_key=None, # Autoencoder mode + chunking_strategy='sliding_window', # Overlapping chunks + overlap=64, # 64-sample overlap + validate_on_init=True + ) + + print("Dataset with custom config:") + print(f" Input key: {dataset.input_key}") + print(f" Target key: {dataset.target_key}") + print(f" Subsequence length: {dataset.subseq_len}") + print(f" Chunking strategy: {dataset.chunking_strategy}") + print(f" Is multi-input: {dataset.is_multi_input}") + print(f" Total subsequences: {len(dataset)}") + print() + + # Show dataset summary + summary = dataset.summary() + print("Dataset summary:") + for key, value in summary.items(): + if key not in ['file_metadata', 'file_paths']: # Skip verbose fields + print(f" {key}: {value}") + print() + + # Inspect multi-key data shapes + print("Data shape inspection:") + input_shapes, target_shapes = dataset.get_sample_shape() + print(f"Input shapes: {input_shapes}") + print(f"Target shapes: {target_shapes}") + + # Get sample data to inspect + sample_inputs, sample_targets = dataset.peek_sample() + print("Sample data types:") + if isinstance(sample_inputs, dict): + for key, tensor in sample_inputs.items(): + print( + f" Input '{key}': shape {tensor.shape}, dtype {tensor.dtype}") + if isinstance(sample_targets, dict): + for key, tensor in sample_targets.items(): + print(f" Target '{key}': shape {tensor.shape}, " + f"dtype {tensor.dtype}") + print() + + # Create DataLoader + loader = DataLoader( + dataset, + batch_size=4, + shuffle=True, + num_workers=1, + worker_init_fn=worker_init_fn + ) + + # Test loading a batch + for batch_idx, (inputs, targets) in enumerate(loader): + print(f"Batch {batch_idx}:") + if isinstance(inputs, dict): + print(f" Input keys: {list(inputs.keys())}") + for key, val in inputs.items(): + print(f" Input '{key}' shape: {val.shape}, " + f"dtype: {val.dtype}") + if isinstance(targets, dict): + print(f" Target keys: {list(targets.keys())}") + for key, val in targets.items(): + print(f" Target '{key}' shape: {val.shape}, " + f"dtype: {val.dtype}") + + if batch_idx == 0: # Only show first batch + break + + return dataset + + +def example_multiple_files(): + """Example with multiple files and advanced features.""" + print("EXAMPLE 3: Multiple Files with Advanced Features") + print("=" * 40) + + # Use glob pattern or directory to find multiple files + file_pattern = "./*.joblib" # Adjust to your path + + dataset = JoblibDataset( + file_paths=file_pattern, # Can be directory, glob pattern, or list + subseq_len=128, + input_key='ece', + target_key=None, # Autoencoder mode + file_pattern="*.joblib", # Pattern for file discovery + max_files=10, # Limit to 10 files + sort_files=True, # Sort files by name + balance_files=True, # Balance samples across files + chunking_strategy='non_overlapping', + validate_on_init=True + ) + + print("Multi-file dataset:") + print(f" Number of files: {dataset.num_files}") + print(f" Total subsequences: {len(dataset)}") + print(f" Balanced: {dataset.balance_files}") + print() + + # Show file statistics + print("File statistics:") + file_stats = dataset.get_file_stats() + for i, stats in enumerate(file_stats[:3]): # Show first 3 files + print(f" File {i}: {Path(stats['file_path']).name}") + print(f" Subsequences: {stats['num_subsequences']}") + print(f" Sequence length: {stats['sequence_length']}") + + if len(file_stats) > 3: + print(f" ... and {len(file_stats) - 3} more files") + print() + + # Inspect data from multiple files + print("Inspecting data from different files:") + for i in range(min(3, dataset.num_files)): + try: + input_shape, target_shape = dataset.get_sample_shape(file_idx=i) + print(f" File {i}: input {input_shape}, target {target_shape}") + except Exception as e: + print(f" File {i}: Error - {e}") + print() + + # Split dataset by files + print("Splitting dataset by files...") + train_ds, val_ds = dataset.split_by_files( + train_ratio=0.8, + val_ratio=0.2, + random_seed=42 + ) + + print("Split results:") + print(f" Train: {len(train_ds)} subsequences from {train_ds.num_files} " + f"files") + print(f" Val: {len(val_ds)} subsequences from {val_ds.num_files} files") + print() + + return train_ds, val_ds + + +def example_autoencoder_training(): + """Example: Train an autoencoder with JoblibDataset.""" + print("EXAMPLE 4: Autoencoder Training") + print("=" * 40) + + # Create dataset for autoencoder training + dataset = JoblibDataset( + file_paths="171348_0.joblib", # Adjust path + subseq_len=128, + input_key='co2', + target_key=None, # Autoencoder mode: input = target + auto_detect_keys=True, + validate_on_init=True + ) + + print(f"Autoencoder dataset: {len(dataset)} samples") + + # Get data shape for model configuration WITHOUT worker_init() + input_shape, target_shape = dataset.get_sample_shape() + print(f"Input shape: {input_shape}") + print(f"Target shape: {target_shape}") + + # Alternative: Get a full sample for inspection + sample_input, sample_target = dataset.peek_sample() + print(f"Sample input shape: {sample_input.shape}") + print(f"Sample target shape: {sample_target.shape}") + print(f"Sample input dtype: {sample_input.dtype}") + + # Get detailed sample information + sample_info = dataset.get_sample_info(0) + print(f"Sample info: {sample_info}") + print() + + # Create model based on data shape + model = BlockBasedAutoencoder( + input_channels=sample_input.shape[0], # Number of channels + hidden_dim=64, + activation='gelu' + ) + + print(f"Model created with {model.parameter_count:,} parameters") + + # Create DataLoader with proper worker init + train_loader = DataLoader( + dataset, + batch_size=16, + shuffle=True, + num_workers=4, + worker_init_fn=worker_init_fn, + collate_fn=collate_fn, + ) + + d = next(iter(train_loader)) + + # Train the model + print("Starting training...") + lightning_model, trainer = train_model( + model=model, + train_dataloader=train_loader, + val_dataloader=None, # Using same data for demo + max_epochs=5, + learning_rate=1e-4, + logger_type="tensorboard", + project_name="joblib-autoencoder" + ) + + print("Training completed!") + print("View logs with: tensorboard --logdir=./logs") + print() + + +def example_multikey_usage(): + """Example showing multi-key input/target usage.""" + print("EXAMPLE 5: Multi-Key Input/Target Usage") + print("=" * 40) + + # Create dataset with multiple input and target keys + dataset = JoblibDataset( + file_paths="171348_0.joblib", # Adjust path + subseq_len=128, + input_key=['co2', 'ece'], # Multiple input keys + target_key=['co2', 'mhr'], + # Multiple target keys (or None for autoencoder) + validate_on_init=True + ) + + print("Multi-key dataset:") + print(f" Input keys: {dataset.input_key}") + print(f" Target keys: {dataset.target_key}") + print(f" Is multi-input: {dataset.is_multi_input}") + print(f" Is multi-target: {dataset.is_multi_target}") + print(f" Total subsequences: {len(dataset)}") + print() + + # Inspect shapes for each key + print("Shape inspection:") + input_shapes, target_shapes = dataset.get_sample_shape() + print("Input shapes by key:") + for key, shape in input_shapes.items(): + print(f" '{key}': {shape}") + print("Target shapes by key:") + for key, shape in target_shapes.items(): + print(f" '{key}': {shape}") + print() + + # Get sample data + sample_inputs, sample_targets = dataset.peek_sample() + print("Sample data inspection:") + print(f"Input data keys: {list(sample_inputs.keys())}") + print(f"Target data keys: {list(sample_targets.keys())}") + + for key, tensor in sample_inputs.items(): + print(f" Input '{key}': shape {tensor.shape}, dtype {tensor.dtype}") + print(f" Min: {tensor.min().item():.4f}, " + f"Max: {tensor.max().item():.4f}") + + for key, tensor in sample_targets.items(): + print(f" Target '{key}': shape {tensor.shape}, dtype {tensor.dtype}") + print(f" Min: {tensor.min().item():.4f}, " + f"Max: {tensor.max().item():.4f}") + print() + + # Test DataLoader + loader = DataLoader(dataset, + batch_size=4, + shuffle=True, + num_workers=2, + worker_init_fn=worker_init_fn, + collate_fn=collate_fn) + + print("DataLoader test:") + for batch_idx, (inputs, targets) in enumerate(loader): + print(f"Batch {batch_idx}:") + print(f" Input batch keys: {list(inputs.keys())}") + print(f" Target batch keys: {list(targets.keys())}") + + for key, tensor in inputs.items(): + print(f" Input '{key}' batch shape: {tensor.shape}") + for key, tensor in targets.items(): + print(f" Target '{key}' batch shape: {tensor.shape}") + + if batch_idx == 0: # Only show first batch + break + + return dataset + + +def example_error_handling(): + """Example showing error handling and debugging.""" + print("EXAMPLE 6: Error Handling and Debugging") + print("=" * 40) + + # Test with mix of valid and invalid files + test_files = [ + "171348_0.joblib", # Replace with your actual valid file + "nonexistent_file.joblib", # This should fail + ] + + print("Testing error handling with mixed file list:") + for f in test_files: + exists = "✓" if Path(f).exists() else "✗" + print(f" {exists} {f}") + print() + + # First, let's validate files BEFORE creating the dataset + print("Manual file validation:") + valid_files = [] + + for file_path in test_files: + try: + # Try to inspect the file directly + from joblib import load + data_dict = load(file_path, mmap_mode='r') + available_keys = list(data_dict.keys()) + del data_dict # Clean up + + print(f" ✓ {Path(file_path).name} - Keys: {available_keys}") + valid_files.append(file_path) + + except Exception as e: + print(f" ✗ {Path(file_path).name} - Error: {e}") + print() + + if not valid_files: + print("No valid files found! Cannot proceed with dataset examples.") + return None + + # Create dataset with only valid files + print(f"Creating dataset with {len(valid_files)} valid files...") + try: + dataset = JoblibDataset( + file_paths=valid_files, # Only use valid files + subseq_len=128, + auto_detect_keys=True, + validate_on_init=True # This should work with valid files + ) + + print("✓ Dataset created successfully!") + print(f" Total subsequences: {len(dataset)}") + print(f" Number of files: {dataset.num_files}") + print(f" Input key: {dataset.input_key}") + print(f" Target key: {dataset.target_key}") + print() + + # Now test file access - this should work + print("Testing file access on valid dataset:") + for i in range(dataset.num_files): + try: + input_shape, target_shape = dataset.get_sample_shape( + file_idx=i) + print( + f" File {i}: ✓ Input {input_shape}, Target {target_shape}") + except Exception as e: + print(f" File {i}: ✗ Error - {e}") + print() + + # Test peek functionality + print("Testing peek functionality:") + try: + sample_input, sample_target = dataset.peek_sample() + print(" ✓ Peek successful!") + s = sample_input.shape \ + if not isinstance(sample_input, dict) \ + else {k: v.shape for k, v in sample_input.items()} + print(f" Input shape: {s}") + s = sample_target.shape \ + if not isinstance(sample_target, dict) \ + else {k: v.shape for k, v in sample_target.items()} + print(f" Target shape: {s}") + except Exception as e: + print(f" ✗ Peek failed: {e}") + print() + + # Show dataset info for debugging + print("Dataset summary:") + info = dataset.summary() + for key, value in info.items(): + if key not in ['file_metadata', 'file_paths']: + print(f" {key}: {value}") + print() + + return dataset + + except Exception as e: + print(f"✗ Failed to create dataset: {e}") + print(f"Error type: {type(e).__name__}") + import traceback + traceback.print_exc() + return None + + +def example_error_handling_advanced(): + """More advanced error handling example.""" + print("EXAMPLE 6B: Advanced Error Handling") + print("=" * 40) + + # Test the validate_files method on a dataset with mixed files + test_files = [ + "171348_0.joblib", # Valid file + "nonexistent_file.joblib", # Invalid file + "another_invalid_file.joblib", # Another invalid file + ] + + try: + # Create dataset without validation to test the validate_files method + dataset = JoblibDataset( + file_paths=test_files, + subseq_len=128, + auto_detect_keys=True, + validate_on_init=False # Don't validate during init + ) + + print("Testing validate_files() method:") + validation_results = dataset.validate_files() + + valid_files = [] + for file_path, is_valid, error_msg in validation_results: + status = "✓" if is_valid else "✗" + print(f" {status} {Path(file_path).name}") + if error_msg: + print(f" Error: {error_msg}") + if is_valid: + valid_files.append(file_path) + print() + + if valid_files: + print(f"Found {len(valid_files)} valid files. " + f"Creating new dataset...") + # Create a new dataset with only valid files + valid_dataset = JoblibDataset( + file_paths=valid_files, + subseq_len=128, + auto_detect_keys=True, + validate_on_init=True + ) + + print(f"✓ Valid dataset created with {len(valid_dataset)} " + f"subsequences") + + # Test this dataset + input_shape, target_shape = valid_dataset.get_sample_shape() + print(f" Input shape: {input_shape}") + print(f" Target shape: {target_shape}") + + return valid_dataset + else: + print("No valid files found!") + return None + + except Exception as e: + print(f"Error in advanced error handling: {e}") + import traceback + traceback.print_exc() + return None + + +def main(): + """Run all examples.""" + print("JoblibDataset Usage Examples") + print("=" * 60) + print() + + # Note: You'll need to replace file paths with actual files + print("NOTE: Replace file paths with your actual joblib files in each " + "example.") + print() + + try: + """ + # Basic usage + dataset1 = example_basic_usage() + + # Custom configuration + dataset2 = example_custom_configuration() + + # Multiple files (comment out if you don't have multiple files) + train_ds, val_ds = example_multiple_files() + + # Autoencoder training (comment out if you don't want to train) + example_autoencoder_training() + """ + # Multi-key usage + dataset3 = example_multikey_usage() + + # Error handling + dataset4 = example_error_handling() + dataset5 = example_error_handling_advanced() + + print("All examples completed successfully!") + + except Exception as e: + print(f"Error running examples: {e}") + print("Make sure to:") + print("1. Replace file paths with real joblib files") + print("2. Install required dependencies: joblib, torch, " + "pytorch-lightning") + print("3. Ensure your files have the expected structure") + print("4. Update the input_key and target_key based on your data") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/examples/Machine_Learning/lightning_trainer_example.py b/examples/Machine_Learning/lightning_trainer_example.py new file mode 100644 index 0000000..427063e --- /dev/null +++ b/examples/Machine_Learning/lightning_trainer_example.py @@ -0,0 +1,569 @@ +"""Examples and tests for the Lightning trainer with autoencoder models.""" + +import torch +import torch.nn.functional as F +from torch.utils.data import DataLoader, TensorDataset +import pytorch_lightning as pl +from pytorch_lightning import Trainer +from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping + +# Assuming your package structure +from src.faith.train.models.autoencoder import BlockBasedAutoencoder +from src.faith.train.training import ( + LightningTrainer, MultimodalLightningTrainer, train_model) + + +def create_dummy_dataset( + batch_size: int = 16, + num_samples: int = 1000, + input_shape: tuple = (80, 100, 128) +) -> tuple: + """Create dummy dataset for testing. + + Parameters + ---------- + batch_size : int, optional + Batch size, by default 16. + num_samples : int, optional + Number of samples, by default 1000. + input_shape : tuple, optional + Shape of input data (C, H, W), by default (80, 100, 128). + + Returns + ------- + tuple + Train and validation dataloaders. + """ + # Create random data + data = torch.randn(num_samples, *input_shape) + + # Split into train/val + train_size = int(0.8 * num_samples) + train_data = data[:train_size] + val_data = data[train_size:] + + # Create datasets (for autoencoders, input = target) + train_dataset = TensorDataset(train_data, train_data) + val_dataset = TensorDataset(val_data, val_data) + + # Create dataloaders + train_loader = DataLoader( + train_dataset, + batch_size=batch_size, + shuffle=True, + num_workers=2 + ) + val_loader = DataLoader( + val_dataset, + batch_size=batch_size, + shuffle=False, + num_workers=2 + ) + + return train_loader, val_loader + + +def create_multimodal_dataset( + batch_size: int = 16, + num_samples: int = 1000 +) -> tuple: + """Create dummy multimodal dataset. + + Parameters + ---------- + batch_size : int, optional + Batch size, by default 16. + num_samples : int, optional + Number of samples, by default 1000. + + Returns + ------- + tuple + Train and validation dataloaders. + """ + # Create multimodal data + audio_data = torch.randn(num_samples, 80, 100, 128) + text_data = torch.randint(0, 1000, (num_samples, 50)) # Token IDs + + # Split into train/val + train_size = int(0.8 * num_samples) + + train_audio = audio_data[:train_size] + train_text = text_data[:train_size] + val_audio = audio_data[train_size:] + val_text = text_data[train_size:] + + # Create datasets as dictionaries + train_data = [] + for i in range(len(train_audio)): + train_data.append({ + 'audio': train_audio[i], + 'text': train_text[i], + 'target_audio': train_audio[i], # Reconstruction target + 'target_text': train_text[i] + }) + + val_data = [] + for i in range(len(val_audio)): + val_data.append({ + 'audio': val_audio[i], + 'text': val_text[i], + 'target_audio': val_audio[i], + 'target_text': val_text[i] + }) + + train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True) + val_loader = DataLoader(val_data, batch_size=batch_size, shuffle=False) + + return train_loader, val_loader + + +class CustomLossAutoencoder(LightningTrainer): + """Example of custom loss computation for specific autoencoder needs.""" + + def __init__(self, model, perceptual_weight: float = 0.1, **kwargs): + """Initialize with perceptual loss component. + + Parameters + ---------- + model : torch.nn.Module + The autoencoder model. + perceptual_weight : float, optional + Weight for perceptual loss, by default 0.1. + **kwargs + Additional arguments passed to parent. + """ + super().__init__(model, **kwargs) + self.perceptual_weight = perceptual_weight + + def compute_loss(self, batch, batch_idx, prefix=""): + """Compute loss with perceptual component. + + Parameters + ---------- + batch : Any + Input batch. + batch_idx : int + Batch index. + prefix : str, optional + Logging prefix, by default "". + + Returns + ------- + Dict[str, torch.Tensor] + Loss components. + """ + inputs, targets = batch + outputs = self.model(inputs) + + if isinstance(outputs, tuple): + reconstructed, latent = outputs + else: + reconstructed = outputs + + # MSE reconstruction loss + recon_loss = F.mse_loss(reconstructed, targets) + + # Simple perceptual loss (L1 in feature space) + # In practice, you'd use a pre-trained network + with torch.no_grad(): + # Downsample for "perceptual" comparison + targets_down = F.avg_pool2d(targets, kernel_size=4, stride=4) + recon_down = F.avg_pool2d(reconstructed, kernel_size=4, stride=4) + + perceptual_loss = F.l1_loss(recon_down, targets_down) + + # Total loss + total_loss = recon_loss + self.perceptual_weight * perceptual_loss + + metrics = { + f'{prefix}loss': total_loss, + f'{prefix}recon_loss': recon_loss, + f'{prefix}perceptual_loss': perceptual_loss, + } + + return metrics + + +class MultimodalAutoencoder(torch.nn.Module): + """Example multimodal autoencoder for testing.""" + + def __init__(self, audio_channels: int = 80, text_vocab_size: int = 1000): + """Initialize multimodal autoencoder. + + Parameters + ---------- + audio_channels : int, optional + Number of audio channels, by default 80. + text_vocab_size : int, optional + Text vocabulary size, by default 1000. + """ + super().__init__() + + # Audio encoder (simplified) + self.audio_encoder = torch.nn.Sequential( + torch.nn.Conv2d(audio_channels, 64, 3, padding=1), + torch.nn.ReLU(), + torch.nn.AdaptiveAvgPool2d((8, 8)), + torch.nn.Flatten(), + torch.nn.Linear(64 * 8 * 8, 256) + ) + + # Text encoder + self.text_encoder = torch.nn.Sequential( + torch.nn.Embedding(text_vocab_size, 128), + torch.nn.LSTM(128, 128, batch_first=True), + ) + + # Shared latent space + self.fusion = torch.nn.Linear(256 + 128, 256) + + # Decoders + self.audio_decoder = torch.nn.Sequential( + torch.nn.Linear(256, 64 * 8 * 8), + torch.nn.Unflatten(1, (64, 8, 8)), + torch.nn.ConvTranspose2d(64, audio_channels, 3, padding=1) + ) + + self.text_decoder = torch.nn.Sequential( + torch.nn.Linear(256, 128), + torch.nn.LSTM(128, 128, batch_first=True), + torch.nn.Linear(128, text_vocab_size) + ) + + def forward(self, batch): + """Forward pass returning losses. + + Parameters + ---------- + batch : dict + Batch containing 'audio', 'text', 'target_audio', 'target_text'. + + Returns + ------- + dict + Dictionary containing losses and outputs. + """ + audio = batch['audio'] + text = batch['text'] + target_audio = batch['target_audio'] + target_text = batch['target_text'] + + # Encode + audio_features = self.audio_encoder(audio) + text_lstm_out, _ = self.text_encoder(text) + text_features = text_lstm_out.mean(dim=1) # Simple pooling + + # Fuse + combined = torch.cat([audio_features, text_features], dim=1) + latent = self.fusion(combined) + + # Decode + audio_reconstructed = self.audio_decoder(latent) + + # Upsample audio reconstruction to match input size + audio_reconstructed = F.interpolate( + audio_reconstructed, + size=(target_audio.shape[-2], target_audio.shape[-1]), + mode='bilinear', + align_corners=False + ) + + text_hidden = self.text_decoder[0]( + latent).unsqueeze(1).repeat(1, text.size(1), 1) + text_lstm_out, _ = self.text_decoder[1](text_hidden) + text_reconstructed = self.text_decoder[2](text_lstm_out) + + # Compute losses + audio_loss = F.mse_loss(audio_reconstructed, target_audio) + text_loss = F.cross_entropy( + text_reconstructed.reshape(-1, text_reconstructed.size(-1)), + target_text.reshape(-1) + ) + + return { + 'audio_reconstructed': audio_reconstructed, + 'text_reconstructed': text_reconstructed, + 'latent': latent, + 'audio_loss': audio_loss, + 'text_loss': text_loss, + } + + +def example_basic_training(): + """Basic training example with BlockBasedAutoencoder.""" + print("=" * 60) + print("EXAMPLE 1: Basic Autoencoder Training") + print("=" * 60) + + # Create autoencoder + autoencoder = BlockBasedAutoencoder(input_channels=80) + print(f"Created autoencoder with " + f"{autoencoder.parameter_count:,} parameters") + + # Create data + train_loader, val_loader = create_dummy_dataset( + batch_size=8, + num_samples=100, # Small for quick testing + input_shape=(80, 100, 128) + ) + print(f"Created dataset with {len(train_loader)} train batches") + + # Use convenience function with TensorBoard logging + lightning_model, trainer = train_model( + model=autoencoder, + train_dataloader=train_loader, + val_dataloader=val_loader, + max_epochs=3, + gpus=0, # CPU for testing + precision="32", + logger_type="tensorboard", # Use TensorBoard instead of wandb + project_name="test-autoencoder", + experiment_name="basic-example", + learning_rate=1e-3 + ) + + print("Training completed!") + print("Logs saved to: ./logs/test-autoencoder/basic-example/") + + # Test the trained model + test_input = torch.randn(1, 80, 100, 128) + lightning_model.eval() + with torch.no_grad(): + output = lightning_model(test_input) + print(f"Test output shape: {output.shape}") + + +def example_custom_trainer(): + """Example with custom trainer class.""" + print("\n" + "=" * 60) + print("EXAMPLE 2: Custom Trainer with Perceptual Loss") + print("=" * 60) + + # Create autoencoder + autoencoder = BlockBasedAutoencoder( + input_channels=80, + activation='gelu' + ) + + # Create custom trainer + lightning_model = CustomLossAutoencoder( + model=autoencoder, + learning_rate=1e-3, + perceptual_weight=0.1, + max_epochs=3 + ) + + # Create data + train_loader, val_loader = create_dummy_dataset( + batch_size=4, + num_samples=50, + input_shape=(80, 100, 128) + ) + + # Manual trainer setup + trainer = Trainer( + max_epochs=3, + accelerator='cpu', + devices=1, + callbacks=[ + ModelCheckpoint(monitor='val_loss', mode='min'), + EarlyStopping(monitor='val_loss', patience=2) + ], + enable_progress_bar=True, + logger=False # Disable logging for testing + ) + + # Train + trainer.fit(lightning_model, train_loader, val_loader) + print("Custom training completed!") + + +def example_multimodal_training(): + """Example with multimodal autoencoder.""" + print("\n" + "=" * 60) + print("EXAMPLE 3: Multimodal Autoencoder Training") + print("=" * 60) + + # Create multimodal model + multimodal_model = MultimodalAutoencoder( + audio_channels=80, + text_vocab_size=1000 + ) + + # Create multimodal trainer with loss weights + lightning_model = MultimodalLightningTrainer( + model=multimodal_model, + loss_weights={'audio_loss': 1.0, 'text_loss': 0.5}, + learning_rate=1e-3, + max_epochs=3 + ) + + # Create multimodal data + train_loader, val_loader = create_multimodal_dataset( + batch_size=4, + num_samples=50 + ) + + # Train + trainer = Trainer( + max_epochs=3, + accelerator='cpu', + devices=1, + enable_progress_bar=True, + logger=False + ) + + trainer.fit(lightning_model, train_loader, val_loader) + print("Multimodal training completed!") + + +def example_configuration_testing(): + """Test different autoencoder configurations.""" + print("\n" + "=" * 60) + print("EXAMPLE 4: Testing Different Configurations") + print("=" * 60) + + # Test configurations similar to your example + configs = [ + { + 'name': 'Default', + 'config': {'input_channels': 80} + }, + { + 'name': 'Custom blocks', + 'config': { + 'input_channels': 80, + 'block_configs': [ + {'out_channels': 64, 'pool_size': (1, 2), 'dropout': 0.2}, + {'out_channels': 128, 'pool_size': (1, 4), 'dropout': 0.3}, + ], + 'activation': 'gelu' + } + }, + { + 'name': 'Large model', + 'config': { + 'input_channels': 80, + 'activation': 'swish' + } + } + ] + + for config_info in configs: + print(f"\nTesting {config_info['name']} configuration:") + + # Create autoencoder + autoencoder = BlockBasedAutoencoder(**config_info['config']) + + # Test forward pass + x = torch.randn(2, 80, 100, 128) + reconstructed, latent = autoencoder(x) + + print(f" Input shape: {x.shape}") + print(f" Latent shape: {latent.shape}") + print(f" Output shape: {reconstructed.shape}") + print(f" Parameters: {autoencoder.parameter_count:,}") + + # Quick training test + lightning_model = LightningTrainer( + model=autoencoder, + learning_rate=1e-3, + max_epochs=1 + ) + + # Create minimal data + train_data = TensorDataset(x, x) + train_loader = DataLoader(train_data, batch_size=2) + + trainer = Trainer( + max_epochs=1, + accelerator='cpu', + devices=1, + enable_progress_bar=False, + logger=False + ) + + trainer.fit(lightning_model, train_loader) + print(" Training test: PASSED") + + +def example_feature_extraction(): + """Example showing feature extraction capabilities.""" + print("\n" + "=" * 60) + print("EXAMPLE 5: Feature Extraction and Analysis") + print("=" * 60) + + # Create and train a small model + autoencoder = BlockBasedAutoencoder(input_channels=80) + + lightning_model = LightningTrainer( + model=autoencoder, + learning_rate=1e-3, + max_epochs=2 + ) + + # Quick training + train_loader, _ = create_dummy_dataset( + batch_size=4, + num_samples=20, + input_shape=(80, 100, 128) + ) + + trainer = Trainer( + max_epochs=2, + accelerator='cpu', + devices=1, + enable_progress_bar=False, + logger=False + ) + + trainer.fit(lightning_model, train_loader) + + # Test feature extraction + test_input = torch.randn(1, 80, 100, 128) + + with torch.no_grad(): + # Get latent representation + latent = autoencoder.encode(test_input) + print(f"Latent representation shape: {latent.shape}") + + # Get reconstruction + reconstruction = autoencoder.decode(latent) + print(f"Reconstruction shape: {reconstruction.shape}") + + # Calculate reconstruction error + mse_error = F.mse_loss(reconstruction, test_input).item() + print(f"Reconstruction MSE: {mse_error:.6f}") + + +def run_all_examples(): + """Run all examples.""" + print("Running Lightning Trainer Examples...") + print("Note: These are minimal examples for demonstration.") + print("For real training, use larger datasets and more epochs.\n") + + try: + example_basic_training() + example_custom_trainer() + example_multimodal_training() + example_configuration_testing() + example_feature_extraction() + + print("\n" + "=" * 60) + print("ALL EXAMPLES COMPLETED SUCCESSFULLY!") + print("=" * 60) + + except Exception as e: + print(f"\nError occurred: {e}") + import traceback + traceback.print_exc() + + +if __name__ == "__main__": + # Set random seed for reproducibility + torch.manual_seed(42) + pl.seed_everything(42) + + # Run examples + run_all_examples() diff --git a/examples/Machine_Learning/mae_example.py b/examples/Machine_Learning/mae_example.py new file mode 100644 index 0000000..6219342 --- /dev/null +++ b/examples/Machine_Learning/mae_example.py @@ -0,0 +1,43 @@ +import torch +from src.faith.train.models import (MaskGenerator, BlockBasedAutoencoder, + MaskedAutoencoder, mae_loss) + +# Example usage and testing +if __name__ == "__main__": + # Test MaskGenerator + print("Testing MaskGenerator...") + mask_gen = MaskGenerator(mask_ratio=0.75) + + shape = (2, 80, 100, 128) + + # Test MaskedAutoencoder + print("\nTesting MaskedAutoencoder...") + + # Create components + autoencoder = BlockBasedAutoencoder(input_channels=80) + mae = MaskedAutoencoder(autoencoder, mask_gen) + + # Test forward pass + x = torch.randn(2, 80, 100, 128) + reconstructed, mask, masked_input = mae(x, mask_type='frequency') + + print(f"Input shape: {x.shape}") + print(f"Mask shape: {mask.shape}") + print(f"Masked input shape: {masked_input.shape}") + print(f"Reconstructed shape: {reconstructed.shape}") + + # Test MAE loss + loss = mae_loss(reconstructed, x, mask, loss_type='mse') + print(f"MAE loss: {loss.item():.6f}") + + # Test different loss types + for loss_type in ['mse', 'l1', 'smooth_l1']: + loss = mae_loss(reconstructed, x, mask, loss_type=loss_type) + print(f"{loss_type} loss: {loss.item():.6f}") + + # Test configuration serialization + config = mae.get_config() + mae_recreated = MaskedAutoencoder.from_config(config) + print(f"Config serialization successful: {mae_recreated}") + + print(f"MAE model: {mae}") diff --git a/examples/Machine_Learning/ray_tune_examples.py b/examples/Machine_Learning/ray_tune_examples.py new file mode 100644 index 0000000..dc53045 --- /dev/null +++ b/examples/Machine_Learning/ray_tune_examples.py @@ -0,0 +1,436 @@ +"""Examples demonstrating hyperparameter tuning with Ray Tune.""" + +import torch +from torch.utils.data import DataLoader, TensorDataset +from ray import tune +import os +import sys +import atexit +from pathlib import Path + +# Add src to path for imports +sys.path.append(os.path.join(os.path.dirname(__file__), '..')) + +from src.faith.train.models.autoencoder import BlockBasedAutoencoder +from src.faith.train.tuning import (RayTuner, SearchSpaces, get_search_space, + CustomSearchSpace) + +# Ensure Ray cleanup on exit +try: + from src import cleanup_ray + + + atexit.register(cleanup_ray) +except ImportError: + pass + + +def get_results_dir(name: str) -> str: + """Get absolute path for results directory. + + Parameters + ---------- + name : str + Directory name. + + Returns + ------- + str + Absolute path. + """ + results_dir = Path.cwd() / "tune_results" / name + results_dir.mkdir(parents=True, exist_ok=True) + return str(results_dir) + + +def create_dummy_dataset(num_samples: int = 1000, batch_size: int = 32): + """Create dummy dataset for tuning examples. + + Parameters + ---------- + num_samples : int, optional + Number of samples, by default 1000. + batch_size : int, optional + Batch size, by default 32. + + Returns + ------- + Dict[str, DataLoader] + Dictionary with train and val dataloaders. + """ + # Create random spectrogram-like data + data = torch.randn(num_samples, 4, 100, 128) + + # Split into train/val + train_size = int(0.8 * num_samples) + train_data = data[:train_size] + val_data = data[train_size:] + + # Create datasets (autoencoder: input = target) + train_dataset = TensorDataset(train_data, train_data) + val_dataset = TensorDataset(val_data, val_data) + + # Create dataloaders + train_loader = DataLoader( + train_dataset, batch_size=batch_size, shuffle=True) + val_loader = DataLoader( + val_dataset, batch_size=batch_size, shuffle=False) + + return {'train': train_loader, 'val': val_loader} + + +def example_basic_tuning(): + """Basic hyperparameter tuning example.""" + print("=" * 60) + print("EXAMPLE 1: Basic Hyperparameter Tuning") + print("=" * 60) + + # Create data + data_loaders = create_dummy_dataset(num_samples=200, batch_size=16) + print(f"Created dataset with {len(data_loaders['train'])} train batches") + + # Base model configuration + model_base_config = { + 'input_channels': 4, + } + + # Create tuner + tuner = RayTuner( + model_class=BlockBasedAutoencoder, + model_base_config=model_base_config, + data_loaders=data_loaders, + num_samples=5, # Small for demo + max_epochs_per_trial=5, # Increased to work with ASHA scheduler + gpus_per_trial=0.0, # CPU only for demo + scheduler_type="asha", + search_algorithm="optuna", + storage_path=get_results_dir("basic_tuning") + ) + + # Define search space + search_space = get_search_space("basic", learning_rate_range=(1e-4, 1e-2)) + + print("Search space:", search_space) + + # Run tuning + print("\nStarting hyperparameter search...") + analysis = tuner.tune(search_space, name="basic_autoencoder_tune") + + # Get best configuration + best_config = tuner.get_best_config(analysis) + print(f"\nBest configuration: {best_config}") + + # Train final model + print("\nTraining final model with best hyperparameters...") + best_model = tuner.train_best_model(analysis, max_epochs=5, + save_path="best_basic_model.pth") + print("Basic tuning completed!") + + +def example_architecture_search(): + """Architecture search example.""" + print("\n" + "=" * 60) + print("EXAMPLE 2: Architecture Search") + print("=" * 60) + + # Create data + data_loaders = create_dummy_dataset(num_samples=150, batch_size=8) + + # Base configuration + model_base_config = { + 'input_channels': 4, + } + + # Create tuner focused on architecture + tuner = RayTuner( + model_class=BlockBasedAutoencoder, + model_base_config=model_base_config, + data_loaders=data_loaders, + num_samples=4, + max_epochs_per_trial=4, # Increased for ASHA compatibility + gpus_per_trial=0.0, + scheduler_type="fifo", # Simple scheduler for architecture search + search_algorithm="random" + ) + + # Architecture-focused search space + search_space = get_search_space("architecture", + learning_rate=1e-3, # Fixed + layer_choices=[2, 3], + width_choices=[32, 64]) + + print("Architecture search space:", search_space) + + # Run tuning + analysis = tuner.tune(search_space, name="architecture_search") + + # Get results + best_config = tuner.get_best_config(analysis) + print(f"\nBest architecture: {best_config}") + + # Compare all trials + df = analysis.dataframe() + print("\nAll trial results:") + print(df[['config/num_layers', 'val_loss']].head()) + + +def example_custom_search_space(): + """Custom search space example.""" + print("\n" + "=" * 60) + print("EXAMPLE 3: Custom Search Space") + print("=" * 60) + + # Create data + data_loaders = create_dummy_dataset(num_samples=100, batch_size=16) + + # Model configuration + model_base_config = { + 'input_channels': 4, + } + + # Create custom search space using builder + custom_space = (CustomSearchSpace() + .add_continuous( + "learning_rate", 1e-5, 1e-2, log_scale=True) + .add_discrete("activation", ["relu", "gelu"]) + .add_continuous("dropout", 0.0, 0.3) + .add_fixed("weight_decay", 1e-5) + .build() + ) + + print("Custom search space:", custom_space) + + # Create tuner + tuner = RayTuner( + model_class=BlockBasedAutoencoder, + model_base_config=model_base_config, + data_loaders=data_loaders, + num_samples=3, + max_epochs_per_trial=4, # Increased for scheduler compatibility + gpus_per_trial=0.0 + ) + + # Run tuning + analysis = tuner.tune(custom_space, name="custom_search") + + # Results + best_config = tuner.get_best_config(analysis) + print(f"\nBest custom configuration: {best_config}") + + +def example_quick_parameter_test(): + """Quick single parameter testing.""" + print("\n" + "=" * 60) + print("EXAMPLE 4: Quick Parameter Testing") + print("=" * 60) + + # Create data + data_loaders = create_dummy_dataset(num_samples=80, batch_size=8) + + # Test different learning rates quickly + model_base_config = {'input_channels': 4} + + tuner = RayTuner( + model_class=BlockBasedAutoencoder, + model_base_config=model_base_config, + data_loaders=data_loaders, + num_samples=3, + max_epochs_per_trial=4, # Minimum for ASHA + gpus_per_trial=0.0 + ) + + # Quick search for learning rate only + search_space = SearchSpaces.quick_search( + param_name="learning_rate", + param_choices=[1e-4, 5e-4, 1e-3], + base_config={"weight_decay": 1e-5} + ) + + print("Quick search space:", search_space) + + # Run tuning + analysis = tuner.tune(search_space, name="quick_lr_test") + + # Show all results + print("\nLearning rate comparison:") + df = analysis.dataframe() + for _, row in df.iterrows(): + lr = row['config/learning_rate'] + val_loss = row['val_loss'] + print(f"LR: {lr:.2e} -> Val Loss: {val_loss:.4f}") + + +def example_advanced_configuration(): + """Advanced tuning configuration example.""" + print("\n" + "=" * 60) + print("EXAMPLE 5: Advanced Configuration") + print("=" * 60) + + # Create data + data_loaders = create_dummy_dataset(num_samples=120, batch_size=12) + + # Model configuration for block-based autoencoder + model_base_config = { + 'input_channels': 4, + } + + # Advanced tuner configuration + tuner = RayTuner( + model_class=BlockBasedAutoencoder, + model_base_config=model_base_config, + data_loaders=data_loaders, + num_samples=4, + max_epochs_per_trial=6, # Sufficient for PBT + gpus_per_trial=0.0, + scheduler_type="pbt", # Population Based Training + search_algorithm="optuna", + metric="val_loss", + mode="min", + storage_path=get_results_dir("advanced_tuning") + ) + + # Block-based autoencoder search space + # (parameters that actually work with the model) + search_space = { + # Training hyperparameters + "learning_rate": tune.loguniform(1e-4, 1e-2), + "weight_decay": tune.loguniform(1e-6, 1e-3), + "scheduler_type": tune.choice(["cosine", "linear", "none"]), + + # Model architecture + "activation": tune.choice(["relu", "gelu", "swish", "leaky_relu"]), + + # Skip architecture parameters that require custom block_configs + # as they are more complex to implement in this example + } + + print("Advanced search space:", search_space) + + # Run tuning with custom name + analysis = tuner.tune(search_space, + name="advanced_block_autoencoder", + resume=False) + + # Detailed analysis + best_trial = analysis.get_best_trial("val_loss", "min") + print("\nBest trial:") + print(f" Config: {best_trial.config}") + print(f" Final val_loss: {best_trial.last_result['val_loss']:.4f}") + print(f" Training time: " + f"{best_trial.last_result.get('time_total_s', 0):.1f}s") + + # Train final model with more epochs + print("\nTraining final model...") + final_model = tuner.train_best_model(analysis, + max_epochs=5, + save_path="advanced_best_model.pth") + + print("Advanced tuning completed!") + print(f"Results saved to: {tuner.storage_path}") + + +def example_model_comparison(): + """Compare different model configurations.""" + print("\n" + "=" * 60) + print("EXAMPLE 6: Model Configuration Comparison") + print("=" * 60) + + # Create data + data_loaders = create_dummy_dataset(num_samples=100, batch_size=16) + + # Test different model configurations + configs_to_test = [ + { + 'name': 'small_model', + 'config': {'input_channels': 4}, + 'search_space': {'learning_rate': 1e-3} + }, + { + 'name': 'medium_model', + 'config': {'input_channels': 4}, + 'search_space': {'learning_rate': 1e-3} + }, + { + 'name': 'large_model', + 'config': {'input_channels': 4}, + 'search_space': {'learning_rate': 5e-4} + } + ] + + results = {} + + for config_info in configs_to_test: + print(f"\nTesting {config_info['name']}...") + + tuner = RayTuner( + model_class=BlockBasedAutoencoder, + model_base_config=config_info['config'], + data_loaders=data_loaders, + num_samples=1, # Single trial per config + max_epochs_per_trial=3, + gpus_per_trial=0.0 + ) + + analysis = tuner.tune(config_info['search_space'], + name=config_info['name']) + + best_trial = analysis.get_best_trial("val_loss", "min") + results[config_info['name']] = { + 'val_loss': best_trial.last_result['val_loss'], + 'params': sum(p.numel() for p in + BlockBasedAutoencoder( + **config_info['config'] + ).parameters()) + } + + # Compare results + print("\n" + "=" * 40) + print("MODEL COMPARISON RESULTS") + print("=" * 40) + for name, result in results.items(): + print(f"{name:12} | Val Loss: {result['val_loss']:.4f} | " + f"Params: {result['params']:,}") + + # Find best model + best_model = min(results.items(), key=lambda x: x[1]['val_loss']) + print(f"\nBest model: {best_model[0]} " + f"(val_loss: {best_model[1]['val_loss']:.4f})") + + +def run_all_examples(): + """Run all tuning examples.""" + print("Running Ray Tune Examples...") + print("Note: These are minimal examples for demonstration.") + print("For real tuning, use larger datasets and more trials.\n") + + try: + example_basic_tuning() + example_architecture_search() + example_custom_search_space() + example_quick_parameter_test() + example_advanced_configuration() + example_model_comparison() + + print("\n" + "=" * 60) + print("ALL TUNING EXAMPLES COMPLETED SUCCESSFULLY!") + print("=" * 60) + print("\nCheck the following directories for results:") + print(" - ./ray_results/") + print(" - ./advanced_tune_results/") + print(" - ./final_training_logs/") + + except ImportError as e: + print(f"\nSkipping examples due to missing dependency: {e}") + print("Install Ray Tune with: pip install ray[tune] optuna hyperopt") + except Exception as e: + print(f"\nError occurred: {e}") + import traceback + traceback.print_exc() + + +if __name__ == "__main__": + # Set random seed for reproducibility + torch.manual_seed(42) + + # Run examples + run_all_examples() diff --git a/src/faith/train/__init__.py b/src/faith/train/__init__.py index e69de29..5d44886 100644 --- a/src/faith/train/__init__.py +++ b/src/faith/train/__init__.py @@ -0,0 +1,12 @@ +# Package metadata +__version__ = "0.1.0" +__author__ = "Peter Steiner" +__email__ = "peter.steiner@princeton.edu" + +# Public API - only these should be imported by users +__all__ = [ + # Metadata + "__version__", + "__author__", + "__email__", +] diff --git a/src/faith/train/blocks/__init__.py b/src/faith/train/blocks/__init__.py index 352d19b..7f06849 100644 --- a/src/faith/train/blocks/__init__.py +++ b/src/faith/train/blocks/__init__.py @@ -3,9 +3,14 @@ from .residual import ResidualBlock from .encoder import EncoderBlock, BlockBasedEncoder from .decoder import DecoderBlock, BlockBasedDecoder -from .base import BaseBlock +from .base import BaseBlock, BlockUtils -__all__ = ["ResidualBlock", - "EncoderBlock", "BlockBasedEncoder", - "DecoderBlock", "BlockBasedDecoder", - "BaseBlock"] +__all__ = [ + "ResidualBlock", + "EncoderBlock", + "BlockBasedEncoder", + "DecoderBlock", + "BlockBasedDecoder", + "BaseBlock", + "BlockUtils", +] diff --git a/src/faith/train/blocks/base.py b/src/faith/train/blocks/base.py index f20c23e..b7332ae 100644 --- a/src/faith/train/blocks/base.py +++ b/src/faith/train/blocks/base.py @@ -66,8 +66,9 @@ def __init__( self.bias = bias @staticmethod - def _normalize_kernel_size(kernel_size: Union[int, tuple[int, int]]) \ - -> tuple[int, int]: + def _normalize_kernel_size( + kernel_size: Union[int, tuple[int, int]] + ) -> tuple[int, int]: """Normalize kernel size to tuple format.""" if isinstance(kernel_size, int): return (kernel_size, kernel_size) @@ -76,8 +77,8 @@ def _normalize_kernel_size(kernel_size: Union[int, tuple[int, int]]) \ @staticmethod def _calculate_padding( kernel_size: Union[int, tuple[int, int]], - padding: Union[int, tuple[int, int], str]) \ - -> tuple[int, ...]: + padding: Union[int, tuple[int, int], str] + ) -> tuple[int, ...]: """Calculate padding based on kernel size and padding specification.""" if padding == 'auto': if isinstance(kernel_size, int): @@ -90,7 +91,10 @@ def _calculate_padding( return padding @abstractmethod - def forward(self, x: torch.Tensor) -> torch.Tensor: + def forward( + self, + x: torch.Tensor + ) -> torch.Tensor: """ Forward pass through the block. @@ -204,59 +208,14 @@ def get_config(self) -> dict[str, Any]: return config @classmethod - def from_config(cls, config: dict[str, Any]) -> 'ConfigurableBlock': + def from_config( + cls, + config: dict[str, Any] + ) -> 'ConfigurableBlock': """Create block instance from configuration dictionary.""" return cls(**config) -class BlockRegistry: - """Registry for different block types. - - This class provides a way to register and retrieve different block - implementations, making it easy to create blocks from string names - or configuration files. - """ - - _registry: dict[str, type] = {} - - @classmethod - def register(cls, name: str, block_class: type) -> None: - """Register a block class with a given name.""" - if not issubclass(block_class, BaseBlock): - raise ValueError( - f"Block class must inherit from BaseBlock, got {block_class}") - cls._registry[name] = block_class - - @classmethod - def get(cls, name: str) -> type: - """Get a block class by name.""" - if name not in cls._registry: - raise KeyError(f"Block '{name}' not found in registry. " - f"Available blocks: {list(cls._registry.keys())}") - return cls._registry[name] - - @classmethod - def create(cls, name: str, **kwargs) -> BaseBlock: - """Create a block instance by name.""" - block_class = cls.get(name) - return block_class(**kwargs) - - @classmethod - def list_blocks(cls) -> list[str]: - """List all registered block names.""" - return list(cls._registry.keys()) - - -def register_block(name: str): - """Decorator to register a block class.""" - - def decorator(block_class: type): - BlockRegistry.register(name, block_class) - return block_class - - return decorator - - class WeightInitializer: """Utilities for weight initialization in blocks.""" @@ -294,8 +253,8 @@ def calculate_output_shape( kernel_size: Union[int, tuple[int, int]], stride: Union[int, tuple[int, int]] = 1, padding: Union[int, tuple[int, int]] = 0, - dilation: Union[int, tuple[int, int]] = 1) \ - -> tuple[int, ...]: + dilation: Union[int, tuple[int, int]] = 1 + ) -> tuple[int, ...]: """Calculate output shape after convolution operation.""" if isinstance(kernel_size, int): kernel_size = (kernel_size, kernel_size) @@ -310,18 +269,21 @@ def calculate_output_shape( height, width = input_shape[2:] out_height = math.floor( - (height + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) - 1) - / stride[0] + 1 + (height + 2 * padding[0] + - dilation[0] * (kernel_size[0] - 1) - 1) / stride[0] + 1 ) out_width = math.floor( - (width + 2 * padding[1] - dilation[1] * (kernel_size[1] - 1) - 1) / - stride[1] + 1 + (width + 2 * padding[1] + - dilation[1] * (kernel_size[1] - 1) - 1) / stride[1] + 1 ) - return (batch_size, channels, out_height, out_width) + return batch_size, channels, out_height, out_width @staticmethod - def count_parameters(block: nn.Module, trainable_only: bool = True) -> int: + def count_parameters( + block: nn.Module, + trainable_only: bool = True + ) -> int: """Count parameters in a block.""" if trainable_only: return sum( @@ -330,55 +292,22 @@ def count_parameters(block: nn.Module, trainable_only: bool = True) -> int: return sum(p.numel() for p in block.parameters()) @staticmethod - def get_memory_usage(block: nn.Module, input_shape: tuple[int, ...]) \ - -> dict[str, float]: + def get_memory_usage( + block: nn.Module, + input_shape: tuple[int, ...] + ) -> dict[str, float]: """Estimate memory usage of a block.""" # This is a simplified estimation - param_memory = BlockUtils.count_parameters( - block) * 4 # 4 bytes per float32 + # 4 bytes per float32 + param_memory = BlockUtils.count_parameters(block) * 4 - # Rough estimation of activation memory + # Estimation of activation memory output_elements = math.prod(input_shape) - activation_memory = output_elements * 4 # 4 bytes per float32 + # 4 bytes per float32 + activation_memory = output_elements * 4 return { 'parameters_mb': param_memory / (1024 * 1024), 'activations_mb': activation_memory / (1024 * 1024), 'total_mb': (param_memory + activation_memory) / (1024 * 1024) } - - -# Example usage and testing -if __name__ == "__main__": - # Example of how the base classes would be used - - class ExampleBlock(BaseBlock): - """Example implementation of BaseBlock.""" - - def __init__(self, in_channels: int, out_channels: int, **kwargs): - super().__init__(in_channels, out_channels, **kwargs) - self.conv = nn.Conv2d(in_channels, out_channels, - kernel_size=self.kernel_size) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return self.conv(x) - - - # Register the block - BlockRegistry.register('example', ExampleBlock) - - # Create block from registry - block = BlockRegistry.create('example', in_channels=64, out_channels=128) - print(f"Created block: {block}") - print(f"Parameter count: {block.parameter_count}") - print(f"Config: {block.get_config()}") - - # Test utility functions - input_shape = (1, 64, 32, 32) - memory_info = BlockUtils.get_memory_usage(block, input_shape) - print(f"Memory usage: {memory_info}") - - output_shape = BlockUtils.calculate_output_shape( - input_shape, kernel_size=3, stride=1, padding=1 - ) - print(f"Output shape: {output_shape}") diff --git a/src/faith/train/blocks/decoder.py b/src/faith/train/blocks/decoder.py index dae1c76..ce9aeca 100644 --- a/src/faith/train/blocks/decoder.py +++ b/src/faith/train/blocks/decoder.py @@ -8,13 +8,11 @@ import torch import torch.nn as nn from typing import Union, Any, Optional -from .base import (SequentialBlock, ConfigurableBlock, register_block, - WeightInitializer) -from torch_training.blocks.residual import ResidualBlock -from . import EncoderBlock +from .base import SequentialBlock, ConfigurableBlock, WeightInitializer +from .residual import ResidualBlock +from .encoder import EncoderBlock -@register_block('decoder') class DecoderBlock(SequentialBlock): # TODO: ConvTranspose2d """Single decoder block: Upsample + ResidualBlock + Dropout. @@ -312,8 +310,8 @@ def __init__( # Validate inputs if output_channels <= 0: - raise ValueError( - f"output_channels must be positive, got {output_channels}") + raise ValueError(f"output_channels must be positive, " + f"got {output_channels}") if bottleneck_channels <= 0: raise ValueError(f"bottleneck_channels must be positive, " @@ -591,77 +589,3 @@ def __repr__(self) -> str: f"num_blocks={len(self.blocks)}, " f"bottleneck_channels={self.bottleneck_channels}, " f"upsampling_mode='{self.upsampling_mode}')") - - -# Example usage and testing -if __name__ == "__main__": - # Test DecoderBlock - print("Testing DecoderBlock...") - decoder_block = DecoderBlock( - in_channels=128, - out_channels=64, - upsample_factor=(1, 2), - dropout=0.3, - upsampling_mode='nearest', - activation='relu' - ) - - x = torch.randn(1, 128, 16, 8) - output = decoder_block(x) - print(f"DecoderBlock - Input: {x.shape}, Output: {output.shape}") - - # Test configuration - config = decoder_block.get_config() - new_block = DecoderBlock.from_config(config) - print(f"Config serialization successful: {new_block}") - - # Test BlockBasedDecoder with mock encoder blocks - print("\nTesting BlockBasedDecoder...") - - # Create mock encoder blocks for testing - from src import EncoderBlock - - - mock_encoder_blocks = [ - EncoderBlock(80, 128, pool_size=(1, 2)), - EncoderBlock(128, 256, pool_size=(1, 4)), - EncoderBlock(256, 128, pool_size=(1, 2)), - ] - - decoder = BlockBasedDecoder( - output_channels=80, - encoder_blocks=mock_encoder_blocks, - bottleneck_channels=64, - upsampling_mode='nearest' - ) - - # Test forward pass - latent = torch.randn(2, 64, 25, 4) - reconstructed = decoder(latent) - print(f"Decoder - Input: {latent.shape}, Output: {reconstructed.shape}") - - # Test feature map extraction - feature_maps = decoder.get_feature_maps(latent) - print(f"Feature maps shapes: {[fm.shape for fm in feature_maps]}") - - # Test from_encoder class method - decoder2 = BlockBasedDecoder.from_encoder( - encoder_blocks=mock_encoder_blocks, - bottleneck_channels=64, - output_channels=80, - upsampling_mode='bilinear' - ) - print(f"Decoder from encoder: {decoder2}") - - # Test registry - from src import BlockRegistry - - - registry_block = BlockRegistry.create( - 'decoder', - in_channels=64, - out_channels=32, - upsample_factor=(2, 2), - upsampling_mode='bilinear' - ) - print(f"Registry block: {registry_block}") diff --git a/src/faith/train/blocks/encoder.py b/src/faith/train/blocks/encoder.py index d1d6735..4dc6e8f 100644 --- a/src/faith/train/blocks/encoder.py +++ b/src/faith/train/blocks/encoder.py @@ -7,14 +7,13 @@ import torch import torch.nn as nn from typing import Union, Any, Optional -from .base import (SequentialBlock, ConfigurableBlock, register_block, - WeightInitializer) +from .base import SequentialBlock, ConfigurableBlock, WeightInitializer from .residual import ResidualBlock -@register_block('encoder') class EncoderBlock(SequentialBlock): - """Single encoder block: ResidualBlock + Dropout + MaxPool. + """ + Single encoder block: ResidualBlock + Dropout + MaxPool. This block represents the fundamental building unit of the encoder, combining feature extraction through ResidualBlock, regularization @@ -357,8 +356,8 @@ def _prepare_block_config( 'bias': config.get('bias', self.bias), 'use_batch_norm': config.get('use_batch_norm', True), 'activation': config.get('activation', 'relu'), - 'residual_init_method': config.get('residual_init_method', - 'kaiming'), + 'residual_init_method': config.get( + 'residual_init_method', 'kaiming'), } return block_config @@ -519,60 +518,3 @@ def __repr__(self) -> str: f"num_blocks={len(self.blocks)}, " f"bottleneck_channels={self.bottleneck_channels}, " f"hidden_dim={self.hidden_dim})") - - -# Example usage and testing -if __name__ == "__main__": - # Test EncoderBlock - print("Testing EncoderBlock...") - encoder_block = EncoderBlock( - in_channels=64, - out_channels=128, - pool_size=(1, 2), - dropout=0.3, - activation='relu' - ) - - x = torch.randn(1, 64, 32, 32) - output = encoder_block(x) - print(f"EncoderBlock - Input: {x.shape}, Output: {output.shape}") - - # Test configuration - config = encoder_block.get_config() - new_block = EncoderBlock.from_config(config) - print(f"Config serialization successful: {new_block}") - - # Test BlockBasedEncoder - print("\nTesting BlockBasedEncoder...") - block_configs = [ - {'out_channels': 128, 'pool_size': (1, 2), 'dropout': 0.2}, - {'out_channels': 256, 'pool_size': (1, 4), 'dropout': 0.3}, - {'out_channels': 128, 'pool_size': (1, 2), 'dropout': 0.4}, - ] - - encoder = BlockBasedEncoder( - input_channels=80, - block_configs=block_configs, - hidden_dim=16, - bottleneck_channels=64 - ) - - x = torch.randn(2, 80, 100, 128) - latent = encoder(x) - print(f"Encoder - Input: {x.shape}, Output: {latent.shape}") - - # Test feature map extraction - feature_maps = encoder.get_feature_maps(x) - print(f"Feature maps shapes: {[fm.shape for fm in feature_maps]}") - - # Test registry - from src import BlockRegistry - - - registry_block = BlockRegistry.create( - 'encoder', - in_channels=32, - out_channels=64, - activation='gelu' - ) - print(f"Registry block: {registry_block}") diff --git a/src/faith/train/blocks/residual.py b/src/faith/train/blocks/residual.py index 1c84372..ca06459 100644 --- a/src/faith/train/blocks/residual.py +++ b/src/faith/train/blocks/residual.py @@ -7,10 +7,9 @@ import torch import torch.nn as nn from typing import Union, Any -from .base import BaseBlock, register_block, WeightInitializer +from .base import BaseBlock, WeightInitializer -@register_block('residual') class ResidualBlock(BaseBlock): """Residual convolutional block with batch normalization and ReLU. @@ -127,8 +126,9 @@ def __init__( # Initialize weights self._initialize_weights() - def _normalize_stride(self, stride: Union[int, tuple[int, int]]) \ - -> tuple[int, int]: + def _normalize_stride(self, + stride: Union[int, tuple[int, int]] + ) -> tuple[int, int]: """Normalize stride to tuple format.""" if isinstance(stride, int): return (stride, stride) @@ -322,8 +322,10 @@ def has_skip_connection(self) -> bool: """Check if this block has a skip connection projection.""" return self.skip_conv is not None - def get_output_shape(self, input_shape: tuple[int, ...]) \ - -> tuple[int, ...]: + def get_output_shape( + self, + input_shape: tuple[int, ...] + ) -> tuple[int, ...]: """Calculate output shape given input shape. Parameters @@ -336,7 +338,7 @@ def get_output_shape(self, input_shape: tuple[int, ...]) \ tuple Output tensor shape. """ - from src import BlockUtils + from src.faith.train.blocks import BlockUtils # Account for stride in the first convolution temp_shape = BlockUtils.calculate_output_shape( @@ -349,41 +351,3 @@ def get_output_shape(self, input_shape: tuple[int, ...]) \ # Update channels batch_size, _, height, width = temp_shape return (batch_size, self.out_channels, height, width) - - -# Example usage and testing -if __name__ == "__main__": - # Test basic functionality - block = ResidualBlock(64, 128, stride=2) - x = torch.randn(1, 64, 32, 32) - output = block(x) - print(f"Input shape: {x.shape}") - print(f"Output shape: {output.shape}") - print(f"Block: {block}") - - # Test configuration serialization - config = block.get_config() - print(f"Config: {config}") - - # Create from config - new_block = ResidualBlock.from_config(config) - print(f"Recreated block: {new_block}") - - # Test registry functionality - from src import BlockRegistry - - - registry_block = BlockRegistry.create( - 'residual', - in_channels=32, - out_channels=64, - activation='gelu' - ) - print(f"Registry block: {registry_block}") - - # Test parameter counting - print(f"Parameter count: {block.parameter_count}") - - # Test output shape calculation - output_shape = block.get_output_shape((1, 64, 32, 32)) - print(f"Calculated output shape: {output_shape}") diff --git a/tests/test_residual_block.py b/src/faith/train/data/__init__.py similarity index 100% rename from tests/test_residual_block.py rename to src/faith/train/data/__init__.py diff --git a/src/faith/train/data/datasets/__init__.py b/src/faith/train/data/datasets/__init__.py new file mode 100644 index 0000000..d7bea1b --- /dev/null +++ b/src/faith/train/data/datasets/__init__.py @@ -0,0 +1,4 @@ +"""Dataset utilities.""" + +from .base import MultiFileDataset, SequentialDataset +from .file_based import JoblibDataset, HDF5Dataset, NumpyDataset diff --git a/src/faith/train/data/datasets/base.py b/src/faith/train/data/datasets/base.py new file mode 100644 index 0000000..9ea4dd3 --- /dev/null +++ b/src/faith/train/data/datasets/base.py @@ -0,0 +1,1411 @@ +"""Base class for datasets that defer file opening until worker processes.""" + +from __future__ import annotations + +import warnings +from abc import ABC, abstractmethod +from pathlib import Path +from typing import Any, Optional, Union + +import torch +from torch.utils.data import Dataset, get_worker_info + + +class LazyFileDataset(Dataset, ABC): + """Abstract base class for datasets that defer file opening. + + This class provides the foundation for datasets that need to: + - Defer file opening until worker processes are initialized + - Handle memory-mapped files safely across multiple workers + - Manage file handles efficiently in multiprocessing environments + + The pattern ensures that each DataLoader worker has its own file handles, + preventing issues with shared file descriptors and memory mapping. + """ + + def __init__( + self, + file_paths: Union[str, list[str]], + validate_on_init: bool = True, + max_open_files: Optional[int] = None, + ) -> None: + """Initialize the lazy file dataset. + + Parameters + ---------- + file_paths : Union[str, list[str]] + Path or list of paths to data files. + validate_on_init : bool, optional + Whether to validate file existence and basic format on + initialization, by default True. + max_open_files : Optional[int], optional + Maximum number of files to keep open simultaneously. If None, + no limit is imposed, by default None. + """ + super().