From 9385b83b4e43e9bd1de4a7e8dedbb188498178fc Mon Sep 17 00:00:00 2001 From: mmaecki Date: Tue, 21 Nov 2023 12:59:53 +0100 Subject: [PATCH 01/10] data analysis --- {{cookiecutter.project_slug}}/EDA.ipynb | 173 ++++++++++++++++++ .../__pycache__/__init__.cpython-310.pyc | Bin 145 -> 0 bytes .../Data analysis/class_images/class_baby.png | Bin 0 -> 28964 bytes .../Data analysis/class_images/class_bowl.png | Bin 0 -> 29335 bytes .../class_images/class_house.png | Bin 0 -> 29373 bytes .../class_images/class_keyboard.png | Bin 0 -> 29438 bytes .../class_images/class_lobster.png | Bin 0 -> 30769 bytes .../class_images/class_motorcycle.png | Bin 0 -> 31344 bytes .../class_images/class_orchid.png | Bin 0 -> 28365 bytes .../Data analysis/class_images/class_ray.png | Bin 0 -> 27898 bytes .../class_images/class_skunk.png | Bin 0 -> 29622 bytes .../Data analysis/class_images/class_worm.png | Bin 0 -> 28662 bytes .../checkpoints/Data analysis/results.json | 1 + {{cookiecutter.project_slug}}/checks.py | 25 +++ {{cookiecutter.project_slug}}/dataset.py | 29 ++- .../lightning_logs/version_0/hparams.yaml | 1 + .../lightning_logs/version_1/hparams.yaml | 1 + .../lightning_logs/version_2/hparams.yaml | 1 + .../models/base_model.py | 5 - .../models/baselines.py | 103 +++++++++++ {{cookiecutter.project_slug}}/run.py | 32 +++- {{cookiecutter.project_slug}}/steps.py | 64 +++++++ 22 files changed, 418 insertions(+), 17 deletions(-) create mode 100644 {{cookiecutter.project_slug}}/EDA.ipynb delete mode 100644 {{cookiecutter.project_slug}}/__pycache__/__init__.cpython-310.pyc create mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_baby.png create mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_bowl.png create mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_house.png create mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_keyboard.png create mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_lobster.png create mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_motorcycle.png create mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_orchid.png create mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_ray.png create mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_skunk.png create mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_worm.png create mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/results.json create mode 100644 {{cookiecutter.project_slug}}/lightning_logs/version_0/hparams.yaml create mode 100644 {{cookiecutter.project_slug}}/lightning_logs/version_1/hparams.yaml create mode 100644 {{cookiecutter.project_slug}}/lightning_logs/version_2/hparams.yaml create mode 100644 {{cookiecutter.project_slug}}/models/baselines.py diff --git a/{{cookiecutter.project_slug}}/EDA.ipynb b/{{cookiecutter.project_slug}}/EDA.ipynb new file mode 100644 index 0000000..640872b --- /dev/null +++ b/{{cookiecutter.project_slug}}/EDA.ipynb @@ -0,0 +1,173 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from dataset import CifarDataModule\n", + "\n", + "from art.project import ArtProject\n", + "from art.checks import CheckResultExists, CheckScoreExists, CheckScoreLessThan, CheckScoreGreaterThan\n", + "from art.steps import EvaluateBaseline, OverfitOneBatch, Overfit#, TransferLearning\n", + "from torchmetrics import Accuracy, Precision, Recall\n", + "import torch.nn as nn\n", + "from lightning.pytorch.callbacks import EarlyStopping\n", + "\n", + "from dataset import CifarDataModule\n", + "from checks import CheckClassImagesExist, CheckLenClassNamesEqualToNumClasses\n", + "from steps import DataAnalysis" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Global seed set to 23\n" + ] + }, + { + "data": { + "text/plain": [ + "23" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "from lightning import seed_everything\n", + "seed_everything(23)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Proper data analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary: \n", + "Step: Data analysis, Model: , Passed: False. Results:\n", + "\n" + ] + } + ], + "source": [ + "project = ArtProject(\"Cifar100\", CifarDataModule(batch_size=32))\n", + "project.add_step(DataAnalysis(), [\n", + " CheckResultExists(\"number_of_classes\"),\n", + " CheckResultExists(\"class_names\"),\n", + " CheckResultExists(\"number_of_examples_in_each_class\"),\n", + " CheckResultExists(\"img_dimensions\"),\n", + " CheckClassImagesExist(),\n", + " CheckLenClassNamesEqualToNumClasses()])\n", + "project.run_all()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from torchmetrics import Accuracy\n", + "import torch.nn as nn\n", + "from art.steps import EvaluateBaseline\n", + "\n", + "NUM_CLASSES = project.get_step(0).get_latest_run()[\"number_of_classes\"]\n", + "accuracy_metric, ce_loss = Accuracy(task=\"multiclass\", num_classes = NUM_CLASSES), nn.CrossEntropyLoss()\n", + "project.register_metrics([accuracy_metric, ce_loss])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from art.metrics import SkippedMetric\n", + "from models.baselines import MlBaseline, HeuristicBaseline, AlreadyExistingSolutionBaseline\n", + "from art.checks import CheckScoreExists\n", + "# baselines = [HeuristicBaseline, MlBaseline, AlreadyExistingSolutionBaseline]\n", + "# for baseline in baselines:\n", + "# project.add_step(\n", + "# step = EvaluateBaseline(baseline), \n", + "# checks = [CheckScoreExists(metric=accuracy_metric)],\n", + "# skipped_metrics=[SkippedMetric(metric=ce_loss)]\n", + "# )\n", + "project.add_step(\n", + " step = EvaluateBaseline(MlBaseline), \n", + " checks = [CheckScoreExists(metric=accuracy_metric)],\n", + " skipped_metrics=[SkippedMetric(metric=ce_loss)]\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary: \n", + "Step: Data analysis, Model: , Passed: False. Results:\n", + "\n" + ] + } + ], + "source": [ + "project.run_all()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "art", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/{{cookiecutter.project_slug}}/__pycache__/__init__.cpython-310.pyc b/{{cookiecutter.project_slug}}/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 3f3f25abe16345f40498699d16033b8619ef541b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 145 zcmd1j<>g`kg7nN?DIoeWh(HF6K#l_t7qb9~6oz01O-8?!3`HPe1o6ux#VRHG47v}F&Mn* 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result["number_of_classes"]: + return ResultOfCheck( + is_positive=False, + error="Number of class names is different than number of classes", + ) + return ResultOfCheck(is_positive=True) \ No newline at end of file diff --git a/{{cookiecutter.project_slug}}/dataset.py b/{{cookiecutter.project_slug}}/dataset.py index c6575da..44763b9 100644 --- a/{{cookiecutter.project_slug}}/dataset.py +++ b/{{cookiecutter.project_slug}}/dataset.py @@ -1,14 +1,29 @@ -from lightning import LightningDataModule +import lightning as pl +from datasets import load_dataset from torch.utils.data import DataLoader +class CifarDataModule(pl.LightningDataModule): + def __init__(self, batch_size: int = 32): + super().__init__() + self.batch_size = batch_size + self.dataset = load_dataset("cifar100").with_format("torch") + self.dataset = self.dataset.rename_columns({"img": "input", "fine_label": "target"}) + self.dataset = self.dataset.remove_columns(["coarse_label"]) + # self.setup() -class MyDataModule(LightningDataModule): - def __init__(self): - super().__init__(...) - self.dataset = ... + def setup(self, stage: str): + self.train = self.dataset["train"] + self.test = self.dataset["test"] def train_dataloader(self): - return DataLoader(...) + return DataLoader(self.dataset["train"], batch_size=self.batch_size) def val_dataloader(self): - return DataLoader(...) + return DataLoader(self.dataset["test"], batch_size=self.batch_size) + + def log_params(self): + return { + "batch_size": self.batch_size, + "train_samples": len(self.dataset["train"]), + "val_samples": len(self.dataset["test"]), + } \ No newline at end of file diff --git a/{{cookiecutter.project_slug}}/lightning_logs/version_0/hparams.yaml b/{{cookiecutter.project_slug}}/lightning_logs/version_0/hparams.yaml new file mode 100644 index 0000000..0967ef4 --- /dev/null +++ b/{{cookiecutter.project_slug}}/lightning_logs/version_0/hparams.yaml @@ -0,0 +1 @@ +{} diff --git a/{{cookiecutter.project_slug}}/lightning_logs/version_1/hparams.yaml b/{{cookiecutter.project_slug}}/lightning_logs/version_1/hparams.yaml new file mode 100644 index 0000000..0967ef4 --- /dev/null +++ b/{{cookiecutter.project_slug}}/lightning_logs/version_1/hparams.yaml @@ -0,0 +1 @@ +{} diff --git a/{{cookiecutter.project_slug}}/lightning_logs/version_2/hparams.yaml b/{{cookiecutter.project_slug}}/lightning_logs/version_2/hparams.yaml new file mode 100644 index 0000000..0967ef4 --- /dev/null +++ b/{{cookiecutter.project_slug}}/lightning_logs/version_2/hparams.yaml @@ -0,0 +1 @@ +{} diff --git a/{{cookiecutter.project_slug}}/models/base_model.py b/{{cookiecutter.project_slug}}/models/base_model.py index 169eaa9..e69de29 100644 --- a/{{cookiecutter.project_slug}}/models/base_model.py +++ b/{{cookiecutter.project_slug}}/models/base_model.py @@ -1,5 +0,0 @@ -from art.core.base_components import ArtModule - - -class Model(ArtModule): - ... diff --git a/{{cookiecutter.project_slug}}/models/baselines.py b/{{cookiecutter.project_slug}}/models/baselines.py new file mode 100644 index 0000000..724f622 --- /dev/null +++ b/{{cookiecutter.project_slug}}/models/baselines.py @@ -0,0 +1,103 @@ +from typing import Dict + +import numpy as np +from einops import rearrange +from sklearn.linear_model import LogisticRegression + +from art.core import ArtModule +from art.utils.enums import BATCH, INPUT, PREDICTION, TARGET + + +class MlBaseline(ArtModule): + name = "ML Baseline" + + def __init__(self, model=LogisticRegression()): + super().__init__() + self.model = model + + def ml_parse_data(self, data): + X = [] + y = [] + for batch in data["dataloader"]: + X.append(batch[INPUT].flatten(start_dim=1).numpy() / 255) + y.append(batch[TARGET].numpy()) + + return {INPUT: np.concatenate(X), TARGET: np.concatenate(y)} + + def baseline_train(self, data): + self.model = self.model.fit(data[INPUT], data[TARGET]) + return {"model": self.model} + + def parse_data(self, data): + """This is first step of your pipeline it always has batch keys inside""" + batch = data[BATCH] + return { + INPUT: batch[INPUT].flatten(start_dim=1).numpy(), + TARGET: batch[TARGET].numpy(), + } + + def predict(self, data): + return {PREDICTION: self.model.predict(data[INPUT]), TARGET: data[TARGET]} + + def log_params(self): + return {"model": self.model.__class__.__name__} + + +class HeuristicBaseline(ArtModule): + name = "Heuristic Baseline" + n_classes = 100 + img_shape = (28, 28) + + def __init__(self): + super().__init__() + + def parse_data(self, data): + """This is first step of your pipeline it always has batch keys inside""" + batch = data[BATCH] + return { + INPUT: batch[INPUT].flatten(start_dim=1).numpy(), + TARGET: batch[TARGET].numpy(), + } + + def baseline_train(self, data): + self.prototypes = np.zeros( + (self.n_classes, self.img_shape[0] * self.img_shape[1]) + ) + self.counts = np.zeros(self.n_classes) + for batch in data["dataloader"]: + for img, label in zip(batch[INPUT], batch[TARGET]): + self.prototypes[label.item()] += img.flatten().numpy() / 255 + self.counts[label.item()] += 1 + + self.prototypes = self.prototypes / self.counts[:, None] + + def predict(self, data): + y_hat = np.argmax((data[INPUT] @ self.prototypes.T), axis=1) + return {PREDICTION: y_hat, TARGET: data[TARGET]} + + def log_params(self): + return {"model": "Heuristic"} + + +class AlreadyExistingSolutionBaseline(ArtModule): + name = "Already Existing Solution Baseline" + + def __init__(self): + from transformers import ResNetForImageClassification + + super().__init__() + self.model = ResNetForImageClassification.from_pretrained( + "sebchw/MNIST_Existing_Baseline" + ) + + def parse_data(self, data): + X = rearrange(data[BATCH][INPUT], "b h w -> b 1 h w").float() + X = (X / 255 - 0.45) / 0.22 + return {INPUT: X, TARGET: data[BATCH][TARGET]} + + def predict(self, data: Dict): + preds = self.model(data[INPUT]).logits.detach().numpy() + return {PREDICTION: preds, TARGET: data[TARGET]} + + def log_params(self): + return {"model": self.model.__class__.__name__} \ No newline at end of file diff --git a/{{cookiecutter.project_slug}}/run.py b/{{cookiecutter.project_slug}}/run.py index a3d9755..746974d 100644 --- a/{{cookiecutter.project_slug}}/run.py +++ b/{{cookiecutter.project_slug}}/run.py @@ -1,14 +1,36 @@ from .models.base_model import Model from .dataset import MyDataModule -from art.experiment.Experiment import ArtProject +from dataset import CifarDataModule + +from art.project import ArtProject +from art.checks import CheckResultExists, CheckScoreExists, CheckScoreLessThan, CheckScoreGreaterThan +from art.steps import EvaluateBaseline, OverfitOneBatch, Overfit, TransferLearning +from torchmetrics import Accuracy, Precision, Recall +import torch.nn as nn +from lightning.pytorch.callbacks import EarlyStopping + + +from lightning import seed_everything +seed_everything(23) + +from dataset import CifarDataModule + +from steps import DataAnalysis def main(): - data_module = MyDataModule() - model = Model() - project = ArtProject("{{cookiecutter.project_slug}}", data_module) - project.add_step(...) + project = ArtProject("Cifar100", CifarDataModule(batch_size=32)) + + # Data Analysis + project.add_step(DataAnalysis(), [ + CheckResultExists("number_of_classes"), + CheckResultExists("class_names"), + CheckResultExists("number_of_examples_in_each_class"), + CheckResultExists("img_dimensions")]) + + # Baseline + project.run_all() diff --git a/{{cookiecutter.project_slug}}/steps.py b/{{cookiecutter.project_slug}}/steps.py index e69de29..84c4eaf 100644 --- a/{{cookiecutter.project_slug}}/steps.py +++ b/{{cookiecutter.project_slug}}/steps.py @@ -0,0 +1,64 @@ +from collections import Counter +import numpy as np +import matplotlib.pyplot as plt +from art.utils.savers import MatplotLibSaver +from art.steps import ExploreData +from random import sample +from art.utils.enums import BATCH, INPUT, PREDICTION, TARGET + +class DataAnalysis(ExploreData): + def do(self, previous_states): + targets = [] + index2label = lambda x: self.datamodule.dataset["train"].features[TARGET].int2str(x) + label2index = lambda x: self.datamodule.dataset["train"].features[TARGET].str2int(x) + # Loop through batches in the cifar_datamodule train dataloader + for batch in self.datamodule.train_dataloader(): + targets.extend(batch[TARGET]) + targets = [index2label(int(x)) for x in targets] + # Calculate the number of unique classes in the targets + number_of_classes = len(np.unique(targets)) + # Now tell me what are the names of these classes + class_names = list(self.datamodule.dataset["train"].features[TARGET].names) + + class_counts = Counter(targets) + + # Now calculate number of images in each class + number_of_examples_in_each_class = [ + class_counts[i] for i in range(number_of_classes) + ] + + # Now tell me dimensions of each image + img_dimensions = self.datamodule.train_dataloader().dataset[0][INPUT].shape + + # Loop through classes and visualize 5 examples for 5 randomly selected classes (out of 100) + for cls in sample(class_names, 5): + class_indices = [i for i, label in enumerate(targets) if label == cls] + class_samples = np.random.choice(class_indices, 5, replace=False).tolist() + + fig, axes = plt.subplots(1, 5, figsize=(15, 5)) + for i, sample_idx in enumerate(class_samples): + img = ( + self.datamodule.train_dataloader() + .dataset[sample_idx][INPUT] + ) + axes[i].imshow(img, cmap="gray") + axes[i].set_title(f"Class: {cls}") + axes[i].axis("off") + + MatplotLibSaver().save( + fig, self.get_full_step_name(), self.get_class_image_path(cls) + ) + + self.results.update( + { + "number_of_classes": number_of_classes, + "class_names": class_names, + "number_of_examples_in_each_class": number_of_examples_in_each_class, + "img_dimensions": img_dimensions, + } + ) + def log_params(self): + return {} + + def get_class_image_path(self, class_name: str): + return f"class_images/class_{class_name}.png" \ No newline at end of file From 8b63fbe9200a006dfb3ab509050fabb631ae351e Mon Sep 17 00:00:00 2001 From: mmaecki Date: Fri, 24 Nov 2023 14:20:14 +0100 Subject: [PATCH 02/10] loss on init --- {{cookiecutter.project_slug}}/.gitignore | 5 + {{cookiecutter.project_slug}}/EDA.ipynb | 162 ++++++++++++++++-- .../Data analysis/class_images/class_baby.png | Bin 28964 -> 0 bytes .../Data analysis/class_images/class_bowl.png | Bin 29335 -> 0 bytes .../class_images/class_house.png | Bin 29373 -> 0 bytes .../class_images/class_keyboard.png | Bin 29438 -> 0 bytes .../class_images/class_lobster.png | Bin 30769 -> 0 bytes .../class_images/class_motorcycle.png | Bin 31344 -> 0 bytes .../class_images/class_orchid.png | Bin 28365 -> 0 bytes .../Data analysis/class_images/class_ray.png | Bin 27898 -> 0 bytes .../class_images/class_skunk.png | Bin 29622 -> 0 bytes .../Data analysis/class_images/class_worm.png | Bin 28662 -> 0 bytes .../checkpoints/Data analysis/results.json | 1 - {{cookiecutter.project_slug}}/dataset.py | 1 - .../models/ResNet.py | 63 +++++++ .../models/baselines.py | 26 +-- {{cookiecutter.project_slug}}/steps.py | 2 +- 17 files changed, 227 insertions(+), 33 deletions(-) delete mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_baby.png delete mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_bowl.png delete mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_house.png delete mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_keyboard.png delete mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_lobster.png delete mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_motorcycle.png delete mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_orchid.png delete mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_ray.png delete mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_skunk.png delete mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_worm.png delete mode 100644 {{cookiecutter.project_slug}}/checkpoints/Data analysis/results.json create mode 100644 {{cookiecutter.project_slug}}/models/ResNet.py diff --git a/{{cookiecutter.project_slug}}/.gitignore b/{{cookiecutter.project_slug}}/.gitignore index b25b609..d6486bf 100644 --- a/{{cookiecutter.project_slug}}/.gitignore +++ b/{{cookiecutter.project_slug}}/.gitignore @@ -100,3 +100,8 @@ instance/ *.bak *.tmp # TODO: Add more patterns based on your specific needs + +*.png +art_checkpoints/ +lightning_logs/ + diff --git a/{{cookiecutter.project_slug}}/EDA.ipynb b/{{cookiecutter.project_slug}}/EDA.ipynb index 640872b..eb9b889 100644 --- a/{{cookiecutter.project_slug}}/EDA.ipynb +++ b/{{cookiecutter.project_slug}}/EDA.ipynb @@ -17,7 +17,8 @@ "\n", "from dataset import CifarDataModule\n", "from checks import CheckClassImagesExist, CheckLenClassNamesEqualToNumClasses\n", - "from steps import DataAnalysis" + "from steps import DataAnalysis\n", + "import math" ] }, { @@ -67,7 +68,7 @@ "output_type": "stream", "text": [ "Summary: \n", - "Step: Data analysis, Model: , Passed: False. Results:\n", + "Step: Data analysis, Model: , Passed: True. Results:\n", "\n" ] } @@ -106,20 +107,15 @@ "outputs": [], "source": [ "from art.metrics import SkippedMetric\n", - "from models.baselines import MlBaseline, HeuristicBaseline, AlreadyExistingSolutionBaseline\n", + "from models.baselines import MlBaseline, HeuristicBaseline, AlreadyExistingResNet20Baseline\n", "from art.checks import CheckScoreExists\n", - "# baselines = [HeuristicBaseline, MlBaseline, AlreadyExistingSolutionBaseline]\n", - "# for baseline in baselines:\n", - "# project.add_step(\n", - "# step = EvaluateBaseline(baseline), \n", - "# checks = [CheckScoreExists(metric=accuracy_metric)],\n", - "# skipped_metrics=[SkippedMetric(metric=ce_loss)]\n", - "# )\n", - "project.add_step(\n", - " step = EvaluateBaseline(MlBaseline), \n", - " checks = [CheckScoreExists(metric=accuracy_metric)],\n", - " skipped_metrics=[SkippedMetric(metric=ce_loss)]\n", - ")\n" + "baselines = [HeuristicBaseline, MlBaseline, AlreadyExistingResNet20Baseline]\n", + "for baseline in baselines:\n", + " project.add_step(\n", + " step = EvaluateBaseline(baseline), \n", + " checks = [CheckScoreExists(metric=accuracy_metric)],\n", + " skipped_metrics=[SkippedMetric(metric=ce_loss)]\n", + " )\n" ] }, { @@ -132,12 +128,144 @@ "output_type": "stream", "text": [ "Summary: \n", - "Step: Data analysis, Model: , Passed: False. Results:\n", - "\n" + "Step: Data analysis, Model: , Passed: True. Results:\n", + "\n", + "Step: Evaluate Baseline, Model: HeuristicBaseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.012299999594688416\n", + "Step: Evaluate Baseline, Model: MlBaseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.15649999678134918\n", + "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.6883000135421753\n" + ] + } + ], + "source": [ + "project.run_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.605170185988091\n" ] } ], "source": [ + "from models.ResNet import ResNet18\n", + "from art.steps import CheckLossOnInit\n", + "from art.checks import CheckScoreCloseTo\n", + "from torch.cuda import is_available\n", + "project.to(\"cuda\" if is_available() else \"cpu\")\n", + "\n", + "EXPECTED_LOSS = -math.log(1/NUM_CLASSES)\n", + "print(EXPECTED_LOSS)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating loss on init\n", + "Validating model ResNet18\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using cache found in C:\\Users\\matri/.cache\\torch\\hub\\facebookresearch_semi-supervised-ImageNet1K-models_master\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c6e91d2fd11340b489221baf6d606871", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃       Validate metric       ┃        DataLoader 0         ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+       "│  CrossEntropyLoss-validate  │      5.054237365722656      │\n",
+       "│ MulticlassAccuracy-validate │    0.008419999852776527     │\n",