__init__() + + # Normalize file paths to list + if isinstance(file_paths, str): + file_paths = [file_paths] + self.file_paths = file_paths + + # Configuration + self.validate_on_init = validate_on_init + self.max_open_files = max_open_files + + # Worker-specific state (None until worker_init is called) + self._opened_files = None + self._worker_id = None + self._is_initialized = False + + # File metadata (populated during construction) + self.file_metadata = [] + + # Initialize metadata and validate files if requested + if self.validate_on_init: + self._initialize_metadata() + + def _initialize_metadata(self) -> None: + """Initialize file metadata without opening files permanently. + + This method should inspect files to gather necessary metadata + (like shapes, sample counts, etc.) while minimizing resource usage. + """ + self.file_metadata = [] + + for file_path in self.file_paths: + try: + metadata = self._inspect_file(file_path) + self.file_metadata.append(metadata) + except Exception as e: + raise ValueError( + f"Failed to inspect file {file_path}: {e}") from e + + @abstractmethod + def _inspect_file(self, file_path: str) -> dict[str, Any]: + """Inspect a single file to extract metadata. + + This method should be implemented by subclasses to extract + necessary metadata from files without keeping them open. + The implementation should examine the actual data available + in the file and extract relevant information like shapes, + available keys, and any other format-specific metadata. + + Parameters + ---------- + file_path : str + Path to the file to inspect. + + Returns + ------- + dict[str, Any] + Dictionary containing file metadata. Should include at minimum: + - 'path': str - The file path + - 'valid': bool - Whether the file is valid + - 'available_keys': list[str] - Keys/datasets available in the file + Additional keys depend on the specific file format and available + data. + """ + pass + + @abstractmethod + def _open_file(self, file_path: str) -> Any: + """Open a file for reading. + + This method should be implemented by subclasses to open files + in the appropriate format (joblib, hdf5, etc.) with proper + memory mapping or lazy loading. + + Parameters + ---------- + file_path : str + Path to the file to open. + + Returns + ------- + Any + Opened file object or data structure. + """ + pass + + @abstractmethod + def _close_file(self, file_handle: Any) -> None: + """Close a file handle. + + This method should be implemented by subclasses to properly + close file handles and free resources. + + Parameters + ---------- + file_handle : Any + File handle to close. + """ + pass + + def worker_init(self) -> None: + """Initialize the dataset in a worker process. + + This method should be called once in each DataLoader worker process + to open files with worker-specific handles. It's typically called + from a worker_init_fn passed to DataLoader. + """ + if self._is_initialized: + warnings.warn( + "Dataset already initialized in this worker. " + "Skipping re-initialization." + ) + return + + # Get worker info if available + worker_info = get_worker_info() + self._worker_id = worker_info.id if worker_info else 0 + + # Open files in this worker + self._opened_files = [] + + try: + for file_path in self.file_paths: + file_handle = self._open_file(file_path) + self._opened_files.append(file_handle) + except Exception as e: + # Clean up any partially opened files + self._cleanup_files() + raise RuntimeError( + f"Failed to open files in worker {self._worker_id}: {e}" + ) from e + + self._is_initialized = True + + def _cleanup_files(self) -> None: + """Clean up opened file handles.""" + if self._opened_files is not None: + for file_handle in self._opened_files: + if file_handle is not None: + try: + self._close_file(file_handle) + except Exception as e: + warnings.warn(f"Failed to close file handle: {e}") + self._opened_files = None + self._is_initialized = False + + def __del__(self) -> None: + """Cleanup when dataset is garbage collected.""" + self._cleanup_files() + + def _ensure_initialized(self) -> None: + """Ensure the dataset is initialized in the current worker. + + Raises + ------ + RuntimeError + If the dataset has not been initialized in the current worker. + """ + if not self._is_initialized or self._opened_files is None: + raise RuntimeError( + f"Dataset not initialized in this worker. Ensure you call " + f"dataset.worker_init() in your worker_init_fn. " + f"Worker ID: {self._worker_id}" + ) + + def get_file_handle(self, file_index: int) -> Any: + """Get the file handle for a specific file index. + + Parameters + ---------- + file_index : int + Index of the file in self.file_paths. + + Returns + ------- + Any + Opened file handle. + + Raises + ------ + RuntimeError + If dataset not initialized or invalid file index. + """ + self._ensure_initialized() + + if not (0 <= file_index < len(self._opened_files)): + raise IndexError( + f"File index {file_index} out of range. " + f"Available files: 0-{len(self._opened_files) - 1}" + ) + + return self._opened_files[file_index] + + def get_file_metadata(self, file_index: int) -> dict[str, Any]: + """Get metadata for a specific file. + + Parameters + ---------- + file_index : int + Index of the file in self.file_paths. + + Returns + ------- + dict[str, Any] + File metadata dictionary. + """ + if not (0 <= file_index < len(self.file_metadata)): + raise IndexError( + f"File index {file_index} out of range. " + f"Available files: 0-{len(self.file_metadata) - 1}" + ) + + return self.file_metadata[file_index] + + @property + def num_files(self) -> int: + """Get the number of files in the dataset. + + Returns + ------- + int + Number of files. + """ + return len(self.file_paths) + + @property + def is_initialized(self) -> bool: + """Check if the dataset is initialized in the current worker. + + Returns + ------- + bool + True if initialized, False otherwise. + """ + return self._is_initialized + + @property + def worker_id(self) -> Optional[int]: + """Get the current worker ID. + + Returns + ------- + Optional[int] + Worker ID if in a worker process, None otherwise. + """ + return self._worker_id + + def get_sample_info(self, sample_idx: int = 0) -> dict[str, Any]: + """Get information about a sample without worker initialization. + + Parameters + ---------- + sample_idx : int, optional + Global sample index, by default 0. + + Returns + ------- + dict[str, Any] + Dictionary with sample information including shapes. + """ + if hasattr(self, "peek_sample"): + # For file-based datasets, use peek_sample + if sample_idx >= len(self): + raise IndexError(f"Sample index {sample_idx} out of range") + + # Find which file and subsequence this sample belongs to + file_idx, start_idx, end_idx = self.subseq_index[sample_idx] + + # Get shapes using peek method + try: + sample_input, sample_target = self.peek_sample( + file_idx=file_idx, subseq_idx=0 + ) + + if isinstance(sample_input, dict): + input_shape = {key: tensor.shape + for key, tensor in sample_input.items()} + else: + input_shape = sample_input.shape + + if isinstance(sample_target, dict): + target_shape = {key: tensor.shape + for key, tensor in sample_target.items()} + else: + target_shape = sample_target.shape + + return { + "sample_idx": sample_idx, + "file_idx": file_idx, + "input_shape": input_shape, + "target_shape": target_shape, + "subseq_start": start_idx, + "subseq_end": end_idx, + "is_multi_input": getattr(self, "is_multi_input", False), + "is_multi_target": getattr(self, "is_multi_target", False), + } + except Exception as e: + return {"sample_idx": sample_idx, + "error": f"Could not peek sample: {e}"} + else: + return {"sample_idx": sample_idx, + "error": "Dataset does not support peeking"} + + def validate_files(self) -> list[tuple[str, bool, Optional[str]]]: + """Validate all files in the dataset. + + Returns + ------- + list[tuple[str, bool, Optional[str]]] + List of tuples containing (file_path, is_valid, error_message). + error_message is None if the file is valid. + """ + results = [] + + for file_path in self.file_paths: + try: + metadata = self._inspect_file(file_path) + is_valid = metadata.get("valid", False) + results.append((file_path, is_valid, None)) + except Exception as e: + results.append((file_path, False, str(e))) + + return results + + def summary(self) -> dict[str, Any]: + """Get a summary of the dataset. + + Returns + ------- + dict[str, Any] + Dictionary containing dataset summary information. + """ + return { + "num_files": self.num_files, + "file_paths": self.file_paths, + "is_initialized": self.is_initialized, + "worker_id": self.worker_id, + "validate_on_init": self.validate_on_init, + "max_open_files": self.max_open_files, + "file_metadata": self.file_metadata, + } + + def __repr__(self) -> str: + """String representation of the dataset. + + Returns + ------- + str + String representation. + """ + return ( + f"{self.__class__.__name__}(" + f"num_files={self.num_files}, " + f"initialized={self.is_initialized}, " + f"worker_id={self.worker_id})" + ) + + +def create_worker_init_fn(dataset: LazyFileDataset) -> callable: + """Create a worker initialization function for a LazyFileDataset. + + This is a convenience function that creates the worker_init_fn + needed by PyTorch's DataLoader to properly initialize lazy datasets + in each worker process. + + Parameters + ---------- + dataset : LazyFileDataset + The dataset that needs worker initialization. + + Returns + ------- + callable + Worker initialization function suitable for DataLoader. + + Examples + -------- + >>> dataset = MyLazyDataset(file_paths=['file1.dat', 'file2.dat']) + >>> worker_init_fn = create_worker_init_fn(dataset) + >>> loader = DataLoader(dataset, batch_size=32, num_workers=4, + ... worker_init_fn=worker_init_fn) + """ + + def worker_init_fn(worker_id: int) -> None: + """Initialize dataset in worker process. + + Parameters + ---------- + worker_id : int + ID of the current worker process. + """ + # Get the dataset from worker info + worker_info = get_worker_info() + if worker_info is not None: + worker_dataset = worker_info.dataset + if isinstance(worker_dataset, LazyFileDataset): + worker_dataset.worker_init() + else: + warnings.warn( + f"Dataset in worker {worker_id} is not a LazyFileDataset. " + f"Got {type(worker_dataset)}." + ) + + return worker_init_fn + + +class SequentialDataset(LazyFileDataset, ABC): + """Abstract base class for sequential/time-series datasets with chunking. + + This class extends LazyFileDataset to handle sequential data that needs to + be divided into subsequences or chunks. It provides the infrastructure for: + - Building indices of available subsequences across files + - Different chunking strategies (non-overlapping, sliding window, etc.) + - Flexible subsequence length handling + - Sample indexing and retrieval + + The class maintains an index of all available subsequences without loading + the actual data, enabling efficient random access to chunks across multiple + files. + """ + + def __init__( + self, + file_paths: Union[str, list[str]], + subseq_len: int, + chunking_strategy: str = "non_overlapping", + overlap: int = 0, + min_seq_len: Optional[int] = None, + validate_on_init: bool = True, + **kwargs, + ) -> None: + """Initialize the sequential dataset. + + Parameters + ---------- + file_paths : Union[str, list[str]] + Path or list of paths to data files. + subseq_len : int + Length of subsequences to extract. Use -1 to use entire sequences. + chunking_strategy : str, optional + Strategy for creating chunks ("non_overlapping", "sliding_window", + "random_crop"), by default "non_overlapping". + overlap : int, optional + Number of samples to overlap between consecutive chunks + (for sliding_window), by default 0. + min_seq_len : Optional[int], optional + Minimum sequence length required to include a file. If None, + uses subseq_len, by default None. + validate_on_init : bool, optional + Whether to validate files and build index on initialization, + by default True. + **kwargs + Additional arguments passed to LazyFileDataset. + """ + # Store chunking parameters before calling super().__init__ + self.subseq_len = subseq_len + self.chunking_strategy = chunking_strategy + self.overlap = overlap + self.min_seq_len = min_seq_len or (subseq_len if subseq_len > 0 else 1) + + # Validate chunking parameters + self._validate_chunking_params() + + # Subsequence index: list of (file_idx, start_idx, end_idx) tuples + self.subseq_index = [] + + # Initialize parent class + super().__init__( + file_paths=file_paths, validate_on_init=validate_on_init, **kwargs + ) + + # Build subsequence index after metadata is available + if validate_on_init: + self._build_subsequence_index() + + def _validate_chunking_params(self) -> None: + """Validate chunking parameters. + + Raises + ------ + ValueError + If chunking parameters are invalid. + """ + valid_strategies = ["non_overlapping", "sliding_window", "random_crop"] + if self.chunking_strategy not in valid_strategies: + raise ValueError( + f"Invalid chunking_strategy '{self.chunking_strategy}'. " + f"Must be one of {valid_strategies}" + ) + + if self.subseq_len <= 0 and self.subseq_len != -1: + raise ValueError( + f"subseq_len must be positive or -1 (for full sequences), " + f"got {self.subseq_len}" + ) + + if self.overlap < 0: + raise ValueError(f"overlap must be non-negative, " + f"got {self.overlap}") + + if (self.chunking_strategy == "sliding_window" + and self.overlap >= self.subseq_len > 0): + raise ValueError( + f"overlap ({self.overlap}) must be less than subseq_len " + f"({self.subseq_len}) for sliding_window strategy" + ) + + @abstractmethod + def _get_sequence_length(self, file_metadata: dict[str, Any]) -> int: + """Get the sequence length from file metadata. + + This method should be implemented by subclasses to extract the + sequence length (number of time steps) from the file metadata. + The implementation should examine the actual data shapes available + in the file and infer the time dimension. + + Parameters + ---------- + file_metadata : dict[str, Any] + Metadata dictionary for a file. + + Returns + ------- + int + Number of time steps in the sequence. + """ + pass + + def _build_subsequence_index(self) -> None: + """Build an index of all available subsequences across files. + + This creates a flat index where each entry represents one subsequence + that can be accessed via __getitem__. The index contains tuples of + (file_index, start_sample, end_sample). + """ + self.subseq_index = [] + + for file_idx, metadata in enumerate(self.file_metadata): + seq_len = self._get_sequence_length(metadata) + + # Skip files that are too short + if seq_len < self.min_seq_len: + warnings.warn( + f"Skipping file {metadata.get('path', file_idx)}: " + f"sequence length {seq_len} < minimum {self.min_seq_len}" + ) + continue + + # Generate subsequences based on strategy + subsequences = self._generate_subsequences(seq_len, file_idx) + self.subseq_index.extend(subsequences) + + def _generate_subsequences(self, seq_len: int, file_idx: int) \ + -> list[tuple[int, int, int]]: + """Generate subsequence indices for a single file. + + Parameters + ---------- + seq_len : int + Total length of the sequence in the file. + file_idx : int + Index of the file. + + Returns + ------- + list[tuple[int, int, int]] + List of (file_idx, start_idx, end_idx) tuples. + """ + subsequences = [] + + # Handle full sequence case + if self.subseq_len == -1: + subsequences.append((file_idx, 0, seq_len)) + return subsequences + + # Handle different chunking strategies + if self.chunking_strategy == "non_overlapping": + subsequences = self._generate_non_overlapping(seq_len, file_idx) + elif self.chunking_strategy == "sliding_window": + subsequences = self._generate_sliding_window(seq_len, file_idx) + elif self.chunking_strategy == "random_crop": + subsequences = self._generate_random_crop(seq_len, file_idx) + + return subsequences + + def _generate_non_overlapping( + self, seq_len: int, file_idx: int + ) -> list[tuple[int, int, int]]: + """Generate non-overlapping subsequences. + + Parameters + ---------- + seq_len : int + Total sequence length. + file_idx : int + File index. + + Returns + ------- + list[tuple[int, int, int]] + List of non-overlapping subsequences. + """ + subsequences = [] + + if seq_len >= self.subseq_len: + n_chunks = seq_len // self.subseq_len + for chunk_idx in range(n_chunks): + start_idx = chunk_idx * self.subseq_len + end_idx = start_idx + self.subseq_len + subsequences.append((file_idx, start_idx, end_idx)) + + return subsequences + + def _generate_sliding_window(self, seq_len: int, file_idx: int) \ + -> list[tuple[int, int, int]]: + """Generate sliding window subsequences. + + Parameters + ---------- + seq_len : int + Total sequence length. + file_idx : int + File index. + + Returns + ------- + list[tuple[int, int, int]] + List of sliding window subsequences. + """ + subsequences = [] + + if seq_len >= self.subseq_len: + step_size = self.subseq_len - self.overlap + start_idx = 0 + + while start_idx + self.subseq_len <= seq_len: + end_idx = start_idx + self.subseq_len + subsequences.append((file_idx, start_idx, end_idx)) + start_idx += step_size + + return subsequences + + def _generate_random_crop(self, seq_len: int, file_idx: int) \ + -> list[tuple[int, int, int]]: + """Generate random crop positions (one per file for now). + + For random cropping, we typically generate crop positions at runtime, + but we still need to register that this file can provide crops. + + Parameters + ---------- + seq_len : int + Total sequence length. + file_idx : int + File index. + + Returns + ------- + list[tuple[int, int, int]] + List containing one entry representing the file's crop potential. + """ + subsequences = [] + + if seq_len >= self.subseq_len: + # For random crop, we store (file_idx, -1, seq_len) to indicate + # that this file can provide random crops + subsequences.append((file_idx, -1, seq_len)) + + return subsequences + + def __len__(self) -> int: + """Get the total number of subsequences. + + Returns + ------- + int + Total number of available subsequences across all files. + """ + return len(self.subseq_index) + + def __getitem__(self, idx: int) \ + -> tuple[ + Union[torch.Tensor, dict[str, torch.Tensor]], + Union[torch.Tensor, dict[str, torch.Tensor]], + ]: + """Get a subsequence by index. + + Parameters + ---------- + idx : int + Index of the subsequence to retrieve. + + Returns + ------- + tuple[Union[torch.Tensor, dict[str, torch.Tensor]], + Union[torch.Tensor, dict[str, torch.Tensor]]] + Tuple of (input_tensor, target_tensor). Each can be either a single + tensor or a dictionary of tensors depending on whether multiple + keys were specified. + + Raises + ------ + IndexError + If index is out of range. + RuntimeError + If dataset is not properly initialized. + """ + if not (0 <= idx < len(self.subseq_index)): + raise IndexError(f"Index {idx} out of range. Dataset has " + f"{len(self.subseq_index)} subsequences.") + + self._ensure_initialized() + + file_idx, start_idx, end_idx = self.subseq_index[idx] + + # Handle random crop case + if start_idx == -1: + start_idx, end_idx = self._generate_random_crop_indices(end_idx) + + # Get data from file + input_tensor, target_tensor = self._extract_subsequence( + file_idx, start_idx, end_idx + ) + + return input_tensor, target_tensor + + def _generate_random_crop_indices(self, seq_len: int) -> tuple[int, int]: + """Generate random crop start and end indices. + + Parameters + ---------- + seq_len : int + Total sequence length available for cropping. + + Returns + ------- + tuple[int, int] + Start and end indices for the random crop. + """ + if seq_len < self.subseq_len: + raise ValueError(f"Sequence length {seq_len} < subsequence length " + f"{self.subseq_len}") + + max_start = seq_len - self.subseq_len + start_idx = torch.randint(0, max_start + 1, (1,)).item() + end_idx = start_idx + self.subseq_len + + return start_idx, end_idx + + @abstractmethod + def _extract_subsequence(self, file_idx: int, + start_idx: int, end_idx: int) \ + -> tuple[ + Union[torch.Tensor, dict[str, torch.Tensor]], + Union[torch.Tensor, dict[str, torch.Tensor]], + ]: + """Extract a subsequence from a file. + + This method should be implemented by subclasses to extract the actual + data subsequence from the opened file. + + Parameters + ---------- + file_idx : int + Index of the file to read from. + start_idx : int + Start index of the subsequence. + end_idx : int + End index of the subsequence. + + Returns + ------- + tuple[Union[torch.Tensor, dict[str, torch.Tensor]], + Union[torch.Tensor, dict[str, torch.Tensor]]] + Tuple of (input_tensor, target_tensor). Each can be either a single + tensor or a dictionary of tensors depending on whether multiple + keys were specified. + """ + pass + + def get_subsequence_info(self, idx: int) -> dict[str, Any]: + """Get information about a specific subsequence. + + Parameters + ---------- + idx : int + Index of the subsequence. + + Returns + ------- + dict[str, Any] + Dictionary containing subsequence information. + """ + if not (0 <= idx < len(self.subseq_index)): + raise IndexError(f"Index {idx} out of range") + + file_idx, start_idx, end_idx = self.subseq_index[idx] + file_metadata = self.get_file_metadata(file_idx) + + return { + "subsequence_idx": idx, + "file_idx": file_idx, + "file_path": file_metadata.get("path", "unknown"), + "start_idx": start_idx, + "end_idx": end_idx, + "length": + end_idx - start_idx if start_idx != -1 else self.subseq_len, + "is_random_crop": start_idx == -1, + } + + def get_file_subsequences(self, file_idx: int) -> list[int]: + """Get all subsequence indices that belong to a specific file. + + Parameters + ---------- + file_idx : int + Index of the file. + + Returns + ------- + list[int] + List of subsequence indices from the specified file. + """ + return [ + idx + for idx, (f_idx, _, _) in enumerate(self.subseq_index) + if f_idx == file_idx + ] + + def summary(self) -> dict[str, Any]: + """Get a summary of the dataset. + + Returns + ------- + dict[str, Any] + Dictionary containing dataset summary information. + """ + base_summary = super().summary() + + # Add sequential-specific information + sequential_info = { + "subseq_len": self.subseq_len, + "chunking_strategy": self.chunking_strategy, + "overlap": self.overlap, + "min_seq_len": self.min_seq_len, + "total_subsequences": len(self.subseq_index), + "subsequences_per_file": [len(self.get_file_subsequences(i)) + for i in range(self.num_files) + ], + } + + base_summary.update(sequential_info) + return base_summary + + def __repr__(self) -> str: + """String representation of the dataset. + + Returns + ------- + str + String representation. + """ + return ( + f"{self.__class__.__name__}(" + f"num_files={self.num_files}, " + f"total_subsequences={len(self.subseq_index)}, " + f"subseq_len={self.subseq_len}, " + f"strategy={self.chunking_strategy})" + ) + + +class MultiFileDataset(SequentialDataset, ABC): + """ + Abstract base class for datasets spanning multiple files with advanced + management. + + This class extends SequentialDataset to provide sophisticated multi-file + handling: + - File discovery and pattern matching + - File sorting and organization + - Load balancing across files + - File filtering and selection + - Memory management for large file collections + + The class is designed to handle datasets where: + - Data is distributed across many files + - Files may have different sizes + - You want to control which files are used + - You need efficient access patterns across files + """ + + def __init__( + self, + file_paths: Union[str, list[str], Path], + subseq_len: int, + file_pattern: Optional[str] = None, + file_filter: Optional[callable] = None, + sort_files: bool = True, + max_files: Optional[int] = None, + balance_files: bool = False, + file_weights: Optional[dict[str, float]] = None, + cache_metadata: bool = True, + **kwargs, + ) -> None: + """Initialize the multi-file dataset. + + Parameters + ---------- + file_paths : Union[str, list[str], Path] + Path(s) to data files. Can be: + - Single file path + - List of file paths + - Directory path (will search for files) + - Glob pattern + subseq_len : int + Length of subsequences to extract. + file_pattern : Optional[str], optional + Glob pattern for file discovery when file_paths is a directory, + by default None (uses "*"). + file_filter : Optional[callable], optional + Function to filter files. Should take file path and return bool, + by default None. + sort_files : bool, optional + Whether to sort files by name, by default True. + max_files : Optional[int], optional + Maximum number of files to use, by default None (use all). + balance_files : bool, optional + Whether to balance subsequences across files, by default False. + file_weights : Optional[dict[str, float]], optional + Weights for sampling from different files, by default None. + cache_metadata : bool, optional + Whether to cache file metadata to disk, by default True. + **kwargs + Additional arguments passed to SequentialDataset. + """ + # Store multi-file parameters + self.file_pattern = file_pattern or "*" + self.file_filter = file_filter + self.sort_files = sort_files + self.max_files = max_files + self.balance_files = balance_files + self.file_weights = file_weights or {} + self.cache_metadata = cache_metadata + + # File management state + self.file_stats = [] + self.file_groups = {} + self.balanced_indices = None + self._metadata_cache = {} + + # Discover and process files + resolved_file_paths = self._discover_files(file_paths) + + # Initialize parent with resolved file paths + super().__init__( + file_paths=resolved_file_paths, subseq_len=subseq_len, **kwargs) + + # Build file statistics and balancing if requested + if self.validate_on_init: + self._build_file_stats() + if self.balance_files: + self._build_balanced_indices() + + def _discover_files(self, file_paths: Union[str, list[str], Path]) \ + -> list[str]: + """Discover and process file paths. + + Parameters + ---------- + file_paths : Union[str, list[str], Path] + Input file paths specification. + + Returns + ------- + list[str] + List of resolved file paths. + """ + if isinstance(file_paths, (str, Path)): + file_paths = Path(file_paths) + + if file_paths.is_dir(): + # Directory: search for files + discovered_files = list(file_paths.glob(self.file_pattern)) + resolved_paths = [ + str(f) for f in discovered_files if f.is_file()] + elif "*" in str(file_paths) or "?" in str(file_paths): + # Glob pattern + from glob import glob + + resolved_paths = glob(str(file_paths)) + else: + # Single file + resolved_paths = [str(file_paths)] + else: + # List of paths + resolved_paths = [str(p) for p in file_paths] + + # Apply file filter if provided + if self.file_filter: + resolved_paths = [f for f in resolved_paths if self.file_filter(f)] + + # Sort files if requested + if self.sort_files: + resolved_paths.sort() + + # Limit number of files + if self.max_files and len(resolved_paths) > self.max_files: + resolved_paths = resolved_paths[: self.max_files] + + if not resolved_paths: + raise ValueError( + f"No files found matching criteria. " + f"Input: {file_paths}, pattern: {self.file_pattern}" + ) + + return resolved_paths + + def _build_file_stats(self) -> None: + """Build statistics for each file.""" + self.file_stats = [] + + for file_idx, metadata in enumerate(self.file_metadata): + file_path = metadata.get("path", f"file_{file_idx}") + seq_len = self._get_sequence_length(metadata) + + # Count subsequences for this file + file_subseqs = self.get_file_subsequences(file_idx) + num_subseqs = len(file_subseqs) + + # Calculate file weight + weight = self.file_weights.get(file_path, 1.0) + + stats = { + "file_idx": file_idx, + "file_path": file_path, + "sequence_length": seq_len, + "num_subsequences": num_subseqs, + "weight": weight, + "subsequence_indices": file_subseqs, + } + + self.file_stats.append(stats) + + def _build_balanced_indices(self) -> None: + """Build balanced indices for equal representation across files. + + This creates a new indexing scheme where each file contributes + roughly the same number of samples, regardless of file size. + """ + if not self.file_stats: + return + + # Find minimum number of subsequences across files + min_subseqs = min( + stats["num_subsequences"] + for stats in self.file_stats + if stats["num_subsequences"] > 0 + ) + + if min_subseqs == 0: + warnings.warn("No files have valid subsequences for balancing") + return + + # Build balanced index + self.balanced_indices = [] + + for stats in self.file_stats: + if stats["num_subsequences"] > 0: + # Sample evenly from this file's subsequences + file_subseqs = stats["subsequence_indices"] + + if len(file_subseqs) >= min_subseqs: + # Sample min_subseqs indices evenly + step = len(file_subseqs) / min_subseqs + selected_indices = [ + file_subseqs[int(i * step)] for i in range(min_subseqs) + ] + else: + # Use all available indices + # (shouldn't happen due to min calculation) + selected_indices = file_subseqs + + self.balanced_indices.extend(selected_indices) + + def __len__(self) -> int: + """Get the total number of subsequences. + + Returns + ------- + int + Total number of subsequences (balanced or unbalanced). + """ + if self.balance_files and self.balanced_indices is not None: + return len(self.balanced_indices) + else: + return super().__len__() + + def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]: + """Get a subsequence by index. + + Parameters + ---------- + idx : int + Index of the subsequence to retrieve. + + Returns + ------- + tuple[torch.Tensor, torch.Tensor] + Tuple of (input_tensor, target_tensor). + """ + # Handle balanced indexing + if self.balance_files and self.balanced_indices is not None: + if not (0 <= idx < len(self.balanced_indices)): + raise IndexError( + f"Balanced index {idx} out of range. Dataset has " + f"{len(self.balanced_indices)} balanced subsequences." + ) + # Map to actual subsequence index + actual_idx = self.balanced_indices[idx] + return super().__getitem__(actual_idx) + else: + return super().__getitem__(idx) + + def get_file_stats(self) -> list[dict[str, Any]]: + """Get statistics for all files. + + Returns + ------- + list[dict[str, Any]] + List of file statistics dictionaries. + """ + return self.file_stats.copy() + + def get_largest_files(self, n: int = 5) -> list[dict[str, Any]]: + """Get the n largest files by subsequence count. + + Parameters + ---------- + n : int, optional + Number of files to return, by default 5. + + Returns + ------- + list[dict[str, Any]] + List of file statistics sorted by subsequence count. + """ + sorted_files = sorted( + self.file_stats, key=lambda x: x["num_subsequences"], reverse=True + ) + return sorted_files[:n] + + def get_files_by_pattern(self, pattern: str) -> list[dict[str, Any]]: + """Get files matching a pattern. + + Parameters + ---------- + pattern : str + Pattern to match against file paths. + + Returns + ------- + list[dict[str, Any]] + List of matching file statistics. + """ + import re + + regex = re.compile(pattern) + + return [ + stats for stats in self.file_stats + if regex.search(stats["file_path"]) + ] + + def filter_files_by_size(self, min_subsequences: int = 1, + max_subsequences: Optional[int] = None) \ + -> "MultiFileDataset": + """Create a new dataset with files filtered by subsequence count. + + Parameters + ---------- + min_subsequences : int, optional + Minimum number of subsequences required, by default 1. + max_subsequences : Optional[int], optional + Maximum number of subsequences allowed, by default None. + + Returns + ------- + MultiFileDataset + New dataset with filtered files. + """ + filtered_paths = [] + + for stats in self.file_stats: + num_subseqs = stats["num_subsequences"] + if num_subseqs >= min_subsequences: + if max_subsequences is None or num_subseqs <= max_subsequences: + filtered_paths.append(stats["file_path"]) + + # Create new dataset with same parameters but filtered files + return self.__class__( + file_paths=filtered_paths, + subseq_len=self.subseq_len, + chunking_strategy=self.chunking_strategy, + overlap=self.overlap, + balance_files=self.balance_files, + file_weights=self.file_weights, + validate_on_init=self.validate_on_init, + ) + + def split_by_files( + self, + train_ratio: float = 0.8, + val_ratio: float = 0.2, + random_seed: Optional[int] = None, + ) -> tuple["MultiFileDataset", "MultiFileDataset"]: + """Split the dataset by files (not by samples). + + This ensures that samples from the same file don't appear in both + training and validation sets, which is important for proper evaluation. + + Parameters + ---------- + train_ratio : float, optional + Fraction of files for training, by default 0.8. + val_ratio : float, optional + Fraction of files for validation, by default 0.1. + random_seed : Optional[int], optional + Random seed for reproducible splits, by default None. + + Returns + ------- + tuple[MultiFileDataset, MultiFileDataset, MultiFileDataset] + Train, validation, and test datasets. + """ + if abs(train_ratio + val_ratio - 1.0) > 1e-6: + raise ValueError("Split ratios must sum to 1.0") + + # Get all file paths + all_paths = [stats["file_path"] for stats in self.file_stats] + + # Shuffle files + if random_seed is not None: + import random + + random.seed(random_seed) + random.shuffle(all_paths) + + # Calculate split points + n_files = len(all_paths) + n_train = int(n_files * train_ratio) + n_val = int(n_files - n_train) + + # Split file paths + train_paths = all_paths[:n_train] + val_paths = all_paths[n_train:] + + if n_train != len(train_paths): + raise ValueError("Training set size does not match expected " + "number of files.") + if n_val != len(val_paths): + raise ValueError("Validation set size does not match expected " + "number of files.") + + # Create split datasets + common_params = { + "subseq_len": self.subseq_len, + "chunking_strategy": self.chunking_strategy, + "overlap": self.overlap, + "balance_files": self.balance_files, + "validate_on_init": self.validate_on_init, + } + + train_dataset = self.__class__(file_paths=train_paths, **common_params) + val_dataset = self.__class__(file_paths=val_paths, **common_params) + + return train_dataset, val_dataset + + def get_memory_usage_estimate(self) -> dict[str, float]: + """Estimate memory usage for different scenarios. + + Returns + ------- + dict[str, float] + Dictionary with memory estimates in GB. + """ + total_subseqs = len(self) + + # Estimate based on first file (rough approximation) + if self.file_metadata: + # This would need to be implemented by subclasses + # based on their specific data types and sizes + try: + sample_input, sample_target = self[0] + + input_size = ( + sample_input.numel() * sample_input.element_size()) + target_size = ( + sample_target.numel() * sample_target.element_size()) + + per_sample_bytes = input_size + target_size + + return { + "per_sample_mb": per_sample_bytes / (1024 * 1024), + "total_dataset_gb": + (total_subseqs * per_sample_bytes) / (1024 ** 3), + "single_batch_mb": (32 * per_sample_bytes) / (1024 * 1024), + # Assume batch=32 + } + except Exception: + pass + + return {"error": "No data available for estimation"} + + def summary(self) -> dict[str, Any]: + """Get a comprehensive summary of the dataset. + + Returns + ------- + dict[str, Any] + Dictionary containing dataset summary information. + """ + base_summary = super().summary() + + # Add multi-file specific information + file_sizes = [stats["num_subsequences"] for stats in self.file_stats] + + multifile_info = { + "file_pattern": self.file_pattern, + "max_files": self.max_files, + "balance_files": self.balance_files, + "actual_num_files": len(self.file_stats), + "file_size_stats": { + "min_subsequences": min(file_sizes) if file_sizes else 0, + "max_subsequences": max(file_sizes) if file_sizes else 0, + "avg_subsequences": sum(file_sizes) / len(file_sizes) + if file_sizes + else 0, + "total_subsequences": sum(file_sizes), + }, + "balanced_length": len(self.balanced_indices) + if self.balanced_indices + else None, + "has_file_weights": bool(self.file_weights), + } + + base_summary.update(multifile_info) + return base_summary + + def __repr__(self) -> str: + """String representation of the dataset. + + Returns + ------- + str + String representation. + """ + balance_info = "" + if self.balance_files: + balanced = len(self.balanced_indices) \ + if self.balanced_indices else 0 + balance_info = f", balanced={balanced}" + + return ( + f"{self.__class__.__name__}(files={len(self.file_stats)}, " + f"subsequences={len(self)}{balance_info}, " + f"subseq_len={self.subseq_len})" + ) diff --git a/src/faith/train/data/datasets/file_based.py b/src/faith/train/data/datasets/file_based.py new file mode 100644 index 0000000..c7d2940 --- /dev/null +++ b/src/faith/train/data/datasets/file_based.py @@ -0,0 +1,1163 @@ +"""Concrete implementations of file-based datasets.""" + +from __future__ import annotations + +import warnings +from collections import Counter +from pathlib import Path +from typing import Any, Optional, Union + +import numpy as np +import torch + +from .base import MultiFileDataset + + +class JoblibDataset(MultiFileDataset): + """Dataset for joblib files with memory-mapped loading. + + Supports flexible key configuration for different file formats. + Can work with input-only data (autoencoders) or input-target pairs. + """ + + def __init__( + self, + file_paths: Union[str, list[str], Path], + subseq_len: int, + input_key: Optional[Union[str, list[str]]] = None, + target_key: Optional[Union[str, list[str]]] = None, + target_slice: Optional[tuple] = None, + auto_detect_keys: bool = True, + **kwargs, + ) -> None: + """Initialize joblib dataset. + + Parameters + ---------- + file_paths : Union[str, list[str], Path] + Path(s) to joblib files. + subseq_len : int + Length of subsequences to extract. + input_key : Optional[Union[str, list[str]]], optional + Key(s) for input data in joblib files. If None, will auto-detect. + If list, returns dictionary of inputs, by default None. + target_key : Optional[Union[str, list[str]]], optional + Key(s) for target data in joblib files. If None, uses input as + target (autoencoder mode). If list, returns dictionary of targets, + by default None. + target_slice : Optional[tuple], optional + Slice to apply to target tensor + (e.g., (slice(None, 48), slice(None), slice(None)) + for [:48, :, :]), by default None. + auto_detect_keys : bool, optional + Whether to automatically detect keys from first valid file, + by default True. + **kwargs + Additional arguments passed to MultiFileDataset. + """ + self.input_key = input_key + self.target_key = target_key + self.target_slice = target_slice + # Auto-detect if no input key provided + self.auto_detect_keys = auto_detect_keys or (input_key is None) + self.is_autoencoder_mode = target_key is None + + # Handle multiple keys + self.is_multi_input = isinstance(input_key, list) + self.is_multi_target = isinstance(target_key, list) + + # Store original keys for auto-detection fallback + self._original_input_key = input_key + self._original_target_key = target_key + + # Track if keys have been detected + self._keys_detected = False + + super().__init__(file_paths, subseq_len, **kwargs) + + def _detect_keys(self, data_dict: dict[str, Any], file_path: str) -> None: + """Auto-detect keys from file contents.""" + available_keys = list(data_dict.keys()) + + print(f"Auto-detecting keys from {file_path}") + print(f"Available keys: {available_keys}") + + # Skip auto-detection if keys are already provided as lists + if self.is_multi_input or self.is_multi_target: + print("Skipping auto-detection since list of keys provided") + return + + # Try to find input key + if not self.is_multi_input and self.input_key is None: + input_candidates = [ + "input", + "data", + "x", + "features", + "spectrogram", + "signal", + ] + for candidate in input_candidates: + if candidate in available_keys: + self.input_key = candidate + print(f"Detected input_key: {self.input_key}") + break + else: + # If no standard key found, use the first array-like key + for key in available_keys: + if ( + hasattr(data_dict[key], "shape") + and len(data_dict[key].shape) >= 2 + ): + self.input_key = key + print(f"Using first array key as input_key: " + f"{self.input_key}") + break + + # Try to find target key + # (only if not in autoencoder mode and not multi-target) + if (not self.is_autoencoder_mode and not self.is_multi_target + and self.target_key is None): + target_candidates = [ + "target", "label", "y", "output", "ground_truth"] + for candidate in target_candidates: + if candidate in available_keys: + self.target_key = candidate + print(f"Detected target_key: {self.target_key}") + break + else: + # If no standard target key found, + # look for second array-like key + array_keys = [ + k for k in available_keys + if hasattr(data_dict[k], "shape") + and len(data_dict[k].shape) >= 2 + ] + if len(array_keys) >= 2: + # Use second array key as target (first is input) + target_candidates_from_arrays = [ + k for k in array_keys if k != self.input_key + ] + if target_candidates_from_arrays: + self.target_key = target_candidates_from_arrays[0] + print(f"Using second array key as target_key: " + f"{self.target_key}") + + def _infer_sequence_length_from_data(self, + data_dict: dict[str, Any]) -> int: + """Infer sequence length by examining data shapes. + + Parameters + ---------- + data_dict : dict[str, Any] + Dictionary containing the loaded data. + + Returns + ------- + int + Inferred sequence length. + """ + # Handle multiple input keys + if self.is_multi_input: + sequence_lengths = [] + for key in self.input_key: + if key in data_dict: + input_data = data_dict[key] + if hasattr(input_data, "shape"): + shape = input_data.shape + # Assume time dimension is the longest dimension or + # axis 2 if >= 3D + if len(shape) >= 3: + # Conventional: (channels, features, time) + seq_len = shape[1] + elif len(shape) == 2: + # Use the larger dimension as time + seq_len = max(shape) + elif len(shape) == 1: + seq_len = shape[0] # 1D: (time,) + else: + continue # Skip scalar values + + sequence_lengths.append((key, seq_len)) + + if sequence_lengths: + # Check if all keys have the same sequence length + unique_lengths = set(length for _, length in sequence_lengths) + if len(unique_lengths) == 1: + return sequence_lengths[0][1] # All keys have same length + else: + # Different sequence lengths - use the most common one or + # smallest + length_counts = Counter( + length for _, length in sequence_lengths) + most_common_length = length_counts.most_common(1)[0][0] + + # Warn about inconsistent lengths + warnings.warn( + f"Inconsistent sequence lengths across input keys: " + f"{dict(sequence_lengths)}. Using most common length: " + f"{most_common_length}" + ) + return most_common_length + + # Handle single input key (original logic) + elif self.input_key is not None and self.input_key in data_dict: + input_data = data_dict[self.input_key] + if hasattr(input_data, "shape"): + shape = input_data.shape + # Assume time dimension is longest dimension or axis 1 if >= 3D + if len(shape) >= 3: + return shape[1] # Conventional: (channels, time, features) + elif len(shape) == 2: + # Use the larger dimension as time + return max(shape) + elif len(shape) == 1: + return shape[0] # 1D: (time,) + + # Fallback: find the largest array and use its time dimension + max_samples = 0 + for key, value in data_dict.items(): + if hasattr(value, "shape") and len(value.shape) >= 1: + shape = value.shape + # Try different conventions for time dimension + if len(shape) >= 3: + # Try axis 2 first (channels, features, time) + candidate_time = shape[2] + max_samples = max(max_samples, candidate_time) + elif len(shape) >= 2: + # Use the larger dimension as time + candidate_time = max(shape) + max_samples = max(max_samples, candidate_time) + elif len(shape) == 1: + # 1D array: assume it's all time + max_samples = max(max_samples, shape[0]) + + return max_samples + + def _validate_keys_in_file(self, + data_dict: dict[str, Any], + file_path: str) -> tuple[bool, str]: + """Validate that required keys exist in the file. + + Parameters + ---------- + data_dict : dict[str, Any] + Dictionary containing the loaded data. + file_path : str + Path to the file being validated. + + Returns + ------- + tuple[bool, str] + Tuple of (is_valid, error_message). Error_message empty if valid. + """ + available_keys = list(data_dict.keys()) + + # Validate input keys + if self.is_multi_input: + missing_input_keys = [ + key for key in self.input_key if key not in data_dict] + if missing_input_keys: + return ( + False, + f"Missing input keys {missing_input_keys}. " + f"Available keys: {available_keys}", + ) + elif self.input_key is not None and self.input_key not in data_dict: + return ( + False, + f"Missing input key '{self.input_key}'. " + f"Available keys: {available_keys}", + ) + + # Validate target keys (if not in autoencoder mode) + if not self.is_autoencoder_mode: + if self.is_multi_target: + missing_target_keys = [ + key for key in self.target_key if key not in data_dict + ] + if missing_target_keys: + return ( + False, + f"Missing target keys {missing_target_keys}. " + f"Available keys: {available_keys}", + ) + elif (self.target_key is not None + and self.target_key not in data_dict): + return ( + False, + f"Missing target key '{self.target_key}'. " + f"Available keys: {available_keys}", + ) + + return True, "" + + def _inspect_file(self, file_path: str) -> dict[str, Any]: + """Inspect joblib file to extract metadata.""" + try: + from joblib import load + except ImportError: + raise ImportError("joblib is required for JoblibDataset. " + "Install with: pip install joblib") + + try: + data_dict = load(file_path, mmap_mode="r") + + # Auto-detect keys from first file if requested + if self.auto_detect_keys and not self._keys_detected: + self._detect_keys(data_dict, file_path) + self._keys_detected = True + + # Get available keys + available_keys = list(data_dict.keys()) + + # Validate keys exist in file + is_valid, error_msg = self._validate_keys_in_file(data_dict, + file_path) + if not is_valid: + return { + "path": file_path, + "valid": False, + "error": error_msg, + "n_samples": 0, + "available_keys": available_keys, + } + + # Infer sequence length from data shapes + n_samples = self._infer_sequence_length_from_data(data_dict) + + # Get data shapes for metadata + input_shape = None + target_shape = None + + if self.is_multi_input: + input_shape = { + key: data_dict[key].shape + for key in self.input_key + if key in data_dict + } + elif self.input_key is not None and self.input_key in data_dict: + input_shape = data_dict[self.input_key].shape + + if not self.is_autoencoder_mode: + if self.is_multi_target: + target_shape = { + key: data_dict[key].shape + for key in self.target_key + if key in data_dict + } + elif (self.target_key is not None + and self.target_key in data_dict): + target_shape = data_dict[self.target_key].shape + + metadata = { + "path": file_path, + "valid": True, + "n_samples": n_samples, + "input_shape": input_shape, + "target_shape": target_shape, + "available_keys": available_keys, + "is_autoencoder_mode": self.is_autoencoder_mode, + "inferred_input_key": self.input_key, + "inferred_target_key": self.target_key, + } + + del data_dict # Close file handle + return metadata + + except Exception as e: + return { + "path": file_path, + "valid": False, + "error": str(e), + "n_samples": 0, + } + + def _get_sequence_length(self, file_metadata: dict[str, Any]) -> int: + """Get sequence length from metadata.""" + return file_metadata.get("n_samples", 0) + + def _open_file(self, file_path: str) -> Any: + """Open joblib file with memory mapping.""" + try: + from joblib import load + except ImportError: + raise ImportError("joblib is required for JoblibDataset. " + "Install with: pip install joblib") + + return load(file_path, mmap_mode="r") + + def _close_file(self, file_handle: Any) -> None: + """Close joblib file handle.""" + del file_handle # Joblib handles cleanup automatically + + def _extract_tensor_from_array(self, array, start_idx: int, end_idx: int) \ + -> torch.Tensor: + """Extract a tensor from an array with flexible shape handling. + + Parameters + ---------- + array : numpy.ndarray + Input array to extract from. + start_idx : int + Start index for extraction. + end_idx : int + End index for extraction. + + Returns + ------- + torch.Tensor + Extracted tensor. + """ + # Handle different array shapes flexibly + if len(array.shape) >= 3: + # Assume (channels, time, features) or similar + sub_array = array[:, start_idx:end_idx, :] + elif len(array.shape) == 2: + # Assume (features, time) or (time, features) + # Check which dimension is longer to determine time axis + if array.shape[0] > array.shape[1]: + # Assume (time, features) + sub_array = array[start_idx:end_idx, :] + else: + # Assume (features, time) + sub_array = array[:, start_idx:end_idx] + else: + # 1D array: (time,) + sub_array = array[start_idx:end_idx] + + return torch.from_numpy(np.array(sub_array)).float() + + def _extract_subsequence(self, file_idx: int, + start_idx: int, end_idx: int) \ + -> tuple[ + Union[torch.Tensor, dict[str, torch.Tensor]], + Union[torch.Tensor, dict[str, torch.Tensor]], + ]: + """Extract subsequence from joblib file.""" + file_handle = self.get_file_handle(file_idx) + + # Get the keys from metadata (they might have been auto-detected) + metadata = self.get_file_metadata(file_idx) + + # Extract input data + if self.is_multi_input: + input_data = {} + for key in self.input_key: + if key in file_handle: + input_data[key] = self._extract_tensor_from_array( + file_handle[key], start_idx, end_idx + ) + else: + raise ValueError(f"Input key '{key}' not found in file") + else: + input_key = metadata.get("inferred_input_key") or self.input_key + if input_key and input_key in file_handle: + input_data = self._extract_tensor_from_array( + file_handle[input_key], start_idx, end_idx + ) + else: + raise ValueError(f"Input key '{input_key}' not found in file") + + # Extract target data + if self.is_autoencoder_mode: + # Autoencoder mode: target = input + if self.is_multi_input: + target_data = {key: tensor.clone() + for key, tensor in input_data.items()} + else: + target_data = input_data.clone() + else: + # Supervised mode: use separate target + if self.is_multi_target: + target_data = {} + for key in self.target_key: + if key in file_handle: + target_data[key] = self._extract_tensor_from_array( + file_handle[key], start_idx, end_idx + ) + else: + raise ValueError( + f"Target key '{key}' not found in file") + else: + target_key = (metadata.get("inferred_target_key") + or self.target_key) + if target_key and target_key in file_handle: + target_data = self._extract_tensor_from_array( + file_handle[target_key], start_idx, end_idx + ) + else: + # Fallback to input if no target found + if self.is_multi_input: + target_data = {key: tensor.clone() + for key, tensor in input_data.items()} + else: + target_data = input_data.clone() + + # Apply target slice if specified (only for single tensor targets) + if self.target_slice is not None and not self.is_multi_target: + target_data = target_data[self.target_slice] + + return input_data, target_data + + def get_sample_shape(self, file_idx: int = 0) -> tuple[Any, Any]: + """Get sample input and target shapes without initializing workers. + + This method temporarily opens a file to inspect data shapes, then + closes it. Useful for model configuration before training. + + Parameters + ---------- + file_idx : int, optional + Index of the file to sample from, by default 0. + + Returns + ------- + tuple[Any, Any] + Tuple of (input_shape, target_shape). For multi-key scenarios, + returns dict of shapes. + """ + if file_idx >= len(self.file_paths): + raise IndexError(f"File index {file_idx} out of range") + + # Temporarily open the file + file_handle = self._open_file(self.file_paths[file_idx]) + + try: + # Get a small subsequence to determine shapes + start_idx = 0 + end_idx = min(self.subseq_len, 10) if self.subseq_len > 0 else 10 + + # Temporarily store the opened file for _extract_subsequence + original_opened_files = self._opened_files + original_initialized = self._is_initialized + + self._opened_files = [None] * len(self.file_paths) + self._opened_files[file_idx] = file_handle + self._is_initialized = True + + # Extract a sample to get shapes + sample_input, sample_target = self._extract_subsequence( + file_idx, start_idx, end_idx + ) + + # Extract shapes + if isinstance(sample_input, dict): + input_shape = { + key: tensor.shape for key, tensor in sample_input.items() + } + else: + input_shape = sample_input.shape + + if isinstance(sample_target, dict): + target_shape = { + key: tensor.shape for key, tensor in sample_target.items() + } + else: + target_shape = sample_target.shape + + return input_shape, target_shape + + finally: + # Always close the file + self._opened_files = original_opened_files + self._is_initialized = original_initialized + self._close_file(file_handle) + + def peek_sample(self, file_idx: int = 0, subseq_idx: int = 0) \ + -> tuple[ + Union[torch.Tensor, dict[str, torch.Tensor]], + Union[torch.Tensor, dict[str, torch.Tensor]], + ]: + """Peek at a sample without worker initialization. + + This method temporarily opens a file, extracts one sample, then closes + it. Useful for data inspection and debugging. + + Parameters + ---------- + file_idx : int, optional + Index of the file to sample from, by default 0. + subseq_idx : int, optional + Index of the subsequence within the file, by default 0. + + Returns + ------- + tuple[Union[torch.Tensor, dict[str, torch.Tensor]], + Union[torch.Tensor, dict[str, torch.Tensor]]] + Sample input and target data. + """ + if file_idx >= len(self.file_paths): + raise IndexError(f"File index {file_idx} out of range") + + # Get file metadata to determine valid subsequence range + file_metadata = self.get_file_metadata(file_idx) + seq_len = self._get_sequence_length(file_metadata) + + # Calculate valid subsequence bounds + if self.subseq_len == -1: + start_idx = 0 + end_idx = seq_len + else: + max_start = max(0, seq_len - self.subseq_len) + start_idx = min(subseq_idx * self.subseq_len, max_start) + end_idx = min(start_idx + self.subseq_len, seq_len) + + # Temporarily open the file + file_handle = self._open_file(self.file_paths[file_idx]) + + try: + # Temporarily store the opened file for _extract_subsequence + original_opened_files = self._opened_files + original_initialized = self._is_initialized + + self._opened_files = [None] * len(self.file_paths) + self._opened_files[file_idx] = file_handle + self._is_initialized = True + + # Extract the sample + sample_input, sample_target = self._extract_subsequence( + file_idx, start_idx, end_idx + ) + + return sample_input, sample_target + + finally: + # Restore original state + self._opened_files = original_opened_files + self._is_initialized = original_initialized + + # Close the temporary file handle + self._close_file(file_handle) + + +class HDF5Dataset(MultiFileDataset): + """Dataset for HDF5 files with configurable keys. + + Supports flexible key configuration for different file formats. + Can work with input-only data (autoencoders) or input-target pairs. + """ + + def __init__( + self, + file_paths: Union[str, list[str], Path], + subseq_len: int, + input_key: Union[str, list[str]] = "input", + target_key: Optional[Union[str, list[str]]] = None, + target_slice: Optional[tuple] = None, + auto_detect_keys: bool = False, + **kwargs) -> None: + """Initialize HDF5 dataset. + + Parameters + ---------- + file_paths : Union[str, list[str], Path] + Path(s) to HDF5 files. + subseq_len : int + Length of subsequences to extract. + input_key : Union[str, list[str]], optional + Key(s) for input data in HDF5 files. If list, returns dictionary of + inputs, by default 'input'. + target_key : Optional[Union[str, list[str]]], optional + Key(s) for target data in HDF5 files. If None, uses input as target + (autoencoder mode). If list, returns dictionary of targets, + by default None. + target_slice : Optional[tuple], optional + Slice to apply to target tensor, by default None. + auto_detect_keys : bool, optional + Whether to automatically detect keys from first valid file, + by default False. + **kwargs + Additional arguments passed to MultiFileDataset. + """ + self.input_key = input_key + self.target_key = target_key + self.target_slice = target_slice + self.auto_detect_keys = auto_detect_keys + self.is_autoencoder_mode = target_key is None + + # Handle multiple keys + self.is_multi_input = isinstance(input_key, list) + self.is_multi_target = isinstance(target_key, list) + + # Store original keys for auto-detection fallback + self._original_input_key = input_key + self._original_target_key = target_key + + # Track if keys have been detected + self._keys_detected = False + + super().__init__(file_paths, subseq_len, **kwargs) + + def _detect_keys(self, file_handle, file_path: str) -> None: + """Auto-detect keys from HDF5 file contents.""" + available_keys = list(file_handle.keys()) + + print(f"Auto-detecting keys from {file_path}") + print(f"Available keys: {available_keys}") + + # Skip auto-detection if keys are already provided as lists + if self.is_multi_input or self.is_multi_target: + print("Skipping auto-detection since list of keys provided") + return + + # Try to find input key + if not self.is_multi_input: + input_candidates = ["input", "data", "x", "features"] + for candidate in input_candidates: + if candidate in available_keys: + self.input_key = candidate + print(f"Detected input_key: {self.input_key}") + break + + # Try to find target key + # (only if not in autoencoder mode and not multi-target) + if not self.is_autoencoder_mode and not self.is_multi_target: + target_candidates = ["target", "label", "y", "output"] + for candidate in target_candidates: + if candidate in available_keys: + self.target_key = candidate + print(f"Detected target_key: {self.target_key}") + break + + def _inspect_file(self, file_path: str) -> dict[str, Any]: + """Inspect HDF5 file to extract metadata.""" + try: + import h5py + except ImportError: + raise ImportError( + "h5py is required for HDF5Dataset. Install with: pip install h5py" + ) + + try: + with (h5py.File(file_path, "r") as f): + # Auto-detect keys from first file if requested + if self.auto_detect_keys and not self._keys_detected: + self._detect_keys(f, file_path) + self._keys_detected = True + + # Validate required keys exist + if self.is_multi_input: + missing_input_keys = [ + key for key in self.input_key if key not in f] + if missing_input_keys: + available_keys = list(f.keys()) + return { + "path": file_path, + "valid": False, + "error": + f"Missing input keys {missing_input_keys}. " + f"Available keys: {available_keys}", + "n_samples": 0, + "available_keys": available_keys, + } + elif self.input_key not in f: + available_keys = list(f.keys()) + return { + "path": file_path, + "valid": False, + "error": + f"Missing input key '{self.input_key}'. " + f"Available keys: {available_keys}", + "n_samples": 0, + "available_keys": available_keys, + } + + # Check target key if not in autoencoder mode + if not self.is_autoencoder_mode: + if self.is_multi_target: + missing_target_keys = [ + key for key in self.target_key if key not in f + ] + if missing_target_keys: + available_keys = list(f.keys()) + return { + "path": file_path, + "valid": False, + "error": + f"Missing target keys " + f"{missing_target_keys}. " + f"Available keys: {available_keys}", + "n_samples": 0, + "available_keys": available_keys, + } + elif self.target_key not in f: + available_keys = list(f.keys()) + return { + "path": file_path, + "valid": False, + "error": + f"Missing target key '{self.target_key}'. " + f"Available keys: {available_keys}", + "n_samples": 0, + "available_keys": available_keys, + } + + # Get sequence length from input shape + # (assume time dimension is axis 1) + if self.is_multi_input: + # Use first input key to determine sequence length + first_key = self.input_key[0] + input_shape = f[first_key].shape + else: + input_shape = f[self.input_key].shape + n_samples = \ + input_shape[1] if len(input_shape) >= 2 else input_shape[0] + + # Get target shape if available + target_shape = None + if not self.is_autoencoder_mode: + if self.is_multi_target: + target_shape = { + key: f[key].shape + for key in self.target_key if key in f + } + elif self.target_key in f: + target_shape = f[self.target_key].shape + + metadata = { + "path": file_path, + "valid": True, + "n_samples": n_samples, + "input_shape": input_shape, + "target_shape": target_shape, + "available_keys": list(f.keys()), + "is_autoencoder_mode": self.is_autoencoder_mode, + } + + return metadata + except Exception as e: + return {"path": file_path, "valid": False, + "error": str(e), "n_samples": 0} + + def _get_sequence_length(self, file_metadata: dict[str, Any]) -> int: + """Get sequence length from metadata.""" + return file_metadata.get("n_samples", 0) + + def _open_file(self, file_path: str) -> Any: + """Open HDF5 file.""" + try: + import h5py + except ImportError: + raise ImportError("h5py is required for HDF5Dataset. " + "Install with: pip install h5py") + + return h5py.File(file_path, "r") + + def _close_file(self, file_handle: Any) -> None: + """Close HDF5 file handle.""" + file_handle.close() + + def _extract_subsequence( + self, file_idx: int, start_idx: int, end_idx: int) \ + -> tuple[ + Union[torch.Tensor, dict[str, torch.Tensor]], + Union[torch.Tensor, dict[str, torch.Tensor]], + ]: + """Extract subsequence from HDF5 file.""" + file_handle = self.get_file_handle(file_idx) + + # Extract input data + if self.is_multi_input: + input_data = {} + for key in self.input_key: + input_arr = file_handle[key][:, start_idx:end_idx, :] + input_data[key] = torch.from_numpy(input_arr).float() + else: + input_arr = file_handle[self.input_key][:, start_idx:end_idx, :] + input_data = torch.from_numpy(input_arr).float() + + # Extract target data + if self.is_autoencoder_mode: + # Autoencoder mode: target = input + if self.is_multi_input: + target_data = {key: tensor.clone() + for key, tensor in input_data.items()} + else: + target_data = input_data.clone() + else: + # Supervised mode: use separate target + if self.is_multi_target: + target_data = {} + for key in self.target_key: + target_arr = file_handle[key][:, start_idx:end_idx, :] + target_data[key] = torch.from_numpy(target_arr).float() + else: + target_arr = \ + file_handle[self.target_key][:, start_idx:end_idx, :] + target_data = torch.from_numpy(target_arr).float() + + # Apply target slice if specified (only for single tensor targets) + if self.target_slice is not None and not self.is_multi_target: + target_data = target_data[self.target_slice] + + return input_data, target_data + + +class NumpyDataset(MultiFileDataset): + """Dataset for NumPy .npz files with configurable keys. + + Supports flexible key configuration for different file formats. + Can work with input-only data (autoencoders) or input-target pairs. + """ + + def __init__( + self, + file_paths: Union[str, list[str], Path], + subseq_len: int, + input_key: Union[str, list[str]] = "input", + target_key: Optional[Union[str, list[str]]] = None, + target_slice: Optional[tuple] = None, + auto_detect_keys: bool = False, + **kwargs, + ) -> None: + """Initialize NumPy dataset. + + Parameters + ---------- + file_paths : Union[str, list[str], Path] + Path(s) to .npz files. + subseq_len : int + Length of subsequences to extract. + input_key : Union[str, list[str]], optional + Key(s) for input data in .npz files. If list, + returns dictionary of inputs, by default 'input'. + target_key : Optional[Union[str, list[str]]], optional + Key(s) for target data in .npz files. If None, uses input as target + (autoencoder mode). If list, returns dictionary of targets, + by default None. + target_slice : Optional[tuple], optional + Slice to apply to target tensor, by default None. + auto_detect_keys : bool, optional + Whether to automatically detect keys from first valid file, + by default False. + **kwargs + Additional arguments passed to MultiFileDataset. + """ + self.input_key = input_key + self.target_key = target_key + self.target_slice = target_slice + self.auto_detect_keys = auto_detect_keys + self.is_autoencoder_mode = target_key is None + + # Handle multiple keys + self.is_multi_input = isinstance(input_key, list) + self.is_multi_target = isinstance(target_key, list) + + # Store original keys for auto-detection fallback + self._original_input_key = input_key + self._original_target_key = target_key + + # Track if keys have been detected + self._keys_detected = False + + super().__init__(file_paths, subseq_len, **kwargs) + + def _detect_keys(self, data_dict: dict[str, Any], file_path: str) -> None: + """Auto-detect keys from NumPy file contents.""" + available_keys = list(data_dict.keys()) + + print(f"Auto-detecting keys from {file_path}") + print(f"Available keys: {available_keys}") + + # Skip auto-detection if keys are already provided as lists + if self.is_multi_input or self.is_multi_target: + print("Skipping auto-detection since list of keys provided") + return + + # Try to find input key + if not self.is_multi_input: + input_candidates = ["input", "data", "x", "features"] + for candidate in input_candidates: + if candidate in available_keys: + self.input_key = candidate + print(f"Detected input_key: {self.input_key}") + break + + # Try to find target key + # (only if not in autoencoder mode and not multi-target) + if not self.is_autoencoder_mode and not self.is_multi_target: + target_candidates = ["target", "label", "y", "output"] + for candidate in target_candidates: + if candidate in available_keys: + self.target_key = candidate + print(f"Detected target_key: {self.target_key}") + break + + def _inspect_file(self, file_path: str) -> dict[str, Any]: + """Inspect NumPy file to extract metadata.""" + try: + with np.load(file_path, mmap_mode="r") as data: + # Auto-detect keys from first file if requested + if self.auto_detect_keys and not self._keys_detected: + self._detect_keys(data, file_path) + self._keys_detected = True + + # Validate required keys exist + if self.is_multi_input: + missing_input_keys = [ + key for key in self.input_key if key not in data] + if missing_input_keys: + available_keys = list(data.keys()) + return { + "path": file_path, + "valid": False, + "error": + f"Missing input keys {missing_input_keys}. " + f"Available keys: {available_keys}", + "n_samples": 0, + "available_keys": available_keys, + } + elif self.input_key not in data: + available_keys = list(data.keys()) + return { + "path": file_path, + "valid": False, + "error": + f"Missing input key '{self.input_key}'. " + f"Available keys: {available_keys}", + "n_samples": 0, + "available_keys": available_keys, + } + + # Check target key if not in autoencoder mode + if not self.is_autoencoder_mode: + if self.is_multi_target: + missing_target_keys = [ + key for key in self.target_key if key not in data + ] + if missing_target_keys: + available_keys = list(data.keys()) + return { + "path": file_path, + "valid": False, + "error": + f"Missing target keys " + f"{missing_target_keys}. " + f"Available keys: {available_keys}", + "n_samples": 0, + "available_keys": available_keys, + } + elif self.target_key not in data: + available_keys = list(data.keys()) + return { + "path": file_path, + "valid": False, + "error": + f"Missing target key '{self.target_key}'. " + f"Available keys: {available_keys}", + "n_samples": 0, + "available_keys": available_keys, + } + + # Get sequence length from input shape + # (assume time dimension is axis 1) + if self.is_multi_input: + # Use first input key to determine sequence length + first_key = self.input_key[0] + input_arr = data[first_key] + else: + input_arr = data[self.input_key] + input_shape = input_arr.shape + n_samples = ( + input_shape[1] + if len(input_shape) >= 2 else input_shape[0]) + + # Get target shape if available + target_shape = None + if not self.is_autoencoder_mode: + if self.is_multi_target: + target_shape = { + key: data[key].shape + for key in self.target_key if key in data + } + elif self.target_key in data: + target_shape = data[self.target_key].shape + + metadata = { + "path": file_path, + "valid": True, + "n_samples": n_samples, + "input_shape": input_shape, + "target_shape": target_shape, + "available_keys": list(data.keys()), + "is_autoencoder_mode": self.is_autoencoder_mode, + } + + return metadata + except Exception as e: + return {"path": file_path, "valid": False, + "error": str(e), "n_samples": 0} + + def _get_sequence_length(self, file_metadata: dict[str, Any]) -> int: + """Get sequence length from metadata.""" + return file_metadata.get("n_samples", 0) + + def _open_file(self, file_path: str) -> Any: + """Open NumPy file with memory mapping.""" + return np.load(file_path, mmap_mode="r") + + def _close_file(self, file_handle: Any) -> None: + """Close NumPy file handle.""" + file_handle.close() + + def _extract_subsequence( + self, file_idx: int, start_idx: int, end_idx: int) \ + -> tuple[ + Union[torch.Tensor, dict[str, torch.Tensor]], + Union[torch.Tensor, dict[str, torch.Tensor]], + ]: + """Extract subsequence from NumPy file.""" + file_handle = self.get_file_handle(file_idx) + + # Extract input data + if self.is_multi_input: + input_data = {} + for key in self.input_key: + input_arr = file_handle[key][:, start_idx:end_idx, :] + input_data[key] = torch.from_numpy(input_arr).float() + else: + input_arr = file_handle[self.input_key][:, start_idx:end_idx, :] + input_data = torch.from_numpy(input_arr).float() + + # Extract target data + if self.is_autoencoder_mode: + # Autoencoder mode: target = input + if self.is_multi_input: + target_data = {key: tensor.clone() + for key, tensor in input_data.items()} + else: + target_data = input_data.clone() + else: + # Supervised mode: use separate target + if self.is_multi_target: + target_data = {} + for key in self.target_key: + target_arr = file_handle[key][:, start_idx:end_idx, :] + target_data[key] = torch.from_numpy(target_arr).float() + else: + target_arr = \ + file_handle[self.target_key][:, start_idx:end_idx, :] + target_data = torch.from_numpy(target_arr).float() + + # Apply target slice if specified (only for single tensor targets) + if self.target_slice is not None and not self.is_multi_target: + target_data = target_data[self.target_slice] + + return input_data, target_data + + +# Helper function for creating worker init function +def worker_init_fn(worker_id: int) -> None: + """Worker initialization function for LazyFileDataset subclasses.""" + from torch.utils.data import get_worker_info + + worker_info = get_worker_info() + if worker_info is not None: + worker_dataset = worker_info.dataset + if hasattr(worker_dataset, "worker_init"): + worker_dataset.worker_init() + else: + warnings.warn( + f"Dataset in worker {worker_id} does not have worker_init " + f"method. Got {type(worker_dataset)}.") diff --git a/src/faith/train/data/loaders/__init__.py b/src/faith/train/data/loaders/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/faith/train/data/loaders/factory.py b/src/faith/train/data/loaders/factory.py new file mode 100644 index 0000000..6be5411 --- /dev/null +++ b/src/faith/train/data/loaders/factory.py @@ -0,0 +1,265 @@ +"""DataLoader factory and worker utilities for lazy file datasets.""" + +import warnings +from torch.utils.data import DataLoader, get_worker_info + + +def worker_init_fn(worker_id: int) -> None: + """Initialize dataset in worker process. + + Parameters + ---------- + worker_id : int + ID of the current worker process. + """ + # Get the dataset from worker info + worker_info = get_worker_info() + if worker_info is not None: + worker_dataset = worker_info.dataset + + # Check if it's a lazy file dataset that needs initialization + if hasattr(worker_dataset, 'worker_init'): + try: + worker_dataset.worker_init() + except Exception as e: + warnings.warn(f"Failed to initialize dataset in worker " + f"{worker_id}: {e}") + else: + # Not a lazy dataset, no initialization needed + pass + else: + # No worker info available (single-threaded mode) + pass + + +def create_dataloader( + dataset, + batch_size: int = 32, + shuffle: bool = True, + num_workers: int = 4, + pin_memory: bool = True, + drop_last: bool = False, + **kwargs +): + """ + Create a DataLoader with automatic worker initialization for lazy datasets. + + This function automatically handles worker initialization for lazy file + datasets while providing sensible defaults for other DataLoader parameters. + + Parameters + ---------- + dataset : Dataset + PyTorch dataset to load from. + batch_size : int, optional + Number of samples per batch, by default 32. + shuffle : bool, optional + Whether to shuffle data, by default True. + num_workers : int, optional + Number of worker processes, by default 4. + pin_memory : bool, optional + Whether to pin memory for GPU transfer, by default True. + drop_last : bool, optional + Whether to drop last incomplete batch, by default False. + **kwargs + Additional arguments passed to DataLoader. + + Returns + ------- + DataLoader + Configured PyTorch DataLoader. + + Examples + -------- + >>> from src import JoblibDataset + >>> dataset = JoblibDataset(['file1.joblib'], subseq_len=128) + >>> loader = create_dataloader(dataset, batch_size=16, num_workers=2) + >>> + >>> # Use in training + >>> for batch in loader: + ... inputs, targets = batch + ... # Training code here + """ + # Determine if we need worker initialization + worker_init_fn = None + if hasattr(dataset, 'worker_init'): + worker_init_fn = create_worker_init_fn(dataset) + + # Auto-adjust num_workers based on dataset type + if hasattr(dataset, 'worker_init') and num_workers == 0: + warnings.warn( + "Using num_workers=0 with lazy file dataset. " + "Consider using num_workers>=1 for better performance." + ) + + return DataLoader( + dataset=dataset, + batch_size=batch_size, + shuffle=shuffle, + num_workers=num_workers, + pin_memory=pin_memory, + drop_last=drop_last, + worker_init_fn=worker_init_fn, + **kwargs + ) + + +def create_train_val_loaders( + train_dataset, + val_dataset, + batch_size: int = 32, + num_workers: int = 4, + pin_memory: bool = True, + **kwargs +): + """Create train and validation DataLoaders with consistent configuration. + + Parameters + ---------- + train_dataset : Dataset + Training dataset. + val_dataset : Dataset + Validation dataset. + batch_size : int, optional + Batch size for both loaders, by default 32. + num_workers : int, optional + Number of workers for both loaders, by default 4. + pin_memory : bool, optional + Whether to pin memory, by default True. + **kwargs + Additional arguments passed to both DataLoaders. + + Returns + ------- + tuple[DataLoader, DataLoader] + Train and validation DataLoaders. + + Examples + -------- + >>> train_ds = JoblibDataset(train_files, subseq_len=128) + >>> val_ds = JoblibDataset(val_files, subseq_len=128) + >>> train_loader, val_loader = create_train_val_loaders( + ... train_ds, val_ds, batch_size=64 + ... ) + """ + train_loader = create_dataloader( + train_dataset, + batch_size=batch_size, + shuffle=True, # Always shuffle training + num_workers=num_workers, + pin_memory=pin_memory, + **kwargs + ) + + val_loader = create_dataloader( + val_dataset, + batch_size=batch_size, + shuffle=False, # Never shuffle validation + num_workers=num_workers, + pin_memory=pin_memory, + drop_last=False, # Don't drop validation samples + **kwargs + ) + + return train_loader, val_loader + + +def create_dataloaders_from_config(config: dict): + """Create DataLoaders from a configuration dictionary. + + This function provides a high-level interface for creating datasets and + DataLoaders from configuration, useful for experiments and hyperparameter + tuning. + + Parameters + ---------- + config : dict + Configuration dictionary containing dataset and loader parameters. + Expected keys: + - 'dataset_type': str ('joblib', 'hdf5', 'numpy') + - 'file_paths': str or list + - 'subseq_len': int + - 'batch_size': int (optional) + - 'num_workers': int (optional) + - Other dataset-specific parameters + + Returns + ------- + DataLoader or tuple[DataLoader, DataLoader] + Single DataLoader or tuple of (train_loader, val_loader) if split is + requested. + + Examples + -------- + >>> config = { + ... 'dataset_type': 'joblib', + ... 'file_paths': '/data/*.joblib', + ... 'subseq_len': 128, + ... 'batch_size': 32, + ... 'split_by_files': True, + ... 'train_ratio': 0.8 + ... } + >>> train_loader, val_loader = create_dataloaders_from_config(config) + """ + # Import here to avoid circular imports + from ..datasets.file_based import JoblibDataset, HDF5Dataset, NumpyDataset + + # Dataset type mapping + dataset_classes = { + 'joblib': JoblibDataset, + 'hdf5': HDF5Dataset, + 'numpy': NumpyDataset + } + + # Extract configuration + dataset_type = config['dataset_type'] + file_paths = config['file_paths'] + subseq_len = config['subseq_len'] + + # DataLoader configuration + batch_size = config.get('batch_size', 32) + num_workers = config.get('num_workers', 4) + + # Dataset-specific configuration + dataset_config = {k: v for k, v in config.items() + if k not in ['dataset_type', 'batch_size', 'num_workers', + 'split_by_files', 'train_ratio', + 'val_ratio', 'test_ratio']} + + # Create dataset + if dataset_type not in dataset_classes: + raise ValueError(f"Unknown dataset type: {dataset_type}") + + dataset_class = dataset_classes[dataset_type] + dataset = dataset_class(file_paths, subseq_len, **dataset_config) + + # Handle splitting + if config.get('split_by_files', False): + train_ratio = config.get('train_ratio', 0.8) + val_ratio = config.get('val_ratio', 0.1) + test_ratio = config.get('test_ratio', 0.1) + + train_ds, val_ds, test_ds = dataset.split_by_files( + train_ratio=train_ratio, + val_ratio=val_ratio, + test_ratio=test_ratio, + random_seed=config.get('random_seed', 42) + ) + + train_loader, val_loader = create_train_val_loaders( + train_ds, val_ds, batch_size=batch_size, num_workers=num_workers + ) + + if test_ratio > 0: + test_loader = create_dataloader( + test_ds, batch_size=batch_size, shuffle=False, + num_workers=num_workers + ) + return train_loader, val_loader, test_loader + else: + return train_loader, val_loader + else: + # Single dataset + return create_dataloader( + dataset, batch_size=batch_size, num_workers=num_workers + ) diff --git a/src/faith/train/models/__init__.py b/src/faith/train/models/__init__.py index f1bffd0..0a814ee 100644 --- a/src/faith/train/models/__init__.py +++ b/src/faith/train/models/__init__.py @@ -13,8 +13,8 @@ Examples -------- Basic usage: ->>> from faith.train.models import BlockBasedAutoencoder, \ -create_block_autoencoder +>>> from faith.train.models import ( +... BlockBasedAutoencoder, create_block_autoencoder) >>> autoencoder = create_block_autoencoder('default', input_channels=80) >>> x = torch.randn(1, 80, 100, 128) >>> reconstructed, latent = autoencoder(x) @@ -44,6 +44,9 @@ get_preset_config, save_model_config, load_model_config, + list_preset_configs, + create_model_from_config_file, + create_autoencoder_from_config, ModelConfig, PRESET_CONFIGS ) @@ -53,14 +56,9 @@ create_mae_model, get_model_info, get_memory_estimate, - validate_input_shape + validate_input_shape, ) -# Package metadata -__version__ = "0.1.0" -__author__ = "Peter Steiner" -__email__ = "peter.steiner@princeton.edu" - # Public API - only these should be imported by users __all__ = [ # Core models @@ -84,9 +82,4 @@ "get_model_info", "get_memory_estimate", "validate_input_shape", - - # Metadata - "__version__", - "__author__", - "__email__", ] diff --git a/src/faith/train/models/autoencoder.py b/src/faith/train/models/autoencoder.py index ad8cea8..810e24f 100644 --- a/src/faith/train/models/autoencoder.py +++ b/src/faith/train/models/autoencoder.py @@ -66,8 +66,8 @@ class BlockBasedAutoencoder(nn.Module): >>> # Custom configuration >>> configs = [ - ... {'out_channels': 64, 'pool_size': (1, 2)}, - ... {'out_channels': 128, 'pool_size': (1, 4)}, + ... {'out_channels': 64, 'pool_size': (1, 2)}, + ... {'out_channels': 128, 'pool_size': (1, 4)}, ... ] >>> autoencoder = BlockBasedAutoencoder( ... input_channels=80, @@ -155,14 +155,19 @@ def __init__( init_method=init_method ) - def _get_default_block_configs(self) -> list[dict[str, Any]]: + def _get_default_block_configs( + self + ) -> list[dict[str, Any]]: """Get default block configuration.""" return [ {'out_channels': 32, 'pool_size': (1, 2)}, {'out_channels': 16, 'pool_size': (1, 2)}, ] - def encode(self, x: torch.Tensor) -> torch.Tensor: + def encode( + self, + x: torch.Tensor + ) -> torch.Tensor: """Encode input to latent representation. Parameters @@ -178,7 +183,10 @@ def encode(self, x: torch.Tensor) -> torch.Tensor: """ return self.encoder(x) - def decode(self, z: torch.Tensor) -> torch.Tensor: + def decode( + self, + z: torch.Tensor + ) -> torch.Tensor: """ Decode latent representation to reconstructed output. @@ -196,7 +204,10 @@ def decode(self, z: torch.Tensor) -> torch.Tensor: """ return self.decoder(z) - def forward(self, inputs: torch.Tensor) -> torch.Tensor: + def forward( + self, + inputs: torch.Tensor + ) -> torch.Tensor: """Forward pass through the complete autoencoder. Parameters @@ -213,8 +224,10 @@ def forward(self, inputs: torch.Tensor) -> torch.Tensor: reconstructed = self.decode(latent) return reconstructed - def latent_with_reconstruction(self, inputs: torch.Tensor) \ - -> tuple[torch.Tensor, torch.Tensor]: + def latent_with_reconstruction( + self, + inputs: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor]: """Forward pass through the complete autoencoder. Parameters @@ -257,7 +270,10 @@ def get_config(self) -> dict[str, Any]: } @classmethod - def from_config(cls, config: dict[str, Any]) -> 'BlockBasedAutoencoder': + def from_config( + cls, + config: dict[str, Any] + ) -> 'BlockBasedAutoencoder': """Create BlockBasedAutoencoder instance from configuration dictionary. Parameters @@ -272,8 +288,10 @@ def from_config(cls, config: dict[str, Any]) -> 'BlockBasedAutoencoder': """ return cls(**config) - def get_output_shape(self, input_shape: tuple[int, ...]) -> tuple[ - int, ...]: + def get_output_shape( + self, + input_shape: tuple[int, ...] + ) -> tuple[int, ...]: """Calculate output shape given input shape. Parameters @@ -291,8 +309,10 @@ def get_output_shape(self, input_shape: tuple[int, ...]) -> tuple[ output_shape = self.decoder.get_output_shape(latent_shape) return output_shape - def get_latent_shape(self, input_shape: tuple[int, ...]) -> tuple[ - int, ...]: + def get_latent_shape( + self, + input_shape: tuple[int, ...] + ) -> tuple[int, ...]: """Calculate latent representation shape given input shape. Parameters @@ -307,8 +327,10 @@ def get_latent_shape(self, input_shape: tuple[int, ...]) -> tuple[ """ return self.encoder.get_output_shape(input_shape) - def get_feature_maps(self, inputs: torch.Tensor) -> dict[ - str, list[torch.Tensor]]: + def get_feature_maps( + self, + inputs: torch.Tensor + ) -> dict[str, list[torch.Tensor]]: """Get intermediate feature maps from encoder and decoder. Useful for visualization and debugging. @@ -374,79 +396,11 @@ def unfreeze_all(self) -> None: def __repr__(self) -> str: """String representation of the BlockBasedAutoencoder.""" - return (f"BlockBasedAutoencoder(" - f"input_channels={self.input_channels}, " - f"encoder_blocks={len(self.encoder.blocks)}, " - f"decoder_blocks={len(self.decoder.blocks)}, " - f"bottleneck_channels={self.encoder.bottleneck_channels}, " - f"parameters={self.parameter_count:,})") - - -# Example usage and testing -if __name__ == "__main__": - # Test basic functionality - print("Testing BlockBasedAutoencoder...") - - # Create autoencoder with default config - autoencoder = BlockBasedAutoencoder(input_channels=80) - - # Test forward pass - x = torch.randn(2, 80, 100, 128) - reconstructed = autoencoder(x) - latent = autoencoder.encode(x) - - print(f"Input shape: {x.shape}") - print(f"Latent shape: {latent.shape}") - print(f"Reconstructed shape: {reconstructed.shape}") - print(f"Autoencoder: {autoencoder}") - - # Test individual methods - latent_only = autoencoder.get_latent_representation(x) - reconstructed_only = autoencoder.reconstruct(x) - - print(f"Latent only shape: {latent_only.shape}") - print(f"Reconstructed only shape: {reconstructed_only.shape}") - - # Test configuration serialization - config = autoencoder.get_config() - print(f"Config keys: {list(config.keys())}") - - new_autoencoder = BlockBasedAutoencoder.from_config(config) - print(f"Recreated autoencoder: {new_autoencoder}") - - # Test shape calculation - output_shape = autoencoder.get_output_shape((1, 80, 100, 128)) - latent_shape = autoencoder.get_latent_shape((1, 80, 100, 128)) - print(f"Calculated output shape: {output_shape}") - print(f"Calculated latent shape: {latent_shape}") - - # Test parameter counting - print(f"Total parameters: {autoencoder.parameter_count:,}") - print(f"Encoder parameters: {autoencoder.encoder_parameter_count:,}") - print(f"Decoder parameters: {autoencoder.decoder_parameter_count:,}") - - # Test feature map extraction - feature_maps = autoencoder.get_feature_maps(x) - print(f"Encoder feature maps: {len(feature_maps['encoder'])}") - print(f"Decoder feature maps: {len(feature_maps['decoder'])}") - - # Test custom configuration - custom_configs = [ - {'out_channels': 64, 'pool_size': (1, 2), 'dropout': 0.2}, - {'out_channels': 128, 'pool_size': (1, 4), 'dropout': 0.3}, - ] - - custom_autoencoder = BlockBasedAutoencoder( - input_channels=80, - block_configs=custom_configs, - activation='gelu' - ) - - x_custom = torch.randn(1, 80, 100, 128) - reconstructed_custom, latent_custom = custom_autoencoder(x_custom) - - print(f"\nCustom autoencoder:") - print(f"Input shape: {x_custom.shape}") - print(f"Latent shape: {latent_custom.shape}") - print(f"Reconstructed shape: {reconstructed_custom.shape}") - print(f"Custom autoencoder: {custom_autoencoder}") + return ( + f"BlockBasedAutoencoder(" + f"input_channels={self.input_channels}, " + f"encoder_blocks={len(self.encoder.blocks)}, " + f"decoder_blocks={len(self.decoder.blocks)}, " + f"bottleneck_channels={self.encoder.bottleneck_channels}, " + f"parameters={self.parameter_count:,})" + ) diff --git a/src/faith/train/models/configs.py b/src/faith/train/models/configs.py index 4e9adba..704051a 100644 --- a/src/faith/train/models/configs.py +++ b/src/faith/train/models/configs.py @@ -581,54 +581,3 @@ def create_model_from_config_file(filepath: Union[str, Path]) -> Union[ return create_mae_from_config(config) else: raise ValueError(f"Unknown model_type: {config.model_type}") - - -# Example usage and testing -if __name__ == "__main__": - # Test preset configurations - print("Available presets:", list_preset_configs()) - - # Test creating models from presets - for preset_name in ['default', 'light', 'mae_default']: - print(f"\nTesting {preset_name} preset:") - - try: - model = create_block_autoencoder(preset_name, input_channels=80) - print(f"Created model: {type(model).__name__}") - - if hasattr(model, 'parameter_count'): - print(f"Parameters: {model.parameter_count:,}") - - except Exception as e: - print(f"Error creating {preset_name}: {e}") - - # Test configuration serialization - print("\nTesting configuration serialization:") - - config = get_preset_config('default') - config = config.update(input_channels=80, hidden_dim=16) - - # Save and load - config.save('test_config.yaml') - loaded_config = ModelConfig.load('test_config.yaml') - - print(f"Original: {config.input_channels}, {config.hidden_dim}") - print( - f"Loaded: {loaded_config.input_channels}, {loaded_config.hidden_dim}") - - # Test model config saving - autoencoder = create_autoencoder_from_config(config) - save_model_config(autoencoder, 'model_config.yaml') - - # Load and recreate model - recreated_model = create_model_from_config_file('model_config.yaml') - print(f"Recreated model: {type(recreated_model).__name__}") - - # Cleanup - import os - - - os.remove('test_config.yaml') - os.remove('model_config.yaml') - - print("Configuration tests completed successfully!") diff --git a/src/faith/train/models/utils.py b/src/faith/train/models/utils.py index 7677232..4e88958 100644 --- a/src/faith/train/models/utils.py +++ b/src/faith/train/models/utils.py @@ -7,8 +7,8 @@ import torch from typing import Union, Optional, Any -from torch_training.models.autoencoder import BlockBasedAutoencoder -from torch_training.models.mae import MaskedAutoencoder, MASK_TYPES +from .autoencoder import BlockBasedAutoencoder +from .mae import MaskedAutoencoder, MASK_TYPES from . import create_block_autoencoder, PRESET_CONFIGS @@ -377,7 +377,7 @@ def analyze_model_architecture( output_shape = base_model.get_output_shape(input_shape) compression_ratio = (input_shape[2] * input_shape[3]) / ( latent_shape[2] * latent_shape[3]) - except Exception as e: + except Exception: latent_shape = None output_shape = None compression_ratio = None @@ -503,7 +503,7 @@ def print_model_summary( print(f"Mask Ratio: {analysis['mask_info']['mask_ratio']:.2f}") print(f"Patch Size: {analysis['mask_info']['patch_size']}") - print(f"\nArchitecture:") + print("\nArchitecture:") print(f" Input Channels: {analysis['architecture']['input_channels']}") print(f" Bottleneck Channels: " f"{analysis['architecture']['bottleneck_channels']}") @@ -516,7 +516,7 @@ def print_model_summary( print(f" Compression Ratio: " f"{analysis['shapes']['compression_ratio']:.1f}x") - print(f"\nShapes:") + print("\nShapes:") print(f" Input: {analysis['shapes']['input']}") print(f" Latent: {analysis['shapes']['latent']}") print(f" Output: {analysis['shapes']['output']}") @@ -542,19 +542,19 @@ def print_model_summary( # Test validation valid_shape = (32, 80, 100, 128) invalid_shape = (80, 100, 128) - print(f"\nShape validation:") + print("\nShape validation:") print(f" {valid_shape}: {validate_input_shape(valid_shape)}") print(f" {invalid_shape}: {validate_input_shape(invalid_shape)}") # Test memory estimation memory = get_memory_estimate(mae, (80, 100, 128), batch_size=32) - print(f"\nMemory estimate for MAE:") + print("\nMemory estimate for MAE:") print(f" Total: {memory['total_mb']:.1f} MB") print(f" Parameters: {memory['parameters_mb']:.1f} MB") print(f" Activations: {memory['activations_mb']:.1f} MB") # Test architecture analysis - print(f"\nModel summary:") + print("\nModel summary:") print_model_summary(mae, (1, 80, 100, 128)) # Test model comparison @@ -563,7 +563,7 @@ def print_model_summary( 'autoencoder': autoencoder } comparison = compare_models(models) - print(f"\nModel comparison:") + print("\nModel comparison:") for name, params in comparison['summary']['parameter_counts'].items(): memory = comparison['summary']['memory_usage'][name] print(f" {name}: {params:,} params, {memory:.1f} MB") diff --git a/src/faith/train/training/__init__.py b/src/faith/train/training/__init__.py new file mode 100644 index 0000000..2d36c4c --- /dev/null +++ b/src/faith/train/training/__init__.py @@ -0,0 +1,13 @@ +"""Training module for autoencoder models using PyTorch Lightning.""" + +from .lightning_trainer import ( + LightningTrainer, + MultimodalLightningTrainer, + train_model +) + +__all__ = [ + 'LightningTrainer', + 'MultimodalLightningTrainer', + 'train_model' +] \ No newline at end of file diff --git a/src/faith/train/training/lightning_trainer.py b/src/faith/train/training/lightning_trainer.py new file mode 100644 index 0000000..025fc26 --- /dev/null +++ b/src/faith/train/training/lightning_trainer.py @@ -0,0 +1,538 @@ +"""PyTorch Lightning trainer for autoencoder models.""" + +import warnings +from typing import Any, Callable, Optional + +import pytorch_lightning as pl +import torch +import torch.nn.functional as F +from torch.optim import AdamW +from torch.optim.lr_scheduler import CosineAnnealingLR, LinearLR, SequentialLR + + +class LightningTrainer(pl.LightningModule): + """PyTorch Lightning wrapper for autoencoder models. + + This class provides a simple interface to train any autoencoder model + from your models/ directory with built-in best practices. + """ + + def __init__( + self, + model: torch.nn.Module, + learning_rate: float = 1e-4, + weight_decay: float = 1e-5, + warmup_epochs: int = 5, + max_epochs: int = 100, + loss_fn: Optional[Callable] = None, + scheduler_type: str = "cosine", + compile_model: bool = False, + **kwargs + ) -> None: + """Initialize the Lightning trainer. + + Parameters + ---------- + model : torch.nn.Module + The autoencoder model to train. + learning_rate : float, optional + Learning rate for optimizer, by default 1e-4. + weight_decay : float, optional + Weight decay for regularization, by default 1e-5. + warmup_epochs : int, optional + Number of warmup epochs, by default 5. + max_epochs : int, optional + Total training epochs (needed for scheduler), by default 100. + loss_fn : Optional[Callable], optional + Custom loss function (defaults to MSE), by default None. + scheduler_type : str, optional + Type of LR scheduler ('cosine', 'linear', 'none'), + by default "cosine". + compile_model : bool, optional + Whether to compile model with torch.compile (PyTorch 2.0+), + by default False. + **kwargs + Additional hyperparameters saved to hparams. + """ + super().__init__() + + # Validate scheduler configuration + self._validate_scheduler_config( + warmup_epochs, max_epochs, scheduler_type) + + # Save hyperparameters + self.save_hyperparameters(ignore=['model', 'loss_fn']) + + # Model setup + self.model = model + if compile_model: + try: + self.model = torch.compile(model) + except Exception as e: + warnings.warn(f"Model compilation failed: {e}") + + # Loss function + self.loss_fn = loss_fn or F.mse_loss + + # Store training config + self.learning_rate = learning_rate + self.weight_decay = weight_decay + self.warmup_epochs = warmup_epochs + self.max_epochs = max_epochs + self.scheduler_type = scheduler_type + + def _validate_scheduler_config( + self, + warmup_epochs: int, + max_epochs: int, + scheduler_type: str + ) -> None: + """Validate scheduler configuration to prevent runtime errors. + + Parameters + ---------- + warmup_epochs : int + Number of warmup epochs. + max_epochs : int + Total training epochs. + scheduler_type : str + Scheduler type. + + Raises + ------ + ValueError + If configuration would cause scheduler errors. + """ + if scheduler_type in ["cosine", "linear"]: + if max_epochs <= 0: + raise ValueError(f"max_epochs must be > 0, got {max_epochs}") + + if scheduler_type == "cosine" and warmup_epochs >= max_epochs: + warnings.warn( + f"warmup_epochs ({warmup_epochs}) >= max_epochs " + f"({max_epochs}). Setting warmup_epochs to " + f"{max(0, max_epochs - 1)}" + ) + # Don't modify the values here, just warn + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass through the model. + + Parameters + ---------- + x : torch.Tensor + Input tensor. + + Returns + ------- + torch.Tensor + Model output. + """ + return self.model(x) + + def compute_loss( + self, + batch: Any, + batch_idx: int, + prefix: str = "" + ) -> dict[str, torch.Tensor]: + """Compute loss for a batch. + + Override this method for custom loss computation. + + Parameters + ---------- + batch : Any + Input batch (can be tensor or dict). + batch_idx : int + Batch index. + prefix : str, optional + Prefix for logging (e.g., "train_", "val_"), by default "". + + Returns + ------- + Dict[str, torch.Tensor] + Dictionary with 'loss' key and optional additional metrics. + """ + # Handle different batch formats + if isinstance(batch, dict): + # Assume input/target are in the batch dict + inputs = batch.get('input', batch.get('x', batch.get('data'))) + targets = batch.get('target', batch.get('y', inputs)) + elif isinstance(batch, (list, tuple)) and len(batch) == 2: + inputs, targets = batch + else: + # Single tensor - autoencoder case where input = target + inputs = targets = batch + + # Forward pass + outputs = self.model(inputs) + + # Handle model outputs (could be tensor or dict) + if isinstance(outputs, dict): + reconstructed = outputs.get( + 'reconstructed', + outputs.get('output', outputs.get('x_hat')) + ) + # Extract additional outputs for logging + additional_losses = { + k: v for k, v in outputs.items() + if k.endswith('_loss') and isinstance(v, torch.Tensor) + } + else: + reconstructed = outputs + additional_losses = {} + + # Main reconstruction loss + recon_loss = self.loss_fn(reconstructed, targets) + + # Total loss (reconstruction + any additional losses) + total_loss = recon_loss + for loss_name, loss_value in additional_losses.items(): + total_loss += loss_value + + # Prepare metrics for logging + metrics = { + f'{prefix}loss': total_loss, + f'{prefix}recon_loss': recon_loss, + } + + # Add additional losses to metrics + for loss_name, loss_value in additional_losses.items(): + metrics[f'{prefix}{loss_name}'] = loss_value + + return metrics + + def training_step(self, batch: Any, batch_idx: int) -> torch.Tensor: + """Training step. + + Parameters + ---------- + batch : Any + Training batch. + batch_idx : int + Batch index. + + Returns + ------- + torch.Tensor + Training loss. + """ + metrics = self.compute_loss(batch, batch_idx, prefix="train_") + + # Log metrics + self.log_dict(metrics, on_step=True, on_epoch=True, prog_bar=True) + + return metrics['train_loss'] + + def validation_step(self, batch: Any, batch_idx: int) -> torch.Tensor: + """Validation step. + + Parameters + ---------- + batch : Any + Validation batch. + batch_idx : int + Batch index. + + Returns + ------- + torch.Tensor + Validation loss. + """ + metrics = self.compute_loss(batch, batch_idx, prefix="val_") + + # Log metrics + self.log_dict(metrics, on_step=False, on_epoch=True, prog_bar=True) + + return metrics['val_loss'] + + def test_step(self, batch: Any, batch_idx: int) -> torch.Tensor: + """Test step. + + Parameters + ---------- + batch : Any + Test batch. + batch_idx : int + Batch index. + + Returns + ------- + torch.Tensor + Test loss. + """ + metrics = self.compute_loss(batch, batch_idx, prefix="test_") + + # Log metrics + self.log_dict(metrics, on_step=False, on_epoch=True) + + return metrics['test_loss'] + + def configure_optimizers(self) -> dict[str, Any]: + """Configure optimizer and learning rate scheduler. + + Returns + ------- + Dict[str, Any] + Dictionary containing optimizer and scheduler configuration. + """ + # Optimizer + optimizer = AdamW( + self.model.parameters(), + lr=self.learning_rate, + weight_decay=self.weight_decay + ) + + if self.scheduler_type == "none": + return optimizer + + # Learning rate scheduler + if self.scheduler_type == "cosine": + # Ensure T_max is always > 0 + cosine_epochs = max(1, self.max_epochs - self.warmup_epochs) + + if self.warmup_epochs > 0: + # Cosine annealing with warmup + warmup_scheduler = LinearLR( + optimizer, + start_factor=0.1, + end_factor=1.0, + total_iters=self.warmup_epochs + ) + cosine_scheduler = CosineAnnealingLR( + optimizer, + T_max=cosine_epochs + ) + scheduler = SequentialLR( + optimizer, + schedulers=[warmup_scheduler, cosine_scheduler], + milestones=[self.warmup_epochs] + ) + else: + # Just cosine annealing without warmup + scheduler = CosineAnnealingLR( + optimizer, + T_max=max(1, self.max_epochs) # Ensure T_max > 0 + ) + elif self.scheduler_type == "linear": + scheduler = LinearLR( + optimizer, + start_factor=1.0, + end_factor=0.1, + total_iters=max(1, self.max_epochs) # Ensure total_iters > 0 + ) + else: + raise ValueError(f"Unknown scheduler type: {self.scheduler_type}") + + return { + "optimizer": optimizer, + "lr_scheduler": { + "scheduler": scheduler, + "interval": "epoch", + "frequency": 1, + }, + } + + def on_train_epoch_end(self) -> None: + """Called at the end of each training epoch.""" + # Log learning rate + current_lr = self.optimizers().param_groups[0]['lr'] + self.log('learning_rate', current_lr, on_epoch=True) + + +class MultimodalLightningTrainer(LightningTrainer): + """Extended trainer for multimodal models with multiple loss components.""" + + def __init__( + self, + model: torch.nn.Module, + loss_weights: Optional[dict[str, float]] = None, + **kwargs + ) -> None: + """Initialize multimodal trainer. + + Parameters + ---------- + model : torch.nn.Module + The multimodal model to train. + loss_weights : Optional[Dict[str, float]], optional + Dictionary of loss component weights, by default None. + **kwargs + Arguments passed to parent class. + """ + super().__init__(model, **kwargs) + self.loss_weights = loss_weights or {} + + def compute_loss( + self, + batch: Any, + batch_idx: int, + prefix: str = "" + ) -> dict[str, torch.Tensor]: + """Compute multimodal loss with weighted components. + + Parameters + ---------- + batch : Any + Input batch. + batch_idx : int + Batch index. + prefix : str, optional + Prefix for logging, by default "". + + Returns + ------- + Dict[str, torch.Tensor] + Dictionary containing loss components and total loss. + """ + # Get model outputs + outputs = self.model(batch) + + if not isinstance(outputs, dict): + # Fallback to parent implementation + return super().compute_loss(batch, batch_idx, prefix) + + # Extract losses from model outputs + total_loss = 0 + metrics = {} + + for key, value in outputs.items(): + if key.endswith('_loss') and isinstance(value, torch.Tensor): + weight = self.loss_weights.get(key, 1.0) + weighted_loss = weight * value + total_loss += weighted_loss + + # Log both weighted and unweighted losses + metrics[f'{prefix}{key}'] = value + metrics[f'{prefix}weighted_{key}'] = weighted_loss + + metrics[f'{prefix}loss'] = total_loss + return metrics + + +def train_model( + model: torch.nn.Module, + train_dataloader, + val_dataloader=None, + max_epochs: int = 100, + gpus: int = 1, + precision: str = "16-mixed", + logger_type: str = "tensorboard", + project_name: str = "autoencoder-training", + experiment_name: Optional[str] = None, + log_dir: str = "./logs", + **trainer_kwargs +): + """Convenience function to train a model with sensible defaults. + + Parameters + ---------- + model : torch.nn.Module + Model to train. + train_dataloader + Training data loader. + val_dataloader, optional + Validation data loader, by default None. + max_epochs : int, optional + Number of epochs, by default 100. + gpus : int, optional + Number of GPUs to use, by default 1. + precision : str, optional + Training precision ("32", "16-mixed", "bf16-mixed"), + by default "16-mixed". + logger_type : str, optional + Logger type ("tensorboard", "csv", "none"), + by default "tensorboard". + project_name : str, optional + Project name for logging, by default "autoencoder-training". + experiment_name : Optional[str], optional + Experiment name, by default None. + log_dir : str, optional + Directory for logs, by default "./logs". + **trainer_kwargs + Additional arguments for LightningTrainer. + + Returns + ------- + tuple + Tuple containing (lightning_model, trainer). + """ + from pytorch_lightning import Trainer + from pytorch_lightning.callbacks import ( + EarlyStopping, + LearningRateMonitor, + ModelCheckpoint + ) + + # Create Lightning module + lightning_model = LightningTrainer( + model=model, + max_epochs=max_epochs, + **trainer_kwargs + ) + + # Callbacks + callbacks = [ + ModelCheckpoint( + monitor='val_loss' if val_dataloader else 'train_loss', + mode='min', + save_top_k=1, + filename=( + 'best-{epoch}-{val_loss:.4f}' if val_dataloader + else 'best-{epoch}-{train_loss:.4f}' + ) + ), + LearningRateMonitor(logging_interval='epoch'), + ] + + if val_dataloader: + callbacks.append( + EarlyStopping(monitor='val_loss', patience=10, mode='min') + ) + + # Configure logger + logger = None + if logger_type == "tensorboard": + try: + from pytorch_lightning.loggers import TensorBoardLogger + logger = TensorBoardLogger( + save_dir=log_dir, + name=project_name, + version=experiment_name + ) + except ImportError: + warnings.warn("TensorBoard not available. " + "Install with: pip install tensorboard") + elif logger_type == "csv": + try: + from pytorch_lightning.loggers import CSVLogger + logger = CSVLogger( + save_dir=log_dir, + name=project_name, + version=experiment_name + ) + except ImportError: + warnings.warn("CSV logger not available.") + elif logger_type == "none": + logger = False + else: + warnings.warn(f"Unknown logger type: {logger_type}. Using no logger.") + logger = False + + # Trainer + trainer = Trainer( + max_epochs=max_epochs, + accelerator='gpu' if gpus > 0 else 'cpu', + devices=gpus if gpus > 0 else 1, # Use 1 CPU core when no GPU + precision=precision, + callbacks=callbacks, + logger=logger, + enable_progress_bar=True, + log_every_n_steps=50, + ) + + # Train + trainer.fit(lightning_model, train_dataloader, val_dataloader) + + return lightning_model, trainer diff --git a/src/faith/train/tuning/__init__.py b/src/faith/train/tuning/__init__.py new file mode 100644 index 0000000..9673ddb --- /dev/null +++ b/src/faith/train/tuning/__init__.py @@ -0,0 +1,33 @@ +"""Hyperparameter tuning module using Ray Tune.""" + +import warnings + +try: + from .ray_tuner import ( + RayTuner, + RayTuneReportCallback, + create_basic_search_space, + suggest_scheduler_config, + cleanup_ray + ) + from .search_spaces import (SearchSpaces, CustomSearchSpace, + get_search_space) + + + __all__ = [ + 'RayTuner', + 'RayTuneReportCallback', + 'SearchSpaces', + 'CustomSearchSpace', + 'get_search_space', + 'create_basic_search_space', + 'suggest_scheduler_config', + 'cleanup_ray' + ] + +except ImportError: + warnings.warn("Ray Tune not available. Hyperparameter tuning " + "functionality disabled. " + "Install with: pip install ray[tune] optuna hyperopt") + + __all__ = [] diff --git a/src/faith/train/tuning/ray_tuner.py b/src/faith/train/tuning/ray_tuner.py new file mode 100644 index 0000000..e9606e5 --- /dev/null +++ b/src/faith/train/tuning/ray_tuner.py @@ -0,0 +1,666 @@ +"""Ray Tune integration for hyperparameter optimization.""" + +"""Ray Tune integration for hyperparameter optimization.""" + +import warnings +from typing import Any, Optional +import tempfile +import os +from pathlib import Path + +try: + import ray + from ray import tune + from ray.tune import CLIReporter + from ray.tune.schedulers import ASHAScheduler, PopulationBasedTraining + from ray.tune.search.optuna import OptunaSearch + from ray.tune.search.hyperopt import HyperOptSearch + + + RAY_AVAILABLE = True +except ImportError: + RAY_AVAILABLE = False + warnings.warn("Ray Tune not available. " + "Install with: pip install ray[tune] optuna hyperopt") + +import torch +import pytorch_lightning as pl +from pytorch_lightning import Trainer +from pytorch_lightning.callbacks import ModelCheckpoint +from pytorch_lightning.loggers import TensorBoardLogger + +from ..training import LightningTrainer + + +def _resolve_path(path: str) -> str: + """Resolve path to absolute path for Ray Tune compatibility. + + Parameters + ---------- + path : str + Input path (relative or absolute). + + Returns + ------- + str + Absolute path. + """ + return str(Path(path).resolve()) + + +def _safe_ray_init(**kwargs): + """Safely initialize Ray with error handling. + + Parameters + ---------- + **kwargs + Arguments passed to ray.init(). + """ + if not RAY_AVAILABLE: + raise ImportError("Ray not available") + + if ray.is_initialized(): + return + + try: + # Default Ray init arguments for stability + default_args = { + 'ignore_reinit_error': True, + 'include_dashboard': False, # Disable dashboard for stability + 'configure_logging': False, # Avoid logging conflicts + } + default_args.update(kwargs) + + ray.init(**default_args) + except Exception as e: + warnings.warn( + f"Ray initialization failed: {e}. Some features may not work.") + + +def cleanup_ray(): + """Safely shutdown Ray.""" + if RAY_AVAILABLE and ray.is_initialized(): + try: + ray.shutdown() + except Exception as e: + warnings.warn(f"Ray shutdown failed: {e}") + + +class RayTuner: + """Ray Tune integration for hyperparameter optimization. + + This class provides a simple interface to tune hyperparameters of your + autoencoder models using Ray Tune's optimization algorithms. + """ + + def __init__( + self, + model_class: type, + model_base_config: dict[str, Any], + data_loaders: dict[str, Any], + num_samples: int = 20, + max_epochs_per_trial: int = 10, + gpus_per_trial: float = 0.25, + cpus_per_trial: int = 1, + scheduler_type: str = "asha", + search_algorithm: str = "optuna", + metric: str = "val_loss", + mode: str = "min", + storage_path: Optional[str] = None + ) -> None: + """Initialize Ray Tuner. + + Parameters + ---------- + model_class : type + Model class to instantiate (e.g., BlockBasedAutoencoder). + model_base_config : Dict[str, Any] + Base configuration for model instantiation. + data_loaders : Dict[str, Any] + Dictionary containing 'train' and optionally 'val' dataloaders. + num_samples : int, optional + Number of hyperparameter configurations to try, by default 20. + max_epochs_per_trial : int, optional + Maximum epochs per trial, by default 10. + gpus_per_trial : float, optional + GPU fraction per trial, by default 0.25. + cpus_per_trial : int, optional + CPU cores per trial, by default 1. + scheduler_type : str, optional + Scheduler type ("asha", "pbt", "fifo"), by default "asha". + search_algorithm : str, optional + Search algorithm ("optuna", "hyperopt", "random"), + by default "optuna". + metric : str, optional + Metric to optimize, by default "val_loss". + mode : str, optional + Optimization mode ("min" or "max"), by default "min". + storage_path : Optional[str], optional + Directory for Ray Tune results, + by default None (uses current dir + ray_results). + """ + if not RAY_AVAILABLE: + raise ImportError( + "Ray Tune is required for hyperparameter tuning. " + "Install with: pip install ray[tune] optuna hyperopt" + ) + + self.model_class = model_class + self.model_base_config = model_base_config + self.data_loaders = data_loaders + self.num_samples = num_samples + self.max_epochs_per_trial = max_epochs_per_trial + self.gpus_per_trial = gpus_per_trial + self.cpus_per_trial = cpus_per_trial + self.scheduler_type = scheduler_type + self.search_algorithm = search_algorithm + self.metric = metric + self.mode = mode + + # Resolve storage_path to absolute path + if storage_path is None: + storage_path = os.path.join(os.getcwd(), "ray_results") + self.storage_path = _resolve_path(storage_path) + + # Ensure directory exists + os.makedirs(self.storage_path, exist_ok=True) + + # Initialize Ray if not already done + _safe_ray_init() + + def _validate_scheduler_params(self) -> None: + """Validate and adjust scheduler parameters for consistency. + + Raises + ------ + ValueError + If configuration is invalid and cannot be automatically fixed. + """ + if self.scheduler_type == "asha": + if self.max_epochs_per_trial < 2: + raise ValueError( + f"ASHA scheduler requires max_epochs_per_trial >= 2, " + f"got {self.max_epochs_per_trial}. " + f"Use scheduler_type='fifo' for single epoch trials." + ) + elif self.scheduler_type == "pbt": + if self.max_epochs_per_trial < 3: + warnings.warn( + f"PBT scheduler works best with max_epochs_per_trial >= 3" + f", got {self.max_epochs_per_trial}. " + f"Consider using 'asha' or 'fifo'." + ) + if self.num_samples < 4: + warnings.warn( + f"PBT scheduler works best with num_samples >= 4, " + f"got {self.num_samples}. Consider using 'asha' scheduler." + ) + + def _create_scheduler(self): + """Create scheduler based on configuration. + + Returns + ------- + ray.tune.schedulers.TrialScheduler + Configured scheduler. + """ + # Validate parameters first + self._validate_scheduler_params() + + if self.scheduler_type == "asha": + # Ensure grace_period is not greater than max_t + grace_period = min(3, max(1, self.max_epochs_per_trial // 2)) + max_t = self.max_epochs_per_trial + + return ASHAScheduler( + metric=self.metric, + mode=self.mode, + max_t=max_t, + grace_period=grace_period, + reduction_factor=2 + ) + elif self.scheduler_type == "pbt": + # For PBT, ensure perturbation_interval is reasonable + perturbation_interval = min(2, + max(1, self.max_epochs_per_trial // 3)) + + return PopulationBasedTraining( + time_attr="training_iteration", + metric=self.metric, + mode=self.mode, + perturbation_interval=perturbation_interval, + hyperparam_mutations={ + "learning_rate": lambda: tune.loguniform(1e-5, + 1e-2).sample(), + "weight_decay": lambda: tune.loguniform(1e-6, + 1e-3).sample(), + } + ) + elif self.scheduler_type == "fifo": + return None # FIFO is default + else: + raise ValueError(f"Unknown scheduler type: {self.scheduler_type}") + + def _has_search_space(self, config: dict[str, Any]) -> bool: + """Check if config contains actual search space or just fixed values. + + Parameters + ---------- + config : Dict[str, Any] + Configuration to check. + + Returns + ------- + bool + True if config contains search space objects, + False if all fixed values. + """ + if not RAY_AVAILABLE: + return False + + for value in config.values(): + # Check if any value is a Ray Tune search space object + if hasattr(value, 'sample') or str(type(value)).startswith( + ' None: + """Training function for Ray Tune trials. + + Parameters + ---------- + config : Dict[str, Any] + Hyperparameter configuration for this trial. + """ + # Merge base config with trial config + model_config = {**self.model_base_config} + + # Extract model parameters + model_params = {} + training_params = {} + + for key, value in config.items(): + if key in ['learning_rate', 'weight_decay', 'batch_size', + 'scheduler_type', 'warmup_epochs']: + training_params[key] = value + else: + model_params[key] = value + + # Update model config + model_config.update(model_params) + + # Create model + model = self.model_class(**model_config) + + # Create Lightning trainer + lightning_model = LightningTrainer( + model=model, + max_epochs=self.max_epochs_per_trial, + **training_params + ) + + # Create temporary directory for this trial + with tempfile.TemporaryDirectory() as temp_dir: + # Setup Lightning trainer + trainer = Trainer( + max_epochs=self.max_epochs_per_trial, + accelerator='gpu' if self.gpus_per_trial > 0 else 'cpu', + devices=1, # Always use 1 device (GPU or CPU) + precision="16-mixed" if self.gpus_per_trial > 0 else "32", + default_root_dir=temp_dir, + enable_progress_bar=False, + logger=False, + enable_checkpointing=False, + callbacks=[ + RayTuneReportCallback( + metrics={ + "loss": "train_loss", + "val_loss": "val_loss", + "epoch": "epoch" + }, + on="validation_end" + ) + ] + ) + + # Train + trainer.fit( + lightning_model, + self.data_loaders['train'], + self.data_loaders.get('val', None) + ) + + def tune( + self, + search_space: dict[str, Any], + name: str = "autoencoder_tune", + resume: bool = False + ) -> Any: + """Run hyperparameter tuning. + + Parameters + ---------- + search_space : Dict[str, Any] + Search space configuration using Ray Tune syntax. + name : str, optional + Name for this tuning experiment, by default "autoencoder_tune". + resume : bool, optional + Whether to resume from previous run, by default False. + + Returns + ------- + ray.tune.ExperimentAnalysis + Results of the tuning experiment. + """ + # Check if we have a real search space + has_search_space = self._has_search_space(search_space) + + # Warn if using advanced search algorithm with fixed values + if not has_search_space and self.search_algorithm in ["optuna", + "hyperopt"]: + warnings.warn(f"Using {self.search_algorithm} with fixed values. " + f"Consider using search_algorithm='random' " + f"for fixed configurations." + ) + + # Create scheduler and search algorithm + scheduler = self._create_scheduler() + search_alg = self._create_search_algorithm(search_space) + + # Configure reporter + reporter = CLIReporter( + parameter_columns=list(search_space.keys())[:4], + # Show first 4 params + metric_columns=[self.metric, "training_iteration"] + ) + + # Run tuning + analysis = tune.run( + self._training_function, + config=search_space, + num_samples=self.num_samples, + scheduler=scheduler, + search_alg=search_alg if self.scheduler_type != 'pbt' else None, + progress_reporter=reporter, + name=name, + storage_path=self.storage_path, + resources_per_trial={ + "cpu": self.cpus_per_trial, + "gpu": self.gpus_per_trial + }, + resume=resume, + raise_on_failed_trial=False + ) + + return analysis + + def get_best_config(self, analysis: Any) -> dict[str, Any]: + """Get best configuration from tuning results. + + Parameters + ---------- + analysis : ray.tune.ExperimentAnalysis + Results from tune.run(). + + Returns + ------- + Dict[str, Any] + Best hyperparameter configuration. + """ + best_trial = analysis.get_best_trial(self.metric, self.mode) + return best_trial.config + + def train_best_model( + self, + analysis: Any, + max_epochs: int = 100, + save_path: Optional[str] = None + ) -> LightningTrainer: + """Train final model with best hyperparameters. + + Parameters + ---------- + analysis : ray.tune.ExperimentAnalysis + Results from tune.run(). + max_epochs : int, optional + Number of epochs for final training, by default 100. + save_path : Optional[str], optional + Path to save final model, by default None. + + Returns + ------- + LightningTrainer + Trained Lightning model with best configuration. + """ + best_config = self.get_best_config(analysis) + + # Separate model and training parameters + model_config = {**self.model_base_config} + training_params = {} + + for key, value in best_config.items(): + if key in ['learning_rate', 'weight_decay', 'batch_size', + 'scheduler_type', 'warmup_epochs']: + training_params[key] = value + else: + model_config[key] = value + + # Create final model + model = self.model_class(**model_config) + + # Create Lightning trainer + lightning_model = LightningTrainer( + model=model, + max_epochs=max_epochs, + **training_params + ) + + # Setup final trainer + callbacks = [ModelCheckpoint(monitor=self.metric, save_top_k=1)] + + trainer = Trainer( + max_epochs=max_epochs, + accelerator='gpu' if torch.cuda.is_available() else 'cpu', + devices=1 if torch.cuda.is_available() else None, + precision="16-mixed" if torch.cuda.is_available() else "32", + callbacks=callbacks, + logger=TensorBoardLogger("./final_training_logs", + name="best_model") + ) + + # Train final model + trainer.fit( + lightning_model, + self.data_loaders['train'], + self.data_loaders.get('val', None) + ) + + # Save model if requested + if save_path: + torch.save(model.state_dict(), save_path) + print(f"Best model saved to: {save_path}") + + return lightning_model + + +class RayTuneReportCallback(pl.Callback): + """Callback to report metrics to Ray Tune.""" + + def __init__( + self, + metrics: dict[str, str] = None, + on: str = "validation_end" + ) -> None: + """Initialize callback. + + Parameters + ---------- + metrics : Dict[str, str], optional + Mapping from Ray Tune metric names to Lightning metric names. + If None, uses default mapping. + on : str, optional + When to report ("validation_end" or "epoch_end"), + by default "validation_end". + """ + if metrics is None: + # Default metrics mapping + metrics = { + "loss": "train_loss", + "val_loss": "val_loss", + "epoch": "epoch" + } + + self.metrics = metrics + self.on = on + + def on_validation_end(self, trainer, pl_module): + """Report metrics after validation.""" + if self.on == "validation_end": + self._report_metrics(trainer, pl_module) + + def