+       "└─────────────────────────────┴─────────────────────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1m Validate metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", + "│\u001b[36m \u001b[0m\u001b[36m CrossEntropyLoss-validate \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 5.054237365722656 \u001b[0m\u001b[35m \u001b[0m│\n", + "│\u001b[36m \u001b[0m\u001b[36mMulticlassAccuracy-validate\u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.008419999852776527 \u001b[0m\u001b[35m \u001b[0m│\n", + "└─────────────────────────────┴─────────────────────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary: \n", + "Step: Data analysis, Model: , Passed: True. Results:\n", + "\n", + "Step: Evaluate Baseline, Model: HeuristicBaseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.012299999594688416\n", + "Step: Evaluate Baseline, Model: MlBaseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.15649999678134918\n", + "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.6883000135421753\n", + "Step: Check Loss On Init, Model: ResNet18, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.008419999852776527\n", + "\tCrossEntropyLoss-validate: 5.054237365722656\n", + "Step: Check Loss On Init, Model: ResNet18, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.008419999852776527\n", + "\tCrossEntropyLoss-validate: 5.054237365722656\n", + "Step: Check Loss On Init, Model: ResNet18, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.008419999852776527\n", + "\tCrossEntropyLoss-validate: 5.054237365722656\n" + ] + } + ], + "source": [ + "project.add_step(\n", + " CheckLossOnInit(ResNet18),\n", + " [CheckScoreCloseTo(metric=ce_loss,\n", + " value=EXPECTED_LOSS, rel_tol=0.1)]\n", + " )\n", + "\n", "project.run_all()" ] }, diff --git a/{{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_baby.png b/{{cookiecutter.project_slug}}/checkpoints/Data analysis/class_images/class_baby.png deleted file mode 100644 index d2027e31e8c636bd361ef4c8240449f4906d3e79..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 28964 zcmeFZX;f3`wl?Z6ORXxqsG47v}F&Mn* z)>`ko=KIV!pJ&e8J?G)J_M2_rEL*m0?Wv!SUs$$m#rU#if1CemCHRWPZTWuipF`{k zzgRB}IyV0D4b-x;mt(JA!^B<-|7Cj|>PAdBCJJ`I{=mWg+pomNUXO9KwvPOd4;;YU z2(z}WZKwhd^7Zwf5i!e_t-rkV_qS*8^6+KLu0K3={OHA-1yVikIVv8{*AuwUg>4*X z<+#{~c5>&zFOfSQ{7(LJAM%DZJ9X6`8y*DR-J3Gl8(P5pC9vij@7ngsb4}k*{$A&` 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zpVP&BUIbfQeXc14*~^z_ebMcAo3XEUF{byJ;%vbJu}W9f`Tf%$*NQolGqpcj<98Jt z4C{Tr{D;Pw6G$R)s8tvM>3vU2S(xd2GFh3%c|`a1oZP>EGHEZy0AE1MT375>MR_rT zcETOa!%7Arm2obg8tRVQ!bJdWv)Q`1yx(NL;4Rv86!L}qW&nK3(asBT1LmTeyn%t& zG(NGnLNNW1k%b))YazH%AeHKyt#W&fDZx*AXD5q>?Yw`A=CAdRdpHmJ`}Jg&XGzEq7P*-xWvyl(dHu^67I_l2+hX?GOj^kLc+!BD=9^JGpBL%==KYr ziL#E`pEz+M-rmkmZ~O0;`hJtu;~&}ei$eZB2>D<2R{f@utAG9R2Os<@9`s)i>R%7) p=Y95HUkf~j|6d-$KP* b c h w") + X = self.preprocess(X) + + return {INPUT: X, TARGET: data[BATCH][TARGET]} + + + + def predict(self, data: Dict): + preds = self.model(data[INPUT]).detach().numpy() + # perhaps softmax will be needed + return {PREDICTION: preds, TARGET: data[TARGET]} + + def compute_loss(self, data): + # Notice that the loss calculation is done in MetricsCalculator! + # We only need to specify which loss (metric) we want to use + loss = data["CrossEntropyLoss"] + return {LOSS: loss} + + def configure_optimizers(self): + return torch.optim.Adam(self.parameters(), lr=self.lr) + + def log_params(self): + # Log relevant parameters + return { + "lr": self.lr, + "model_name": self.model.__class__.__name__, + "n_parameters": sum(p.numel() for p in self.parameters() if p.requires_grad), + } \ No newline at end of file diff --git a/{{cookiecutter.project_slug}}/models/baselines.py b/{{cookiecutter.project_slug}}/models/baselines.py index 724f622..8f94c8a 100644 --- a/{{cookiecutter.project_slug}}/models/baselines.py +++ b/{{cookiecutter.project_slug}}/models/baselines.py @@ -46,7 +46,7 @@ def log_params(self): class HeuristicBaseline(ArtModule): name = "Heuristic Baseline" n_classes = 100 - img_shape = (28, 28) + img_shape = (32, 32, 3) def __init__(self): super().__init__() @@ -61,7 +61,7 @@ def parse_data(self, data): def baseline_train(self, data): self.prototypes = np.zeros( - (self.n_classes, self.img_shape[0] * self.img_shape[1]) + (self.n_classes, self.img_shape[0] * self.img_shape[1] * self.img_shape[2]) ) self.counts = np.zeros(self.n_classes) for batch in data["dataloader"]: @@ -79,24 +79,24 @@ def log_params(self): return {"model": "Heuristic"} -class AlreadyExistingSolutionBaseline(ArtModule): - name = "Already Existing Solution Baseline" - +class AlreadyExistingResNet20Baseline(ArtModule): + name = "Already Existing ResNet20 Baseline" + def __init__(self): - from transformers import ResNetForImageClassification - super().__init__() - self.model = ResNetForImageClassification.from_pretrained( - "sebchw/MNIST_Existing_Baseline" - ) + import torch + self.model = torch.hub.load("chenyaofo/pytorch-cifar-models", "cifar100_resnet20", pretrained=True) def parse_data(self, data): - X = rearrange(data[BATCH][INPUT], "b h w -> b 1 h w").float() - X = (X / 255 - 0.45) / 0.22 + mean = np.asarray([0.5071, 0.4867, 0.4408], dtype=np.float32) + std = np.asarray([0.2675, 0.2565, 0.2761], dtype=np.float32) + X = data[BATCH][INPUT] + X = (X / 255 - mean) / std + X = rearrange(X, "b h w c -> b c h w") return {INPUT: X, TARGET: data[BATCH][TARGET]} def predict(self, data: Dict): - preds = self.model(data[INPUT]).logits.detach().numpy() + preds = self.model(data[INPUT]).detach().numpy() return {PREDICTION: preds, TARGET: data[TARGET]} def log_params(self): diff --git a/{{cookiecutter.project_slug}}/steps.py b/{{cookiecutter.project_slug}}/steps.py index 84c4eaf..c945581 100644 --- a/{{cookiecutter.project_slug}}/steps.py +++ b/{{cookiecutter.project_slug}}/steps.py @@ -31,7 +31,7 @@ def do(self, previous_states): img_dimensions = self.datamodule.train_dataloader().dataset[0][INPUT].shape # Loop through classes and visualize 5 examples for 5 randomly selected classes (out of 100) - for cls in sample(class_names, 5): + for cls in class_names: class_indices = [i for i, label in enumerate(targets) if label == cls] class_samples = np.random.choice(class_indices, 5, replace=False).tolist() From a5f845a9ff1894cb84e74861a18aec3247467456 Mon Sep 17 00:00:00 2001 From: mmaecki Date: Fri, 24 Nov 2023 16:37:45 +0100 Subject: [PATCH 03/10] cuda fix --- {{cookiecutter.project_slug}}/EDA.ipynb | 170 ++---------------- .../{dataset.py => MyDataset.py} | 0 .../models/ResNet.py | 8 +- 3 files changed, 19 insertions(+), 159 deletions(-) rename {{cookiecutter.project_slug}}/{dataset.py => MyDataset.py} (100%) diff --git a/{{cookiecutter.project_slug}}/EDA.ipynb b/{{cookiecutter.project_slug}}/EDA.ipynb index eb9b889..1d12651 100644 --- a/{{cookiecutter.project_slug}}/EDA.ipynb +++ b/{{cookiecutter.project_slug}}/EDA.ipynb @@ -2,11 +2,11 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "from dataset import CifarDataModule\n", + "from MyDataset import CifarDataModule\n", "\n", "from art.project import ArtProject\n", "from art.checks import CheckResultExists, CheckScoreExists, CheckScoreLessThan, CheckScoreGreaterThan\n", @@ -15,7 +15,7 @@ "import torch.nn as nn\n", "from lightning.pytorch.callbacks import EarlyStopping\n", "\n", - "from dataset import CifarDataModule\n", + "from MyDataset import CifarDataModule\n", "from checks import CheckClassImagesExist, CheckLenClassNamesEqualToNumClasses\n", "from steps import DataAnalysis\n", "import math" @@ -23,27 +23,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Global seed set to 23\n" - ] - }, - { - "data": { - "text/plain": [ - "23" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -60,19 +42,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Summary: \n", - "Step: Data analysis, Model: , Passed: True. Results:\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "project = ArtProject(\"Cifar100\", CifarDataModule(batch_size=32))\n", "project.add_step(DataAnalysis(), [\n", @@ -87,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -102,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -120,48 +92,23 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Summary: \n", - "Step: Data analysis, Model: , Passed: True. Results:\n", - "\n", - "Step: Evaluate Baseline, Model: HeuristicBaseline, Passed: True. Results:\n", - "\tMulticlassAccuracy-validate: 0.012299999594688416\n", - "Step: Evaluate Baseline, Model: MlBaseline, Passed: True. Results:\n", - "\tMulticlassAccuracy-validate: 0.15649999678134918\n", - "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", - "\tMulticlassAccuracy-validate: 0.6883000135421753\n" - ] - } - ], + "outputs": [], "source": [ "project.run_all()" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4.605170185988091\n" - ] - } - ], + "outputs": [], "source": [ "from models.ResNet import ResNet18\n", "from art.steps import CheckLossOnInit\n", "from art.checks import CheckScoreCloseTo\n", "from torch.cuda import is_available\n", - "project.to(\"cuda\" if is_available() else \"cpu\")\n", "\n", "EXPECTED_LOSS = -math.log(1/NUM_CLASSES)\n", "print(EXPECTED_LOSS)" @@ -169,96 +116,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating loss on init\n", - "Validating model ResNet18\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using cache found in C:\\Users\\matri/.cache\\torch\\hub\\facebookresearch_semi-supervised-ImageNet1K-models_master\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c6e91d2fd11340b489221baf6d606871", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

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-       "│  CrossEntropyLoss-validate  │      5.054237365722656      │\n",
-       "│ MulticlassAccuracy-validate │    0.008419999852776527     │\n",
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Results:\n", - "\n", - "Step: Evaluate Baseline, Model: HeuristicBaseline, Passed: True. Results:\n", - "\tMulticlassAccuracy-validate: 0.012299999594688416\n", - "Step: Evaluate Baseline, Model: MlBaseline, Passed: True. Results:\n", - "\tMulticlassAccuracy-validate: 0.15649999678134918\n", - "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", - "\tMulticlassAccuracy-validate: 0.6883000135421753\n", - "Step: Check Loss On Init, Model: ResNet18, Passed: True. Results:\n", - "\tMulticlassAccuracy-validate: 0.008419999852776527\n", - "\tCrossEntropyLoss-validate: 5.054237365722656\n", - "Step: Check Loss On Init, Model: ResNet18, Passed: True. Results:\n", - "\tMulticlassAccuracy-validate: 0.008419999852776527\n", - "\tCrossEntropyLoss-validate: 5.054237365722656\n", - "Step: Check Loss On Init, Model: ResNet18, Passed: True. Results:\n", - "\tMulticlassAccuracy-validate: 0.008419999852776527\n", - "\tCrossEntropyLoss-validate: 5.054237365722656\n" - ] - } - ], + "outputs": [], "source": [ "project.add_step(\n", " CheckLossOnInit(ResNet18),\n", diff --git a/{{cookiecutter.project_slug}}/dataset.py b/{{cookiecutter.project_slug}}/MyDataset.py similarity index 100% rename from {{cookiecutter.project_slug}}/dataset.py rename to {{cookiecutter.project_slug}}/MyDataset.py diff --git a/{{cookiecutter.project_slug}}/models/ResNet.py b/{{cookiecutter.project_slug}}/models/ResNet.py index 8f12083..afdddcc 100644 --- a/{{cookiecutter.project_slug}}/models/ResNet.py +++ b/{{cookiecutter.project_slug}}/models/ResNet.py @@ -23,16 +23,15 @@ def __init__(self, num_classes=100, lr=1e-3): self.lr = lr self.model.fc = nn.Linear(self.model.fc.in_features, num_classes) self.preprocess = transforms.Compose([ + transforms.Normalize(mean=[0.5071, 0.4867, 0.4408], std=[0.2675, 0.2565, 0.2761]), transforms.Resize(256), transforms.CenterCrop(224), ]) def parse_data(self, data): """This is first step of your pipeline it always has batch keys inside""" - mean = np.asarray([0.5071, 0.4867, 0.4408], dtype=np.float32) - std = np.asarray([0.2675, 0.2565, 0.2761], dtype=np.float32) X = data[BATCH][INPUT] - X = (X / 255 - mean) / std + X = X / 255 X = rearrange(X, "b h w c -> b c h w") X = self.preprocess(X) @@ -41,7 +40,8 @@ def parse_data(self, data): def predict(self, data: Dict): - preds = self.model(data[INPUT]).detach().numpy() + # preds = self.model(data[INPUT]).detach().numpy() + preds = self.model(data[INPUT]).detach() # perhaps softmax will be needed return {PREDICTION: preds, TARGET: data[TARGET]} From fb2834b00a852cf8416bfff4baed0a47d06a3512 Mon Sep 17 00:00:00 2001 From: mmaecki Date: Fri, 24 Nov 2023 21:59:00 +0100 Subject: [PATCH 04/10] lightning logs delete + predict fix --- {{cookiecutter.project_slug}}/.gitignore | 1 - {{cookiecutter.project_slug}}/EDA.ipynb | 13 +++++++++++++ {{cookiecutter.project_slug}}/models/ResNet.py | 13 ++++++------- 3 files changed, 19 insertions(+), 8 deletions(-) diff --git a/{{cookiecutter.project_slug}}/.gitignore b/{{cookiecutter.project_slug}}/.gitignore index d6486bf..a28a5ed 100644 --- a/{{cookiecutter.project_slug}}/.gitignore +++ b/{{cookiecutter.project_slug}}/.gitignore @@ -104,4 +104,3 @@ instance/ *.png art_checkpoints/ lightning_logs/ - diff --git a/{{cookiecutter.project_slug}}/EDA.ipynb b/{{cookiecutter.project_slug}}/EDA.ipynb index 1d12651..b3b6f29 100644 --- a/{{cookiecutter.project_slug}}/EDA.ipynb +++ b/{{cookiecutter.project_slug}}/EDA.ipynb @@ -129,6 +129,19 @@ "project.run_all()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from art.steps import OverfitOneBatch\n", + "from art.checks import CheckScoreLessThan\n", + "project.add_step(OverfitOneBatch(ResNet18, number_of_steps=40),\n", + " [CheckScoreLessThan(metric=ce_loss, value=0.05)])\n", + "project.run_all()" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/{{cookiecutter.project_slug}}/models/ResNet.py b/{{cookiecutter.project_slug}}/models/ResNet.py index afdddcc..0b8bd87 100644 --- a/{{cookiecutter.project_slug}}/models/ResNet.py +++ b/{{cookiecutter.project_slug}}/models/ResNet.py @@ -27,6 +27,8 @@ def __init__(self, num_classes=100, lr=1e-3): transforms.Resize(256), transforms.CenterCrop(224), ]) + # for name, para in self.model.named_parameters(): + # para.requires_grad = True def parse_data(self, data): """This is first step of your pipeline it always has batch keys inside""" @@ -34,16 +36,13 @@ def parse_data(self, data): X = X / 255 X = rearrange(X, "b h w c -> b c h w") X = self.preprocess(X) - - return {INPUT: X, TARGET: data[BATCH][TARGET]} + target = data[BATCH][TARGET].long() + return {INPUT: X, TARGET: target} - def predict(self, data: Dict): - # preds = self.model(data[INPUT]).detach().numpy() - preds = self.model(data[INPUT]).detach() - # perhaps softmax will be needed - return {PREDICTION: preds, TARGET: data[TARGET]} + def predict(self, data: Dict): + return {PREDICTION: self.model(data[INPUT]), TARGET: data[TARGET]} def compute_loss(self, data): # Notice that the loss calculation is done in MetricsCalculator! From 5644898cbd5b9862a46dc16b94a599f330172d0f Mon Sep 17 00:00:00 2001 From: mmaecki Date: Sat, 25 Nov 2023 10:51:08 +0100 Subject: [PATCH 05/10] transfer learning step --- {{cookiecutter.project_slug}}/EDA.ipynb | 471 +++++++++++++++++++++++- 1 file changed, 455 insertions(+), 16 deletions(-) diff --git a/{{cookiecutter.project_slug}}/EDA.ipynb b/{{cookiecutter.project_slug}}/EDA.ipynb index b3b6f29..a7597aa 100644 --- a/{{cookiecutter.project_slug}}/EDA.ipynb +++ b/{{cookiecutter.project_slug}}/EDA.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -23,9 +23,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Global seed set to 23\n" + ] + }, + { + "data": { + "text/plain": [ + "23" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -42,9 +60,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary: \n", + "Step: Data analysis, Model: , Passed: True. Results:\n", + "\n" + ] + } + ], "source": [ "project = ArtProject(\"Cifar100\", CifarDataModule(batch_size=32))\n", "project.add_step(DataAnalysis(), [\n", @@ -59,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -74,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -92,18 +120,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary: \n", + "Step: Data analysis, Model: , Passed: True. Results:\n", + "\n", + "Step: Evaluate Baseline, Model: HeuristicBaseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.012299999594688416\n", + "Step: Evaluate Baseline, Model: MlBaseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.15649999678134918\n", + "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.6883000135421753\n" + ] + } + ], "source": [ "project.run_all()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.605170185988091\n" + ] + } + ], "source": [ "from models.ResNet import ResNet18\n", "from art.steps import CheckLossOnInit\n", @@ -116,9 +168,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary: \n", + "Step: Data analysis, Model: , Passed: True. Results:\n", + "\n", + "Step: Evaluate Baseline, Model: HeuristicBaseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.012299999594688416\n", + "Step: Evaluate Baseline, Model: MlBaseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.15649999678134918\n", + "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.6883000135421753\n", + "Step: Check Loss On Init, Model: ResNet18, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.015399999916553497\n", + "\tCrossEntropyLoss-validate: 5.087794780731201\n", + "Code of the following steps was changed: ResNet18_Check Loss On Init\n", + " Rerun could be needed.\n" + ] + } + ], "source": [ "project.add_step(\n", " CheckLossOnInit(ResNet18),\n", @@ -131,9 +204,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary: \n", + "Step: Data analysis, Model: , Passed: True. Results:\n", + "\n", + "Step: Evaluate Baseline, Model: HeuristicBaseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.012299999594688416\n", + "Step: Evaluate Baseline, Model: MlBaseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.15649999678134918\n", + "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.6883000135421753\n", + "Step: Check Loss On Init, Model: ResNet18, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.015399999916553497\n", + "\tCrossEntropyLoss-validate: 5.087794780731201\n", + "Step: Overfit One Batch, Model: ResNet18, Passed: True. Results:\n", + "\tMulticlassAccuracy-train: 1.0\n", + "\tCrossEntropyLoss-train: 0.00015361519763246179\n", + "Code of the following steps was changed: ResNet18_Check Loss On Init\n", + " Rerun could be needed.\n" + ] + } + ], "source": [ "from art.steps import OverfitOneBatch\n", "from art.checks import CheckScoreLessThan\n", @@ -142,12 +239,354 @@ "project.run_all()" ] }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from art.steps import Overfit\n", + "from art.checks import CheckScoreGreaterThan\n", + "\n", + "project.add_step(Overfit(ResNet18, max_epochs=10),\n", + " [CheckScoreGreaterThan(metric=accuracy_metric, value=0.8)])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overfitting model\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\logger_connector\\logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", + " warning_cache.warn(\n", + "Using cache found in C:\\Users\\matri/.cache\\torch\\hub\\facebookresearch_semi-supervised-ImageNet1K-models_master\n", + "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n", + "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\configuration_validator.py:71: PossibleUserWarning: You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.\n", + " rank_zero_warn(\n", + "\n", + " | Name | Type | Params\n", + "-------------------------------------------\n", + "0 | model | ResNet | 11.2 M\n", + "1 | loss | CrossEntropyLoss | 0 \n", + "-------------------------------------------\n", + "11.2 M Trainable params\n", + "0 Non-trainable params\n", + "11.2 M Total params\n", + "44.911 Total estimated model params size (MB)\n", + "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\data_connector.py:442: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", + " rank_zero_warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3e48f6451a2a4a5abcd9dc1a336620d0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\torchvision\\transforms\\functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validating overfitted model\n", + "Validating model ResNet18\n", + "Error while executing step Overfit!\n", + "\u001b[33m\u001b[1mTraceback (most recent call last):\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\\u001b[0m\u001b[32m\u001b[1mrunpy.py\u001b[0m\", line \u001b[33m196\u001b[0m, in \u001b[35m_run_module_as_main\u001b[0m\n", + " \u001b[35m\u001b[1mreturn\u001b[0m \u001b[1m_run_code\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode\u001b[0m\u001b[1m,\u001b[0m \u001b[1mmain_globals\u001b[0m\u001b[1m,\u001b[0m \u001b[36m\u001b[1mNone\u001b[0m\u001b[1m,\u001b[0m\n", + " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m{'__name__': '__main__', '__doc__': 'Entry point for launching an IPython kernel.\\n\\nThis is separate from the ipykernel pack...\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m at 0x000001DF8D1F3050, file \"c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel_launcher.py\"...\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\\u001b[0m\u001b[32m\u001b[1mrunpy.py\u001b[0m\", line \u001b[33m86\u001b[0m, in \u001b[35m_run_code\u001b[0m\n", + " \u001b[1mexec\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode\u001b[0m\u001b[1m,\u001b[0m \u001b[1mrun_globals\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m{'__name__': '__main__', '__doc__': 'Entry point for launching an IPython kernel.\\n\\nThis is separate from the ipykernel pack...\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m at 0x000001DF8D1F3050, file \"c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel_launcher.py\"...\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\\u001b[0m\u001b[32m\u001b[1mipykernel_launcher.py\u001b[0m\", line \u001b[33m17\u001b[0m, in \u001b[35m\u001b[0m\n", + " \u001b[1mapp\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mlaunch_new_instance\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m>\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\traitlets\\config\\\u001b[0m\u001b[32m\u001b[1mapplication.py\u001b[0m\", line \u001b[33m1053\u001b[0m, in \u001b[35mlaunch_instance\u001b[0m\n", + " \u001b[1mapp\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mstart\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelapp.py\u001b[0m\", line \u001b[33m737\u001b[0m, in \u001b[35mstart\u001b[0m\n", + " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mio_loop\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mstart\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\tornado\\platform\\\u001b[0m\u001b[32m\u001b[1masyncio.py\u001b[0m\", line \u001b[33m215\u001b[0m, in \u001b[35mstart\u001b[0m\n", + " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1masyncio_loop\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_forever\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m<_WindowsSelectorEventLoop running=True closed=False debug=False>\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\asyncio\\\u001b[0m\u001b[32m\u001b[1mbase_events.py\u001b[0m\", line \u001b[33m603\u001b[0m, in \u001b[35mrun_forever\u001b[0m\n", + " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_run_once\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m<_WindowsSelectorEventLoop running=True closed=False debug=False>\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\asyncio\\\u001b[0m\u001b[32m\u001b[1mbase_events.py\u001b[0m\", line \u001b[33m1909\u001b[0m, in \u001b[35m_run_once\u001b[0m\n", + " \u001b[1mhandle\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_run\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m, ...],))>)>\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\asyncio\\\u001b[0m\u001b[32m\u001b[1mevents.py\u001b[0m\", line \u001b[33m80\u001b[0m, in \u001b[35m_run\u001b[0m\n", + " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_context\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun\u001b[0m\u001b[1m(\u001b[0m\u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_callback\u001b[0m\u001b[1m,\u001b[0m \u001b[35m\u001b[1m*\u001b[0m\u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_args\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m│ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m, ...],))>)>\u001b[0m\n", + " \u001b[36m│ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1m, ...],))>)>\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m, ...],))>)>\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelbase.py\u001b[0m\", line \u001b[33m524\u001b[0m, in \u001b[35mdispatch_queue\u001b[0m\n", + " \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mprocess_one\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelbase.py\u001b[0m\", line \u001b[33m513\u001b[0m, in \u001b[35mprocess_one\u001b[0m\n", + " \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mdispatch\u001b[0m\u001b[1m(\u001b[0m\u001b[35m\u001b[1m*\u001b[0m\u001b[1margs\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m([, , >\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelbase.py\u001b[0m\", line \u001b[33m418\u001b[0m, in \u001b[35mdispatch_shell\u001b[0m\n", + " \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mresult\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelbase.py\u001b[0m\", line \u001b[33m758\u001b[0m, in \u001b[35mexecute_request\u001b[0m\n", + " \u001b[1mreply_content\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mreply_content\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mipkernel.py\u001b[0m\", line \u001b[33m426\u001b[0m, in \u001b[35mdo_execute\u001b[0m\n", + " \u001b[1mres\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mshell\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_cell\u001b[0m\u001b[1m(\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mzmqshell.py\u001b[0m\", line \u001b[33m549\u001b[0m, in \u001b[35mrun_cell\u001b[0m\n", + " \u001b[35m\u001b[1mreturn\u001b[0m \u001b[1msuper\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_cell\u001b[0m\u001b[1m(\u001b[0m\u001b[35m\u001b[1m*\u001b[0m\u001b[1margs\u001b[0m\u001b[1m,\u001b[0m \u001b[35m\u001b[1m**\u001b[0m\u001b[1mkwargs\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m{'store_history': True, 'silent': False, 'cell_id': 'vscode-notebook-cell:/c%3A/Users/matri/Polibuda/diploma/ART-Templates/%7...