on_train_epoch_end(self, trainer, pl_module): + """Report metrics after training epoch.""" + if self.on == "epoch_end": + self._report_metrics(trainer, pl_module) + + def _report_metrics(self, trainer, pl_module): + """Report metrics to Ray Tune.""" + # Import here to avoid issues if Ray not available + try: + from ray import train + except ImportError: + return + + metrics_to_report = {} + + # Get logged metrics from trainer + logged_metrics = trainer.logged_metrics + callback_metrics = trainer.callback_metrics + + # Combine both metric sources + all_metrics = {**logged_metrics, **callback_metrics} + + for tune_name, lightning_name in self.metrics.items(): + if lightning_name in all_metrics: + value = all_metrics[lightning_name] + if isinstance(value, torch.Tensor): + value = float(value.detach().cpu()) + metrics_to_report[tune_name] = value + + # Always include epoch information + metrics_to_report["training_iteration"] = trainer.current_epoch + + # Report to Ray Tune using positional argument + if metrics_to_report: + # Use the correct Ray Train reporting format + train.report(metrics_to_report) + + +def suggest_scheduler_config( + max_epochs_per_trial: int, + num_samples: int, + training_time_per_epoch: str = "medium" +) -> dict[str, str]: + """Suggest appropriate scheduler configuration. + + Parameters + ---------- + max_epochs_per_trial : int + Maximum epochs per trial. + num_samples : int + Number of samples/trials. + training_time_per_epoch : str, optional + Expected training time ("fast", "medium", "slow"), by default "medium". + + Returns + ------- + Dict[str, str] + Suggested configuration with reasoning. + """ + suggestions = {} + + if max_epochs_per_trial < 2: + suggestions["scheduler_type"] = "fifo" + suggestions["reason"] = "FIFO recommended for single epoch trials" + elif max_epochs_per_trial < 4: + suggestions["scheduler_type"] = "fifo" + suggestions["reason"] = \ + "FIFO recommended for very short trials (< 4 epochs)" + elif max_epochs_per_trial >= 10 and num_samples >= 8: + if training_time_per_epoch in ["fast", "medium"]: + suggestions["scheduler_type"] = "asha" + suggestions["reason"] = \ + "ASHA recommended for efficient early stopping" + else: + suggestions["scheduler_type"] = "pbt" + suggestions["reason"] = \ + "PBT recommended for longer training with population evolution" + elif num_samples >= 4 and max_epochs_per_trial >= 6: + suggestions["scheduler_type"] = "pbt" + suggestions["reason"] = "PBT recommended for medium-scale experiments" + else: + suggestions["scheduler_type"] = "asha" + suggestions["reason"] = "ASHA recommended as general-purpose scheduler" + + return suggestions + + +def create_basic_search_space( + learning_rate_range: tuple = (1e-5, 1e-2), + weight_decay_range: tuple = (1e-6, 1e-3), + batch_size_choices: list = [16, 32, 64] +) -> dict[str, Any]: + """Create a basic search space for autoencoder tuning. + + Parameters + ---------- + learning_rate_range : tuple, optional + Learning rate range (min, max), by default (1e-5, 1e-2). + weight_decay_range : tuple, optional + Weight decay range (min, max), by default (1e-6, 1e-3). + batch_size_choices : list, optional + Batch size choices, by default [16, 32, 64]. + + Returns + ------- + Dict[str, Any] + Search space configuration. + """ + if not RAY_AVAILABLE: + raise ImportError("Ray Tune is required to create search spaces") + + return { + "learning_rate": tune.loguniform(*learning_rate_range), + "weight_decay": tune.loguniform(*weight_decay_range), + "batch_size": tune.choice(batch_size_choices), + "scheduler_type": tune.choice(["cosine", "linear"]), + "warmup_epochs": tune.choice([0, 3, 5]) + } diff --git a/src/faith/train/tuning/schedulers.py b/src/faith/train/tuning/schedulers.py new file mode 100644 index 0000000..e69de29 diff --git a/src/faith/train/tuning/search_spaces.py b/src/faith/train/tuning/search_spaces.py new file mode 100644 index 0000000..7319c8b --- /dev/null +++ b/src/faith/train/tuning/search_spaces.py @@ -0,0 +1,360 @@ +"""Predefined search spaces for common hyperparameter tuning scenarios.""" + +from typing import Any + + +try: + from ray import tune + + + RAY_AVAILABLE = True +except ImportError: + RAY_AVAILABLE = False + + +class SearchSpaces: + """Collection of predefined search spaces for different scenarios.""" + + @staticmethod + def basic_autoencoder( + learning_rate_range: tuple[float, float] = (1e-5, 1e-2), + weight_decay_range: tuple[float, float] = (1e-6, 1e-3), + activation_choices: list[str] = None + ) -> dict[str, Any]: + """Basic autoencoder search space. + + Parameters + ---------- + learning_rate_range : Tuple[float, float], optional + Learning rate range, by default (1e-5, 1e-2). + weight_decay_range : Tuple[float, float], optional + Weight decay range, by default (1e-6, 1e-3). + activation_choices : List[str], optional + Activation function choices, by default ["relu", "gelu", "swish"]. + + Returns + ------- + Dict[str, Any] + Search space configuration. + """ + if not RAY_AVAILABLE: + raise ImportError("Ray Tune required for search spaces") + + if activation_choices is None: + activation_choices = ["relu", "gelu", "swish"] + + return { + "learning_rate": tune.loguniform(*learning_rate_range), + "weight_decay": tune.loguniform(*weight_decay_range), + "activation": tune.choice(activation_choices), + "scheduler_type": tune.choice(["cosine", "linear"]), + "warmup_epochs": tune.choice([0, 3, 5, 10]) + } + + @staticmethod + def block_based_autoencoder( + learning_rate_range: tuple[float, float] = (1e-5, 1e-2), + dropout_range: tuple[float, float] = (0.0, 0.5), + num_blocks_range: tuple[int, int] = (2, 6) + ) -> dict[str, Any]: + """Search space for BlockBasedAutoencoder architecture. + + Parameters + ---------- + learning_rate_range : Tuple[float, float], optional + Learning rate range, by default (1e-5, 1e-2). + dropout_range : Tuple[float, float], optional + Dropout range, by default (0.0, 0.5). + num_blocks_range : Tuple[int, int], optional + Number of blocks range, by default (2, 6). + + Returns + ------- + Dict[str, Any] + Search space configuration. + """ + if not RAY_AVAILABLE: + raise ImportError("Ray Tune required for search spaces") + + return { + # Training hyperparameters + "learning_rate": tune.loguniform(*learning_rate_range), + "weight_decay": tune.loguniform(1e-6, 1e-3), + "scheduler_type": tune.choice(["cosine", "linear", "none"]), + + # Model architecture + "activation": tune.choice(["relu", "gelu", "swish", "leaky_relu"]), + "dropout": tune.uniform(*dropout_range), + + # Block configuration (for custom block_configs) + "base_channels": tune.choice([32, 64, 128]), + "channel_multiplier": tune.choice([1.5, 2.0, 2.5]), + "pool_size": tune.choice([(1, 2), (2, 2), (1, 4)]), + } + + @staticmethod + def quick_search( + param_name: str, + param_choices: list[Any], + base_config: dict[str, Any] = None + ) -> dict[str, Any]: + """Quick search space for testing single parameters. + + Parameters + ---------- + param_name : str + Name of parameter to search. + param_choices : List[Any] + Choices for the parameter. + base_config : Dict[str, Any], optional + Base configuration to extend, by default None. + + Returns + ------- + Dict[str, Any] + Search space configuration. + """ + if not RAY_AVAILABLE: + raise ImportError("Ray Tune required for search spaces") + + if base_config is None: + base_config = {"learning_rate": 1e-4} + + search_space = base_config.copy() + search_space[param_name] = tune.choice(param_choices) + + return search_space + + @staticmethod + def regularization_focused( + base_lr: float = 1e-4, + dropout_range: tuple[float, float] = (0.0, 0.5), + weight_decay_range: tuple[float, float] = (1e-6, 1e-2) + ) -> dict[str, Any]: + """Search space focused on regularization parameters. + + Parameters + ---------- + base_lr : float, optional + Fixed learning rate, by default 1e-4. + dropout_range : Tuple[float, float], optional + Dropout range, by default (0.0, 0.5). + weight_decay_range : Tuple[float, float], optional + Weight decay range, by default (1e-6, 1e-2). + + Returns + ------- + Dict[str, Any] + Search space configuration. + """ + if not RAY_AVAILABLE: + raise ImportError("Ray Tune required for search spaces") + + return { + "learning_rate": base_lr, # Fixed + "weight_decay": tune.loguniform(*weight_decay_range), + "dropout": tune.uniform(*dropout_range), + + # Regularization techniques + "label_smoothing": tune.uniform(0.0, 0.2), + "gradient_clip_val": tune.choice([0.5, 1.0, 2.0, None]), + + # Data augmentation (if applicable) + "noise_factor": tune.uniform(0.0, 0.1), + "mixup_alpha": tune.uniform(0.0, 0.4), + } + + @staticmethod + def architecture_search( + learning_rate: float = 1e-4, + layer_choices: list[int] = None, + width_choices: list[int] = None + ) -> dict[str, Any]: + """Search space focused on architecture parameters. + + Parameters + ---------- + learning_rate : float, optional + Fixed learning rate, by default 1e-4. + layer_choices : List[int], optional + Number of layers choices, by default [2, 3, 4, 5]. + width_choices : List[int], optional + Layer width choices, by default [64, 128, 256, 512]. + + Returns + ------- + Dict[str, Any] + Search space configuration. + """ + if not RAY_AVAILABLE: + raise ImportError("Ray Tune required for search spaces") + + if layer_choices is None: + layer_choices = [2, 3, 4, 5] + if width_choices is None: + width_choices = [64, 128, 256, 512] + + return { + "learning_rate": learning_rate, # Fixed + "weight_decay": 1e-5, # Fixed + + # Architecture parameters + "num_layers": tune.choice(layer_choices), + "bottleneck_dim": tune.choice([16, 32, 64, 128]), + + # Activation and normalization + "activation": tune.choice(["relu", "gelu", "swish", "leaky_relu"]), + "use_batch_norm": tune.choice([True, False]), + "use_layer_norm": tune.choice([True, False]), + + # Skip connections + "use_skip_connections": tune.choice([True, False]), + "skip_type": tune.choice(["add", "concat"]), + } + + +class CustomSearchSpace: + """Builder for custom search spaces.""" + + def __init__(self): + """Initialize custom search space builder.""" + if not RAY_AVAILABLE: + raise ImportError("Ray Tune required for custom search spaces") + + self.space = {} + + def add_continuous( + self, + name: str, + low: float, + high: float, + log_scale: bool = False + ) -> 'CustomSearchSpace': + """Add continuous parameter. + + Parameters + ---------- + name : str + Parameter name. + low : float + Lower bound. + high : float + Upper bound. + log_scale : bool, optional + Use log scale, by default False. + + Returns + ------- + CustomSearchSpace + Self for chaining. + """ + if log_scale: + self.space[name] = tune.loguniform(low, high) + else: + self.space[name] = tune.uniform(low, high) + return self + + def add_discrete(self, name: str, choices: list[Any]) \ + -> 'CustomSearchSpace': + """Add discrete parameter. + + Parameters + ---------- + name : str + Parameter name. + choices : List[Any] + List of choices. + + Returns + ------- + CustomSearchSpace + Self for chaining. + """ + self.space[name] = tune.choice(choices) + return self + + def add_integer(self, name: str, low: int, + high: int) -> 'CustomSearchSpace': + """Add integer parameter. + + Parameters + ---------- + name : str + Parameter name. + low : int + Lower bound. + high : int + Upper bound. + + Returns + ------- + CustomSearchSpace + Self for chaining. + """ + self.space[name] = tune.randint(low, high + 1) + return self + + def add_fixed(self, name: str, value: Any) -> 'CustomSearchSpace': + """Add fixed parameter. + + Parameters + ---------- + name : str + Parameter name. + value : Any + Fixed value. + + Returns + ------- + CustomSearchSpace + Self for chaining. + """ + self.space[name] = value + return self + + def build(self) -> dict[str, Any]: + """Build the search space. + + Returns + ------- + Dict[str, Any] + Complete search space configuration. + """ + return self.space.copy() + + +# Convenience function for quick access +def get_search_space(name: str, **kwargs) -> dict[str, Any]: + """Get predefined search space by name. + + Parameters + ---------- + name : str + Search space name ("basic", "block_based", "multimodal", + "regularization", "architecture"). + **kwargs + Additional parameters for the search space. + + Returns + ------- + Dict[str, Any] + Search space configuration. + + Examples + -------- + >>> space = get_search_space("basic", learning_rate_range=(1e-4, 1e-2)) + >>> space = get_search_space("architecture", layer_choices=[2, 3, 4]) + """ + spaces = { + "basic": SearchSpaces.basic_autoencoder, + "block_based": SearchSpaces.block_based_autoencoder, + "regularization": SearchSpaces.regularization_focused, + "architecture": SearchSpaces.architecture_search, + } + + if name not in spaces: + available = ", ".join(spaces.keys()) + raise ValueError( + f"Unknown search space '{name}'. Available: {available}") + + return spaces[name](**kwargs) diff --git a/tests/test_autoencoder.py b/tests/test_autoencoder.py new file mode 100644 index 0000000..2fa7aab --- /dev/null +++ b/tests/test_autoencoder.py @@ -0,0 +1,69 @@ +import torch +from src.faith.train.models import BlockBasedAutoencoder + +# Test basic functionality +print("Testing BlockBasedAutoencoder...") + +# Create autoencoder with default config +autoencoder = BlockBasedAutoencoder(input_channels=80) + +# Test forward pass +x = torch.randn(2, 80, 100, 128) +reconstructed = autoencoder(x) +latent = autoencoder.encode(x) + +print(f"Input shape: {x.shape}") +print(f"Latent shape: {latent.shape}") +print(f"Reconstructed shape: {reconstructed.shape}") +print(f"Autoencoder: {autoencoder}") + +# Test individual methods +latent_only = autoencoder.get_latent_representation(x) +reconstructed_only = autoencoder.reconstruct(x) + +print(f"Latent only shape: {latent_only.shape}") +print(f"Reconstructed only shape: {reconstructed_only.shape}") + +# Test configuration serialization +config = autoencoder.get_config() +print(f"Config keys: {list(config.keys())}") + +new_autoencoder = BlockBasedAutoencoder.from_config(config) +print(f"Recreated autoencoder: {new_autoencoder}") + +# Test shape calculation +output_shape = autoencoder.get_output_shape((1, 80, 100, 128)) +latent_shape = autoencoder.get_latent_shape((1, 80, 100, 128)) +print(f"Calculated output shape: {output_shape}") +print(f"Calculated latent shape: {latent_shape}") + +# Test parameter counting +print(f"Total parameters: {autoencoder.parameter_count:,}") +print(f"Encoder parameters: {autoencoder.encoder_parameter_count:,}") +print(f"Decoder parameters: {autoencoder.decoder_parameter_count:,}") + +# Test feature map extraction +feature_maps = autoencoder.get_feature_maps(x) +print(f"Encoder feature maps: {len(feature_maps['encoder'])}") +print(f"Decoder feature maps: {len(feature_maps['decoder'])}") + +# Test custom configuration +custom_configs = [ + {'out_channels': 64, 'pool_size': (1, 2), 'dropout': 0.2}, + {'out_channels': 128, 'pool_size': (1, 4), 'dropout': 0.3}, +] + +custom_autoencoder = BlockBasedAutoencoder( + input_channels=80, + block_configs=custom_configs, + activation='gelu' +) + +x_custom = torch.randn(1, 80, 100, 128) +reconstructed_custom, latent_custom = custom_autoencoder(x_custom) + +print("\nCustom autoencoder:") +print(f"Input shape: {x_custom.shape}") +print(f"Latent shape: {latent_custom.shape}") +print(f"Reconstructed shape: {reconstructed_custom.shape}") +print(f"Custom autoencoder: {custom_autoencoder}") diff --git a/tests/test_train_blocks_base.py b/tests/test_train_blocks_base.py new file mode 100644 index 0000000..77c9626 --- /dev/null +++ b/tests/test_train_blocks_base.py @@ -0,0 +1,26 @@ +import torch +from src.faith.train.blocks import BaseBlock, BlockUtils + + +# Example of how the base classes would be used + +class ExampleBlock(BaseBlock): + """Example implementation of BaseBlock.""" + + def __init__(self, in_channels: int, out_channels: int, **kwargs): + super().__init__(in_channels, out_channels, **kwargs) + self.conv = nn.Conv2d(in_channels, out_channels, + kernel_size=self.kernel_size) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.conv(x) + +# Test utility functions +input_shape = (1, 64, 32, 32) +memory_info = BlockUtils.get_memory_usage(block, input_shape) +print(f"Memory usage: {memory_info}") + +output_shape = BlockUtils.calculate_output_shape( + input_shape, kernel_size=3, stride=1, padding=1 +) +print(f"Output shape: {output_shape}") diff --git a/tests/test_train_blocks_decoder.py b/tests/test_train_blocks_decoder.py new file mode 100644 index 0000000..6040c5b --- /dev/null +++ b/tests/test_train_blocks_decoder.py @@ -0,0 +1,60 @@ +import torch +from src.faith.train.blocks import DecoderBlock, BlockBasedDecoder,EncoderBlock + + +# Example usage and testing +# Test DecoderBlock +print("Testing DecoderBlock...") +decoder_block = DecoderBlock( + in_channels=128, + out_channels=64, + upsample_factor=(1, 2), + dropout=0.3, + upsampling_mode='nearest', + activation='relu' +) + +x = torch.randn(1, 128, 16, 8) +output = decoder_block(x) +print(f"DecoderBlock - Input: {x.shape}, Output: {output.shape}") + +# Test configuration +config = decoder_block.get_config() +new_block = DecoderBlock.from_config(config) +print(f"Config serialization successful: {new_block}") + +# Test BlockBasedDecoder with mock encoder blocks +print("\nTesting BlockBasedDecoder...") + +# Create mock encoder blocks for testing + +mock_encoder_blocks = [ + EncoderBlock(80, 128, pool_size=(1, 2)), + EncoderBlock(128, 256, pool_size=(1, 4)), + EncoderBlock(256, 128, pool_size=(1, 2)), +] + +decoder = BlockBasedDecoder( + output_channels=80, + encoder_blocks=mock_encoder_blocks, + bottleneck_channels=64, + upsampling_mode='nearest' +) + +# Test forward pass +latent = torch.randn(2, 64, 25, 4) +reconstructed = decoder(latent) +print(f"Decoder - Input: {latent.shape}, Output: {reconstructed.shape}") + +# Test feature map extraction +feature_maps = decoder.get_feature_maps(latent) +print(f"Feature maps shapes: {[fm.shape for fm in feature_maps]}") + +# Test from_encoder class method +decoder2 = BlockBasedDecoder.from_encoder( + encoder_blocks=mock_encoder_blocks, + bottleneck_channels=64, + output_channels=80, + upsampling_mode='bilinear' +) +print(f"Decoder from encoder: {decoder2}") diff --git a/tests/test_train_blocks_encoder.py b/tests/test_train_blocks_encoder.py new file mode 100644 index 0000000..d3f9639 --- /dev/null +++ b/tests/test_train_blocks_encoder.py @@ -0,0 +1,46 @@ +# Example usage and testing +import torch +from src.faith.train.blocks import EncoderBlock, BlockBasedEncoder + + +# Test EncoderBlock +print("Testing EncoderBlock...") +encoder_block = EncoderBlock( + in_channels=64, + out_channels=128, + pool_size=(1, 2), + dropout=0.3, + activation='relu' +) + +x = torch.randn(1, 64, 32, 32) +output = encoder_block(x) +print(f"EncoderBlock - Input: {x.shape}, Output: {output.shape}") + +# Test configuration +config = encoder_block.get_config() +new_block = EncoderBlock.from_config(config) +print(f"Config serialization successful: {new_block}") + +# Test BlockBasedEncoder +print("\nTesting BlockBasedEncoder...") +block_configs = [ + {'out_channels': 128, 'pool_size': (1, 2), 'dropout': 0.2}, + {'out_channels': 256, 'pool_size': (1, 4), 'dropout': 0.3}, + {'out_channels': 128, 'pool_size': (1, 2), 'dropout': 0.4}, +] + +encoder = BlockBasedEncoder( + input_channels=80, + block_configs=block_configs, + hidden_dim=16, + bottleneck_channels=64 +) + +x = torch.randn(2, 80, 100, 128) +latent = encoder(x) +print(f"Encoder - Input: {x.shape}, Output: {latent.shape}") + +# Test feature map extraction +feature_maps = encoder.get_feature_maps(x) +print(f"Feature maps shapes: {[fm.shape for fm in feature_maps]}") diff --git a/tests/test_train_blocks_residual.py b/tests/test_train_blocks_residual.py new file mode 100644 index 0000000..04d6f54 --- /dev/null +++ b/tests/test_train_blocks_residual.py @@ -0,0 +1,20 @@ +import torch +from src.faith.train.blocks import ResidualBlock + + +# Example usage and testing +# Test basic functionality +block = ResidualBlock(64, 128, stride=2) +x = torch.randn(1, 64, 32, 32) +output = block(x) +print(f"Input shape: {x.shape}") +print(f"Output shape: {output.shape}") +print(f"Block: {block}") + +# Test configuration serialization +config = block.get_config() +print(f"Config: {config}") + +# Create from config +new_block = ResidualBlock.from_config(config) +print(f"Recreated block: {new_block}") diff --git a/tests/test_train_configuration.py b/tests/test_train_configuration.py new file mode 100644 index 0000000..ad6ade6 --- /dev/null +++ b/tests/test_train_configuration.py @@ -0,0 +1,58 @@ +from src.faith.train.models import ( + get_preset_config, + create_block_autoencoder, + list_preset_configs, + save_model_config, + create_model_from_config_file, + create_autoencoder_from_config, + ModelConfig, +) + +# Example usage and testing +# Test preset configurations +print("Available presets:", list_preset_configs()) + +# Test creating models from presets +for preset_name in ['default', 'light', 'mae_default']: + print(f"\nTesting {preset_name} preset:") + + try: + model = create_block_autoencoder(preset_name, input_channels=80) + print(f"Created model: {type(model).__name__}") + + if hasattr(model, 'parameter_count'): + print(f"Parameters: {model.parameter_count:,}") + + except Exception as e: + print(f"Error creating {preset_name}: {e}") + +# Test configuration serialization +print("\nTesting configuration serialization:") + +config = get_preset_config('default') +config = config.update(input_channels=80, hidden_dim=16) + +# Save and load +config.save('test_config.yaml') +loaded_config = ModelConfig.load('test_config.yaml') + +print(f"Original: {config.input_channels}, {config.hidden_dim}") +print( + f"Loaded: {loaded_config.input_channels}, {loaded_config.hidden_dim}") + +# Test model config saving +autoencoder = create_autoencoder_from_config(config) +save_model_config(autoencoder, 'model_config.yaml') + +# Load and recreate model +recreated_model = create_model_from_config_file('model_config.yaml') +print(f"Recreated model: {type(recreated_model).__name__}") + +# Cleanup +import os + + +os.remove('test_config.yaml') +os.remove('model_config.yaml') + +print("Configuration tests completed successfully!") From 732830313d0564bbaf2aded884e3b935d7030223 Mon Sep 17 00:00:00 2001 From: Peter Steiner <61472983+renierts@users.noreply.github.com> Date: Tue, 15 Jul 2025 20:29:54 -0400 Subject: [PATCH 051/103] Added tests for residual block and fixed bugs for it. --- src/faith/__init__.py | 1 - src/faith/train/blocks/__init__.py | 4 +- src/faith/train/blocks/base.py | 27 ++- src/faith/train/blocks/residual.py | 30 +-- tests/test_train_blocks_base.py | 4 +- tests/test_train_blocks_residual.py | 277 ++++++++++++++++++++++++++-- 6 files changed, 304 insertions(+), 39 deletions(-) diff --git a/src/faith/__init__.py b/src/faith/__init__.py index 83986cb..e69de29 100644 --- a/src/faith/__init__.py +++ b/src/faith/__init__.py @@ -1 +0,0 @@ -from . import core \ No newline at end of file diff --git a/src/faith/train/blocks/__init__.py b/src/faith/train/blocks/__init__.py index 7f06849..02cf1a9 100644 --- a/src/faith/train/blocks/__init__.py +++ b/src/faith/train/blocks/__init__.py @@ -3,7 +3,7 @@ from .residual import ResidualBlock from .encoder import EncoderBlock, BlockBasedEncoder from .decoder import DecoderBlock, BlockBasedDecoder -from .base import BaseBlock, BlockUtils +from .base import BaseConvBlock, BlockUtils __all__ = [ "ResidualBlock", @@ -11,6 +11,6 @@ "BlockBasedEncoder", "DecoderBlock", "BlockBasedDecoder", - "BaseBlock", + "BaseConvBlock", "BlockUtils", ] diff --git a/src/faith/train/blocks/base.py b/src/faith/train/blocks/base.py index b7332ae..b02f36a 100644 --- a/src/faith/train/blocks/base.py +++ b/src/faith/train/blocks/base.py @@ -13,8 +13,8 @@ import math -class BaseBlock(nn.Module, ABC): - """Abstract base class for all neural network blocks. +class BaseConvBlock(nn.Module, ABC): + """Abstract base class for all convolutional-based neural network blocks. This class defines the common interface that all blocks should implement, ensuring consistency across different block types in the autoencoder @@ -60,6 +60,21 @@ def __init__( raise ValueError( f"out_channels must be positive, got {out_channels}") + if not isinstance(in_channels, int): + raise TypeError( + f"in_channels must be an int, got {type(in_channels)}") + + if not isinstance(out_channels, int): + raise TypeError( + f"out_channels must be an int, got {type(out_channels)}") + + if isinstance(kernel_size, int) and kernel_size <= 0: + raise ValueError( + f"kernel_size must be positive, got {kernel_size}") + if isinstance(kernel_size, tuple) and any(k <= 0 for k in kernel_size): + raise ValueError( + f"kernel_size must be positive, got {kernel_size}") + self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = self._normalize_kernel_size(kernel_size) @@ -82,11 +97,11 @@ def _calculate_padding( """Calculate padding based on kernel size and padding specification.""" if padding == 'auto': if isinstance(kernel_size, int): - return (kernel_size // 2, kernel_size // 2) + return kernel_size // 2, kernel_size // 2 else: return tuple(k // 2 for k in kernel_size) elif isinstance(padding, int): - return (padding, padding) + return padding, padding else: return padding @@ -140,7 +155,7 @@ def __repr__(self) -> str: f"bias={self.bias})") -class SequentialBlock(BaseBlock): +class SequentialBlock(BaseConvBlock): """Base class for blocks that apply operations sequentially. This class provides common functionality for blocks that consist of @@ -181,7 +196,7 @@ def add_operation(self, operation: nn.Module) -> None: self.operations.add_module(str(len(self.operations)), operation) -class ConfigurableBlock(BaseBlock): +class ConfigurableBlock(BaseConvBlock): """ Base class for blocks with extensive configuration options. diff --git a/src/faith/train/blocks/residual.py b/src/faith/train/blocks/residual.py index ca06459..44862af 100644 --- a/src/faith/train/blocks/residual.py +++ b/src/faith/train/blocks/residual.py @@ -7,10 +7,10 @@ import torch import torch.nn as nn from typing import Union, Any -from .base import BaseBlock, WeightInitializer +from .base import BaseConvBlock, WeightInitializer -class ResidualBlock(BaseBlock): +class ResidualBlock(BaseConvBlock): """Residual convolutional block with batch normalization and ReLU. This block implements a standard residual connection with two convolutional @@ -27,9 +27,6 @@ class ResidualBlock(BaseBlock): Size of the convolving kernel. stride : int or tuple of int, default=1 Stride of the convolution. - padding : int, tuple of int, or str, default='auto' - Padding added to all four sides of the input. If 'auto', padding is - calculated to maintain spatial dimensions when stride=1. bias : bool, default=True If True, adds a learnable bias to the output. use_batch_norm : bool, default=True @@ -78,7 +75,6 @@ def __init__( out_channels: int, kernel_size: Union[int, tuple[int, int]] = 3, stride: Union[int, tuple[int, int]] = 1, - padding: Union[int, tuple[int, int], str] = 'auto', bias: bool = True, use_batch_norm: bool = True, activation: str = 'relu', @@ -96,8 +92,6 @@ def __init__( Size of the convolving kernel. stride : int or tuple of int, default=1 Stride of the convolution. - padding : int, tuple of int, or str, default='auto' - Padding specification. bias : bool, default=True Whether to use bias in convolutions. use_batch_norm : bool, default=True @@ -110,9 +104,20 @@ def __init__( # Initialize base class super().__init__(in_channels, out_channels, kernel_size, bias) + if isinstance(stride, int) and stride < 1: + raise ValueError(f"Stride must be a positive integer or tuple, " + f"got {stride}") + if isinstance(stride, tuple) and any(s < 1 for s in stride): + raise ValueError(f"Stride must be a positive integer or tuple, " + f"got {stride}") + if (isinstance(stride, float) or isinstance(stride, tuple) + and any(isinstance(s, float) for s in stride)): + raise TypeError(f"Stride must be an integer or tuple, " + f"got float {stride}") + # Normalize stride and padding self.stride = self._normalize_stride(stride) - self.padding = self._calculate_padding(self.kernel_size, padding) + self.padding = self._calculate_padding(self.kernel_size, "auto") self.use_batch_norm = use_batch_norm self.activation_name = activation self.init_method = init_method @@ -239,8 +244,9 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: Returns ------- torch.Tensor - Output tensor with shape (batch_size, out_channels, height', width') - where height' and width' depend on stride. + Output tensor with shape + (batch_size, out_channels, height', width') where height' + and 'width' depend on stride. """ # Store input for residual connection residual = x @@ -350,4 +356,4 @@ def get_output_shape( # Update channels batch_size, _, height, width = temp_shape - return (batch_size, self.out_channels, height, width) + return batch_size, self.out_channels, height, width diff --git a/tests/test_train_blocks_base.py b/tests/test_train_blocks_base.py index 77c9626..1e135e2 100644 --- a/tests/test_train_blocks_base.py +++ b/tests/test_train_blocks_base.py @@ -1,10 +1,10 @@ import torch -from src.faith.train.blocks import BaseBlock, BlockUtils +from src.faith.train.blocks import BaseConvBlock, BlockUtils # Example of how the base classes would be used -class ExampleBlock(BaseBlock): +class ExampleBlock(BaseConvBlock): """Example implementation of BaseBlock.""" def __init__(self, in_channels: int, out_channels: int, **kwargs): diff --git a/tests/test_train_blocks_residual.py b/tests/test_train_blocks_residual.py index 04d6f54..a0f1710 100644 --- a/tests/test_train_blocks_residual.py +++ b/tests/test_train_blocks_residual.py @@ -2,19 +2,264 @@ from src.faith.train.blocks import ResidualBlock -# Example usage and testing -# Test basic functionality -block = ResidualBlock(64, 128, stride=2) -x = torch.randn(1, 64, 32, 32) -output = block(x) -print(f"Input shape: {x.shape}") -print(f"Output shape: {output.shape}") -print(f"Block: {block}") - -# Test configuration serialization -config = block.get_config() -print(f"Config: {config}") - -# Create from config -new_block = ResidualBlock.from_config(config) -print(f"Recreated block: {new_block}") +import pytest + + +def test_kernel_size_constant_channels(): + """Test the kernel size of the ResidualBlock.""" + block = ResidualBlock(4, 4, kernel_size=3) + + # Test that the block was created successfully + assert block is not None + + # Test that the kernel size is correctly set in the convolutional layers + # Assuming ResidualBlock has conv layers with the specified kernel size + for module in block.modules(): + if isinstance(module, torch.nn.Conv2d): + assert module.kernel_size == (3, 3), ( + f"Expected kernel size (3, 3), got {module.kernel_size}" + ) + + +def test_kernel_size_changing_channels(): + """Test the kernel size of the ResidualBlock.""" + block = ResidualBlock(4, 6, kernel_size=3) + + # Test that the block was created successfully + assert block is not None + + # Test that the kernel size is correctly set in the convolutional layers + # Assuming ResidualBlock has conv layers with the specified kernel size + for module in block.modules(): + if isinstance(module, torch.nn.Conv2d): + assert (module.kernel_size == (3, 3) + or module.kernel_size == (1, 1)), ( + f"Expected kernel size (3, 3) or (1, 1), " + f"got {module.kernel_size}" + ) + + +def test_kernel_size_different_values(): + """Test ResidualBlock with different kernel sizes.""" + test_cases = [ + (1, (1, 1)), + (3, (3, 3)), + (5, (5, 5)), + (7, (7, 7)), + ] + + for kernel_size, expected in test_cases: + block = ResidualBlock(4, 8, kernel_size=kernel_size) + + # Check that conv layers have the correct kernel size + conv_layers = [ + module for module in block.modules() + if isinstance(module, torch.nn.Conv2d) + ] + + assert len(conv_layers) == 3, ("ResidualBlock should contain Conv2d " + "layers") + + for conv_layer in conv_layers: + assert (conv_layer.kernel_size == expected + or conv_layer.kernel_size == (1, 1)), ( + f"For kernel_size={kernel_size}, expected {expected}, " + f"got {conv_layer.kernel_size}" + ) + + block = ResidualBlock(4, 4, kernel_size=kernel_size) + + # Check that conv layers have the correct kernel size + conv_layers = [ + module for module in block.modules() + if isinstance(module, torch.nn.Conv2d) + ] + + assert len(conv_layers) == 2, ("ResidualBlock should contain Conv2d " + "layers") + + for conv_layer in conv_layers: + assert conv_layer.kernel_size == expected, ( + f"For kernel_size={kernel_size}, expected {expected}, " + f"got {conv_layer.kernel_size}" + ) + + +def test_kernel_size_with_forward_pass(): + """Test that different kernel sizes work in forward pass.""" + batch_size = 2 + channels = 2 + height, width = 32, 32 + + input_tensor = torch.randn(batch_size, channels, height, width) + + # Test different kernel sizes + for kernel_size in [1, 3, 5]: + block = ResidualBlock(2, 4, kernel_size=kernel_size) + block.eval() # Set to evaluation mode + + with torch.no_grad(): + output = block(input_tensor) + + # Check output shape is reasonable + output_shape = output.shape[:1] + output.shape[2:] + input_shape = input_tensor.shape[:1] + input_tensor.shape[2:] + assert output_shape == input_shape, ( + f"Input shape should be preserved, got {output.shape[0]}" + ) + assert output.shape[1] == 4, ( + f"Output channels should be 4, got {output.shape[1]}" + ) + + assert len(output.shape) == 4, ( + f"Output should be 4D tensor, got shape {output.shape}" + ) + + +def test_invalid_kernel_size(): + """Test that invalid kernel sizes raise appropriate errors.""" + with pytest.raises(ValueError): + ResidualBlock(2, 4, kernel_size=0) + + with pytest.raises(ValueError): + ResidualBlock(2, 4, kernel_size=-1) + + +def test_kernel_size_parameter_types(): + """Test that kernel_size accepts different parameter types.""" + # Test integer + block1 = ResidualBlock(2, 4, kernel_size=3) + assert block1 is not None + + # Test tuple (if supported) + block2 = ResidualBlock(2, 4, kernel_size=(3, 3)) + assert block2 is not None + + +def test_invalid_channels(): + """Test that invalid channel numbers raise appropriate errors.""" + # Test zero input channels + with pytest.raises(ValueError): + ResidualBlock(0, 2, kernel_size=3) + + # Test negative input channels + with pytest.raises(ValueError): + ResidualBlock(-64, 2, kernel_size=3) + + # Test zero output channels + with pytest.raises(ValueError): + ResidualBlock(2, 0, kernel_size=3) + + # Test negative output channels + with pytest.raises(ValueError): + ResidualBlock(2, -128, kernel_size=3) + + # Test non-integer channels + with pytest.raises(TypeError): + ResidualBlock(64.5, 128, kernel_size=3) + + with pytest.raises(TypeError): + ResidualBlock(64, 128.5, kernel_size=3) + + +def test_valid_channels(): + """Test that valid channel numbers work correctly.""" + valid_channel_pairs = [ + (1, 1), + (1, 64), + (64, 1), + (32, 64), + (64, 128), + (128, 256), + (512, 512), + ] + + for in_channels, out_channels in valid_channel_pairs: + block = ResidualBlock(in_channels, out_channels, kernel_size=3) + assert block is not None + + # Test forward pass with appropriate input + input_tensor = torch.randn(1, in_channels, 8, 8) + with torch.no_grad(): + output = block(input_tensor) + assert output.shape[1] == out_channels + + +def test_invalid_stride(): + """Test that invalid stride values raise appropriate errors.""" + # Test zero stride + with pytest.raises(ValueError): + ResidualBlock(64, 128, kernel_size=3, stride=0) + + # Test negative stride + with pytest.raises(ValueError): + ResidualBlock(64, 128, kernel_size=3, stride=-1) + + # Test non-integer stride + with pytest.raises(TypeError): + ResidualBlock(64, 128, kernel_size=3, stride=1.5) + + +def test_valid_stride(): + """Test that valid stride values work correctly.""" + valid_strides = [1, 2, 3, 4] + + for stride in valid_strides: + block = ResidualBlock(64, 128, kernel_size=3, stride=stride) + assert block is not None + + # Test that stride affects conv layers + conv_layers = [ + module for module in block.modules() + if isinstance(module, torch.nn.Conv2d) + ] + + # At least one conv layer should have the specified stride + stride_found = any( + conv.stride == (stride, stride) or conv.stride == stride + for conv in conv_layers + ) + assert stride_found, f"No conv layer found with stride {stride}" + + +def test_stride_output_shape(): + """Test that stride correctly affects output dimensions.""" + input_size = 32 + input_tensor = torch.randn(1, 2, input_size, input_size) + + for stride in [1, 2]: + block = ResidualBlock(2, 4, kernel_size=3, stride=stride) + block.eval() + + with torch.no_grad(): + output = block(input_tensor) + + # Output spatial dimensions are affected by stride + if stride == 1: + # Might be same size + assert output.shape[2] == input_size + assert output.shape[3] == input_size + elif stride == 2: + # Should be half the size + assert output.shape[2] <= input_size // stride + assert output.shape[3] <= input_size // stride + + +def test_combined_invalid_parameters(): + """Test combinations of invalid parameters.""" + invalid_combinations = [ + # (in_channels, out_channels, kernel_size, stride) + (0, 0, 0, 0), # All invalid + (-1, 128, 3, 1), # Invalid in_channels + (64, -1, 3, 1), # Invalid out_channels + (64, 128, -1, 1), # Invalid kernel_size + (64, 128, 3, -1), # Invalid stride + ] + + for in_ch, out_ch, k_size, stride in invalid_combinations: + with pytest.raises((ValueError, TypeError, RuntimeError)): + ResidualBlock( + in_ch, out_ch, + kernel_size=k_size, + stride=stride, + ) From 9ede5339ff9c11d3aba7b9fca8bc79c2c0116bd5 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Wed, 16 Jul 2025 07:59:34 -0400 Subject: [PATCH 052/103] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f810b36..24c496f 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ This repository serves as a centralized platform for fusion-related machine lear Go to your scratch directory while you are on the HEAD node (so you need internet access, which computing nodes do not have). -We will be using Python 3.12. +We will be using Python 3.12 and uv as a package manageer. Since uv isn't on Stellar, for now we will install it via pip /scratch/gpfs/[username] From 13df06a4d7aa4570c976631d96767070f2b15657 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Wed, 16 Jul 2025 08:04:11 -0400 Subject: [PATCH 053/103] Create python-package.yml --- .github/workflows/python-package.yml | 40 ++++++++++++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 .github/workflows/python-package.yml diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml new file mode 100644 index 0000000..81088ca --- /dev/null +++ b/.github/workflows/python-package.yml @@ -0,0 +1,40 @@ +# This workflow will install Python dependencies, run tests and lint with a variety of Python versions +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python + +name: Python package + +on: + push: + branches: [ "foundation25" ] + pull_request: + branches: [ "foundation25" ] + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"] + + steps: + - uses: actions/checkout@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v3 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install flake8 pytest + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + pytest From b244c089fd8bf94c568fee25336482e5f6115e35 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Wed, 16 Jul 2025 08:07:02 -0400 Subject: [PATCH 054/103] Update .gitignore --- .gitignore | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 4b8399c..eb21c69 100644 --- a/.gitignore +++ b/.gitignore @@ -159,8 +159,12 @@ cython_debug/ # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ +# Ruff cache +.ruff_cache/ + +# Fusion AI Hub data/ logs/ outputs/ *.pkl -*.joblib \ No newline at end of file +*.joblib From ff49f7bfd067690160d15d779901c3ccb7d4addf Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Wed, 16 Jul 2025 08:14:48 -0400 Subject: [PATCH 055/103] Update pyproject.toml --- pyproject.toml | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 69ff4d7..fb6bd15 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,6 +39,11 @@ dependencies = [ "jupyter", ] +[dependency-groups] +dev = [ + "pytest >=8.1.1,<9" +] + [tool.uv.sources] torch = [ { index = "pytorch-cpu" }, @@ -55,6 +60,10 @@ explicit = true [tool.uv] package = true +[tool.ruff] +select = ["E", "F", "I", "B", "ANN"] +line-length = 88 # TODO + [project.urls] Homepage = "https://github.com" Documentation = "https://readthedocs.org" From 22e37fa2b4094edc7cf66ecb91f7c1bbb0476b2a Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Wed, 16 Jul 2025 08:15:08 -0400 Subject: [PATCH 056/103] Create .pre-commit-config.yaml --- .pre-commit-config.yaml | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 .pre-commit-config.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..78c0d57 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,7 @@ +repos: +- repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.2.0 + hooks: + - id: ruff + args: [--fix] + - id: ruff-format From 9ca5efb99e1a33883ae2e2b1e99d746b44e0a9ad Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Wed, 16 Jul 2025 08:16:06 -0400 Subject: [PATCH 057/103] Update pyproject.toml --- pyproject.toml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index fb6bd15..23ecee3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,7 +41,8 @@ dependencies = [ [dependency-groups] dev = [ - "pytest >=8.1.1,<9" + "pytest >=8.1.1,<9", + "pre-commit", ] [tool.uv.sources] @@ -62,7 +63,7 @@ package = true [tool.ruff] select = ["E", "F", "I", "B", "ANN"] -line-length = 88 # TODO +line-length = 79 [project.urls] Homepage = "https://github.com" From c7c461979b6557a5c1008b29954e70fcb07bd1b2 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Wed, 16 Jul 2025 08:18:32 -0400 Subject: [PATCH 058/103] Update .pre-commit-config.yaml --- .pre-commit-config.yaml | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 78c0d57..e8e12a6 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,7 +1,12 @@ repos: -- repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.2.0 - hooks: - - id: ruff - args: [--fix] - - id: ruff-format +- repo: https://github.com/astral-sh/ruff-pre-commit + # Ruff version. + rev: v0.12.3 + hooks: + # Run the linter. + - id: ruff-check + types_or: [ python, pyi ] + args: [ --fix ] + # Run the formatter. + - id: ruff-format + types_or: [ python, pyi ] From 29a9c7aae6bd182a0f5ab7ad92dbb0f7dfa9b221 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen <79379842+nathanchenseanwalter@users.noreply.github.com> Date: Wed, 16 Jul 2025 08:19:00 -0400 Subject: [PATCH 059/103] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 24c496f..f4d1758 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ This repository serves as a centralized platform for fusion-related machine lear Go to your scratch directory while you are on the HEAD node (so you need internet access, which computing nodes do not have). -We will be using Python 3.12 and uv as a package manageer. Since uv isn't on Stellar, for now we will install it via pip +We will be using Python 3.12 and uv as a package manager. Since uv isn't on Stellar, for now we will install it via pip /scratch/gpfs/[username] From 1058c64c0cbc3bcc2e1d7f1ce866c32e2b60bb7a Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Wed, 16 Jul 2025 09:29:57 -0400 Subject: [PATCH 060/103] Refactor project configuration and enhance linting rules - Changed project name from "Fusion Artificial InTelligence Hub" to "faith" in pyproject.toml. - Updated linting rules in the ruff configuration to include additional checks for code quality. - Expanded Python version resolution markers in uv.lock to support more specific platform and version combinations. - Added new package dependencies in uv.lock, including aiohappyeyeballs and aiohttp with their respective versions and sources. - Imported nn module from torch in the test file to support neural network operations. --- pyproject.toml | 13 +- tests/test_train_blocks_base.py | 1 + uv.lock | 3269 +++++++++++++++++++++++++++++-- 3 files changed, 3124 insertions(+), 159 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 23ecee3..68fa05b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,5 +1,5 @@ [project] -name = "Fusion Artificial InTelligence Hub" +name = "faith" version = "0.0.1-alpha" authors = [ { name = "Peter Steiner", email = "peter.steiner@princeton.edu" }, @@ -62,7 +62,16 @@ explicit = true package = true [tool.ruff] -select = ["E", "F", "I", "B", "ANN"] +select = [ + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # pyflakes + "I", # isort + "B", # flake8-bugbear + "C4", # flake8-comprehensions + "UP", # pyupgrade + "ANN", # flake8-annotations + ] line-length = 79 [project.urls] diff --git a/tests/test_train_blocks_base.py b/tests/test_train_blocks_base.py index 1e135e2..c3ddbf2 100644 --- a/tests/test_train_blocks_base.py +++ b/tests/test_train_blocks_base.py @@ -1,4 +1,5 @@ import torch +import torch.nn as nn from src.faith.train.blocks import BaseConvBlock, BlockUtils diff --git a/uv.lock b/uv.lock index b805b42..d6b9e52 100644 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@@ -41,8 +41,8 @@ dependencies = [ [dependency-groups] dev = [ - "pytest >=8.1.1,<9", - "pre-commit", + "pytest >=8.1.1,<9", + "pre-commit", ] [tool.uv.sources] diff --git a/src/faith/train/blocks/base.py b/src/faith/train/blocks/base.py index b02f36a..20f5aaf 100644 --- a/src/faith/train/blocks/base.py +++ b/src/faith/train/blocks/base.py @@ -86,13 +86,13 @@ def _normalize_kernel_size( ) -> tuple[int, int]: """Normalize kernel size to tuple format.""" if isinstance(kernel_size, int): - return (kernel_size, kernel_size) + return kernel_size, kernel_size return kernel_size @staticmethod def _calculate_padding( kernel_size: Union[int, tuple[int, int]], - padding: Union[int, tuple[int, int], str] + padding: Union[int, tuple[int, int], str] = 'auto' ) -> tuple[int, ...]: """Calculate padding based on kernel size and padding specification.""" if padding == 'auto': diff --git a/src/faith/train/blocks/decoder.py b/src/faith/train/blocks/decoder.py index ce9aeca..5c1a900 100644 --- a/src/faith/train/blocks/decoder.py +++ b/src/faith/train/blocks/decoder.py @@ -34,9 +34,6 @@ class DecoderBlock(SequentialBlock): stride : int or tuple of int, default=1 Stride for convolutions in ResidualBlock. The DecoderBlock uses stride=1 and relies on Upsample for dimension changes. - padding : int, tuple of int, or str, default='auto' - Padding for convolutions in ResidualBlock. 'auto' calculates - padding to maintain spatial dimensions. dropout : float, default=0.3 Dropout probability. Must be between 0.0 and 1.0. bias : bool, default=True @@ -89,7 +86,6 @@ def __init__( upsample_factor: tuple[int, int] = (1, 2), kernel_size: Union[int, tuple[int, int]] = 3, stride: Union[int, tuple[int, int]] = 1, - padding: Union[int, tuple[int, int], str] = 'auto', dropout: float = 0.3, bias: bool = True, use_batch_norm: bool = True, @@ -124,7 +120,7 @@ def __init__( # Build the sequential operations operations = self._build_operations( - in_channels, out_channels, kernel_size, stride, padding, + in_channels, out_channels, kernel_size, stride, bias, use_batch_norm, activation, residual_init_method ) @@ -148,7 +144,6 @@ def _build_operations( out_channels: int, kernel_size: Union[int, tuple[int, int]], stride: Union[int, tuple[int, int]], - padding: Union[int, tuple[int, int], str], bias: bool, use_batch_norm: bool, activation: str, @@ -171,7 +166,6 @@ def _build_operations( out_channels=out_channels, kernel_size=kernel_size, stride=stride, - padding=padding, bias=bias, use_batch_norm=use_batch_norm, activation=activation, @@ -196,7 +190,6 @@ def get_config(self) -> dict[str, Any]: 'activation': self.activation_name, 'residual_init_method': self.residual_init_method, 'stride': getattr(self.residual_block, 'stride', 1), - 'padding': getattr(self.residual_block, 'padding', 'auto'), }) return config @@ -421,7 +414,6 @@ def _create_decoder_block_config( 'upsample_factor': encoder_block.pool_size, # Mirror the pooling 'kernel_size': self.kernel_size, 'stride': 1, # Always use stride=1 in decoder - 'padding': 'auto', 'dropout': encoder_block.dropout_prob, # Match encoder dropout 'bias': self.bias, 'use_batch_norm': self.use_batch_norm, diff --git a/src/faith/train/blocks/encoder.py b/src/faith/train/blocks/encoder.py index 4dc6e8f..97876f2 100644 --- a/src/faith/train/blocks/encoder.py +++ b/src/faith/train/blocks/encoder.py @@ -32,9 +32,6 @@ class EncoderBlock(SequentialBlock): stride : int or tuple of int, default=1 Stride for convolutions in ResidualBlock. The EncoderBlock uses stride=1 and relies on MaxPool for downsampling. - padding : int, tuple of int, or str, default='auto' - Padding for convolutions in ResidualBlock. 'auto' calculates - padding to maintain spatial dimensions. dropout : float, default=0.3 Dropout probability. Must be between 0.0 and 1.0. bias : bool, default=True @@ -81,7 +78,6 @@ def __init__( pool_size: tuple[int, int] = (1, 2), kernel_size: Union[int, tuple[int, int]] = 3, stride: Union[int, tuple[int, int]] = 1, - padding: Union[int, tuple[int, int], str] = 'auto', dropout: float = 0.3, bias: bool = True, use_batch_norm: bool = True, @@ -108,7 +104,7 @@ def __init__( # Build the sequential operations operations = self._build_operations( - in_channels, out_channels, kernel_size, stride, padding, + in_channels, out_channels, kernel_size, stride, bias, use_batch_norm, activation, residual_init_method ) @@ -132,7 +128,6 @@ def _build_operations( out_channels: int, kernel_size: Union[int, tuple[int, int]], stride: Union[int, tuple[int, int]], - padding: Union[int, tuple[int, int], str], bias: bool, use_batch_norm: bool, activation: str, @@ -148,7 +143,6 @@ def _build_operations( out_channels=out_channels, kernel_size=kernel_size, stride=stride, - padding=padding, bias=bias, use_batch_norm=use_batch_norm, activation=activation, @@ -176,7 +170,6 @@ def get_config(self) -> dict[str, Any]: 'activation': self.activation_name, 'residual_init_method': self.residual_init_method, 'stride': getattr(self.residual_block, 'stride', 1), - 'padding': getattr(self.residual_block, 'padding', 'auto'), }) return config @@ -221,7 +214,7 @@ class BlockBasedEncoder(ConfigurableBlock): Parameters ---------- - input_channels : int + in_channels : int Number of input channels in the data. block_configs : list of dict Configuration for each encoder block. Each dict should contain: @@ -262,7 +255,7 @@ class BlockBasedEncoder(ConfigurableBlock): def __init__( self, - input_channels: int, + in_channels: int, block_configs: list[dict[str, Any]], bottleneck_channels: Optional[int] = None, hidden_dim: Optional[int] = None, @@ -276,8 +269,8 @@ def __init__( # Initialize ConfigurableBlock super().__init__( - in_channels=input_channels, - out_channels=input_channels, # Will be updated after building + in_channels=in_channels, + out_channels=in_channels, # Will be updated after building kernel_size=kernel_size, bias=bias, block_configs=block_configs, @@ -292,11 +285,11 @@ def __init__( if not block_configs: raise ValueError("block_configs cannot be empty") - if input_channels <= 0: + if in_channels <= 0: raise ValueError( - f"input_channels must be positive, got {input_channels}") + f"in_channels must be positive, got {in_channels}") - self.input_channels = input_channels + self.in_channels = in_channels self.block_configs = block_configs self.hidden_dim = hidden_dim self.bottleneck_activation = bottleneck_activation @@ -316,7 +309,7 @@ def __init__( def _build_encoder_blocks(self) -> nn.ModuleList: """Build the sequence of encoder blocks.""" blocks = [] - current_channels = self.input_channels + current_channels = self.in_channels for i, config in enumerate(self.block_configs): if 'out_channels' not in config: @@ -351,7 +344,6 @@ def _prepare_block_config( 'pool_size': config.get('pool_size', (1, 2)), 'kernel_size': config.get('kernel_size', self.kernel_size), 'stride': config.get('stride', 1), - 'padding': config.get('padding', 'auto'), 'dropout': config.get('dropout', 0.3), 'bias': config.get('bias', self.bias), 'use_batch_norm': config.get('use_batch_norm', True), @@ -374,7 +366,7 @@ def _build_bottleneck( if self.blocks: current_channels = self.blocks[-1].out_channels else: - current_channels = self.input_channels + current_channels = self.in_channels # Optional adaptive pooling if self.hidden_dim is not None: @@ -450,7 +442,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: Parameters ---------- x : torch.Tensor - Input tensor with shape (batch_size, input_channels, height, width) + Input tensor with shape (batch_size, in_channels, height, width) Returns ------- @@ -514,7 +506,7 @@ def get_output_shape( def __repr__(self) -> str: """String representation of the BlockBasedEncoder.""" return (f"BlockBasedEncoder(" - f"input_channels={self.input_channels}, " + f"in_channels={self.in_channels}, " f"num_blocks={len(self.blocks)}, " f"bottleneck_channels={self.bottleneck_channels}, " f"hidden_dim={self.hidden_dim})") diff --git a/src/faith/train/blocks/residual.py b/src/faith/train/blocks/residual.py index 44862af..d4ec436 100644 --- a/src/faith/train/blocks/residual.py +++ b/src/faith/train/blocks/residual.py @@ -4,9 +4,11 @@ following the established patterns and interfaces defined in the base module. """ +from typing import Any, Union + import torch import torch.nn as nn -from typing import Union, Any + from .base import BaseConvBlock, WeightInitializer @@ -141,7 +143,8 @@ def _normalize_stride(self, def _validate_parameters(self) -> None: """Validate input parameters.""" - valid_activations = {'relu', 'leaky_relu', 'gelu', 'swish', 'mish'} + valid_activations = {'tanh', 'sigmoid', 'relu', 'leaky_relu', 'gelu', + 'swish', 'mish'} if self.activation_name not in valid_activations: raise ValueError(f"activation must be one of {valid_activations}, " f"got {self.activation_name}") @@ -210,6 +213,8 @@ def _build_layers(self) -> None: def _create_activation(self) -> nn.Module: """Create activation function based on name.""" activations = { + 'tanh': nn.Tanh(), + 'sigmoid': nn.Sigmoid(), 'relu': nn.ReLU(inplace=True), 'leaky_relu': nn.LeakyReLU(0.1, inplace=True), 'gelu': nn.GELU(), diff --git a/tests/test_train_blocks_encoder.py b/tests/test_train_blocks_encoder.py index d3f9639..d41df8c 100644 --- a/tests/test_train_blocks_encoder.py +++ b/tests/test_train_blocks_encoder.py @@ -1,13 +1,428 @@ -# Example usage and testing +import pytest import torch -from src.faith.train.blocks import EncoderBlock, BlockBasedEncoder +import torch.nn as nn +from src.faith.train.blocks import EncoderBlock, ResidualBlock + +class TestEncoderBlockIntegration: + """Integration tests using actual ResidualBlock implementation.""" + + def test_initialization_with_residual_block(self): + """Test EncoderBlock initialization with real ResidualBlock.""" + block = EncoderBlock(in_channels=4, out_channels=8) + + # Test basic attributes + assert block.in_channels == 4 + assert block.out_channels == 8 + assert block.pool_size == (1, 2) + assert block.dropout_prob == 0.3 + assert block.use_batch_norm is True + assert block.activation_name == "relu" + assert block.residual_init_method == "kaiming" + + # Test that operations are correctly built + assert len(block.operations) == 3 + assert isinstance(block.operations[0], ResidualBlock) + assert isinstance(block.operations[1], nn.Dropout) + assert isinstance(block.operations[2], nn.MaxPool2d) + + # Test component references + assert block.residual_block is block.operations[0] + assert block.dropout is block.operations[1] + assert block.pool is block.operations[2] + + def test_custom_initialization_with_real_components(self): + """Test custom initialization parameters.""" + block = EncoderBlock( + in_channels=2, + out_channels=4, + pool_size=(2, 2), + kernel_size=5, + stride=2, + dropout=0.5, + bias=False, + use_batch_norm=False, + activation="gelu", + residual_init_method="xavier", + ) + + # Test that ResidualBlock received correct parameters + residual = block.residual_block + assert residual.in_channels == 2 + assert residual.out_channels == 4 + # Add more assertions based on your ResidualBlock's interface + + # Test other components + assert block.dropout.p == 0.5 + assert block.pool.kernel_size == (2, 2) + + def test_forward_pass_integration(self): + """Test actual forward pass with real tensors and components.""" + block = EncoderBlock(in_channels=3, out_channels=16, pool_size=(2, 2)) + + # Create real input tensor + input_tensor = torch.randn(2, 3, 32, 32) + + # Set to eval mode to make dropout deterministic + block.eval() + + # Forward pass + with torch.no_grad(): + output = block(input_tensor) + + # Check output properties + assert isinstance(output, torch.Tensor) + assert output.shape == (2, 16, 16, 16) # Pooled by (2,2) + assert not torch.isnan(output).any() + assert not torch.isinf(output).any() + + # Test that output has reasonable values + assert output.std() > 0 # Should have some variation + + def test_output_shape_calculation_integration(self): + """Test output shape calculation with real ResidualBlock.""" + block = EncoderBlock(in_channels=4, out_channels=8, pool_size=(2, 4)) + + input_shape = (1, 64, 32, 64) + output_shape = block.get_output_shape(input_shape) + + # The calculation should work with real ResidualBlock + expected_shape = (1, 8, 16, 16) # 32//2=16, 64//4=16 + assert output_shape == expected_shape + + def test_different_pool_sizes_integration(self): + """Test various pooling configurations with real components.""" + test_cases = [ + ((1, 1), (1, 2, 32, 32), (1, 4, 32, 32)), # No pooling + ((1, 2), (1, 2, 32, 32), (1, 4, 32, 16)), # Width pooling only + ((2, 1), (1, 2, 32, 32), (1, 4, 16, 32)), # Height pooling only + ((2, 2), (1, 2, 32, 32), (1, 4, 16, 16)), # Both dimensions + ((4, 4), (1, 2, 64, 64), (1, 4, 16, 16)), # Aggressive pooling + ] + + for pool_size, input_shape, expected_output in test_cases: + block = EncoderBlock(in_channels=2, out_channels=4, + pool_size=pool_size) + + output_shape = block.get_output_shape(input_shape) + assert output_shape == expected_output, \ + f"Failed for pool_size {pool_size}" + + def test_activation_functions_integration(self): + """Test different activation functions with real ResidualBlock.""" + activations = ["relu", "gelu", "tanh", "sigmoid"] + + for activation in activations: + block = EncoderBlock(in_channels=4, out_channels=8, + activation=activation) + + # Test that activation is stored correctly + assert block.activation_name == activation + + # Test forward pass works + input_tensor = torch.randn(1, 4, 8, 8) + block.eval() + + with torch.no_grad(): + output = block(input_tensor) + + assert output.shape == (1, 8, 8, 4) # Default pool_size=(1,2) + assert not torch.isnan(output).any() + + def test_batch_norm_integration(self): + """Test batch normalization configurations.""" + # With batch norm + block_bn = EncoderBlock(in_channels=32, out_channels=64, + use_batch_norm=True) + + # Without batch norm + block_no_bn = EncoderBlock( + in_channels=32, out_channels=64, use_batch_norm=False + ) + + input_tensor = torch.randn(4, 32, 16, 16) # Batch size > 1 for BN + + # Test both configurations work + with torch.no_grad(): + output_bn = block_bn(input_tensor) + output_no_bn = block_no_bn(input_tensor) + + assert output_bn.shape == output_no_bn.shape + assert not torch.isnan(output_bn).any() + assert not torch.isnan(output_no_bn).any() + + # Outputs should be different due to batch norm + assert not torch.allclose(output_bn, output_no_bn, atol=1e-6) + + def test_dropout_behavior_integration(self): + """Test dropout behavior in training vs evaluation mode.""" + block = EncoderBlock(in_channels=32, out_channels=64, dropout=0.5) + input_tensor = torch.randn(1, 32, 16, 16) + + # Training mode - dropout should introduce randomness + block.train() + outputs_train = [] + for _ in range(3): + with torch.no_grad(): + output = block(input_tensor) + outputs_train.append(output.clone()) + + # Outputs should be different due to dropout randomness + assert not torch.allclose( + outputs_train[0], outputs_train[1], atol=1e-6) + assert not torch.allclose( + outputs_train[1], outputs_train[2], atol=1e-6) + + # Evaluation mode - should be deterministic + block.eval() + outputs_eval = [] + for _ in range(3): + with torch.no_grad(): + output = block(input_tensor) + outputs_eval.append(output.clone()) + + # Outputs should be identical in eval mode + assert torch.allclose(outputs_eval[0], outputs_eval[1]) + assert torch.allclose(outputs_eval[1], outputs_eval[2]) + + def test_configuration_serialization_integration(self): + """Test configuration serialization with real components.""" + original_config = { + "in_channels": 4, + "out_channels": 8, + "pool_size": (3, 3), + "kernel_size": 5, + "stride": 2, + "dropout": 0.4, + "bias": False, + "use_batch_norm": False, + "activation": "gelu", + "residual_init_method": "xavier", + } + + # Create block + block = EncoderBlock(**original_config) + + # Get configuration + saved_config = block.get_config() + + # Verify important parameters are preserved + assert saved_config["pool_size"] == (3, 3) + assert saved_config["dropout"] == 0.4 + assert saved_config["use_batch_norm"] is False + assert saved_config["activation"] == "gelu" + assert saved_config["residual_init_method"] == "xavier" + + # Test from_config + recreated_block = EncoderBlock.from_config(saved_config) + + # Verify recreated block has same configuration + assert recreated_block.pool_size == block.pool_size + assert recreated_block.dropout_prob == block.dropout_prob + assert recreated_block.activation_name == block.activation_name + + def test_gradient_flow_integration(self): + """Test that gradients flow correctly through the block.""" + block = EncoderBlock(in_channels=6, out_channels=8) + input_tensor = torch.randn(1, 6, 8, 8, requires_grad=True) + + # Forward pass + output = block(input_tensor) + + # Create a simple loss + loss = output.sum() + + # Backward pass + loss.backward() + + # Check that input has gradients + assert input_tensor.grad is not None + assert not torch.isnan(input_tensor.grad).any() + + # Check that block parameters have gradients + for param in block.parameters(): + if param.requires_grad: + assert param.grad is not None + assert not torch.isnan(param.grad).any() + + @pytest.mark.parametrize("batch_size", [1, 4, 16]) + @pytest.mark.parametrize("spatial_size", [8, 16, 32]) + def test_various_input_sizes_integration(self, batch_size, spatial_size): + """Test with various input sizes.""" + block = EncoderBlock(in_channels=8, out_channels=16, pool_size=(2, 2)) + + input_tensor = torch.randn(batch_size, 8, spatial_size, spatial_size) + + block.eval() + with torch.no_grad(): + output = block(input_tensor) + + expected_spatial = spatial_size // 2 + expected_shape = (batch_size, 16, expected_spatial, expected_spatial) + + assert output.shape == expected_shape + assert not torch.isnan(output).any() + + @pytest.mark.parametrize("dropout_val", [0.0, 0.3, 0.7, 1.0]) + def test_dropout_values_integration(self, dropout_val): + """Test various dropout values.""" + block = EncoderBlock(in_channels=16, out_channels=32, + dropout=dropout_val) + + assert block.dropout_prob == dropout_val + assert block.dropout.p == dropout_val + + # Test forward pass works + input_tensor = torch.randn(1, 16, 8, 8) + block.eval() + + with torch.no_grad(): + output = block(input_tensor) + + assert not torch.isnan(output).any() + + # For dropout=1.0 in training mode, output should be zeros + if dropout_val == 1.0: + block.train() + with torch.no_grad(): + output_train = block(input_tensor) + # Note: with dropout=1.0, the dropout layer zeros everything + # but the residual block and pooling still contribute + + def test_memory_efficiency_integration(self): + """Test memory usage with real components.""" + import gc + + # Create block + block = EncoderBlock(in_channels=4, out_channels=8, pool_size=(4, 4)) + + # Large input tensor + large_input = torch.randn(8, 4, 64, 64) + + block.eval() + + # Forward pass + with torch.no_grad(): + output = block(large_input) + + # Check output shape + assert output.shape == (8, 8, 16, 16) + + # Clean up + del large_input, output + gc.collect() + + def test_device_compatibility_integration(self): + """Test that the block works on different devices.""" + block = EncoderBlock(in_channels=16, out_channels=32) + input_tensor = torch.randn(1, 16, 8, 8) + + # Test on CPU + block_cpu = block.to("cpu") + input_cpu = input_tensor.to("cpu") + + with torch.no_grad(): + output_cpu = block_cpu(input_cpu) + + assert output_cpu.device.type == "cpu" + assert not torch.isnan(output_cpu).any() + + # Test on GPU if available + if torch.cuda.is_available(): + block_gpu = block.to("cuda") + input_gpu = input_tensor.to("cuda") + + with torch.no_grad(): + output_gpu = block_gpu(input_gpu) + + assert output_gpu.device.type == "cuda" + assert not torch.isnan(output_gpu).any() + + +class TestEncoderBlockErrorHandling: + """Test error handling with real components.""" + + def test_invalid_parameters_real(self): + """Test parameter validation with real ResidualBlock.""" + # Test invalid dropout + with pytest.raises( + ValueError, match="Dropout must be between 0.0 and 1.0"): + EncoderBlock(in_channels=4, out_channels=8, dropout=-0.1) + + with pytest.raises( + ValueError, match="Dropout must be between 0.0 and 1.0"): + EncoderBlock(in_channels=4, out_channels=8, dropout=1.1) + + # Test invalid pool_size + with pytest.raises( + ValueError, match="pool_size must be a tuple of length 2"): + EncoderBlock(in_channels=4, out_channels=8, pool_size=(1,)) + + with pytest.raises( + ValueError, match="pool_size must be a tuple of length 2"): + EncoderBlock(in_channels=4, out_channels=8, pool_size=(1, 2, 3)) + + def test_incompatible_tensor_shapes(self): + """Test behavior with incompatible tensor shapes.""" + block = EncoderBlock(in_channels=16, out_channels=32) + + # Wrong number of channels + wrong_channels = torch.randn(1, 8, 16, 16) # 8 channels instead of 16 + + with pytest.raises(RuntimeError): + block(wrong_channels) + + # Wrong number of dimensions + wrong_dims = torch.randn(1, 16, 16) # 3D instead of 4D + + with pytest.raises((RuntimeError, IndexError)): + block(wrong_dims) + + +class TestEncoderBlockPerformance: + """Performance tests with real components.""" + + def test_inference_speed(self): + """Basic inference speed test.""" + import time + + block = EncoderBlock(in_channels=4, out_channels=8) + input_tensor = torch.randn(16, 4, 32, 32) + + block.eval() + + # Warmup + with torch.no_grad(): + for _ in range(5): + _ = block(input_tensor) + + # Time inference + start_time = time.time() + with torch.no_grad(): + for _ in range(10): + output = block(input_tensor) + end_time = time.time() + + avg_time = (end_time - start_time) / 10 + + # Should complete in reasonable time (adjust threshold as needed) + assert avg_time < 1e-2 # Less than 10 milliseconds per forward pass + assert output.shape == (16, 8, 32, 16) # Verify correctness + + +if __name__ == "__main__": + pytest.main([__file__, "-v", "--tb=short"]) + + + +######################################################################## +""" # Test EncoderBlock print("Testing EncoderBlock...") encoder_block = EncoderBlock( - in_channels=64, - out_channels=128, + in_channels=2, + out_channels=4, pool_size=(1, 2), dropout=0.3, activation='relu' @@ -44,3 +459,4 @@ # Test feature map extraction feature_maps = encoder.get_feature_maps(x) print(f"Feature maps shapes: {[fm.shape for fm in feature_maps]}") +""" From 4218067515821e5b2a7f182234e2f014743a5fe6 Mon Sep 17 00:00:00 2001 From: Peter Steiner <61472983+renierts@users.noreply.github.com> Date: Wed, 16 Jul 2025 21:25:56 -0400 Subject: [PATCH 062/103] Added tests for decoder and fixed bugs. --- pyproject.toml | 1 + src/faith/train/blocks/decoder.py | 220 +++++++++------- tests/test_train_blocks_decoder.py | 400 ++++++++++++++++++++++++++++- tests/test_train_blocks_encoder.py | 51 ---- 4 files changed, 521 insertions(+), 151 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 6e75ff3..c84fc37 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,6 +37,7 @@ dependencies = [ "tables", "pyyaml", "jupyter", + "typing", ] [dependency-groups] diff --git a/src/faith/train/blocks/decoder.py b/src/faith/train/blocks/decoder.py index 5c1a900..4fc7d67 100644 --- a/src/faith/train/blocks/decoder.py +++ b/src/faith/train/blocks/decoder.py @@ -5,21 +5,23 @@ The decoder creates a symmetric reconstruction path to the encoder. """ +from typing import Any, Optional, Union + import torch import torch.nn as nn -from typing import Union, Any, Optional -from .base import SequentialBlock, ConfigurableBlock, WeightInitializer -from .residual import ResidualBlock + +from .base import ConfigurableBlock, SequentialBlock, WeightInitializer from .encoder import EncoderBlock +from .residual import ResidualBlock class DecoderBlock(SequentialBlock): - # TODO: ConvTranspose2d - """Single decoder block: Upsample + ResidualBlock + Dropout. + """ + Single decoder block: ConvTranspose2d + Dropout + ResidualBlock. This block represents the fundamental building unit of the decoder, - combining spatial upsampling, feature refinement through ResidualBlock, - and regularization through Dropout. + combining spatial upsampling through ConvTranspose2d, regularization + through Dropout, and feature refinement through ResidualBlock. Parameters ---------- @@ -33,7 +35,7 @@ class DecoderBlock(SequentialBlock): Kernel size for convolutions in ResidualBlock. stride : int or tuple of int, default=1 Stride for convolutions in ResidualBlock. The DecoderBlock uses - stride=1 and relies on Upsample for dimension changes. + stride=1 and relies on ConvTranspose2d for upsampling. dropout : float, default=0.3 Dropout probability. Must be between 0.0 and 1.0. bias : bool, default=True @@ -42,26 +44,21 @@ class DecoderBlock(SequentialBlock): Whether to use batch normalization in ResidualBlock. activation : str, default='relu' Activation function for ResidualBlock. - upsampling_mode : str, default='nearest' - Upsampling algorithm. Options: 'nearest', 'linear', 'bilinear', - 'bicubic', 'trilinear', 'area'. residual_init_method : str, default='kaiming' Weight initialization method for ResidualBlock. Attributes ---------- - upsample : nn.Upsample - Upsampling layer for spatial dimension restoration. + conv_transpose : nn.ConvTranspose2d + Transposed convolution layer for spatial upsampling. + dropout_layer : nn.Dropout + Dropout layer for regularization. residual_block : ResidualBlock The residual convolutional block for feature refinement. - dropout : nn.Dropout - Dropout layer for regularization. upsample_factor : tuple of int - Stored upsampling factor. - dropout_prob : float + Stored upsampling factor for encoder symmetry. + dropout : float Stored dropout probability. - upsampling_mode : str - Stored upsampling mode. Examples -------- @@ -74,54 +71,52 @@ class DecoderBlock(SequentialBlock): >>> # Custom configuration >>> block = DecoderBlock( ... in_channels=128, out_channels=64, - ... upsample_factor=(2, 2), upsampling_mode='bilinear', - ... activation='gelu' + ... upsample_factor=(2, 2), dropout=0.5, activation='gelu' ... ) """ def __init__( - self, - in_channels: int, - out_channels: int, - upsample_factor: tuple[int, int] = (1, 2), - kernel_size: Union[int, tuple[int, int]] = 3, - stride: Union[int, tuple[int, int]] = 1, - dropout: float = 0.3, - bias: bool = True, - use_batch_norm: bool = True, - activation: str = 'relu', - upsampling_mode: str = 'nearest', - residual_init_method: str = 'kaiming' + self, + in_channels: int, + out_channels: int, + upsample_factor: tuple[int, int] = (1, 2), + kernel_size: Union[int, tuple[int, int]] = 3, + stride: Union[int, tuple[int, int]] = 1, + dropout: float = 0.3, + bias: bool = True, + use_batch_norm: bool = True, + activation: str = "relu", + residual_init_method: str = "kaiming", ) -> None: """Initialize DecoderBlock.""" # Validate parameters if not 0.0 <= dropout <= 1.0: raise ValueError( - f"Dropout must be between 0.0 and 1.0, got {dropout}") + f"Dropout must be between 0.0 and 1.0, got {dropout}" + ) if len(upsample_factor) != 2: raise ValueError(f"upsample_factor must be a tuple of length 2, " f"got {upsample_factor}") - valid_modes = { - 'nearest', 'linear', 'bilinear', 'bicubic', 'trilinear', 'area'} - if upsampling_mode not in valid_modes: - raise ValueError(f"upsampling_mode must be one of {valid_modes}, " - f"got {upsampling_mode}") - # Store configuration self.upsample_factor = upsample_factor - self.dropout_prob = dropout - self.upsampling_mode = upsampling_mode + self.dropout = dropout self.use_batch_norm = use_batch_norm self.activation_name = activation self.residual_init_method = residual_init_method # Build the sequential operations operations = self._build_operations( - in_channels, out_channels, kernel_size, stride, - bias, use_batch_norm, activation, residual_init_method + in_channels, + out_channels, + kernel_size, + stride, + bias, + use_batch_norm, + activation, + residual_init_method, ) # Initialize SequentialBlock with operations @@ -130,37 +125,52 @@ def __init__( out_channels=out_channels, operations=operations, kernel_size=kernel_size, - bias=bias + bias=bias, ) # Store individual components for introspection - self.upsample = self.operations[0] - self.residual_block = self.operations[1] - self.dropout = self.operations[2] + self.conv_transpose = self.operations[0] + self.dropout_layer = self.operations[1] + self.residual_block = self.operations[2] def _build_operations( - self, - in_channels: int, - out_channels: int, - kernel_size: Union[int, tuple[int, int]], - stride: Union[int, tuple[int, int]], - bias: bool, - use_batch_norm: bool, - activation: str, - init_method: str + self, + in_channels: int, + out_channels: int, + kernel_size: Union[int, tuple[int, int]], + stride: Union[int, tuple[int, int]], + bias: bool, + use_batch_norm: bool, + activation: str, + init_method: str, ) -> list[nn.Module]: """Build the list of operations for this decoder block.""" operations = [] - # 1. Upsample for spatial dimension restoration - upsample_layer = nn.Upsample( - scale_factor=self.upsample_factor, - mode=self.upsampling_mode + # 1. ConvTranspose2d for upsampling + # Calculate kernel size and padding based on upsample factor + # For stride=1 in any dimension, we need to handle it carefully + + # Determine kernel size and padding for each dimension + kernel_h = 3 if self.upsample_factor[0] == 1 else 4 + kernel_w = 3 if self.upsample_factor[1] == 1 else 4 + + conv_transpose_layer = nn.ConvTranspose2d( + in_channels=in_channels, + out_channels=in_channels, # Keep same channels for upsampling + kernel_size=(kernel_h, kernel_w), + stride=self.upsample_factor, + padding=(1, 1), # Use padding=1 for both cases + bias=bias, ) - operations.append(upsample_layer) + operations.append(conv_transpose_layer) + + # 2. Dropout for regularization + dropout_layer = nn.Dropout(p=self.dropout) + operations.append(dropout_layer) - # 2. ResidualBlock for feature refinement + # 3. ResidualBlock for feature refinement residual_block = ResidualBlock( in_channels=in_channels, out_channels=out_channels, @@ -169,59 +179,80 @@ def _build_operations( bias=bias, use_batch_norm=use_batch_norm, activation=activation, - init_method=init_method + init_method=init_method, ) operations.append(residual_block) - # 3. Dropout for regularization - dropout_layer = nn.Dropout(p=self.dropout_prob) - operations.append(dropout_layer) - return operations def get_config(self) -> dict[str, Any]: """Get configuration dictionary for this block.""" config = super().get_config() - config.update({ - 'upsample_factor': self.upsample_factor, - 'dropout': self.dropout_prob, - 'upsampling_mode': self.upsampling_mode, - 'use_batch_norm': self.use_batch_norm, - 'activation': self.activation_name, - 'residual_init_method': self.residual_init_method, - 'stride': getattr(self.residual_block, 'stride', 1), - }) + config.update( + { + "upsample_factor": self.upsample_factor, + "dropout": self.dropout, + "use_batch_norm": self.use_batch_norm, + "activation": self.activation_name, + "residual_init_method": self.residual_init_method, + "stride": getattr(self.residual_block, "stride", 1), + } + ) return config @classmethod - def from_config(cls, config: dict[str, Any]) -> 'DecoderBlock': + def from_config(cls, config: dict[str, Any]) -> "DecoderBlock": """Create DecoderBlock instance from configuration dictionary.""" return cls(**config) def get_output_shape( - self, - input_shape: tuple[int, ...] + self, input_shape: tuple[int, ...] ) -> tuple[int, ...]: """Calculate output shape given input shape.""" + + # Get shape after ConvTranspose2d upsampling batch_size, channels, height, width = input_shape - # Apply upsampling - upsampled_height = height * self.upsample_factor[0] - upsampled_width = width * self.upsample_factor[1] + # Calculate upsampled dimensions using ConvTranspose2d formula + # output_size = (input_size - 1) * stride - 2 * padding + kernel_size + + # Use appropriate kernel for each dimension based on upsample factor + kernel_h = 3 if self.upsample_factor[0] == 1 else 4 + kernel_w = 3 if self.upsample_factor[1] == 1 else 4 + padding = 1 # Always use padding=1 + + upsampled_height = ( + (height - 1) * self.upsample_factor[0] - 2 * padding + kernel_h + ) + upsampled_width = ( + (width - 1) * self.upsample_factor[1] - 2 * padding + kernel_w + ) + + # Shape after ConvTranspose2d (channels remain the same) + conv_transpose_output_shape = ( + batch_size, + channels, + upsampled_height, + upsampled_width, + ) + + # Apply ResidualBlock (changes channels to out_channels) + residual_output_shape = self.residual_block.get_output_shape( + conv_transpose_output_shape + ) - # ResidualBlock changes channels but maintains spatial dimensions - return (batch_size, self.out_channels, upsampled_height, - upsampled_width) + return residual_output_shape def __repr__(self) -> str: """String representation of the DecoderBlock.""" - return (f"DecoderBlock(" - f"in_channels={self.in_channels}, " - f"out_channels={self.out_channels}, " - f"upsample_factor={self.upsample_factor}, " - f"dropout={self.dropout_prob}, " - f"upsampling_mode='{self.upsampling_mode}', " - f"activation='{self.activation_name}')") + return ( + f"DecoderBlock(" + f"in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"upsample_factor={self.upsample_factor}, " + f"dropout={self.dropout}, " + f"activation='{self.activation_name}')" + ) class BlockBasedDecoder(ConfigurableBlock): @@ -414,11 +445,10 @@ def _create_decoder_block_config( 'upsample_factor': encoder_block.pool_size, # Mirror the pooling 'kernel_size': self.kernel_size, 'stride': 1, # Always use stride=1 in decoder - 'dropout': encoder_block.dropout_prob, # Match encoder dropout + 'dropout': encoder_block.dropout, # Match encoder dropout 'bias': self.bias, 'use_batch_norm': self.use_batch_norm, 'activation': self.activation_name, - 'upsampling_mode': self.upsampling_mode, 'residual_init_method': self.init_method, } diff --git a/tests/test_train_blocks_decoder.py b/tests/test_train_blocks_decoder.py index 6040c5b..5052269 100644 --- a/tests/test_train_blocks_decoder.py +++ b/tests/test_train_blocks_decoder.py @@ -1,16 +1,406 @@ +import pytest import torch -from src.faith.train.blocks import DecoderBlock, BlockBasedDecoder,EncoderBlock +import torch.nn as nn +from src.faith.train.blocks import DecoderBlock, ResidualBlock + +class TestDecoderBlockInitialization: + """Test DecoderBlock initialization and parameter validation.""" + + def test_basic_initialization(self): + """Test basic DecoderBlock initialization with default parameters.""" + block = DecoderBlock(in_channels=4, out_channels=2) + + assert block.in_channels == 4 + assert block.out_channels == 2 + assert block.upsample_factor == (1, 2) + assert block.dropout == 0.3 + assert block.use_batch_norm is True + assert block.activation_name == "relu" + assert block.residual_init_method == "kaiming" + + def test_custom_initialization(self): + """Test DecoderBlock initialization with custom parameters.""" + block = DecoderBlock( + in_channels=4, + out_channels=2, + upsample_factor=(2, 2), + kernel_size=5, + stride=2, + dropout=0.5, + bias=False, + use_batch_norm=False, + activation='gelu', + residual_init_method='xavier' + ) + + assert block.in_channels == 4 + assert block.out_channels == 2 + assert block.upsample_factor == (2, 2) + assert block.kernel_size == (5, 5) + assert block.dropout == 0.5 + assert block.bias is False + assert block.use_batch_norm is False + assert block.activation_name == "gelu" + assert block.residual_init_method == "xavier" + + def test_operations_creation(self): + """Test that all operations are created correctly.""" + block = DecoderBlock(in_channels=4, out_channels=2) + + assert hasattr(block, "conv_transpose") + assert hasattr(block, "dropout") + assert hasattr(block, "residual_block") + assert len(block.operations) == 3 + + # Check types + assert isinstance(block.conv_transpose, nn.ConvTranspose2d) + assert isinstance(block.dropout_layer, nn.Dropout) + assert isinstance(block.residual_block, ResidualBlock) + + def test_conv_transpose_parameters(self): + """Test ConvTranspose2d layer parameters.""" + block = DecoderBlock( + in_channels=8, + out_channels=4, + upsample_factor=(2, 2), + bias=False, + ) + + conv_transpose = block.conv_transpose + assert conv_transpose.in_channels == 8 + assert conv_transpose.out_channels == 8 # Same as in_channels + assert conv_transpose.kernel_size == (4, 4) + assert conv_transpose.stride == (2, 2) + assert conv_transpose.padding == (1, 1) # (4-1)//2 = 1 + assert conv_transpose.bias is None # bias=False + + def test_operations_order(self): + """Test that operations are in correct order.""" + block = DecoderBlock(in_channels=8, out_channels=4) + + # Should be: ConvTranspose2d, Dropout, ResidualBlock + assert isinstance(block.operations[0], nn.ConvTranspose2d) + assert isinstance(block.operations[1], nn.Dropout) + assert isinstance(block.operations[2], ResidualBlock) + + +class TestDecoderBlockValidation: + """Test parameter validation in DecoderBlock.""" + + def test_invalid_dropout_values(self): + """Test that invalid dropout values raise ValueError.""" + with pytest.raises( + ValueError, match="Dropout must be between 0.0 and 1.0" + ): + DecoderBlock(in_channels=4, out_channels=2, dropout=-0.1) + + with pytest.raises( + ValueError, match="Dropout must be between 0.0 and 1.0" + ): + DecoderBlock(in_channels=4, out_channels=2, dropout=1.5) + + def test_invalid_upsample_factor(self): + """Test that invalid upsample_factor raises ValueError.""" + with pytest.raises( + ValueError, match="upsample_factor must be a tuple of length 2" + ): + DecoderBlock(in_channels=4, out_channels=2, upsample_factor=(2,)) + + with pytest.raises( + ValueError, match="upsample_factor must be a tuple of length 2" + ): + DecoderBlock( + in_channels=4, out_channels=2, upsample_factor=(1, 2, 3) + ) + + def test_boundary_dropout_values(self): + """Test boundary dropout values (0.0 and 1.0).""" + block1 = DecoderBlock(in_channels=4, out_channels=2, dropout=0.0) + assert block1.dropout == 0.0 + + block2 = DecoderBlock(in_channels=4, out_channels=2, dropout=1.0) + assert block2.dropout == 1.0 + + +class TestDecoderBlockForwardPass: + """Test DecoderBlock forward pass functionality.""" + + def test_forward_pass_basic(self): + """Test basic forward pass with default parameters.""" + block = DecoderBlock(in_channels=8, out_channels=4) + x = torch.randn(2, 8, 16, 8) + + output = block(x) + + assert output.shape[0] == 2 # batch size + assert output.shape[1] == 4 # out_channels from ResidualBlock + assert output.shape[2] == 16 # height should be positive + assert output.shape[3] == 16 # width should be positive + + def test_forward_pass_2x1_upsampling(self): + """Test forward pass with 2x1 upsampling.""" + + block = DecoderBlock(in_channels=8, out_channels=4, + upsample_factor=(2, 1)) + x = torch.randn(1, 8, 8, 8) + + output = block(x) + + assert output.shape[0] == 1 + assert output.shape[1] == 4 + # Should approximately double the spatial dimensions + assert output.shape[2] == 16 + assert output.shape[3] == 8 + + def test_forward_pass_no_dropout(self): + """Test forward pass with dropout disabled.""" + block = DecoderBlock(in_channels=4, out_channels=2, dropout=0.0) + x = torch.randn(2, 4, 8, 8) + + output = block(x) + assert output.shape[1] == 2 + + def test_dropout_parameters(self): + """Test that Dropout is created with correct parameters.""" + block = DecoderBlock(in_channels=4, out_channels=2, dropout=0.7) + + dropout_layer = block.dropout_layer + assert dropout_layer.p == 0.7 + + +class TestDecoderBlockConfiguration: + """Test DecoderBlock configuration methods.""" + + def test_get_config(self): + """Test get_config method returns complete configuration.""" + block = DecoderBlock( + in_channels=8, + out_channels=4, + upsample_factor=(2, 2), + dropout=0.5, + activation="gelu", + ) + + config = block.get_config() + + # Check all important parameters are in config + assert config["in_channels"] == 8 + assert config["out_channels"] == 4 + assert config["upsample_factor"] == (2, 2) + assert config["dropout"] == 0.5 + assert config["activation"] == "gelu" + + def test_from_config(self): + """Test from_config class method creates equivalent block.""" + original_block = DecoderBlock( + in_channels=8, + out_channels=4, + upsample_factor=(2, 2), + dropout=0.4, + activation="gelu", + ) + + config = original_block.get_config() + reconstructed_block = DecoderBlock.from_config(config) + + # Check key attributes match + assert reconstructed_block.in_channels == original_block.in_channels + assert reconstructed_block.out_channels == original_block.out_channels + assert ( + reconstructed_block.upsample_factor + == original_block.upsample_factor + ) + assert reconstructed_block.dropout == original_block.dropout + assert ( + reconstructed_block.activation_name + == original_block.activation_name + ) + + def test_config_roundtrip(self): + """Test that config -> block -> config roundtrip works.""" + original_config = { + "in_channels": 4, + "out_channels": 2, + "upsample_factor": (2, 2), + "dropout": 0.3, + "activation": "relu", + } + + block = DecoderBlock.from_config(original_config) + reconstructed_config = block.get_config() + + # Check that key parameters survive roundtrip + for key in original_config: + assert reconstructed_config[key] == original_config[key] + + +class TestDecoderBlockShapeCalculation: + """Test DecoderBlock shape calculation methods.""" + + def test_get_output_shape_basic(self): + """Test get_output_shape with basic parameters.""" + block = DecoderBlock(in_channels=8, out_channels=4) + + input_shape = (2, 8, 16, 8) + output_shape = block.get_output_shape(input_shape) + + assert output_shape[0] == 2 # batch size + assert output_shape[1] == 4 # out_channels from ResidualBlock + assert output_shape[2] == 16 # height should be positive + assert output_shape[3] == 16 # width should be positive + + def test_get_output_shape_2x2_upsampling(self): + """Test get_output_shape with 2x2 upsampling.""" + block = DecoderBlock( + in_channels=4, out_channels=2, upsample_factor=(2, 2) + ) + + input_shape = (1, 4, 8, 8) + output_shape = block.get_output_shape(input_shape) + + assert output_shape[0] == 1 + assert output_shape[1] == 2 + # Should approximately double both dimensions + assert output_shape[2] == 16 + assert output_shape[3] == 16 + + +class TestDecoderBlockRepresentation: + """Test DecoderBlock string representation.""" + + def test_repr_basic(self): + """Test __repr__ method with basic parameters.""" + block = DecoderBlock(in_channels=8, out_channels=4) + repr_str = repr(block) + + assert "DecoderBlock(" in repr_str + assert "in_channels=8" in repr_str + assert "out_channels=4" in repr_str + assert "upsample_factor=(1, 2)" in repr_str + assert "dropout=0.3" in repr_str + assert "activation='relu'" in repr_str + + def test_repr_custom(self): + """Test __repr__ method with custom parameters.""" + block = DecoderBlock( + in_channels=6, + out_channels=8, + upsample_factor=(2, 2), + dropout=0.5, + activation="gelu", + ) + repr_str = repr(block) + + assert "in_channels=6" in repr_str + assert "out_channels=8" in repr_str + assert "upsample_factor=(2, 2)" in repr_str + assert "dropout=0.5" in repr_str + assert "activation='gelu'" in repr_str + + +class TestDecoderBlockIntegration: + """Test DecoderBlock integration aspects.""" + + def test_sequential_block_inheritance(self): + """Test that DecoderBlock inherits from SequentialBlock correctly.""" + block = DecoderBlock(in_channels=8, out_channels=4) + + # Should have SequentialBlock attributes + assert hasattr(block, "operations") + assert hasattr(block, "in_channels") + assert hasattr(block, "out_channels") + + def test_component_access(self): + """Test that individual components can be accessed.""" + block = DecoderBlock(in_channels=8, out_channels=4) + + # Should be able to access individual components + assert hasattr(block, "conv_transpose") + assert hasattr(block, "dropout_layer") + assert hasattr(block, "residual_block") + + # Components should be the same as operations + assert block.conv_transpose is block.operations[0] + assert block.dropout_layer is block.operations[1] + assert block.residual_block is block.operations[2] + + +class TestDecoderBlockEdgeCases: + """Test edge cases and error conditions.""" + + def test_single_channel_input_output(self): + """Test with single channel input and output.""" + block = DecoderBlock(in_channels=1, out_channels=1) + x = torch.randn(1, 1, 16, 16) + + output = block(x) + assert output.shape[1] == 1 + + def test_large_upsampling_factor(self): + """Test with large upsampling factors.""" + block = DecoderBlock( + in_channels=4, out_channels=2, upsample_factor=(4, 4) + ) + x = torch.randn(1, 4, 2, 2) + + output = block(x) + assert output.shape[1] == 2 + assert output.shape[2] == 6 + assert output.shape[3] == 6 + + def test_minimal_spatial_dimensions(self): + """Test with minimal spatial dimensions.""" + block = DecoderBlock(in_channels=4, out_channels=2) + x = torch.randn(1, 4, 1, 1) + + output = block(x) + assert output.shape[1] == 2 + assert output.shape[2] == 1 + assert output.shape[3] == 2 + + +# Fixtures for common test data +@pytest.fixture +def sample_input(): + """Fixture providing sample input tensor.""" + return torch.randn(2, 128, 16, 8) + + +@pytest.fixture +def basic_decoder_block(): + """Fixture providing basic DecoderBlock instance.""" + return DecoderBlock(in_channels=8, out_channels=4) + + +@pytest.fixture +def custom_decoder_block(): + """Fixture providing custom DecoderBlock instance.""" + return DecoderBlock( + in_channels=256, + out_channels=128, + upsample_factor=(2, 2), + dropout=0.4, + activation='gelu' + ) + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) + + + +####################################################################### +""" # Example usage and testing # Test DecoderBlock print("Testing DecoderBlock...") decoder_block = DecoderBlock( - in_channels=128, - out_channels=64, + in_channels=8, + out_channels=4, upsample_factor=(1, 2), dropout=0.3, - upsampling_mode='nearest', activation='relu' ) @@ -55,6 +445,6 @@ encoder_blocks=mock_encoder_blocks, bottleneck_channels=64, output_channels=80, - upsampling_mode='bilinear' ) print(f"Decoder from encoder: {decoder2}") +""" diff --git a/tests/test_train_blocks_encoder.py b/tests/test_train_blocks_encoder.py index d41df8c..57f744d 100644 --- a/tests/test_train_blocks_encoder.py +++ b/tests/test_train_blocks_encoder.py @@ -409,54 +409,3 @@ def test_inference_speed(self): # Should complete in reasonable time (adjust threshold as needed) assert avg_time < 1e-2 # Less than 10 milliseconds per forward pass assert output.shape == (16, 8, 32, 16) # Verify correctness - - -if __name__ == "__main__": - pytest.main([__file__, "-v", "--tb=short"]) - - - -######################################################################## -""" -# Test EncoderBlock -print("Testing EncoderBlock...") -encoder_block = EncoderBlock( - in_channels=2, - out_channels=4, - pool_size=(1, 2), - dropout=0.3, - activation='relu' -) - -x = torch.randn(1, 64, 32, 32) -output = encoder_block(x) -print(f"EncoderBlock - Input: {x.shape}, Output: {output.shape}") - -# Test configuration -config = encoder_block.get_config() -new_block = EncoderBlock.from_config(config) -print(f"Config serialization successful: {new_block}") - -# Test BlockBasedEncoder -print("\nTesting BlockBasedEncoder...") -block_configs = [ - {'out_channels': 128, 'pool_size': (1, 2), 'dropout': 0.2}, - {'out_channels': 256, 'pool_size': (1, 4), 'dropout': 0.3}, - {'out_channels': 128, 'pool_size': (1, 2), 'dropout': 0.4}, -] - -encoder = BlockBasedEncoder( - input_channels=80, - block_configs=block_configs, - hidden_dim=16, - bottleneck_channels=64 -) - -x = torch.randn(2, 80, 100, 128) -latent = encoder(x) -print(f"Encoder - Input: {x.shape}, Output: {latent.shape}") - -# Test feature map extraction -feature_maps = encoder.get_feature_maps(x) -print(f"Feature maps shapes: {[fm.shape for fm in feature_maps]}") -""" From fdcc7024c3dfcefa1ca409f92389293bf4332c5e Mon Sep 17 00:00:00 2001 From: Peter Steiner <61472983+renierts@users.noreply.github.com> Date: Wed, 16 Jul 2025 22:23:08 -0400 Subject: [PATCH 063/103] Reworked on BlockBasedEncoder and BlockBasedDecoder. Still needs to be tested. --- src/faith/train/blocks/base.py | 42 +- src/faith/train/blocks/decoder.py | 543 ++++++++------- src/faith/train/blocks/encoder.py | 362 ++++------ .../test_train_blocks_block_based_decoder.py | 617 ++++++++++++++++++ .../test_train_blocks_block_based_encoder.py | 588 +++++++++++++++++ 5 files changed, 1593 insertions(+), 559 deletions(-) create mode 100644 tests/test_train_blocks_block_based_decoder.py create mode 100644 tests/test_train_blocks_block_based_encoder.py diff --git a/src/faith/train/blocks/base.py b/src/faith/train/blocks/base.py index 20f5aaf..7569be3 100644 --- a/src/faith/train/blocks/base.py +++ b/src/faith/train/blocks/base.py @@ -6,11 +6,12 @@ patterns for initialization, forward passes, and configuration. """ +import math +from abc import ABC, abstractmethod +from typing import Any, Optional, Union + import torch import torch.nn as nn -from abc import ABC, abstractmethod -from typing import Union, Any, Optional -import math class BaseConvBlock(nn.Module, ABC): @@ -196,41 +197,6 @@ def add_operation(self, operation: nn.Module) -> None: self.operations.add_module(str(len(self.operations)), operation) -class ConfigurableBlock(BaseConvBlock): - """ - Base class for blocks with extensive configuration options. - - This class provides utilities for blocks that need to handle complex - configuration dictionaries and parameter validation. - """ - - def __init__(self, **kwargs) -> None: - # Extract base parameters - in_channels = kwargs.pop('in_channels') - out_channels = kwargs.pop('out_channels') - kernel_size = kwargs.pop('kernel_size', 3) - bias = kwargs.pop('bias', True) - - super().__init__(in_channels, out_channels, kernel_size, bias) - - # Store additional configuration - self._config = kwargs - - def get_config(self) -> dict[str, Any]: - """Get full configuration including additional parameters.""" - config = super().get_config() - config.update(self._config) - return config - - @classmethod - def from_config( - cls, - config: dict[str, Any] - ) -> 'ConfigurableBlock': - """Create block instance from configuration dictionary.""" - return cls(**config) - - class WeightInitializer: """Utilities for weight initialization in blocks.""" diff --git a/src/faith/train/blocks/decoder.py b/src/faith/train/blocks/decoder.py index 4fc7d67..7128904 100644 --- a/src/faith/train/blocks/decoder.py +++ b/src/faith/train/blocks/decoder.py @@ -5,13 +5,13 @@ The decoder creates a symmetric reconstruction path to the encoder. """ -from typing import Any, Optional, Union +from typing import Any, Union import torch import torch.nn as nn -from .base import ConfigurableBlock, SequentialBlock, WeightInitializer -from .encoder import EncoderBlock +from .base import SequentialBlock +from .encoder import BlockBasedEncoder from .residual import ResidualBlock @@ -255,359 +255,336 @@ def __repr__(self) -> str: ) -class BlockBasedDecoder(ConfigurableBlock): +class BlockBasedDecoder(SequentialBlock): """Decoder architecture built from a sequence of DecoderBlocks. - This decoder mirrors the encoder architecture by using the encoder's - block configuration to create a symmetric upsampling path. Each decoding - stage consists of a DecoderBlock (Upsample + ResidualBlock + Dropout). + This decoder provides a flexible architecture where each decoding stage + consists of a DecoderBlock with configurable parameters. The blocks are + automatically chained together with matching input/output channels. Parameters ---------- - output_channels : int - Number of channels in the final output (should match encoder input). - encoder_blocks : list of EncoderBlock - List of encoder blocks to create symmetric decoder from. - bottleneck_channels : int - Number of channels from the encoder's bottleneck. - kernel_size : int or tuple of int, default=3 - Default kernel size for convolutions. - bias : bool, default=True - Default bias setting for convolutions. - upsampling_mode : str, default='nearest' - Upsampling algorithm for all decoder blocks. - use_batch_norm : bool, default=True - Whether to use batch normalization in blocks. - activation : str, default='relu' - Default activation function for blocks. - init_method : str, default='kaiming' - Weight initialization method. - reconstruction_kernel_size : int or tuple of int, optional - Kernel size for final reconstruction layer. If None, uses kernel_size. + in_channels : int + Number of input channels (typically from encoder bottleneck). + block_configs : list of dict + Configuration for each decoder block. Each dict should contain: + - 'out_channels' (int): Output channels for the block (required) + - 'upsample_factor' (tuple, optional): Upsampling factor, + default (1, 2) + - 'dropout' (float, optional): Dropout probability, default 0.3 + - 'kernel_size' (int/tuple, optional): Conv kernel size, default 3 + - 'activation' (str, optional): Activation function, default 'relu' + - 'use_batch_norm' (bool, optional): Use batch norm, default True + - 'bias' (bool, optional): Use bias in convolutions, default True Attributes ---------- - decoder_start : nn.Sequential - Initial layers to process bottleneck output. - blocks : nn.ModuleList - List of DecoderBlock modules. - reconstruction : nn.Conv2d - Final reconstruction convolution layer. - output_channels : int - Number of output channels. - upsampling_mode : str - Upsampling mode used throughout decoder. + blocks : list of DecoderBlock + List of DecoderBlock modules (accessed via self.operations). + block_configs : list of dict + Stored block configurations. + + Examples + -------- + >>> # Simple 3-block decoder (reverse of encoder) + >>> configs = [ + ... {'out_channels': 128, 'upsample_factor': (2, 2)}, + ... {'out_channels': 64, 'upsample_factor': (1, 2)}, + ... {'out_channels': 3} # Final output channels + ... ] + >>> decoder = BlockBasedDecoder(in_channels=256, block_configs=configs) + >>> z = torch.randn(1, 256, 8, 4) + >>> output = decoder(z) + + >>> # Create decoder that mirrors an encoder + >>> encoder_configs = [ + ... {'out_channels': 64}, + ... {'out_channels': 128, 'pool_size': (2, 2)}, + ... {'out_channels': 256, 'pool_size': (1, 2)} + ... ] + >>> decoder_configs = BlockBasedDecoder.reverse_encoder_configs( + ... encoder_configs, final_out_channels=3 + ... ) + >>> decoder = BlockBasedDecoder(in_channels=256, + ... block_configs=decoder_configs) """ def __init__( - self, - output_channels: int, - encoder_blocks: list[EncoderBlock], - bottleneck_channels: int, - kernel_size: Union[int, tuple[int, int]] = 3, - bias: bool = True, - upsampling_mode: str = 'nearest', - use_batch_norm: bool = True, - activation: str = 'relu', - init_method: str = 'kaiming', - reconstruction_kernel_size: - Optional[Union[int, tuple[int, int]]] = None, - **kwargs + self, + in_channels: int, + block_configs: list[dict[str, Any]], + kernel_size: Union[int, tuple[int, int]] = 3, + bias: bool = True, ) -> None: """Initialize BlockBasedDecoder.""" - # Initialize ConfigurableBlock - super().__init__( - in_channels=bottleneck_channels, - out_channels=output_channels, - kernel_size=kernel_size, - bias=bias, - encoder_blocks=encoder_blocks, - bottleneck_channels=bottleneck_channels, - upsampling_mode=upsampling_mode, - use_batch_norm=use_batch_norm, - activation=activation, - init_method=init_method, - reconstruction_kernel_size=reconstruction_kernel_size, - **kwargs - ) - # Validate inputs - if output_channels <= 0: - raise ValueError(f"output_channels must be positive, " - f"got {output_channels}") - - if bottleneck_channels <= 0: - raise ValueError(f"bottleneck_channels must be positive, " - f"got {bottleneck_channels}") - - self.output_channels = output_channels - self.encoder_blocks = encoder_blocks - self.bottleneck_channels = bottleneck_channels - self.upsampling_mode = upsampling_mode - self.use_batch_norm = use_batch_norm - self.activation_name = activation - self.init_method = init_method - - if reconstruction_kernel_size is None: - reconstruction_kernel_size = kernel_size - self.reconstruction_kernel_size = reconstruction_kernel_size - - # Build decoder components - self.decoder_start = self._build_decoder_start(kernel_size, bias) - self.blocks = self._build_decoder_blocks() - self.reconstruction = self._build_reconstruction_layer() - - # Initialize weights - self._initialize_weights() - - def _build_decoder_start( - self, - kernel_size: Union[int, tuple[int, int]], - bias: bool - ) -> nn.Sequential: - """Build the initial decoder layers to process bottleneck output.""" - # Calculate padding for convolution - if isinstance(kernel_size, int): - padding = kernel_size // 2 - else: - padding = tuple(k // 2 for k in kernel_size) - - # Determine first block channels - first_block_channels = ( - self.encoder_blocks[-1].out_channels - if self.encoder_blocks - else self.bottleneck_channels - ) + if not block_configs: + raise ValueError("block_configs cannot be empty") - layers = [ - nn.Conv2d( - self.bottleneck_channels, - first_block_channels, - kernel_size=kernel_size, - padding=padding, - bias=bias and not self.use_batch_norm + if in_channels <= 0: + raise ValueError( + f"in_channels must be positive, got {in_channels}" ) - ] - if self.use_batch_norm: - layers.append(nn.BatchNorm2d(first_block_channels)) + # Validate that all configs have out_channels + for i, config in enumerate(block_configs): + if "out_channels" not in config: + raise ValueError( + f"Block {i} missing required 'out_channels' key" + ) + if config["out_channels"] <= 0: + raise ValueError(f"out_channels must be positive, " + f"got {config['out_channels']} in block {i}") - layers.append(self._create_activation(self.activation_name)) + self.block_configs = block_configs - return nn.Sequential(*layers) + # Build decoder blocks + operations = self._build_decoder_blocks(in_channels, kernel_size, bias) - def _build_decoder_blocks(self) -> nn.ModuleList: - """Build decoder blocks by mirroring encoder blocks.""" - blocks = [] + # Get final output channels from last block + final_out_channels = block_configs[-1]["out_channels"] - # Get the output channels from decoder_start - current_channels = ( - self.encoder_blocks[-1].out_channels - if self.encoder_blocks - else self.bottleneck_channels + # Initialize SequentialBlock with operations + super().__init__( + in_channels=in_channels, + out_channels=final_out_channels, + operations=operations, + kernel_size=kernel_size, + bias=bias, ) - # Create symmetric decoder by reversing encoder blocks - for i, encoder_block in enumerate(reversed(self.encoder_blocks)): - # Determine output channels for this decoder block - if i == len(self.encoder_blocks) - 1: - # Last block outputs to final channels - out_channels = self.output_channels - else: - # Use the input channels of the corresponding encoder block - corresponding_encoder_idx = len(self.encoder_blocks) - 2 - i - out_channels = ( - self.encoder_blocks[corresponding_encoder_idx].in_channels) - - # Create decoder block configuration - block_config = self._create_decoder_block_config( - encoder_block, current_channels, out_channels - ) + def _build_decoder_blocks( + self, + in_channels: int, + default_kernel_size: Union[int, tuple[int, int]], + default_bias: bool, + ) -> list[nn.Module]: + """ + Build the sequence of decoder blocks with automatic channel chaining. + """ + blocks = [] + current_channels = in_channels + + for i, config in enumerate(self.block_configs): + # Prepare block configuration with defaults + block_config = { + "in_channels": current_channels, + "out_channels": config["out_channels"], + "upsample_factor": config.get("upsample_factor", (1, 2)), + "kernel_size": config.get("kernel_size", default_kernel_size), + "stride": config.get("stride", 1), + "dropout": config.get("dropout", 0.3), + "bias": config.get("bias", default_bias), + "use_batch_norm": config.get("use_batch_norm", True), + "activation": config.get("activation", "relu"), + "residual_init_method": config.get( + "residual_init_method", "kaiming" + ), + } # Create decoder block - decoder_block = DecoderBlock(**block_config) - blocks.append(decoder_block) - current_channels = out_channels + block = DecoderBlock(**block_config) + blocks.append(block) - return nn.ModuleList(blocks) + # Update current channels for next block + current_channels = config["out_channels"] - def _create_decoder_block_config( - self, - encoder_block: EncoderBlock, - in_channels: int, - out_channels: int - ) -> dict[str, Any]: - """Create configuration for a decoder block based on encoder block.""" - return { - 'in_channels': in_channels, - 'out_channels': out_channels, - 'upsample_factor': encoder_block.pool_size, # Mirror the pooling - 'kernel_size': self.kernel_size, - 'stride': 1, # Always use stride=1 in decoder - 'dropout': encoder_block.dropout, # Match encoder dropout - 'bias': self.bias, - 'use_batch_norm': self.use_batch_norm, - 'activation': self.activation_name, - 'residual_init_method': self.init_method, - } - - def _build_reconstruction_layer(self) -> nn.Conv2d: - """Build the final reconstruction convolution layer.""" - # Calculate padding for reconstruction layer - if isinstance(self.reconstruction_kernel_size, int): - padding = self.reconstruction_kernel_size // 2 - else: - padding = tuple(k // 2 for k in self.reconstruction_kernel_size) - - return nn.Conv2d( - self.output_channels, - self.output_channels, - kernel_size=self.reconstruction_kernel_size, - padding=padding, - bias=self.bias - ) + return blocks + + def get_feature_maps(self, x: torch.Tensor) -> list[torch.Tensor]: + """Get intermediate feature maps from each decoder block. - def _create_activation(self, activation: str) -> nn.Module: - """Create activation function based on name.""" - activations = { - 'relu': nn.ReLU(inplace=True), - 'leaky_relu': nn.LeakyReLU(0.1, inplace=True), - 'gelu': nn.GELU(), - 'swish': nn.SiLU(), - 'mish': nn.Mish(), - } - if activation not in activations: - raise ValueError(f"Unknown activation: {activation}") - return activations[activation] - - def _initialize_weights(self) -> None: - """Initialize weights according to the specified method.""" - if self.init_method == 'kaiming': - self.apply(WeightInitializer.kaiming_normal_) - elif self.init_method == 'xavier': - self.apply(WeightInitializer.xavier_uniform_) - - # Always properly initialize batch norm - if self.use_batch_norm: - self.apply(WeightInitializer.init_batch_norm_) - - def forward(self, z: torch.Tensor) -> torch.Tensor: - """Forward pass through the decoder. + Useful for visualization, debugging, and skip connections. Parameters ---------- - z : torch.Tensor - Latent representation with shape - (batch_size, bottleneck_channels, height, width). + x : torch.Tensor + Input tensor. Returns ------- - torch.Tensor - Reconstructed output with shape - (batch_size, output_channels, height', width') where height' - and width' are restored to approximate original input dimensions. + list of torch.Tensor + Feature maps after each decoder block. """ - # Process through decoder start - x = self.decoder_start(z) + feature_maps = [] - # Process through decoder blocks - for block in self.blocks: + for block in self.operations: x = block(x) + feature_maps.append(x.clone()) - # Final reconstruction - x = self.reconstruction(x) + return feature_maps - return x + def get_channel_progression(self) -> list[int]: + """Get the channel count progression through the decoder. - def get_feature_maps(self, z: torch.Tensor) -> list[torch.Tensor]: - """Get intermediate feature maps from each decoder block. + Returns + ------- + list of int + Channel counts: [in_channels, block1_out, block2_out, ...] + """ + channels = [self.in_channels] + for config in self.block_configs: + channels.append(config["out_channels"]) + return channels + + def get_config(self) -> dict[str, Any]: + """Get configuration dictionary for this decoder.""" + config = super().get_config() + config.update( + { + "block_configs": self.block_configs, + } + ) + return config + + @classmethod + def from_config(cls, config: dict[str, Any]) -> "BlockBasedDecoder": + """Create BlockBasedDecoder instance from configuration dictionary.""" + return cls(**config) + + @classmethod + def reverse_encoder_configs( + cls, + encoder_configs: list[dict[str, Any]], + final_out_channels: int + ) -> list[dict[str, Any]]: + """Create decoder configs that reverse an encoder's configuration. - Useful for visualization and debugging. + This method helps create symmetric encoder-decoder architectures by + automatically generating decoder configs that mirror the encoder. Parameters ---------- - z : torch.Tensor - Latent representation. + encoder_configs : list of dict + Encoder block configurations with 'out_channels' and optional + 'pool_size'. + final_out_channels : int + Number of output channels for the final decoder block. Returns ------- - list of torch.Tensor - Feature maps after each decoder block. + list of dict + Decoder block configurations with reversed channel progression + and mirrored upsampling factors. + + Examples + -------- + >>> encoder_configs = [ + ... {'out_channels': 64, 'pool_size': (1, 2)}, + ... {'out_channels': 128, 'pool_size': (2, 2)}, + ... {'out_channels': 256, 'pool_size': (1, 2)} + ... ] + >>> decoder_configs = BlockBasedDecoder.reverse_encoder_configs( + ... encoder_configs, final_out_channels=3 + ... ) + >>> # Result: [ + >>> # {'out_channels': 128, 'upsample_factor': (1, 2)}, + >>> # {'out_channels': 64, 'upsample_factor': (2, 2)}, + >>> # {'out_channels': 3, 'upsample_factor': (1, 2)} + >>> # ] """ - feature_maps = [] + if not encoder_configs: + raise ValueError("encoder_configs cannot be empty") - # Process through decoder start - x = self.decoder_start(z) - feature_maps.append(x.clone()) + # Get channel progression from encoder + encoder_channels = [] + for config in encoder_configs: + encoder_channels.append(config["out_channels"]) - # Process through decoder blocks - for block in self.blocks: - x = block(x) - feature_maps.append(x.clone()) + # Create reversed decoder configs + decoder_configs = [] - return feature_maps + # Reverse the encoder configs + for i, encoder_config in enumerate(reversed(encoder_configs)): + # Determine output channels for this decoder block + if i == len(encoder_configs) - 1: + # Last decoder block outputs final channels + out_channels = final_out_channels + else: + # Use the input channels from the corresponding encoder stage + # For encoder: input -> block1 -> block2 -> block3 + # For decoder: block3_out -> block2_in -> block1_in -> input + corresponding_encoder_idx = len(encoder_configs) - 2 - i + if corresponding_encoder_idx == 0: + # This would be the original input channels to the encoder + # We'll use the final_out_channels as a reasonable guess + out_channels = final_out_channels + else: + out_channels = encoder_configs[ + corresponding_encoder_idx - 1 + ]["out_channels"] + + # Create decoder config + decoder_config = { + "out_channels": out_channels, + "upsample_factor": encoder_config.get("pool_size", (1, 2)), + } - def get_output_shape( - self, - input_shape: tuple[int, ...] - ) -> tuple[int, ...]: - """Calculate output shape given input shape.""" - current_shape = input_shape - - # Apply decoder start (changes channels but not spatial dims) - batch_size, _, height, width = current_shape - first_channels = ( - self.encoder_blocks[-1].out_channels - if self.encoder_blocks - else self.bottleneck_channels - ) - current_shape = (batch_size, first_channels, height, width) + # Copy other relevant parameters + for key in [ + "dropout", + "kernel_size", + "activation", + "use_batch_norm", + "bias", + ]: + if key in encoder_config: + decoder_config[key] = encoder_config[key] - # Apply each decoder block - for block in self.blocks: - current_shape = block.get_output_shape(current_shape) + decoder_configs.append(decoder_config) - # Final reconstruction doesn't change shape - return current_shape + return decoder_configs @classmethod def from_encoder( cls, - encoder_blocks: list[EncoderBlock], - bottleneck_channels: int, - output_channels: int, + encoder: "BlockBasedEncoder", + final_out_channels: int, **kwargs - ) -> 'BlockBasedDecoder': - """Create decoder that mirrors the given encoder blocks. + ) -> "BlockBasedDecoder": + """Create decoder that mirrors a BlockBasedEncoder. Parameters ---------- - encoder_blocks : list of EncoderBlock - Encoder blocks to mirror. - bottleneck_channels : int - Number of channels from encoder bottleneck. - output_channels : int - Number of output channels. + encoder : BlockBasedEncoder + Encoder to mirror. + final_out_channels : int + Number of output channels for the decoder. **kwargs Additional arguments for decoder configuration. Returns ------- BlockBasedDecoder - Configured decoder instance. + Configured decoder instance that mirrors the encoder. + + Examples + -------- + >>> encoder = BlockBasedEncoder(in_channels=3, block_configs=[...]) + >>> decoder = BlockBasedDecoder.from_encoder(encoder, + ... final_out_channels=3) """ + # Create reversed configs from encoder + decoder_configs = cls.reverse_encoder_configs( + encoder.block_configs, final_out_channels + ) + return cls( - output_channels=output_channels, - encoder_blocks=encoder_blocks, - bottleneck_channels=bottleneck_channels, - **kwargs + in_channels=encoder.out_channels, + block_configs=decoder_configs, + **kwargs, ) + @property + def blocks(self) -> list[nn.Module]: + """Access to decoder blocks for compatibility.""" + return list(self.operations) + def __repr__(self) -> str: """String representation of the BlockBasedDecoder.""" + channel_progression = ' → '.join( + map(str, self.get_channel_progression())) return (f"BlockBasedDecoder(" - f"output_channels={self.output_channels}, " - f"num_blocks={len(self.blocks)}, " - f"bottleneck_channels={self.bottleneck_channels}, " - f"upsampling_mode='{self.upsampling_mode}')") + f"blocks={len(self.operations)}, " + f"channels={channel_progression})") diff --git a/src/faith/train/blocks/encoder.py b/src/faith/train/blocks/encoder.py index 97876f2..1374267 100644 --- a/src/faith/train/blocks/encoder.py +++ b/src/faith/train/blocks/encoder.py @@ -4,10 +4,12 @@ inherit from the base classes, following established patterns and interfaces. """ +from typing import Any, Union + import torch import torch.nn as nn -from typing import Union, Any, Optional -from .base import SequentialBlock, ConfigurableBlock, WeightInitializer + +from .base import SequentialBlock from .residual import ResidualBlock @@ -193,24 +195,26 @@ def get_output_shape(self, input_shape: tuple[int, ...]) \ pooled_height = height // self.pool_size[0] pooled_width = width // self.pool_size[1] - return (batch_size, channels, pooled_height, pooled_width) + return batch_size, channels, pooled_height, pooled_width def __repr__(self) -> str: """String representation of the EncoderBlock.""" - return (f"EncoderBlock(" - f"in_channels={self.in_channels}, " - f"out_channels={self.out_channels}, " - f"pool_size={self.pool_size}, " - f"dropout={self.dropout_prob}, " - f"activation='{self.activation_name}')") + return ( + f"EncoderBlock(" + f"in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"pool_size={self.pool_size}, " + f"dropout={self.dropout_prob}, " + f"activation='{self.activation_name}')" + ) -class BlockBasedEncoder(ConfigurableBlock): +class BlockBasedEncoder(SequentialBlock): """Encoder architecture built from a sequence of EncoderBlocks. This encoder provides a flexible architecture where each encoding stage - consists of an EncoderBlock (ResidualBlock + Dropout + MaxPool) followed - by an optional bottleneck compression layer. + consists of an EncoderBlock with configurable parameters. The blocks are + automatically chained together with matching input/output channels. Parameters ---------- @@ -218,252 +222,121 @@ class BlockBasedEncoder(ConfigurableBlock): Number of input channels in the data. block_configs : list of dict Configuration for each encoder block. Each dict should contain: - - 'out_channels' (int): Output channels for the block + - 'out_channels' (int): Output channels for the block (required) - 'pool_size' (tuple, optional): MaxPool kernel size, default (1, 2) - 'dropout' (float, optional): Dropout probability, default 0.3 - 'kernel_size' (int/tuple, optional): Conv kernel size, default 3 + - 'activation' (str, optional): Activation function, default 'relu' + - 'use_batch_norm' (bool, optional): Use batch norm, default True - 'bias' (bool, optional): Use bias in convolutions, default True - - Other ResidualBlock parameters (activation, use_batch_norm, etc.) - bottleneck_channels : int, optional - Number of channels in the bottleneck layer. If None, defaults to - max(16, last_block_channels // 2). - hidden_dim : int, optional - Target frequency dimension after adaptive pooling. If None, no - adaptive pooling is applied. - kernel_size : int or tuple of int, default=3 - Default kernel size for blocks that don't specify one. - bias : bool, default=True - Default bias setting for blocks that don't specify one. - bottleneck_activation : str, default='relu' - Activation function for bottleneck layer. - bottleneck_init_method : str, default='kaiming' - Weight initialization method for bottleneck. Attributes ---------- - blocks : nn.ModuleList - List of EncoderBlock modules. - bottleneck : nn.Sequential - Bottleneck compression layers. - bottleneck_channels : int - Number of channels in the bottleneck output. + blocks : list of EncoderBlock + List of EncoderBlock modules (accessed via self.operations). block_configs : list of dict Stored block configurations. - hidden_dim : int or None - Stored target frequency dimension. + + Examples + -------- + >>> # Simple 3-block encoder + >>> configs = [ + ... {'out_channels': 64}, + ... {'out_channels': 128, 'pool_size': (2, 2)}, + ... {'out_channels': 256, 'dropout': 0.5} + ... ] + >>> encoder = BlockBasedEncoder(in_channels=3, block_configs=configs) + >>> x = torch.randn(1, 3, 32, 64) + >>> output = encoder(x) """ def __init__( - self, - in_channels: int, - block_configs: list[dict[str, Any]], - bottleneck_channels: Optional[int] = None, - hidden_dim: Optional[int] = None, - kernel_size: Union[int, tuple[int, int]] = 3, - bias: bool = True, - bottleneck_activation: str = 'relu', - bottleneck_init_method: str = 'kaiming', - **kwargs + self, + in_channels: int, + block_configs: list[dict[str, Any]], + kernel_size: Union[int, tuple[int, int]] = 3, + bias: bool = True, ) -> None: """Initialize BlockBasedEncoder.""" - # Initialize ConfigurableBlock - super().