\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m('project.run_all()',)\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3024\u001b[0m, in \u001b[35mrun_cell\u001b[0m\n", + " \u001b[1mresult\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_run_cell\u001b[0m\u001b[1m(\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3079\u001b[0m, in \u001b[35m_run_cell\u001b[0m\n", + " \u001b[1mresult\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mrunner\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcoro\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1masync_helpers.py\u001b[0m\", line \u001b[33m129\u001b[0m, in \u001b[35m_pseudo_sync_runner\u001b[0m\n", + " \u001b[1mcoro\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1msend\u001b[0m\u001b[1m(\u001b[0m\u001b[36m\u001b[1mNone\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3284\u001b[0m, in \u001b[35mrun_cell_async\u001b[0m\n", + " \u001b[1mhas_raised\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_ast_nodes\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode_ast\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mbody\u001b[0m\u001b[1m,\u001b[0m \u001b[1mcell_name\u001b[0m\u001b[1m,\u001b[0m\n", + " \u001b[36m │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m'C:\\\\Users\\\\matri\\\\AppData\\\\Local\\\\Temp\\\\ipykernel_21408\\\\2062359450.py'\u001b[0m\n", + " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m[]\u001b[0m\n", + " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3466\u001b[0m, in \u001b[35mrun_ast_nodes\u001b[0m\n", + " \u001b[35m\u001b[1mif\u001b[0m \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_code\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode\u001b[0m\u001b[1m,\u001b[0m \u001b[1mresult\u001b[0m\u001b[1m,\u001b[0m \u001b[1masync_\u001b[0m\u001b[35m\u001b[1m=\u001b[0m\u001b[1masy\u001b[0m\u001b[1m)\u001b[0m\u001b[1m:\u001b[0m\n", + " \u001b[36m │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1mFalse\u001b[0m\n", + " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m at 0x000001DFCA92B3C0, file \"C:\\Users\\matri\\AppData\\Local\\Temp\\ipykernel_21408\\2062359450.py\", line 1>\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3526\u001b[0m, in \u001b[35mrun_code\u001b[0m\n", + " \u001b[1mexec\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode_obj\u001b[0m\u001b[1m,\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1muser_global_ns\u001b[0m\u001b[1m,\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1muser_ns\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m{'__name__': '__main__', '__doc__': 'Automatically created module for IPython interactive environment', '__package__': None, ...\u001b[0m\n", + " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m at 0x000001DFCA92B3C0, file \"C:\\Users\\matri\\AppData\\Local\\Temp\\ipykernel_21408\\2062359450.py\", line 1>\u001b[0m\n", + "\n", + " File \"\u001b[32mC:\\Users\\matri\\AppData\\Local\\Temp\\ipykernel_21408\\\u001b[0m\u001b[32m\u001b[1m2062359450.py\u001b[0m\", line \u001b[33m1\u001b[0m, in \u001b[35m\u001b[0m\n", + " \u001b[1mproject\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_all\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1mproject.py\u001b[0m\", line \u001b[33m201\u001b[0m, in \u001b[35mrun_all\u001b[0m\n", + " \u001b[1mstep\u001b[0m\u001b[1m(\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1msteps.py\u001b[0m\", line \u001b[33m249\u001b[0m, in \u001b[35m__call__\u001b[0m\n", + " \u001b[1msuper\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m__call__\u001b[0m\u001b[1m(\u001b[0m\u001b[1mprevious_states\u001b[0m\u001b[1m,\u001b[0m \u001b[1mdatamodule\u001b[0m\u001b[1m,\u001b[0m \u001b[1mmetric_calculator\u001b[0m\u001b[1m,\u001b[0m \u001b[1mrun_id\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m'2023-11-25_10-34-23_1f1eb008-32fa-4b8c-ab2a-eeaa8921961e'\u001b[0m\n", + " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1mdefaultdict(. at 0x000001DFA8FA2CB0>, {'': defaultdict( File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1msteps.py\u001b[0m\", line \u001b[33m78\u001b[0m, in \u001b[35m__call__\u001b[0m\n", + " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mdo\u001b[0m\u001b[1m(\u001b[0m\u001b[1mprevious_states\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1mdefaultdict(. at 0x000001DFA8FA2CB0>, {'': defaultdict(\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1msteps.py\u001b[0m\", line \u001b[33m497\u001b[0m, in \u001b[35mdo\u001b[0m\n", + " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mvalidate\u001b[0m\u001b[1m(\u001b[0m\u001b[1mtrainer_kwargs\u001b[0m\u001b[35m\u001b[1m=\u001b[0m\u001b[1m{\u001b[0m\u001b[36m\"datamodule\"\u001b[0m\u001b[1m:\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mdatamodule\u001b[0m\u001b[1m}\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1msteps.py\u001b[0m\", line \u001b[33m303\u001b[0m, in \u001b[35mvalidate\u001b[0m\n", + " \u001b[1mresult\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mtrainer\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mvalidate\u001b[0m\u001b[1m(\u001b[0m\u001b[1mmodel\u001b[0m\u001b[35m\u001b[1m=\u001b[0m\u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1minitialize_model\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[1m,\u001b[0m \u001b[35m\u001b[1m**\u001b[0m\u001b[1mtrainer_kwargs\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m │ │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m{'datamodule': }\u001b[0m\n", + " \u001b[36m │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\\u001b[0m\u001b[32m\u001b[1mtrainer.py\u001b[0m\", line \u001b[33m633\u001b[0m, in \u001b[35mvalidate\u001b[0m\n", + " \u001b[35m\u001b[1mreturn\u001b[0m \u001b[1mcall\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_call_and_handle_interrupt\u001b[0m\u001b[1m(\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m)\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m>\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\\u001b[0m\u001b[32m\u001b[1mtrainer.py\u001b[0m\", line \u001b[33m673\u001b[0m, in \u001b[35m_validate_impl\u001b[0m\n", + " \u001b[1mckpt_path\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_checkpoint_connector\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_select_ckpt_path\u001b[0m\u001b[1m(\u001b[0m\n", + " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\\u001b[0m\u001b[32m\u001b[1mcheckpoint_connector.py\u001b[0m\", line \u001b[33m108\u001b[0m, in \u001b[35m_select_ckpt_path\u001b[0m\n", + " \u001b[1mckpt_path\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_parse_ckpt_path\u001b[0m\u001b[1m(\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\\u001b[0m\u001b[32m\u001b[1mcheckpoint_connector.py\u001b[0m\", line \u001b[33m175\u001b[0m, in \u001b[35m_parse_ckpt_path\u001b[0m\n", + " \u001b[35m\u001b[1mraise\u001b[0m \u001b[1mValueError\u001b[0m\u001b[1m(\u001b[0m\n", + "\n", + "\u001b[31m\u001b[1mValueError\u001b[0m:\u001b[1m `.validate(ckpt_path=\"best\")` is set but `ModelCheckpoint` is not configured to save the best model.\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\call.py:53: UserWarning: Detected KeyboardInterrupt, attempting graceful shutdown...\n", + " rank_zero_warn(\"Detected KeyboardInterrupt, attempting graceful shutdown...\")\n", + "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\checkpoint_connector.py:149: UserWarning: `.validate(ckpt_path=None)` was called without a model. The best model of the previous `fit` call will be used. You can pass `.validate(ckpt_path='best')` to use the best model or `.validate(ckpt_path='last')` to use the last model. If you pass a value, this warning will be silenced.\n", + " rank_zero_warn(\n" + ] + }, + { + "ename": "ValueError", + "evalue": "`.validate(ckpt_path=\"best\")` is set but `ModelCheckpoint` is not configured to save the best model.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Users\\matri\\Polibuda\\diploma\\ART-Templates\\{{cookiecutter.project_slug}}\\EDA.ipynb Cell 12\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> 1\u001b[0m project\u001b[39m.\u001b[39;49mrun_all()\n", + "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\project.py:218\u001b[0m, in \u001b[0;36mArtProject.run_all\u001b[1;34m(self, force_rerun)\u001b[0m\n\u001b[0;32m 216\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprint_summary()\n\u001b[0;32m 217\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m--> 218\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[0;32m 219\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m 220\u001b[0m remove_logger(logger_id)\n", + "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\project.py:201\u001b[0m, in \u001b[0;36mArtProject.run_all\u001b[1;34m(self, force_rerun)\u001b[0m\n\u001b[0;32m 199\u001b[0m \u001b[39mcontinue\u001b[39;00m\n\u001b[0;32m 200\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 201\u001b[0m step(\n\u001b[0;32m 202\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstate\u001b[39m.\u001b[39;49mstep_states,\n\u001b[0;32m 203\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdatamodule,\n\u001b[0;32m 204\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmetric_calculator,\n\u001b[0;32m 205\u001b[0m run_id,\n\u001b[0;32m 206\u001b[0m )\n\u001b[0;32m 207\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcheck_checks(step, checks)\n\u001b[0;32m 208\u001b[0m \u001b[39mexcept\u001b[39;00m CheckFailedException \u001b[39mas\u001b[39;00m e:\n", + "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:249\u001b[0m, in \u001b[0;36mModelStep.__call__\u001b[1;34m(self, previous_states, datamodule, metric_calculator, run_id)\u001b[0m\n\u001b[0;32m 245\u001b[0m curr_device \u001b[39m=\u001b[39m (\n\u001b[0;32m 246\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mcuda\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39maccelerator, CUDAAccelerator) \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mcpu\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 247\u001b[0m )\n\u001b[0;32m 248\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmetric_calculator\u001b[39m.\u001b[39mto(curr_device)\n\u001b[1;32m--> 249\u001b[0m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(previous_states, datamodule, metric_calculator, run_id)\n\u001b[0;32m 250\u001b[0m \u001b[39mdel\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\n\u001b[0;32m 251\u001b[0m gc\u001b[39m.\u001b[39mcollect()\n", + "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:82\u001b[0m, in \u001b[0;36mStep.__call__\u001b[1;34m(self, previous_states, datamodule, metric_calculator, run_id)\u001b[0m\n\u001b[0;32m 80\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m 81\u001b[0m art_logger\u001b[39m.\u001b[39mexception(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mError while executing step \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname\u001b[39m}\u001b[39;00m\u001b[39m!\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m---> 82\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[0;32m 83\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m 84\u001b[0m remove_logger(logger_id)\n", + "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:78\u001b[0m, in \u001b[0;36mStep.__call__\u001b[1;34m(self, previous_states, datamodule, metric_calculator, run_id)\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdatamodule \u001b[39m=\u001b[39m datamodule\n\u001b[0;32m 77\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfill_basic_results()\n\u001b[1;32m---> 78\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdo(previous_states)\n\u001b[0;32m 79\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfinalized \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m 80\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n", + "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:497\u001b[0m, in \u001b[0;36mOverfit.do\u001b[1;34m(self, previous_states)\u001b[0m\n\u001b[0;32m 495\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrain(trainer_kwargs\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39mtrain_dataloaders\u001b[39m\u001b[39m\"\u001b[39m: train_loader})\n\u001b[0;32m 496\u001b[0m art_logger\u001b[39m.\u001b[39minfo(\u001b[39m\"\u001b[39m\u001b[39mValidating overfitted model\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m--> 497\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mvalidate(trainer_kwargs\u001b[39m=\u001b[39;49m{\u001b[39m\"\u001b[39;49m\u001b[39mdatamodule\u001b[39;49m\u001b[39m\"\u001b[39;49m: \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdatamodule})\n", + "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:303\u001b[0m, in \u001b[0;36mModelStep.validate\u001b[1;34m(self, trainer_kwargs)\u001b[0m\n\u001b[0;32m 295\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 296\u001b[0m \u001b[39mValidate the model using the provided trainer arguments.\u001b[39;00m\n\u001b[0;32m 297\u001b[0m \n\u001b[0;32m 298\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[0;32m 299\u001b[0m \u001b[39m trainer_kwargs (Dict): Arguments to be passed to the trainer for validating the model.\u001b[39;00m\n\u001b[0;32m 300\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 301\u001b[0m art_logger\u001b[39m.\u001b[39minfo(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mValidating model \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel_name\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m--> 303\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39mvalidate(model\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39minitialize_model(), \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mtrainer_kwargs)\n\u001b[0;32m 304\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mresults[\u001b[39m\"\u001b[39m\u001b[39mscores\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mupdate(result[\u001b[39m0\u001b[39m])\n", + "File \u001b[1;32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\trainer.py:633\u001b[0m, in \u001b[0;36mTrainer.validate\u001b[1;34m(self, model, dataloaders, ckpt_path, verbose, datamodule)\u001b[0m\n\u001b[0;32m 631\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstrategy\u001b[39m.\u001b[39m_lightning_module \u001b[39m=\u001b[39m model\n\u001b[0;32m 632\u001b[0m _verify_strategy_supports_compile(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlightning_module, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstrategy)\n\u001b[1;32m--> 633\u001b[0m \u001b[39mreturn\u001b[39;00m call\u001b[39m.\u001b[39;49m_call_and_handle_interrupt(\n\u001b[0;32m 634\u001b[0m \u001b[39mself\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_impl, model, dataloaders, ckpt_path, verbose, datamodule\n\u001b[0;32m 635\u001b[0m )\n", + "File \u001b[1;32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\call.py:43\u001b[0m, in \u001b[0;36m_call_and_handle_interrupt\u001b[1;34m(trainer, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m 41\u001b[0m \u001b[39mif\u001b[39;00m trainer\u001b[39m.\u001b[39mstrategy\u001b[39m.\u001b[39mlauncher \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 42\u001b[0m \u001b[39mreturn\u001b[39;00m trainer\u001b[39m.\u001b[39mstrategy\u001b[39m.\u001b[39mlauncher\u001b[39m.\u001b[39mlaunch(trainer_fn, \u001b[39m*\u001b[39margs, trainer\u001b[39m=\u001b[39mtrainer, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m---> 43\u001b[0m \u001b[39mreturn\u001b[39;00m trainer_fn(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 45\u001b[0m \u001b[39mexcept\u001b[39;00m _TunerExitException:\n\u001b[0;32m 46\u001b[0m _call_teardown_hook(trainer)\n", + "File \u001b[1;32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\trainer.py:673\u001b[0m, in \u001b[0;36mTrainer._validate_impl\u001b[1;34m(self, model, dataloaders, ckpt_path, verbose, datamodule)\u001b[0m\n\u001b[0;32m 670\u001b[0m \u001b[39m# links data to the trainer\u001b[39;00m\n\u001b[0;32m 671\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_data_connector\u001b[39m.\u001b[39mattach_data(model, val_dataloaders\u001b[39m=\u001b[39mdataloaders, datamodule\u001b[39m=\u001b[39mdatamodule)\n\u001b[1;32m--> 673\u001b[0m ckpt_path \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_checkpoint_connector\u001b[39m.\u001b[39;49m_select_ckpt_path(\n\u001b[0;32m 674\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstate\u001b[39m.\u001b[39;49mfn, ckpt_path, model_provided\u001b[39m=\u001b[39;49mmodel_provided, model_connected\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlightning_module \u001b[39mis\u001b[39;49;00m \u001b[39mnot\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m\n\u001b[0;32m 675\u001b[0m )\n\u001b[0;32m 676\u001b[0m results \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_run(model, ckpt_path\u001b[39m=\u001b[39mckpt_path)\n\u001b[0;32m 677\u001b[0m \u001b[39m# remove the tensors from the validation results\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\checkpoint_connector.py:108\u001b[0m, in \u001b[0;36m_CheckpointConnector._select_ckpt_path\u001b[1;34m(self, state_fn, ckpt_path, model_provided, model_connected)\u001b[0m\n\u001b[0;32m 106\u001b[0m ckpt_path \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ckpt_path\n\u001b[0;32m 107\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 108\u001b[0m ckpt_path \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parse_ckpt_path(\n\u001b[0;32m 109\u001b[0m state_fn,\n\u001b[0;32m 110\u001b[0m ckpt_path,\n\u001b[0;32m 111\u001b[0m model_provided\u001b[39m=\u001b[39;49mmodel_provided,\n\u001b[0;32m 112\u001b[0m model_connected\u001b[39m=\u001b[39;49mmodel_connected,\n\u001b[0;32m 113\u001b[0m )\n\u001b[0;32m 114\u001b[0m \u001b[39mreturn\u001b[39;00m ckpt_path\n", + "File \u001b[1;32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\checkpoint_connector.py:175\u001b[0m, in \u001b[0;36m_CheckpointConnector._parse_ckpt_path\u001b[1;34m(self, state_fn, ckpt_path, model_provided, model_connected)\u001b[0m\n\u001b[0;32m 170\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39mfast_dev_run:\n\u001b[0;32m 171\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 172\u001b[0m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mYou cannot execute `.\u001b[39m\u001b[39m{\u001b[39;00mfn\u001b[39m}\u001b[39;00m\u001b[39m(ckpt_path=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mbest\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m)` with `fast_dev_run=True`.\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m 173\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m Please pass an exact checkpoint path to `.\u001b[39m\u001b[39m{\u001b[39;00mfn\u001b[39m}\u001b[39;00m\u001b[39m(ckpt_path=...)`\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 174\u001b[0m )\n\u001b[1;32m--> 175\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 176\u001b[0m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m`.\u001b[39m\u001b[39m{\u001b[39;00mfn\u001b[39m}\u001b[39;00m\u001b[39m(ckpt_path=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mbest\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m)` is set but `ModelCheckpoint` is not configured to save the best model.\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m 177\u001b[0m )\n\u001b[0;32m 178\u001b[0m \u001b[39m# load best weights\u001b[39;00m\n\u001b[0;32m 179\u001b[0m ckpt_path \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39mcheckpoint_callback, \u001b[39m\"\u001b[39m\u001b[39mbest_model_path\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)\n", + "\u001b[1;31mValueError\u001b[0m: `.validate(ckpt_path=\"best\")` is set but `ModelCheckpoint` is not configured to save the best model." + ] + } + ], + "source": [ + "project.run_all()" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "from art.steps import TransferLearning\n", + "from art import NeptuneLogger\n", + "\n", + "logger = NeptuneLogger(project_name=\"mmaecki/transfer-learning\", api_token=\"XXX\")\n", + "early_stopping = EarlyStopping('CrossEntropyLoss-validate', patience=6)\n", + "project.add_step(TransferLearning(ResNet18,\n", + " freezed_trainer_kwargs={\"max_epochs\": 40,\n", + " \"check_val_every_n_epoch\": 2,\n", + " \"callbacks\": [early_stopping]},\n", + " unfreezed_trainer_kwargs={\"max_epochs\": 50,\n", + " \"check_val_every_n_epoch\": 2,\n", + " \"callbacks\": [early_stopping]},\n", + " keep_unfrozen=1,\n", + " logger=logger\n", + " ),\n", + " [CheckScoreGreaterThan(metric=accuracy_metric, value=0.70)])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "project.run_all()" + ] } ], "metadata": { From c041a10aee5052f4838f147cf243b3b483dff31e Mon Sep 17 00:00:00 2001 From: mmaecki Date: Tue, 28 Nov 2023 13:04:51 +0100 Subject: [PATCH 06/10] update on transfer learning + normalization fix --- {{cookiecutter.project_slug}}/EDA.ipynb | 536 +++++++++--------- {{cookiecutter.project_slug}}/MyDataset.py | 2 + .../models/ResNet.py | 2 +- {{cookiecutter.project_slug}}/steps.py | 1 - 4 files changed, 278 insertions(+), 263 deletions(-) diff --git a/{{cookiecutter.project_slug}}/EDA.ipynb b/{{cookiecutter.project_slug}}/EDA.ipynb index a7597aa..be4357e 100644 --- a/{{cookiecutter.project_slug}}/EDA.ipynb +++ b/{{cookiecutter.project_slug}}/EDA.ipynb @@ -69,7 +69,9 @@ "text": [ "Summary: \n", "Step: Data analysis, Model: , Passed: True. Results:\n", - "\n" + "\n", + "Code of the following steps was changed: Data analysis\n", + " Rerun could be needed.\n" ] } ], @@ -135,7 +137,9 @@ "Step: Evaluate Baseline, Model: MlBaseline, Passed: True. Results:\n", "\tMulticlassAccuracy-validate: 0.15649999678134918\n", "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", - "\tMulticlassAccuracy-validate: 0.6883000135421753\n" + "\tMulticlassAccuracy-validate: 0.6883000135421753\n", + "Code of the following steps was changed: Data analysis\n", + " Rerun could be needed.\n" ] } ], @@ -187,7 +191,7 @@ "Step: Check Loss On Init, Model: ResNet18, Passed: True. Results:\n", "\tMulticlassAccuracy-validate: 0.015399999916553497\n", "\tCrossEntropyLoss-validate: 5.087794780731201\n", - "Code of the following steps was changed: ResNet18_Check Loss On Init\n", + "Code of the following steps was changed: Data analysis, ResNet18_Check Loss On Init\n", " Rerun could be needed.\n" ] } @@ -226,7 +230,7 @@ "Step: Overfit One Batch, Model: ResNet18, Passed: True. Results:\n", "\tMulticlassAccuracy-train: 1.0\n", "\tCrossEntropyLoss-train: 0.00015361519763246179\n", - "Code of the following steps was changed: ResNet18_Check Loss On Init\n", + "Code of the following steps was changed: Data analysis, ResNet18_Check Loss On Init, ResNet18_Overfit One Batch\n", " Rerun could be needed.\n" ] } @@ -241,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -252,6 +256,15 @@ " [CheckScoreGreaterThan(metric=accuracy_metric, value=0.8)])" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "project.run_all()" + ] + }, { "cell_type": "code", "execution_count": 11, @@ -261,55 +274,76 @@ "name": "stderr", "output_type": "stream", "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n" + "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\neptune\\common\\warnings.py:62: NeptuneWarning: The following monitoring options are disabled by default in interactive sessions: 'capture_stdout', 'capture_stderr', 'capture_traceback', and 'capture_hardware_metrics'. To enable them, set each parameter to 'True' when initializing the run. The monitoring will continue until you call run.stop() or the kernel stops. Also note: Your source files can only be tracked if you pass the path(s) to the 'source_code' argument. For help, see the Neptune docs: https://docs.neptune.ai/logging/source_code/\n", + " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Overfitting model\n" + "https://app.neptune.ai/mmaecki/transfer-learning/e/TRAN-5\n" ] - }, + } + ], + "source": [ + "from art.steps import TransferLearning\n", + "from art.loggers import NeptuneLoggerAdapter\n", + "\n", + "logger = NeptuneLoggerAdapter(project=\"mmaecki/transfer-learning\", api_token=\"\")\n", + "early_stopping = EarlyStopping('CrossEntropyLoss-validate', patience=6)\n", + "project.add_step(TransferLearning(ResNet18,\n", + " freezed_trainer_kwargs={\"max_epochs\": 4,\n", + " \"check_val_every_n_epoch\": 2,\n", + " \"callbacks\": [early_stopping]},\n", + " unfreezed_trainer_kwargs={\"max_epochs\": 50,\n", + " \"check_val_every_n_epoch\": 2,\n", + " \"callbacks\": [early_stopping]},\n", + " keep_unfrozen=1,\n", + " logger = logger,\n", + " ),\n", + " [CheckScoreGreaterThan(metric=accuracy_metric, value=0.70)])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\logger_connector\\logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", - " warning_cache.warn(\n", + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", "Using cache found in C:\\Users\\matri/.cache\\torch\\hub\\facebookresearch_semi-supervised-ImageNet1K-models_master\n", "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", " warnings.warn(\n", "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", " warnings.warn(msg)\n", - "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\configuration_validator.py:71: PossibleUserWarning: You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.\n", - " rank_zero_warn(\n", "\n", " | Name | Type | Params\n", "-------------------------------------------\n", "0 | model | ResNet | 11.2 M\n", "1 | loss | CrossEntropyLoss | 0 \n", "-------------------------------------------\n", - "11.2 M Trainable params\n", - "0 Non-trainable params\n", + "100 Trainable params\n", + "11.2 M Non-trainable params\n", "11.2 M Total params\n", - "44.911 Total estimated model params size (MB)\n", - "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\data_connector.py:442: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", - " rank_zero_warn(\n" + "44.911 Total estimated model params size (MB)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3e48f6451a2a4a5abcd9dc1a336620d0", + "model_id": "8e251ec73820468f94bb4c83398d3c83", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Training: 0it [00:00, ?it/s]" + "Sanity Checking: 0it [00:00, ?it/s]" ] }, "metadata": {}, @@ -319,236 +353,242 @@ "name": "stderr", "output_type": "stream", "text": [ + "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\data_connector.py:442: PossibleUserWarning: The dataloader, val_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", + " rank_zero_warn(\n", "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\torchvision\\transforms\\functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n", - " warnings.warn(\n" + " warnings.warn(\n", + "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\data_connector.py:442: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", + " rank_zero_warn(\n" ] }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "355a47ccd7b64123aeca48cb419480f3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f3699618a688453c8a3fb3c6625424a6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "216b851e36f0424daf4303df10a4d7b2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9250ceacdf704b8f83b84719781063f9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2b5ce9bc901d40d4b7c517dc989a358a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5f88c244f2fe4d93948f4fe25c641562", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9672598ad6d54d0397c02d54b040b65c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stdout", "output_type": "stream", "text": [ - "Validating overfitted model\n", - "Validating model ResNet18\n", - "Error while executing step Overfit!\n", - "\u001b[33m\u001b[1mTraceback (most recent call last):\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\\u001b[0m\u001b[32m\u001b[1mrunpy.py\u001b[0m\", line \u001b[33m196\u001b[0m, in \u001b[35m_run_module_as_main\u001b[0m\n", - " \u001b[35m\u001b[1mreturn\u001b[0m \u001b[1m_run_code\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode\u001b[0m\u001b[1m,\u001b[0m \u001b[1mmain_globals\u001b[0m\u001b[1m,\u001b[0m \u001b[36m\u001b[1mNone\u001b[0m\u001b[1m,\u001b[0m\n", - " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m{'__name__': '__main__', '__doc__': 'Entry point for launching an IPython kernel.\\n\\nThis is separate from the ipykernel pack...\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m at 0x000001DF8D1F3050, file \"c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel_launcher.py\"...\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\\u001b[0m\u001b[32m\u001b[1mrunpy.py\u001b[0m\", line \u001b[33m86\u001b[0m, in \u001b[35m_run_code\u001b[0m\n", - " \u001b[1mexec\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode\u001b[0m\u001b[1m,\u001b[0m \u001b[1mrun_globals\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m{'__name__': '__main__', '__doc__': 'Entry point for launching an IPython kernel.\\n\\nThis is separate from the ipykernel pack...\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m at 0x000001DF8D1F3050, file \"c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel_launcher.py\"...\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\\u001b[0m\u001b[32m\u001b[1mipykernel_launcher.py\u001b[0m\", line \u001b[33m17\u001b[0m, in \u001b[35m\u001b[0m\n", - " \u001b[1mapp\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mlaunch_new_instance\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m>\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\traitlets\\config\\\u001b[0m\u001b[32m\u001b[1mapplication.py\u001b[0m\", line \u001b[33m1053\u001b[0m, in \u001b[35mlaunch_instance\u001b[0m\n", - " \u001b[1mapp\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mstart\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelapp.py\u001b[0m\", line \u001b[33m737\u001b[0m, in \u001b[35mstart\u001b[0m\n", - " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mio_loop\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mstart\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\tornado\\platform\\\u001b[0m\u001b[32m\u001b[1masyncio.py\u001b[0m\", line \u001b[33m215\u001b[0m, in \u001b[35mstart\u001b[0m\n", - " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1masyncio_loop\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_forever\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m<_WindowsSelectorEventLoop running=True closed=False debug=False>\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\asyncio\\\u001b[0m\u001b[32m\u001b[1mbase_events.py\u001b[0m\", line \u001b[33m603\u001b[0m, in \u001b[35mrun_forever\u001b[0m\n", - " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_run_once\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m<_WindowsSelectorEventLoop running=True closed=False debug=False>\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\asyncio\\\u001b[0m\u001b[32m\u001b[1mbase_events.py\u001b[0m\", line \u001b[33m1909\u001b[0m, in \u001b[35m_run_once\u001b[0m\n", - " \u001b[1mhandle\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_run\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m, ...],))>)>\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\asyncio\\\u001b[0m\u001b[32m\u001b[1mevents.py\u001b[0m\", line \u001b[33m80\u001b[0m, in \u001b[35m_run\u001b[0m\n", - " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_context\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun\u001b[0m\u001b[1m(\u001b[0m\u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_callback\u001b[0m\u001b[1m,\u001b[0m \u001b[35m\u001b[1m*\u001b[0m\u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_args\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m│ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m, ...],))>)>\u001b[0m\n", - " \u001b[36m│ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1m, ...],))>)>\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m, ...],))>)>\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelbase.py\u001b[0m\", line \u001b[33m524\u001b[0m, in \u001b[35mdispatch_queue\u001b[0m\n", - " \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mprocess_one\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelbase.py\u001b[0m\", line \u001b[33m513\u001b[0m, in \u001b[35mprocess_one\u001b[0m\n", - " \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mdispatch\u001b[0m\u001b[1m(\u001b[0m\u001b[35m\u001b[1m*\u001b[0m\u001b[1margs\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m([, , >\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelbase.py\u001b[0m\", line \u001b[33m418\u001b[0m, in \u001b[35mdispatch_shell\u001b[0m\n", - " \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mresult\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelbase.py\u001b[0m\", line \u001b[33m758\u001b[0m, in \u001b[35mexecute_request\u001b[0m\n", - " \u001b[1mreply_content\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mreply_content\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mipkernel.py\u001b[0m\", line \u001b[33m426\u001b[0m, in \u001b[35mdo_execute\u001b[0m\n", - " \u001b[1mres\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mshell\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_cell\u001b[0m\u001b[1m(\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mzmqshell.py\u001b[0m\", line \u001b[33m549\u001b[0m, in \u001b[35mrun_cell\u001b[0m\n", - " \u001b[35m\u001b[1mreturn\u001b[0m \u001b[1msuper\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_cell\u001b[0m\u001b[1m(\u001b[0m\u001b[35m\u001b[1m*\u001b[0m\u001b[1margs\u001b[0m\u001b[1m,\u001b[0m \u001b[35m\u001b[1m**\u001b[0m\u001b[1mkwargs\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m{'store_history': True, 'silent': False, 'cell_id': 'vscode-notebook-cell:/c%3A/Users/matri/Polibuda/diploma/ART-Templates/%7...\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m('project.run_all()',)\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3024\u001b[0m, in \u001b[35mrun_cell\u001b[0m\n", - " \u001b[1mresult\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_run_cell\u001b[0m\u001b[1m(\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3079\u001b[0m, in \u001b[35m_run_cell\u001b[0m\n", - " \u001b[1mresult\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mrunner\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcoro\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1masync_helpers.py\u001b[0m\", line \u001b[33m129\u001b[0m, in \u001b[35m_pseudo_sync_runner\u001b[0m\n", - " \u001b[1mcoro\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1msend\u001b[0m\u001b[1m(\u001b[0m\u001b[36m\u001b[1mNone\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3284\u001b[0m, in \u001b[35mrun_cell_async\u001b[0m\n", - " \u001b[1mhas_raised\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_ast_nodes\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode_ast\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mbody\u001b[0m\u001b[1m,\u001b[0m \u001b[1mcell_name\u001b[0m\u001b[1m,\u001b[0m\n", - " \u001b[36m │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m'C:\\\\Users\\\\matri\\\\AppData\\\\Local\\\\Temp\\\\ipykernel_21408\\\\2062359450.py'\u001b[0m\n", - " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m[]\u001b[0m\n", - " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3466\u001b[0m, in \u001b[35mrun_ast_nodes\u001b[0m\n", - " \u001b[35m\u001b[1mif\u001b[0m \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_code\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode\u001b[0m\u001b[1m,\u001b[0m \u001b[1mresult\u001b[0m\u001b[1m,\u001b[0m \u001b[1masync_\u001b[0m\u001b[35m\u001b[1m=\u001b[0m\u001b[1masy\u001b[0m\u001b[1m)\u001b[0m\u001b[1m:\u001b[0m\n", - " \u001b[36m │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1mFalse\u001b[0m\n", - " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m at 0x000001DFCA92B3C0, file \"C:\\Users\\matri\\AppData\\Local\\Temp\\ipykernel_21408\\2062359450.py\", line 1>\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3526\u001b[0m, in \u001b[35mrun_code\u001b[0m\n", - " \u001b[1mexec\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode_obj\u001b[0m\u001b[1m,\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1muser_global_ns\u001b[0m\u001b[1m,\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1muser_ns\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m{'__name__': '__main__', '__doc__': 'Automatically created module for IPython interactive environment', '__package__': None, ...\u001b[0m\n", - " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m at 0x000001DFCA92B3C0, file \"C:\\Users\\matri\\AppData\\Local\\Temp\\ipykernel_21408\\2062359450.py\", line 1>\u001b[0m\n", - "\n", - " File \"\u001b[32mC:\\Users\\matri\\AppData\\Local\\Temp\\ipykernel_21408\\\u001b[0m\u001b[32m\u001b[1m2062359450.py\u001b[0m\", line \u001b[33m1\u001b[0m, in \u001b[35m\u001b[0m\n", - " \u001b[1mproject\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_all\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1mproject.py\u001b[0m\", line \u001b[33m201\u001b[0m, in \u001b[35mrun_all\u001b[0m\n", - " \u001b[1mstep\u001b[0m\u001b[1m(\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1msteps.py\u001b[0m\", line \u001b[33m249\u001b[0m, in \u001b[35m__call__\u001b[0m\n", - " \u001b[1msuper\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m__call__\u001b[0m\u001b[1m(\u001b[0m\u001b[1mprevious_states\u001b[0m\u001b[1m,\u001b[0m \u001b[1mdatamodule\u001b[0m\u001b[1m,\u001b[0m \u001b[1mmetric_calculator\u001b[0m\u001b[1m,\u001b[0m \u001b[1mrun_id\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m'2023-11-25_10-34-23_1f1eb008-32fa-4b8c-ab2a-eeaa8921961e'\u001b[0m\n", - " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1mdefaultdict(. at 0x000001DFA8FA2CB0>, {'': defaultdict( File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1msteps.py\u001b[0m\", line \u001b[33m78\u001b[0m, in \u001b[35m__call__\u001b[0m\n", - " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mdo\u001b[0m\u001b[1m(\u001b[0m\u001b[1mprevious_states\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1mdefaultdict(. at 0x000001DFA8FA2CB0>, {'': defaultdict(\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1msteps.py\u001b[0m\", line \u001b[33m497\u001b[0m, in \u001b[35mdo\u001b[0m\n", - " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mvalidate\u001b[0m\u001b[1m(\u001b[0m\u001b[1mtrainer_kwargs\u001b[0m\u001b[35m\u001b[1m=\u001b[0m\u001b[1m{\u001b[0m\u001b[36m\"datamodule\"\u001b[0m\u001b[1m:\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mdatamodule\u001b[0m\u001b[1m}\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1msteps.py\u001b[0m\", line \u001b[33m303\u001b[0m, in \u001b[35mvalidate\u001b[0m\n", - " \u001b[1mresult\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mtrainer\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mvalidate\u001b[0m\u001b[1m(\u001b[0m\u001b[1mmodel\u001b[0m\u001b[35m\u001b[1m=\u001b[0m\u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1minitialize_model\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[1m,\u001b[0m \u001b[35m\u001b[1m**\u001b[0m\u001b[1mtrainer_kwargs\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m │ │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m{'datamodule': }\u001b[0m\n", - " \u001b[36m │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\\u001b[0m\u001b[32m\u001b[1mtrainer.py\u001b[0m\", line \u001b[33m633\u001b[0m, in \u001b[35mvalidate\u001b[0m\n", - " \u001b[35m\u001b[1mreturn\u001b[0m \u001b[1mcall\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_call_and_handle_interrupt\u001b[0m\u001b[1m(\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m)\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m>\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\\u001b[0m\u001b[32m\u001b[1mtrainer.py\u001b[0m\", line \u001b[33m673\u001b[0m, in \u001b[35m_validate_impl\u001b[0m\n", - " \u001b[1mckpt_path\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_checkpoint_connector\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_select_ckpt_path\u001b[0m\u001b[1m(\u001b[0m\n", - " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\\u001b[0m\u001b[32m\u001b[1mcheckpoint_connector.py\u001b[0m\", line \u001b[33m108\u001b[0m, in \u001b[35m_select_ckpt_path\u001b[0m\n", - " \u001b[1mckpt_path\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_parse_ckpt_path\u001b[0m\u001b[1m(\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\\u001b[0m\u001b[32m\u001b[1mcheckpoint_connector.py\u001b[0m\", line \u001b[33m175\u001b[0m, in \u001b[35m_parse_ckpt_path\u001b[0m\n", - " \u001b[35m\u001b[1mraise\u001b[0m \u001b[1mValueError\u001b[0m\u001b[1m(\u001b[0m\n", - "\n", - "\u001b[31m\u001b[1mValueError\u001b[0m:\u001b[1m `.validate(ckpt_path=\"best\")` is set but `ModelCheckpoint` is not configured to save the best model.\u001b[0m\n" + "Experiencing connection interruptions. Will try to reestablish communication with Neptune. Internal exception was: RequestsFutureAdapterTimeout\n", + "Communication with Neptune restored!\n" ] }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7ff72e8e6e854a999c8f7c36ecc1473a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ec9bac2ba2d94fa69be326fc053b7ada", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8e8186dc69a047109e3cb2ec79823312", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e6e7c248acc04c6182b96ff095616e23", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "18881a8514e64557a5c4b612d318a7a1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\call.py:53: UserWarning: Detected KeyboardInterrupt, attempting graceful shutdown...\n", " rank_zero_warn(\"Detected KeyboardInterrupt, attempting graceful shutdown...\")\n", - "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\checkpoint_connector.py:149: UserWarning: `.validate(ckpt_path=None)` was called without a model. The best model of the previous `fit` call will be used. You can pass `.validate(ckpt_path='best')` to use the best model or `.validate(ckpt_path='last')` to use the last model. If you pass a value, this warning will be silenced.\n", - " rank_zero_warn(\n" + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "Using cache found in C:\\Users\\matri/.cache\\torch\\hub\\facebookresearch_semi-supervised-ImageNet1K-models_master\n", + "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\callbacks\\model_checkpoint.py:617: UserWarning: Checkpoint directory c:\\Users\\matri\\Polibuda\\diploma\\ART-Templates\\{{cookiecutter.project_slug}}\\.neptune\\Untitled\\TRAN-5\\checkpoints exists and is not empty.\n", + " rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n", + "\n", + " | Name | Type | Params\n", + "-------------------------------------------\n", + "0 | model | ResNet | 11.2 M\n", + "1 | loss | CrossEntropyLoss | 0 \n", + "-------------------------------------------\n", + "11.2 M Trainable params\n", + "0 Non-trainable params\n", + "11.2 M Total params\n", + "44.911 Total estimated model params size (MB)\n" ] }, { - "ename": "ValueError", - "evalue": "`.validate(ckpt_path=\"best\")` is set but `ModelCheckpoint` is not configured to save the best model.", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Users\\matri\\Polibuda\\diploma\\ART-Templates\\{{cookiecutter.project_slug}}\\EDA.ipynb Cell 12\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> 1\u001b[0m project\u001b[39m.\u001b[39;49mrun_all()\n", - "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\project.py:218\u001b[0m, in \u001b[0;36mArtProject.run_all\u001b[1;34m(self, force_rerun)\u001b[0m\n\u001b[0;32m 216\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprint_summary()\n\u001b[0;32m 217\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m--> 218\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[0;32m 219\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m 220\u001b[0m remove_logger(logger_id)\n", - "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\project.py:201\u001b[0m, in \u001b[0;36mArtProject.run_all\u001b[1;34m(self, force_rerun)\u001b[0m\n\u001b[0;32m 199\u001b[0m \u001b[39mcontinue\u001b[39;00m\n\u001b[0;32m 200\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 201\u001b[0m step(\n\u001b[0;32m 202\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstate\u001b[39m.