__init__( - in_channels=in_channels, - out_channels=in_channels, # Will be updated after building - kernel_size=kernel_size, - bias=bias, - block_configs=block_configs, - bottleneck_channels=bottleneck_channels, - hidden_dim=hidden_dim, - bottleneck_activation=bottleneck_activation, - bottleneck_init_method=bottleneck_init_method, - **kwargs - ) - # Validate inputs if not block_configs: raise ValueError("block_configs cannot be empty") if in_channels <= 0: raise ValueError( - f"in_channels must be positive, got {in_channels}") + f"in_channels must be positive, got {in_channels}" + ) + + # Validate that all configs have out_channels + for i, config in enumerate(block_configs): + if "out_channels" not in config: + raise ValueError( + f"Block {i} missing required 'out_channels' key" + ) + if config["out_channels"] <= 0: + raise ValueError(f"out_channels must be positive, " + f"got {config['out_channels']} in block {i}") - self.in_channels = in_channels self.block_configs = block_configs - self.hidden_dim = hidden_dim - self.bottleneck_activation = bottleneck_activation - self.bottleneck_init_method = bottleneck_init_method # Build encoder blocks - self.blocks = self._build_encoder_blocks() + operations = self._build_encoder_blocks(in_channels, kernel_size, bias) - # Build bottleneck - self.bottleneck, self.bottleneck_channels = self._build_bottleneck( - bottleneck_channels, kernel_size, bias - ) + # Get final output channels from last block + final_out_channels = block_configs[-1]["out_channels"] - # Update out_channels after building - self.out_channels = self.bottleneck_channels + # Initialize SequentialBlock with operations + super().__init__( + in_channels=in_channels, + out_channels=final_out_channels, + operations=operations, + kernel_size=kernel_size, + bias=bias, + ) - def _build_encoder_blocks(self) -> nn.ModuleList: - """Build the sequence of encoder blocks.""" + def _build_encoder_blocks( + self, + in_channels: int, + default_kernel_size: Union[int, tuple[int, int]], + default_bias: bool, + ) -> list[nn.Module]: + """ + Build the sequence of encoder blocks with automatic channel chaining. + """ blocks = [] - current_channels = self.in_channels + current_channels = in_channels for i, config in enumerate(self.block_configs): - if 'out_channels' not in config: - raise ValueError( - f"Block {i} missing required 'out_channels' key") - - # Extract config with defaults - block_config = self._prepare_block_config(config, current_channels) - - # Validate channels - out_channels = block_config['out_channels'] - if out_channels <= 0: - raise ValueError(f"out_channels must be positive, " - f"got {out_channels} in block {i}") + # Prepare block configuration with defaults + block_config = { + "in_channels": current_channels, + "out_channels": config["out_channels"], + "pool_size": config.get("pool_size", (1, 2)), + "kernel_size": config.get("kernel_size", default_kernel_size), + "stride": config.get("stride", 1), + "dropout": config.get("dropout", 0.3), + "bias": config.get("bias", default_bias), + "use_batch_norm": config.get("use_batch_norm", True), + "activation": config.get("activation", "relu"), + "residual_init_method": config.get( + "residual_init_method", "kaiming" + ), + } # Create encoder block block = EncoderBlock(**block_config) blocks.append(block) - current_channels = out_channels - return nn.ModuleList(blocks) - - def _prepare_block_config( - self, - config: dict[str, Any], - current_channels: int - ) -> dict[str, Any]: - """Prepare block configuration with defaults.""" - block_config = { - 'in_channels': current_channels, - 'out_channels': config['out_channels'], - 'pool_size': config.get('pool_size', (1, 2)), - 'kernel_size': config.get('kernel_size', self.kernel_size), - 'stride': config.get('stride', 1), - 'dropout': config.get('dropout', 0.3), - 'bias': config.get('bias', self.bias), - 'use_batch_norm': config.get('use_batch_norm', True), - 'activation': config.get('activation', 'relu'), - 'residual_init_method': config.get( - 'residual_init_method', 'kaiming'), - } - return block_config - - def _build_bottleneck( - self, - bottleneck_channels: Optional[int], - kernel_size: Union[int, tuple[int, int]], - bias: bool - ) -> tuple[nn.Sequential, int]: - """Build the bottleneck compression layers.""" - bottleneck_layers = [] - - # Get input channels from last block - if self.blocks: - current_channels = self.blocks[-1].out_channels - else: - current_channels = self.in_channels - - # Optional adaptive pooling - if self.hidden_dim is not None: - if self.hidden_dim <= 0: - raise ValueError(f"hidden_dim must be positive, " - f"got {self.hidden_dim}") - bottleneck_layers.append( - nn.AdaptiveAvgPool2d((None, self.hidden_dim))) - - # Channel compression - if bottleneck_channels is None: - bottleneck_channels = max(16, current_channels // 2) - - if bottleneck_channels <= 0: - raise ValueError(f"bottleneck_channels must be positive, " - f"got {bottleneck_channels}") - - # Calculate padding for bottleneck convolution - if isinstance(kernel_size, int): - padding = kernel_size // 2 - else: - padding = tuple(k // 2 for k in kernel_size) - - # Add compression layers - bottleneck_layers.extend([ - nn.Conv2d( - current_channels, - bottleneck_channels, - kernel_size=kernel_size, - padding=padding, - bias=bias - ), - nn.BatchNorm2d(bottleneck_channels), - self._create_activation(self.bottleneck_activation), - ]) - - bottleneck = nn.Sequential(*bottleneck_layers) - - # Initialize bottleneck weights - self._initialize_bottleneck_weights(bottleneck) - - return bottleneck, bottleneck_channels - - def _create_activation(self, activation: str) -> nn.Module: - """Create activation function based on name.""" - activations = { - 'relu': nn.ReLU(inplace=True), - 'leaky_relu': nn.LeakyReLU(0.1, inplace=True), - 'gelu': nn.GELU(), - 'swish': nn.SiLU(), - 'mish': nn.Mish(), - } - if activation not in activations: - raise ValueError(f"Unknown activation: {activation}") - return activations[activation] - - def _initialize_bottleneck_weights( - self, - bottleneck: nn.Sequential - ) -> None: - """Initialize bottleneck weights.""" - if self.bottleneck_init_method == 'kaiming': - bottleneck.apply(WeightInitializer.kaiming_normal_) - elif self.bottleneck_init_method == 'xavier': - bottleneck.apply(WeightInitializer.xavier_uniform_) + # Update current channels for next block + current_channels = config["out_channels"] - # Always properly initialize batch norm - bottleneck.apply(WeightInitializer.init_batch_norm_) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """Forward pass through the encoder. - - Parameters - ---------- - x : torch.Tensor - Input tensor with shape (batch_size, in_channels, height, width) - - Returns - ------- - torch.Tensor - Encoded latent representation with shape - (batch_size, bottleneck_channels, height', width') where height' - and width' depend on the pooling operations and hidden_dim. - """ - # Pass through encoder blocks - for block in self.blocks: - x = block(x) - - # Pass through bottleneck - x = self.bottleneck(x) - - return x + return blocks def get_feature_maps(self, x: torch.Tensor) -> list[torch.Tensor]: """Get intermediate feature maps from each encoder block. - Useful for visualization and debugging. + Useful for visualization, debugging, and skip connections. Parameters ---------- @@ -477,36 +350,49 @@ def get_feature_maps(self, x: torch.Tensor) -> list[torch.Tensor]: """ feature_maps = [] - for block in self.blocks: + for block in self.operations: x = block(x) feature_maps.append(x.clone()) return feature_maps - def get_output_shape( - self, - input_shape: tuple[int, ...] - ) -> tuple[int, ...]: - """Calculate output shape given input shape.""" - current_shape = input_shape + def get_channel_progression(self) -> list[int]: + """Get the channel count progression through the encoder. + + Returns + ------- + list of int + Channel counts: [in_channels, block1_out, block2_out, ...] + """ + channels = [self.in_channels] + for config in self.block_configs: + channels.append(config["out_channels"]) + return channels - # Apply each encoder block - for block in self.blocks: - current_shape = block.get_output_shape(current_shape) + def get_config(self) -> dict[str, Any]: + """Get configuration dictionary for this encoder.""" + config = super().get_config() + config.update( + { + "block_configs": self.block_configs, + } + ) + return config - # Apply adaptive pooling if present - if self.hidden_dim is not None: - batch_size, channels, height, _ = current_shape - current_shape = (batch_size, channels, height, self.hidden_dim) + @classmethod + def from_config(cls, config: dict[str, Any]) -> "BlockBasedEncoder": + """Create BlockBasedEncoder instance from configuration dictionary.""" + return cls(**config) - # Apply bottleneck channel reduction - batch_size, _, height, width = current_shape - return (batch_size, self.bottleneck_channels, height, width) + @property + def blocks(self) -> list[nn.Module]: + """Access to encoder blocks for compatibility.""" + return list(self.operations) def __repr__(self) -> str: """String representation of the BlockBasedEncoder.""" + channel_progression = ' → '.join( + map(str, self.get_channel_progression())) return (f"BlockBasedEncoder(" - f"in_channels={self.in_channels}, " - f"num_blocks={len(self.blocks)}, " - f"bottleneck_channels={self.bottleneck_channels}, " - f"hidden_dim={self.hidden_dim})") + f"blocks={len(self.operations)}, " + f"channels={channel_progression})") diff --git a/tests/test_train_blocks_block_based_decoder.py b/tests/test_train_blocks_block_based_decoder.py new file mode 100644 index 0000000..c1cc246 --- /dev/null +++ b/tests/test_train_blocks_block_based_decoder.py @@ -0,0 +1,617 @@ + +import pytest +import torch + +# Assuming these imports exist in your codebase +from src.faith.train.blocks import BlockBasedDecoder + + +class TestBlockBasedDecoderInitialization: + """Test BlockBasedDecoder initialization and parameter validation.""" + + def test_basic_initialization(self): + """Test basic BlockBasedDecoder initialization.""" + configs = [ + {"out_channels": 128, "upsample_factor": (2, 2)}, + {"out_channels": 64, "upsample_factor": (1, 2)}, + {"out_channels": 3}, + ] + decoder = BlockBasedDecoder(in_channels=256, block_configs=configs) + + assert decoder.in_channels == 256 + assert decoder.out_channels == 3 # Last block's out_channels + assert len(decoder.operations) == 3 + assert len(decoder.block_configs) == 3 + + def test_custom_initialization(self): + """Test BlockBasedDecoder with custom parameters.""" + configs = [ + {"out_channels": 128, "upsample_factor": (2, 2), "dropout": 0.5}, + {"out_channels": 64, "activation": "gelu"}, + {"out_channels": 32, "kernel_size": 5, "bias": False}, + ] + decoder = BlockBasedDecoder( + in_channels=256, block_configs=configs, kernel_size=7, bias=True + ) + + assert decoder.in_channels == 256 + assert decoder.out_channels == 32 + assert len(decoder.operations) == 3 + assert decoder.kernel_size == 7 + assert decoder.bias is True + + def test_single_block_decoder(self): + """Test decoder with single block.""" + configs = [{"out_channels": 3}] + decoder = BlockBasedDecoder(in_channels=128, block_configs=configs) + + assert decoder.in_channels == 128 + assert decoder.out_channels == 3 + assert len(decoder.operations) == 1 + + def test_channel_progression_setup(self): + """Test that blocks are configured with correct channel progression.""" + configs = [ + {"out_channels": 128}, + {"out_channels": 64}, + {"out_channels": 32}, + ] + decoder = BlockBasedDecoder(in_channels=256, block_configs=configs) + + # Check channel progression + progression = decoder.get_channel_progression() + assert progression == [256, 128, 64, 32] + + # Check individual block configurations + assert decoder.operations[0].in_channels == 256 + assert decoder.operations[0].out_channels == 128 + assert decoder.operations[1].in_channels == 128 + assert decoder.operations[1].out_channels == 64 + assert decoder.operations[2].in_channels == 64 + assert decoder.operations[2].out_channels == 32 + + +class TestBlockBasedDecoderValidation: + """Test parameter validation in BlockBasedDecoder.""" + + def test_empty_block_configs(self): + """Test that empty block_configs raises ValueError.""" + with pytest.raises(ValueError, match="block_configs cannot be empty"): + BlockBasedDecoder(in_channels=256, block_configs=[]) + + def test_invalid_in_channels(self): + """Test that invalid in_channels raises ValueError.""" + configs = [{"out_channels": 64}] + + with pytest.raises(ValueError, match="in_channels must be positive"): + BlockBasedDecoder(in_channels=0, block_configs=configs) + + with pytest.raises(ValueError, match="in_channels must be positive"): + BlockBasedDecoder(in_channels=-5, block_configs=configs) + + def test_missing_out_channels(self): + """Test that missing out_channels in config raises ValueError.""" + configs = [ + {"out_channels": 128}, + {"dropout": 0.5}, # Missing out_channels + {"out_channels": 32}, + ] + + with pytest.raises( + ValueError, match="Block 1 missing required 'out_channels' key" + ): + BlockBasedDecoder(in_channels=256, block_configs=configs) + + def test_invalid_out_channels(self): + """Test that invalid out_channels raises ValueError.""" + configs = [ + {"out_channels": 128}, + {"out_channels": 0}, # Invalid + {"out_channels": 32}, + ] + + with pytest.raises( + ValueError, match="out_channels must be positive, got 0 in block 1" + ): + BlockBasedDecoder(in_channels=256, block_configs=configs) + + configs = [ + {"out_channels": -64} # Invalid + ] + + with pytest.raises( + ValueError, + match="out_channels must be positive, got -64 in block 0", + ): + BlockBasedDecoder(in_channels=256, block_configs=configs) + + +class TestBlockBasedDecoderForwardPass: + """Test BlockBasedDecoder forward pass functionality.""" + + def test_forward_pass_basic(self): + """Test basic forward pass.""" + configs = [ + {"out_channels": 128, "upsample_factor": (1, 2)}, + {"out_channels": 64, "upsample_factor": (2, 1)}, + {"out_channels": 3}, + ] + decoder = BlockBasedDecoder(in_channels=256, block_configs=configs) + z = torch.randn(2, 256, 8, 4) + + output = decoder(z) + + assert output.shape[0] == 2 # batch size + assert output.shape[1] == 3 # final out_channels + # Spatial dimensions should be larger due to upsampling + + def test_forward_pass_single_block(self): + """Test forward pass with single block.""" + configs = [{"out_channels": 32}] + decoder = BlockBasedDecoder(in_channels=128, block_configs=configs) + z = torch.randn(1, 128, 8, 8) + + output = decoder(z) + + assert output.shape[0] == 1 + assert output.shape[1] == 32 + + def test_forward_pass_gradient_flow(self): + """Test that gradients flow properly through the decoder.""" + configs = [{"out_channels": 64}, {"out_channels": 32}] + decoder = BlockBasedDecoder(in_channels=128, block_configs=configs) + z = torch.randn(1, 128, 8, 8, requires_grad=True) + + output = decoder(z) + loss = output.sum() + loss.backward() + + assert z.grad is not None + assert z.grad.shape == z.shape + + +class TestBlockBasedDecoderConfiguration: + """Test BlockBasedDecoder configuration methods.""" + + def test_get_config(self): + """Test get_config method returns complete configuration.""" + configs = [ + {"out_channels": 128, "dropout": 0.4}, + {"out_channels": 64, "activation": "gelu"}, + {"out_channels": 3}, + ] + decoder = BlockBasedDecoder( + in_channels=256, block_configs=configs, kernel_size=5, bias=False + ) + + config = decoder.get_config() + + assert config["in_channels"] == 256 + assert config["out_channels"] == 3 + assert config["block_configs"] == configs + assert config["kernel_size"] == 5 + assert config["bias"] is False + + def test_from_config(self): + """Test from_config class method creates equivalent decoder.""" + original_configs = [ + {"out_channels": 128, "dropout": 0.3}, + {"out_channels": 64, "upsample_factor": (2, 2)}, + {"out_channels": 3}, + ] + original_decoder = BlockBasedDecoder( + in_channels=256, block_configs=original_configs, kernel_size=7 + ) + + config = original_decoder.get_config() + reconstructed_decoder = BlockBasedDecoder.from_config(config) + + assert ( + reconstructed_decoder.in_channels == original_decoder.in_channels + ) + assert ( + reconstructed_decoder.out_channels == original_decoder.out_channels + ) + assert ( + reconstructed_decoder.block_configs + == original_decoder.block_configs + ) + assert ( + reconstructed_decoder.kernel_size == original_decoder.kernel_size + ) + + def test_config_roundtrip(self): + """Test that config -> decoder -> config roundtrip works.""" + original_config = { + "in_channels": 128, + "block_configs": [ + {"out_channels": 64, "dropout": 0.2}, + {"out_channels": 32, "activation": "relu"}, + {"out_channels": 3}, + ], + "kernel_size": 3, + "bias": True, + } + + decoder = BlockBasedDecoder.from_config(original_config) + reconstructed_config = decoder.get_config() + + for key in original_config: + assert reconstructed_config[key] == original_config[key] + + +class TestBlockBasedDecoderChannelProgression: + """Test BlockBasedDecoder channel progression functionality.""" + + def test_get_channel_progression_basic(self): + """Test get_channel_progression with basic configuration.""" + configs = [ + {"out_channels": 128}, + {"out_channels": 64}, + {"out_channels": 32}, + ] + decoder = BlockBasedDecoder(in_channels=256, block_configs=configs) + + progression = decoder.get_channel_progression() + assert progression == [256, 128, 64, 32] + + def test_get_channel_progression_single_block(self): + """Test get_channel_progression with single block.""" + configs = [{"out_channels": 3}] + decoder = BlockBasedDecoder(in_channels=128, block_configs=configs) + + progression = decoder.get_channel_progression() + assert progression == [128, 3] + + +class TestBlockBasedDecoderReverseConfigs: + """Test BlockBasedDecoder encoder reversal functionality.""" + + def test_reverse_encoder_configs_basic(self): + """Test reverse_encoder_configs with basic encoder configuration.""" + encoder_configs = [ + {"out_channels": 64, "pool_size": (1, 2)}, + {"out_channels": 128, "pool_size": (2, 2)}, + {"out_channels": 256, "pool_size": (1, 2)}, + ] + + decoder_configs = BlockBasedDecoder.reverse_encoder_configs( + encoder_configs, final_out_channels=3 + ) + + assert len(decoder_configs) == 3 + # Should reverse the channel progression + assert decoder_configs[0]["out_channels"] == 128 # 256 -> 128 + assert decoder_configs[1]["out_channels"] == 64 # 128 -> 64 + assert decoder_configs[2]["out_channels"] == 3 # 64 -> 3 + + # Should mirror the pool sizes as upsample factors + assert decoder_configs[0]["upsample_factor"] == (1, 2) + assert decoder_configs[1]["upsample_factor"] == (2, 2) + assert decoder_configs[2]["upsample_factor"] == (1, 2) + + def test_reverse_encoder_configs_single_block(self): + """Test reverse_encoder_configs with single encoder block.""" + encoder_configs = [{"out_channels": 128, "pool_size": (2, 2)}] + + decoder_configs = BlockBasedDecoder.reverse_encoder_configs( + encoder_configs, final_out_channels=3 + ) + + assert len(decoder_configs) == 1 + assert decoder_configs[0]["out_channels"] == 3 + assert decoder_configs[0]["upsample_factor"] == (2, 2) + + def test_reverse_encoder_configs_empty_raises_error(self): + """Test that empty encoder_configs raises ValueError.""" + with pytest.raises( + ValueError, match="encoder_configs cannot be empty" + ): + BlockBasedDecoder.reverse_encoder_configs([], final_out_channels=3) + + +class TestBlockBasedDecoderFromEncoder: + """Test BlockBasedDecoder encoder mirroring functionality.""" + + def test_from_encoder_basic(self): + """Test from_encoder creates decoder that mirrors encoder.""" + + # Create a mock encoder for testing + class MockEncoder: + def __init__(self): + self.out_channels = 256 + self.block_configs = [ + {"out_channels": 64, "pool_size": (1, 2)}, + {"out_channels": 128, "pool_size": (2, 2)}, + {"out_channels": 256, "pool_size": (1, 2)}, + ] + + encoder = MockEncoder() + decoder = BlockBasedDecoder.from_encoder(encoder, final_out_channels=3) + + assert decoder.in_channels == 256 # encoder.out_channels + assert decoder.out_channels == 3 + assert len(decoder.block_configs) == 3 + + def test_from_encoder_with_kwargs(self): + """Test from_encoder with additional keyword arguments.""" + + class MockEncoder: + def __init__(self): + self.out_channels = 128 + self.block_configs = [ + {"out_channels": 64, "pool_size": (2, 2)} + ] + + encoder = MockEncoder() + decoder = BlockBasedDecoder.from_encoder( + encoder, final_out_channels=3, kernel_size=5, bias=False + ) + + assert decoder.kernel_size == 5 + assert decoder.bias is False + + +class TestBlockBasedDecoderShapeCalculation: + """Test BlockBasedDecoder shape calculation methods.""" + + def test_get_output_shape_basic(self): + """Test get_output_shape with basic configuration.""" + configs = [ + {"out_channels": 128, "upsample_factor": (1, 2)}, + {"out_channels": 64, "upsample_factor": (2, 1)}, + {"out_channels": 3}, + ] + decoder = BlockBasedDecoder(in_channels=256, block_configs=configs) + input_shape = (2, 256, 8, 4) + + output_shape = decoder.get_output_shape(input_shape) + + assert output_shape[0] == 2 # batch size + assert output_shape[1] == 3 # final out_channels + assert output_shape[2] > 0 # height should be positive + assert output_shape[3] > 0 # width should be positive + + def test_get_output_shape_matches_forward(self): + """Test that get_output_shape matches actual forward pass output.""" + configs = [ + {"out_channels": 64, "upsample_factor": (1, 2)}, + {"out_channels": 32, "upsample_factor": (2, 1)}, + {"out_channels": 3}, + ] + decoder = BlockBasedDecoder(in_channels=128, block_configs=configs) + input_shape = (1, 128, 8, 8) + + predicted_shape = decoder.get_output_shape(input_shape) + + z = torch.randn(*input_shape) + actual_output = decoder(z) + + assert predicted_shape == actual_output.shape + + +class TestBlockBasedDecoderFeatureMaps: + """Test BlockBasedDecoder feature map extraction.""" + + def test_get_feature_maps_basic(self): + """Test get_feature_maps returns correct number of maps.""" + configs = [ + {"out_channels": 128}, + {"out_channels": 64}, + {"out_channels": 32}, + ] + decoder = BlockBasedDecoder(in_channels=256, block_configs=configs) + z = torch.randn(1, 256, 8, 8) + + feature_maps = decoder.get_feature_maps(z) + + assert len(feature_maps) == 3 # One per block + assert feature_maps[0].shape[1] == 128 # First block output + assert feature_maps[1].shape[1] == 64 # Second block output + assert feature_maps[2].shape[1] == 32 # Third block output + + def test_get_feature_maps_consistency(self): + """Test that get_feature_maps gives same result as forward pass.""" + configs = [{"out_channels": 64}, {"out_channels": 32}] + decoder = BlockBasedDecoder(in_channels=128, block_configs=configs) + z = torch.randn(1, 128, 8, 8) + + feature_maps = decoder.get_feature_maps(z) + final_output = decoder(z) + + # Last feature map should match forward pass output + assert torch.allclose(feature_maps[-1], final_output, atol=1e-6) + + +class TestBlockBasedDecoderRepresentation: + """Test BlockBasedDecoder string representation.""" + + def test_repr_basic(self): + """Test __repr__ method with basic configuration.""" + configs = [ + {"out_channels": 128}, + {"out_channels": 64}, + {"out_channels": 3}, + ] + decoder = BlockBasedDecoder(in_channels=256, block_configs=configs) + repr_str = repr(decoder) + + assert "BlockBasedDecoder(" in repr_str + assert "blocks=3" in repr_str + assert "channels=256 → 128 → 64 → 3" in repr_str + + def test_repr_single_block(self): + """Test __repr__ method with single block.""" + configs = [{"out_channels": 3}] + decoder = BlockBasedDecoder(in_channels=128, block_configs=configs) + repr_str = repr(decoder) + + assert "blocks=1" in repr_str + assert "channels=128 → 3" in repr_str + + +class TestBlockBasedDecoderCompatibility: + """Test BlockBasedDecoder compatibility and integration.""" + + def test_sequential_block_inheritance(self): + """Test that BlockBasedDecoder properly inherits from SequentialBlock.""" + configs = [ + {"out_channels": 128}, + {"out_channels": 64}, + {"out_channels": 3}, + ] + decoder = BlockBasedDecoder(in_channels=256, block_configs=configs) + + # Should have SequentialBlock attributes + assert hasattr(decoder, "operations") + assert hasattr(decoder, "in_channels") + assert hasattr(decoder, "out_channels") + assert len(decoder.operations) == 3 + + def test_blocks_property(self): + """Test blocks property for backward compatibility.""" + configs = [{"out_channels": 64}, {"out_channels": 32}] + decoder = BlockBasedDecoder(in_channels=128, block_configs=configs) + + assert hasattr(decoder, "blocks") + assert len(decoder.blocks) == 2 + assert decoder.blocks[0] is decoder.operations[0] + assert decoder.blocks[1] is decoder.operations[1] + + +class TestBlockBasedDecoderEdgeCases: + """Test edge cases and boundary conditions.""" + + def test_large_channel_counts(self): + """Test with large channel counts.""" + configs = [ + {"out_channels": 512}, + {"out_channels": 256}, + {"out_channels": 3}, + ] + decoder = BlockBasedDecoder(in_channels=1024, block_configs=configs) + z = torch.randn(1, 1024, 4, 4) + + output = decoder(z) + assert output.shape[1] == 3 + + def test_single_channel_output(self): + """Test with single channel output.""" + configs = [ + {"out_channels": 32}, + {"out_channels": 16}, + {"out_channels": 1}, # Single channel output + ] + decoder = BlockBasedDecoder(in_channels=128, block_configs=configs) + z = torch.randn(1, 128, 8, 8) + + output = decoder(z) + assert output.shape[1] == 1 + + def test_no_upsampling_decoder(self): + """Test decoder with no upsampling (all factors = (1,1)).""" + configs = [ + {"out_channels": 64, "upsample_factor": (1, 1)}, + {"out_channels": 32, "upsample_factor": (1, 1)}, + {"out_channels": 3, "upsample_factor": (1, 1)}, + ] + decoder = BlockBasedDecoder(in_channels=128, block_configs=configs) + z = torch.randn(1, 128, 16, 16) + + output = decoder(z) + assert output.shape[1] == 3 + + +class TestBlockBasedDecoderIntegration: + """Test BlockBasedDecoder integration with encoders.""" + + def test_encoder_decoder_symmetry(self): + """Test that decoder can process encoder output.""" + # Create encoder + encoder_configs = [ + {"out_channels": 64, "pool_size": (1, 2)}, + {"out_channels": 128, "pool_size": (2, 2)}, + ] + + class MockEncoder: + def __init__(self): + self.out_channels = 128 + self.block_configs = encoder_configs + + encoder = MockEncoder() + + # Create symmetric decoder + decoder = BlockBasedDecoder.from_encoder(encoder, final_out_channels=3) + + # Test forward pass + z = torch.randn(1, 128, 8, 4) # Typical encoder output shape + output = decoder(z) + + assert output.shape[1] == 3 + # Output should have larger spatial dimensions due to upsampling + assert output.shape[2] >= z.shape[2] + assert output.shape[3] >= z.shape[3] + + +class TestBlockBasedDecoderErrorHandling: + """Test error handling and edge cases.""" + + def test_channel_mismatch(self): + """Test behavior with channel count mismatch.""" + configs = [{"out_channels": 32}] + decoder = BlockBasedDecoder(in_channels=128, block_configs=configs) + + # Input with wrong number of channels + z_wrong_channels = torch.randn(1, 64, 8, 8) # Should be 128 channels + + with pytest.raises(RuntimeError): + decoder(z_wrong_channels) + + +# Fixtures for common test data +@pytest.fixture +def sample_latent(): + """Fixture providing sample latent tensor.""" + return torch.randn(2, 256, 8, 4) + + +@pytest.fixture +def basic_decoder(): + """Fixture providing basic BlockBasedDecoder instance.""" + configs = [ + {"out_channels": 128, "upsample_factor": (1, 2)}, + {"out_channels": 64, "upsample_factor": (2, 1)}, + {"out_channels": 3}, + ] + return BlockBasedDecoder(in_channels=256, block_configs=configs) + + +@pytest.fixture +def complex_decoder(): + """Fixture providing complex BlockBasedDecoder instance.""" + configs = [ + {"out_channels": 128, "upsample_factor": (2, 2), "dropout": 0.2}, + {"out_channels": 64, "upsample_factor": (1, 2), "activation": "gelu"}, + {"out_channels": 32, "kernel_size": 5, "bias": False}, + {"out_channels": 3}, + ] + return BlockBasedDecoder(in_channels=256, block_configs=configs) + + +@pytest.fixture +def mock_encoder(): + """Fixture providing mock encoder for testing from_encoder method.""" + class MockEncoder: + def __init__(self): + self.out_channels = 128 + self.block_configs = [ + {'out_channels': 32, 'pool_size': (1, 2)}, + {'out_channels': 64, 'pool_size': (2, 2)}, + {'out_channels': 128, 'pool_size': (1, 2)} + ] + + return MockEncoder() + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/tests/test_train_blocks_block_based_encoder.py b/tests/test_train_blocks_block_based_encoder.py new file mode 100644 index 0000000..027ac37 --- /dev/null +++ b/tests/test_train_blocks_block_based_encoder.py @@ -0,0 +1,588 @@ +import pytest +import torch + +from src.faith.train.blocks import BlockBasedEncoder + + +class TestBlockBasedEncoderInitialization: + """Test BlockBasedEncoder initialization and parameter validation.""" + + def test_basic_initialization(self): + """Test basic BlockBasedEncoder initialization.""" + configs = [{"out_channels": 64}, {"out_channels": 128}] + encoder = BlockBasedEncoder(in_channels=3, block_configs=configs) + + assert encoder.in_channels == 3 + assert encoder.out_channels == 128 # Last block's out_channels + assert len(encoder.operations) == 2 + assert len(encoder.block_configs) == 2 + + def test_custom_initialization(self): + """Test BlockBasedEncoder with custom parameters.""" + configs = [ + {"out_channels": 64, "pool_size": (2, 2), "dropout": 0.5}, + {"out_channels": 128, "activation": "gelu"}, + {"out_channels": 256, "kernel_size": 5, "bias": False}, + ] + encoder = BlockBasedEncoder( + in_channels=16, block_configs=configs, kernel_size=7, bias=True + ) + + assert encoder.in_channels == 16 + assert encoder.out_channels == 256 + assert len(encoder.operations) == 3 + assert encoder.kernel_size == 7 + assert encoder.bias is True + + def test_single_block_encoder(self): + """Test encoder with single block.""" + configs = [{"out_channels": 32}] + encoder = BlockBasedEncoder(in_channels=8, block_configs=configs) + + assert encoder.in_channels == 8 + assert encoder.out_channels == 32 + assert len(encoder.operations) == 1 + + def test_channel_progression_setup(self): + """Test that blocks are configured with correct channel progression.""" + configs = [ + {"out_channels": 64}, + {"out_channels": 128}, + {"out_channels": 256}, + ] + encoder = BlockBasedEncoder(in_channels=3, block_configs=configs) + + # Check channel progression + progression = encoder.get_channel_progression() + assert progression == [3, 64, 128, 256] + + # Check individual block configurations + assert encoder.operations[0].in_channels == 3 + assert encoder.operations[0].out_channels == 64 + assert encoder.operations[1].in_channels == 64 + assert encoder.operations[1].out_channels == 128 + assert encoder.operations[2].in_channels == 128 + assert encoder.operations[2].out_channels == 256 + + +class TestBlockBasedEncoderValidation: + """Test parameter validation in BlockBasedEncoder.""" + + def test_empty_block_configs(self): + """Test that empty block_configs raises ValueError.""" + with pytest.raises(ValueError, match="block_configs cannot be empty"): + BlockBasedEncoder(in_channels=3, block_configs=[]) + + def test_invalid_in_channels(self): + """Test that invalid in_channels raises ValueError.""" + configs = [{"out_channels": 64}] + + with pytest.raises(ValueError, match="in_channels must be positive"): + BlockBasedEncoder(in_channels=0, block_configs=configs) + + with pytest.raises(ValueError, match="in_channels must be positive"): + BlockBasedEncoder(in_channels=-5, block_configs=configs) + + def test_missing_out_channels(self): + """Test that missing out_channels in config raises ValueError.""" + configs = [ + {"out_channels": 64}, + {"dropout": 0.5}, # Missing out_channels + {"out_channels": 256}, + ] + + with pytest.raises( + ValueError, match="Block 1 missing required 'out_channels' key" + ): + BlockBasedEncoder(in_channels=3, block_configs=configs) + + def test_invalid_out_channels(self): + """Test that invalid out_channels raises ValueError.""" + configs = [ + {"out_channels": 64}, + {"out_channels": 0}, # Invalid + {"out_channels": 256}, + ] + + with pytest.raises( + ValueError, match="out_channels must be positive, got 0 in block 1" + ): + BlockBasedEncoder(in_channels=3, block_configs=configs) + + configs = [ + {"out_channels": -32} # Invalid + ] + + with pytest.raises( + ValueError, + match="out_channels must be positive, got -32 in block 0", + ): + BlockBasedEncoder(in_channels=3, block_configs=configs) + + +class TestBlockBasedEncoderForwardPass: + """Test BlockBasedEncoder forward pass functionality.""" + + def test_forward_pass_basic(self): + """Test basic forward pass.""" + configs = [{"out_channels": 64}, {"out_channels": 128}] + encoder = BlockBasedEncoder(in_channels=3, block_configs=configs) + x = torch.randn(2, 3, 32, 64) + + output = encoder(x) + + assert output.shape[0] == 2 # batch size + assert output.shape[1] == 128 # final out_channels + # Spatial dimensions depend on pooling operations + + def test_forward_pass_single_block(self): + """Test forward pass with single block.""" + configs = [{"out_channels": 32}] + encoder = BlockBasedEncoder(in_channels=8, block_configs=configs) + x = torch.randn(1, 8, 16, 16) + + output = encoder(x) + + assert output.shape[0] == 1 + assert output.shape[1] == 32 + + def test_forward_pass_multiple_blocks(self): + """Test forward pass with multiple blocks.""" + configs = [ + {"out_channels": 32, "pool_size": (1, 2)}, + {"out_channels": 64, "pool_size": (2, 2)}, + {"out_channels": 128, "pool_size": (1, 1)}, + ] + encoder = BlockBasedEncoder(in_channels=16, block_configs=configs) + x = torch.randn(1, 16, 32, 32) + + output = encoder(x) + + assert output.shape[0] == 1 + assert output.shape[1] == 128 + + def test_forward_pass_different_input_sizes(self): + """Test forward pass with different input sizes.""" + configs = [{"out_channels": 64}, {"out_channels": 128}] + encoder = BlockBasedEncoder(in_channels=3, block_configs=configs) + + # Test various input sizes + for h, w in [(8, 8), (16, 32), (32, 64), (64, 128)]: + x = torch.randn(1, 3, h, w) + output = encoder(x) + + assert output.shape[0] == 1 + assert output.shape[1] == 128 + assert output.shape[2] > 0 + assert output.shape[3] > 0 + + def test_forward_pass_gradient_flow(self): + """Test that gradients flow properly through the encoder.""" + configs = [{"out_channels": 32}, {"out_channels": 64}] + encoder = BlockBasedEncoder(in_channels=8, block_configs=configs) + x = torch.randn(1, 8, 16, 16, requires_grad=True) + + output = encoder(x) + loss = output.sum() + loss.backward() + + assert x.grad is not None + assert x.grad.shape == x.shape + + def test_forward_pass_with_custom_parameters(self): + """Test forward pass with custom block parameters.""" + configs = [ + {"out_channels": 32, "dropout": 0.5, "activation": "gelu"}, + {"out_channels": 64, "pool_size": (2, 2), "kernel_size": 5}, + ] + encoder = BlockBasedEncoder(in_channels=16, block_configs=configs) + x = torch.randn(1, 16, 32, 32) + + output = encoder(x) + + assert output.shape[0] == 1 + assert output.shape[1] == 64 + + +class TestBlockBasedEncoderConfiguration: + """Test BlockBasedEncoder configuration methods.""" + + def test_get_config(self): + """Test get_config method returns complete configuration.""" + configs = [ + {"out_channels": 64, "dropout": 0.4}, + {"out_channels": 128, "activation": "gelu"}, + ] + encoder = BlockBasedEncoder( + in_channels=3, block_configs=configs, kernel_size=5, bias=False + ) + + config = encoder.get_config() + + assert config["in_channels"] == 3 + assert config["out_channels"] == 128 + assert config["block_configs"] == configs + assert config["kernel_size"] == 5 + assert config["bias"] is False + + def test_from_config(self): + """Test from_config class method creates equivalent encoder.""" + original_configs = [ + {"out_channels": 64, "dropout": 0.3}, + {"out_channels": 128, "pool_size": (2, 2)}, + ] + original_encoder = BlockBasedEncoder( + in_channels=16, block_configs=original_configs, kernel_size=7 + ) + + config = original_encoder.get_config() + reconstructed_encoder = BlockBasedEncoder.from_config(config) + + assert ( + reconstructed_encoder.in_channels == original_encoder.in_channels + ) + assert ( + reconstructed_encoder.out_channels == original_encoder.out_channels + ) + assert ( + reconstructed_encoder.block_configs + == original_encoder.block_configs + ) + assert ( + reconstructed_encoder.kernel_size == original_encoder.kernel_size + ) + + def test_config_roundtrip(self): + """Test that config -> encoder -> config roundtrip works.""" + original_config = { + "in_channels": 8, + "block_configs": [ + {"out_channels": 32, "dropout": 0.2}, + {"out_channels": 64, "activation": "relu"}, + ], + "kernel_size": 3, + "bias": True, + } + + encoder = BlockBasedEncoder.from_config(original_config) + reconstructed_config = encoder.get_config() + + for key in original_config: + assert reconstructed_config[key] == original_config[key] + + +class TestBlockBasedEncoderChannelProgression: + """Test BlockBasedEncoder channel progression functionality.""" + + def test_get_channel_progression_basic(self): + """Test get_channel_progression with basic configuration.""" + configs = [ + {"out_channels": 32}, + {"out_channels": 64}, + {"out_channels": 128}, + ] + encoder = BlockBasedEncoder(in_channels=8, block_configs=configs) + + progression = encoder.get_channel_progression() + assert progression == [8, 32, 64, 128] + + def test_get_channel_progression_single_block(self): + """Test get_channel_progression with single block.""" + configs = [{"out_channels": 64}] + encoder = BlockBasedEncoder(in_channels=16, block_configs=configs) + + progression = encoder.get_channel_progression() + assert progression == [16, 64] + + def test_get_channel_progression_complex(self): + """Test get_channel_progression with complex configuration.""" + configs = [ + {"out_channels": 128}, + {"out_channels": 64}, # Decreasing channels + {"out_channels": 256}, # Then increasing + {"out_channels": 32}, # Then decreasing again + ] + encoder = BlockBasedEncoder(in_channels=3, block_configs=configs) + + progression = encoder.get_channel_progression() + assert progression == [3, 128, 64, 256, 32] + + +class TestBlockBasedEncoderShapeCalculation: + """Test BlockBasedEncoder shape calculation methods.""" + + def test_get_output_shape_basic(self): + """Test get_output_shape with basic configuration.""" + configs = [{"out_channels": 64}, {"out_channels": 128}] + encoder = BlockBasedEncoder(in_channels=3, block_configs=configs) + input_shape = (2, 3, 32, 32) + + output_shape = encoder.get_output_shape(input_shape) + + assert output_shape[0] == 2 # batch size + assert output_shape[1] == 128 # final out_channels + assert output_shape[2] > 0 # height should be positive + assert output_shape[3] > 0 # width should be positive + + def test_get_output_shape_matches_forward(self): + """Test that get_output_shape matches actual forward pass output.""" + configs = [ + {"out_channels": 32, "pool_size": (1, 2)}, + {"out_channels": 64, "pool_size": (2, 1)}, + ] + encoder = BlockBasedEncoder(in_channels=8, block_configs=configs) + input_shape = (1, 8, 16, 16) + + predicted_shape = encoder.get_output_shape(input_shape) + + x = torch.randn(*input_shape) + actual_output = encoder(x) + + assert predicted_shape == actual_output.shape + + def test_get_output_shape_different_pool_sizes(self): + """Test get_output_shape with different pool sizes.""" + configs = [ + {"out_channels": 32, "pool_size": (2, 2)}, + {"out_channels": 64, "pool_size": (1, 4)}, + ] + encoder = BlockBasedEncoder(in_channels=16, block_configs=configs) + input_shape = (1, 16, 32, 32) + + output_shape = encoder.get_output_shape(input_shape) + + assert output_shape[0] == 1 + assert output_shape[1] == 64 + # Height should be reduced by factor of 2 from first block + # Width should be reduced by factors 2 and 4 from both blocks + + +class TestBlockBasedEncoderFeatureMaps: + """Test BlockBasedEncoder feature map extraction.""" + + def test_get_feature_maps_basic(self): + """Test get_feature_maps returns correct number of maps.""" + configs = [ + {"out_channels": 32}, + {"out_channels": 64}, + {"out_channels": 128}, + ] + encoder = BlockBasedEncoder(in_channels=8, block_configs=configs) + x = torch.randn(1, 8, 16, 16) + + feature_maps = encoder.get_feature_maps(x) + + assert len(feature_maps) == 3 # One per block + assert feature_maps[0].shape[1] == 32 # First block output + assert feature_maps[1].shape[1] == 64 # Second block output + assert feature_maps[2].shape[1] == 128 # Third block output + + def test_get_feature_maps_single_block(self): + """Test get_feature_maps with single block.""" + configs = [{"out_channels": 64}] + encoder = BlockBasedEncoder(in_channels=16, block_configs=configs) + x = torch.randn(1, 16, 32, 32) + + feature_maps = encoder.get_feature_maps(x) + + assert len(feature_maps) == 1 + assert feature_maps[0].shape[1] == 64 + + def test_get_feature_maps_consistency(self): + """Test that get_feature_maps gives same result as forward pass.""" + configs = [{"out_channels": 32}, {"out_channels": 64}] + encoder = BlockBasedEncoder(in_channels=8, block_configs=configs) + x = torch.randn(1, 8, 16, 16) + + feature_maps = encoder.get_feature_maps(x) + final_output = encoder(x) + + # Last feature map should match forward pass output + assert torch.allclose(feature_maps[-1], final_output, atol=1e-6) + + def test_get_feature_maps_independence(self): + """Test that feature maps are independent copies.""" + configs = [{"out_channels": 32}, {"out_channels": 64}] + encoder = BlockBasedEncoder(in_channels=8, block_configs=configs) + x = torch.randn(1, 8, 16, 16) + + feature_maps = encoder.get_feature_maps(x) + + # Modify one feature map + original_value = feature_maps[0][0, 0, 0, 0].item() + feature_maps[0][0, 0, 0, 0] = 999.0 + + # Get feature maps again + new_feature_maps = encoder.get_feature_maps(x) + + # Should not be affected by previous modification + assert new_feature_maps[0][0, 0, 0, 0].item() == original_value + + +class TestBlockBasedEncoderRepresentation: + """Test BlockBasedEncoder string representation.""" + + def test_repr_basic(self): + """Test __repr__ method with basic configuration.""" + configs = [{"out_channels": 64}, {"out_channels": 128}] + encoder = BlockBasedEncoder(in_channels=3, block_configs=configs) + repr_str = repr(encoder) + + assert "BlockBasedEncoder(" in repr_str + assert "blocks=2" in repr_str + assert "channels=3 → 64 → 128" in repr_str + + def test_repr_single_block(self): + """Test __repr__ method with single block.""" + configs = [{"out_channels": 32}] + encoder = BlockBasedEncoder(in_channels=8, block_configs=configs) + repr_str = repr(encoder) + + assert "blocks=1" in repr_str + assert "channels=8 → 32" in repr_str + + def test_repr_complex(self): + """Test __repr__ method with complex configuration.""" + configs = [ + {"out_channels": 16}, + {"out_channels": 32}, + {"out_channels": 64}, + {"out_channels": 128}, + ] + encoder = BlockBasedEncoder(in_channels=3, block_configs=configs) + repr_str = repr(encoder) + + assert "blocks=4" in repr_str + assert "channels=3 → 16 → 32 → 64 → 128" in repr_str + + +class TestBlockBasedEncoderCompatibility: + """Test BlockBasedEncoder compatibility and integration.""" + + def test_sequential_block_inheritance(self): + """ + Test that BlockBasedEncoder properly inherits from SequentialBlock. + """ + configs = [{"out_channels": 64}, {"out_channels": 128}] + encoder = BlockBasedEncoder(in_channels=3, block_configs=configs) + + # Should have SequentialBlock attributes + assert hasattr(encoder, "operations") + assert hasattr(encoder, "in_channels") + assert hasattr(encoder, "out_channels") + assert len(encoder.operations) == 2 + + def test_blocks_property(self): + """Test blocks property for backward compatibility.""" + configs = [{"out_channels": 32}, {"out_channels": 64}] + encoder = BlockBasedEncoder(in_channels=16, block_configs=configs) + + assert hasattr(encoder, "blocks") + assert len(encoder.blocks) == 2 + assert encoder.blocks[0] is encoder.operations[0] + assert encoder.blocks[1] is encoder.operations[1] + + def test_module_list_functionality(self): + """Test that operations work like ModuleList.""" + configs = [{"out_channels": 32}, {"out_channels": 64}] + encoder = BlockBasedEncoder(in_channels=8, block_configs=configs) + + # Should be able to iterate over operations + block_count = 0 + for block in encoder.operations: + assert hasattr(block, "forward") + block_count += 1 + assert block_count == 2 + + def test_parameter_count(self): + """Test that parameter count is reasonable.""" + configs = [{"out_channels": 32}, {"out_channels": 64}] + encoder = BlockBasedEncoder(in_channels=16, block_configs=configs) + + total_params = sum(p.numel() for p in encoder.parameters()) + trainable_params = sum( + p.numel() for p in encoder.parameters() if p.requires_grad + ) + + assert total_params > 0 + assert ( + trainable_params == total_params + ) # All parameters should be trainable + + +class TestBlockBasedEncoderEdgeCases: + """Test edge cases and boundary conditions.""" + + def test_large_channel_counts(self): + """Test with large channel counts.""" + configs = [{"out_channels": 512}, {"out_channels": 1024}] + encoder = BlockBasedEncoder(in_channels=256, block_configs=configs) + x = torch.randn(1, 256, 8, 8) + + output = encoder(x) + assert output.shape[1] == 1024 + + def test_single_channel_input(self): + """Test with single channel input.""" + configs = [{"out_channels": 16}, {"out_channels": 32}] + encoder = BlockBasedEncoder(in_channels=1, block_configs=configs) + x = torch.randn(1, 1, 32, 32) + + output = encoder(x) + assert output.shape[1] == 32 + + def test_minimal_spatial_dimensions(self): + """Test with minimal spatial dimensions.""" + configs = [{"out_channels": 64}] + encoder = BlockBasedEncoder(in_channels=32, block_configs=configs) + x = torch.randn(1, 32, 1, 1) + + output = encoder(x) + assert output.shape[1] == 64 + assert output.shape[2] > 0 + assert output.shape[3] > 0 + + def test_decreasing_channels(self): + """Test with decreasing channel progression.""" + configs = [ + {"out_channels": 128}, + {"out_channels": 64}, + {"out_channels": 32}, + ] + encoder = BlockBasedEncoder(in_channels=256, block_configs=configs) + x = torch.randn(1, 256, 16, 16) + + output = encoder(x) + assert output.shape[1] == 32 + + progression = encoder.get_channel_progression() + assert progression == [256, 128, 64, 32] + + +# Fixtures for common test data +@pytest.fixture +def sample_input(): + """Fixture providing sample input tensor.""" + return torch.randn(2, 16, 32, 32) + + +@pytest.fixture +def basic_encoder(): + """Fixture providing basic BlockBasedEncoder instance.""" + configs = [{"out_channels": 64}, {"out_channels": 128}] + return BlockBasedEncoder(in_channels=16, block_configs=configs) + + +@pytest.fixture +def complex_encoder(): + """Fixture providing complex BlockBasedEncoder instance.""" + configs = [ + {'out_channels': 32, 'pool_size': (1, 2), 'dropout': 0.2}, + {'out_channels': 64, 'pool_size': (2, 2), 'activation': 'gelu'}, + {'out_channels': 128, 'kernel_size': 5, 'bias': False} + ] + return BlockBasedEncoder(in_channels=8, block_configs=configs) + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) From 080f69eb77d20425d5d447af8a04633ccc36c815 Mon Sep 17 00:00:00 2001 From: Peter Steiner <61472983+renierts@users.noreply.github.com> Date: Thu, 17 Jul 2025 10:47:59 -0400 Subject: [PATCH 064/103] BlockBasedEncoder is tested. --- src/faith/train/blocks/encoder.py | 9 + .../test_train_blocks_block_based_encoder.py | 164 +++++++++++------- 2 files changed, 106 insertions(+), 67 deletions(-) diff --git a/src/faith/train/blocks/encoder.py b/src/faith/train/blocks/encoder.py index 1374267..2f9a024 100644 --- a/src/faith/train/blocks/encoder.py +++ b/src/faith/train/blocks/encoder.py @@ -256,6 +256,7 @@ def __init__( block_configs: list[dict[str, Any]], kernel_size: Union[int, tuple[int, int]] = 3, bias: bool = True, + **kwargs ) -> None: """Initialize BlockBasedEncoder.""" @@ -356,6 +357,14 @@ def get_feature_maps(self, x: torch.Tensor) -> list[torch.Tensor]: return feature_maps + def get_output_shape(self, input_shape: tuple[int, ...]) \ + -> tuple[int, ...]: + """Calculate output shape given input shape.""" + shape = input_shape + for block in self.operations: + shape = block.get_output_shape(shape) + return shape + def get_channel_progression(self) -> list[int]: """Get the channel count progression through the encoder. diff --git a/tests/test_train_blocks_block_based_encoder.py b/tests/test_train_blocks_block_based_encoder.py index 027ac37..e8bd991 100644 --- a/tests/test_train_blocks_block_based_encoder.py +++ b/tests/test_train_blocks_block_based_encoder.py @@ -9,29 +9,29 @@ class TestBlockBasedEncoderInitialization: def test_basic_initialization(self): """Test basic BlockBasedEncoder initialization.""" - configs = [{"out_channels": 64}, {"out_channels": 128}] + configs = [{"out_channels": 4}, {"out_channels": 8}] encoder = BlockBasedEncoder(in_channels=3, block_configs=configs) assert encoder.in_channels == 3 - assert encoder.out_channels == 128 # Last block's out_channels + assert encoder.out_channels == 8 # Last block's out_channels assert len(encoder.operations) == 2 assert len(encoder.block_configs) == 2 def test_custom_initialization(self): """Test BlockBasedEncoder with custom parameters.""" configs = [ - {"out_channels": 64, "pool_size": (2, 2), "dropout": 0.5}, - {"out_channels": 128, "activation": "gelu"}, - {"out_channels": 256, "kernel_size": 5, "bias": False}, + {"out_channels": 4, "pool_size": (2, 2), "dropout": 0.5}, + {"out_channels": 8, "activation": "gelu"}, + {"out_channels": 16, "kernel_size": 5, "bias": False}, ] encoder = BlockBasedEncoder( - in_channels=16, block_configs=configs, kernel_size=7, bias=True + in_channels=3, block_configs=configs, kernel_size=7, bias=True ) - assert encoder.in_channels == 16 - assert encoder.out_channels == 256 + assert encoder.in_channels == 3 + assert encoder.out_channels == 16 assert len(encoder.operations) == 3 - assert encoder.kernel_size == 7 + assert encoder.kernel_size == (7, 7) assert encoder.bias is True def test_single_block_encoder(self): @@ -42,27 +42,30 @@ def test_single_block_encoder(self): assert encoder.in_channels == 8 assert encoder.out_channels == 32 assert len(encoder.operations) == 1 + assert encoder.kernel_size == (3, 3) + assert encoder.bias is True + assert len(encoder.operations) == 1 def test_channel_progression_setup(self): """Test that blocks are configured with correct channel progression.""" configs = [ - {"out_channels": 64}, - {"out_channels": 128}, - {"out_channels": 256}, + {"out_channels": 4}, + {"out_channels": 8}, + {"out_channels": 16}, ] encoder = BlockBasedEncoder(in_channels=3, block_configs=configs) # Check channel progression progression = encoder.get_channel_progression() - assert progression == [3, 64, 128, 256] + assert progression == [3, 4, 8, 16] # Check individual block configurations assert encoder.operations[0].in_channels == 3 - assert encoder.operations[0].out_channels == 64 - assert encoder.operations[1].in_channels == 64 - assert encoder.operations[1].out_channels == 128 - assert encoder.operations[2].in_channels == 128 - assert encoder.operations[2].out_channels == 256 + assert encoder.operations[0].out_channels == 4 + assert encoder.operations[1].in_channels == 4 + assert encoder.operations[1].out_channels == 8 + assert encoder.operations[2].in_channels == 8 + assert encoder.operations[2].out_channels == 16 class TestBlockBasedEncoderValidation: @@ -125,15 +128,20 @@ class TestBlockBasedEncoderForwardPass: def test_forward_pass_basic(self): """Test basic forward pass.""" - configs = [{"out_channels": 64}, {"out_channels": 128}] + configs = [{"out_channels": 4}, {"out_channels": 8}] encoder = BlockBasedEncoder(in_channels=3, block_configs=configs) x = torch.randn(2, 3, 32, 64) output = encoder(x) assert output.shape[0] == 2 # batch size - assert output.shape[1] == 128 # final out_channels - # Spatial dimensions depend on pooling operations + assert output.shape[1] == 8 # final out_channels + assert output.shape[2] == 32 # Height should be preserved + assert output.shape[3] == 16 # Width pooling of 2 in every block + + progression = encoder.get_channel_progression() + assert progression == [3, 4, 8] + def test_forward_pass_single_block(self): """Test forward pass with single block.""" @@ -145,25 +153,33 @@ def test_forward_pass_single_block(self): assert output.shape[0] == 1 assert output.shape[1] == 32 + assert output.shape[2] == 16 # Height should be preserved + assert output.shape[3] == 8 # Width pooling of 2 def test_forward_pass_multiple_blocks(self): """Test forward pass with multiple blocks.""" configs = [ - {"out_channels": 32, "pool_size": (1, 2)}, - {"out_channels": 64, "pool_size": (2, 2)}, - {"out_channels": 128, "pool_size": (1, 1)}, + {"out_channels": 4, "pool_size": (1, 2)}, + {"out_channels": 8, "pool_size": (2, 2)}, + {"out_channels": 16, "pool_size": (1, 1)}, ] - encoder = BlockBasedEncoder(in_channels=16, block_configs=configs) - x = torch.randn(1, 16, 32, 32) + encoder = BlockBasedEncoder(in_channels=3, block_configs=configs) + x = torch.randn(1, 3, 32, 32) output = encoder(x) assert output.shape[0] == 1 - assert output.shape[1] == 128 + assert output.shape[1] == 16 + assert output.shape[2] == 16 + # Width pooling of 2 in first and second block + assert output.shape[3] == 8 + + progression = encoder.get_channel_progression() + assert progression == [3, 4, 8, 16] def test_forward_pass_different_input_sizes(self): """Test forward pass with different input sizes.""" - configs = [{"out_channels": 64}, {"out_channels": 128}] + configs = [{"out_channels": 4}, {"out_channels": 8}] encoder = BlockBasedEncoder(in_channels=3, block_configs=configs) # Test various input sizes @@ -172,13 +188,14 @@ def test_forward_pass_different_input_sizes(self): output = encoder(x) assert output.shape[0] == 1 - assert output.shape[1] == 128 - assert output.shape[2] > 0 - assert output.shape[3] > 0 + assert output.shape[1] == 8 + assert output.shape[2] == h + # Width pooling of 2 in every block + assert output.shape[3] == w // 4 def test_forward_pass_gradient_flow(self): """Test that gradients flow properly through the encoder.""" - configs = [{"out_channels": 32}, {"out_channels": 64}] + configs = [{"out_channels": 8}, {"out_channels": 16}] encoder = BlockBasedEncoder(in_channels=8, block_configs=configs) x = torch.randn(1, 8, 16, 16, requires_grad=True) @@ -186,22 +203,27 @@ def test_forward_pass_gradient_flow(self): loss = output.sum() loss.backward() + progression = encoder.get_channel_progression() + assert progression == [8, 8, 16] + assert x.grad is not None assert x.grad.shape == x.shape def test_forward_pass_with_custom_parameters(self): """Test forward pass with custom block parameters.""" configs = [ - {"out_channels": 32, "dropout": 0.5, "activation": "gelu"}, - {"out_channels": 64, "pool_size": (2, 2), "kernel_size": 5}, + {"out_channels": 2, "dropout": 0.5, "activation": "gelu"}, + {"out_channels": 4, "pool_size": (2, 2), "kernel_size": 5}, ] - encoder = BlockBasedEncoder(in_channels=16, block_configs=configs) - x = torch.randn(1, 16, 32, 32) + encoder = BlockBasedEncoder(in_channels=1, block_configs=configs) + x = torch.randn(1, 1, 32, 32) output = encoder(x) assert output.shape[0] == 1 - assert output.shape[1] == 64 + assert output.shape[1] == 4 + assert output.shape[2] == 16 # One height pooling of 2 in first block + assert output.shape[3] == 8 # Width pooling of 2 in every block class TestBlockBasedEncoderConfiguration: @@ -222,7 +244,7 @@ def test_get_config(self): assert config["in_channels"] == 3 assert config["out_channels"] == 128 assert config["block_configs"] == configs - assert config["kernel_size"] == 5 + assert config["kernel_size"] == (5, 5) assert config["bias"] is False def test_from_config(self): @@ -260,7 +282,7 @@ def test_config_roundtrip(self): {"out_channels": 32, "dropout": 0.2}, {"out_channels": 64, "activation": "relu"}, ], - "kernel_size": 3, + "kernel_size": (3, 3), "bias": True, } @@ -268,7 +290,8 @@ def test_config_roundtrip(self): reconstructed_config = encoder.get_config() for key in original_config: - assert reconstructed_config[key] == original_config[key] + if key != "out_channels": + assert reconstructed_config[key] == original_config[key] class TestBlockBasedEncoderChannelProgression: @@ -321,8 +344,8 @@ def test_get_output_shape_basic(self): assert output_shape[0] == 2 # batch size assert output_shape[1] == 128 # final out_channels - assert output_shape[2] > 0 # height should be positive - assert output_shape[3] > 0 # width should be positive + assert output_shape[2] == 32 # height should be positive + assert output_shape[3] == 8 # width should be positive def test_get_output_shape_matches_forward(self): """Test that get_output_shape matches actual forward pass output.""" @@ -349,12 +372,19 @@ def test_get_output_shape_different_pool_sizes(self): encoder = BlockBasedEncoder(in_channels=16, block_configs=configs) input_shape = (1, 16, 32, 32) - output_shape = encoder.get_output_shape(input_shape) + predicted_shape = encoder.get_output_shape(input_shape) - assert output_shape[0] == 1 - assert output_shape[1] == 64 + assert predicted_shape[0] == 1 + assert predicted_shape[1] == 64 # Height should be reduced by factor of 2 from first block + assert predicted_shape[2] == 16 # Width should be reduced by factors 2 and 4 from both blocks + assert predicted_shape[3] == 4 + + x = torch.randn(*input_shape) + actual_output = encoder(x) + + assert predicted_shape == actual_output.shape class TestBlockBasedEncoderFeatureMaps: @@ -373,9 +403,9 @@ def test_get_feature_maps_basic(self): feature_maps = encoder.get_feature_maps(x) assert len(feature_maps) == 3 # One per block - assert feature_maps[0].shape[1] == 32 # First block output - assert feature_maps[1].shape[1] == 64 # Second block output - assert feature_maps[2].shape[1] == 128 # Third block output + assert feature_maps[0].shape == (1, 32, 16, 8) + assert feature_maps[1].shape == (1, 64, 16, 4) + assert feature_maps[2].shape == (1, 128, 16, 2) def test_get_feature_maps_single_block(self): """Test get_feature_maps with single block.""" @@ -386,7 +416,7 @@ def test_get_feature_maps_single_block(self): feature_maps = encoder.get_feature_maps(x) assert len(feature_maps) == 1 - assert feature_maps[0].shape[1] == 64 + assert feature_maps[0].shape == (1, 64, 32, 16) def test_get_feature_maps_consistency(self): """Test that get_feature_maps gives same result as forward pass.""" @@ -394,8 +424,10 @@ def test_get_feature_maps_consistency(self): encoder = BlockBasedEncoder(in_channels=8, block_configs=configs) x = torch.randn(1, 8, 16, 16) - feature_maps = encoder.get_feature_maps(x) - final_output = encoder(x) + encoder.eval() + with torch.no_grad(): + feature_maps = encoder.get_feature_maps(x) + final_output = encoder(x) # Last feature map should match forward pass output assert torch.allclose(feature_maps[-1], final_output, atol=1e-6) @@ -404,16 +436,18 @@ def test_get_feature_maps_independence(self): """Test that feature maps are independent copies.""" configs = [{"out_channels": 32}, {"out_channels": 64}] encoder = BlockBasedEncoder(in_channels=8, block_configs=configs) + encoder.eval() x = torch.randn(1, 8, 16, 16) - feature_maps = encoder.get_feature_maps(x) + with torch.no_grad(): + feature_maps = encoder.get_feature_maps(x) - # Modify one feature map - original_value = feature_maps[0][0, 0, 0, 0].item() - feature_maps[0][0, 0, 0, 0] = 999.0 + # Modify one feature map + original_value = feature_maps[0][0, 0, 0, 0].item() + feature_maps[0][0, 0, 0, 0] = 999.0 - # Get feature maps again - new_feature_maps = encoder.get_feature_maps(x) + # Get feature maps again + new_feature_maps = encoder.get_feature_maps(x) # Should not be affected by previous modification assert new_feature_maps[0][0, 0, 0, 0].item() == original_value @@ -505,9 +539,7 @@ def test_parameter_count(self): ) assert total_params > 0 - assert ( - trainable_params == total_params - ) # All parameters should be trainable + assert trainable_params == total_params # All parameters are trainable class TestBlockBasedEncoderEdgeCases: @@ -520,7 +552,7 @@ def test_large_channel_counts(self): x = torch.randn(1, 256, 8, 8) output = encoder(x) - assert output.shape[1] == 1024 + assert output.shape == (1, 1024, 8, 2) def test_single_channel_input(self): """Test with single channel input.""" @@ -529,7 +561,7 @@ def test_single_channel_input(self): x = torch.randn(1, 1, 32, 32) output = encoder(x) - assert output.shape[1] == 32 + assert output.shape == (1, 32, 32, 8) def test_minimal_spatial_dimensions(self): """Test with minimal spatial dimensions.""" @@ -537,10 +569,8 @@ def test_minimal_spatial_dimensions(self): encoder = BlockBasedEncoder(in_channels=32, block_configs=configs) x = torch.randn(1, 32, 1, 1) - output = encoder(x) - assert output.shape[1] == 64 - assert output.shape[2] > 0 - assert output.shape[3] > 0 + with pytest.raises(ValueError): + _ = encoder(x) def test_decreasing_channels(self): """Test with decreasing channel progression.""" @@ -553,7 +583,7 @@ def test_decreasing_channels(self): x = torch.randn(1, 256, 16, 16) output = encoder(x) - assert output.shape[1] == 32 + assert output.shape == (1, 32, 16, 2) progression = encoder.get_channel_progression() assert progression == [256, 128, 64, 32] From 59ab46093e876f566009147b573d25a7c490036c Mon Sep 17 00:00:00 2001 From: Peter Steiner <61472983+renierts@users.noreply.github.com> Date: Thu, 17 Jul 2025 11:46:26 -0400 Subject: [PATCH 065/103] BlockBasedDecoder is tested. --- src/faith/train/blocks/decoder.py | 9 ++++++ .../test_train_blocks_block_based_decoder.py | 30 ++++++++----------- 2 files changed, 22 insertions(+), 17 deletions(-) diff --git a/src/faith/train/blocks/decoder.py b/src/faith/train/blocks/decoder.py index 7128904..7dcd364 100644 --- a/src/faith/train/blocks/decoder.py +++ b/src/faith/train/blocks/decoder.py @@ -315,6 +315,7 @@ def __init__( block_configs: list[dict[str, Any]], kernel_size: Union[int, tuple[int, int]] = 3, bias: bool = True, + **kwargs ) -> None: """Initialize BlockBasedDecoder.""" @@ -392,6 +393,14 @@ def _build_decoder_blocks( return blocks + def get_output_shape(self, input_shape: tuple[int, ...]) \ + -> tuple[int, ...]: + """Calculate output shape given input shape.""" + shape = input_shape + for block in self.operations: + shape = block.get_output_shape(shape) + return shape + def get_feature_maps(self, x: torch.Tensor) -> list[torch.Tensor]: """Get intermediate feature maps from each decoder block. diff --git a/tests/test_train_blocks_block_based_decoder.py b/tests/test_train_blocks_block_based_decoder.py index c1cc246..312d6f8 100644 --- a/tests/test_train_blocks_block_based_decoder.py +++ b/tests/test_train_blocks_block_based_decoder.py @@ -1,4 +1,3 @@ - import pytest import torch @@ -37,7 +36,7 @@ def test_custom_initialization(self): assert decoder.in_channels == 256 assert decoder.out_channels == 32 assert len(decoder.operations) == 3 - assert decoder.kernel_size == 7 + assert decoder.kernel_size == (7, 7) assert decoder.bias is True def test_single_block_decoder(self): @@ -141,9 +140,7 @@ def test_forward_pass_basic(self): output = decoder(z) - assert output.shape[0] == 2 # batch size - assert output.shape[1] == 3 # final out_channels - # Spatial dimensions should be larger due to upsampling + assert output.shape == (2, 3, 16, 16) def test_forward_pass_single_block(self): """Test forward pass with single block.""" @@ -153,8 +150,7 @@ def test_forward_pass_single_block(self): output = decoder(z) - assert output.shape[0] == 1 - assert output.shape[1] == 32 + assert output.shape == (1, 32, 8, 16) def test_forward_pass_gradient_flow(self): """Test that gradients flow properly through the decoder.""" @@ -189,7 +185,7 @@ def test_get_config(self): assert config["in_channels"] == 256 assert config["out_channels"] == 3 assert config["block_configs"] == configs - assert config["kernel_size"] == 5 + assert config["kernel_size"] == (5, 5) assert config["bias"] is False def test_from_config(self): @@ -229,7 +225,7 @@ def test_config_roundtrip(self): {"out_channels": 32, "activation": "relu"}, {"out_channels": 3}, ], - "kernel_size": 3, + "kernel_size": (3, 3), "bias": True, } @@ -237,7 +233,8 @@ def test_config_roundtrip(self): reconstructed_config = decoder.get_config() for key in original_config: - assert reconstructed_config[key] == original_config[key] + if key != "out_channels": + assert reconstructed_config[key] == original_config[key] class TestBlockBasedDecoderChannelProgression: @@ -348,7 +345,7 @@ def __init__(self): encoder, final_out_channels=3, kernel_size=5, bias=False ) - assert decoder.kernel_size == 5 + assert decoder.kernel_size == (5, 5) assert decoder.bias is False @@ -367,10 +364,7 @@ def test_get_output_shape_basic(self): output_shape = decoder.get_output_shape(input_shape) - assert output_shape[0] == 2 # batch size - assert output_shape[1] == 3 # final out_channels - assert output_shape[2] > 0 # height should be positive - assert output_shape[3] > 0 # width should be positive + assert output_shape == (2, 3, 16, 16) def test_get_output_shape_matches_forward(self): """Test that get_output_shape matches actual forward pass output.""" @@ -416,8 +410,10 @@ def test_get_feature_maps_consistency(self): decoder = BlockBasedDecoder(in_channels=128, block_configs=configs) z = torch.randn(1, 128, 8, 8) - feature_maps = decoder.get_feature_maps(z) - final_output = decoder(z) + decoder.eval() + with torch.no_grad(): + feature_maps = decoder.get_feature_maps(z) + final_output = decoder(z) # Last feature map should match forward pass output assert torch.allclose(feature_maps[-1], final_output, atol=1e-6) From b670085613f74c27278fd7287d22549a45378e94 Mon Sep 17 00:00:00 2001 From: Kouroche Bouchiat Date: Thu, 17 Jul 2025 13:15:48 -0400 Subject: [PATCH 066/103] Base implementations for `Residual{Encoding,Decoding}{1d,2d}` --- src/faith/train/blocks/base.py | 95 +++++++++++++++++++++++++++++++++- 1 file changed, 94 insertions(+), 1 deletion(-) diff --git a/src/faith/train/blocks/base.py b/src/faith/train/blocks/base.py index 7569be3..acbb871 100644 --- a/src/faith/train/blocks/base.py +++ b/src/faith/train/blocks/base.py @@ -8,7 +8,7 @@ import math from abc import ABC, abstractmethod -from typing import Any, Optional, Union +from typing import Any, ClassVar, Literal, Optional, Union import torch import torch.nn as nn @@ -292,3 +292,96 @@ def get_memory_usage( 'activations_mb': activation_memory / (1024 * 1024), 'total_mb': (param_memory + activation_memory) / (1024 * 1024) } + +# Kouroche's implementation. +def padding_for_conv( + size: int, mode: Literal["valid", "same", "full"] = "same" +) -> int: + if size % 2 == 0 and mode in ("same", "full"): + raise ValueError(f'Kernel size must be odd for "{mode}" convolution.') + + if mode == "valid": + return 0 + elif mode == "same": + return size // 2 + elif mode == "full": + return size - 1 + else: + raise ValueError(f'Invalid mode: "{mode}"') + +class _ResidualBlock(ABC, nn.Module): + _conv_type: ClassVar[ + type[nn.Conv1d] + | type[nn.Conv2d] + | type[nn.ConvTranspose1d] + | type[nn.ConvTranspose2d] + ] + _norm_type: ClassVar[type[nn.BatchNorm1d] | type[nn.BatchNorm2d]] + + conv_1: nn.Sequential + conv_2: nn.Sequential + downsample: nn.Sequential | None + activation: nn.ReLU + + def __init__( + self, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1 + ) -> None: + super().