\u001b[39;49mstep_states,\n\u001b[0;32m 203\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdatamodule,\n\u001b[0;32m 204\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmetric_calculator,\n\u001b[0;32m 205\u001b[0m run_id,\n\u001b[0;32m 206\u001b[0m )\n\u001b[0;32m 207\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcheck_checks(step, checks)\n\u001b[0;32m 208\u001b[0m \u001b[39mexcept\u001b[39;00m CheckFailedException \u001b[39mas\u001b[39;00m e:\n", - "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:249\u001b[0m, in \u001b[0;36mModelStep.__call__\u001b[1;34m(self, previous_states, datamodule, metric_calculator, run_id)\u001b[0m\n\u001b[0;32m 245\u001b[0m curr_device \u001b[39m=\u001b[39m (\n\u001b[0;32m 246\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mcuda\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39maccelerator, CUDAAccelerator) \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mcpu\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 247\u001b[0m )\n\u001b[0;32m 248\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmetric_calculator\u001b[39m.\u001b[39mto(curr_device)\n\u001b[1;32m--> 249\u001b[0m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(previous_states, datamodule, metric_calculator, run_id)\n\u001b[0;32m 250\u001b[0m \u001b[39mdel\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\n\u001b[0;32m 251\u001b[0m gc\u001b[39m.\u001b[39mcollect()\n", - "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:82\u001b[0m, in \u001b[0;36mStep.__call__\u001b[1;34m(self, previous_states, datamodule, metric_calculator, run_id)\u001b[0m\n\u001b[0;32m 80\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m 81\u001b[0m art_logger\u001b[39m.\u001b[39mexception(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mError while executing step \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname\u001b[39m}\u001b[39;00m\u001b[39m!\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m---> 82\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[0;32m 83\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m 84\u001b[0m remove_logger(logger_id)\n", - "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:78\u001b[0m, in \u001b[0;36mStep.__call__\u001b[1;34m(self, previous_states, datamodule, metric_calculator, run_id)\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdatamodule \u001b[39m=\u001b[39m datamodule\n\u001b[0;32m 77\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfill_basic_results()\n\u001b[1;32m---> 78\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdo(previous_states)\n\u001b[0;32m 79\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfinalized \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m 80\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n", - "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:497\u001b[0m, in \u001b[0;36mOverfit.do\u001b[1;34m(self, previous_states)\u001b[0m\n\u001b[0;32m 495\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrain(trainer_kwargs\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39mtrain_dataloaders\u001b[39m\u001b[39m\"\u001b[39m: train_loader})\n\u001b[0;32m 496\u001b[0m art_logger\u001b[39m.\u001b[39minfo(\u001b[39m\"\u001b[39m\u001b[39mValidating overfitted model\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m--> 497\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mvalidate(trainer_kwargs\u001b[39m=\u001b[39;49m{\u001b[39m\"\u001b[39;49m\u001b[39mdatamodule\u001b[39;49m\u001b[39m\"\u001b[39;49m: \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdatamodule})\n", - "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:303\u001b[0m, in \u001b[0;36mModelStep.validate\u001b[1;34m(self, trainer_kwargs)\u001b[0m\n\u001b[0;32m 295\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 296\u001b[0m \u001b[39mValidate the model using the provided trainer arguments.\u001b[39;00m\n\u001b[0;32m 297\u001b[0m \n\u001b[0;32m 298\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[0;32m 299\u001b[0m \u001b[39m trainer_kwargs (Dict): Arguments to be passed to the trainer for validating the model.\u001b[39;00m\n\u001b[0;32m 300\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 301\u001b[0m art_logger\u001b[39m.\u001b[39minfo(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mValidating model \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel_name\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m--> 303\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39mvalidate(model\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39minitialize_model(), \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mtrainer_kwargs)\n\u001b[0;32m 304\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mresults[\u001b[39m\"\u001b[39m\u001b[39mscores\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mupdate(result[\u001b[39m0\u001b[39m])\n", - "File \u001b[1;32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\trainer.py:633\u001b[0m, in \u001b[0;36mTrainer.validate\u001b[1;34m(self, model, dataloaders, ckpt_path, verbose, datamodule)\u001b[0m\n\u001b[0;32m 631\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstrategy\u001b[39m.\u001b[39m_lightning_module \u001b[39m=\u001b[39m model\n\u001b[0;32m 632\u001b[0m _verify_strategy_supports_compile(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlightning_module, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstrategy)\n\u001b[1;32m--> 633\u001b[0m \u001b[39mreturn\u001b[39;00m call\u001b[39m.\u001b[39;49m_call_and_handle_interrupt(\n\u001b[0;32m 634\u001b[0m \u001b[39mself\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_impl, model, dataloaders, ckpt_path, verbose, datamodule\n\u001b[0;32m 635\u001b[0m )\n", - "File \u001b[1;32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\call.py:43\u001b[0m, in \u001b[0;36m_call_and_handle_interrupt\u001b[1;34m(trainer, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m 41\u001b[0m \u001b[39mif\u001b[39;00m trainer\u001b[39m.\u001b[39mstrategy\u001b[39m.\u001b[39mlauncher \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 42\u001b[0m \u001b[39mreturn\u001b[39;00m trainer\u001b[39m.\u001b[39mstrategy\u001b[39m.\u001b[39mlauncher\u001b[39m.\u001b[39mlaunch(trainer_fn, \u001b[39m*\u001b[39margs, trainer\u001b[39m=\u001b[39mtrainer, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m---> 43\u001b[0m \u001b[39mreturn\u001b[39;00m trainer_fn(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 45\u001b[0m \u001b[39mexcept\u001b[39;00m _TunerExitException:\n\u001b[0;32m 46\u001b[0m _call_teardown_hook(trainer)\n", - "File \u001b[1;32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\trainer.py:673\u001b[0m, in \u001b[0;36mTrainer._validate_impl\u001b[1;34m(self, model, dataloaders, ckpt_path, verbose, datamodule)\u001b[0m\n\u001b[0;32m 670\u001b[0m \u001b[39m# links data to the trainer\u001b[39;00m\n\u001b[0;32m 671\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_data_connector\u001b[39m.\u001b[39mattach_data(model, val_dataloaders\u001b[39m=\u001b[39mdataloaders, datamodule\u001b[39m=\u001b[39mdatamodule)\n\u001b[1;32m--> 673\u001b[0m ckpt_path \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_checkpoint_connector\u001b[39m.\u001b[39;49m_select_ckpt_path(\n\u001b[0;32m 674\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstate\u001b[39m.\u001b[39;49mfn, ckpt_path, model_provided\u001b[39m=\u001b[39;49mmodel_provided, model_connected\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlightning_module \u001b[39mis\u001b[39;49;00m \u001b[39mnot\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m\n\u001b[0;32m 675\u001b[0m )\n\u001b[0;32m 676\u001b[0m results \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_run(model, ckpt_path\u001b[39m=\u001b[39mckpt_path)\n\u001b[0;32m 677\u001b[0m \u001b[39m# remove the tensors from the validation results\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\checkpoint_connector.py:108\u001b[0m, in \u001b[0;36m_CheckpointConnector._select_ckpt_path\u001b[1;34m(self, state_fn, ckpt_path, model_provided, model_connected)\u001b[0m\n\u001b[0;32m 106\u001b[0m ckpt_path \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ckpt_path\n\u001b[0;32m 107\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 108\u001b[0m ckpt_path \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parse_ckpt_path(\n\u001b[0;32m 109\u001b[0m state_fn,\n\u001b[0;32m 110\u001b[0m ckpt_path,\n\u001b[0;32m 111\u001b[0m model_provided\u001b[39m=\u001b[39;49mmodel_provided,\n\u001b[0;32m 112\u001b[0m model_connected\u001b[39m=\u001b[39;49mmodel_connected,\n\u001b[0;32m 113\u001b[0m )\n\u001b[0;32m 114\u001b[0m \u001b[39mreturn\u001b[39;00m ckpt_path\n", - "File \u001b[1;32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\checkpoint_connector.py:175\u001b[0m, in \u001b[0;36m_CheckpointConnector._parse_ckpt_path\u001b[1;34m(self, state_fn, ckpt_path, model_provided, model_connected)\u001b[0m\n\u001b[0;32m 170\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39mfast_dev_run:\n\u001b[0;32m 171\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 172\u001b[0m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mYou cannot execute `.\u001b[39m\u001b[39m{\u001b[39;00mfn\u001b[39m}\u001b[39;00m\u001b[39m(ckpt_path=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mbest\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m)` with `fast_dev_run=True`.\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m 173\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m Please pass an exact checkpoint path to `.\u001b[39m\u001b[39m{\u001b[39;00mfn\u001b[39m}\u001b[39;00m\u001b[39m(ckpt_path=...)`\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 174\u001b[0m )\n\u001b[1;32m--> 175\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 176\u001b[0m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m`.\u001b[39m\u001b[39m{\u001b[39;00mfn\u001b[39m}\u001b[39;00m\u001b[39m(ckpt_path=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mbest\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m)` is set but `ModelCheckpoint` is not configured to save the best model.\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m 177\u001b[0m )\n\u001b[0;32m 178\u001b[0m \u001b[39m# load best weights\u001b[39;00m\n\u001b[0;32m 179\u001b[0m ckpt_path \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39mcheckpoint_callback, \u001b[39m\"\u001b[39m\u001b[39mbest_model_path\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)\n", - "\u001b[1;31mValueError\u001b[0m: `.validate(ckpt_path=\"best\")` is set but `ModelCheckpoint` is not configured to save the best model." - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0e51f689e33a44809e224ba265ea4323", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sanity Checking: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5426b450053746578fa344a35f6cd4db", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -560,33 +600,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "from art.steps import TransferLearning\n", - "from art import NeptuneLogger\n", - "\n", - "logger = NeptuneLogger(project_name=\"mmaecki/transfer-learning\", api_token=\"XXX\")\n", - "early_stopping = EarlyStopping('CrossEntropyLoss-validate', patience=6)\n", - "project.add_step(TransferLearning(ResNet18,\n", - " freezed_trainer_kwargs={\"max_epochs\": 40,\n", - " \"check_val_every_n_epoch\": 2,\n", - " \"callbacks\": [early_stopping]},\n", - " unfreezed_trainer_kwargs={\"max_epochs\": 50,\n", - " \"check_val_every_n_epoch\": 2,\n", - " \"callbacks\": [early_stopping]},\n", - " keep_unfrozen=1,\n", - " logger=logger\n", - " ),\n", - " [CheckScoreGreaterThan(metric=accuracy_metric, value=0.70)])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "project.run_all()" - ] + "source": [] } ], "metadata": { diff --git a/{{cookiecutter.project_slug}}/MyDataset.py b/{{cookiecutter.project_slug}}/MyDataset.py index e41b49e..560e5fb 100644 --- a/{{cookiecutter.project_slug}}/MyDataset.py +++ b/{{cookiecutter.project_slug}}/MyDataset.py @@ -8,7 +8,9 @@ def __init__(self, batch_size: int = 32): self.batch_size = batch_size self.dataset = load_dataset("cifar100").with_format("torch") self.dataset = self.dataset.rename_columns({"img": "input", "fine_label": "target"}) + # self.dataset = self.dataset.rename_columns({"img": "input", "coarse_label": "target"}) self.dataset = self.dataset.remove_columns(["coarse_label"]) + # self.dataset = self.dataset.remove_columns(["fine_label"]) def setup(self, stage: str): self.train = self.dataset["train"] diff --git a/{{cookiecutter.project_slug}}/models/ResNet.py b/{{cookiecutter.project_slug}}/models/ResNet.py index 0b8bd87..26563e5 100644 --- a/{{cookiecutter.project_slug}}/models/ResNet.py +++ b/{{cookiecutter.project_slug}}/models/ResNet.py @@ -23,7 +23,7 @@ def __init__(self, num_classes=100, lr=1e-3): self.lr = lr self.model.fc = nn.Linear(self.model.fc.in_features, num_classes) self.preprocess = transforms.Compose([ - transforms.Normalize(mean=[0.5071, 0.4867, 0.4408], std=[0.2675, 0.2565, 0.2761]), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), transforms.Resize(256), transforms.CenterCrop(224), ]) diff --git a/{{cookiecutter.project_slug}}/steps.py b/{{cookiecutter.project_slug}}/steps.py index c945581..4e5ef43 100644 --- a/{{cookiecutter.project_slug}}/steps.py +++ b/{{cookiecutter.project_slug}}/steps.py @@ -30,7 +30,6 @@ def do(self, previous_states): # Now tell me dimensions of each image img_dimensions = self.datamodule.train_dataloader().dataset[0][INPUT].shape - # Loop through classes and visualize 5 examples for 5 randomly selected classes (out of 100) for cls in class_names: class_indices = [i for i, label in enumerate(targets) if label == cls] class_samples = np.random.choice(class_indices, 5, replace=False).tolist() From 4c10a65fa8f7356933105ce78b694cbcbed3b6d0 Mon Sep 17 00:00:00 2001 From: mmaecki Date: Wed, 29 Nov 2023 20:45:08 +0100 Subject: [PATCH 07/10] Adding thext for the tutorial + small cleanup --- {{cookiecutter.project_slug}}/.gitignore | 3 + .../{EDA.ipynb => Tutorial.ipynb} | 105 +++++++++++------- .../models/base_model.py | 0 {{cookiecutter.project_slug}}/run.py | 38 ------- {{cookiecutter.project_slug}}/steps.py | 4 +- 5 files changed, 72 insertions(+), 78 deletions(-) rename {{cookiecutter.project_slug}}/{EDA.ipynb => Tutorial.ipynb} (89%) delete mode 100644 {{cookiecutter.project_slug}}/models/base_model.py diff --git a/{{cookiecutter.project_slug}}/.gitignore b/{{cookiecutter.project_slug}}/.gitignore index a28a5ed..de3971c 100644 --- a/{{cookiecutter.project_slug}}/.gitignore +++ b/{{cookiecutter.project_slug}}/.gitignore @@ -104,3 +104,6 @@ instance/ *.png art_checkpoints/ lightning_logs/ + +.neptune/ +*.zip diff --git a/{{cookiecutter.project_slug}}/EDA.ipynb b/{{cookiecutter.project_slug}}/Tutorial.ipynb similarity index 89% rename from {{cookiecutter.project_slug}}/EDA.ipynb rename to {{cookiecutter.project_slug}}/Tutorial.ipynb index be4357e..43484ab 100644 --- a/{{cookiecutter.project_slug}}/EDA.ipynb +++ b/{{cookiecutter.project_slug}}/Tutorial.ipynb @@ -1,54 +1,63 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## This is a showcase of usage of ART - a package inspirated by Andrej Karpathy's blogpost.\n", + "\n", + "### In this tutorial, you will be presented with how to use ART to perform very popular form of training model, which is transfer learning, while following steps recommended by Andrej Karpathy." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start with the dataset. Since ART is based on Pytorch-Lightning, we need to create LightningDataModule.\\\n", + "For this tutorial, I decided to use very well known dataset called Cifar100 and I wrapped it into LightningDataModule creating [CifarDataModule](https://github.com/SebChw/ART-Templates/blob/cv_transfer_learning_tutorial/%7B%7Bcookiecutter.project_slug%7D%7D/MyDataset.py)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay, we have datamodule, but how do we use it?\\\n", + "Let me introduce you, to the core of ART, the ArtProject." + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from MyDataset import CifarDataModule\n", - "\n", "from art.project import ArtProject\n", - "from art.checks import CheckResultExists, CheckScoreExists, CheckScoreLessThan, CheckScoreGreaterThan\n", - "from art.steps import EvaluateBaseline, OverfitOneBatch, Overfit#, TransferLearning\n", - "from torchmetrics import Accuracy, Precision, Recall\n", - "import torch.nn as nn\n", - "from lightning.pytorch.callbacks import EarlyStopping\n", - "\n", - "from MyDataset import CifarDataModule\n", - "from checks import CheckClassImagesExist, CheckLenClassNamesEqualToNumClasses\n", - "from steps import DataAnalysis\n", - "import math" + "project_name = \"Cifar100\"\n", + "dataset = CifarDataModule(batch_size=32)\n", + "project = ArtProject(project_name, dataset)" ] }, { - "cell_type": "code", - "execution_count": 2, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, that we have a Datamodule and ArtProject let's do a first step: \\\n", + "`Become one with the data`\\\n", + "To achieve this, let's perform some data analysis.\\\n", + "Let's create a first step of out project - Data analysis" + ] + }, + { + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Global seed set to 23\n" - ] - }, - { - "data": { - "text/plain": [ - "23" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "from lightning import seed_everything\n", - "seed_everything(23)" + "In this class, we check multiple characteristics of the data:\n", + "* Number of classes\n", + "* Names of calsses\n", + "* Whether dataset is balanced - number of examples in each class\n", + "* The dimentions of images\n", + "* We get to know the actual visaluzations of the images (10 visualizations per class)" ] }, { @@ -76,7 +85,9 @@ } ], "source": [ - "project = ArtProject(\"Cifar100\", CifarDataModule(batch_size=32))\n", + "from checks import CheckClassImagesExist, CheckLenClassNamesEqualToNumClasses\n", + "from steps import DataAnalysis\n", + "from art.checks import CheckResultExists\n", "project.add_step(DataAnalysis(), [\n", " CheckResultExists(\"number_of_classes\"),\n", " CheckResultExists(\"class_names\"),\n", @@ -87,6 +98,21 @@ "project.run_all()\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i, result in enumerate(project.get_step(0).get_latest_run()):\n", + " if i < 4:\n", + " print(result)\n", + " else:\n", + " for j, fig in enumerate(result):\n", + " if j % 20 == 0:\n", + " fig.show()" + ] + }, { "cell_type": "code", "execution_count": 4, @@ -592,7 +618,8 @@ } ], "source": [ - "project.run_all()" + "project.run_all()\n", + "logger.stop()" ] }, { diff --git a/{{cookiecutter.project_slug}}/models/base_model.py b/{{cookiecutter.project_slug}}/models/base_model.py deleted file mode 100644 index e69de29..0000000 diff --git a/{{cookiecutter.project_slug}}/run.py b/{{cookiecutter.project_slug}}/run.py index 746974d..e69de29 100644 --- a/{{cookiecutter.project_slug}}/run.py +++ b/{{cookiecutter.project_slug}}/run.py @@ -1,38 +0,0 @@ -from .models.base_model import Model -from .dataset import MyDataModule - -from dataset import CifarDataModule - -from art.project import ArtProject -from art.checks import CheckResultExists, CheckScoreExists, CheckScoreLessThan, CheckScoreGreaterThan -from art.steps import EvaluateBaseline, OverfitOneBatch, Overfit, TransferLearning -from torchmetrics import Accuracy, Precision, Recall -import torch.nn as nn -from lightning.pytorch.callbacks import EarlyStopping - - -from lightning import seed_everything -seed_everything(23) - -from dataset import CifarDataModule - -from steps import DataAnalysis - - -def main(): - project = ArtProject("Cifar100", CifarDataModule(batch_size=32)) - - # Data Analysis - project.add_step(DataAnalysis(), [ - CheckResultExists("number_of_classes"), - CheckResultExists("class_names"), - CheckResultExists("number_of_examples_in_each_class"), - CheckResultExists("img_dimensions")]) - - # Baseline - - project.run_all() - - -if __name__ == "__main__": - main() diff --git a/{{cookiecutter.project_slug}}/steps.py b/{{cookiecutter.project_slug}}/steps.py index 4e5ef43..8c8dcef 100644 --- a/{{cookiecutter.project_slug}}/steps.py +++ b/{{cookiecutter.project_slug}}/steps.py @@ -29,7 +29,7 @@ def do(self, previous_states): # Now tell me dimensions of each image img_dimensions = self.datamodule.train_dataloader().dataset[0][INPUT].shape - + figures = [] for cls in class_names: class_indices = [i for i, label in enumerate(targets) if label == cls] class_samples = np.random.choice(class_indices, 5, replace=False).tolist() @@ -47,6 +47,7 @@ def do(self, previous_states): MatplotLibSaver().save( fig, self.get_full_step_name(), self.get_class_image_path(cls) ) + figures.append(fig) self.results.update( { @@ -54,6 +55,7 @@ def do(self, previous_states): "class_names": class_names, "number_of_examples_in_each_class": number_of_examples_in_each_class, "img_dimensions": img_dimensions, + "images": figures, } ) def log_params(self): From 1b740ecf84c50de0b79fb155ef306ef622d92f08 Mon Sep 17 00:00:00 2001 From: mmaecki Date: Fri, 1 Dec 2023 21:59:59 +0100 Subject: [PATCH 08/10] 1st version of tutorial + new model --- {{cookiecutter.project_slug}}/MyDataset.py | 7 +- {{cookiecutter.project_slug}}/Tutorial.ipynb | 458 ++++++++++++++++-- .../models/EffiNet.py | 59 +++ .../models/ResNet.py | 4 +- {{cookiecutter.project_slug}}/steps.py | 11 +- 5 files changed, 477 insertions(+), 62 deletions(-) create mode 100644 {{cookiecutter.project_slug}}/models/EffiNet.py diff --git a/{{cookiecutter.project_slug}}/MyDataset.py b/{{cookiecutter.project_slug}}/MyDataset.py index 560e5fb..7902f36 100644 --- a/{{cookiecutter.project_slug}}/MyDataset.py +++ b/{{cookiecutter.project_slug}}/MyDataset.py @@ -3,7 +3,7 @@ from torch.utils.data import DataLoader class CifarDataModule(pl.LightningDataModule): - def __init__(self, batch_size: int = 32): + def __init__(self, batch_size: int = 32, num_workers: int = 4): super().__init__() self.batch_size = batch_size self.dataset = load_dataset("cifar100").with_format("torch") @@ -11,16 +11,17 @@ def __init__(self, batch_size: int = 32): # self.dataset = self.dataset.rename_columns({"img": "input", "coarse_label": "target"}) self.dataset = self.dataset.remove_columns(["coarse_label"]) # self.dataset = self.dataset.remove_columns(["fine_label"]) + self.num_workers = num_workers def setup(self, stage: str): self.train = self.dataset["train"] self.test = self.dataset["test"] def train_dataloader(self): - return DataLoader(self.dataset["train"], batch_size=self.batch_size) + return DataLoader(self.dataset["train"], batch_size=self.batch_size, num_workers=self.num_workers) def val_dataloader(self): - return DataLoader(self.dataset["test"], batch_size=self.batch_size) + return DataLoader(self.dataset["test"], batch_size=self.batch_size, num_workers=self.num_workers) def log_params(self): return { diff --git a/{{cookiecutter.project_slug}}/Tutorial.ipynb b/{{cookiecutter.project_slug}}/Tutorial.ipynb index 43484ab..0baa3c5 100644 --- a/{{cookiecutter.project_slug}}/Tutorial.ipynb +++ b/{{cookiecutter.project_slug}}/Tutorial.