__init__() + + padding = padding_for_conv(kernel_size) + + self.conv_1 = nn.Sequential( + self._conv_type(in_channels, out_channels, kernel_size, stride, padding), + self._norm_type(out_channels), + nn.ReLU(inplace=True), + ) + self.conv_2 = nn.Sequential( + self._conv_type(out_channels, out_channels, kernel_size, padding=padding), + self._norm_type(out_channels), + ) + + if in_channels != out_channels or stride != 1: + self.downsample = nn.Sequential( + self._conv_type( + in_channels, out_channels, kernel_size=1, stride=stride + ), + self._norm_type(out_channels), + ) + else: + self.downsample = None + + self.activation = nn.ReLU(inplace=True) + + + def forward(self, input: torch.Tensor) -> torch.Tensor: + residual = input + + output = self.conv_1(input) + output = self.conv_2(output) + + if self.downsample: + residual = self.downsample(residual) + + output = output + residual + output = self.activation(output) + return output + + +class ResidualEncoding1d(_ResidualBlock): + _conv_type = nn.Conv1d + _norm_type = nn.BatchNorm1d + + +class ResidualEncoding2d(_ResidualBlock): + _conv_type = nn.Conv2d + _norm_type = nn.BatchNorm2d + + +class ResidualDecoding1d(_ResidualBlock): + _conv_type = nn.ConvTranspose1d + _norm_type = nn.BatchNorm1d + + +class ResidualDecoding2d(_ResidualBlock): + _conv_type = nn.ConvTranspose2d + _norm_type = nn.BatchNorm2d From afb8dc864b4a2c62f00f3fdcc36718d49cce44c3 Mon Sep 17 00:00:00 2001 From: Peter Steiner <61472983+renierts@users.noreply.github.com> Date: Thu, 17 Jul 2025 16:04:30 -0400 Subject: [PATCH 067/103] Started refactoring the ResidualBlock. --- src/faith/train/blocks/__init__.py | 16 +- src/faith/train/blocks/base.py | 247 ++++++++---- src/faith/train/blocks/decoder.py | 14 +- src/faith/train/blocks/encoder.py | 160 +++----- src/faith/train/blocks/residual.py | 364 ------------------ ...test_train_blocks_residual_encoding_1d.py} | 119 +++--- 6 files changed, 304 insertions(+), 616 deletions(-) delete mode 100644 src/faith/train/blocks/residual.py rename tests/{test_train_blocks_residual.py => test_train_blocks_residual_encoding_1d.py} (65%) diff --git a/src/faith/train/blocks/__init__.py b/src/faith/train/blocks/__init__.py index 02cf1a9..97dd186 100644 --- a/src/faith/train/blocks/__init__.py +++ b/src/faith/train/blocks/__init__.py @@ -1,16 +1,14 @@ -"""Neural network blocks for building autoencoders.""" +"""Neural network blocks.""" -from .residual import ResidualBlock -from .encoder import EncoderBlock, BlockBasedEncoder -from .decoder import DecoderBlock, BlockBasedDecoder -from .base import BaseConvBlock, BlockUtils +from .base import BlockUtils +from .decoder import BlockBasedDecoder, DecoderBlock +from .encoder import EncoderBlock1d, ResidualEncoding1d, ResidualEncoding2d __all__ = [ - "ResidualBlock", - "EncoderBlock", - "BlockBasedEncoder", + "ResidualEncoding1d", + "ResidualEncoding2d", + "EncoderBlock1d", "DecoderBlock", "BlockBasedDecoder", - "BaseConvBlock", "BlockUtils", ] diff --git a/src/faith/train/blocks/base.py b/src/faith/train/blocks/base.py index acbb871..07a85a7 100644 --- a/src/faith/train/blocks/base.py +++ b/src/faith/train/blocks/base.py @@ -5,10 +5,11 @@ It ensures consistency across different block types and provides common patterns for initialization, forward passes, and configuration. """ +from __future__ import annotations import math from abc import ABC, abstractmethod -from typing import Any, ClassVar, Literal, Optional, Union +from typing import Any, ClassVar, Optional, Union import torch import torch.nn as nn @@ -90,22 +91,6 @@ def _normalize_kernel_size( return kernel_size, kernel_size return kernel_size - @staticmethod - def _calculate_padding( - kernel_size: Union[int, tuple[int, int]], - padding: Union[int, tuple[int, int], str] = 'auto' - ) -> tuple[int, ...]: - """Calculate padding based on kernel size and padding specification.""" - if padding == 'auto': - if isinstance(kernel_size, int): - return kernel_size // 2, kernel_size // 2 - else: - return tuple(k // 2 for k in kernel_size) - elif isinstance(padding, int): - return padding, padding - else: - return padding - @abstractmethod def forward( self, @@ -293,95 +278,221 @@ def get_memory_usage( 'total_mb': (param_memory + activation_memory) / (1024 * 1024) } -# Kouroche's implementation. -def padding_for_conv( - size: int, mode: Literal["valid", "same", "full"] = "same" -) -> int: - if size % 2 == 0 and mode in ("same", "full"): - raise ValueError(f'Kernel size must be odd for "{mode}" convolution.') - - if mode == "valid": - return 0 - elif mode == "same": - return size // 2 - elif mode == "full": - return size - 1 - else: - raise ValueError(f'Invalid mode: "{mode}"') +class _Identity(nn.Module): + """Identity block that returns the input tensor unchanged.""" + + def __init__(self): + super().__init__() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Identity function that returns the input tensor unchanged.""" + return x + + +def _calculate_padding( + kernel_size: int | tuple[int, int], + padding: int | tuple[int, int] | str = 'auto' +) -> int | tuple[int, int]: + """Calculate padding based on kernel size and padding specification.""" + if isinstance(kernel_size, int) and kernel_size % 2 == 0: + raise ValueError(f"Kernel size must be odd, got {kernel_size} (even).") + if isinstance(kernel_size, tuple) and any(k % 2 == 0 for k in kernel_size): + raise ValueError(f"Kernel size must be odd, got {kernel_size} (even).") + if padding == 'auto': + if isinstance(kernel_size, int): + return kernel_size // 2 + else: + return tuple(k // 2 for k in kernel_size) + return padding + + +def _create_activation(activation_name: str = "relu") -> nn.Module: + """Create activation function based on name.""" + activations = { + 'tanh': nn.Tanh(), + 'sigmoid': nn.Sigmoid(), + 'relu': nn.ReLU(inplace=True), + 'leaky_relu': nn.LeakyReLU(0.1, inplace=True), + 'gelu': nn.GELU(), + 'swish': nn.SiLU(), # SiLU is the same as Swish + 'mish': nn.Mish(), + } + return activations[activation_name] + + +# Kouroche's implementation. class _ResidualBlock(ABC, nn.Module): + """ + Abstract base class for residual blocks in neural networks. + """ _conv_type: ClassVar[ - type[nn.Conv1d] - | type[nn.Conv2d] - | type[nn.ConvTranspose1d] - | type[nn.ConvTranspose2d] - ] - _norm_type: ClassVar[type[nn.BatchNorm1d] | type[nn.BatchNorm2d]] + type[nn.Conv1d] | type[nn.Conv2d] | + type[nn.ConvTranspose1d] | type[nn.ConvTranspose2d]] + _norm_type: ClassVar[type[nn.BatchNorm1d] | type[nn.BatchNorm2d] | None] conv_1: nn.Sequential conv_2: nn.Sequential - downsample: nn.Sequential | None - activation: nn.ReLU + mixing: nn.Sequential | None def __init__( - self, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1 + self, + in_channels: int, + out_channels: int, + kernel_size: int | tuple[int, int] = 3, + stride: int | tuple[int, int] = 1, + bias: bool = True, + activation_name: str = "relu", + weight_init_method: str = "kaiming", ) -> None: super().__init__() - padding = padding_for_conv(kernel_size) + if isinstance(kernel_size, int) and kernel_size % 2 == 0: + raise ValueError(f"Kernel size must be odd, got {kernel_size} (even).") + if isinstance(kernel_size, tuple) and any(k % 2 == 0 for k in kernel_size): + raise ValueError(f"Kernel size must be odd, got {kernel_size} (even).") + if isinstance(kernel_size, int) and kernel_size <= 0: + raise ValueError(f"Kernel size must be positive, got {kernel_size}.") + if isinstance(kernel_size, tuple) and any(k <= 0 for k in kernel_size): + raise ValueError(f"Kernel size must be positive, got {kernel_size}.") + + if in_channels <= 0: + raise ValueError(f"in_channels must be positive, got {in_channels}.") + if out_channels <= 0: + raise ValueError(f"out_channels must be positive, got {out_channels}.") + + if not isinstance(in_channels, int): + raise TypeError(f"in_channels must be an int, got {type(in_channels)}.") + if not isinstance(out_channels, int): + raise TypeError(f"out_channels must be an int, got {type(out_channels)}.") + + if isinstance(stride, int) and stride <= 0: + raise ValueError(f"stride must be positive, got {stride}.") + if isinstance(stride, tuple) and any(s <= 0 for s in stride): + raise ValueError(f"stride must be positive, got {stride}.") + if not isinstance(stride, int) and not isinstance(stride, tuple): + raise TypeError(f"stride must be an int or tuple, got {type(stride)}.") + + self.padding = _calculate_padding(kernel_size, padding="auto") + self.kernel_size = kernel_size + self.stride = stride + self.bias = bias + self.out_channels = out_channels self.conv_1 = nn.Sequential( - self._conv_type(in_channels, out_channels, kernel_size, stride, padding), - self._norm_type(out_channels), - nn.ReLU(inplace=True), + self._conv_type( + in_channels=in_channels, out_channels=out_channels, + kernel_size=kernel_size, stride=stride, padding=self.padding, bias=bias + ), + self._norm_type(out_channels) if self._norm_type is not None \ + else _Identity(), + _create_activation(activation_name), ) self.conv_2 = nn.Sequential( - self._conv_type(out_channels, out_channels, kernel_size, padding=padding), - self._norm_type(out_channels), + self._conv_type( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=kernel_size, + padding=self.padding, + bias=bias, + ), + self._norm_type(out_channels) if self._norm_type is not None \ + else _Identity(), ) if in_channels != out_channels or stride != 1: - self.downsample = nn.Sequential( + self.mixing = nn.Sequential( self._conv_type( in_channels, out_channels, kernel_size=1, stride=stride ), - self._norm_type(out_channels), + self._norm_type(out_channels) + if self._norm_type is not None + else _Identity(), ) else: - self.downsample = None - - self.activation = nn.ReLU(inplace=True) + self.mixing = None + self.final_activation = _create_activation(activation_name) + self.init_method = weight_init_method + self._initialize_weights() def forward(self, input: torch.Tensor) -> torch.Tensor: + """ + Forward pass through the residual block. + + Parameters + ---------- + input : torch.Tensor + Input tensor of shape (batch, channels, height, width). + + Returns + ------- + torch.Tensor + Output tensor after applying the residual block. + """ residual = input output = self.conv_1(input) output = self.conv_2(output) - if self.downsample: - residual = self.downsample(residual) + if self.mixing: + residual = self.mixing(residual) output = output + residual - output = self.activation(output) + output = self.final_activation(output) return output + def _initialize_weights(self) -> None: + """Initialize weights according to the specified method.""" + if self.init_method == 'kaiming': + self.apply(WeightInitializer.kaiming_normal_) + elif self.init_method == 'xavier': + self.apply(WeightInitializer.xavier_uniform_) + elif self.init_method == 'default': + pass # Use PyTorch's default initialization -class ResidualEncoding1d(_ResidualBlock): - _conv_type = nn.Conv1d - _norm_type = nn.BatchNorm1d - + # Always properly initialize batch norm + if self._norm_type is not None: + self.apply(WeightInitializer.init_batch_norm_) -class ResidualEncoding2d(_ResidualBlock): - _conv_type = nn.Conv2d - _norm_type = nn.BatchNorm2d + def __repr__(self) -> str: + """String representation of the ResidualBlock.""" + return (f"ResidualBlock(" + f"in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}, " + f"stride={self.stride}, " + f"bias={self.bias}, " + f"activation_name='{self.activation_name}', " + f"weight_init_method={self.init_method})") + def get_output_shape( + self, + input_shape: tuple[int, ...] + ) -> tuple[int, ...]: + """ + Calculate output shape given input shape. -class ResidualDecoding1d(_ResidualBlock): - _conv_type = nn.ConvTranspose1d - _norm_type = nn.BatchNorm1d + Parameters + ---------- + input_shape : tuple + Input tensor shape (batch, channels, height, width). + Returns + ------- + tuple + Output tensor shape. + """ + from src.faith.train.blocks import BlockUtils + + # Account for stride in the first convolution + temp_shape = BlockUtils.calculate_output_shape( + input_shape, + kernel_size=self.kernel_size, + stride=self.stride, + padding=self.padding + ) -class ResidualDecoding2d(_ResidualBlock): - _conv_type = nn.ConvTranspose2d - _norm_type = nn.BatchNorm2d + # Update channels + batch_size, _, height, width = temp_shape + return batch_size, self.out_channels, height, width diff --git a/src/faith/train/blocks/decoder.py b/src/faith/train/blocks/decoder.py index 7dcd364..3f2dbb1 100644 --- a/src/faith/train/blocks/decoder.py +++ b/src/faith/train/blocks/decoder.py @@ -10,9 +10,19 @@ import torch import torch.nn as nn -from .base import SequentialBlock +from .base import SequentialBlock, _ResidualBlock from .encoder import BlockBasedEncoder -from .residual import ResidualBlock + + +class ResidualDecoding1d(_ResidualBlock): + _conv_type = nn.ConvTranspose1d + _norm_type = nn.BatchNorm1d + + +class ResidualDecoding2d(_ResidualBlock): + _conv_type = nn.ConvTranspose2d + _norm_type = nn.BatchNorm2d + class DecoderBlock(SequentialBlock): diff --git a/src/faith/train/blocks/encoder.py b/src/faith/train/blocks/encoder.py index 2f9a024..7237602 100644 --- a/src/faith/train/blocks/encoder.py +++ b/src/faith/train/blocks/encoder.py @@ -3,23 +3,32 @@ This module implements the EncoderBlock and BlockBasedEncoder classes that inherit from the base classes, following established patterns and interfaces. """ +from __future__ import annotations -from typing import Any, Union +from typing import Any import torch import torch.nn as nn -from .base import SequentialBlock -from .residual import ResidualBlock +from .base import SequentialBlock, _ResidualBlock -class EncoderBlock(SequentialBlock): +class ResidualEncoding1d(_ResidualBlock): + _conv_type = nn.Conv1d + _norm_type = nn.BatchNorm1d + + +class ResidualEncoding2d(_ResidualBlock): + _conv_type = nn.Conv2d + _norm_type = nn.BatchNorm2d + + +class EncoderBlock1d(nn.Module): """ - Single encoder block: ResidualBlock + Dropout + MaxPool. + Single encoder block with 1D convolutions: ResidualEncoding1D + Dropout + MaxPool. - This block represents the fundamental building unit of the encoder, - combining feature extraction through ResidualBlock, regularization - through Dropout, and spatial downsampling through MaxPooling. + This block combines feature extraction through ResidualEncoding1D, regularization + through Dropout, and downsampling through MaxPooling. Parameters ---------- @@ -27,23 +36,21 @@ class EncoderBlock(SequentialBlock): Number of input channels. out_channels : int Number of output channels from the ResidualBlock. - pool_size : tuple of int, default=(1, 2) - Kernel size for MaxPool2d operation. Format: (height, width). kernel_size : int or tuple of int, default=3 Kernel size for convolutions in ResidualBlock. stride : int or tuple of int, default=1 Stride for convolutions in ResidualBlock. The EncoderBlock uses stride=1 and relies on MaxPool for downsampling. - dropout : float, default=0.3 - Dropout probability. Must be between 0.0 and 1.0. bias : bool, default=True Whether to use bias in convolution layers. - use_batch_norm : bool, default=True - Whether to use batch normalization in ResidualBlock. - activation : str, default='relu' + activation_name : str, default='relu' Activation function for ResidualBlock. - residual_init_method : str, default='kaiming' + weight_init_method : str, default='kaiming' Weight initialization method for ResidualBlock. + pool_size : tuple of int, default=(1, 2) + Kernel size for MaxPool2d operation. Format: (height, width). + dropout : float, default=0.3 + Dropout probability. Must be between 0.0 and 1.0. Attributes ---------- @@ -53,21 +60,17 @@ class EncoderBlock(SequentialBlock): Dropout layer for regularization. pool : nn.MaxPool2d Max pooling layer for spatial downsampling. - pool_size : tuple of int - Stored pooling size for decoder symmetry. - dropout_prob : float - Stored dropout probability. Examples -------- - >>> block = EncoderBlock(in_channels=64, out_channels=128) + >>> block = EncoderBlock1d(in_channels=64, out_channels=128) >>> x = torch.randn(1, 64, 32, 32) >>> out = block(x) >>> print(out.shape) torch.Size([1, 128, 32, 16]) >>> # Custom configuration - >>> block = EncoderBlock( + >>> block = EncoderBlock1d( ... in_channels=64, out_channels=128, ... pool_size=(2, 2), dropout=0.5, activation='gelu' ... ) @@ -77,90 +80,36 @@ def __init__( self, in_channels: int, out_channels: int, + kernel_size: int | tuple[int, int] = 3, + stride: int | tuple[int, int] = 1, + bias: bool = True, + activation_name: str = 'relu', + weight_init_method: str = 'kaiming', pool_size: tuple[int, int] = (1, 2), - kernel_size: Union[int, tuple[int, int]] = 3, - stride: Union[int, tuple[int, int]] = 1, dropout: float = 0.3, - bias: bool = True, - use_batch_norm: bool = True, - activation: str = 'relu', - residual_init_method: str = 'kaiming' ) -> None: """Initialize EncoderBlock.""" - - # Validate parameters - if not 0.0 <= dropout <= 1.0: - raise ValueError( - f"Dropout must be between 0.0 and 1.0, got {dropout}") - - if len(pool_size) != 2: - raise ValueError( - f"pool_size must be a tuple of length 2, got {pool_size}") - - # Store configuration - self.pool_size = pool_size - self.dropout_prob = dropout - self.use_batch_norm = use_batch_norm - self.activation_name = activation - self.residual_init_method = residual_init_method - - # Build the sequential operations - operations = self._build_operations( - in_channels, out_channels, kernel_size, stride, - bias, use_batch_norm, activation, residual_init_method - ) - - # Initialize SequentialBlock with operations - super().__init__( - in_channels=in_channels, - out_channels=out_channels, - operations=operations, - kernel_size=kernel_size, - bias=bias + super().__init__() + + self.encoder_block = nn.Sequential( + ResidualEncoding1d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + bias=bias, + activation_name=activation_name, + weight_init_method=weight_init_method + ), + nn.Dropout(p=dropout), + nn.MaxPool1d(kernel_size=pool_size, stride=pool_size) ) # Store individual components for introspection - self.residual_block = self.operations[0] - self.dropout = self.operations[1] - self.pool = self.operations[2] - - def _build_operations( - self, - in_channels: int, - out_channels: int, - kernel_size: Union[int, tuple[int, int]], - stride: Union[int, tuple[int, int]], - bias: bool, - use_batch_norm: bool, - activation: str, - init_method: str - ) -> list[nn.Module]: - """Build the list of operations for this encoder block.""" - - operations = [] - - # 1. ResidualBlock for feature extraction - residual_block = ResidualBlock( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=kernel_size, - stride=stride, - bias=bias, - use_batch_norm=use_batch_norm, - activation=activation, - init_method=init_method - ) - operations.append(residual_block) - - # 2. Dropout for regularization - dropout_layer = nn.Dropout(p=self.dropout_prob) - operations.append(dropout_layer) - - # 3. MaxPool for downsampling - pool_layer = nn.MaxPool2d(kernel_size=self.pool_size) - operations.append(pool_layer) - - return operations + self.residual_block = self.encoder_block[0] + self.dropout = self.encoder_block[1] + self.pool = self.encoder_block[2] + self.pool_size = pool_size if isinstance(pool_size, tuple) else (1, pool_size) def get_config(self) -> dict[str, Any]: """Get configuration dictionary for this block.""" @@ -175,18 +124,11 @@ def get_config(self) -> dict[str, Any]: }) return config - @classmethod - def from_config(cls, config: dict[str, Any]) -> 'EncoderBlock': - """Create EncoderBlock instance from configuration dictionary.""" - return cls(**config) - - def get_output_shape(self, input_shape: tuple[int, ...]) \ - -> tuple[int, ...]: + def get_output_shape(self, input_shape: tuple[int, ...]) -> tuple[int, ...]: """Calculate output shape given input shape.""" # Get shape after residual block - residual_output_shape = ( - self.residual_block.get_output_shape(input_shape)) + residual_output_shape = (self.residual_block.get_output_shape(input_shape)) # Apply pooling batch_size, channels, height, width = residual_output_shape @@ -389,7 +331,7 @@ def get_config(self) -> dict[str, Any]: return config @classmethod - def from_config(cls, config: dict[str, Any]) -> "BlockBasedEncoder": + def from_config(cls, config: dict[str, Any]) -> BlockBasedEncoder: """Create BlockBasedEncoder instance from configuration dictionary.""" return cls(**config) diff --git a/src/faith/train/blocks/residual.py b/src/faith/train/blocks/residual.py deleted file mode 100644 index d4ec436..0000000 --- a/src/faith/train/blocks/residual.py +++ /dev/null @@ -1,364 +0,0 @@ -"""Residual block implementation derived from base classes. - -This module implements the ResidualBlock class that inherits from BaseBlock, -following the established patterns and interfaces defined in the base module. -""" - -from typing import Any, Union - -import torch -import torch.nn as nn - -from .base import BaseConvBlock, WeightInitializer - - -class ResidualBlock(BaseConvBlock): - """Residual convolutional block with batch normalization and ReLU. - - This block implements a standard residual connection with two convolutional - layers, batch normalization, and ReLU activation. It includes an optional - projection layer for dimension matching in the skip connection. - - Parameters - ---------- - in_channels : int - Number of input channels. - out_channels : int - Number of output channels. - kernel_size : int or tuple of int, default=3 - Size of the convolving kernel. - stride : int or tuple of int, default=1 - Stride of the convolution. - bias : bool, default=True - If True, adds a learnable bias to the output. - use_batch_norm : bool, default=True - Whether to use batch normalization layers. - activation : str, default='relu' - Activation function to use ('relu', 'leaky_relu', 'gelu', etc.). - init_method : str, default='kaiming' - Weight initialization method ('kaiming', 'xavier', 'default'). - - Attributes - ---------- - conv1 : torch.nn.Conv2d - First convolutional layer. - batch_norm_1 : torch.nn.BatchNorm2d or None - First batch normalization layer. - activation_fn : torch.nn.Module - Activation function. - conv2 : torch.nn.Conv2d - Second convolutional layer. - batch_norm_2 : torch.nn.BatchNorm2d or None - Second batch normalization layer. - skip_conv : torch.nn.Conv2d or None - Optional 1x1 convolution for dimension matching. - stride : tuple of int - Stored stride values. - padding : tuple of int - Stored padding values. - - Examples - -------- - >>> block = ResidualBlock(64, 128) - >>> x = torch.randn(1, 64, 32, 32) - >>> out = block(x) - >>> print(out.shape) - torch.Size([1, 128, 32, 32]) - - >>> # Custom configuration - >>> block = ResidualBlock(64, 128, stride=2, activation='gelu') - >>> config = block.get_config() - >>> new_block = ResidualBlock.from_config(config) - """ - - def __init__( - self, - in_channels: int, - out_channels: int, - kernel_size: Union[int, tuple[int, int]] = 3, - stride: Union[int, tuple[int, int]] = 1, - bias: bool = True, - use_batch_norm: bool = True, - activation: str = 'relu', - init_method: str = 'kaiming' - ) -> None: - """Initialize ResidualBlock. - - Parameters - ---------- - in_channels : int - Number of input channels. - out_channels : int - Number of output channels. - kernel_size : int or tuple of int, default=3 - Size of the convolving kernel. - stride : int or tuple of int, default=1 - Stride of the convolution. - bias : bool, default=True - Whether to use bias in convolutions. - use_batch_norm : bool, default=True - Whether to use batch normalization. - activation : str, default='relu' - Activation function name. - init_method : str, default='kaiming' - Weight initialization method. - """ - # Initialize base class - super().__init__(in_channels, out_channels, kernel_size, bias) - - if isinstance(stride, int) and stride < 1: - raise ValueError(f"Stride must be a positive integer or tuple, " - f"got {stride}") - if isinstance(stride, tuple) and any(s < 1 for s in stride): - raise ValueError(f"Stride must be a positive integer or tuple, " - f"got {stride}") - if (isinstance(stride, float) or isinstance(stride, tuple) - and any(isinstance(s, float) for s in stride)): - raise TypeError(f"Stride must be an integer or tuple, " - f"got float {stride}") - - # Normalize stride and padding - self.stride = self._normalize_stride(stride) - self.padding = self._calculate_padding(self.kernel_size, "auto") - self.use_batch_norm = use_batch_norm - self.activation_name = activation - self.init_method = init_method - - # Validate parameters - self._validate_parameters() - - # Build the block layers - self._build_layers() - - # Initialize weights - self._initialize_weights() - - def _normalize_stride(self, - stride: Union[int, tuple[int, int]] - ) -> tuple[int, int]: - """Normalize stride to tuple format.""" - if isinstance(stride, int): - return (stride, stride) - return stride - - def _validate_parameters(self) -> None: - """Validate input parameters.""" - valid_activations = {'tanh', 'sigmoid', 'relu', 'leaky_relu', 'gelu', - 'swish', 'mish'} - if self.activation_name not in valid_activations: - raise ValueError(f"activation must be one of {valid_activations}, " - f"got {self.activation_name}") - - valid_init_methods = {'kaiming', 'xavier', 'default'} - if self.init_method not in valid_init_methods: - raise ValueError(f"init_method must be one of {valid_init_methods}" - f", got {self.init_method}") - - def _build_layers(self) -> None: - """Build the convolutional layers and other components.""" - # First convolutional layer - self.conv1 = nn.Conv2d( - self.in_channels, - self.out_channels, - kernel_size=self.kernel_size, - stride=self.stride, - padding=self.padding, - bias=self.bias and not self.use_batch_norm - # No bias if using batch norm - ) - - # First batch normalization (optional) - if self.use_batch_norm: - self.batch_norm_1 = nn.BatchNorm2d(self.out_channels) - else: - self.batch_norm_1 = None - - # Activation function - self.activation_fn = self._create_activation() - - # Second convolutional layer (always stride=1 to maintain dimensions) - self.conv2 = nn.Conv2d( - self.out_channels, - self.out_channels, - kernel_size=self.kernel_size, - stride=1, - padding=self.padding, - bias=self.bias and not self.use_batch_norm - ) - - # Second batch normalization (optional) - if self.use_batch_norm: - self.batch_norm_2 = nn.BatchNorm2d(self.out_channels) - else: - self.batch_norm_2 = None - - # Projection for skip connection if dimensions don't match - if self.in_channels != self.out_channels or self.stride != (1, 1): - self.skip_conv = nn.Conv2d( - self.in_channels, - self.out_channels, - kernel_size=1, - stride=self.stride, - padding=0, - bias=self.bias and not self.use_batch_norm - ) - if self.use_batch_norm: - self.skip_batch_norm = nn.BatchNorm2d(self.out_channels) - else: - self.skip_batch_norm = None - else: - self.skip_conv = None - self.skip_batch_norm = None - - def _create_activation(self) -> nn.Module: - """Create activation function based on name.""" - activations = { - 'tanh': nn.Tanh(), - 'sigmoid': nn.Sigmoid(), - 'relu': nn.ReLU(inplace=True), - 'leaky_relu': nn.LeakyReLU(0.1, inplace=True), - 'gelu': nn.GELU(), - 'swish': nn.SiLU(), # SiLU is the same as Swish - 'mish': nn.Mish(), - } - return activations[self.activation_name] - - def _initialize_weights(self) -> None: - """Initialize weights according to the specified method.""" - if self.init_method == 'kaiming': - self.apply(WeightInitializer.kaiming_normal_) - elif self.init_method == 'xavier': - self.apply(WeightInitializer.xavier_uniform_) - elif self.init_method == 'default': - pass # Use PyTorch's default initialization - - # Always properly initialize batch norm - if self.use_batch_norm: - self.apply(WeightInitializer.init_batch_norm_) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """Forward pass through the residual block. - - TODO: What does ResNet do for initializing the residual connections? - - Parameters - ---------- - x : torch.Tensor - Input tensor with shape (batch_size, in_channels, height, width). - - Returns - ------- - torch.Tensor - Output tensor with shape - (batch_size, out_channels, height', width') where height' - and 'width' depend on stride. - """ - # Store input for residual connection - residual = x - - # First conv block - out = self.conv1(x) - if self.batch_norm_1 is not None: - out = self.batch_norm_1(out) - out = self.activation_fn(out) - - # Second conv block - out = self.conv2(out) - if self.batch_norm_2 is not None: - out = self.batch_norm_2(out) - - # Apply skip connection with optional projection - if self.skip_conv is not None: - residual = self.skip_conv(residual) - if self.skip_batch_norm is not None: - residual = self.skip_batch_norm(residual) - - # Add residual connection - out += residual - - # Final activation - out = self.activation_fn(out) - - return out - - def get_config(self) -> dict[str, Any]: - """Get configuration dictionary for this block. - - Returns - ------- - dict - Configuration dictionary containing all parameters needed - to reconstruct this block. - """ - config = super().get_config() - config.update({ - 'stride': self.stride, - 'padding': self.padding, - 'use_batch_norm': self.use_batch_norm, - 'activation': self.activation_name, - 'init_method': self.init_method, - }) - return config - - @classmethod - def from_config(cls, config: dict[str, Any]) -> 'ResidualBlock': - """Create ResidualBlock instance from configuration dictionary. - - Parameters - ---------- - config : dict - Configuration dictionary. - - Returns - ------- - ResidualBlock - New ResidualBlock instance. - """ - return cls(**config) - - def __repr__(self) -> str: - """String representation of the ResidualBlock.""" - return (f"ResidualBlock(" - f"in_channels={self.in_channels}, " - f"out_channels={self.out_channels}, " - f"kernel_size={self.kernel_size}, " - f"stride={self.stride}, " - f"padding={self.padding}, " - f"bias={self.bias}, " - f"use_batch_norm={self.use_batch_norm}, " - f"activation='{self.activation_name}')") - - @property - def has_skip_connection(self) -> bool: - """Check if this block has a skip connection projection.""" - return self.skip_conv is not None - - def get_output_shape( - self, - input_shape: tuple[int, ...] - ) -> tuple[int, ...]: - """Calculate output shape given input shape. - - Parameters - ---------- - input_shape : tuple - Input tensor shape (batch, channels, height, width). - - Returns - ------- - tuple - Output tensor shape. - """ - from src.faith.train.blocks import BlockUtils - - # Account for stride in the first convolution - temp_shape = BlockUtils.calculate_output_shape( - input_shape, - kernel_size=self.kernel_size, - stride=self.stride, - padding=self.padding - ) - - # Update channels - batch_size, _, height, width = temp_shape - return batch_size, self.out_channels, height, width diff --git a/tests/test_train_blocks_residual.py b/tests/test_train_blocks_residual_encoding_1d.py similarity index 65% rename from tests/test_train_blocks_residual.py rename to tests/test_train_blocks_residual_encoding_1d.py index a0f1710..7c04c11 100644 --- a/tests/test_train_blocks_residual.py +++ b/tests/test_train_blocks_residual_encoding_1d.py @@ -1,13 +1,12 @@ +import pytest import torch -from src.faith.train.blocks import ResidualBlock - -import pytest +from src.faith.train.blocks import ResidualEncoding1d def test_kernel_size_constant_channels(): """Test the kernel size of the ResidualBlock.""" - block = ResidualBlock(4, 4, kernel_size=3) + block = ResidualEncoding1d(4, 4, kernel_size=3) # Test that the block was created successfully assert block is not None @@ -15,15 +14,15 @@ def test_kernel_size_constant_channels(): # Test that the kernel size is correctly set in the convolutional layers # Assuming ResidualBlock has conv layers with the specified kernel size for module in block.modules(): - if isinstance(module, torch.nn.Conv2d): - assert module.kernel_size == (3, 3), ( - f"Expected kernel size (3, 3), got {module.kernel_size}" + if isinstance(module, torch.nn.Conv1d): + assert module.kernel_size == (3, ), ( + f"Expected kernel size (3, ), got {module.kernel_size}" ) def test_kernel_size_changing_channels(): """Test the kernel size of the ResidualBlock.""" - block = ResidualBlock(4, 6, kernel_size=3) + block = ResidualEncoding1d(4, 6, kernel_size=3) # Test that the block was created successfully assert block is not None @@ -31,52 +30,47 @@ def test_kernel_size_changing_channels(): # Test that the kernel size is correctly set in the convolutional layers # Assuming ResidualBlock has conv layers with the specified kernel size for module in block.modules(): - if isinstance(module, torch.nn.Conv2d): - assert (module.kernel_size == (3, 3) - or module.kernel_size == (1, 1)), ( - f"Expected kernel size (3, 3) or (1, 1), " - f"got {module.kernel_size}" + if isinstance(module, torch.nn.Conv1d): + assert (module.kernel_size == (3, ) + or module.kernel_size == (1, )), ( + f"Expected kernel size (3, ) or (1, ), got {module.kernel_size}" ) def test_kernel_size_different_values(): """Test ResidualBlock with different kernel sizes.""" test_cases = [ - (1, (1, 1)), - (3, (3, 3)), - (5, (5, 5)), - (7, (7, 7)), + (1, (1, )), + (3, (3, )), + (5, (5, )), + (7, (7, )), ] for kernel_size, expected in test_cases: - block = ResidualBlock(4, 8, kernel_size=kernel_size) + block = ResidualEncoding1d(4, 8, kernel_size=kernel_size) # Check that conv layers have the correct kernel size conv_layers = [ - module for module in block.modules() - if isinstance(module, torch.nn.Conv2d) + module for module in block.modules()if isinstance(module, torch.nn.Conv1d) ] - assert len(conv_layers) == 3, ("ResidualBlock should contain Conv2d " - "layers") + assert len(conv_layers) == 3, "ResidualBlock should contain 3 Conv1d layers" for conv_layer in conv_layers: assert (conv_layer.kernel_size == expected - or conv_layer.kernel_size == (1, 1)), ( + or conv_layer.kernel_size == (1, )), ( f"For kernel_size={kernel_size}, expected {expected}, " f"got {conv_layer.kernel_size}" ) - block = ResidualBlock(4, 4, kernel_size=kernel_size) + block = ResidualEncoding1d(4, 4, kernel_size=kernel_size) # Check that conv layers have the correct kernel size conv_layers = [ - module for module in block.modules() - if isinstance(module, torch.nn.Conv2d) + module for module in block.modules() if isinstance(module, torch.nn.Conv1d) ] - assert len(conv_layers) == 2, ("ResidualBlock should contain Conv2d " - "layers") + assert len(conv_layers) == 2, "ResidualBlock should contain Conv1d layers" for conv_layer in conv_layers: assert conv_layer.kernel_size == expected, ( @@ -89,50 +83,49 @@ def test_kernel_size_with_forward_pass(): """Test that different kernel sizes work in forward pass.""" batch_size = 2 channels = 2 - height, width = 32, 32 + width = 32 - input_tensor = torch.randn(batch_size, channels, height, width) + input_tensor = torch.randn(batch_size, channels, width) # Test different kernel sizes for kernel_size in [1, 3, 5]: - block = ResidualBlock(2, 4, kernel_size=kernel_size) + block = ResidualEncoding1d(2, 4, kernel_size=kernel_size) block.eval() # Set to evaluation mode with torch.no_grad(): output = block(input_tensor) # Check output shape is reasonable - output_shape = output.shape[:1] + output.shape[2:] - input_shape = input_tensor.shape[:1] + input_tensor.shape[2:] - assert output_shape == input_shape, ( - f"Input shape should be preserved, got {output.shape[0]}" - ) + assert output.shape[0] == input_tensor.shape[0], ( + f"Batch size {input_tensor.shape[0]} should be preserved, got " + f"{output.shape[0]}") + assert output.shape[2] == input_tensor.shape[2], ( + f"Width {input_tensor.shape[2]} should be preserved, got " + f"{output.shape[2]}") assert output.shape[1] == 4, ( - f"Output channels should be 4, got {output.shape[1]}" - ) + f"Output channels should be 4, got {output.shape[1]}") - assert len(output.shape) == 4, ( - f"Output should be 4D tensor, got shape {output.shape}" - ) + assert len(output.shape) == 3, ( + f"Output should be 4D tensor, got shape {output.shape}") def test_invalid_kernel_size(): """Test that invalid kernel sizes raise appropriate errors.""" with pytest.raises(ValueError): - ResidualBlock(2, 4, kernel_size=0) + ResidualEncoding1d(2, 4, kernel_size=0) with pytest.raises(ValueError): - ResidualBlock(2, 4, kernel_size=-1) + ResidualEncoding1d(2, 4, kernel_size=-1) def test_kernel_size_parameter_types(): """Test that kernel_size accepts different parameter types.""" # Test integer - block1 = ResidualBlock(2, 4, kernel_size=3) + block1 = ResidualEncoding1d(2, 4, kernel_size=3) assert block1 is not None # Test tuple (if supported) - block2 = ResidualBlock(2, 4, kernel_size=(3, 3)) + block2 = ResidualEncoding1d(2, 4, kernel_size=(3, 3)) assert block2 is not None @@ -140,26 +133,26 @@ def test_invalid_channels(): """Test that invalid channel numbers raise appropriate errors.""" # Test zero input channels with pytest.raises(ValueError): - ResidualBlock(0, 2, kernel_size=3) + ResidualEncoding1d(0, 2, kernel_size=3) # Test negative input channels with pytest.raises(ValueError): - ResidualBlock(-64, 2, kernel_size=3) + ResidualEncoding1d(-64, 2, kernel_size=3) # Test zero output channels with pytest.raises(ValueError): - ResidualBlock(2, 0, kernel_size=3) + ResidualEncoding1d(2, 0, kernel_size=3) # Test negative output channels with pytest.raises(ValueError): - ResidualBlock(2, -128, kernel_size=3) + ResidualEncoding1d(2, -128, kernel_size=3) # Test non-integer channels with pytest.raises(TypeError): - ResidualBlock(64.5, 128, kernel_size=3) + ResidualEncoding1d(64.5, 128, kernel_size=3) with pytest.raises(TypeError): - ResidualBlock(64, 128.5, kernel_size=3) + ResidualEncoding1d(64, 128.5, kernel_size=3) def test_valid_channels(): @@ -175,29 +168,29 @@ def test_valid_channels(): ] for in_channels, out_channels in valid_channel_pairs: - block = ResidualBlock(in_channels, out_channels, kernel_size=3) + block = ResidualEncoding1d(in_channels, out_channels, kernel_size=3) assert block is not None # Test forward pass with appropriate input - input_tensor = torch.randn(1, in_channels, 8, 8) + input_tensor = torch.randn(1, in_channels, 8) with torch.no_grad(): output = block(input_tensor) - assert output.shape[1] == out_channels + assert output.shape == (1, out_channels, 8) def test_invalid_stride(): """Test that invalid stride values raise appropriate errors.""" # Test zero stride with pytest.raises(ValueError): - ResidualBlock(64, 128, kernel_size=3, stride=0) + ResidualEncoding1d(64, 128, kernel_size=3, stride=0) # Test negative stride with pytest.raises(ValueError): - ResidualBlock(64, 128, kernel_size=3, stride=-1) + ResidualEncoding1d(64, 128, kernel_size=3, stride=-1) # Test non-integer stride with pytest.raises(TypeError): - ResidualBlock(64, 128, kernel_size=3, stride=1.5) + ResidualEncoding1d(64, 128, kernel_size=3, stride=1.5) def test_valid_stride(): @@ -205,19 +198,17 @@ def test_valid_stride(): valid_strides = [1, 2, 3, 4] for stride in valid_strides: - block = ResidualBlock(64, 128, kernel_size=3, stride=stride) + block = ResidualEncoding1d(64, 128, kernel_size=3, stride=stride) assert block is not None # Test that stride affects conv layers conv_layers = [ - module for module in block.modules() - if isinstance(module, torch.nn.Conv2d) + module for module in block.modules() if isinstance(module, torch.nn.Conv1d) ] # At least one conv layer should have the specified stride stride_found = any( - conv.stride == (stride, stride) or conv.stride == stride - for conv in conv_layers + conv.stride == (stride, ) or conv.stride == stride for conv in conv_layers ) assert stride_found, f"No conv layer found with stride {stride}" @@ -228,7 +219,7 @@ def test_stride_output_shape(): input_tensor = torch.randn(1, 2, input_size, input_size) for stride in [1, 2]: - block = ResidualBlock(2, 4, kernel_size=3, stride=stride) + block = ResidualEncoding1d(2, 4, kernel_size=3, stride=stride) block.eval() with torch.no_grad(): @@ -258,7 +249,7 @@ def test_combined_invalid_parameters(): for in_ch, out_ch, k_size, stride in invalid_combinations: with pytest.raises((ValueError, TypeError, RuntimeError)): - ResidualBlock( + ResidualEncoding1d( in_ch, out_ch, kernel_size=k_size, stride=stride, From 0802d6bed1642fa132cab4433d5456059a70fda5 Mon Sep 17 00:00:00 2001 From: Nathaniel Chen Date: Fri, 18 Jul 2025 11:07:55 -0400 Subject: [PATCH 068/103] Enhance dataset preparation and configuration - Added `hydra-core` dependency to `pyproject.toml` and updated `uv.lock`. - Refactored `prepare_data.sh` to use the new preprocessing command. - Updated Jupyter notebook to reflect changes in data paths and execution counts. - Modified `preprocess.py` to accept Hydra configuration and improved logging. - Adjusted output directory structure in `default.yaml` and removed obsolete `raw.yaml`. - Enhanced processing pipeline to include time column and linear resampling. - Cleaned up unused code and improved dataset indexing logic. --- commands/prepare_data.sh | 4 +- notebooks/accessing_data.ipynb | 69 ++- notebooks/data_preparation_ts.ipynb | 566 ++++++++++++++++++ notebooks/output.wav | Bin 0 -> 11331542 bytes pyproject.toml | 1 + src/faith/preprocess/__main__.py | 12 +- src/faith/preprocess/config/default.yaml | 8 +- .../config/{raw.yaml => magnetics.yaml} | 36 +- src/faith/preprocess/config/signals.yaml | 63 ++ src/faith/preprocess/config/spectrogram.yaml | 65 ++ .../preprocess/pipelines/processing_v0.py | 14 +- src/faith/preprocess/preprocess.py | 105 +--- .../preprocess/transform/signal_processing.py | 29 +- uv.lock | 47 ++ 14 files changed, 889 insertions(+), 130 deletions(-) create mode 100644 notebooks/data_preparation_ts.ipynb create mode 100644 notebooks/output.wav rename src/faith/preprocess/config/{raw.yaml => magnetics.yaml} (61%) create mode 100644 src/faith/preprocess/config/signals.yaml create mode 100644 src/faith/preprocess/config/spectrogram.yaml diff --git a/commands/prepare_data.sh b/commands/prepare_data.sh index 7e69625..3335557 100644 --- a/commands/prepare_data.sh +++ b/commands/prepare_data.sh @@ -4,7 +4,7 @@ #SBATCH --ntasks=1 # total number of tasks across all nodes #SBATCH --cpus-per-task=96 # cpu-cores per task (>1 if multi-threaded tasks) #SBATCH --mem=500GB # memory per node -#SBATCH --time=010:00:00 # maximum time needed (HH:MM:SS) +#SBATCH --time=1:00:00 # maximum time needed (HH:MM:SS) #SBATCH --output=logs/%A_%a.out #SBATCH --error=logs/%A_%a.err @@ -13,4 +13,4 @@ module purge source .venv/bin/activate # Run pipeline -srun python -m fusionaihub.datasets.prepare --config config/raw.yaml --log-level DEBUG \ No newline at end of file +srun python -m faith.preprocess --config-name signals \ No newline at end of file diff --git a/notebooks/accessing_data.ipynb b/notebooks/accessing_data.ipynb index 4cd7659..82fa2dd 100644 --- a/notebooks/accessing_data.ipynb +++ b/notebooks/accessing_data.ipynb @@ -2,10 +2,19 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 29, "id": "914fa271", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%load_ext autoreload\n", "%autoreload 2" @@ -13,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "id": "836b5c67", "metadata": {}, "outputs": [], @@ -26,17 +35,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 32, "id": "d564239f", "metadata": {}, "outputs": [], "source": [ - "files = pd.read_csv(\"/scratch/gpfs/EKOLEMEN/hackathon/foundation25/train/index.csv\").values[:,0]" + "files = pd.read_csv(\"/scratch/gpfs/nc1514/FusionAIHub/data/debug/index.csv\").values[:,0]\n", + "# files = pd.read_csv(\"/scratch/gpfs/EKOLEMEN/hackathon/foundation25/index.csv\").values[:,0]" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 37, "id": "434b288f", "metadata": {}, "outputs": [ @@ -44,21 +54,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "/scratch/gpfs/EKOLEMEN/hackathon/foundation25/train/170000_0.joblib\n", - "mhr (8, 11066, 513)\n", - "ece (48, 11066, 513)\n", - "co2 (4, 11066, 513)\n", - "gas (5, 11066, 1)\n", - "ech (11, 11066, 1)\n", - "pin (8, 11066, 1)\n", - "tin (8, 11066, 1)\n" + "../data/debug/170000_0.joblib\n", + "mhr (8, 513, 11066)\n", + "time_ms (1, 1, 11066)\n", + "ece (48, 513, 11066)\n", + "co2 (4, 513, 11066)\n", + "gas (5, 1, 11066)\n", + "ech (11, 1, 11066)\n", + "pin (8, 1, 11066)\n", + "tin (8, 1, 11066)\n" ] }, { "data": { - "image/png": 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", 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" ] }, "metadata": {}, @@ -71,14 +82,18 @@ "data = joblib.load(file_name)\n", "for key, value in data.items():\n", " print(key, value.shape)\n", - "plt.subplot(3, 1, 1)\n", - "plt.imshow(data['mhr'][4].T, aspect='auto', origin='lower')\n", + "plt.subplot(4, 1, 1)\n", + "plt.plot(data['time_ms'][0,0,:])\n", + "plt.xlim(0, len(data['time_ms'][0,0,:]))\n", + "plt.title('time_ms')\n", + "plt.subplot(4, 1, 2)\n", + "plt.imshow(np.log(data['mhr'][4] + 1e-6), aspect='auto', origin='lower')\n", "plt.title('mhrb4')\n", - "plt.subplot(3, 1, 2)\n", - "plt.imshow(data['co2'][0].T, aspect='auto', origin='lower')\n", + "plt.subplot(4, 1, 3)\n", + "plt.imshow(np.log(data['co2'][0] + 1e-6), aspect='auto', origin='lower')\n", "plt.title('co2r0')\n", - "plt.subplot(3, 1, 3)\n", - "plt.imshow(data['pin'][:,:,0], aspect='auto', origin='lower', interpolation='none')\n", + "plt.subplot(4, 1, 4)\n", + "plt.imshow(data['pin'][:,0,:], aspect='auto', origin='lower', interpolation='none')\n", "plt.title('pin')\n", "plt.tight_layout()\n", "plt.show()" @@ -87,17 +102,15 @@ { "cell_type": "code", "execution_count": null, - "id": "7fbe52bc", + "id": "180be0ec", "metadata": {}, "outputs": [], - "source": [ - "data = joblib.load('../data/170000/170000.pkl')" - ] + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -111,7 +124,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/notebooks/data_preparation_ts.ipynb b/notebooks/data_preparation_ts.ipynb new file mode 100644 index 0000000..15473a4 --- /dev/null +++ b/notebooks/data_preparation_ts.ipynb @@ -0,0 +1,566 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "e23296a9", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1321d5df", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from pathlib import Path\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import yaml\n", + "import joblib" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9b8f64ca", + "metadata": {}, + "outputs": [], + "source": [ + "shot_number = 170000\n", + "yaml_path = \"../src/faith/preprocess/config/default.yaml\"\n", + "with open(yaml_path, 'r') as f:\n", + " cfg = yaml.safe_load(f)\n", + "\n", + "from faith.preprocess.extract.data_extraction import (\n", + " extract_signal, \n", + " extract_running_time, \n", + " align_signal,\n", + ")\n", + "from faith.preprocess.transform.sample_processing import (\n", + " split_samples,\n", + " remove_empty_samples,\n", + " save_sample,\n", + ")\n", + "from faith.preprocess.transform.signal_processing import (\n", + " identity_transform,\n", + " stft_transform,\n", + " resample_transform,\n", + " resample_linear_transform,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c8e825ce", + "metadata": {}, + "outputs": [], + "source": [ + "start_time, end_time = extract_running_time(\n", + " shot_number=shot_number,\n", + " directory=Path(cfg[\"raw_data_dir\"]),\n", + " ip_threshold=cfg[\"ip_threshold\"],\n", + " start_time=cfg[\"start_time\"],\n", + " end_time=cfg[\"end_time\"],\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9d6c90be", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12.75 , 5678.5\n" + ] + } + ], + "source": [ + "print(start_time, \",\", end_time)\n", + "# note, they are offset by 0.05ms" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b4c3b00c", + "metadata": {}, + "outputs": [], + "source": [ + "cfg['signal'] = {'magnetics_high_resolution': cfg['signal']['magnetics_high_resolution']}" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "02b2448a", + "metadata": {}, + "outputs": [], + "source": [ + "cfg['fs_khz'] = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a68aaa6f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "magnetics_high_resolution\n", + "{'abbr': 'mhr', 'make_stft': True, 'expected_channels': 8}\n" + ] + } + ], + "source": [ + "dfs = []\n", + "missing_signals = []\n", + "for signal in cfg['signal'].items():\n", + " print(signal[0])\n", + " print(signal[1])\n", + " try:\n", + " df = extract_signal(\n", + " shot_number=shot_number,\n", + " directory=Path(cfg[\"raw_data_dir\"]),\n", + " signal=signal[0], \n", + " start_time=start_time,\n", + " end_time=end_time,\n", + " )\n", + " df.columns = [\n", + " f\"{signal[1]['abbr']}_{col}\" if col != \"time\" else col\n", + " for col in range(len(df.columns))\n", + " ]\n", + " df = align_signal(\n", + " df=df,\n", + " start_time=start_time,\n", + " end_time=end_time,\n", + " fs=cfg[\"fs_khz\"],\n", + " )\n", + " dfs.append(df)\n", + " except Exception as e:\n", + " for channel in range(int(signal[1]['expected_channels'])):\n", + " missing_signals.append((signal[1]['abbr'], channel))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fe35d6ca", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.concat(dfs, axis=1, join='inner')\n", + "for signal_abbr, channel in missing_signals:\n", + " df[f\"{signal_abbr}_{channel}\"] = np.nan\n", + " df[f\"{signal_abbr}_{channel}_state\"] = False" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2175aa46", + "metadata": {}, + "outputs": [], + "source": [ + "import scipy.io.wavfile as wavfile" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "f642c6a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-155.95071 133.22498\n", + "Saved WAV file with 5665749 samples at 22400 Hz\n" + ] + } + ], + "source": [ + "wav_vals = df['mhr_4'].values\n", + "wav_vals = wav_vals[~np.isnan(wav_vals)]\n", + "print(wav_vals.min(), wav_vals.max())\n", + "\n", + "wav_vals = (wav_vals - wav_vals.min()) / (wav_vals.max() - wav_vals.min())\n", + "wav_vals = wav_vals - 0.5\n", + "\n", + "wav_vals = np.int16(wav_vals * 32767)\n", + "\n", + "sample_rate = int(22400) # Convert kHz to Hz\n", + "wavfile.write('output.wav', sample_rate, wav_vals)\n", + "print(f\"Saved WAV file with {len(wav_vals)} samples at {sample_rate} Hz\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "09ff5b99", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Removed 109982 outliers, 1987169 samples remaining\n", + "Saved WAV file with 1987169 samples at 44100 Hz\n" + ] + } + ], + "source": [ + "wav_vals = df[df['mhr_4_state'] == True]['mhr_4'].values\n", + "wav_vals_mean = wav_vals.mean()\n", + "wav_vals_std = wav_vals.std()\n", + "wav_vals = (wav_vals - wav_vals_mean) / wav_vals_std\n", + "# Remove outliers using z-score method (values beyond 3 standard deviations)\n", + "z_scores = np.abs((wav_vals - wav_vals.mean()) / wav_vals.std())\n", + "outlier_mask = z_scores < 2\n", + "wav_vals = wav_vals[outlier_mask]\n", + "print(f\"Removed {np.sum(~outlier_mask)} outliers, {len(wav_vals)} samples remaining\")\n", + "\n", + "wav_vals = wav_vals - 0.5\n", + "wav_vals = np.int16(wav_vals * 32767)\n", + "\n", + "sample_rate = int(44100) # Convert kHz to Hz\n", + "wavfile.write('output.wav', sample_rate, wav_vals)\n", + "print(f\"Saved WAV file with {len(wav_vals)} samples at {sample_rate} Hz\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "01210a63", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(wav_vals)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3bf95002", + "metadata": {}, + "outputs": [], + "source": [ + "# Create timedelta column\n", + "time_duration = end_time - start_time # Duration in milliseconds\n", + "df['timedelta'] = np.linspace(start_time, end_time, len(df))\n", + "# Convert timedelta to proper timedelta format\n", + "# df['timedelta'] = pd.to_timedelta(df['timedelta'], unit='ms')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "87a1b47e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "samples = split_samples(\n", + " df=df,\n", + " shot_number=shot_number,\n", + " window_ms=cfg[\"window_ms\"],\n", + " hop_ms=cfg[\"hop_ms\"],\n", + " fs_khz=cfg[\"fs_khz\"],\n", + ")\n", + "len(samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "40750cb0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "samples = remove_empty_samples(samples)\n", + "len(samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "de25ce4f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mhr (8, 1, 3272613)\n", + "ece (48, 1, 3272613)\n", + "co2 (4, 1, 3272613)\n", + "gas (5, 1, 3272613)\n", + "ech (11, 1, 3272613)\n", + "pin (8, 1, 3272613)\n", + "tin (8, 1, 3272613)\n" + ] + } + ], + "source": [ + "for sample in samples:\n", + " transformed_samples = {}\n", + " for key, value in sample.items():\n", + " for signal in cfg['signal'].items():\n", + " abbr = signal[1]['abbr']\n", + " cols = [col for col in value.columns if abbr in col]\n", + " transformed_samples[abbr] = identity_transform(\n", + " x=value[cols].to_numpy().T)\n", + " print(abbr, transformed_samples[abbr].shape)\n", + " transformed_samples['time_ms'] = identity_transform(\n", + " x=np.array([value['timedelta'].to_numpy().T])\n", + " )\n", + " save_sample(transformed_samples, Path('../data'), key)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "bc37f8e0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 5665749) (1, 513, 22132)\n" + ] + } + ], + "source": [ + "first_arr = np.array([list(samples[0].values())[0].iloc[:, 0].values])\n", + "transform_shape = stft_transform(x=first_arr).shape\n", + "print(first_arr.shape, transform_shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "f6cae5ac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mhr (8, 513, 22132)\n", + "(1, 5665749)\n", + "(1, 513, 22132)\n", + "(5665749,)\n", + "(22132,)\n", + "(1, 5665749)\n" + ] + } + ], + "source": [ + "for sample in samples:\n", + " transformed_samples = {}\n", + " for key, value in sample.items():\n", + " for signal in cfg['signal'].items():\n", + " abbr = signal[1]['abbr']\n", + " cols = [col for col in value.columns if abbr in col]\n", + " if signal[1]['make_stft']:\n", + " transformed_samples[abbr] = stft_transform(\n", + " x=value[cols].to_numpy().T,\n", + " n_fft=cfg[\"stft\"][\"n_fft\"],\n", + " hop_length=cfg[\"stft\"][\"hop_length\"],\n", + " )\n", + " else:\n", + " transformed_samples[abbr] = resample_transform(\n", + " x=value[cols].to_numpy().T,\n", + " ref_shape=transform_shape,\n", + " )\n", + " print(abbr, transformed_samples[abbr].shape)\n", + " transformed_samples['time_ms'] = resample_linear_transform(\n", + " x=np.array([value['timedelta'].to_numpy().T]),\n", + " ref_shape=transform_shape,\n", + " )\n", + " save_sample(transformed_samples, Path('../data'), key)\n", + " break\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "9c4fc5c7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "../data/170000_0.joblib\n", + "mhr (8, 513, 22132)\n", + "time_ms (1, 1, 22132)\n" + ] + }, + { + "data": { + "image/png": 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sKxbP1axpE+mQ+bMZDLsBRuwMBoNhgYP92TRJm+fPNjmLbbOP0Z9tB6chmD+bwbB3YMTOYDAY9mOwP1uTsPXmRY+aP5vBcGDAiJ3BYDDso9jV/mxLR9rNZLo7IG7mz2Yw7N8wYmcwGAx7Adt71Tz/tY3pRAS+bv5sBoPh0cKIncFgMOxC7NCfbQeJdR+xP1vh5x1dZf5sBoPhwWDEzmAwGB4h6kC4L+Vne6jo0Ufqz7YonTe6Iy0bp/wwfzaDwfBoYMTOYDAYAPSrgM2Ts+LPlpPpzsgpCObPZjAY9nUYsTMYDAse2p+Nfdh2hT/bjo+wMn82g8Gw92DEzmAw7LcgImydGTRyse0o5cfO+rNp06j5sxkMhn0ZRuwMBsM+CfNnMxgMhkcPI3YGg2GPQ/uzsXl0bsqPx+LPJj5so11Jsmv+bAaD4UCCETuDwbBLMd2vsmm04c/Wk0AE82czGAyG3QMjdgaD4RGh4c+mE+tuncXGST4VYRaTj9afbQeEzfzZDAaD4bHBiJ3BYGj4sz3UaQizg0fmzzbSLnDw+NAODofP5G3pSNv82QwGg2EXw4idwbDAwf5sOfhAk7dI2u7e1kNl/mwGg8Gw38OIncGwH4P92XIyXX2MVfRru3eq94iexf5sO0qma/5sBoPBsH/AiJ3BsA9iT/izNUyjY10sX9QxfzaDwWDYz2HEzmDYw2B/tk0PYhrdPNnDxq0z5s9mMBgMhkcNI3YGwy5Evwq4e5syh+6AtG2enH1U/mw7Tqabfzd/NoPBYDAwjNgZDI8Q0/2qkUx3fvTo7C7zZ5tIWjfzZzMYDAbDo4ERO8MBD/Zna5hGmcBNcmDCzKPyZ1s51sHBo0Pmz2YwGAyGPQojdoYFjRAI927vzdOszT0V4dH4s3Hy3AcLRjB/NoPBYDDsLRixM+y3mOvPtqM8bebPZjAYDIYDCUbsDPskdrU/2/LFHUyMDZk/m8FgMBgWNIzYGfYoiAiTMxU2Ts40E+vOiR59rP5s+XD4fBqC+bMZDAaD4UCBETvDLoP5sxkMBoPBsHdhxM7wiKD92ZrHVmXi9mj82ZYMt6JpdAfJdPl382czGAwGg+HRwYidoeHPtuPTEGZx3/Ye6BFwtofyZ2MCZ/5sBoPBYDDsHhixW8DQ/mw6EGFu9OjWmcEjep72Z1s5pg+HN382g8FgMBj2BRix20+h/dm0pm3zHBPpzKB+RM/T/mwPlvLD/NkMBoPBYNi3sU8Tuw996EO4/PLLsWnTJjz1qU/FBz7wAZx00kl7u1i7HezPpjVr5s9mMBgMBoPh4bDPErvPfOYzuOCCC/DRj34Uq1evxvvf/36sW7cOt9xyC1asWLG3i/eYIf5sO8jLxtfvnXr0/mwTo50HjR41fzaDwWAwGA4MOKJHQiH2PFavXo1nPvOZ+OAHPwgACCHg0EMPxZ/92Z/hz//8zx/2+5OTkxgbG8PWrVsxOjq6u4sr/myc0mN3+bPpYATzZzMYDAaDYeHj0XCafVJj1+/3cdNNN+Giiy6Sa957rF27Fhs2bNjhd3q9Hnq9fBLB5OTkbi/nx778c9x4yz1C3h6pP9twu1Dm0CHzZzMYDAaDwbBLsE8Su3vvvRd1XWPlypWN6ytXrsRPf/rTHX5n/fr1uPTSS/dE8QQ/v2cKG35xX+Ma+7NNjHbEr61hGh3rYnGnNNJmMBgMBoNhl2OfJHaPBRdddBEuuOAC+XtychKHHnrobn3nfznxUPzmE5aZP5vBYDAYDIZ9AvsksVu2bBmKosDmzZsb1zdv3oyJiYkdfqfT6aDT6cjf7Dq4O02yRy0tcdTSxemvGv2Z7ejP7LbXGQwGg8FgOADBXOaRhEXsk8Su3W7jhBNOwPXXX4+XvOQlAGLwxPXXX4/zzz//ET1j27ZtALDbtXYGg8FgMBgMewLbtm3D2NjYQ96zTxI7ALjgggtw1lln4cQTT8RJJ52E97///di+fTte85rXPKLvr1q1CnfeeScWL1682/zZ2Nx755137pHIW8Puh/XpwoL158KC9efCgvXnIwcRYdu2bVi1atXD3rvPErvTTz8d99xzD971rndh06ZNeNrTnoZrr712XkDFg8F7j0MOOWQ3lzJidHTUBuUCg/XpwoL158KC9efCgvXnI8PDaeoY+yyxA4Dzzz//EZteDQaDwWAwGA50WHZbg8FgMBgMhgUCI3Y7gU6ng4svvrgRjWvYv2F9urBg/bmwYP25sGD9uXuwzx4pZjAYDAaDwWB4dDCNncFgMBgMBsMCgRE7g8FgMBgMhgUCI3YGg8FgMBgMCwRG7AwGg8FgMBgWCIzY7QQ+9KEP4YgjjkC328Xq1avx7W9/e28X6YDHJZdcAudc49/RRx8tn8/OzuK8887DQQcdhEWLFuFlL3vZvDOJ77jjDpx66qkYHh7GihUr8Pa3vx1VVTXuufHGG/GMZzwDnU4HT3ziE3HFFVfsieoteHzlK1/Bi1/8YqxatQrOOVx11VWNz4kI73rXu3DwwQdjaGgIa9euxa233tq45/7778eZZ56J0dFRjI+P4+yzz8bU1FTjnh/84Ad47nOfi263i0MPPRTvec975pXlyiuvxNFHH41ut4vjjz8e11xzzS6v70LHw/Xnq1/96nnz9eSTT27cY/2572D9+vV45jOficWLF2PFihV4yUtegltuuaVxz55cY00GPwjI8Jjw6U9/mtrtNn384x+nH/3oR/S6172OxsfHafPmzXu7aAc0Lr74Yjr22GNp48aN8u+ee+6Rz9/whjfQoYceStdffz39x3/8Bz3rWc+i3/zN35TPq6qi4447jtauXUvf/e536ZprrqFly5bRRRddJPf84he/oOHhYbrgggvoxz/+MX3gAx+goijo2muv3aN1XYi45ppr6C//8i/pc5/7HAGgz3/+843PL7vsMhobG6OrrrqKvv/979Pv//7v05FHHkkzMzNyz8knn0xPfepT6Zvf/CZ99atfpSc+8Yl0xhlnyOdbt26llStX0plnnkk333wzfepTn6KhoSH62Mc+Jvd8/etfp6Io6D3veQ/9+Mc/pne+853UarXohz/84W5vg4WEh+vPs846i04++eTGfL3//vsb91h/7jtYt24dfeITn6Cbb76Zvve979Hv/u7v0mGHHUZTU1Nyz55aY00GPziM2D1GnHTSSXTeeefJ33Vd06pVq2j9+vV7sVSGiy++mJ761Kfu8LMtW7ZQq9WiK6+8Uq795Cc/IQC0YcMGIoqCyHtPmzZtkns+8pGP0OjoKPV6PSIiesc73kHHHnts49mnn346rVu3bhfX5sDGXCIQQqCJiQm6/PLL5dqWLVuo0+nQpz71KSIi+vGPf0wA6Dvf+Y7c82//9m/knKP//M//JCKiD3/4w7RkyRLpTyKiCy+8kI466ij5++UvfzmdeuqpjfKsXr2azjnnnF1axwMJD0bsTjvttAf9jvXnvo27776bANCXv/xlItqza6zJ4AeHmWIfA/r9Pm666SasXbtWrnnvsXbtWmzYsGEvlswAALfeeitWrVqFxz/+8TjzzDNxxx13AABuuukmDAaDRr8dffTROOyww6TfNmzYgOOPP75xJvG6deswOTmJH/3oR3KPfgbfY32/e3H77bdj06ZNjbYfGxvD6tWrG/03Pj6OE088Ue5Zu3YtvPf41re+Jfc873nPQ7vdlnvWrVuHW265BQ888IDcY328Z3DjjTdixYoVOOqoo3Duuefivvvuk8+sP/dtbN26FQCwdOlSAHtujTUZ/NAwYvcYcO+996Ku68bABICVK1di06ZNe6lUBgBYvXo1rrjiClx77bX4yEc+gttvvx3Pfe5zsW3bNmzatAntdhvj4+ON7+h+27Rp0w77lT97qHsmJycxMzOzm2pm4PZ/qHm3adMmrFixovF5WZZYunTpLuljm9+7FieffDL++Z//Gddffz3+/u//Hl/+8pdxyimnoK5rANaf+zJCCHjzm9+MZz/72TjuuOMAYI+tsSaDHxrl3i6AwbArccopp8jvT3nKU7B69Wocfvjh+OxnP4uhoaG9WDKDwTAXr3jFK+T3448/Hk95ylPwhCc8ATfeeCNe8IIX7MWSGR4O5513Hm6++WZ87Wtf29tFMcyBaeweA5YtW4aiKOZF+mzevBkTExN7qVSGHWF8fBxPfvKTcdttt2FiYgL9fh9btmxp3KP7bWJiYof9yp891D2jo6NGHncjuP0fat5NTEzg7rvvbnxeVRXuv//+XdLHNr93Lx7/+Mdj2bJluO222wBYf+6rOP/88/GFL3wBN9xwAw455BC5vqfWWJPBDw0jdo8B7XYbJ5xwAq6//nq5FkLA9ddfjzVr1uzFkhnmYmpqCj//+c9x8MEH44QTTkCr1Wr02y233II77rhD+m3NmjX44Q9/2BAm1113HUZHR3HMMcfIPfoZfI/1/e7FkUceiYmJiUbbT05O4lvf+laj/7Zs2YKbbrpJ7vnSl76EEAJWr14t93zlK1/BYDCQe6677jocddRRWLJkidxjfbzn8etf/xr33XcfDj74YADWn/saiAjnn38+Pv/5z+NLX/oSjjzyyMbne2qNNRn8MNjb0Rv7Kz796U9Tp9OhK664gn784x/T61//ehofH29E+hj2PN761rfSjTfeSLfffjt9/etfp7Vr19KyZcvo7rvvJqIYin/YYYfRl770JfqP//gPWrNmDa1Zs0a+z6H4L3rRi+h73/seXXvttbR8+fIdhuK//e1vp5/85Cf0oQ99yNKd7CJs27aNvvvd79J3v/tdAkDvfe976bvf/S796le/IqKY7mR8fJz+7//9v/SDH/yATjvttB2mO3n6059O3/rWt+hrX/saPelJT2qkx9iyZQutXLmSXvnKV9LNN99Mn/70p2l4eHheeoyyLOkf/uEf6Cc/+QldfPHFlh7jMeCh+nPbtm30tre9jTZs2EC33347ffGLX6RnPOMZ9KQnPYlmZ2flGdaf+w7OPfdcGhsboxtvvLGRomZ6elru2VNrrMngB4cRu53ABz7wATrssMOo3W7TSSedRN/85jf3dpEOeJx++ul08MEHU7vdpsc97nF0+umn02233Safz8zM0J/+6Z/SkiVLaHh4mP7gD/6ANm7c2HjGL3/5SzrllFNoaGiIli1bRm9961tpMBg07rnhhhvoaU97GrXbbXr84x9Pn/jEJ/ZE9RY8brjhBgIw799ZZ51FRDHlyV/91V/RypUrqdPp0Ate8AK65ZZbGs+477776IwzzqBFixbR6OgoveY1r6Ft27Y17vn+979Pz3nOc6jT6dDjHvc4uuyyy+aV5bOf/Sw9+clPpna7TcceeyxdffXVu63eCxUP1Z/T09P0ohe9iJYvX06tVosOP/xwet3rXjdPMFt/7jvYUV8CaKx/e3KNNRm8Yzgioj2tJTQYDAaDwWAw7HqYj53BYDAYDAbDAoERO4PBYDAYDIYFAiN2BoPBYDAYDAsERuwMBoPBYDAYFgiM2BkMBoPBYDAsEBixMxgMBoPBYFggMGJnMBgMBoPBsEBgxM5gMBgMBoNhgcCIncFgMBgMBsMCgRE7g8FgMBgMhgUCI3YGg8FgMBgMCwRG7AwGg8FgMBgWCIzYGQwGg8FgMCwQGLEzGAwGg8FgWCAwYmcwGAwGg8GwQGDEzmAwGAwGg2GBwIidwWAwGAwGwwKBETuDwWDYAW688UY45/B//s//2annHHHEEfi93/u9XVQqg8FgeGgYsTMYDIZ9EJ/85CfhnMOiRYv2dlEMBsN+BCN2BoPBsI9hamoK73jHOzAyMrK3i2IwGPYzGLEzGAyG3YDp6enH/N13v/vdWLx4MV7ykpfsugIZDIYDAkbsDAbDgsUll1wC5xx+9rOf4Y//+I8xNjaG5cuX46/+6q9ARLjzzjtx2mmnYXR0FBMTE/jHf/zHec8IIeBv//Zvccghh6Db7eIFL3gBbrvttsY9v/Vbv4XjjjsON910E573vOdheHgYf/EXf9G459///d/xtKc9Dd1uF8cccww+97nP7bDMt956K973vvfhve99L8qy3HWNYTAYDggYsTMYDAsep59+OkIIuOyyy7B69Wq8+93vxvvf/3688IUvxOMe9zj8/d//PZ74xCfibW97G77yla80vnvZZZfh85//PN72trfhoosuwje/+U2ceeaZ895x33334ZRTTsHTnvY0vP/978dv//Zvy2e33norTj/9dJxyyilYv349yrLEf/kv/wXXXXfdvOe8+c1vxm//9m/jd3/3d3d9QxgMhgUP2w4aDIYFj5NOOgkf+9jHAACvf/3rccQRR+Ctb30r1q9fjwsvvBAAcMYZZ2DVqlX4+Mc/juc973ny3dnZWXzve99Du90GACxZsgRvetObcPPNN+O4446T+zZt2oSPfvSjOOecc+a9/2c/+xn+9V//FS996UsBAGeffTaOPvpoXHjhhXjhC18o91199dX493//d3z/+9/f9Y1gMBgOCJjGzmAwLHi89rWvld+LosCJJ54IIsLZZ58t18fHx3HUUUfhF7/4ReO7r3nNa4TUAcBzn/tcAJh3X6fTwWte85odvn/VqlX4gz/4A/l7dHQUr3rVq/Dd734XmzZtAgD0+3285S1vwRve8AYcc8wxj7GmBoPhQIcRO4PBsOBx2GGHNf4eGxtDt9vFsmXL5l1/4IEHHvK7S5YsAYB59z3ucY9rEECNJz7xiXDONa49+clPBgD88pe/BAC8733vw7333otLL730EdTIYDAYdgwzxRoMhgWPoige0TUAIKLHdN/Q0NBjLB2wdetWvPvd78af/umfYnJyEpOTkwBi2hMiwi9/+UsMDw9jxYoVj/kdBoPhwIARO4PBYNjNuO2220BEDa3dz372MwDxZIoHHngAU1NTeM973oP3vOc9875/5JFH4rTTTsNVV121p4psMBj2UxixMxgMht2Mu+66C5///OcleGJychL//M//jKc97WmYmJjA9PQ0Pv/5z8/73n/9r/8VGzZswKc+9SkcfPDBe7rYBoNhP4QRO4PBYNjNePKTn4yzzz4b3/nOd7By5Up8/OMfx+bNm/GJT3wCADA8PLzDZMRXXXUVvv3tb1uiYoPB8IhhwRMGg8Gwm/GkJz0Jn/nMZ3DNNdfgz//8zzEYDPCZz3wG69at29tFMxgMCwyO5noAGwwGg8FgMBj2S5jGzmAwGAwGg2GBwIidwWAwGAwGwwKBETuDwWAwGAyGBQIjdgaDwWAwGAwLBEbsDAaDwWAwGBYIdjmxu+SSS+Cca/w7+uij5fPZ2Vmcd955OOigg7Bo0SK87GUvw+bNmxvPuOOOO3DqqafKETpvf/vbUVXVri6qwWAwGAwGw4LCbklQfOyxx+KLX/xifkmZX/OWt7wFV199Na688kqMjY3h/PPPx0tf+lJ8/etfBwDUdY1TTz0VExMT+MY3voGNGzfiVa96FVqtFv7u7/7uEZchhIC77roLixcvnnf4tsFgMBgMBsP+AiLCtm3bsGrVKnj/MDo52sW4+OKL6alPfeoOP9uyZQu1Wi268sor5dpPfvITAkAbNmwgIqJrrrmGvPe0adMmuecjH/kIjY6OUq/Xe8TluPPOOwmA/bN/9s/+2T/7Z//s34L4d+eddz4s/9ktGrtbb70Vq1atQrfbxZo1a7B+/XocdthhuOmmmzAYDLB27Vq59+ijj8Zhhx2GDRs24FnPehY2bNiA448/HitXrpR71q1bh3PPPRc/+tGP8PSnP32H7+z1euj1evI3pbzLz8HvokRrd1TzgAe1HO79/54EIgfnCERRM0oEFJ4Q0t/OEUBAIAfvCaw/pXgZziH9wh8A3hOq2sP79F249HG+0TuSZwAODgSCk/dTepwDAEdAKk+gdL+j+Awg1gHI30llJnKAAxwI3sXPAzl4RyiLGoO6UPVxoFQuyLtjmQI5FD4gpPdI28iN8X2tImBQ+1xwcoAj+Y53sV2KIqD0AYOqQFEE1LVHu6zjd2OREYJDUQQ4EOpQoPA1AvlGmfj3wgXUwcP5WPYQvLQN14cAFC72K9eD+905Qggu9mUqbx08Ch9Qh1juoiA4IlTBgxCvBYr91S5que5A4M4oHKEir9qKyxLv855QOEKdyhTHmQcQy9Uqa9TBN8YXl9c7kjLqtsjjyEkbAECZ6sKdE8dzQAgezlFsi+DS75C68Tu9XE9jF7G8jsuR+tF7LpsDkMZNcDJOAah7PEpfowoFCh9k3JVFjd6gBe9CrFPqEz1+5Fmp/WJBHQpfoyYf21XqC2l3IoeyqBGCl/FR1bEN2kWNfl3AuVRnH6S9SxcwCAVKH0BpfDjEMcBGFcf9m67p+Q3kdi0cSX0HdSGftYoaBKCqC+mzKvU/rw3eU+oXbhdS83x+/6DZBFKadlmhX5UgPTZcnDsOBOcJpQ/oVyVaRZybzkHa1ac1yoMQ0nxAahduY+njIsSyO0JvUMJ5gk/zxAGoKY/Xskjrgg8Y1AVaRS39w+3n03jl9WVQFyhcQFEEVHUxp90g63Yr9W+RvsNl5vkf2zaN+7RuhjR/ud8ByPpRpPfwGAGAwgVUoZC+DMHLe0sfUPiA3qBMaxuPh4Dxl/98fkctIFQY4Gu4BosXL37Ye3c5sVu9ejWuuOIKHHXUUdi4cSMuvfRSPPe5z8XNN9+MTZs2od1uY3x8vPGdlStXYtOmTQCATZs2NUgdf86fPRjWr1+PSy+9dN71Ei2Uzojd7gA5Bz/UBVEiZw3pCRRpYucFkwVJSCTIye+a1LHwawGyELl0XZM3Jn1xMc2LCj+IBRkLDv47CzEkoe4T2YtOp4QokIAskPn5AIDg4V1AWdagJEA0KWShrMvrE9msEmFylIWtfrZzhFIRZO/jwu4dwaX7ivQeB4IPPpLo4OGKGkWI7Vr4uEAXRQ0ihwJRoCAtqHXw8CwoEwGkkMiaJ3hpR0g5uYcLAD6RWBEOIBRJEEcSG+voHQHBwTuIcKdQICTSVdVRePgioAjZvCBtiQBPXgn+ODacIuZMrgouUxIszsXnIqS/0wM04SiCg/OEQsYwpF5BESvwZqFmMoNUr0j4ZQywIHSJIiqBxfcgxGfyRqDwsW9dei73u0vCmgCUaRPD5Y/fB0Ae3oe4dSUmMgV8UaNsFfCex39sK6Q6l0VAvypQJuFIiZQ4R2j5AJcIL9VFntaKFBW+lLHMc4IIKMoKRV00BLznjZEnFGrDwHOg1sR4ziaR5xORkzlVOhLCSuRQ8lxxsZO9I7ThUNepnYJPZEaNLN6MUGyLoOZcgSah1+DxHsiBfI12F6hSWyERXSd1AODjfPY+oGTC55A2AHG94Wd6JqtVkcmUozyuU1uXba/IpEfL1/DkIoFK88HXRezz2sMXNQryskZ5XnvUvC5DnLPeESitbwUp4g3ENQKI9VFrCRNtXlc91MbVEVK3yBpH5MS53zmCV5sl5+JGjYIHfIhtSQ6+SOOqCHHOtUqpQ+niOrHg5XwakI/EtWyXE7tTTjlFfn/KU56C1atX4/DDD8dnP/tZDA0N7erXCS666CJccMEF8vfk5CQOPfTQ3fY+Q0RcbJoqN6K8CPHnUdiFRLfSQqU0MyIAWUimxY810F6N5Uwk+UIWOEyk5BnpfVGDEzmIaGxYq5iIVtrEg29kskcukh0mBLw4Fy6gch4uVx0hxMW1oRUitTvnBV8eHMsQdVWsPYiCjuAQAhoaBQ2uW01IJI1AKh4qa3SiFjBqr8rYQqxh49ZKRETKJW2dF2j+Wwth+RyxvXgh5++wYOc+o/Qun/rfpdd5RxikDvAu62edS8pMpfHkugVCEiY+a1ylfPH3kISGaAIpDRhS409GL2sX0l+p34Tgoc4aPVJaYSFcelzO19ZpDbV3hDpphPhaZhIulSVrsBrtn67FNiAhf0yqhQQ05oim5kqznMiHaMKS8OXNRAgOrSJk8oKoEfWo1TiUYieilxojtwQC+dh+5BCgxoPSarpEqOpEcnlDCLmf0vd5TiNrhtKlOjgUPhELhzlrCuWNmjw/E18KTsorxXd5c5j+lBbkDWbUiPsG+eIvh7h8KWuGXshy2+V6OnluSEQ9tvl8TX+RiF18hRrIBDWuUv0DRBsvcyU4gDeUYA1znDO+cI1retNM5AAhsrKYyRgjB5mrlDZ93GY1ZS1g4dOa55zq31hOXvN4ra15c0ex5SOJ5/qo+hsA7IF0J+Pj43jyk5+M2267DRMTE+j3+9iyZUvjns2bN2NiYgIAMDExMS9Klv/me3aETqeD0dHRxj/D7kdQuy1AE7q8IPLUZ5U8kpYjkgsvCxmAvCAhmjKiwEo7ZLCWj7Kg47ezMFeEAOozJEFSh/wu3mnyYio1UUKahY/TdUuLXE0eIURBwwsuUtnYVMbPCsGpRZ3JhnofYtmDkFAupxJgYEGYNYpANpUyeWCB3Kh/er5ouVT7eU+isYlkN71P9UkIriFchKQGVRdCXtxF25SEW4NARVRJU0PEpDC3CpMUvUHgdtR9EclvLoMW0EAan6kuWcgLnW0ISv1+eQKxpiFvHLzP/doog8tNBjTHICGTDb6LCSMTUmEN6bPGeE5EIgj957Ln+RWFPcnzshlTy/v8Hpe0Uvwul36P2kZFeHT7a4KjK4tc/KgJ88zixfwf0kbNJ5Mf3+tE60lqA+DSnMnld2nNcEmwZ0Lr0hj1Mj5ltUjzt2BiLW3Cmy9uoDT+FRnkNsllzPNSNilzxo2sFYgbWTbpzl1D4lxEGjNqE5mIEBN9Lp4ml0GNEV4ieI2Eeq50daobUXNcMFHSGzmZb2pUNV1e0tqon8HjDXljwP3GbQCwO02zLfNEy+1asMmW0iYijUFZknhKz10fYGDsdmI3NTWFn//85zj44INxwgknoNVq4frrr5fPb7nlFtxxxx1Ys2YNAGDNmjX44Q9/iLvvvlvuue666zA6OopjjjlmdxfX8BjAk1X8LGQRzQvGjn9P2hnRAEEWKV6QeWGJi0Fo7sAzBQQFN+fdzV1w/BkFH+8+HaLGRzRlUsC4COkyZ1NN9vUrXGgI76BNiUpL11SdZKKZfZwC5kFpOvX3mdDVwaff86IbTVpZgGsS1dBeUTZr8e98Y53qIMIB+Xl68aSQzeFBhB3ld1MWEvzOTPayJiCTbO1HpwmJFjrRHMTfjcIzd3TDt8fxmEwaAKc+10JYce083lJbu+j7RCGPQy57kLEKEVS6rlCaWU24+WUi2BtXFDFUzNLpNtXtr9smsYBYf5K6gIC69tI3TNq4L5wQ3Xh7mKNhYbIHJA0lMXGL7dTsxyYp0MTNuWhmFNJGmRnxmJXNEeXvhjRu2WVANHPks2lP2ouyViq1L5t5A2Xyzj/nNnU2v+YP6+BkU8abm1zTJqHIWtXcc7k/89hJXQQ2herVId6T/eG4WevaC7nzLj8fiNp6fn+TiDvxp5PxwG0whyxJX0GbTFUfyHdJ2p9byjWajNKmIbduU5ueibtYNqTsaNyX53V+fCaeqrxSPwNjlxO7t73tbfjyl7+MX/7yl/jGN76BP/iDP0BRFDjjjDMwNjaGs88+GxdccAFuuOEG3HTTTXjNa16DNWvW4FnPehYA4EUvehGOOeYYvPKVr8T3v/99/L//9//wzne+E+eddx46nc6uLq5hJ8GTlDetererJyYvczwhmYxkjYSa1MgmE/6Q38NaG0DtzFloJc1KY1HkcvpsogXyIsjCjAUBa168LHdo7Jw14eAFWExJWrgja+kaO0ne+Srywe1HIuziLlqLbvYnZNIg96dtuUN21temPzYFcV0zmUQStqQEWiaZoh1zus9Uo2pCkC6EpMHk9uf6y/elDbIPkmhH82Pm+0MlsFM5C42gFnPWXjTKCqg2zEKbtSFMVvlV3I+s1eV+EV9JIfVImo5c/iZ3p1THSMQpPYM3C1lrGkSqCh1xahyldmHzb5wPedw2Xunyc0TDTVzfpMUkwJES1C7Ptzx/VfeCBTD3USbI8vk8garmBZracS5T1Ch76V0eV96RmB7Z/9N7SoQykz4JhlFVl7ZirRZrmB0p7SfPg7m+fCRlZCLDV7ke2kogBITndyLZIfkZMqHTbgwOWdtcuCCfC6lCrlL0O851C+p5cdrFD3mcChlPY1jMp645nuZvtOb2HDLhdpngxc9oXps7aTd+pnSR9KnMI0dqzPBNuf5cPumj1J9x/mRXAzSeodrPzanMAY5d7mP361//GmeccQbuu+8+LF++HM95znPwzW9+E8uXLwcAvO9974P3Hi972cvQ6/Wwbt06fPjDH5bvF0WBL3zhCzj33HOxZs0ajIyM4KyzzsJf//Vf7+qiGnYR5go28QNi4dNgRWnSssP/nEXHyY2ZwDDys0gEswicdA8LlZCcgdnvS8xzlBc1x6TGRV82IPtUSWDGPKGdFmrK0WVwDs4F1KFo7EKlXgSQy2WNmj2StuJ3ZOKhovTSYqc1bHnxcw1h431AHUphSt4B4rmsK8FtGBxEscOCk4VWEvKiIWWCyp8DyWTrGk7RUl9Vf6IoBLmtCVEDESMruQ48HvQCz32ltKpMUtHcqYuZLpEGHn9wkOhSLpeQFxEG0XTo2ecHkP/FZ8wBejRKUIKQHSQNViqzy1ox9gfkcQ9ioeiZH6ZS5LpKOVV93Zy+4fHMHcJBQuxLml0Kstmydj6Ts9TuEpmItKFJz2Giw+3BrgdcH+lj1abSP9S8zpoafrdPkb68JrD7Am+y4nMVsRBtWCYsuR20xEej7aJW3ct3594oRK4xPSjOYzVPa/l9DrtJV6K3mabD7H+ZiCZvtpA3kQ0qkuYYVF9zmZmwancMEMTsyQQ4a5YDgrRfNoFz/wA56le07sjldKCGRpi1ujyOYr8GhFCkftG0NM9FXhuqOj5LrxNM/rz6Hs+X/KA8yHgs6Y0ybwido6iVhoGxy4ndpz/96Yf8vNvt4kMf+hA+9KEPPeg9hx9+OK655ppdXTTDrgYTNdc0UbAgoGRhdEi7UOcawguIAp+1aSy0ZNWTScwSLj2PfyrhwuSk4aOjhEyMLlWeIy7fJ9Uh1mTF17EAkUXdcV1IdtFRoAKel6gk1IG40/aExiIeF9RsDgOaJIIXvMQG09KczSnkMpGWuquFMhJWWcajFsFp8xukrYFs3hLtKOllWhHihiDNbZhN7iQLLKchAPRnsT240GIOd3m8xNeQlItN2TwGChck+EU7TMtYEqFAjfJyfUjeq0kP1yW7EoSUXqIOLgnI9C4XAOca9VXDT7RRbN7ncjPZk7IoQsAsJGuo8zjTpnr2z2u8O/U1jyFOEZS1aiSCV6ZVIpVZgxW/3zTj58hj4rFBSmsocyRrWFhAs7ZL2h9qA8R1cgFEHD6TxrlLgQ8upKtZ48jzJVAm/kzItMaN+4FJiNbiZiKrCBJyZ2htkPiepXaSFnZJo+9D7kT1Tq6fV4SLfUczb2sSGe5fIpdcBggEFY2a5rYOuiJ5G6Q+LrUUb0yJgBp+3p5ONjjU7P84BtRGkVPCqM2j3qQE8tL24uPLLg8haxkl2Emlv+L66MVG+9el1k4ct2luZSKa1/F4vSxyMI9hD/jYGRYw0mrFiwowd1ccEQWTz+YFNkEy8UI2J2Tyxr+rJSxNYjbFsimxKci4aNR4TlALNQvduX4+sfxApjUsGNIzlOlj7o6aS9o0dzAJy6Yf9k1p6iKb5hPxW0JeGPk7DfMsazQ1wRDiw5oBanwX0BG0TszXAIkQUjIuayXUxWxKSX2J3AdMnDKRZT/E7E8kz0rXG5ovRf70BoDvl7ZWpjGSD/KDG2ZTN6e9kE2sbM5m4es9Ncyn/FJuL+4rJi5z20me5aDGYhRYLrUxmwk5iCXPAU2Wcz2lLpTfEc2teU6BcjCQLpP4YSUTexxfPkfEojnGmCaJtjZPyZSaI94gZj7oACr2eUwaSmT/SyDPLW3ia3QqQUzEUhZiounzTel64bTbRdMvC0j+tPJZ1vLp/udZLsSJkIhLIiES9ORkfGfCSGIylr89u0BkX1XZiKQ5qVOraNMtt6OH8lUGwIEduq+yNi1rhrmMkD7XDLZJaPVGmtdB1lgzOP1R7qqU0iS1o6S04ftVu0UyFsuQ8wbmTUX6rTGutbsHr5vcttrXMq3s4kYCbiODwIidYeeQZiYhayxYo8B5mJjUZAJE4LxNfLWQvFT5sUyUWIiwD4vOkcXv5TIAetesnMQV6SNEAah9y/i5ul56MSbiiK9sLhDeqdrAAbLYcTCFRI7OEaAc1s/kSISDC7L75RxgTC6kbFARvumyV89iAZ93u7ms2gQp7eMyqWCtjzaLcr2JOJ+Va8hkAbFJkv2UcpSgtJEiehDyqQmsIhb8O2Id2M8uf4/FVHwGC3v9DB5/OhWIaJaY5JISHqq83IYihCmWhSNad0zumuRMyDF3gctBASxUhSQpoqnfz/81TNE+yJjlRM2aJHFZdWszyWkU2zX9U9ObMvlFc8OTiS5XKDdA1MrqjQ9kvEbNsyaCBI4i103I64gmyDx/ZQ2Rcjkhmo1NUCov51bjuVeIViyP+1iHHO0MNb95TvL4477ittREjb8gWkVVn7lmdUZOI6K07lw/ArTGDC63SzZHu0YkO49v3aDajMsbCe3fJ9pvva4BkpUgR5Y7mUu8pjTr4zK51c+mvGZyu0tdSbmYkN44k/QbzxkOjOGAmDz7ZWQaEozYGXYKWShnQSvLtuzE4k/tx6EXL95Fp7uVxidHdjE5Y9832TMrIRIdrZMpRxaMJEiDjrrMmgTe8WX/DR0ViVSQvHToBYvrEs1HEbVK4aHJJNQuXJ5DWf+mfcWosbChcY8IN8qCh9tMgidU/+jfPbJZgwmEXkTlPiWnWEhnDZlraGgyPct9IH5mUn4koZTLnimHGieqtLoP+QXZ5yu/I2vG0tiCIl+UhVU2e6l2TQKXSRq/Xmt4WPyJlmFee8TnsDY3k79cppyOJpNWbbKTt7KwhIp+JEXglTDnp4ego51zGwrhm6MFKVQENgtZHg9SLpdMbchj0YtQVWZOJjSkzGM8nxXBiXMyQpMvIGuEJHJXPYvJGQdeqCaVPuVNT4PE8txQ5F36SUhG3lQxacnRznmOZr8/TsOhNgnECdahiE6uP8/HsohtzuZN3UekxrSUmevjdYSrVEN6L9anuc7SnHYipe3jIc6BSUI+XSZoorNTZMyzq0zqS46cb26aFUGn3K9MaHXXiXuMENYmEc3fy5sQ3tRzW+n3OMwdVwYjdoadhhaSvJDwIqUFfDaP5h0/m8dYKLCzejavqD2ZU5OYySI5eTcLQd6BN8mfjoDjoAfI9awdUnncZCXNDszxr0xodK6+uWZoWbBZ+6YFOaEZ0JHKKcmM9bOS4OH3cVCJaBikTbOPIEdcNhbT9JOP8CFinxbXILiFr3PbNurFC20W7oxmzrmkgWUCR046mP3FRKDqKqaWZUES78+Eh53FmfSwsItjLgtH1rE4l4/EYqHAZdEaAP4Gky7ZUMxJKcKpZjQB1Roz1v6IthpMQuMzOIhDa03SAwE48XPjtiFAjtJjQdccl80yyJxRn3M7xn7N5GGuGwQTbpLWixGqorlDUwPf0K5r0i3jMc8damgmCVBtyp0vqVAo9xd/f25KGyBbBpj4qIaUvmCyweXnwKr4/fx6Hgd6g6brIr5jqS1D8A1fQH1EGc8X2Rx4Eh/dHWmonCPJIal2PY3xkbVWueflNBq5pqKAhXDp5+lsArmteE2VOaHaMxM13cbNNYU3njkDgBMi3nS9yffz2hWJc5DyNYb1nHEuFp85az//NFrXhBE7w06BFwL2Pck7v0w29N18f9ZQ8bLEfm9NwSmkLREESZqabtEmCdY68AKQd3HUWAzEcVftSvm+pimMnxe/z2SJhZQmRkD2o2IBWQfWkUHuZzMGXN7Ba+h8aEhtKGTBsUmIyTNL4iygJfUACxnkMvKiy8/RSV61czv7bmXypfsjEoc6RR3rRTv3JZNydV1Ih+5XJXQokzo5EcNxu2W6ps2qmWArXx8m5ZTHjvdBCAXnnxNhxu9xUP5piSg6HpfZROs9j4WmaY5NTnozAYdG23J7aR/N7Nek/bOUNk/1ryS/fhApJkKffONa9i1jEpPnTCS/zTx3HJlaKc0Mn+vKrgGNcjSEPjXeqckjXN4scOkIUPNW5ZjksQPtg8ll1204R4uaxhblV0g/FGkc6DRKzbaTrWd+QF46MrlAPv+00f7E787jNV9Pa0SRz85tbG4ok73sB0j56DjpQ143lbUhPUA2BEx4kJ+T+ySvu6J11cRZ/Sf+hWr81CH3hbQLeI0mIbzNLVH8fiS4MkPSJqvp6yfEF/NJm9LVNeRIHDc7yAV6AMOInWGnoBcMLz5oWsiRnIuYTR9psaFMANgEykKWNSZ69WWzB/ukxGt5seV36ig8KGEkvnqkTE2KGOidK383qL/5O0xmCE6EYvw8C6+88OQdPMC/ZyGgiRsvdpq08nf4+UG1BxM1ZnCsTQNyzqx5JirHMYZ5kfSYTzAdmmRZCyMmz0IkglP9QuBkrHN9Cuc+v6lByYRIHKaDE9Kg2zBqn3IwAGvvuLxzNWJysoIj0Vo1HLhT23OldBSojDERmpCxoH865OhGJixc0zwGcr9LHxITKNVWLn/WIPpp3HMARt70ZHgXVLnVGEJu03nEUGlS4r15DBLmbBZSXRqHSfEGSQhW9hXkxuHk4hL5zcmGCXINQE5/weNJlXVusWXj1ig7lB8faxbn+HGlOsszSW+oXIMY6j7jU3KEKKtnAqovqOnzx+uOZpsyJ+GUhrqphZfm42ZwaIzruXN2LiF26t38SO2KkMubn8cbmfw8krVdJ4iO59JCvhfN0ZBNAZwyz/O6LqMQsjliglkoLSuXkzeCEqyi1wIpq2nsdgQjdoadQlALmzbZ8WpL5FQ2+HwPG0gYEnSBvKpmzVraYZJy6lc79yAEMS3WjqPy5p/+kAlELEv0OXKyY+SdMGspeLGK9UOupyOJNNSRYCyMeV/Ki1UmoJloAE0ft6y7yYs8I/uXoOFTJWQa2YeO2yBT3qSZoXymKpu+WJsm9Uttzt/JZ6VSQ4DqsunoOyHkxD0eoY9yy2YuTX8gGjBxEPek7s+EXY6aculkACVYWfPEbQ8o7VSqQ6M/iTWd6sQNqA2L1BFZQIkgy2ZibmdAjXHKddUEI4+BWAC98ZDcZCARbjwWdds5VRYNTWZ5vrHfnOcxoPrMgeBU+RsbjlSHQo/DJLRF+0pMn2JR5pq+edzrsnrMiYx1PG6yfyqTGK5zCF7mZ9NfVj8m9+88IqHu0akzdL15LuZIe17JeK1S4zW9XBNC/j9qdZn58RyEbA54XMX5GxolzGQtr1pZa6nqidxJmgBrH03tBwxkn7xI+HL5g6qC83kj6HxeT+a+g39KW6m1q7HR5e+Ck3InQpYIdkN7zevbDrS63GesmWuuDXmsGCKM2Bl2CnpxZf8fMZmBRLPCi7vedWmzrfjoJTIGJaB18AQLkrmrupjMGuQyCzbt98FkxqdIXFlEtdBF1i5oPzZ2aNd+Rrzss2M5LzY6dYmQT3mO0ualcmnyx3XiBZGFnw7O8OIzFe9m4a8lBQt0vi9rCjIp53c32lNpBtiMyYtx7t+0W4faMSfCms1imXTpTuP24LJq4aLLlU3qrtEfjWejqeXI4ywLaE1WWdyLL5aMNO1fiUbf12oDw+/gXF68eXDIJFRAesMTr7MwzBodHdAw5/1Qvm3qM0jb52tRKHoRwlnrDcDRPKd3gBrpRXgc8NjkccN+n0zWue1ZO9MwkTF54fKJplJprfnnHE2+EME5Y9Hzc3cATf60CVRImSZw6WfWsjeDMvR6FJ+pr6uxRPleybrGJB+ZWOvcc1lzym2v2gDQLajmVnY78D7IHNGma5D2w8xrKy9K/FTvc+vw9abZN5Ylp3dhZLNrHhvKj1PmZJ5Dczcb0paK9PHmSIKO0rMkUEaWeJUXEPnEEi43B1Xo4xwNRuwMuwCi+ufJq3LLAZDdMABJEuxcSs9ASgCQzt2Ung1CUWTBp5MZyx4xrUN8divvvGURcmrhJbVwsqBXu8b00qwRAeR+IZtKTotuzjefz8SJb+fyumSSYhLIfns5YpI1NyG/FwBHiLGmDdCReLl+3HDZbJl9F8v0TALkwHRN2pgwzTfvMRnODGKurxgTMtFiySJOQpLzau1EUOR36bbKpiS+1jQ7Nf2kmFR5R3NSyzTrJaZl5L9JPwR5c6G7mftaC6XcNqSy9KvDzbkdHZeXgwOyBkW3gd6UaA0foKOBdTtxG2ciqwMYeE7q0zTkfeB8enH5b/karCEN5Bqbsdz++V0NHzZuHsqEn9QawFnEtVuCTr/Dmngx+0nf5ecCrMnMAl5M3onEyGZAEdWApkacx6VPUbp6jdD1zK4k/DOPU25nRh5Lzc0qk8Zc6tyg3BZ6rSKkdlcEkH3gvFNrUKMf4/XSBXku/+R7cjsqLZhaM9KfTS22Ivp6HDp139y1UW969MThTTK/RxP8vK5kv+Umgc8WnKYWb077kkMjstZgxM6wE0iLrl4MAGW6S0I8/h8nXl6ksuZEa/L0At1I0IkmWeF7tdCBLBhZDycLGDVJi/cpEEIFfGhtAee/iqQrLrhCVPk7LtdLa7cCNcupF3deCPP65xo7a22+0BoAvkPSLLCzcCIBTBVkt84chHL7iIlL6CgaGswsyDBHGKdXpaLwWZ15d54FFRd1LhnIZjSlqaXsv8XP10Iwjw91XigLRGTyLj55LOSVMBGyjay5kT5Rkk4oqNoUsC9bHbTWQJeXJBgjeV82NJn5/kwyWZhJahpira8Wwrqcqc5KGMvnpDdPypyY+iWVKI8bZEKYpbcab+mOEHyO9i2CjJG55kAmiUx8cgoQdrxvlk9M5ZrUhey3yu2kN0iZ6isywS0jYxkyDkWbzMRWnpu/L2uDbmtqJo1u9rFLbZ3Xqkzl1dxGdpPgd/u0LtXqujxHEcbcd7mGvAHMPo55SRVTO7RLQJrfinQ1CCG3Pxw4cEwHk4kmlfJay/XWZly9GWOSry0QrIGNa2cmbo0NgWuSyELKlcsex0L+W+SEmi983TR2TVhrGB47ZBFJ5jwdup5mLGfTz1eaO0Xxf3LJHKeFgX4GmqZL+VQttJiz4DYWCVkzkqALfE0L27R4Qy36ThFKfqBrkoz5ph8ODpmjEeR24J21MuHpna8mhtlnLN4XtSnUuD+2USK1KRt7PlEil4Xfn9NHZDIiAs41ibrWYHEb8PfFBOlyOfipc4U6kAWj9hHkz3PErxYcud5y5i8TpFQnSbOQiEtTC5jbu+mbxmOEB0Umc1wBNkUWRU7D0TDbMT3iSyrnmPZNyhoZ7vdIZkpPEmGbTaf8+lwBzxraNKibQppbG1K/0gcRpFJH19SIiNlTjb9MgPI1bgt9moikE0J2lM9zIH0mmiNkDY309Q76BtnnimS8NPtRm+2yBpapHPdJLjjP64bmWs8VeR+bO+O38thvBsBowsS9z2WN7g7ZVMnkubEpUo+SlTCtE/oc6LyJyYFB7A8X/QzzuiSBTUAjdZL0sVqv9NiJbcDt3yRNOrceE6vsD5nrzHOt0ZeAfI/IKesKyVopPpeqz7gvc7vxxpn9Al1DLuT+tUjYB4MRO8NOIWsbHOq6Gd0nGgo0d7ZZaGjtjZrsLu/ChThCEwgV2Sm7yTz59eLD5eB3ZhNh1ghmYZIXShHGyAuRmKEUqeMABhaiczV4mtzJAerByQIG5N0s1zT7q+SgAyG+UAuhYyac25aflQM0spAB2O/PS30YDd+XJIiZOOoFvFY7YxbIctQP9wOS/x8LBC4nt5LjftaLtSKwqv4gNt+nzUMyZed703cVIefCO8dnvzKxyH3DZvDsg+ca79bCl02ThQ/xtAdFinTKCT1Gmn2fyT+PFP2WLIhzH2lTI9MIgtJ8itY2a2W01o/nkviEIZJPOYZMtwf/o7xBEP89HgtMYNPPQvo093nhY7oLUmcyZxN8Mg82kjI3Shu1npRJGMnvOXhFf0XGjiYt6Tv6WC5pT2quBUhtL+3j8nN1P/L40lp57rfSB2USTPNUVjyIiwb3RV37OT6VaZ4KsczrQjTP53nP64zWvOVNcu6nRvJo1cdZ20syb/Qaku/LYzebd/OzuPx6fWUCz291yOmvtHk8r1tz4HIduFy8hjveYEFZTQh5LGMHzzvAYcTOsFPgNUmbMBzy5A/En6MxMYFMkmSiu7zQApQXMuSFlEUj5O9EyJBJW57sefcnZgRdBgJKSaESLwjBRJNUMsnTUWBAihik3Aa8EPFDFL2MpApMfOb7xbCgduAs90I3GoKfSZE4UavduUdzIQWyj1BeqJsLZ+wLFlpZaBByihmG5FoLTpKkcj21hrEhCJEJD+cSm0fiHNczCwpe7J3LaTAyeXaN7zJh0IIpmqypoXHQwqrRO4rUFEVAWdRgd4A6+Bi9mEiBOHijSeIAavRJljlC+xA1nknwSfu7RvoPgOdPDlDQ6UNkM6POTM6BDDyWsrDmo+vykVA8z5LGSA505/5XKXvS2BEnfzVe9eaFNV6aNImJj3KAiWiW1O8s7GvK9WFtpn6faONpzvPhwFr0fIg8oDU+DgA8l0fuaGgKQWpNEvLD72qmCuH3MWnn9YfU/fmZTHj0MYp5w+UQTd5If3MeQ2l7IW25Lfg4Oh6PmDPeeA3S/nlzxxA3VPaddODNtmxQc2+B16acazKbgYUgC5FPz01tzPOXXQV4PdXrRXx2ELcLkSkO4MTEPH69JsyNUWcAjNgZdhJZrGfhzRB1fr6SJ2QiH3mpyQtk+qOR1oIndTynFLKwqEfDgSPeMkHRwl8f7i2fpXsLMUHkWjE4rxmgHNmTMNXpNuReoKHpIrDwc2mB0uQym19YcMbn6uCGTKC5nQj6dAdeQJuLnFM9xAJWlxUgOVtVNCuNvkMWykLSCPm8TYjwFSEfshCbiyiQ2D8PYHOTFiRz/RP14s+axkwk8n0apD4TjRU3N5MbJqIAWmUtdQKQtHxefo9fzCbV/B6nnpnbnq/z2b3cC2JmdqTKg8bcabQl8rZCayaFZLhcRx5vgZxENjIpzn5I2v/OKeGb+57HAZNhHuOcHJmJjJCPxvh0UladS076P3U8tz/cHH+29E+O3qPkO+XUNRfvZLOdjAHZwHn1NDSInkMeB6ztYU19/opajSi3HZdStNMcAS/PbGpcswtKtiowacsES7lMyLqSSkuQunC/svsI0juLdIpLQwupyHBj44ZMzLX21TVMr5l8S22EFHppxzr4uF7yM7jeKtcdzzNeLzn/Jqfc4WfOM7O6nMJI1hfuPzX/shY8E0VDhhE7w2MH6UWtKRAawlPtdHX6hbxzzqSKKGkY9GSHzpXW9ImTyFDkyc/gHaVoldLOL2v19AIU/9bCSC+Mus58kbUk/A7Once+JCxMmBwBEGEAeX/+XTRexK/Jvmf6yCWCy8c0qWLxb0xwSf7i9tCCiOVYfp9Ub46gyAQjog4qmbSQlewLxEQx5zDL/cuaGL4PqS6kFn4h9MiahIYvI/epkFr+PVPfJg1m2sH+h6Gh4RvUhXzLJQ0dG8u47jldTqatmZSQ2ogoQuuYjGbtC5uGa0WAmeRIO7HGg7UkWhC7PAR5bGkNofi5cbkoE11fhIZ2hQVq42QSNf7jCRYumV1dJsVOjSvi4KJMnJjESjll3kHaQY7p0hp7bjbXzG8GeVbWIrLm1ks7QcYqZIxEYqg1bTIqVFtrbbgm4rIegRprhJ6D3O6iBeVxBNUN8jxqECEmffy5rn/DfxI55Y3e0Da0ZaoM2urAD3dqfHEdIPflHJGOx4laEQL3MXID5vtzyRv+1KlMOcI6+y/y5oHbPY+FXP+ocW6uXTI3uGSk/EgNDRixMzx2pPnEmdOzz0NeiPUCL9cbpM3JzlEwR2ujd3Rs2uXdMJCFiWzxkIMMtE8YC3W+y6lFBo2/IQ+MO0u1m3a5XnlhTULH5QVP53fiNVqbx+a2IUPveCXdgUqlIkI5kQY2tbDGhsvAh707VTdtthXHd9WW7A/pXCYaQFpApezZrOYdqWPR4ne4PpIUFdx3TQmWySNJ3+f0NFmoMrFiX0Zuf4mGJa3ZUBonRRrEzwd57NW1z32LbF4WTSsBpAgsE858ygZEA6d9LVlgAWhETzePX1NjFrmvue/0+GRBzWPNuzx+CzfniCwoYiy9HwdtHdQL0zh2kkiXCVcsXz2HoGZSksmpDF9lMg2EefNNtJDI7cV1ZpOmEDhFCgofxJ+SSZom7znIAWBTNRPdvLmMS4kOFNDjT0fBx/nLfzMhzmORWzKbSAl63RE/WZqbOiQTGCF/Lo93LhePy6y5InU/ZAPF6xoTGtaQ5/xyOahJnyGs+1iPA50TT/t1ynxJgyVHz+Y1rQ4uat+kPtkFB5TXHu53nQOUxwyPVx5LTR/IXFY9RrTbwIP67B3gMGJn2CnoXWT0ocpO6dknJk9VJoGy2+ZdJGGeMOEFqlCaBn4Pv1tfl4U+CUrv1VmL1BRIQBbORPmYJd6DihBTAqNpZk4/gXgkFOVUHnGxd/AQMZQFoi4CKZMa8s6/ULnueIEVYYG8ILLJQpvEWMNQSK4uoApRI8WLPwsh74NaGDMJc6n83F6ieVPaWF64tfCbC0K+T1ecBQibnZn4MsniMcHEE9ye0tdZg8ZEVBO3uUdmiUzQKVO81vimZymTM5Kgr5OGjQWgRCkmgjm3jbL5PSf11fXMQjAn49WJptnVQBNMLfjzc3Jb6tx8WStEQlh5DjXbP7crzx39fT5fl9T7weVPXy84kES0M1p7lOqpiBDk+Xkicd9nUhZ9GQuXzXdNzXneZIkGbg55lKohE+bGONAkVbSQ1NC45vx3lOetnr6USZEmUM7nFDy5Tdk3VRFiteHi8SAPTpsKeZWqQzzW0MkzeE5K9LWaT9wu+hxVJlX8Pjbx8nhtbG6lH+PfVYO0s7dwvle2CEzg3fz0Tfx8XmPg8hhhn8+iqKWVmqZ6RUq5DXa0WTYYsTPsHLKPShRQQkbUTisLnXRN/uMFPf4mpxE4SgQxLwTaRNVkR1wOAJQjF2XiJ6FaFKEpjB12uIjN3V2zs78214j/D2WhDmSNHJHOW5XLx2XJL0wkLv3OWdSjRjCbebmdefHjxVPKjLy7pyRNtC8gR3JqoVfqY6JUyhDxV3HZlFoH3/hu9mlhQs4P0uSXpL845xyTgqbpKCetZgmf9U3pGr8q/Ro1lUmQi/DOWkidzNSlJxBF7QYPxsC+mIpQs09cNtNmvy7vQsNnUsaEJruJwMpGBZlMshZXopaVdiabT7OGlq8HNLWTQkag51N8NlHKB8Y8Wrd1up/HliZ9GUyq47t1Mmwu21xSmIkRpE+brg9M8JWAF9LZTOLL2htuSTAJZ/Loso+aJpR6OYhrkJfvFS4TKmlzNyd1itJi5bnUbG9+DedKzOtS1tYJWQHAqZea1om8FvI6J02ptFByE7eXEOzYUkx0azkGL29Y5iZRZ59W8DuZFqbPC9lYqBNOVFsg9Utcj9EcU4pUNlKP6P4AzVt75E69oUztIWl5uF2Q+06UAi4qD3jMSLsZBEbsDDsNbSZkR18WIjpcf66Ph5glHAvKvMgSsrasqn3DjDPXj0QWKl6EWVik30XYEPIChvh74fP5nPmZeYHRaJzdimjulEPXlfDhMmYS5/L1BlHJjtksrPS5nFGQ5HbjHHVs9hOfLVKmWLB/nz6KLbcll4lNc0wEY/RoEM2C9nn0Df8+3eCZaEeBqUky5Dsu9YUkEaXsXM/14g0C+09pQpe1lU5pRXNi1bkmbh5DPL5EM4tcHr4ntnvSXqZ6REf9nACaybo8GxBNl/TwHN9BPjlEyktIpl02EyOPHeT5ExQp4RQh2vcIgJgotYuAFtZefU+b2Rwom+jVeMy0OmuvctAJaxuzg7oQM0h3CsGv6nxsmTYjCikXx/hMBkUDlcY/IR/7lTWx2eQIvbGZ0+cMwhyTcfrpPSHUXh0nl29wjTvz50ziRYtHmVyz+4l26RBSOoegZS1orhO3CZOVpua9SWrZsuDUeNGEDy4RNdmc6pQ+c4isarccIMT3NQNReI3gfHg8Z1kbmjVwaX1LZXLqu40NBvcNsf9m7j/x3ZW566Tfqjr7qwJN142WYy2fATBiZ9hJiK8REwJZiOPn2kmdBTQnIubJXiYzEU/2+P3sVB6/Pzf6NL9DgzUjPPkDOXTKar7ZJO3+tNAXk5NjM1PT3MQLaeO0BGTBJIJOkc7APijCiLL4EC0TAdpRWJ4tAlqZWVhThLxb5bbg3SwAOWYI4OjZ/N3C5zN8GyDWnIQsfKB8l7SWZE45M6nM18OccnNbS1s5JglsqooXG2Y7IfSkWi9/Twtnp/qoqfHIGwd+txYOXEYR3JolcTldjkLkujXq0xi7ul1YgDshTvzegKYWTEyRyO2m2zI3XNI48hhXeeP0BgjIZJ9SG2u/Mvk89YP0L5Nx5A0DnDYfQzYkmURmDTP3Sy7bnF5zXM48brjs2ecxkzeJnnTZb9Un4sH38wZO1h29yVIkVMYJ5iQlli7P41DK45pzsElU8mZWakJqjYMiXtQkm3qcJL17Q7vF9ZS1kL+j3++QSaRaUyUJONQpNam8VSLNzpFYFoBmqpioaVPrAMXk17zOy1m4+cFSB9kg8dghte6pDVNOhdUkmjwn4lgLQgR5TGr/aiClUNphfsQDF0bsDDuFuf5VsoNUO0s2q/JCV6qdq+PvgFDK8UVZUOgdpFYRsCaLF/xCmRu1Cde7kN6nyoa8IGvzVHq0LKZlUeedL5sreOfMGh44IaTcDGUjR11z0dKkRWuMmPiy0OGyCJkM+TOtufTJAd65bIbT5E+Eb7rXSx+pPkTiz5okgdRh6c3+ZZNlIG3myZ8DyteMIGdz5qz2+b36u0wWnPpU+y95lxfwWgfq8JdUc2tiIFo61e+Q27PgKlRgAvvfgfJmQaNhTk4/RVB5rU1LxDG9m38nuNzmLmvA2HyXTVKUhTayqZ01MdxGXL+c/1ARAX43v7cxTvJ80YEO3N76u9xmXF4+6xlobnZCuocjbDNZThsLtUEIirRkMybkNAaHHDAiJBM8ZyG/AyqHYGqBqJ1P7USKQAjP4LakxljJJCTPUz2mtMaM5wFvTnVghLhHpH7Om0vW9Ofy53mHtKHMa4XkUFTrhybE7GrBGnwOauLngQlsIkVetbf3Mfkvkz8m/w0in56htaa8Rgqppajl1kEW+exbXrdiQ8h3PaEU4hdLmzeDaKwVRTpRJZNd/oaqp0FgxM6w0/CNyakmGQtb/qEmJiNzNSdCw7umwy8nWBWhkhblvEPWvklz/ICcplaKTLDGha8rUugTUWQhGe+PX+ZFFORQ+hoENPzwWBPD3xECoCP1SGssVD1ZiHCZ55qseGeu75UFkN8fb6n4DE40A0DA2gEl47CDxZEz5nMXarNyQA5ymLuiNrSM0OaubB7K5DuTbE24MsHNRIwo+RQp4iiEnN/huNz8PShiBumr7PekNLZKIPG7a45qBfs/Km2ibjBFhpD6TWvKwKTRZSKTBV0WYEziWPBxfUBK802ZSGqTqPgcKg2bEAHhKipAhscUtOZo/k/vg7Sd9E8iXXy8WGN+pbkhDvmKwMCpzQfXSxEDmecyl3Nfa8g85RxwarxxuTn1hvQBbxrcnOeKpm0OmVPI5wQnVwmHHJyE2EY6P2NsVzWGXdzseTV2eO1iojS3flwnXSYHFYyCvFZqn1Ndfu0WIycAKQ1kKmjeYMt8yC4RTPQB3c/pM599Ip3+lNQGb075efxx+5VFHZ+X1iYgtpV2j9Fjj+ut+6bckfXhAIcRO8NOgTUMPNkK1lZALyJ8byZmejfs1ULE9+vDtEWQuUxiohYom5ZYYDMh5IWUfTgo5MWFof8SGa2IE5tGxFwXXMO0pBdIXkR16D77AzEKtbhzzeL3UnmUudODo2shBAnI/lmaIPPP0gXRlohzN7Jw0z54QvSQnabFf0URomxSD0pbE9uu8DnfVdYupPae05biN5MaW5vV2UQuRFSdcsBl1EE07GuTn61Ikpig0mcqpUfu8xxJzD49hdZwAPNMO1kjzH2fBSxra0WTkb7TIMP8PWSzG0OfucuEtfBBSdJMknO5M6Hh+0rPEbWKy6m/9Rmkc03r/BwiJ1HZbOLU/ZvnLxNnJyRrrkkN0IST2ySVI5FCHdWtNy5au8bEgceGRBszeUf2OXVcX9fUaskGU2lvI+HLJIn7TBN4aRvkNUubCvnZmggVRXOs5PEc3+d57HhqjAveUMoK4XRfZ5/ZJilrzu14XWkA9QYj7QSzdjU+l++VgC/H7h3NKGxtcvYujk/ehDU3E0yuXaP9uO48HrS7B8997RqRN/HaDYAa47bwAZ2y2rFryQEMI3aGnUKrCGKG0ERDi1K9+3bIkW3NTOrUmOjasVr7Q+XH5sADggoOSItTmY6F6pSVCAy9aMRnkTw7m3Hy4s6O3o3EtIlg8eKkz01kIpu1NfEoHRYk/I8FbHyvz6ZqZMLAgpIjwWL9AYd87BjvptlRH4BEAeoFMGuGcvOxAIrmGy9tQMgLuQhnIDs5g1Q6Fi9txp/FkwKyDx9rYCDtk4lYTinB5CNrn6QtVFCCT6SzLLIfGjd+DLBpRmeS6tN5GoX0/OzDyX2f6yJ580hHoipCzYQEmYOJRpL92eS61jLNIXAKTLC4f0M69SFvJqhJvlzuKxF4LpdDa620VpSJOiiNU0UKWRMakE8+AGXto4P6m+ctoSFwudPy2KVkws9n/+pNhpP5mMmZhhBaTg2jPmv0bXp1Hnc8lnL7NsYe5Y2S1qJyDXhM8HoxX9vMhK25qeR+lw0B5rQbWJuV1h9F9HOwgwNHmofU7pyWh/2I8/mxuUzRJKqieVXAjHad4dWP/+l+5nEHyidBOMR1gNe2WD9lpeBxq9brwgfJdsCkmhsgmuWz7OB2KZw+TrG57stm0vHzCcOtAVregic0jNgZdgo1OfSrUhbKdpEnmCx6SlBpoQSnNXN5Ma+Vcy8wPxcSi2L21+EFGMgCtKp91NolMyn7aIDLAcwjVPGpityhadZlrWAmC0DplCkJ1DA9CZFMf2dBqNtHkRght5kg6khd1lyJI7PsrJv1YiElZI993LRASoWM7U8iEAsXJL2JmMSYLKY+4EW9xX3tCOyvw9GUEjWqCKJT75U6gxfuWOfC5chgyXHH7cTCzGXtBpOlwje1uNl0Q0JWGn2iBCdrV8qizjm1nNaiKOGJHMHM5llu3+xfGNu16WvZRMP/r9EvWXsKZP/U7DMX381tT8iEgiPCWcsm0cZpDgaVHkM2Gr457gqdd66xEYA8Mx/bFcvTLus8XtEk725ue3MfQH2WKqKGhpDTuRrhpvYskQOvxr0iHtmKkJP6+jS+M7mAOuVAa4r1fOI1KG8cJI+ikKk87tjvN/e/akO14SiLWgg51530mEee544vAOBoUn5u6eqUvkeRUpf7WfvGNdItqTYnNWbi+OcxHzdSotkFZI6LO4D0J/dBJsuB3HxtuBrrvHHXbVDw3Fbzp1SkOWoM43s7RYXS1fPM2Qc6jNgZdgrxIPh8ZmnFvkiNhTtHUImWAZDFif2OtJmRFyTWKGiCxYscmwpILqJhyqiDz8JXkZMm08rvk0siiOLvkM+yYAKyNqxUGjOoOninCREaBJc1FLoMTFbi7+wsnN7sMmllLU+nqJBPe4jRbqSIcp0cqUsWbI4UscmN6aRBnSyoYnYCwOfbAilZtJDqHGygF2HvKeaqUkJPm2xES4McaKC7hMk4O3ln07SOcM1Cm4mvJpLcjkxOWbBxxKOX8sfnlEUthG1H2jWtUeb6+OQcHsQXT2l2M3eQmun8aznogmQusMaECQDAmkzuJ1J1y/VoCu35RIPfJ9om1TcyD5HHJL+ekI+Eiu2Ve0k/u1MO0CkrdFsDtBJZYfrfkgAkRXCdJktZI8Oa5+zHyoQjR49rUgAo8qra0vuQfdochLQRWJPKARVqTeGxnsrOVgg9Z/QmjZ8oriiqt2vlj1nwuqfaTY8hPT6API+AuFFpl1V8v0rRxO1QuBw0wQEWvInxjvJ5tmrs6Pmn667XwLmuCWy5kFM0VD45OCCnTM7jRrevNqHq9R9opl/SFgYxmfMcTvOR+zWkrm37GlUojNjNgRE7w06DBW4gJ2eUYs7CHdRE5UVJC5psNkBD+8FLRmNnzMI8LVhyuHlCkfKxBXKoQkyJ0C6Vdkmtatq0wwsIC0oWZjsqD9J3nM+aI23i4M99IlWszQALdEDSjpSyKOf3d8sqpSzJyZq9p2hySMSj9HUmSG6O8ETeaUdhG4MPvKNGgCcvpNF3KTS0MSAXc6/NMRdym+nIT4kYdJD244U9sFZLCeu5Wio2pcwVeJlq5KhVoKnZiDn4lLYCOi+gSjDNRKQ1EE0cE3HeJPD7tYZJIv0U2c0kLAtO1himg46V8GsKM27zQuqcNyNcj8JxAFGOEmQSy0SMyY5odllwKzO4aDF90w+OlGk59ym3CcHxuOTnOGqQPK3hbvsaI+0+OkUlfcTkYqgcSJly3ZquG0IEgEzquEzc7umZcd6ERvRkIDQ2V/M3gSQEiX36monH1c/0PZ2qR2uO+axpqDKL9hA54p/biglI9C/L3wFyMJD2CQTy5mioNcg383hUz+B5Vs8JYBDil9q7VHOLiSFAoq3lNafhR5rGlT5aMG8+ciXYPaIRMJZ+sktGoOiSwmNx/hxqtgvPfzZpZ41/2uyldwQA7aLCQJ0HbIgwYmfYacgOn4UFkBfgoPIZQU369Acvzmx0EZ8jWTRJK7bki3r3WaRdu+RhoibRaxU12kXV8EXLC7UmlZnUMTmMu9cYpcU7ZFmIlXajITAjG8kEUfmJCfnx+X7WavgUieuSQOy2BnCIu9lWih7j95RFLeaJTlmBEIMnSBGaMv3TyTuzRrC5a4+auGhal77y1EibkE/W4KZLhCuZQTnvH4MTropmgdtD/eNFm0mnTh6dTU1BFv9CLfYsIIiirydSG2qHeE4no8diHbzkMGPCzcSeSaguG79HTgkhHutsEsv3Zc1Efn4moM08YdyuHDXJ2i42f3M/UjJneSUUA7FLQ95YsTncpTGP9FwdScljXTRpyH6iWiPikTddXEeOhhWNexpfVSiiCZ/YhSKRDNnooDGvNJnKbaDbMRN38Vnl7yriy8/iDYyb84/He+FI+pVN/bw6dMoKzsWfEkSkSFYz4ElpkPQ4V5uKuf6kTO7lGYmg14k8FYlwN6PUIRuyOJ/VublQRMflzQmPY6/epbXWTKRiepO80SDkgBRtZeG24PGjtexM2Jhokiozf1c2o3wvIjFtF9FsWqZjImW8qfnM5dJrLbcpE2ceA6WP427ucZEHOozYGXYePAEB0VBEuanyECXhMahVNnNKZgtkh2bR5vCjMV+rxhn8m+RJE4r4rLIIEjWl5EyD/PAutkkE+KzVqBUriyACQAQvWIOQTYnyLKeJnGsscrJrDtk0xMRHm6ZHWj2MtPpgc06niHUofYAHoe1rLGr10juUJkn7s6R3dYsKgNImJELAJk9eYDUZ5HLyLj3vtqMwaqmAmWhGyQJHfPrYBKo0jt4HZeaK44IjQHkhL32tInXnRN6lyglhSmOmDkzsXSYlTgsHFjj5KCImRCzwubx8kL1sUJRAZcEPNeaaQoykXbzLY4LHWtOxPmuKKBFOIG8Y5pNLdmCPglKTpE5RNdqfNS/yHHVd+1zOJdgsWIO0EZJWkzdEud6tJKQJwEHdaYy2ZwFEDV6riON0uJU1dnrTV6aNBKcVapBjpgqK+HHbdcpKNmrcN2URpCz6H5DLL+2hxjWTxUL1iQRWqfVCCIvL/aL7JBK07PMoWnPRwDP1yW0nhEYRG/4uP599l1te5XBTZWXTO5vIZTMq47YZVAUALV9nS0mqT1mErDlLz+Y1UWtJ47xUGlU1dnM7xUbgdajl2Y+Qg0gyAWPCyWuuXkN50yrzN7UduyFwGQahyFYig6Dc2wUw7N9wQMOPSecggyNJwso7NpcueEcgB4R0rCsvHuJzocxispAAWbAiT26oMnjEAA7nCIvaPfl8ZtCG9wHdcoB+XQAu7/jiTjcLveHWIAoORxiEIi5eiJq4mUEpO2MgHYzt4q638EGCNup0WHfcjUcTJwtL5yl9ngUWayvZEXioGKDta9yzfSRrQRTb9Y7QSfUsFTFqarIiuR0p+3H3D6DlAoJ36FdF1FyohZOJWx28mAGJtUSqHwoX4hmmSvfEi/EgFLGImpTkXoJHM9WJT2OkLELsFyQiSNls5lKoKkcugyhpinzSpGUNjHcBwRWiNeUxpSOWC9EWOSDE99VwKF1A5byQ2RqKKDPxRtZ8sGB1IDjPhCwLNASkdkrd5iBkYq5fptbG1Im0hbpoEIoi1ZnLw+dwzv0n5kMRqHHsMYGuKEe7ao0JABlLro4Eu/Bl08yr/tVwEiw1CF60MeKP6aKJnYlz6QN6iBudOgDtskIrXYtjI60NntBCnc3Pjv0h87hhQk7BwyNqt6o0V3k+lj6Ot8IHtIsKvaoF1i+VPgChiP2Q6hDHBxP/rJ1rmglT3dVZw7rvWkUNqkvAheTakIOCKDEnz2TIUdKyhhTpHUnxwBfSh+2ywlAxwGSvK+WRiHPHWvMghInXMNlIqA1YWdTJJ1ppSn3TjI80lzXx49RNLvVnnU49ETP+vHWY1474NL2JEpJL2bTLY4bnFpNJ9rtuFVnj3y5q9KoAIPvVBXJ6aBhgxM6wC8ATTO9WeVJn82i8t+RoPmLfK+VTpUyivNsN6br2y8i+JrygOPFXA0VNQukDynTqxPZBG1Uy9XXLCtsHAUNJi6fPZ3SOMNzuY8XwNrR91AjM1pHIDUJcbCdnh0AiUOOCWgWnFnfAeQB1JEAeMRVATTmSjXfSvHuvyWGoNRANQOkDWr5G11WyGC5q9TA521XavQAPTqLsRPtTsvbJEUABnaISklq6gJFWH1P9TmPXDZd28tKjhbyjmuO/4tJ/lM5TFSElfZ6FTwhN0xAbWXIEXdbyFi7A+5SfjrKPFpOPkMwwde3BKTO02VQCaai5wlMiVjXU0UM1y4HskB3qZoSjEGQCyrIGQjOilCtcJkLh07Mo5A1DgAMlAhA3KZkwcqocBy9tkMf+/BQq+RSIqOkbBC9zSwKGVLm1kOTvDeoCQ+UArs7ktA4O5LN2LlAkuFlrFYU5R122ihqDuogaKirk3TNVS7SRmfgQKoobnXbS3AyCR+EC2j4n0ZZoc7DPVAF4L76zPH7q4GQdCZQ2GC5ShUFdxN5mglqQbLaK0IzILXw+H7UKRfR1QySyOuqcyyfjmSJZ5L87RQ2kDUjL16jIo1NUsp7FueoaY7wmjyKRuBC8yneZTepxTYT4pgU4IWXc3pGQB7hEHNu+Bp9byxvBVlHDVbFhi7QeFkXTh5Q3aUKgkTSgvkYvlGJpkejWkLWsPEc5HUydxk4ND1Bs53ZRYbYqETWLUbPaDwUigWfyWUdXDGTNbjwNg+ARyXuZNgjaMiE+x4gR32Z+zLC2MOwcXBbsrL2Li6Fs3LNKP2lnoAR7FvlZlMlzWMOnJjM/H8hEsnAhOQiTRDfWwaEfSszWJfqhmJf0tkiCl81l/K9bVGj7Gr067nmWtGcw3p7BQZ3tGGvPJHMAiRaiLSkysnD2yJpA9qdhDSZXkNOisPlivDODkVZ0QAeiZo3JZasIGGvNAEjaJigfIEUImNB1ykoIIAsahygMuuUgLbBBTJN8YoccIJ5+6ihOzHlnFbJGVUe/itmNHLzPGirW6nF6CRE8XO5kkmby66UMOfVMJJGx56PPYx492jeKTZ9F8glkjbCYjVweOw4k2mGtmZC6+qy10aSJj7RTD0c+vi6O1ypE0rajvFxM/HgzwpsCNt3nPk6mOCZoLm+E+HkhbRq0FpCJIAs+rhtvRniOcrvzcxwIBZKJ16PRplw+Pe6Y+FTkI0Fy2ezcLmpQ0up1y4Fo2Vq+RittNrzLJCuSAy9EPQc4xLpyTsdacj+mlUM0lNk3L6eeyRo69knj8ZEDCSB9oP1Bs39tNt/zPHfIVgXZ0CGvW7E9vWqv7Kum0+Cw3yT3TccPGlpd3gjGsZjNxnl8Ri1/mfzwvE8WC0WIJXpd7XlaRS2JlLmd9H0El3xbmybS7FozJ6pX9ZnWtNbkG0E7Ps13Pq5RItGVZSD2afKtVW0LSuPPqe+AtX1582kwjZ1hJ8GEpfBBUp8EJSh5p69D8UH5OCLHu72QT6lgTYyAkhaM2NyRNDeAaPVYo5MXi0juKpRinuDFAYiLXZ8KeReTsNm6xD2zI1FYtfrohTIvcMoXhKMWvQNKhJjeIgn/uEttIRAw0uqjHwr0Q7pBNJdBDuP2jrCkM40qFJgatDMZpKyV6RQVWmmnzwttRVmzxoIBgGiPojmqFu1Ju6jQcjm5bwE+iB5wPqBQZDoK0uQQDyjzcm5rPqYIyESMNYxCusVXhn0BkylKaUZC0NGd7HcDuCTEWWjzqRpAFKp5BFJjzGjyVRYhji0PGS+FqxspVpD82zhVTBTI2bxLyH5vDs1ggGhKjoIlUNRGxWjsQu5lUhocZS1f0jAhzR/RdM3ZaxMlc64P0Z8QOYG2lI8yse0UFWaqFqoQ3Q2q5IPkHAEFkw9kYgLWhqWzd9MQ5b9DcCjKqIGbqwYI5OBTv00P2hgqB+D8hXUiW1FbE4NjZqsSNXnMcL+n+SC506AJQDah85rA4wxpDEXNTxxUMka0GTppierggaJGCA512szpyGyXIpgDJQ0rgAr5tA/uB553OjJWmy9Z+xjXJAePdIxc8IijGTI/o2k2b8p4zeyUFfwgzrUaruF/K9sY3kggE6cAL4EWulyUxiJ/N2r98pqh5w3PCUra0KiBywEgzsVE4EBu40iOU1opJMKf+qpM5DkTwJiSKQZrcF956eeAbJlxgLh1aF9Jdo2JFpA4N/gZfLfBNHaGnQQveFUyE7A2JhK4rM1pmmjnPwPqU15oWkXyz0mmARb8S7vT4lzPO3R2/udFYhBYtxUXs5FWH94HSezKvkESjp/K1/Y1+nWJijz6ocDUoINtgw629IawvWonrUiOKmRCAmTfmZavJQih9Mqp2+dAAb3Acixxp6hkJzpdt7G9aksbDULRKGcVPKarltSBtUpM/DifYECsBzuXDxJZ1LmnCFH4VMGnyEbtz5YFGvvRcXoJnaCVtSVEQCtpOLgdovYgtkfHD1IKGohPHWs3OeoNaBInNjtVoUC7qLGo3YdjTR7lA+N1AAUPKfZ5ZK1ZCNEszgSJ6xDIiZAE5TZlX72aXCNimMcVJ64tkuaJI2drcom4Q56ddJao6kI2E1G7HMkXn6Na1x5Vrfqboum7ZgHIAhhO5tpAtNJZkFJwYk4EuBxKow4mYFmzVNVx3rB2MwColYaPvx/HSiQNPDZ7dSRu2jQ/qAvMViVmqpZoBKtQoF/HOlfBo18VqEKBQF6ucS44bjuXCF2tno3UBhwMJF5djiNfM+HjduD0R+yryO8h1rymNskJpPPJE6wndYm4sRaXSQml50ZLQPZXJUQfRO63QSJaQmhSvQM/B07K5B1htm6lcvkUYJP9YutEGrlNomuIxyDVi4k6j+Uq+cdxWiNJUSX3OtHAiltDIviDushzgsuPTJCZ8EHep8i4hsskkj+v0kaC/VTj5sLlsotmlnOnxvnWq8t57hcGI3aGnQQL/2yaycKPd17xJzW+kyNdk+bOZV+WrP1hE0W8jzU2k72O7CBZM1QnYVCrBbxfF7KAj3dmopYNmRgB+XxE1rTARUISfVaiIOwlosemNU3oXNo18kLNueCYWGVBx7vuTCYiCYr+JVv6XfRDgYqigJscdLG96kTiTMB03VY76ijIp6s2quDRTv5xpYvvFjMTopDaXrcxYKIAyCLO/cV9xYKpDj46cydBjiQQOGqzkV/KZe0j61hDInc1efSqEpzOA4BopNg8PEgCPqRFnDU9/B1toildQFUX6NWFaEdYkLCpHeDNhhNCKoKSnJSVNwQM9vepap9NzKmOlLQC0QzoGkKLy16nPq0T4eHvgnisKCLMxBlKK6gEYmxckqPeuA6iAUzvd4jCLQSHfupf1nLwWMkkMGviHHLesTqRMjldhM1hLpU1vUvGemq3gHhmb0Ve5lAcY2luKRJM5NCvSyEQha9lzsA1o39Z+zqvTZCJUByXiQiFdPJNyG0e39v0zevXRaMO/FxuHzbLhtAkfNpvk108eEPBOdV4zHKEvybYvGngzQ4/a1BzkFHW9nE7SZQ9ci5OOMiJPTyHtctCJIZeCLkDn0QCMWHKJiOtjYO6aIwx1rTJWibPdTL2mcBGH1qvNlN6zOXE9YNQZO0roDR03Baqv1P/BaSURA3CmjYwdRk3WancVSK6OuWMwYidYReCF1q9gLEg0Ek1xQSTzIOck4r9mWR3WRdxN1/HBYSJ22zVSn+75O/lRTsgfmGI16vgMVQOQOTQ8dF/bbw70/BL4nISOcwMWqjISxi97ErBGhKSBUo0LsT7zEj7eCFijRBS/bMwV4Ii7cK3DzrY0h8C+5Bt70dNIbOPmaqVTLReSMggCSt2/gaU755EKXvUoYjmRvLohVIElPSPy+VjIjmovZhhmRgBSJqkaFar0sLer4pEMjKZrSnu8AOi9gDIWiztt8d9xEKkTlGOrHXwDkmbkwSIi2XgcaRNkpqpsUmoDj6eI8uEGtEsLJsHsPbHib8Ta3VCyKQGJF5uDcLE2grWelWhEEf1HMmc/b6iaTSbF9l8Fv2R8okYQuIAaec4b5wQaPHf8jl/I6lG4Pp5QAjaXI0KEw/R+HJ7Ehptyxpbfg6/hXMDujQeiXiexv6vk+8dBzvEPi+Q09QkDVrtpX8k3YzLpnkmpFkL30xQrf0OnaovuzuonUwao5mMyYaQ8tol8x0qMbRqZ5/IN2vw2GdUE2hZB6m5FtSqD3j0cV/26jIRJi9tR4ko8WaUN7iS5ojYp5KkXlJWsOmWREPObVsnQs9jOZo3o6ZaH1Emmm3uG4fsYgMn+TM1gYQaV0RONl68OWbtXtaEZnjdJqntqrRh4SA4thQFtjBYHrsGzMfOsNPIprucUJJ3zmy6YZ86z4uAiwItRlolZ/O0AKSgKtlBsqYJen1OC0Z/zq66VdTRAb+IJlUCMNzqY3vVxmS/i245wAMzw1jU7mGoxdnys+akFnISy5EPW08RWuDFixcfjnJlIpS1K35OWpNuOUC/imUKqT2YRHhH6NWlLGqBXNR2cbn41IjUDrx75nZmoeLSu0oX0KNIQ+KOn2RhBaKwZ01FSJGRHC0ofmzBp7xX7LPUJDRI76uDQ6vF0c7R1AhH2cGaHPppQWfBwMcd8T3cTkJSWUik9uBUH6whYMLNGwIdMZlTPZCQBAYTV++bgo4FbOGizyYHdIgWIZHewgXULhOeaNZ24pNUVZH4UhEFVFX7vGmpHIhyAIcet0wEPCB9xdeRokoLH8kTyIESoWciOtQaoKoLeCGUJCRQNDANYpejUbn2vKFySGdA11n7Nwgepfc5ups3KOn7g7rU3Cn7u9VMYr1o5gunzm0GiWB2SIEXQfWby2bxkIjQoC7gikwUKfUDf4fngUtsfKiMG7peXQh5bhcV3yHaZ+6POo1hCmyRaEaGM/GNdYq+jyFkkyaRQ1w+SEgfz4069QO3K4JPKYcy2eRQmbjBKrImWVwuUtRxEcsdo4jR0K4yMeQxiop9PvPGU/vIcsRtNnsTXKqHQ3RpaBV57HAbwAF9daSXDjzq12Val5Q/Y2pB3mBxO8wfn/H3VkFAyJt93hwzgWeNXU3efOwUTGNn2CnwcueS6UimLrGjLd/n1MKifF9qL/42bApl4c/+edkXCiKIglocZTEFnykY0C5qlL5Gp6hA5LCt3xETHBDTmEwP2pipWuimJKqRjDVTfHBgRx08akQn4bzIk+weeVHvlpWUkXebrIVpFezrpUxeycTRT9qOwoWk6UgasrRT79Vl1IiAhWb2DWols7HuD9aW9OpSnj0IWXtGilgEuKTdij/zgeiEXlWKpo3JO++6mYQWRRaqrLEtlBln7lhhba1oNVR/AlkTwd9hGsVaNtHwJgdw1j5q0x0LNQ52cchRmqXqYxboDtGXjLUdnPWfC1EUIX0/l80rDXMkwTnKkM3u/CytReGIWM5fyFqZxoZB2ir2R6uoxdwrEcAcfaxIR9SG5TknEaYuRyuzyXCQ/OlEc0msndY5zSBpZth8yKZuJlpASj8TckJkbqfsG5X8ICmXidS4YmI5SO4T4gcZInlkTTm3LW9YmFxyn2sNULusZXPZTznr2inBcSDle4jsc8fpb7qtAbplJetI4XJOOq41awPZb1fGkoujWvzqaA6ZkrmfwRtebhtJXZPIYeHzyS7iguAgvph6w8WuBHqNLHzS5iVLB5vwObCGtYx8P5t7RSMLisFFqa3hEAPGKBPjvOHK0JsXnptBzU3WxPEdLV+Lvx1v5rSpmmUK+zHGuWMJiufCNHaGnQLnKnMAJOGmIzkEnc0aLu06yWfNCmtBWJBUwUteJd6xZkdikh1/lRK3srofBNGu9Kq4S+xVZUyYWxe4b3YYRPk4GtboRA0iYbrfTqdUzAkoIHbchgiQOjh0ygqDusBwu4+Zqg2OhqwQgxTYbMYLbFVnJ/rSB/SrQsjdIPnG9KoSVFQoVfr3QRLS5KL2jgiYrVqiAeVnz9alEMDCkUQfsi9eOy2WgRwoOa5r4cKBBUDsC/ar4wW1SO2oNUri+wOIZsijmZcwRw4GEOXExVG4xwAW0VSlqE82bVFwqBKpYa0fpVMqOBG0NuVwRn72bYpt70VbFEIylaUx1SkqdMsKW2aHpL+ZlPSrEp2UaoYoJUmuY/+1UluICcwHCTjgh3C/awLEWgYuJ2thAMh5m0ifi9mRnNRXB5fwmA/Bo1XW4oslfmouEWtE8szv5AhkrVVhsy/PN61ZafmAXp0FOY9x1vrILgM5B56QdMrEgIk+m/hQaM0vEOqc09EjBT7U2dQaNYhzNwnpzFfUQqwANLSwMRo6+8T1Uy5KPsKLcwzymHdcndSRnNfOI6cf4cTctcydTIDbZU4z4gB0UvAXO/unsAfZDOpTStj3NPue8VzxiFncI3gu8Dt5PeuHUtZaIG8SdB9oKsnkEUjt4IP8zcSV+1KsMeDAkmRZIZLNtphbCzaLOqm7EEwVuCR9pAhorF+a1z7npuQeYS239q9rpQh301A1YcTOsFNgv5waTX8UJnWAEraS5TwmIq1T2Pug9qgDL7ApaovTobA2AQ6UTgnQp9hH05nLiY8RSZBW6fcGbZQpIII1Re2yFqHGBKlV1skkSCmFRVo4k8mnTAk2S59NgRIwgLjCTQ9aDY3FIBTxsxBTP7RdDV5ieTH0cs5malPkiMusZeGUH1E4FulEiyigOf1IFMqlj4mJO0nj0PYVtva6yQRVgwLQDyVKX6NXt8AmsNguMaEopZW0VdTpfdkMpLWZ7NjdStG9lUrRElOkZEdu3mG3fS3n4E4PYlBI4VI6EEQCMUAhwRP87thOBXpVTGEz1BrI81spWrfla6DMGwLWMIojfe1RFpEMcDZ/FiujnVkM6gJbE6GHy8mRgXi8XKsImKlK0a50fI2RcoAosh1mqlbDPF64gNIRhlt9PNAbUuZCPoIsnRJALNgTYUhEgsApHRwWtXuYTP0YCWRAHQqQi9HcTH5Kz5pFCAlG6gM+PaPla1QuaqFFC+myVjT2b5AoaCaG/ZDT/hRFiOZ5ZFLMJEDOQoZKCeLU0WsuSGLkvDHk8Z83FKzxLBDQ4eS6iATbOcJM1UoBOtSYN60iyDwIcGgrDbL3IbtfkE6zk/35crRwJIg++b11ygoVB26Qgy97UatUEKqqQLccyAaOgHTCSzSlD1w0hxaO0GnVopGLm4lImMUUimbkKG8Ooh+Dk+MAWdtcJGIWkj+jdyHx0+RSUClCla7LZly9g2+S+nmCTv3DaxtvnEqnTr3w6aQeBzinzvwm7tWs0ZVi8NhDNvkX0h95TEnCeh5fqTyclsepehiM2Bl2Erwg846OkvZGkzo2kbDwA0jMOuz0Kv5pwcEXccHjCCxe+CXRr4J3mehpp1oRTj7mZ+uWFZa2t8eFPjgMiBdZPrORJKUKR5h1igpL2tMxoWrSat09vShFeqXnc0ESYR3URUoQCklh4V2MFGMCHKPECjGpiTlszuIU65NzxxFidvyWjwlJg3cYbg0AQJIbs8N129foB4/tgwJUxICRlq8xXcV8Y/1+mb5TY/ugjW5RYXG7l1IMOPTqSKz6dSGnK7STxqvla4wM9ZOJOC623ZQUmdBGHTyGW33M1mUiBJFIcbLkoiDMDGIqF/aJLHwNDNqSMLdKpxq0ixqzIaZ7iKQ0tlW3qDBbl+CUOj5l+e+UNahiARn7lpP0Drf6mBm0pc/jM7Ov1aD2mKlaGOvMxpx/SeNS+oBF7R5KF08x4QhHNvvPpKOq2kUdtbmhwHCrnwhh1sx2igquBEoXE2BXoQBVUWBrX6TSUSSqyYTrACzpTGN71Y7BRuoczkEdxBw4SIEbgyqZ7BNVaZxLSjE5LCFGM+ekz0jXM7HMAjhrXEof8wJ6l0854YjcxppADr4IKNLzKZGRmklvKifP3Tq1T1nUclQaj/squUGAcrl4M1S6AF9kba33HnwO7FAxQJkiM5nksf6HTZA6+Ij9/eJGBPJZDAKK7hKj7dkYWEU5MGtAvTgmWsBQOUDZD5iu2gDiusIn07SLWvxdy+S7ymQaDkIGhczwupKScUdLBpPe5rnW0f+T9WzK/CmEMI9XJO2yS6l9mETziGmQbd2n/Dt/nogpa5aZ0OaAlbgmOk+o6xglS+CEn/l5+txeTfTi3InRt94HUJ3z8ckmgU/DcAGGDCN2hp1C4evkpM5RpnVa+KKWyqfjcVj1L74qyVGffY94lxYXnuRDhJzLKIb2x3eKqaioozmPzQOUBY4HRHMx3plFIIfZ0EI/lFjc6mG0PYNBKNEuKtw3OyIL6qJ2D1P9DgjASKsn2qZZSlqsVA9e2FnTwQsVL8p89FLp67iTdjkDewHCIMQTJVq+Qj9EAjSoCwwVA1lY67Qos/nHIZowO60qmbcCZuoWiuDlSKdt/Y7kVINL2ocimmS1n9yS7nQkgEUVffQQ/RB51zzano1ENQxFwhjSsT4unmYxOehgpOyzJS6S3SIeWdZLjvSFIxSpjwYUzVqjrR629rtY3Inn+PYqLhckhxzBoV1UMQ9gUaNLA+nXCgEjRYUqeDl6jdsdYHNiTBrNuQiZuEz1O1jc7mGy38Widg+PW7QF982OxFxtIR/tta3fwYqhvhCixe0etva6KHzAUBnLwo7h3aLCzKAdBSLiqSXsKzZTt0Tr6CimemkVkdRJ4umSTaQejgjD5QAtX2O2bqHF2jxHmBp0RNvD2oxByodXOELb11jc7slZw95F7UnhAvrpFBUSEhw3G0woOTqcx6smvkDUBvIRThQgLguioU/zgDWE7L/HnmWFB7yrpT6s9XbqFAggtkOrCOjXXuYQnzcbEP1POddi6QParhbNMgv2bYMO2Ow9CAWcj5GUFJpmPe5rbbKMT4rzOQSfclWmU1EQ0oYjRXBTTAbdKSrQILoNELlI6Bww1pkRslMHn9apqAmM5nyP4aRxrpOWm8cbkyPJCwrerLJvHUlZ2S+3hhcSH3N/Zo1w9odMWvTACZNJXETiO5JZNwVs8CklXvqHE3u7BqmMG/dI2lgDSchJvaOSMZnYAwFJA1/45PeZNOqc+5O1eqIxDpEMFp6PF4suBbwxGbgiaqAfsdRa+DBiZ9gpHDQ8jUAO2/ttLB2axqJWD7+aXBqJliI7UZuHFGkaF3znUqg8svkFTvnWpMWAF95WWaF0AZ2yFhMbm5FYk8C5nWJ+sRJ1SmIZc705DBUD3DOzCDMhmnBG27NYNbIFFRUxipaAblGhSovd5KCTNG5Ja4G4JPK5j7yQ+eScLKaUJD0cIKSTAtBKvu3sUB9312lxLXL2+UAOqIv4e1pI2VTSq0r4FonZxCEFFLiA8c4MKopkpvQBfVeg42tsH3SiNsFXqEKB2bqM2oQ6mo+4zQtH6JHD9kEHraLGks4M+qHAWGcmJjuuCgCRiFZUYGbQUoKyEO1dFWJSaDlqCsBsVYo5ezaZz4CoSStcwPakTSRy2F5FzdpQMYgasKIfk0ynRMV3zy5ukJfh1kCc7KMzfIGWqzFc9CVCsF3E8zyHW32JlI7nUUayelBnO6aqDrb2uhikGLsiHe02ksrYSURciKcPWDE8BT7FYUt/CO0i3j/cHogGeagY4P7eSCIyKeVKESNdS2Q3AqR2HS77mK7aybwaNZQjrbjpqJEEoYtm4QoAEtksyiTIkfPDDbX6ovnO+dPy3GoljSqT29KHZOoDuuUAfGQTm3H5udF3MpFqzyefJHO9b55bzOZsziRZhagJ42AhDophDLUGknqDNzGErBl2qb5xU0Top/7ivJAhOCzq9DBc9DFU9gHEAAqt9QNUag3E5LcxmXHMlSgpgNLaxc+VCE1XxKCm4NFJx3MNFQNsG3TiyTPIRFwn6y5cgC+j6XRRq59IaDzrlE+NaftK6s7aLF5LkfqoLAJaFMdO9KXM7c5BFzGaPbcrr4+UiLi0hyfpS5fGVuXTNwNkrSp9EHeLgnJuxsITfOANKVRS8LRuUvYPZbcK55BOv0He9PocRMJBMvHc2IAi9ZHn9zn2Ha0bGxGDETvDTmJQF/EsVjhM9rsx+zzv4OfcKwmF0//NdAtOjheLfyftV5rgZVmJgJhNARI1RfNMIAcox2b2IQrwCM5hJAmJWZX+ZKZqoUjO+BunxzCcFth4zmuNdiINZdI0AClH3BBFEyCbRdIiJToMyj47TpmhGn4ziQQMQgHHp034vEMd9GMtilQnjvrk0P7os1MJKebzZZkU1ahSqgXgID/AcNHHtqqD8fYMhoo+7u+NoB8KDJcD9BNB7RYDeHIYKfvohUJ8kLYnn8EKLTG3Iwn8uMuOvoHddPbnZK+LXh0DNmaTZqrlagTv0HLRpMnCxLloouHI3sWt2ZizDg7jnRkJEFna2Y7tgw629ockxyALSlJChP0hq+DhfPYZKn1AC7WQu+jT6bF90MZopydBMlv7Q6jJYXG7F33WWn20iwocbNH2NSYHHYy1Z6ImLJQporlMgRU1FrV6qCgnuY3EMGrgChfQLSs4EBaVPdzXGwFRPHSdXBb8nSKac7upP4kcxtqRYLfLGr06+tF5UDpRJQpiFuBak9OrIwnN6SOiebnlQ5xHvsZwq48+lWLmZ9LIAlSbCwGgnTZY3bJKvog16qBM245PsIjzuB+KeCSer1FSTt2CNHZn61Z6Z41uMUi+Y+lkC/LJLzU9qy4AxP5v+Xg/+3sB2TwJOHSL6O/WCy10/KChrQo8kF0+Ko9LvKg1C0JXNm+MVqgx0uqnROBx1zbk+9EEn9wgvAsYKqPWukhryGxdopvmaB+lRFwHculMaifmyG5RYXoQk/OyxpL7IVDOH8d+j3XwCEXUvFehDTa/c4od1qryfOP24c1YWrJkLgHRUsKksHQBFTyc0mwyyfeOGms614HzbGp/vJhLEvPIZjMNCuRamdw+XHLiFM2kiz/7yZVGm+wNGUbsDDuF7YO2EJnZqpSIQaDpTM2mQZ+0WTW55PBdyCLjffZ9KdWiytqRXjJ/cZqG0gXUcGhzGhGKqTtGWlEwTw06KJzHUNnHlt4QuilnHZs+2unw7OjHE8tdIKCmUhbAynlUFJJ2okQvxIjT4VYfXR+1aW7QQa9KWkFfi9mlcNFUSJWTyLrCBSBFWLYRfbLaqFO+uHwMFyEKb9awIPngjHdmkuYqaiyXdqPGtF3UGCnjubQelBy8PaaqDiryOGzkAQDxBItl3SkU/WEMlwMMimgyXFT2sLI7GU+7AJ+kAEwNOhhK2qOhYgCHaC7sFgNMVR0MF4Oo0WvPiLP5bN3CSKuPbf2OEKsaAUs721PQRsD9vWF4Rxgu+0lb61JkXyT5U4OO+EnN1G0hV0XyfQSQNDAORCSRoTGrXDqFw+X0H9HEXGAQPPp1NMcf1N2OQTrpIxBQeKDjKmyrulg6/EDDD3Nq0EXlPVrJHDc56GKoiGbTmaTdZR8qSq4Gg+DTKRltIU2D5IQ/3pqJ/lkhJmSGj0ESneQrB+QIwMKTHC/HaTuyWdHBEedajJGYPH4KF7CI+ugnX89INgKqELVMcPF5VTpztSxifjk2l3pAzMNseq7hJK1L6aLPHtVljKoE+6e5pIWN2tPYLsmvy8Vx1a/zUWRwKXWIJ0xXsa/bPs75tq/R8lUiel5805hkEJBM23WKxI0nsrSKGkXgSO4YdDNIG8E6vTNq0lKgABzqtBCx20JZchRxbGmCk+O9OJhhdGgGG2eHUAziJmysNYupqhMJfnJfWFr20fLRytAKcS0JyXrAQT7s28pBQb007wfJj88h+l4WZS1J3btp7rV81ES3ywodX4m2i4/+A2JwUdS8ckqVuKzwBo5N0IUjtMv8DEr3sKmUqHniBUd0e08oKW4YQu3FNC4EMW0meBPu49EdsQw+wNXZLQAAKpf8o10AihRsRx4uaXdLlTpKb5wNEUbsDDsFnk6sQgdyni6eu4SYByxwGof0Rc5nlM2AAUPtaHrrJA0Q72r7SdhRMgc4kEx2JghBFp0Y9TnemUFNMU/eorKPxa1ZEa7dskLbV1E4J1+pTlpo+4GFvcrblszGfM7jdNUGFVF4Fi4mHwaifxrBYagcYLpqYXE5GyPpkhAbbc1gquoK0QCimaeX/KDG/AyWdKJGaEt/CAd1pyUlgHfx3MjZZFoufUjEaxAd+IkSCQLarsYAfGC2x/Y6kqyKPGbQRuEJsyEmRO4WAwyowO3bD0K/LpN5LmDI9zHaJtE6cd6wAIe7ZxfHIIxQoCKPzTOjGCoH6BYVhsoBavJY0plGSKbpjqtwX28Ei8oetg66OKizHXDA9qqDkaIXiS2c+FBxhCkopamAQ121onYvkdh2iJoj7se2z8S962NQyYAKTNet5PdZ4IH+EJa0p7GiM4W2r3DH9FKMd2fQdhVWdiYxU7WxcXYsaiAHhMWtWXSLCt2k+Wy5Gpv7o2i1ZjBcDjDemsbwcBx7wTn8auYgDBXxOgv/0gWMlTO4fWZZ9IUiwraqE/33Wj306lK01ayxG2vNYOtgKJIM8lhSTmOsNYN7eotQu0hWW0krWlGBRWUvnSoS2ysG8cRxN5x8IZ0boABF364QScp4ewYczTwLoJsCDtgHbbQ9m/3iXI12ETVhQ8UAk4MYodstBzI2WNvIhI39aDspp2RFMUWFK3OELJsi20x+Kea1nBx0xAWB/SXZVMymUfYp5A1h4QMW+1mUnjDWmo4a+BSxPR3aYubzCNmXLZGQkFYr7wjbBt3k11cKgdjaLzBSRo1zn0ohp0PFAB1foeUrLCp7MdAilJIOaHvVxqHDW6JbAKIfXj+U0CZmPifau5A2pNFEXrgg2s46RG11ry7jeckuku6hso+KCviqJabeTjJZV8ldoE5mU9byi58IUoCDo+jH5gK8y4ElrHXNGjqSTWoLNRzlYIeoncubDDmmMRHmmKPOoUDc+OSMB0haRyZ2yWc7+WByBHncfNQp4XJ8pqSicQGDnZRlCwlG7Aw7BdnFAeIM7R3BER9oHQMg2OEZiMSr5l2fD+KUz7nYmND161JMOhwxBkRTEKcuCRQd2uPfAbN1S/xTShfNgAe1p+AAPLF7N2bqFo4a2gRCjIxl010n7VLvmh5L/mq1CCfPkbNp0ZR0Fmmx5fMxYwLWKLjYAbkOXhZ57wiTgyERVoPg0fEx4hSJTIG1B6FIO+iAdhEw2e+iIicOzHywOZBNvYUnFBREC9lxUbPV8jW2DbqRDCZy2kuas1neAae+YWfoijym67YkI+ZcdlwPTvkwU7Wl7IEcKpToV9EU268jKVnU6idhFQnDSKsfn52iGrfXbXQo+q4xuWFyP1z2QSlXYMdX2FZ10K9LEdYAMFsN4YHBMNoppQ2b/hyixq/rB9EsRoQnLb4bd/cWY1vVwQP9pZgYmsSS1nbMVC3cNTuOmjwOGXoAW+phHNTZjuEikraDh7aipkjcjhy5D7OhRAeRqGzsjclcOGRoC6oQiWVOxVLjgcEIhss+Du5sRR08HqiHMQgFtlVddIsKHpE8L21PYZHvYbIewqKiBz4xc7joYza0cNSizehTmQW4q2RsTna6UQMZfCxfUWF71cFsaKHlK9zfH0kCPPq6jZQ9zNYlFpU9dNMcYjPyss4UeiGaCUdbPWwbdDFcDlAQYbiIrg0caarP5m25Omm/fDpWjZJJ0idtPgAH9OqWnFjAJ1HwRofXimWd7RgqBhjyffF364WoNfeOMFZGQs7Rq2w65bTXHV9hNrTipgbA0nK7rFsDipGtw2UfvUSyum4gmrTFxSzarkYgoOvjJtMR0EeBYR999ghAhSKRxzoG7MDj0O4DsuOtyGO2bqFTVNhax7nfbkXyOlL0ZdPiQGi5WlKjbB9ETXtFBUqKUbYcFLKoFSO0O76KG7m0nsR1MG52AvKpE5z01yczJq8dhQ9Cupp9FYNyuq4S3012keHIVX4m++fxelP66PfH2jcOtohreI1W4eElQU68VoUClMgrn9FNZU68zuMhEEnQVLuoJOE46gKlRcU2YMTOsFPgkH1CzBEWnVrjhGN/kOgDVKnUJYQyqeE5y/hMMgdxahDxu3DRBFGmFCKdROo45UdNDi1XA4g5raLfXRHTZYRIyLZVXdTB4Y7pJWj7GJXY8jXarsby7jYc1JqKCwqA8UXbxYTEixmbvRwIk1UX/VAm0uowU7dAdTuaHNmBuhxELYzLQQ3OU9qRRuf/mbqFtq8xXMaAgG5RoV1U6BYDbKs60YepGGBq0EmnRERtwvLWNEp0cdjw/fAgbK2GcFBrexRMPhKEwhF6ocSQH6DrK3R9hZu2HYLHdSZxUGs7fja9HPcOFmF5exum6za21R2sbG+DB2G46OM/Z8fRp0jOtvSHsKjsYbLqYklrGgCwuDWLg1oew0Uf9xUj6NcFDh3agoo87uqNoQ5dPK67Bb+eHUfpAibak9iMxVjRmUKfChzaeQArWtvQ9RX+Y9thqMhjtJzF9tAW38P7MRKJeUrd2in66PgKpc/m85m6BaJCAk4YkXhGbeXWQYktGEYVosbnbrcYvTqmeil8wGTVxbaqg6lBR9Jh3LJ9JQ7uTmJbIp+FI/xoalRI46+ml2IQCixtb0dFBSbrbkx9UUQi0XI17gmLMFPn1CpsHt86GBJCsaQ1jUO6D6AfSnTcAJ2iwsbeOO7uj0ZfwrpEp4h9uLEfyaMEC4Q4hpe0pjEIBQ5qT2EQCowUPUxSW8jOcNnHEe170Q8ljhy6N87ZRLzu74/EXIbUwjI3hXt7i7Cisw2lr7F1MIRFyGmBRluzGC1nsa3qYqw1LeOTc1RuHQwBiMJ4quomYhs1TN1igPFyBt121Kk4F0mMQ9QQ8vnFhSOMlD3ZQG0ZDCdyGbXag1BgyA8wUsRo9am6gy1hCDMhmqljAE48YaZV1BjykehM1R3ZMHFQAZ9u004afN68hJBNvry+sDWC56zka3SEZe0p3NtfJK4RfNLNaGsmpTwBxstpbAtx/jgHDPuepCUa8T0ULuAWmsBsaGGk6GNb1cVBnSkMF30M0jysO+ms1aTNjmZcYKw1k+4rsa3qJFIWtfClj/5+BSiZ/bPbSxV4rQ7JPzRIG7Z9hZYP4o4xnerWLeLmC4ibWtei5JMcxJrQ9pWcklG6HBTU9nFjFjXqjo/ZjXXzNUofx3pMws2m32h2r8lLUAwQv8PMpVVUKZrbiJ3GPk3sPvShD+Hyyy/Hpk2b8NSnPhUf+MAHcNJJJ+3tYhkUQpqk5PLxW+2C0PIDVK6AVyH/fG4lEzz2u+P8XUAidGlB4RMRCp8XD96ZVcGjn3bL/VCiWwxSKo4+CA69FK3JpmBeHHhX33Y1+hQ1V7+cOUgiQ9k8FB2xvfgCApCdecvFneVQMcBEZxKzoSWmJCAuaDHay6FASEEHkRwWiDnaZkIrLXQ58g8AFpU9PK79AEaKfozcTdqJFiJZHmvNoAoes9TCVN3BwZ2t6FMZfX8ccH9/JAYiFFHLUpHH4rKHY0c2Y5ZK/Lo/hicM3YunLvpPBPIYUEyhUiBgMnRx32AYxyzaiEW+h6FigHv7i7C0NY17ByMY9gMMFX10/QB39caxqJzFca5CL7SSsOnjqOG7ozD3AxwzsgktlwIbkkhp+Qp3Dxbj1pnl6IcWnjR0N7aHaHJbWm6X+h4zvBGc/HqQ+ovg0KdCBC6TgdIFzIQytT9hJrTR9hUqihrDXog54/pUYLpuwwFY0p7GWDmNe/uLsaQVff/Gyhlsr9u4p78YI2UPfSqwsj2JYd9H1YkaHib0BQJWtCfRcgEzoYWZOmou7xsswmgxg/FyBv1QYjq0YpRrkbWUNcW8bLOhhRIBs+SwpR7GYFCg6ysc0bkPI0UPs6GFySqaBFe0JmMQTN1FK6X5KBxhsu6CiujDOR3aqOExWQ9FcpM0cFFr1UZFRSQSSesyUvSwvY6a1NnQwqqhrdFvUpmICxAWl7PRpcAPIhnt3I/Z0MLWehjjZXQV6FGJmbqNRQUHj8QUIDXFYJheXUqQgoPDtrqF2bolGxKCw3DZw93TiyWlSKBIYKLmMpKpft0R8/6I72O8PQOf5hQAlJ1agqiWtKYTcfGSQ1KYTfoRzbAxX2SNvIFjEhG1edEU/OveUoyWM+j4ClNVBwFxc7O8vQ1tFzcFQ2ljNhPasZ/gsLUawqruVhAcPEVd2pZqCP1Q4AGMoCKP5e0p9EOBPsVxvGUwjMlqKBHfgAf6w6IZLVy0UixpTcf52d4ua0fHVyhdjbodtX3sKuFddPcYpP4IyezKvszRN08lYk/asl4oUbquaMpG0AcBGCn7KVAsR0LXwSfiGdHyOWl5KwUH9UOJ0tWS+47dd/ohEjsg1pED10oXMrmHQ7eIQUgDX2CmbmO47EcNvs+njxj2YWL3mc98BhdccAE++tGPYvXq1Xj/+9+PdevW4ZZbbsGKFSv2dvEMADhvkhyBhOivMd6aFs1C1KA5DBd92c2y/xz7UjnE3R7nbOuHEi1XR38kR2LqDeQwW5cxbUfSggxCPMYrLkAx0g9IR9/AY5COtyl9jdH2LLYP2inSkXen0aTbT1o8ncKEazlI5pKWrzBddVNdY50KF6JWgwgDKiT9RjzSKZJCn+rFCxy/j3NzcbCCB7CyO4mtg7igz4YWEKI5cTbE9mkNakwOhjDamoEDcEfVkQCDJa1pbE3CYLIaiu8JBQYo8IuZZaIBiKZDUgtokPcFcvh5Mmd1iwolagymC4yWs9g6GMJsiKa7B/rDGEq+VYMUeMKpSZh4DRcDiV7rp6SuY60ZTFZdDBcxAOSmbYchwCc/vxjwUSQ/I5eiDqfqmJtspOzHvH1JC9oPRfJtyrv5kbKHfihFM+CTiYs/O6jYLrntbtk+gUO6D+D+agSDUOK+agQ1eSwqegjkcczwRmwPHUzWQ+j6qCFholyRx696B6F0AVsHQyIMV7QmMVkP4Z50xi5jc38U463pmA/QBSxvTWHI9/HLmYMw7Psoi4Dhoo/RYhbToYVtoZt8MmdRprrOUiuRNY7fDFja2i6bneXtbWi5GtMhktca0bF8yEcfyh7F9CBDPpp1p+s2RssZLC5msb3uYCbEIBrWchEchn0fi4tZDPnoG7gd8XrHVzjITQGIrhX3DxYhIOaK3FoNoeMHMm9LV2NZayoRnQg2pXd8OskBsd8XDffS5imgR2zmjKbFSrTznJ7EoaICsxR9RduuxpZ6SN5Z+gBHhKnQRdtXAEUtVYGAWWqBCOj4KvreIee2K1xASTm9CBOgRWUPE61JFK4WU+7ScjrOezV22y4GMQyowPa6I4EIJYKk6JhobRUySqk+HnFN63Uj4e2FFmZScm4aclhU9NAPBUaSFq7tB5iquxjyfUzWQ+hTgTr1wQAxb2IcPzVADr0yHpVHBDm2Eci/ewTZWA35gYyzRWUvukb4rBXn33WiaL2ec2RzkYjZSJpTFXxMSwWOaI2raT+UGC1nMUh9XsGj7Sohx/F6wPa6g7aPAU7TdTtpXAsZF4aIfZbYvfe978XrXvc6vOY1rwEAfPSjH8XVV1+Nj3/84/jzP//zvVw6A4Mdqx3yAd8HdbajWwzQSkIvIJpRZup2Cl4oMFO1o28F4mLaSWYuAsQhOuhjvSS/Ufw5S6WkFuFIVudInPw5aWudfDwKFzBSRC1ElfJnDRfRIT6+P5JN9i+RHFJpkSoQUCDvcGP0ap3OjUznnSZtXzsloZXTOAAMFX1Z5IligAY7RjsH9NKZrj4RksXlLHxNkWyggHcB01UHy4opBHJYVPZA5KKPlo8Rqh0fUz90fYWZuhVJja/R8ZXs0LmPZkIrpSfgUz+iuatk38LUn1N1JC139xdLihggC1Y+xqqVnLRn6pbssPuhRJW0Af0QU5/06pwDrw7RD3KmLkWocOoVTmmS09/kwIoYwRp9/gahSEQconHYXrXhRTlDUr9yUIu2peViwuRbp1diuIh+Vkwo7/XxxIQ7Z5diuIxmv+k6amBm6paY2g/t3o8t1TAeGAyLeXFb3REtw3TVjqlYEMn/vYNFKF1MfTJS9tHxA4wUffQovvueajHu7C1FO/mGzaaAGiay3sWo0W4xwEwdzXaDZIpu+wq9Omodt1ZDovHuJzOVJB0mYNrnY+8GVMb8hIkc8Xj1yD5q/RQl3nb51IsBFZgO7ajpAWF5exsCOYwUPYwnssNtTxz8oFJUbAuFBOp0fCUm2/toBNOhjdIFTNdtFI4wVMQUI/cNRuTc1XbS8rHPaEVRYz4gj5k6+qMtbW+HB+GBwXC8j3JuQtZwx7FZiPaXNU98BOFM2nC2XMC2qoufpo0ER3Av72zDPb3F0W+NHBYnEhQ3qT7NJ48jhu6NfsTkMZr66IFqBEM+bnCGEznr+AE29cZQk4tuFG6Awsd3E2J+x5CI9fL2FKbrNg7ubIEHMFrMxo1O8veDI5SI59CWqNGjMq1fACccKVwc694F9ELUoi4uZyRlTE0O06EtFgZQNIW23QAtx0eNOfGb4/Mrov8yyTox5HsgeFkbChdQgtBLY6Lro19j1vbGzeUAHnzcW4GAB+ohjPg++lRgKnQQ4LCktQT394Z3oVTb/7FPErt+v4+bbroJF110kVzz3mPt2rXYsGHDDr/T6/XQ6/Xk761btwIAKgxy6KZhl8IRcGLnFkniyaHqxSAg9KM5daTuiWNzlSLIanIg50AF0iIaBc/AFRI4wfmTUKTdrNrd6f50BISUTgQh58GLJuJ8riUBWNzrYVHYEv1EAjCCHpYOpjEI/WgiSYu6D3nB4uzs7Nw+U7dijimqUVKdhHZc5HmHW1D2wSkoapOGaBDTMCTtEgchtKuUbJk8yhAwTH20qiGM9mYxHAosDi14iqRhe93GkmoaI/UQVvSnYlTrYDsW0yza1TYs6U2jqLZjGDEpby+UGCoGKEKAq6YxRCluLO3Y468papEqiWTuhRKt5ATdVzntBi5quhbVPYyGISzyPfQomneKOmDY9TBDHTkJpPQxo94wBpihFhCALgapDb1obAZUYNhFUyUAtCkKIR8ChusBJqtuJKiooinVETqo0IuHwuajmUDx+aGlaod8jjEFdClqo5gYxCCNqJ0oE7l0Ibb3gAr4iuCIsAgeI0UvasPq+Oxqm8Ni18MQbYUD0PYVfBWF2mJlzmPHdNZczNRttENK2UIlvKvRCwVavo9WGtctcliEnDKodFGjN1l10a2jlmsYPWyvuohiGhiQx+J6FkUVoypnQ4mliQQ6EEaKPgBge93GUNmLvnBlDwMEhCode1X20PUVtvdH0PUV6lDC9WcwCG3AVagC4R7PY6IHJB+tXoikvyocZmtgQICHQ4/P/kQ6vgwxlUkgjxCiv+xs8ICrMJsiVVsUtaNDNBsFuo+57UZDXzZpbRej0DmtD2+s+qHEorQZQz+aDAf9fvKhdZLmYzwlFGeXCSczHnIfwWFY9cEwNZOPA8BimkFZb4d3MZ1Mp65Eaw/w6TBtzAziRrAmjwecw4AG6NBkCswK2Fq30PJTIASMh0rmJwdULK5non9eij5uuRpDdQ8+tOCqWcyEEn3n0XIFukWNKrQxVPRkDWs5wvbgUCSyWbiBzP2oLfSoKcBjFls9J59OlhLMoItp2ZT3AQQfMJP8FjliP74nhauiwmxqrwIBU9EugSqNgx5SMmbEc5anQZhKo79AAK/2bCCPKWKAEQzQdhXaAFb6GMRzhPs1fkIr8WNanjtmAaJKcb9ED09o9klid++996Kua6xcubJxfeXKlfjpT3+6w++sX78el1566bzrX8M1u6WMBgAD4Ibn7+1CGAwGg+HAxi/2dgH2GLZt24axsbGHvGefJHaPBRdddBEuuOAC+XvLli04/PDDcccddzxsIyxETE5O4tBDD8Wdd96J0dHRvV2cPYoDue6A1d/qb/W3+lv9F1r9iQjbtm3DqlWrHvbefZLYLVu2DEVRYPPmzY3rmzdvxsTExA6/0+l00Ol05l0fGxtbUJ37aDE6OnrA1v9Arjtg9bf6W/2t/lb/hYRHqqTyD3/Lnke73cYJJ5yA66+/Xq6FEHD99ddjzZo1e7FkBoPBYDAYDPsu9kmNHQBccMEFOOuss3DiiSfipJNOwvvf/35s375domQNBoPBYDAYDE3ss8Tu9NNPxz333IN3vetd2LRpE572tKfh2muvnRdQ8WDodDq4+OKLd2iePRBwINf/QK47YPW3+lv9rf5W/wO1/gDg6JHEzhoMBoPBYDAY9nnskz52BoPBYDAYDIZHDyN2BoPBYDAYDAsERuwMBoPBYDAYFgiM2BkMBoPBYDAsECxIYvehD30IRxxxBLrdLlavXo1vf/vbe7tIjxrr16/HM5/5TCxevBgrVqzAS17yEtxyyy2Ne37rt34LzrnGvze84Q2Ne+644w6ceuqpGB4exooVK/D2t78dVVU17rnxxhvxjGc8A51OB0984hNxxRVX7O7qPSwuueSSeXU7+uij5fPZ2Vmcd955OOigg7Bo0SK87GUvm5fQen+tOwAcccQR8+rvnMN5550HYOH1/Ve+8hW8+MUvxqpVq+Ccw1VXXdX4nIjwrne9CwcffDCGhoawdu1a3HrrrY177r//fpx55pkYHR3F+Pg4zj77bExNTTXu+cEPfoDnPve56Ha7OPTQQ/Ge97xnXlmuvPJKHH300eh2uzj++ONxzTW7/1jCh6r/YDDAhRdeiOOPPx4jIyNYtWoVXvWqV+Guu+5qPGNHY+ayyy5r3LMv1v/h+v7Vr371vHqdfPLJjXsWat8D2OE64JzD5ZdfLvfsr30PPDJZtyfX+4XAH0ALDJ/+9Kep3W7Txz/+cfrRj35Er3vd62h8fJw2b968t4v2qLBu3Tr6xCc+QTfffDN973vfo9/93d+lww47jKampuSe5z//+fS6172ONm7cKP+2bt0qn1dVRccddxytXbuWvvvd79I111xDy5Yto4suukju+cUvfkHDw8N0wQUX0I9//GP6wAc+QEVR0LXXXrtH6zsXF198MR177LGNut1zzz3y+Rve8AY69NBD6frrr6f/+I//oGc961n0m7/5m/L5/lx3IqK77767UffrrruOANANN9xARAuv76+55hr6y7/8S/rc5z5HAOjzn/984/PLLruMxsbG6KqrrqLvf//79Pu///t05JFH0szMjNxz8skn01Of+lT65je/SV/96lfpiU98Ip1xxhny+datW2nlypV05pln0s0330yf+tSnaGhoiD72sY/JPV//+tepKAp6z3veQz/+8Y/pne98J7VaLfrhD3+41+q/ZcsWWrt2LX3mM5+hn/70p7RhwwY66aST6IQTTmg84/DDD6e//uu/bowJvV7sq/V/uL4/66yz6OSTT27U6/7772/cs1D7noga9d64cSN9/OMfJ+cc/fznP5d79te+J3pksm5PrfcLhT8sOGJ30kkn0XnnnSd/13VNq1atovXr1+/FUu087r77bgJAX/7yl+Xa85//fHrTm970oN+55ppryHtPmzZtkmsf+chHaHR0lHq9HhERveMd76Bjjz228b3TTz+d1q1bt2sr8Chx8cUX01Of+tQdfrZlyxZqtVp05ZVXyrWf/OQnBIA2bNhARPt33XeEN73pTfSEJzyBQghEtLD7fq5wCyHQxMQEXX755XJty5Yt1Ol06FOf+hQREf34xz8mAPSd73xH7vm3f/s3cs7Rf/7nfxIR0Yc//GFasmSJ1J+I6MILL6SjjjpK/n75y19Op556aqM8q1evpnPOOWeX1vGhsCPhPhff/va3CQD96le/kmuHH344ve9973vQ7+wP9X8wYnfaaac96HcOtL4/7bTT6Hd+53ca1xZC3zPmyro9ud4vFP6woEyx/X4fN910E9auXSvXvPdYu3YtNmzYsBdLtvPYunUrAGDp0qWN65/85CexbNkyHHfccbjoooswPT0tn23YsAHHH398I6nzunXrMDk5iR/96Edyj24vvmdfaK9bb70Vq1atwuMf/3iceeaZuOOOOwAAN910EwaDQaPcRx99NA477DAp9/5ed41+v4///b//N/7kT/4Ezjm5vpD7XuP222/Hpk2bGmUdGxvD6tWrG/09Pj6OE088Ue5Zu3YtvPf41re+Jfc873nPQ7vdlnvWrVuHW265BQ888IDcsz+0ydatW+Gcw/j4eOP6ZZddhoMOOghPf/rTcfnllzdMUftz/W+88UasWLECRx11FM4991zcd9998tmB1PebN2/G1VdfjbPPPnveZwul7+fKuj213i8k/rDPnjzxWHDvvfeirut5p1OsXLkSP/3pT/dSqXYeIQS8+c1vxrOf/Wwcd9xxcv2P/uiPcPjhh2PVqlX4wQ9+gAsvvBC33HILPve5zwEANm3atMO24M8e6p7JyUnMzMxgaGhod1btQbF69WpcccUVOOqoo7Bx40ZceumleO5zn4ubb74ZmzZtQrvdnifUVq5c+bD14s8e6p69Xfe5uOqqq7Blyxa8+tWvlmsLue/ngsu7o7LquqxYsaLxeVmWWLp0aeOeI488ct4z+LMlS5Y8aJvwM/YFzM7O4sILL8QZZ5zROOT8jW98I57xjGdg6dKl+MY3voGLLroIGzduxHvf+14A+2/9Tz75ZLz0pS/FkUceiZ///Of4i7/4C5xyyinYsGEDiqI4oPr+f/2v/4XFixfjpS99aeP6Qun7Hcm6PbXeP/DAAwuGPywoYrdQcd555+Hmm2/G1772tcb117/+9fL78ccfj4MPPhgveMEL8POf/xxPeMIT9nQxdylOOeUU+f0pT3kKVq9ejcMPPxyf/exn9xnCsafwP//n/8Qpp5yCVatWybWF3PeGB8dgMMDLX/5yEBE+8pGPND674IIL5PenPOUpaLfbOOecc7B+/fr9+nilV7ziFfL78ccfj6c85Sl4whOegBtvvBEveMEL9mLJ9jw+/vGP48wzz0S3221cXyh9/2CyzvDosKBMscuWLUNRFPOiZTZv3oyJiYm9VKqdw/nnn48vfOELuOGGG3DIIYc85L2rV68GANx2220AgImJiR22BX/2UPeMjo7uUwRqfHwcT37yk3HbbbdhYmIC/X4fW7Zsadyj+3mh1P1Xv/oVvvjFL+K1r33tQ963kPuey/tQ83piYgJ333134/OqqnD//ffvkjGxL6wfTOp+9atf4brrrmto63aE1atXo6oq/PKXvwSw/9ef8fjHPx7Lli1rjPWF3vcA8NWvfhW33HLLw64FwP7Z9w8m6/bUer+Q+MOCInbtdhsnnHACrr/+erkWQsD111+PNWvW7MWSPXoQEc4//3x8/vOfx5e+9KV5avQd4Xv/fzt3EwpdG8YB/H6fmDGTGMxgohH5CAtfRVOaDU1JkZUskAXFlslKykIWYiHJQiwsZGehiGYm+Sya4yOT0GhSSk0NI0T8n8XTnJ4TL72Lx2PO+//VbGauc9zXuab7vsJ9S5IQQgiz2SyEEMJqtYrDw0PFpBdeEAoKCuSY359XOOa7Pa+7uztxfn4uzGazKCsrE9HR0Ypxn5ycCL/fL49bLblPT0+L5ORkUVtb+2GcmmufmZkpUlNTFWO9vb0VOzs7inoHg0Gxt7cnxzidTvH6+io3vVarVaytrYnn52c5ZmVlReTl5YmEhAQ55js+k3BTd3p6KlZXV0VSUtKn10iSJH78+CH/mTKS8//d5eWlCAQCiu+6mmsfNjU1JcrKykRRUdGnsZFU+8/Wuq+a79XUP6huV+zc3By0Wi1mZmZwfHyMjo4OGAwGxW6ZSNDZ2Yn4+Hi43W7FFvb7+3sAwNnZGQYGBrC7uwufz4eFhQVkZWXBZrPJ9whvAbfb7ZAkCUtLSzCZTO9uAXc4HPB6vRgfH/8WR350d3fD7XbD5/NhY2MD1dXVMBqNuL6+BvBr+7vFYoHT6cTu7i6sViusVqt8fSTnHvby8gKLxYLe3l7F+2qsfSgUgsfjgcfjgRACIyMj8Hg88q7PoaEhGAwGLCws4ODgAPX19e8ed1JSUoKdnR2sr68jJydHceRFMBhESkoKmpubcXR0hLm5Oej1+jdHPkRFRWF4eBherxf9/f1fcuTDR/k/PT2hrq4O6enpkCRJMR+Ed/xtbm5idHQUkiTh/Pwcs7OzMJlMaGlp+fb5f5R7KBRCT08Ptra24PP5sLq6itLSUuTk5ODx8VG+h1prH3ZzcwO9Xo+JiYk310dy7YHP1zrg6+Z7tfQPqmvsAGBsbAwWiwUajQbl5eXY3t7+20P6z4QQ776mp6cBAH6/HzabDYmJidBqtcjOzobD4VCcZQYAFxcXqKmpgU6ng9FoRHd3N56fnxUxLpcLxcXF0Gg0yMrKkn/G39TY2Aiz2QyNRoO0tDQ0Njbi7OxM/vzh4QFdXV1ISEiAXq9HQ0MDrq6uFPeI1NzDlpeXIYTAycmJ4n011t7lcr37fW9tbQXw68iTvr4+pKSkQKvVoqqq6s1zCQQCaGpqQmxsLOLi4tDW1oZQKKSI2d/fR2VlJbRaLdLS0jA0NPRmLPPz88jNzYVGo0FhYSEWFxf/WN5hH+Xv8/n+dT4In2u4t7eHiooKxMfHIyYmBvn5+RgcHFQ0P981/49yv7+/h91uh8lkQnR0NDIyMtDe3v5moVVr7cMmJyeh0+kQDAbfXB/JtQc+X+uAr53v1dA//AMAf+iXgURERET0hVT1P3ZERERE/2ds7IiIiIhUgo0dERERkUqwsSMiIiJSCTZ2RERERCrBxo6IiIhIJdjYEREREakEGzsiIiIilWBjR0RERKQSbOyIiIiIVIKNHREREZFKsLEjIiIiUomf2n5FGOlDLGMAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "file_name = f'../data/{shot_number}_0.joblib'\n", + "print(file_name)\n", + "data = joblib.load(file_name)\n", + "for key, value in data.items():\n", + " print(key, value.shape)\n", + "plt.subplot(4, 1, 1)\n", + "plt.plot(data['time_ms'][0,0,:].T)\n", + "# plt.xlim(0, len(data['time_ms'][0,0,:]))\n", + "plt.title('time_ms')\n", + "plt.subplot(4, 1, 2)\n", + "plt.imshow(np.log(data['mhr'][4] + 1e-6), aspect='auto', origin='lower')\n", + "plt.title('mhrb4')\n", + "# plt.subplot(4, 1, 3)\n", + "# plt.imshow(np.log(data['co2'][0].T + 1e-6), aspect='auto', origin='lower')\n", + "# plt.title('co2r0')\n", + "# plt.subplot(4, 1, 4)\n", + "# plt.imshow(data['pin'][:,:,0], aspect='auto', origin='lower', interpolation='none')\n", + "# plt.title('pin')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "160156ad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "../data/180245_0.joblib\n", + "mhr (8, 1, 3634770)\n", + "ece (48, 1, 3634770)\n", + "co2 (4, 1, 3634770)\n", + "gas (5, 1, 3634770)\n", + "ech (11, 1, 3634770)\n", + "pin (8, 1, 3634770)\n", + "tin (8, 1, 3634770)\n", + "time_ms (1, 1, 3634770)\n" + ] + } + ], + "source": [ + "file_name = f'../data/{shot_number}_0.joblib'\n", + "print(file_name)\n", + "data = joblib.load(file_name)\n", + "for key, value in data.items():\n", + " print(key, value.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "246cd261", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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