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## This is a showcase of usage of ART - a package inspirated by Andrej Karpathy's blogpost.\n", + "## This is a showcase of usage of ART - a package inspired by Andrej Karpathy's blogpost.\n", "\n", - "### In this tutorial, you will be presented with how to use ART to perform very popular form of training model, which is transfer learning, while following steps recommended by Andrej Karpathy." + "### In this tutorial, you will be presented with how to use ART to perform very popular form of model training - the transfer learning, while following steps recommended by Andrej Karpathy." ] }, { @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -42,10 +42,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, that we have a Datamodule and ArtProject let's do a first step: \\\n", - "`Become one with the data`\\\n", - "To achieve this, let's perform some data analysis.\\\n", - "Let's create a first step of out project - Data analysis" + "Now, that we have a Datamodule and ArtProject let's perform a first step: \\\n", + "`Become one with the data`\n", + "\n", + "\n", + "To achieve this, we'll perform some data analysis.\\\n", + "Firstly, create a new step for our project - Data analysis\\\n", + "Here is how it could look like: [Data analysis step](https://github.com/SebChw/ART-Templates/blob/4c10a65fa8f7356933105ce78b694cbcbed3b6d0/%7B%7Bcookiecutter.project_slug%7D%7D/steps.py#L9)" ] }, { @@ -64,12 +67,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Proper data analysis" + "Let's insert it into the ArtProject" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -78,9 +81,7 @@ "text": [ "Summary: \n", "Step: Data analysis, Model: , Passed: True. Results:\n", - "\n", - "Code of the following steps was changed: Data analysis\n", - " Rerun could be needed.\n" + "\n" ] } ], @@ -98,19 +99,109 @@ "project.run_all()\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see, that except for the data analysis step, there are some checks.\\\n", + "Those checks, are run after the step, so that we can be sure, that everything went as planned.\\\n", + "`You`, as the user of ART, are responsible for creating checks that will suit your experiments.\\\n", + "We used only two `custom` [checks](https://github.com/SebChw/ART-Templates/blob/cv_transfer_learning_tutorial/%7B%7Bcookiecutter.project_slug%7D%7D/checks.py) and one universal check implemented into art [CheckResultExists](https://github.com/SebChw/Actually-Robust-Training/blob/main/art/checks.py#L82).\n", + "\n", + "\n", + "Let's see the results:" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of classes: 100\n", + "All classes names: ['apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle', 'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel', 'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock', 'cloud', 'cockroach', 'couch', 'cra', 'crocodile', 'cup', 'dinosaur', 'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster', 'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion', 'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain', 'mouse', 'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree', 'pear', 'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy', 'porcupine', 'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose', 'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake', 'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', 'table', 'tank', 'telephone', 'television', 'tiger', 'tractor', 'train', 'trout', 'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf', 'woman', 'worm']\n", + "Number of examples in each class: {'cattle': 500, 'dinosaur': 500, 'apple': 500, 'boy': 500, 'aquarium_fish': 500, 'telephone': 500, 'train': 500, 'cup': 500, 'cloud': 500, 'elephant': 500, 'keyboard': 500, 'willow_tree': 500, 'sunflower': 500, 'castle': 500, 'sea': 500, 'bicycle': 500, 'wolf': 500, 'squirrel': 500, 'shrew': 500, 'pine_tree': 500, 'rose': 500, 'television': 500, 'table': 500, 'possum': 500, 'oak_tree': 500, 'leopard': 500, 'maple_tree': 500, 'rabbit': 500, 'chimpanzee': 500, 'clock': 500, 'streetcar': 500, 'cockroach': 500, 'snake': 500, 'lobster': 500, 'mountain': 500, 'palm_tree': 500, 'skyscraper': 500, 'tractor': 500, 'shark': 500, 'butterfly': 500, 'bottle': 500, 'bee': 500, 'chair': 500, 'woman': 500, 'hamster': 500, 'otter': 500, 'seal': 500, 'lion': 500, 'mushroom': 500, 'girl': 500, 'sweet_pepper': 500, 'forest': 500, 'crocodile': 500, 'orange': 500, 'tulip': 500, 'mouse': 500, 'camel': 500, 'caterpillar': 500, 'man': 500, 'skunk': 500, 'kangaroo': 500, 'raccoon': 500, 'snail': 500, 'rocket': 500, 'whale': 500, 'worm': 500, 'turtle': 500, 'beaver': 500, 'plate': 500, 'wardrobe': 500, 'road': 500, 'fox': 500, 'flatfish': 500, 'tiger': 500, 'ray': 500, 'dolphin': 500, 'poppy': 500, 'porcupine': 500, 'lamp': 500, 'cra': 500, 'motorcycle': 500, 'spider': 500, 'tank': 500, 'orchid': 500, 'lizard': 500, 'beetle': 500, 'bridge': 500, 'baby': 500, 'lawn_mower': 500, 'house': 500, 'bus': 500, 'couch': 500, 'bowl': 500, 'pear': 500, 'bed': 500, 'plain': 500, 'trout': 500, 'bear': 500, 'pickup_truck': 500, 'can': 500}\n", + "Demansions of images: [32, 32, 3]\n", + "art_checkpoints\\Data analysis\\class_images\\class_chair.png\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import random\n", + "import matplotlib.pyplot as plt\n", + "from PIL import Image\n", + "from os.path import normpath\n", + "results = project.get_step(0).get_latest_run()\n", + "print(\"Number of classes: \", results[\"number_of_classes\"])\n", + "print(\"All classes names: \", results[\"class_names\"])\n", + "print(\"Number of examples in each class: \", results[\"number_of_examples_in_each_class\"])\n", + "print(\"Demansions of images: \", results[\"img_dimensions\"])\n", + "for i in range(3):\n", + " img_class = results[\"class_names\"][random.randint(0, 100)]\n", + " img_path = normpath(f\"art_checkpoints/Data analysis/{project.get_step(0).get_class_image_path(img_class)}\")\n", + " print(img_path)\n", + " image = Image.open(img_path)\n", + " plt.imshow(image)\n", + " plt.axis('off')\n", + " plt.show()\n", + "\n", + " \n" + ] + }, + { + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "for i, result in enumerate(project.get_step(0).get_latest_run()):\n", - " if i < 4:\n", - " print(result)\n", - " else:\n", - " for j, fig in enumerate(result):\n", - " if j % 20 == 0:\n", - " fig.show()" + "We can see, that the dataset is perfectly balanced, and indeed has 100 classes as it's name suggests.\n", + "\n", + "Now that we know something about our dataset, we can proceed to the next steps. It is time to decide, what `metrics` will we use in our project." ] }, { @@ -125,9 +216,19 @@ "\n", "NUM_CLASSES = project.get_step(0).get_latest_run()[\"number_of_classes\"]\n", "accuracy_metric, ce_loss = Accuracy(task=\"multiclass\", num_classes = NUM_CLASSES), nn.CrossEntropyLoss()\n", + "# Register metrics, that you want to be calculated for every model that you will be using\n", "project.register_metrics([accuracy_metric, ce_loss])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having registered the metrics, we can proceed to the next step which is `Add baselines`. We want to make sure, that everything we did until this moment, works as it sholud, and there are no bugs in our DataLoader.\\\n", + "This we used three [baselines](https://github.com/SebChw/ART-Templates/blob/cv_transfer_learning_tutorial/%7B%7Bcookiecutter.project_slug%7D%7D/models/baselines.py).\\\n", + "Feel free to implement yours however you like, but remember to follow the structure of ArtModule." + ] + }, { "cell_type": "code", "execution_count": 5, @@ -163,9 +264,7 @@ "Step: Evaluate Baseline, Model: MlBaseline, Passed: True. Results:\n", "\tMulticlassAccuracy-validate: 0.15649999678134918\n", "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", - "\tMulticlassAccuracy-validate: 0.6883000135421753\n", - "Code of the following steps was changed: Data analysis\n", - " Rerun could be needed.\n" + "\tMulticlassAccuracy-validate: 0.6883000135421753\n" ] } ], @@ -173,6 +272,21 @@ "project.run_all()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nice, we can see that MLBaseline is better than random selection, and that pretrained ResNet20 performs quite well." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, that we are sure, that our pipeline works as intended, we can start implementing our model.\\\n", + "We will start with yet another good pracitce which is `check loss on init` - we want to make sure, that our model, which didn't see any of the Cifar100 data, will give us `loss` equal to `-log(1/100)`." + ] + }, { "cell_type": "code", "execution_count": 7, @@ -187,44 +301,271 @@ } ], "source": [ - "from models.ResNet import ResNet18\n", + "# from models.ResNet import ResNet\n", + "from models.EffiNet import EffiNet\n", "from art.steps import CheckLossOnInit\n", "from art.checks import CheckScoreCloseTo\n", - "from torch.cuda import is_available\n", + "import math\n", "\n", "EXPECTED_LOSS = -math.log(1/NUM_CLASSES)\n", "print(EXPECTED_LOSS)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll add new stop to the project `CheckLossOnInit`." + ] + }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Summary: \n", - "Step: Data analysis, Model: , Passed: True. Results:\n", + "Calculating loss on init\n", + "Validating model EfiNet\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\logger_connector\\logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", + " warning_cache.warn(\n", + "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\data_connector.py:442: PossibleUserWarning: The dataloader, val_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", + " rank_zero_warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0d00232f67324dfd9dc475061b48b248", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Validation: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\torchvision\\transforms\\functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error while executing step Check Loss On Init!\n", + "\u001b[33m\u001b[1mTraceback (most recent call last):\u001b[0m\n", "\n", - "Step: Evaluate Baseline, Model: HeuristicBaseline, Passed: True. Results:\n", - "\tMulticlassAccuracy-validate: 0.012299999594688416\n", - "Step: Evaluate Baseline, Model: MlBaseline, Passed: True. Results:\n", - "\tMulticlassAccuracy-validate: 0.15649999678134918\n", - "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", - "\tMulticlassAccuracy-validate: 0.6883000135421753\n", - "Step: Check Loss On Init, Model: ResNet18, Passed: True. Results:\n", - "\tMulticlassAccuracy-validate: 0.015399999916553497\n", - "\tCrossEntropyLoss-validate: 5.087794780731201\n", - "Code of the following steps was changed: Data analysis, ResNet18_Check Loss On Init\n", - " Rerun could be needed.\n" + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\\u001b[0m\u001b[32m\u001b[1mrunpy.py\u001b[0m\", line \u001b[33m196\u001b[0m, in \u001b[35m_run_module_as_main\u001b[0m\n", + " \u001b[35m\u001b[1mreturn\u001b[0m \u001b[1m_run_code\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode\u001b[0m\u001b[1m,\u001b[0m \u001b[1mmain_globals\u001b[0m\u001b[1m,\u001b[0m \u001b[36m\u001b[1mNone\u001b[0m\u001b[1m,\u001b[0m\n", + " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m{'__name__': '__main__', '__doc__': 'Entry point for launching an IPython kernel.\\n\\nThis is separate from the ipykernel pack...\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m at 0x0000022AB3583050, file \"c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel_launcher.py\"...\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\\u001b[0m\u001b[32m\u001b[1mrunpy.py\u001b[0m\", line \u001b[33m86\u001b[0m, in \u001b[35m_run_code\u001b[0m\n", + " \u001b[1mexec\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode\u001b[0m\u001b[1m,\u001b[0m \u001b[1mrun_globals\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m{'__name__': '__main__', '__doc__': 'Entry point for launching an IPython kernel.\\n\\nThis is separate from the ipykernel pack...\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m at 0x0000022AB3583050, file \"c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel_launcher.py\"...\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\\u001b[0m\u001b[32m\u001b[1mipykernel_launcher.py\u001b[0m\", line \u001b[33m17\u001b[0m, in \u001b[35m\u001b[0m\n", + " \u001b[1mapp\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mlaunch_new_instance\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m>\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\traitlets\\config\\\u001b[0m\u001b[32m\u001b[1mapplication.py\u001b[0m\", line \u001b[33m1053\u001b[0m, in \u001b[35mlaunch_instance\u001b[0m\n", + " \u001b[1mapp\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mstart\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelapp.py\u001b[0m\", line \u001b[33m737\u001b[0m, in \u001b[35mstart\u001b[0m\n", + " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mio_loop\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mstart\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\tornado\\platform\\\u001b[0m\u001b[32m\u001b[1masyncio.py\u001b[0m\", line \u001b[33m215\u001b[0m, in \u001b[35mstart\u001b[0m\n", + " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1masyncio_loop\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_forever\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m<_WindowsSelectorEventLoop running=True closed=False debug=False>\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\asyncio\\\u001b[0m\u001b[32m\u001b[1mbase_events.py\u001b[0m\", line \u001b[33m603\u001b[0m, in \u001b[35mrun_forever\u001b[0m\n", + " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_run_once\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m<_WindowsSelectorEventLoop running=True closed=False debug=False>\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\asyncio\\\u001b[0m\u001b[32m\u001b[1mbase_events.py\u001b[0m\", line \u001b[33m1909\u001b[0m, in \u001b[35m_run_once\u001b[0m\n", + " \u001b[1mhandle\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_run\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m, ...],))>)>\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\asyncio\\\u001b[0m\u001b[32m\u001b[1mevents.py\u001b[0m\", line \u001b[33m80\u001b[0m, in \u001b[35m_run\u001b[0m\n", + " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_context\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun\u001b[0m\u001b[1m(\u001b[0m\u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_callback\u001b[0m\u001b[1m,\u001b[0m \u001b[35m\u001b[1m*\u001b[0m\u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_args\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m│ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m, ...],))>)>\u001b[0m\n", + " \u001b[36m│ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1m, ...],))>)>\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m, ...],))>)>\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelbase.py\u001b[0m\", line \u001b[33m524\u001b[0m, in \u001b[35mdispatch_queue\u001b[0m\n", + " \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mprocess_one\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelbase.py\u001b[0m\", line \u001b[33m513\u001b[0m, in \u001b[35mprocess_one\u001b[0m\n", + " \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mdispatch\u001b[0m\u001b[1m(\u001b[0m\u001b[35m\u001b[1m*\u001b[0m\u001b[1margs\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m([, , >\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelbase.py\u001b[0m\", line \u001b[33m418\u001b[0m, in \u001b[35mdispatch_shell\u001b[0m\n", + " \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mresult\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelbase.py\u001b[0m\", line \u001b[33m758\u001b[0m, in \u001b[35mexecute_request\u001b[0m\n", + " \u001b[1mreply_content\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mreply_content\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mipkernel.py\u001b[0m\", line \u001b[33m426\u001b[0m, in \u001b[35mdo_execute\u001b[0m\n", + " \u001b[1mres\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mshell\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_cell\u001b[0m\u001b[1m(\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mzmqshell.py\u001b[0m\", line \u001b[33m549\u001b[0m, in \u001b[35mrun_cell\u001b[0m\n", + " \u001b[35m\u001b[1mreturn\u001b[0m \u001b[1msuper\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_cell\u001b[0m\u001b[1m(\u001b[0m\u001b[35m\u001b[1m*\u001b[0m\u001b[1margs\u001b[0m\u001b[1m,\u001b[0m \u001b[35m\u001b[1m**\u001b[0m\u001b[1mkwargs\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m{'store_history': True, 'silent': False, 'cell_id': 'vscode-notebook-cell:/c%3A/Users/matri/Polibuda/diploma/ART-Templates/%7...\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m('project.add_step(\\n CheckLossOnInit(EfiNet),\\n [CheckScoreCloseTo(metric=ce_loss,\\n ...\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3024\u001b[0m, in \u001b[35mrun_cell\u001b[0m\n", + " \u001b[1mresult\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_run_cell\u001b[0m\u001b[1m(\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3079\u001b[0m, in \u001b[35m_run_cell\u001b[0m\n", + " \u001b[1mresult\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mrunner\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcoro\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1masync_helpers.py\u001b[0m\", line \u001b[33m129\u001b[0m, in \u001b[35m_pseudo_sync_runner\u001b[0m\n", + " \u001b[1mcoro\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1msend\u001b[0m\u001b[1m(\u001b[0m\u001b[36m\u001b[1mNone\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3284\u001b[0m, in \u001b[35mrun_cell_async\u001b[0m\n", + " \u001b[1mhas_raised\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_ast_nodes\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode_ast\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mbody\u001b[0m\u001b[1m,\u001b[0m \u001b[1mcell_name\u001b[0m\u001b[1m,\u001b[0m\n", + " \u001b[36m │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m'C:\\\\Users\\\\matri\\\\AppData\\\\Local\\\\Temp\\\\ipykernel_33072\\\\1791217716.py'\u001b[0m\n", + " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m[, ]\u001b[0m\n", + " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3466\u001b[0m, in \u001b[35mrun_ast_nodes\u001b[0m\n", + " \u001b[35m\u001b[1mif\u001b[0m \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_code\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode\u001b[0m\u001b[1m,\u001b[0m \u001b[1mresult\u001b[0m\u001b[1m,\u001b[0m \u001b[1masync_\u001b[0m\u001b[35m\u001b[1m=\u001b[0m\u001b[1masy\u001b[0m\u001b[1m)\u001b[0m\u001b[1m:\u001b[0m\n", + " \u001b[36m │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1mFalse\u001b[0m\n", + " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m at 0x0000022AF3C56340, file \"C:\\Users\\matri\\AppData\\Local\\Temp\\ipykernel_33072\\1791217716.py\", line 1>\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3526\u001b[0m, in \u001b[35mrun_code\u001b[0m\n", + " \u001b[1mexec\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode_obj\u001b[0m\u001b[1m,\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1muser_global_ns\u001b[0m\u001b[1m,\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1muser_ns\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m{'__name__': '__main__', '__doc__': 'Automatically created module for IPython interactive environment', '__package__': None, ...\u001b[0m\n", + " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m at 0x0000022AF3C56340, file \"C:\\Users\\matri\\AppData\\Local\\Temp\\ipykernel_33072\\1791217716.py\", line 1>\u001b[0m\n", + "\n", + " File \"\u001b[32mC:\\Users\\matri\\AppData\\Local\\Temp\\ipykernel_33072\\\u001b[0m\u001b[32m\u001b[1m1791217716.py\u001b[0m\", line \u001b[33m7\u001b[0m, in \u001b[35m\u001b[0m\n", + " \u001b[1mproject\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_all\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1mproject.py\u001b[0m\", line \u001b[33m201\u001b[0m, in \u001b[35mrun_all\u001b[0m\n", + " \u001b[1mstep\u001b[0m\u001b[1m(\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1msteps.py\u001b[0m\", line \u001b[33m249\u001b[0m, in \u001b[35m__call__\u001b[0m\n", + " \u001b[1msuper\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m__call__\u001b[0m\u001b[1m(\u001b[0m\u001b[1mprevious_states\u001b[0m\u001b[1m,\u001b[0m \u001b[1mdatamodule\u001b[0m\u001b[1m,\u001b[0m \u001b[1mmetric_calculator\u001b[0m\u001b[1m,\u001b[0m \u001b[1mrun_id\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m'2023-12-01_15-38-42_e12e7153-d0bc-4a18-aef0-ff8409199751'\u001b[0m\n", + " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1mdefaultdict(. at 0x0000022ACF270430>, {'': defaultdict( File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1msteps.py\u001b[0m\", line \u001b[33m78\u001b[0m, in \u001b[35m__call__\u001b[0m\n", + " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mdo\u001b[0m\u001b[1m(\u001b[0m\u001b[1mprevious_states\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1mdefaultdict(. at 0x0000022ACF270430>, {'': defaultdict(\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1msteps.py\u001b[0m\", line \u001b[33m428\u001b[0m, in \u001b[35mdo\u001b[0m\n", + " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mvalidate\u001b[0m\u001b[1m(\u001b[0m\u001b[1mtrainer_kwargs\u001b[0m\u001b[35m\u001b[1m=\u001b[0m\u001b[1m{\u001b[0m\u001b[36m\"dataloaders\"\u001b[0m\u001b[1m:\u001b[0m \u001b[1mtrain_loader\u001b[0m\u001b[1m}\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + " File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1msteps.py\u001b[0m\", line \u001b[33m304\u001b[0m, in \u001b[35mvalidate\u001b[0m\n", + " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mresults\u001b[0m\u001b[1m[\u001b[0m\u001b[36m\"scores\"\u001b[0m\u001b[1m]\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mupdate\u001b[0m\u001b[1m(\u001b[0m\u001b[1mresult\u001b[0m\u001b[1m[\u001b[0m\u001b[34m\u001b[1m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n", + " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1mNone\u001b[0m\n", + " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m{'scores': {}, 'parameters': {'lr': 0.001, 'model_name': 'EfficientNet', 'n_parameters': 17727916, 'batch_size': 32, 'train_s...\u001b[0m\n", + " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "\n", + "\u001b[31m\u001b[1mTypeError\u001b[0m:\u001b[1m 'NoneType' object is not subscriptable\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\call.py:53: UserWarning: Detected KeyboardInterrupt, attempting graceful shutdown...\n", + " rank_zero_warn(\"Detected KeyboardInterrupt, attempting graceful shutdown...\")\n" + ] + }, + { + "ename": "TypeError", + "evalue": "'NoneType' object is not subscriptable", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Users\\matri\\Polibuda\\diploma\\ART-Templates\\{{cookiecutter.project_slug}}\\Tutorial.ipynb Cell 20\u001b[0m line \u001b[0;36m7\n\u001b[0;32m 1\u001b[0m project\u001b[39m.\u001b[39madd_step(\n\u001b[0;32m 2\u001b[0m CheckLossOnInit(EfiNet),\n\u001b[0;32m 3\u001b[0m [CheckScoreCloseTo(metric\u001b[39m=\u001b[39mce_loss,\n\u001b[0;32m 4\u001b[0m value\u001b[39m=\u001b[39mEXPECTED_LOSS, rel_tol\u001b[39m=\u001b[39m\u001b[39m0.1\u001b[39m)]\n\u001b[0;32m 5\u001b[0m )\n\u001b[1;32m----> 7\u001b[0m project\u001b[39m.\u001b[39;49mrun_all()\n", + "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\project.py:218\u001b[0m, in \u001b[0;36mArtProject.run_all\u001b[1;34m(self, force_rerun)\u001b[0m\n\u001b[0;32m 216\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprint_summary()\n\u001b[0;32m 217\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m--> 218\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[0;32m 219\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m 220\u001b[0m remove_logger(logger_id)\n", + "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\project.py:201\u001b[0m, in \u001b[0;36mArtProject.run_all\u001b[1;34m(self, force_rerun)\u001b[0m\n\u001b[0;32m 199\u001b[0m \u001b[39mcontinue\u001b[39;00m\n\u001b[0;32m 200\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 201\u001b[0m step(\n\u001b[0;32m 202\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstate\u001b[39m.\u001b[39;49mstep_states,\n\u001b[0;32m 203\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdatamodule,\n\u001b[0;32m 204\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmetric_calculator,\n\u001b[0;32m 205\u001b[0m run_id,\n\u001b[0;32m 206\u001b[0m )\n\u001b[0;32m 207\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcheck_checks(step, checks)\n\u001b[0;32m 208\u001b[0m \u001b[39mexcept\u001b[39;00m CheckFailedException \u001b[39mas\u001b[39;00m e:\n", + "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:249\u001b[0m, in \u001b[0;36mModelStep.__call__\u001b[1;34m(self, previous_states, datamodule, metric_calculator, run_id)\u001b[0m\n\u001b[0;32m 245\u001b[0m curr_device \u001b[39m=\u001b[39m (\n\u001b[0;32m 246\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mcuda\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39maccelerator, CUDAAccelerator) \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mcpu\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 247\u001b[0m )\n\u001b[0;32m 248\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmetric_calculator\u001b[39m.\u001b[39mto(curr_device)\n\u001b[1;32m--> 249\u001b[0m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(previous_states, datamodule, metric_calculator, run_id)\n\u001b[0;32m 250\u001b[0m \u001b[39mdel\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\n\u001b[0;32m 251\u001b[0m gc\u001b[39m.\u001b[39mcollect()\n", + "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:82\u001b[0m, in \u001b[0;36mStep.__call__\u001b[1;34m(self, previous_states, datamodule, metric_calculator, run_id)\u001b[0m\n\u001b[0;32m 80\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m 81\u001b[0m art_logger\u001b[39m.\u001b[39mexception(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mError while executing step \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname\u001b[39m}\u001b[39;00m\u001b[39m!\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m---> 82\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[0;32m 83\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m 84\u001b[0m remove_logger(logger_id)\n", + "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:78\u001b[0m, in \u001b[0;36mStep.__call__\u001b[1;34m(self, previous_states, datamodule, metric_calculator, run_id)\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdatamodule \u001b[39m=\u001b[39m datamodule\n\u001b[0;32m 77\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfill_basic_results()\n\u001b[1;32m---> 78\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdo(previous_states)\n\u001b[0;32m 79\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfinalized \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m 80\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n", + "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:428\u001b[0m, in \u001b[0;36mCheckLossOnInit.do\u001b[1;34m(self, previous_states)\u001b[0m\n\u001b[0;32m 426\u001b[0m train_loader \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdatamodule\u001b[39m.\u001b[39mtrain_dataloader()\n\u001b[0;32m 427\u001b[0m art_logger\u001b[39m.\u001b[39minfo(\u001b[39m\"\u001b[39m\u001b[39mCalculating loss on init\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m--> 428\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mvalidate(trainer_kwargs\u001b[39m=\u001b[39;49m{\u001b[39m\"\u001b[39;49m\u001b[39mdataloaders\u001b[39;49m\u001b[39m\"\u001b[39;49m: train_loader})\n", + "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:304\u001b[0m, in \u001b[0;36mModelStep.validate\u001b[1;34m(self, trainer_kwargs)\u001b[0m\n\u001b[0;32m 301\u001b[0m art_logger\u001b[39m.\u001b[39minfo(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mValidating model \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel_name\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 303\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39mvalidate(model\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39minitialize_model(), \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mtrainer_kwargs)\n\u001b[1;32m--> 304\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mresults[\u001b[39m\"\u001b[39m\u001b[39mscores\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mupdate(result[\u001b[39m0\u001b[39;49m])\n", + "\u001b[1;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable" ] } ], "source": [ "project.add_step(\n", - " CheckLossOnInit(ResNet18),\n", + " CheckLossOnInit(EffiNet),\n", " [CheckScoreCloseTo(metric=ce_loss,\n", " value=EXPECTED_LOSS, rel_tol=0.1)]\n", " )\n", @@ -232,9 +573,17 @@ "project.run_all()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great, the loss looks good!\\\n", + "The next step we want to perform, is to `overfit one batch` of our data. Thanks to this, we can be sure that our model is properly implemented." + ] + }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -264,11 +613,18 @@ "source": [ "from art.steps import OverfitOneBatch\n", "from art.checks import CheckScoreLessThan\n", - "project.add_step(OverfitOneBatch(ResNet18, number_of_steps=40),\n", + "project.add_step(OverfitOneBatch(EffiNet, number_of_steps=40),\n", " [CheckScoreLessThan(metric=ce_loss, value=0.05)])\n", "project.run_all()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we will do the same, for the whole dataset." + ] + }, { "cell_type": "code", "execution_count": null, @@ -278,7 +634,7 @@ "from art.steps import Overfit\n", "from art.checks import CheckScoreGreaterThan\n", "\n", - "project.add_step(Overfit(ResNet18, max_epochs=10),\n", + "project.add_step(Overfit(EffiNet, max_epochs=10),\n", " [CheckScoreGreaterThan(metric=accuracy_metric, value=0.8)])" ] }, @@ -291,9 +647,16 @@ "project.run_all()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, that the model passed all steps and checks we can finaly proceed with the `transfer learning`!" + ] + }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -315,10 +678,11 @@ "source": [ "from art.steps import TransferLearning\n", "from art.loggers import NeptuneLoggerAdapter\n", + "from lightning.pytorch.callbacks import EarlyStopping\n", "\n", - "logger = NeptuneLoggerAdapter(project=\"mmaecki/transfer-learning\", api_token=\"\")\n", + "logger = NeptuneLoggerAdapter(project=\"mmaecki/transfer-learning\")\n", "early_stopping = EarlyStopping('CrossEntropyLoss-validate', patience=6)\n", - "project.add_step(TransferLearning(ResNet18,\n", + "project.add_step(TransferLearning(EffiNet,\n", " freezed_trainer_kwargs={\"max_epochs\": 4,\n", " \"check_val_every_n_epoch\": 2,\n", " \"callbacks\": [early_stopping]},\n", @@ -333,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [ { diff --git a/{{cookiecutter.project_slug}}/models/EffiNet.py b/{{cookiecutter.project_slug}}/models/EffiNet.py new file mode 100644 index 0000000..106845b --- /dev/null +++ b/{{cookiecutter.project_slug}}/models/EffiNet.py @@ -0,0 +1,59 @@ +from typing import Dict +from art.core import ArtModule +import torch +import timm +import torch.nn as nn +from torchvision import transforms +import numpy as np +from einops import rearrange +from art.utils.enums import ( + BATCH, + INPUT, + LOSS, + PREDICTION, + TARGET, + TRAIN_LOSS, + VALIDATION_LOSS, +) + +class EffiNet(ArtModule): + def __init__(self, num_classes=100, lr=1e-3): + super().__init__() + self.model = timm.create_model('efficientnet_b2.ra_in1k', pretrained=True, num_classes=100) + self.loss = torch.nn.CrossEntropyLoss() + self.lr = lr + self.preprocess = transforms.Compose([ + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), + transforms.Resize(256), + ]) + + def parse_data(self, data): + """This is first step of your pipeline it always has batch keys inside""" + X = data[BATCH][INPUT] + X = X / 255 + X = rearrange(X, "b h w c -> b c h w") + X = self.preprocess(X) + target = data[BATCH][TARGET].long() + return {INPUT: X, TARGET: target} + + + + def predict(self, data: Dict): + return {PREDICTION: self.model(data[INPUT]), TARGET: data[TARGET]} + + def compute_loss(self, data): + # Notice that the loss calculation is done in MetricsCalculator! + # We only need to specify which loss (metric) we want to use + loss = data["CrossEntropyLoss"] + return {LOSS: loss} + + def configure_optimizers(self): + return torch.optim.Adam(self.parameters(), lr=self.lr) + + def log_params(self): + # Log relevant parameters + return { + "lr": self.lr, + "model_name": self.model.__class__.__name__, + "n_parameters": sum(p.numel() for p in self.parameters() if p.requires_grad), + } \ No newline at end of file diff --git a/{{cookiecutter.project_slug}}/models/ResNet.py b/{{cookiecutter.project_slug}}/models/ResNet.py index 26563e5..0e07ee3 100644 --- a/{{cookiecutter.project_slug}}/models/ResNet.py +++ b/{{cookiecutter.project_slug}}/models/ResNet.py @@ -15,7 +15,7 @@ VALIDATION_LOSS, ) -class ResNet18(ArtModule): +class ResNet(ArtModule): def __init__(self, num_classes=100, lr=1e-3): super().__init__() self.model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnet18_swsl') @@ -27,8 +27,6 @@ def __init__(self, num_classes=100, lr=1e-3): transforms.Resize(256), transforms.CenterCrop(224), ]) - # for name, para in self.model.named_parameters(): - # para.requires_grad = True def parse_data(self, data): """This is first step of your pipeline it always has batch keys inside""" diff --git a/{{cookiecutter.project_slug}}/steps.py b/{{cookiecutter.project_slug}}/steps.py index 8c8dcef..460d833 100644 --- a/{{cookiecutter.project_slug}}/steps.py +++ b/{{cookiecutter.project_slug}}/steps.py @@ -20,16 +20,11 @@ def do(self, previous_states): # Now tell me what are the names of these classes class_names = list(self.datamodule.dataset["train"].features[TARGET].names) - class_counts = Counter(targets) - # Now calculate number of images in each class - number_of_examples_in_each_class = [ - class_counts[i] for i in range(number_of_classes) - ] + class_counts = Counter(targets) # Now tell me dimensions of each image img_dimensions = self.datamodule.train_dataloader().dataset[0][INPUT].shape - figures = [] for cls in class_names: class_indices = [i for i, label in enumerate(targets) if label == cls] class_samples = np.random.choice(class_indices, 5, replace=False).tolist() @@ -47,15 +42,13 @@ def do(self, previous_states): MatplotLibSaver().save( fig, self.get_full_step_name(), self.get_class_image_path(cls) ) - figures.append(fig) self.results.update( { "number_of_classes": number_of_classes, "class_names": class_names, - "number_of_examples_in_each_class": number_of_examples_in_each_class, + "number_of_examples_in_each_class": class_counts, "img_dimensions": img_dimensions, - "images": figures, } ) def log_params(self): From aed727d4a4d0aeaeff23e45cbdec94c619dfbeab Mon Sep 17 00:00:00 2001 From: mmaecki Date: Sat, 2 Dec 2023 18:58:41 +0100 Subject: [PATCH 09/10] Tutorial almost done. Waiting for some change suggestions. --- {{cookiecutter.project_slug}}/.gitignore | 1 - {{cookiecutter.project_slug}}/Tutorial.ipynb | 634 +++--------------- .../results.json | 1 + .../Data analysis/results.json | 1 + .../EffiNet_Check Loss On Init/results.json | 1 + .../EffiNet_Overfit One Batch/results.json | 1 + .../EffiNet_Overfit/results.json | 1 + .../EffiNet_TransferLearning/results.json | 1 + .../results.json | 1 + .../MlBaseline_Evaluate Baseline/results.json | 1 + 10 files changed, 93 insertions(+), 550 deletions(-) create mode 100644 {{cookiecutter.project_slug}}/art_checkpoints/AlreadyExistingResNet20Baseline_Evaluate Baseline/results.json create mode 100644 {{cookiecutter.project_slug}}/art_checkpoints/Data analysis/results.json create mode 100644 {{cookiecutter.project_slug}}/art_checkpoints/EffiNet_Check Loss On Init/results.json create mode 100644 {{cookiecutter.project_slug}}/art_checkpoints/EffiNet_Overfit One Batch/results.json create mode 100644 {{cookiecutter.project_slug}}/art_checkpoints/EffiNet_Overfit/results.json create mode 100644 {{cookiecutter.project_slug}}/art_checkpoints/EffiNet_TransferLearning/results.json create mode 100644 {{cookiecutter.project_slug}}/art_checkpoints/HeuristicBaseline_Evaluate Baseline/results.json create mode 100644 {{cookiecutter.project_slug}}/art_checkpoints/MlBaseline_Evaluate Baseline/results.json diff --git a/{{cookiecutter.project_slug}}/.gitignore b/{{cookiecutter.project_slug}}/.gitignore index de3971c..107b2d4 100644 --- a/{{cookiecutter.project_slug}}/.gitignore +++ b/{{cookiecutter.project_slug}}/.gitignore @@ -102,7 +102,6 @@ instance/ # TODO: Add more patterns based on your specific needs *.png -art_checkpoints/ lightning_logs/ .neptune/ diff --git a/{{cookiecutter.project_slug}}/Tutorial.ipynb b/{{cookiecutter.project_slug}}/Tutorial.ipynb index 0baa3c5..2da6d56 100644 --- a/{{cookiecutter.project_slug}}/Tutorial.ipynb +++ b/{{cookiecutter.project_slug}}/Tutorial.ipynb @@ -125,12 +125,12 @@ "All classes names: ['apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle', 'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel', 'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock', 'cloud', 'cockroach', 'couch', 'cra', 'crocodile', 'cup', 'dinosaur', 'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster', 'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion', 'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain', 'mouse', 'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree', 'pear', 'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy', 'porcupine', 'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose', 'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake', 'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', 'table', 'tank', 'telephone', 'television', 'tiger', 'tractor', 'train', 'trout', 'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf', 'woman', 'worm']\n", "Number of examples in each class: {'cattle': 500, 'dinosaur': 500, 'apple': 500, 'boy': 500, 'aquarium_fish': 500, 'telephone': 500, 'train': 500, 'cup': 500, 'cloud': 500, 'elephant': 500, 'keyboard': 500, 'willow_tree': 500, 'sunflower': 500, 'castle': 500, 'sea': 500, 'bicycle': 500, 'wolf': 500, 'squirrel': 500, 'shrew': 500, 'pine_tree': 500, 'rose': 500, 'television': 500, 'table': 500, 'possum': 500, 'oak_tree': 500, 'leopard': 500, 'maple_tree': 500, 'rabbit': 500, 'chimpanzee': 500, 'clock': 500, 'streetcar': 500, 'cockroach': 500, 'snake': 500, 'lobster': 500, 'mountain': 500, 'palm_tree': 500, 'skyscraper': 500, 'tractor': 500, 'shark': 500, 'butterfly': 500, 'bottle': 500, 'bee': 500, 'chair': 500, 'woman': 500, 'hamster': 500, 'otter': 500, 'seal': 500, 'lion': 500, 'mushroom': 500, 'girl': 500, 'sweet_pepper': 500, 'forest': 500, 'crocodile': 500, 'orange': 500, 'tulip': 500, 'mouse': 500, 'camel': 500, 'caterpillar': 500, 'man': 500, 'skunk': 500, 'kangaroo': 500, 'raccoon': 500, 'snail': 500, 'rocket': 500, 'whale': 500, 'worm': 500, 'turtle': 500, 'beaver': 500, 'plate': 500, 'wardrobe': 500, 'road': 500, 'fox': 500, 'flatfish': 500, 'tiger': 500, 'ray': 500, 'dolphin': 500, 'poppy': 500, 'porcupine': 500, 'lamp': 500, 'cra': 500, 'motorcycle': 500, 'spider': 500, 'tank': 500, 'orchid': 500, 'lizard': 500, 'beetle': 500, 'bridge': 500, 'baby': 500, 'lawn_mower': 500, 'house': 500, 'bus': 500, 'couch': 500, 'bowl': 500, 'pear': 500, 'bed': 500, 'plain': 500, 'trout': 500, 'bear': 500, 'pickup_truck': 500, 'can': 500}\n", "Demansions of images: [32, 32, 3]\n", - "art_checkpoints\\Data analysis\\class_images\\class_chair.png\n" + "art_checkpoints\\Data analysis\\class_images\\class_train.png\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -142,12 +142,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "art_checkpoints\\Data analysis\\class_images\\class_lizard.png\n" + "art_checkpoints\\Data analysis\\class_images\\class_bus.png\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -159,12 +159,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "art_checkpoints\\Data analysis\\class_images\\class_bee.png\n" + "art_checkpoints\\Data analysis\\class_images\\class_apple.png\n" ] }, { "data": { - "image/png": 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", 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PyTAWw9iAhVKG14ffnXPje1JKiAh37tzhV37lV/hv/9v/Fu89/+v/+r/y3ve+l2effXZ8r4iM7x8+462I3bm3O1ZXnXsA/9P/9D/x3/13/x37+/t85jOf4XOf+xw///M/f2Hu7X7WW3ns3ui59+DBA/7RP/pH/Pf//X+P957/8//8P/nrf/2v8453vOOhe3f4rLfy+L2Rc09E+J//5/+Zv//3/z7Xr1/nT//0T/nDP/xD/sv/8r/8Szf3vhOYjIG3IFSVu3fvcvPmTZxzrNdrYoxvWTr7zxsD7Xvjxg1UlQcPHoze74RvDVXl3r173LhxA+ccm82Grus4PDz8Th/aWwIxRk5OTrh58+YYElgsFn8pqi/ebAxz7+joCO89bduyXq+nJME3CJMx8C2Qc+bDH/4wx8fHAG+5GtZXs4qvch5vf/vb+bEf+7FH+pvjY+XBfQURQAAFLb/aET7ycfzFxuXzs5e8h2efA++//fNVVX7rt36Ll19+uXhJALuP9oMIgpQxtmsuDP+2nQPeeXvvMCcuT4Hy8vDPsvvk4lt23nzx+fbpq8y7h36xJ1qGjWFeqqIomjN9jDz77DP8tZ/48Ufy8gbvcGB2LjNxw/dcPtZv5954paPQ4aSGaV6+yzlHSpl79+6xXK748Id/F9XM7ds3ERHe855/h+vXrzGbzXHe27W89MmvfTQ7V2bnfIZzflR2QVX5+hc/zfLkAbV3eBE0Z3LKJFEiit+r8Qc1zilDoY2KELvMyZ01JHAbO7J9L3gR9psFIkIXM1mVZWc5ErP9itAEkARkKu9xzrFaR/o+QRCoBHVK8pGqCjTzmpQyd148QTLspcbGznkcQqN2H2zmFdnZ36pAlQOCkLtM6jLqI9knQqgIIdCmSJ8STjy1m7O3f8Bz3/32x4JhmEoLvwVyznzsYx8bk3/eqhgm8usxZn7iJ37ikY2BszOwsPNA310+risfzl9IvPLwKlUFTz8jPEqFmJUA/j6f//znLyzmw+8izn52ngPldcE52XmfUFXVhfe9EgajYzQILhsD43N5hX+/9NrlTZZXmIPl9zy8Vuhgo4WVGCNt2/LDP/xX+Ws/8WjJYMNG+Eqvb492ewzf1p2h24eL9p5e+ISsGc2KiCshg8S9e/e4d+8+v/L/+1VSSrzr+/4K3nueeuoWde0JVaBy5cguGyzf8ugE8Bfu8V0K/qr45le+wL1vfJW9EAjOkWMkx0iH0jqlujWnfnIf75VQDSPg2KwiX//cMdKDP7aktCcqR+2E24c38M5zvomknLm7PCZr5vD2nPlBA9KBSzRVTRUC9x6sWW8iVILseZKPxLqnmdccHu3TtZHPfebrSITb3QEOgVDjEQ7VA8LZtTmxciR7yiw1eBz9sieuErFqyVVHM5vRNDPOupZ17AlSseePuPXkkzz33W+/8ji+lTAZA98m3mqswIDdWB1c/Tyu9Heq5DwsTtt44e5x2eJ1pUP6CwFzLi96Y3DRALOXHv0kQ6io62bHALi4wZshMGz+tsnbo+D97nPwPuy872FPcpd5EHl9hsB47sOYjGMzPG7nwu4P5Uc1j++JMRqrcUW8ukcnD//6GnNcX+X33c/ZcmC5fG5GNbFcnrFanaFkfHA888wzhFBxcHCNZrZAnEcpxprsHMrWannFLxxZoDd4eXKt4lYZFxLOZVwAqSuEhBARErlf40JAmwBZYdMjscenFklQqUewx5AFjRuyeMgJ0UwtLZlE5TzBCUlbUu7x4gjOsa9KnUGTEttIHxIqET+rkCB4FfavCZJgv82gQte2SFI2ZzYgqyoSK0+uFRwEMoonxY7Yd+ATIolQNYQGnHaorkha0UZPH/ff2IH9C4zJGHgNvJqH8VbBrqfw530e201Av6VRMvz6VjMKLm9uF/9Nx/dcddhDqKiqGuf8SPsPhoG7YBBsjYHBYLhsQAyfMYYXkAub964BcPn5gEdiBoZxGH6KUbQ7VvkCEwBaGIGc8zhoIQScv1rR06sf/4VXy/8tZLANZbzCRfuW8/PSP8p45qhmNpsVm80KyHhfcfv2E1RVxWKxT9PMcc6DOHQIqY0fV4yK15hEr3Rur4fadp3iN+CqhHM2f3xdIQpCsv9iZ8dbVxAVST2SenzskSQEVRxCUAgIGjuyFEtHM0E6lEzleipvGzTa42RG5ZQFMFOIKdP1itNEHzKOjKsEh7DYF1yCea2QlNQlVDPdqkcVNnPokzP2xsPcB0QCKW3IsUPUwhw+ZKpGkNhD2pBTpI2OPm2uPIZvNUzGwIQ3HY9DvO0y3ggDzDk3/tjGL69qCFxkDS6HE7DNBgsf2PFdNB5sD7KI9eXnl8/p4Y3/1Q2B4VEBzZfYIedAtRyLoiIoW8PRq+Kcx32L0Maj4OFrURirkRm4eNw7f/jw67L7wsAG7f4N4+eqQrtpadse5zwhVOzt7VPXNSGEkiew491f+Gx7ouq237N7EEPOyBuM85NTjl++iy4qquBwMeCloteePnf0Uek6cP0Ml2dIUqqTjm7Zsbp7DxcFPQ8IsPae3gmpOUdEWGI5A2fpBNVMmrcsZEamJ2uml0CImdXJGd1pR/JKH5S2ypzPexrtkH1HTgnSBjLjj8tKipnV8Smqyqlv6WpPuB6QIEif8CrEs4607KjxVM4jlZBcposrkm7Q5OlXmU27esPH9i8qJmNgwpuGy0vUZXbgccBuDP5RUdcNTTMfxWsuGwPOOcS9kjHwcM6AK1T7xXDDDjtQjAQ3Pt8aC/BwcuC4AT30fHv8urO57rJEw6OqovliqMBCBBZzjzGiCFVRTnwUvNpcU1Xy7q4rUpx4edgIKA76EHnXnc1YEAsFlNeccQv2ugrgQC0kk7Ny585dTk5Omc332N/b413vfBdN03C4d0DlPHmbO7lzDuXb1I9HMB7A7kF+izG4Kv70j/8NX/z4x3n7MzfYm9fItRq53ZBjR44bztKak7gk3loQby2oEtw6h3jacucTLxCicO28QRQ2IniBzilZlPtVIjrlwWxFkszb/soTHN0+YNHMqEOA5iZ9WPC1P/kyJy8do17IwdHWynKemN3e5+DeLZxTrh1Em3t5hiRH09Wsz1q++kefI6XMl488m1q49f1PUM0rzr52TNxEZJOQTeTmc9e5/vQh/niGm9f4/YSfZ9arzItfaZHw1pOCviomY+CquBQHVtld8nZxOZ4nlzYH/RYM4CWX5RXfMSxig4ejO/9m/9fLf6+v+uTi174BHsduMtrlxflKi9W4weye58Mb0fj2Vxm6h5b+KxzL5T95ow0d5xze+zHLfGsMSElOM3bANn5XDA+/wwzsPvflM7chgodYgV3joHze5fkqI4X9cN4Bl67DrjGwOz7jY1bUqSUQqpI1g1qNvjq7wt6HK/eFeNX5dcED1+39M16/7c6sYuGLIY9hm+NSUiJF7R4s9H7G299ni1Hn5Mgx03U9fR+ZNTWzWUNTVzR1wDkFyTtf64oRcoktGM8JUBl/lx0G4o1E7Fv69YrUzkk+oW2PrntcjkiKSN8iXUs8h3WTSRG6UyWdd4S2w/eCW5chzmY29T6RRYn7QvLgZgLeE6Vn068IorgUWLenQEe/2ZBjj2ZBszEBKfX0jbC5X+EDSGPGbk4JiQ5tE7QtrushZeK6o4vQrddkiZyfnRE3PVWEKkLXrWnbANKjybM3qwjOA5E+bkjp8REumoyBK8NxUcAxYhvyzt1bPAN7LJBhzSnxxAufOWxrw/uHx60HcoF/HNOVhtcHvmznyx7Kfb6I7b/k8RV9M1aXCY+Mqm5oZsYMXIz/u1d8fOVcgctMwUVjYAwblMTCgRFwcpEZGBmAbysOb3ilhLvdPItX/MnGCuScTT1ShbqevZ5hfAiFeOdipr4CyV7LsWz+pl6ZUkQ1EfuenBOqCVC8d3hvhph3AXCI1IDDSUWOjozSrVse3DtltVrz5K3bHB7uc/PGXgkTJGCzExlwZRUYEj3tdzcaB7JNKdBd4+yNxerBy5ze+TrreUu1mdFtlM0ms+cqDkKDXy3x52esjuGll6HuFXc3Elpl764Selgce0QVNi05Z87chuhh9Y4DdOZZvO02NJ5lOmV5fpdbLNiXmtWdb+CW0G0yOWpZ2oQ+bdh052y+6Tm9U1HPPE//6E28hy5mJDp4aQ89jczvn5BSZnW+4tgnuN4TFhVf/eJXaZctR/MFR/MFbrEkVSec5451jrxz9gwHt2+hbsPZ8i6rzfEbP7h/QTEZA1fGJSZAASnlczLEEBXRjLgtvWfexLDx5vH1cdMeGQcZPYCxdGn0tAbXq2QTM7xVC3U5vorRlTAaFpfinDsHf+m1NwYPmyID1TpaRQ95mnr5GHfYhQGv9NuFv3m191xgPYpPOMT3y/8eWlvH1155fPRNsp18YQYGJbWrGgPApeqCi9UHxiC4kR0YWYIxJl3wiAbBBT9bd56/UqiAYgg4R06m3ocqPvjRYHlUPKwrsHPMJQBgxxDtzunXoBlyz65hrTmScyb1HSn2pNSTcsLZkFkegK8Q5xHX4F1F8HNydqTUsVkvOTs7YbPZUAWoAqS0Kb0iOpwzIsFu/WKUMRhjvrBAHifeEkFLMqgTX67JqzMnajUKjz54ogjZfjQRRJgHz9x5Fj6gLpCl4jj25HWP9uA2CR+Fxnm8A1/WQeeM7UgC0Slhr0EXgbDfIDMPfbQ8yTWkvidvMrLKVGGGmwVSn4nrnpCgjpDaRH+WIXpCjHiESoyX0RTJKdEoZBV8VhxKv2lJksgxkWPGAcE5M+gcSFZQu6ZVEKrKUdeeKjw+iv2TMXBlpPIj2x8tG4zaQiIu4r1SeTXDwBlzIBLtuRjlOBgLQ8b1mEytoCpGk6l9h2abnJrL89FgcOOjjpu/2zkuSyDbrocDM3GZTXjjoK9gYOxs+bzCtvvqGBwlNzwtf7szVrsb+fi4c3nAhqKEiBnGY/jJ7PyiF67sBQ7m4jleOrk3kLatm4bZbHYlZuDhnIHLz7evw9ZI2DIE23DBOI7jST78/JVGZWvUbeedsjUC2Pl9zB0oEto5Z2JVkTVfSZ3v1UI2hRcZjyZrpN1Ystnm5AU09fi8RlCq2sY954SitOsVXd9yvjyjbTfE2JFzwodAPZvhfEXd7FPXexzuP0nXKesVnJye8YXPf5qUEz/07ndwsKesly8Qu8Cm35A10/WWVCdSmIHCJvpQEUJF08yZNXPq2n6c89T1AnCIVuaIjOe460RcDU4izvd4WjywV82Y7804cDOuyZy2D2x8xXJ1jz+7/wCNMDv1zCTwRL2PSia5DaIQZhVZYc2SzsP1d97GHzZU33sdGkdo57jYon/6gPZ4RbqzQu/1vP373sW1WzdZn6x4cHqX0EHYCKtVy4O75+S9wOI9+9Qzx/5+bezBekm9ydxOto7u9Zm1JE6+eZcYlPZsQ+4y4cCxN2tYNIFZI6w1QmppKmVvEdBOuXVjweHBo+ervFUxGQNXxS47z7DcCU50rM2tQsQ5aELZbpw1DHLeQgpuZAx2YqgKMdqCmBIWSx02eBXUld+dsRCjmAuu/O6KV+ExVqDsahpQBOViQsywKX6rU3x9+FafMmwIF334i3+RS9wxg6iJn2hCUx4p5XHz1lf7jELJDNl8xVO5+ChQMu4Fb3+iA8fySmzDxcV2Z9+7mif2CnCvwQyIc7hXNQa2OQMwqB9uX999HL5LEHCy80hhBuTieb0KQ3BhLC6RZrsGwW6PjfHHFYPAyagcqGr68lfNGciqOJFtcl6hJ0St/CzFc3Lq6Zcv2zG0x2jqEa84Gch5my8K+BDwmu0xBWLqyJogK10PPieQgODpuhV9D32v5LTh4GCGauba4T77+4ty/hTrdMwQQHTLRqCQSUSNOIyxyLEjdhu8D2iOOBeo6j0zBiSMzM4wvmXoHxkJJamSUybHhGRoJNC4irlvUHpir7DJxFUkZUeIHidKlIhkqGa1XfxoFnuvji4oXS34xuGDIEFwBKqgbEToUx6t9ZQyqY/kLiJtxCdlroGskVodtTokJohl7JwwmzeEPlHLyly1mFAi/SoTgyIJXBZISo4JJ1BXgbnMyJUQvCPFRE4Z77bG8uOAyRh4vSj3m3fgRKmqzGK/o6oSe/sdwUNT2RtzbkGUUA9NSMxIiH0JVRZjYL3aEGOmaztSUpIrG6YKORvtb+yBklLZUDHREqQpxkADCJqaQtHWxVjYMxZBhvcDhRAcXLnvTNbA1qXe8hggmAGV4oqUezbnD+i7NavzM2LXkqMtxqKCU1u8nbjxugxeJwg+BBDBNw3iHGHWICKE+QznPdViH+c9vt4D8TZGF7jub/dMrkjNXkJdVcxms9EYeEh8qDz3u8aAH3QIPA8zBeB27SERhqo9MwbYMgJjNcFQhWBMkww09ki57J737uOr/JteMgK+Zc5AIMVEVT06M5Ax3s4MguEACu2TNuS4YfXgy6RuyfLOFwClYmWMwPUbeF9RyRMgAfUNWSqyq3BNJDlwtafLK2MH+kTc9DhXUVdL6npOTrZZti20Xcf3f99TOOf4/u//bpqmQfDkBOQAOeMVnGbQDtVM7FdmsJRxWRdm0JWQQQgVB9dvUtUzDq4/g3M1vrpmc8IVw0vt3vE8ekZ8p5m1JtZtS41yrd/jJnOuVQfcnF/j+G4kPziGk47V8YYgnnk1wwks5Zw61Fx/4kkAlucbck4cp4p1yHDkCdccYSH4CupqwcILp5+7x+mmZ9YLVfZ0y5aNP6c/XsGDFXMf2K/2OFeHaEedHJy3kDzh+owqeI6eeoK+6dm4U1LOuGVLThvOVh2tKPtujhOHbiKb8zVB9jjcX3AwO0Rrx6LxbM6WxA3MqsqqGx4TPD5n+kZj5OSKtzEssC6bYpczz39rqOu48NprFiYYFqkLGcoXvuCy95l3FtaB4DYvyr5goPwTqEOGuGd5bkukbsVN7IguPLBjGLx+lOO78OxbvHf04sp5ZGNRUt+Tc2dx274j9609DsZAMSHMi93mRwwbi1Gv5nFKSfrKvni8waHJ4asKzQFxNeIGBTnZxhbk4hUCdvIFdsfrDTKldjxwufT7hdLAV3jtodLB0anXnee68++5kCeX32/ns0sgPRw6eLXzHlirQk6V++ShWP6A8gYVvXi+3+ZwvdrnjToBmrGcgFjyAPrigds9gWQzhLUYDZqx/B7dIZa2G/LwA4LTjB/i+lKqP0QQUZw4qirgpDA9hYUZKjmcSMkLlGJGlut24RbcrXoYyi+jhTByIpPwvJECaSWENjgpr/AzJjrs3tQ7Xz+cgwysUllydPfUZJe1G0kBYzEHxa6RoZORrRnGSHV3zWCgWy6eyqWXRGXHKL14vHa+eVse+hhhMgauDHM3QsiEkNnb65g1PXUduXZtjffKfJ65TCyLQAi2gMQYyVkhJ5ucKdtWry2QSKk1yirnCzd5zrr9ScPiXu6ycQMr2c2YV6XJkVVo2wZVh4QDEIev9nC+xkmNjtPh4eX3aguyPvRMyyKze5u5EuQARfs15MT6wR1y7Dj+5vPkFDm5+w025yeszu7Rtys26zNi35FyJGtmvphz7foRVQjU9QxNSu4T7WbD6mw5OoUiQvCBbTIB+KpGnGN+/QgfKvafeTuhmXHjue8h1A3V3nXC/NC8Q/ElrWobQpByDq9vrB5GVdU0TUPY6SvgdhiBsdRwoDNF8CW5zI9hAvMORVqEjHdrRJSqas0LDhERY7ZkaDpjfLVdj4GJUk/WABpQrTHuxsZRXGVXVCozxNycbWY9UHTihymc9WKYIF/KG8iFHQgle7+6Skc/1bLx26GRI3FznxTXnLz8eWMEvvlv8Bo5zKeIwMHBwjZrHJID2ld2jvU1JEATZjSuYlEvUM08cfQMOUVyhtSDVRHYPeRkOD+7d7/rmWcRhKqal2tW2ZhUEVRR3ZjBnkE1kWODki1ZMfXEGIkp0vfl9w3cPX0BFxpO779E3exzdPud+NAwP7h1hdl2EdHX9GHO6XkmSUedluyv79Pub1gfrOlfPKW/u8H1yoKGWXJoNFaiaoSFq3lidgAKL68zrUbqpqKvMmt6Ao5ZVVHVgVCmU96fk6/ts6pbcJHbqx7yinkrHDU3qEPFXjOnySd0aYOvYL3uiSiHIUASlvfvEk8iqq3NgZQhYiEFUXz0CI60VlrXsz5fcX4WOGiusTdbsFyec3q2xuWGmbtBLY/PFvn4nOnrwUAz6tY+H3xd79WadYREVdlPCIngIYSB/tzxtkTwXorTIuPiO/pQu9lwal6Jah7jncO/jbRq8SWG1AB7h3lDMmbdbcMMmi3LVnKN4FBfl1h5Ll7jjifFG7Ox6YXfL8fgL55rjh2aI3GzJPcd3fKUHHvas2M2Zye0Z8f07Ypuc06MHVHNGHDa088DpJogmRxzoW/X9OtzO4hk355Lx6Bhc/KhKol1aglbB4eEbka/Pre4bD3H1xEVbz/sMgJSPMkyVm/UoGGUvewqEA4JgJeSBE1vYJhPQy6AYNe0hKQkAhnnekQyznXlfCOC4n0JIZRk12Ge5cFbzQ5yQKlQzSV8YBuavd82AjMGkl1XKcbwkPyqw3iVTx8SCXfm8phYiO4oMF5xQJXC3Nk9lFNH6lv69ozULUntGUJCXGsGEbUF2zSWKdnb3xf2QLC8FT9UZFBbVn2GVExCwe4lNJXzselQN4NB4+w+3LmPEd0+NzcYGQQHi2GWJeNIOBKSo4UIS25R364Q8aS+NdZGdxKCXyWv4zUhDsSVc4PUZ8viD5G27smd5e2QwZWqpmIhm++u1jsA2PryhQlRVVLJGxkrsQdq1TtySYbUnNGYkOwJ4gni8OKMmSn3a8oZpzZPBSsDzXlb5i3YvK685QF5tZwqwZyqlBIpxuJwlc6MyVihamBoHhNMxsBrYVgYR60Ae17PWuq65/Baz95eYrHoaJoeL0oTrMrA4cGL/SBUocZqkqvy2Z018vFn5hERbXOTCJJxFeAhdUbJpZyJsbAII4Nmm7crtBcpja8LQuWTrSmF0uvbNSkpXapQFerZTUK1RzO7SQh7iJuBVCVtb7uR6xVuioucyOXfFUcmaCT1LeuTe+SU+MYXP0W/WXH+wp9aKOD8GM2Zzfkpfbsh9i05RcRbZ9NVu6aLkZR6pBb2FnPme870y4mEsMbJuS32hZ/ULqFZ6dsWgC7bUvzgpS+ZzfClT4EPXPuud+GbGU+94we5+dw72bv1LPPrt1EXQMLD+/63Fwv5tkevqmqa2YwQqouMgOwaAWYADIujVbYnVM9QWmK+awxAfYJzicXcWKvFIuI9zGYZKYulsQtmeGruUZS+S2XeQdtC7B1d5xECToxRqet9xHlCfQASEH+I5bDsAULOR8ZU5X3MKDDdgMzADDirmhmEfVKyTa6zbP26voICYWGCSD2qHbFbcfern6DvTnjwwseQ3DLv7+Gco5kf2JhunIngpGiJuvUKFQfVHoQZLhwgfo7kCsHjNCBqiboqttmos8ZEWVtyjqS0tsNILYptPhfnymCs2wtOU6HFzaCS3CO5x6eE5AQp4tSMgYCSk9CdnBKXc1J3zmxxRKgst6OeXR9zPh4ZfgbVPt2qw6fMKinnbcuy2nC3vk+zzDSbTFe0GFSVdc40CNdzRWiV/qX79lGSqUTxOeNSZn3vDLcJtPcO0CZQHy1gUeF8hW8aWmnpkrI6WTJnjadhX6+RU2TTrVj7jnUAkrJed9Quc6hKyIp2a3JMpDqhCW4f7eF1zve9/RlcU3P/7pK+T2z6JZt+yfH9Yzb+nP2TcxaHC6qDhmoejFULOhr6jwMmY+DbwKjhN9LwhQ2oMlWVqepEVWVCUKsmGHIDhh2hZGQ7b1apKxUBliWdt9an7OwmQ9nhYE3L1nPaGgMWMxvisRbwKlutKoizOGn5THtLQslj+aLmDk1V8X4i6OgLFq14xlje64E+9Fvx2FIkx55+syKnRHt+SrdZ0Z6fGEuwWULOpN4ai2iKkNNYASCFLRnp5WFwdgTLpajEmbc1eL3ZwjNATvZ3MUYySq8ZnGd9foLvNqxXZ7TrJU0xRFSKpj47e/7o7b1xYQIpyoKvqiNQwgOuhA+245uBCNoDG2OPZINIwvsW54zJcs7YKxEI3mr7VYZmQSa8k3w0zzQXxkCGktVgDIO4Emby+NBb3N11WH+4HrsLyoYh1lluYAgY48KDMVDYAswrdN6NkstXgrKNraeOvj0ntuek9hynHWhrnnxO5pnmDCJo6lF15NTaPEuFSSKU4Y2oBgvBqS9sSEAlo6VLoeqmxPQ3ZTxbm6OjMTDcB2WxyINRUDxmChuYI5Ijotbpz2myCgZsrbHSYyUnR+zWxGpGTr3FvxmCcldAaZFtScvYd8ShFDniIwS1BE0tDFJCjSEpa9Fg+GhtORHOO7yzNUqTkmIiOUGTotnuZdFtUoEZiql8ul2bpJBFS1qxnV1USOV+zFJCrSWHuAqOBmF/3hBmDZtZR+8dSYU2Killui5Z34hNjzQBXwXyUJD1GGEyBl4DMtStlT2kaTJ1E7n9ROTGzcRinpk14FzAOW8LaLJFULHXq2BZ63XTIAjee1QV521x6Hpvnmq0BL+h730ItkAO64dIQrXfWTjtpsmKJdIp5N4WXlvfHMnbQufESgvrprG8H+nK8Z0DLamLaJxR1beRsA9uwUAD2wg8ejKNXnocx1Qzklpiu+LBi19meXKfL33qY2hO3P/aF8l9Sx03iFp5DxRmwlFKMyrbkAX26zl7glUDUKEdnD44N6pUE05hvneIE2EWajQr7bolpUSrxqpsVmuSZnpNZJS4OUOB8698FnWek7NTnn/+z/iu73sPT779e7l28xkW126WBdNYnpECL8f6eg0CEairmtlOzsDDrYwxQy1HyJGcO2J/D9U1Wb+Bcy317GVEYH+/JwRlf9/2t/ki4ERoGqs6Cb7GScWQIBmTCfGkvEZTgtiTtS2bVaJqGhaLGc559g9nOBdo5qkc0wbFkbQzNqrvyOpJXYdqRY43UPUoNarBflfZyR0w46zrAjHGq+kM5Awx0p4/oD35Bt36hJc++4eQljTxa3gyVUh4CbRdbQZ5b/dwqpLZKvMTuxDBmT5+DiYkFD05O3IOqDqbl15Rl8nOOvFp7rc5GGC5BApDnwEZdppcYaWFtW2CwXI+im1g460JUUVyxmfbGFWNBfJi3x3zivMHa/rNdarZAucCt55trDVvsGv8KJjP5uzvHeDOOzQmesmsnFKlRNUlznPino/ciytWcUUSx8s+sZDAjVgRA3yjakGE+I49UuO5efjdHHi4GyIpw4PnjxGvNDEwP2rw32xpnl8yP3dIXuDShnWOhLTkvDsn+oq2XvBglrhXe6SB5uAa1cxxb9NSqVBfq9AqEZ9wkJTvyTM6hKe++4h6b87JM46UI198cc2ffTNy0jruPg+n9yNhtubwBiwOMrNFIjyzpItT18IJlzEs9iW0FQJUlT1axZot0OZBDaoD7Hj3g6a8XPD4hyQwlW2e+jbTe+tAXGTph0/ffdxSjcPfDMlZtkENn2MtVUYygmFxKcxAiRIPXzr4um8sWTbESzM5Why3XZ2jORtD0Hd4est4UNsAS4bbdlCxhLkxpu4GT8a8mIEBUGTM3nbO2zg7Z/SfbEMhY2L0Tvw6xR6VRNduCJt1KWXsUS0x8RLv3S602+v+RsBO9dX0A8YLeIF1Ma80otqhdEBv4QOXxrlrbEIuc6+Ea2VbLmhDU1il8fWdayda8g6sYsb7Eu71FPageJCaUBVcjpCV7BJkC2eI2JiLOHI2BoScUSnJmaolMdK9vrhtTuQUTTmw3yCp3c7zrBYWyRan16RkctG0wLxyZ6Jfxhgk08iPzoyC7FH1jHkJLhVjwK6BZCyWBWARaIaKFyWZF5wxZmTQB8mYhgjDNR6ckXJfDo8U0TId7mUzQHK2c1W/TV68Cq83rFcDRsZGhxVHSaKFv7DvspqGPL43WryDVAm5EnxTWQm22vzIMVpKRlJL7o2KJMWp2CiN96RdkyyOrOb557J+ZufIzpFU8QrqBHUCHhShKnN6Fjx18HSNJ6lSV2JOV2ehltirGVW9MSA5Wq+Mx6mx2mQMvBbUg3q8t03z+nXl+k04OkrsH3YEn7CyrEHmVQDzNKzJih+11ZumKQaDTTKrH4eqMk+jqqzJSdf1OCekIV9hR9bTB09KaglypaJguEuHmzJnpWsjZrwYS1HXg5ESCjtRmAtJZbNY43xEmVkCjrO4pTMf6g0zB4RMTpHu5B6rk/t86TOfZH36gJe/+gXQjJ4fQ85sSlZ8Pbfs62Y2p2pqfB1w5cb2zrE/s05nDiEI9CnSdu1oNCzmC64fHNoyLI6+jzz/9a/TtR132xbNyqbtiDlZzTimSgdqWikinJ/cZd13LA4PEacE75jVAedrwuwaxUe7cJZvhEEQqoqqqnaqCez1nDMpJfq+JfYdMS5JcQm0iNzB+5bZ/C51lbl2zYyAxZ7DeQjVsBFnSEK76RCEXpxdb6kR8cRsm3XfOVLK5OzxviKEmvmeYzabs9jbxznP3v7NIn5zDUsknBtBXToBxmgGRtdacmjfnaHqyWnf6PjcAJ6svoSutomEdd0SqkdfpnLc0K7vc378NR5847Ok9px0/jJeO2vJK2YQKp51LLkjyUIvvinhwN6MAjURAGIyXY+YhJQp7EaxpryQNZFyP4Y5QgjjPV95M6y8s7yhoTxTS1hHJaI41IFmhwsmNazqxuoVJYME+ytRcuqLzVuYAzakeMrp6Qs45zm8/SxVXtA01x85j3C+v8/e9evovQf0bWajkbO4plElaab1mXWAer/hiTrgFXwGsmO5yYiDdUjghHDgYOFx1xuCExatI2lmtTojk6wz4TKxeumM/s6Sp/1NDq/tsVlBjBuQSIwbWkmcpA1L5+kWFdRCdA6PcL/vaUQ4aGbkCPdTh2alaQ5pXEC7NVEiVd0SJHPjaMZz4RbLr0XOjyNpI2QHSWs07xFjxf6qtiZGjwkenzO9IobIsJOME1jMlaMjYe8gM19Yja/F6mHQzLVN3oRBvPejnOxAd8ZoCUBmOLjyflNbyzmPAjGM0bgtw2DvKbG4MRt7PNjx9ZhiWSjK8Tvr/iZ+t3tdOUNJONfhXAZdl/DDAiEU3zpc0Rh4lRUoJ7r1ktXZCXe+8TXas2PO730TVGk0WiWYayxePi+1xVVNmM9p5jWhCszrQB08N/f2WdQ1LmeCKqvNhpOzMmA+cO3gkKeeeIKSVclm03Lnmy+hMZm6Wc70MRJTIqZELnI15rRFVIS4PkNj5OzBHeZ7e2yefJrc3YKQkeagnJQfz/dqvtjDMOMvjCp8g5dnzXIyfd/Rbtb03QkxniKyIYS7VHWP9+eECuYzKSEqZ6xAkbPPal5l1GT5FBrNYHIBwY3GQIoUoSuHc4EQPHVT0cwWzPcOEPE0s4My3/dtk5P5lnVRCL4vuS09OQuwQdWTYmMeX7bxy4V9M2MZsiZCFfDu0YO3OfekfslmdZ/l8QvkboW2ZyipZKTbRVIR+hLWi7pGxFHhimSHSdupS+CUPloyZR/VQnMIW/EQRy55J8N9p6GiKvLhoTZmS0JJThsLfYrmR+leaBoMO3Sg2JiM9QZDokq2mslRj0MzDkXThs3qPs5X9N05Q/b+o6JqGurFgo13ZIGeTJuth4BX6J0SnUkNH+43SFZcF5EIbWtJqa3L4IXZ3CFzhywC3jlqVVJOnJz2ZI2sTte43hFPNuTzjsW84mixx/24IpMKq2THsNFI6xypCWgFUSzgsoyR6IRNgOSVs8JGLUKgDjUaO7JEfB0Rr+ztVdyoDwjfXNJ2mTYJvQp7e4H5rEF8oO08ff/4aA1MxsBrQQXUsX9gMddr1xLXDiM+RMi9haZH5902dydVUQlr8N7TlLKioS/9sIl7P6i7aREs0hKCsOyVlMy7d27IPxBSsr9PKZGSVRcASC4xhVIak1IuXpYdVyoZ2n7Q7hz0CUqYwjsl+NIthIQyx/JwGrLUV2cGBja+HIz2G/rNGffufI3T+3e4f+cFUkkeFNVROtZ5gZIgJ34rC6rFS+ozaMqc9z29ljyEaJ3uepHilVV4D7HfgCqp7+i7Dskt5JYYN6Z0l3vLGSifr0XG2ZgBCxekrJwc30WD59qtJ5nv79PMD6j3rpdM8u2t9MbwApgh6b3RsjnT9S1d17I8P7Of5TGr5QlVdU6oVlR15ubNNU2jHB4KdS3MF/b3dW1MS1XtqDQiJXFNRj0BP5QoiilWVuyNoSKcjWtVN4Qwo673EPFU1SGCx7k92/Ao2f9lM3MUY6DuiyKglXHFfmXzMjtUK8sfwKHZjwmxVVWN982joO/OWJ5+jeXpn3F+/Dkk9dR6RhCosrFNYjI9RNfZcWpnhndXKgM2OpDgAHTlnhsUsHEOLXR6CNahT0oyak4ZVyki6y3dL85CKGJSvCCor+0bvMmJSzDWwJXKI1PGFkhmiGi2HgYqufQnUSSLlRzKBlWha49xLnB2+jJ1s+b60buw/J9vH9X+HvOj66xnFf1KaDWz1JZOHV0WQlXT7NU8/fQB3/XcNTRnYrsmLjtOPncHgqJFfiISEVVOT++RRairGc7D3qwha+Dk7jn3Vqdcv79h3jpcrRZaEszIcp7eB1pgKcqmgrjwZK+crTaERtifzclOuCeZ6DPfWLeQoHY98+A4mEVC5Wj2apwIB3sL1DvmexVUpyQO6NMe5+sD5MGCphN40XF06/HJIpyMgdeELezNTKkrZT63pMFUvLNBW3wo5RsMAhGPE/NqBjnZYfMfNubdGPDYJ12GWKn95GwbtsO6ftnCss2ez3nQ8i4GyRDzLlUHuXTfsfghqCZ2JcBM1UtK7FdRtS1RaYGKjN9RK7gidBBhsbhm6jecnz1gefaA5fkxdG3RV77kVY8Z8+5S7FpIqqBCmzIqpeY5pbHzHd4RqlBivhEtFQmp76yOPFs5oo1hIuXdlk12PYu9ZIaXKqvVGXJaszw/Zr06xao1YjlUHY/tjcJQZz9kmKfY07Ubzs9POTm+z/npXZbnD1jsrZnvGdVtoQBhNgtUlbPNX4QQyqMvyYgEyk5dztHGX5xdK5xHXABnYQNxHhcqvA9U9QznaoK3RjlO9grtPS+P9WgQKeCdaRMQhvLBWIiaDpxVlKAU1sCX5Dkh5UQIAecf3TvLsaXbPKBb36Vbv4TLiTkbgnp8XpTj9CDZvM9C1wuQkgcVCgFX7iGIcTtP7DZXMwic1dUL4LLVr5OKLkAom1rE3puDST1LKnM6UxohbHOInLPxLuqGKo4sEck2x8nZDFYnkBPmG1t1QdZIiiuyC7SbM8uFuQIzEGYNYTGHypO9EFOiG+L1eBbOMW8aDq8fcv3pJ0g5sd4sWZ2suFO9ZL0mnIIHjyXyLjcrEspRZSxlXQUUz/H5CWd318yXjnlyxjIMygGD3oH3RJQOpQ9CbhxJMquuJzhHrAI4WKrSS+akS2hSVl1GcmbR2TE4PF48TV2zP2+omxb8Bk175HydTT/HrWd0mgnHmeVyYgYmFAyb5OE1mDWmKuglonhynhmFVZKABiNAvDOrXS2hMMa0EyYQqlBdaNZiQhkD/VsStNwQT9yWsA0L7FbAo5QdYl6ylky4rHmbIDjQjGp0rHOFFi60tsOPP6KlnaeY6lhmjcORSsnho8O8Kley6xUl9hu6bm1e7foMlQjOUo/Mf3KomIcKSttuLNfBKV3XUnczfBVGxb2z4plZi9mIU8WRqLxnUQeaKrBfV2YcxY4UI8fHx3R9X8Iv23DJUJU4CDjnIvySyMSsbNoN+fyMs/NTTk+PyYW9kSL680ZjSJxaLk/tuE8ecPzgHg8e3OHB/bvkdE5O58zmlsxa147FombWCHXtCMGNapdVmOHEUTe2EXo/s3MX+/dctBa8zBEJODdHpELcHBEPUiGuMbldXyFS4aSIDtFg0jLD82FZGfpWWhKjugBkgq7Rsq0415LcCk0e5/dRHC57LGxgYYnBiH4U9Jtzzu5+nXh2lzqu8arMQoUXh/gi5uUjKkoYGoihiELqrAww9kN1Q6nqyZlUkg1t6Kzs0XmHUwuppWyGhddMIBNKa2hXvgHpjUkq3ymhtkRAb2a5OmNTvJdSUunI4vBSlaTXiEZrXNSLxeRzEXvy6tEs+AyiyvLkhKo2A/lRcXDtgBtPHHGyX9MvHRKtsU+blHU01mNRg7qenM/Jmujcktj0+CcO6FU5wdYx58EFmO3NESccXd/DiXD3zjkpJbrTnrOXN8TlHOkca845XbVsYqTPyfQUXEUi0UqiC9D5RHaKC1YKWF/bJ3jIqSVlIe01EJW2TfhNh1vMqJJwrblOtaiQYPZZ5WeEaoHkGyS9xSY2pFzRoCz6RN8fvOZY/WXBZAy8Bkw3QJnNlMUC6qpoCWDU5ujRFroVsfp8S/4pzH1KjC1iRUamALjg4Q/ypbsYFcmG6gMoWeHbqgRK1cAQF7ds8GI4lL1ujEWOGeJhpItNS91kOp3YzZs14YhkjWwb/V4Vuj3O3JNSR9ut6fuNxUmHnGQxIZqxPbFYf3PEBGFyyTYPgyGFjkZVSpYD4FCCKJUTuspTe0dbhXIhenJKrNdr+iG8snuUO8zEkDlNYW0sEa6HrmXTbti0G6quJedUxu9NQPHq2s2GGHvOz044Pr7Hg/t3eXD/Dt5vCL5FcyD4ihAcdVVRVbaZeL81Gr2rcM5RBfPefVW8Y2dMQSphLicznASc28NJhcgCK5+sEJlhBm9VHku5XElKZWAb2M15MSVMMwotPOB8D2SyduYha0em42LfDGNFvPcXstq/XaS+o1sek9slIUc8UBVxplLQbhuyKN7aU9qGXeL+ZlzmwlhQmIIhpo/t6zoYAs5YAhRGDYCiRyjD/ezKuZmhbo+CuLJhlg19iDYOGhLs5CVYqoDpQSTF1hjESjlytl4JCr4sAv16jSZ/JWagmTcs9heEJuBqh4hpcvSqRKdEjzE95fqpJqLrSCHhDhpImc3GEnkXDoKDelbhvbDYqxl0AFWF2Ca6ZSS3iiRHXLe0viNSap0ECN4UDlwmOssLyKKI97jgCfMG7yGnhG4yWgdUlLjO9DEhnYVg52FO08xY0RMk4lyF9w24BeoO6XNFTgGpMn3MxDy1MJ5QUIVMHRL7e8JiAVUNKoms5i0m1SJraSUypjAIyZsymfOCakAk0YZ+pPlzznSdeap9b5K6xhCkC4aBamnbm62CQFPCyotKjbEzQQ+S3VgpxjFOL4D4YXEejA8p1G05QSmJS1gClJdQjIJMpkfoEd0g9I88doMcybAQihQREU2WHxGEetGQvNKt7D2xz1ZS1Hd2zEVGNKH4vqftOsS50fhJhU3JyfIFLOJvY1OJUHnHLJS4eckai73lAAwViCWwU5YexqNOhS3IYmwFzhnrA8SUSUOYRraNoS8HC16PCbVeLzk/P+Xll79J2665d+8l7r78IuvVCd3mhL39zGymzOee+RzmM2MIqkqLsqvpByBCVS0Q5/FhHxGPL70pxM8xI8A2eEdjIS7MCBDmWA+CAAwdMa1N79BzYKyZZ9DQpVhSqTBXPYMkk5BxEo1sHlp45x5YAjNEatRZ7X0IgvcOf4UwQew2rE/u4TdrGnGl2sSXls9Y7N4XRqDcGy4bzdYP/Yli2obchgoHVTMAxBGc3Sc21mbQup0L7kW2Jaw63mo2YuV9paVRCcXpWJono6Ru6T7J0GahZLWIlns0g5T7KRfB0zL71psNOftyLR4Ni0XN4cGMeh7wteUxqAq9wnnOLHyiqyLRbWwdlEwOLbmCNBf6HpYb0yeep0iIyqKZUVWepgk4wKkZ+C4FXKxwqYLkR0nsm4c3cLM5IUbqrqXKLSlFkkBSq1g43J9R7wVm84BzSmqBYA6NNYHs0S7RP1jjq8jqhQfEvYZ+z8PCU2mgqea4OCfFGSINQk0u5bHK4pHH7q2KyRj4FhDsJg8+U9dC0wje2129W1+raip2Y3w+W0zUuUhQT3LmBcQ+FyPfqPwUk3m0xQDYZQi2BkH5jmxiLJQscAdlg7J+A+rsxip1Bjthgm2QAYY6+C1rMGY272xjIn7cVK2mvuiyXxk6GkFDDwLEDBVfechFW1ghRQsXaI5lmSx/21t8nxhB5MKYbRMqB0PJFswABCejMTCvwgVGZiBTKCbAcKxa/l/SEkoG97bef/DY89AR0b2eLf9VRkyh6zvabsP58pTNesXZ6THnZ8f03TkpbRBx1LWjqpW6pjACJRmVgU0qCWmusooS3xgr5Et835V4v9QWHqDGqkfmhe6flUcPYzggYB0xh+VjN6tkl1uJhRkQrCrGjAAzMjLqepBETskUAbEyVy2UjHNmDFxFZyCnnrhZ4WJPEGe6QMXkg2Hu2zz0uAvKd1LCbUN28CCCNNDtgm30XhTvrDeDw8oQnQzBkTEV4ALlNJASMhoBMobR7LOHuWQb6aijvzPKhWMoBn/5QNWx+6GXQoTF3sISjzx6lnA6mxnb5IOzRFMvxKR0DnqvZJ9Jzj4/k1EXTZyp8mQVOgCULlmyo1VWeUJwIzMgKkh2OPU2p9QD1jxrMZ8z279mOUWAj9mSTRkYU2E+C1SN5cfglNQrWiRJcEDMaK+kdU/qle5kDX0iS4PUM5w6Kl8jrkJdTdYaRzOs7jxq4uVbGZMx8BqQwsLtNuE1Kr+YAzsJe7mU+1A6xaVkNcFD7/iua0uc1oyBOBgDKY2tdofmGfaYx9fGRhqlo9tQUjiuMwNLueuYDZTtxTO68DCc04Vno11Q5EyHDfz1QIevLbR1iQX7qgLN+Loo+ZUsviLRXhZke55ykW4W+91ElYbEyFzYAvsyp1pEcKFLRs26mEfGRBViHrf9C2c3XumyP0hhBFzwOG85IUPo5pXwxoQMLF9ivV6xWa1Yr5d03YYUO6zhkIn9mPcsRUxohxEac0rKZjr8UErcxJgrGeP+NebxW83/9vlOh0Kq4sPa33LBGNgdvWG+DK+n8vvwfKvCZyxU4Q2G+TaEuHZEkR4Vogo54jTh0LJ5F9Gw0upb2W7ONskYbyaLcpQbapC5HowCZ8mFWpwCh8OZyk2xJ8yQFFVkCMuNI1U8+3KTWZ5CERAqvijjd2eGosJdY6HUu1iiZ7kiY5ftcv0RBpKdK927w30UanxoSBF6TfQobQnqdGSyqCUJes/BfoNfm/RyyubsiBNSzLgoxD7ixHpOOBVy0hJVsRvN8jKMbY3FEJPSKjnnYpwPI6AmThS8EBzk3hQfJSoumiOgmMCRZNBon9GvTH+gnznSPKCprKMlZ8uP87cM4ht0N78VMBkD3wqyVRgcrXwyg9bW7iaScyrlfAJYHDln8M4Re6stNmNg8MQzm82GnBNda15vP9S9d33RzO7NYIgJzSWbOZaywR2DYCw1LseyNQSGx+2mMP435hTYCjhO+XHTHjyTBCSukkD4UImdmgqg99ZmOM86mr09UhXQ/gBVpXcbK6NbmSDQIK9M7LdeHSaaMy5xsmOq6Q6dC/RZ6Uu9YlfitK58SC7vT1qYHhlYAaxzWilndFXAhUDdNNRNYxnuQze9HSPsKpvWt8Lp8QPu3b3DvXsvsVmvWC2P6boV3kdCgKYRZjNHU8s4T71YeMDJkA9izIA4qwgQH4wZKOJCFgYQhD0g4EpYQEZGYIcNwNpic8Eg2F0wh9FLDJ7tdoJmzMvKCDVKQsQ2aie5xNAH99kuUHCOUDQWHhkakbzB01M7G5e6zHtXjjsPGaKDYR1t7mwjaMXQL1UquTBzmYxkR1IHxav1pXRzyBXKKEEzTi0rvuS7lkfdvp6zqUEWY8hTcibUHARxNvbDyPqy5jgp6ntqyYeaQb1dkeRNR8JpssqZK0CKkVI3+zSL66zyivVSWWrkjJ4DgRWJayHgaghNxZPPPc3JccvnPvs8sUsmfCbQbqznx3rZkjrHUgSnQuqUHIFkOhYpQZ8zbc5scoaUCcUQSNmqTTIWrig3L4vGESpIq6XNxC7j28wMB5IJOeMz5FUiiXB25xxfO3pRYuXJG4rzkUipw0uZ5yLY1ZiqCSYUDB6WRZAx2123XmnOSk66jR9TGrGImDjQkHAkg/wqhVbeJioNVu+gKDjoBAyvM26CQ+y9RLZLoyLVLe29XZyHgMDOZB42rMEb2vl9oB93gwoD3SAD7XCV8Sv/MVCjYiVToaqsG58PaEhIsGxpV9nC66tBCrQ0s7mgtDhsO4PPtDXKhkifef+WlDgYS5Y7Zl7iEAKwK1oYADEJVDdmdJsf50JAqoAPoRx3KCVv20TQcXy3l+t1o+9bunZN33em75+HpEfZtvctGe0XDJHLNNE4OvLwj2wj1kNprFyYDUNbXl8MgmIMyGVDYPd7dr9xK9Gsxa/b9XQZEmNl2zp5qIAxL/eyHPK3BycQnO6YMIIv0fThPsK5wgLYhbM48bZA2DzTPP4+ZESMIyPD45Z1ELEWQ+Q88AV2PONoDuOi5d7bMgAwfIeOY0YJBdhn2u9Otpk4lGMzm8tKnccrOLBFjzx6sGkjq3WHb/aZHSQk9Ei4QXt+hp4cE4E2QxvVGv5IZn3es1lG8jqR2wRdyRnpsrEpnZXo9m3EqdB3mRgzsax1kUzUTKdmEJyvV7bxx0TuOlrtaXOmTxBzBnVkEkmFfiN2vVaJdJ7QLkNSqqqmVkH7YA2gC+vY9ZF207Fulc3GEpuDZLwkhL70lAmEK1SyvFUxGQOvAYtbmtRoLtQjOZbNX+ljJEWb1OY4mFWf8KRkmb8x2TDnUsM/ZFqnFMdwgarS98YCtG0kxkTsU9HZt7jhGOccbu+S2JRzWQcyhf7VcdM3KliwQJpYApNQdNmLx6LW3tdji5otSAONOjAHVzEGyrI6dHsUIYSKKjTM5gdoToRmhjhHmC/K5u+KVoA1b8IbQ6DRYstaWJGBp0jluIYFdEuK2v8lGxUL1qpYxLzEbY8IxmMb9NiHDHYfvMVgmwYJFbPFnGY+Yzaf0TQNdV2PmzI7l+WKdtNDWC7POD19wGp5StdaiEBgJ/Ya8KH0Zihfb/FtKY+ZYdvYckJFClccVlY4JAFuN31jBoZtdDdMMPw+bEO7xsblk94apVsDYOhAl9Cdv7G8kK3y3iCO6cXhnd9WlzwCvFOaoDQKTRI8jppq5NNVtKgtFhkrVWK2+2lc/suNJTlvN94yfxwQxPIaghMqobAxpufVasaTCcJoJIxsHIw9BURTEQ3aJtqaxPFw25R7r2hhuFK66FBwQspCVGMCNGfIlgYqCMHDVfey49M1L909pz58lqp5CqjJOqP/+ld5vv0cG7fmtDtnsVH2VxHnIOqK5VlH/6AjdRE5NzEnPYvkXulPO6gc62Qzbnnek6LSdpGYE21ObFQ4T5EsmXTvZRru4Z1nVtUcEzmXnth7+ujITon0aBaWD3okKdwHPY/k8wgJFnvXWSwq9FjpI5DsGp2vOpanyoPjyIPjTKpuMAsJT4dTpfYN81lDXU85AxNG7AYDzNuyuH0atQEGNUDrLiigoSQnyejdgjEJW9pUi/hQtpIlCjNQPPxh6S7O8I6jV3b5HVaAEvMaE+JKNz6KR25xznL8Q2ZzeToyAyOtPAgElTOWqxoCBhkWtsHrLHXqdTMnxo5QF5ralyjfEI/3Jggj3jOkaDsR6xdfvALV0sZ1uDpaKhFKIudoMexu1IPLBLtKsvY2V7ZLb8pyGiyC6Er5kveBUNWEUFNVM0KoERfK5rodpTeIGNjmiOiQp1JGsWxCuxuMvZ/CFpkPquMmP1D7F49snJmyMx/YevAMtP3lv5ILD2yNgeFe2f15ZdtoNBDKPB3yG4bft8wAV2IGxDlC5fDZRGYcDlL5nkIhuzqYwRTTYEkBZZ6J4KUINDpnDEGxUoK3KiHvPT44gnfWDEvMcUDAq1VBOLM8R12MwfiUMjLD4zAmrhgBusOUMPQbz2UO7Bjouz/GcsiWURjkUa+ALjna5FmvI6kVYoI+wWlbkdw+UZU2tqw7OF9lHJF+taJdJXznCb1S53KihRnABFVJvRlDXZuI0apyrLQ4kyTTC3RAI4onW1MiTPRoo5mUhS4mKie0MeIRtDPhgP5+B6uM5KLLUFlZbCc9WZRY8odO28TJecs6BdQHqioQ6kDtG4JrmO/NWMwWNPVUWjihYKtLV4RKNKE5jgZAHzv6PtF32bpvIQhVWdgSgsM5P9K4llzUX6gY6HvrVWAJhUXV0HxAoBgLijUoymw1DdRihTnD0CJh12gYep2PZO24upb9VbZbhC+lUl5cWbBk3AvilZmB4ZzLcieCCw1Vndg/tOY2s/3r9O2a9ebUDKcYTVWtiOO7bMYMfjACttndWbX4mqWIMZsxIKlYAXlIBGO7WYsMmrtF9W3LWlgilnU5VBGrowZ8XeOqino2o5ktmC0OmC0OaWYHOG8GgW2ou0v760dMiT6aNsKgp2CGgL/U0W/b0joPsdXssNK/Qu3rthSwkNQ7HvuOQXHBk798FV/p+eXN/7IhcNkgGIzhPE5YOwdfchhM9wIs3+GqXQu9dzSziiZXVLHCqUNaMwQsxANhz6E50q66YrTbHPOuZEk4V0JIljQ4aHuEysa/rk0dMXhvBoITXHA4NZGsUBlzI+KQUNiYIXlAtBhARWdgGG836JSUUdRs5bhFeVB2MoRduU5DxUIuhoITyCrGOlwxZ2DZB443FXeOO9olrFo432TWmznRPcU6B87alnDa40KH9pF8fwNRqM9ryMJ+MmZNlhH6jGyALPQlF+PstCPGTBcT6kxGuCezEls5vcslfGd6qKeaOM2R2Gf6VgkKx60Skme/r8mbxP0/PSd0whP9zOb1/hwNFefHiZgiROsm+eLJipeWGx5sbkJ9nb29hma2YH/vkHmzoJ7NuH79Bod7h1cav7ciJmPgNVCSWclq8XxLRNYh2bfIkMrWSyqe+FCKJ6XsZ7uyWq09qtuM+LzTR2DXEFAtHvCQP6BFh6DkFeRXqC4YG6GPq0l5Xh4HSlm2nqUbcxBskR7OZsiVuJr6YBkMGbYd7LzEIS5QNwti7Jgt9k1gJdSgGef9mMEPgwCNbdLqpNRY2TJo3ukgw6pjMx8vFgseuN0tKVC8zW3t3ZgrMHzHdoyMIhERnPf4EKiqhqaZU9Vz6npOqGrE+UK5y3j9ZfdLX4dlMF6bnc1QSma6jDF7V4zCklg1MAO7j+OBFAUFTQhmgIqkEk6wjH+5YPxa8uhWAzOW79s1BAaDWe39quWzlKGqRktCodV3pPJ8eE3te8WNhsDgve/S6o8K5wQfAi55MwSylA7YatERpyPjpLK9V4ckP1PBHPIp7H4f2z8XQ8yX0kfnBDcIPHlB1JmmQfC4YHNjYAiGRIPLbKMl7Flop9yN5Vq4LWMwzAGGkNgOM1BsWmP2CsMlWw2FR0VSR9RArw2dBtbJc9Z6+lgRdUbMNT0V67bj9LSDBGHt8NkxL0qVB80cBc763kqp+2zHE2zd7LtI1xuzmktKdnRaZpkQJdOXhCYh02KJhTEpXWf9V9etEGJCtCOvM+uzRIhCijYG664np0yriShK33comdPUc5Yivmm4tn/E0bVbHBw8ybXDa+wt9sg4+qIZ87hgMgZeAykrMZvAUFTFpZLkMyS0MWjnD8yBklMsC3H592HjBQZjYJcZsJyBsudmV36s7CanXD5vkCuOxJSJfTamIA2ljWWNzltOfFhU7LikHEsY4+K20ZWYN1s5YyfOYvECSsWgp3BVXPA+fcDRsHd4AxcC1248yXp1yunJHax5zYacIrTRSrlKklcuhtGFnUEG2pztBjUoCelFAZjLkLJ5D4/jhitidHBJLhAn+Koi1A2zxT77hzfY27/OYu86oV4gvgZxo57++PlXHq0t/KDAVzafgRESGa6ZK8biwAIJOQmahJzL81zOUU0zIudoCazaIeLJ2iLi0NJ2e+RySeXR8ly2G/fAWA3YDQ8M7ymCOWYtA10xGNpiCLTlb4aeAB5HVRp8VQjBWALJY1+KR4XznnrWEPoWRzDbZji1oNZ6LxTq3RehH5dxqoTSGKzyzvT12YZmRuVPB3Ww3A0peUXixMrsSq6DeEeoTZXRVaUlsTfPP+1scpazUBQJx1ydhCkSmrIgxRHJpTImo/jCrAx2Bs4YAbBbIHixLpWPPHrQ5ppValjmio3Cgzbz0lnGsTDVibyicnPiyTnnp0t8dlzb7DF3nqcXB6jLzEJFJnO2/AY9ibjskahI1aDA6nxD2yW6ridropdMJ0oQM3HWklGXS6WP45TMqSb6TmmXSujg4LTHi2O58eR15vTFjiZ7nnBWsnqfJcEJnZqS4YP1GTFHXvKJl0Pm4O2HvP3Z7+a7nnoXT9/6fm7fPuL69UPuHy/51//266Zf8JhgMga+DQzJuruPfJuPY3hghx3Y1SYYS+FgZFe1fMnw+kPvvfSohY2wv5cxJrm7F357C8LugSu7h3VlFIr+IZQYsfcB7wPOeVS3bMVQpnX5PF4T5Q++1fluyyovbt56+Y/Gf7djcaV5jBvKNGWb1zGcbNmu3whi4KHEMyhzgd25sfszePCXv1u3k3LcuJVBonroh7ENEeSyKe3muLgLjzKerb76j1zMIdhNGhzaMV+Uyr14AUb77GrDxxgTYxjHh9+x/fphzmFnVjbY3Xv48mP5BC7kAuxQ/OMseNUT2F0w9JUfZXd92NbOmP37arNr6ww8VN77baMYQeP4uRLd2IqTZy1B1GJ4D1c5FWbJeTc6RLvnNQxz8I7k88hebM3Jkg9VzlHFkjKtW2R5PZdqqqL3kPpEjroz1bWsA24r6iT2WUMeCLt5Us6atYlkkIg4K33dNiD7y4/JGPgWUIWYhD45ut7jvFoWt3ekONQfFyq2SJlu4/eDF5XHjPch7ttHyxkYFM002mOMls3c9+bx930Ryug2JTyQyCmSkhKjkpM1MUMhx8HzL7F+N9CG9rr3rkix2g0gzm4Q70ywxpTrBl0By09IClETgwbh60dZnlygmR3gfeCpp9/OcnnC8vy+dRaMLbHvrWgjJWLRXhChSDGzs4LLkA85hjoGUaeypr/KdS1MxbhWlwVvTGMviYOhAhHq2R7NYp9rR09w+4nnuHb0JHv7N3B+EOyRS983XPvXh6aZsVgsaOoKTT2xT8RMCRMpKQmaAylZqoVzrjza9XPRumuKCCl3KAlJHskeXInRS13YEQfEEhQIZUm2xkLb0sKKITSxk6q2c+KlzR+xMAJDmGCFtXta2qNacfcQxhAqkAXONbiihDiGDoYN9hHhXcWsWlBJT82sbKQdoES1hMEcQdWBr+1eqPvCjFm+Soze5HKHzatyliDoAFGcL2yCx0SpHLiAaQMEVx5tzqsv4kOSQUzGeMvAMIoTodYvwYgZQTWh9GWcYLQ6dgw6g2zjG8UAD840Ja4yFYcqC+/2qFxg0QjX9oWu91aSl5Q+tjTaM1OrmphViSzKN+PLzJuGt996kqxKdfqirVdtR5bEtfo63ju+95kjYsx8pb/HvXVk7ZReteg7CJusZA9tVs6Al1W5D7gu404VCUr3fAsKL728RHphtprjUHJlNNDh/gGzynM3LdGcWM+gE8juJjN3xLWjW9w8qnnySeGJp5TF3gPC7Ji6X7O3/xKz2VRaOKFg8Lpy6VxWNIDZLnyy/bnkWm5v1B0vDi1U30B9b716Ezah5AGwlUEdfnYqBgY24IITsf3m8ViGTW/r4+54DcVE3+bQbY+T4mluvc3Xsbkp23i87tSzi8MH0xuoqtqa/riAcxlxzlTaZMdjk2ERvOiX2mZWNngtBsGOR7e9Alxy9cb/bR93WIORnXAe5wI+VPiqKtS9L3kN5f36Cp//OuG9L9+1TaR7mBGSC3PC5ulFzwqGuWJ0tCJYTMqMVVWxuHnpeaGyZQakRKaNAbHQgZT5tRVkGU5+W75xsapgYBlGv44h5GD/3+YMbCsJtnPzSptZYZ6GHyhS4LrzucO8lqIgMBqUjKk7uw2/xilYfpGd45Pxta3ewPCeh01DvfjrZctVFZXiag9G03BP787pHXYIhntMxsdxHK+E4drZeieDQxHdyHwU3R/7fpHR27YMgBLiUR1zGIY1DFVEoQmBIJnae4JzkBkTgof8CinhMJNQGJQqrZwzOKids89MaatmCCU8NqxlhXkQcHUwZ0grVKsSXkuk1BNTS4zgI6TcIhIRSa8wNn85MRkDr4G+F7pW6DaV6a4tZiaIIy1IR5aIYlFPy+bdOq8x6djQaIAqdN2QK2C5ANpbc6EUS3JgYQhSyQfo2pZc+s2jpaV5hBSx9wKairaAs5vFlxXCedNdDyUDPRSRGu/tBnFDybnTsqkak9HHGTFXZJmDn3MVje7LJL+xd7aJOALiGvb2bxCqhiefeQc5JTbrFX27RmMkpUho221nQtJYZjUsLtvF9lLsfxc7NOvIIO6+baARB6W7kk/hK9NAmO9fZ//aLa7deIrrN59mtneErxYgbjSc2Fk0RAXLUnt9OLp+ndXtJzh98BLBC8vzSNclYkpo19N3jr6r6Vqh3ZgR0Ha2GXS9nWjsLBTQt2vLD/FDzkEF4lFZb5kB16NEhEAuj9AXpmDboninBmUY4PK4VX8wxPKvLWYI9GjZKBjJ7oDIHo5riJtjPRLccFnKnvboVK24Chf28US8rs3LDTZTMtbSOmvxfkvZTGxLLk8xXpwvCaZl8xoYgiE+74MlDDrvCLUZh76qxo1sOxf1omGza9MOxzu+1ZgDTbFQ8UJW04SQEkobmoopuShx2gaaxDbMTIUipFyaPl3BmkrRdP5jC3EDgZrD+YyzvGK9Gc+qsBwVoQoc3jiAnFgvz3HOk4peYl3NyeJo15k+KuvjFXXl+Su3b9j7TjJ173n5ZMX5ZsP1Zg6VsH/tkINZxXnMbNaJqlvhzjfc2N/j7c/epK497/6+Z+i7xCcefMkSDlEkO5atFdWeLBNdBWvvSc7z5Pd8F7Ko+dPnM9/8hnLnzjln7Vd5cNqx//zXuXmr4dr1ihgFFYfKVE0woWDwjgdhH7TUQ+/EjEdve8jcHry2oQJgKHVjywzY68XDi7YYDUbAUEq4W2Uwbjo7TMA2h2BrAats44nD8cHg4Wxrt4fXLno3w+eVPGV16Njx44qJNGXR2+7HwrARaPHKLHegYsghSL6yqgK12m7bXLeeOjvnNzglWwdrm6c9HkJxEUbn/cLaqKMhYc7i9h/Haosioex8wA26AmVVH87rQunlG8QMDIxJCINa49bAuKg8uZNAmHfKTdXiuTK8b6g6YXuNB89TSKiWVrXDc8rjmLuRylgprzwfLupoWOWCvb7NPxgmAmzn1W6vhIse7e58fTSIfab4cr0UKS2CR2aKoti3c29c+gQGVm1QKbx4v8hDP1sKYec02TIRNlcvmcmXfxlZneF+uYgLeUO7fydlHuzSFXK1+1ZLOGqgSETE9BpcEXMe17+B0XFjBdBwNwzVUsOA5Ww2c4qZPJQyO0ftPbUPpekTSBAkOFzlcZUjiFBnoVZPXTmayjGrHFXw1N4jAWZ1RZ8zWXpELPFbgS5nXITkpPBaAdFAVmsQ10fF95Guj3Sxp4+OvldSciQ1HYrHBZMx8BrI0ZP7wPlJT7+GJtT4fUceWvyWKiwpOQMkyL2V/nVtT0yJ9dq6bpkOgTIkBW3Wpq7XbawkK5cKhdHPSiWdZig3KutsjpB7YwZi8QCNPbO6fESsfMc5qpLFXPmS9RwsdyAEW7Ss5/12/4qpI+WeGK+TdA+VBcgc5NGZgQuJkeX4U9kTYjnXnDyZmvneETlnrh09Qd+u6danxL5ls16VKgpbjDWaut6gG781LS4QrdtjeJXHC9e4ZHIPnyNlEXWhQbxntrjG/rVbzPaOCLNDstS0sYy3M01977ZhI1UdWYaRRn1EiAi3n3oaV1WcnT9geXaKeGG1XpJzT0o9XZfZbDLNzFE3FsfvOttcNxszJjcb6wAX6kLz9n1pvNSWBbx0ctSMcxXqOkQCSl8eOwSP6fJVZYQuty4eRnb42YYD7OpYRYKqdTEk27IjugfMcO4QJweINBgDVZiBrdn66APoa2iugUTI54DV3NtmbJ/vSimlaG2bq/eFLTDUVVWO087DlypSF0qVSW2lg+Idrq6M1vbeTJ+sO6rNJmk9sCm7Sadb48xYrpxtnueko+c9bLTi3FjlOxrtQ2evgT0RRy5MTtI5GmZXGr/VOnF2FonJARV1vWBvdh1Y0ndzJC1wcZ8qRXzq8CHgwwJNCR96lMC9+6d2z0ePakO36hEHx3fXzCrPO649SV0FnpofUF/zpJxwQTl8Yo+DfVMAbIJnHmqenB3wxOqE2X3Htf2Kp29ZjkZIZwTn+LEfegebZc+nH/wZqVPunxp9sTxe4YDZ0RO4ULN8PpFCx50HFSfnNedpg1+u8HNl72jGapPIsiYlWLXCrfXykcfurYrJGPg2MFjoQ++Brdm/Y/6PsamdRXCw4EuYYHvjl4cifzp4bDoI7Izfe3HD2zIAw2JwcaMZJQ3KL5eXUhm8n51TgIE1oHxL8TjHv9zlN68ydsUz2D2vS4/meTjbXIeFbyfmuo0fb3UBRmZgCBlwcdwuHMMrHdf4zd8C43eX2OUQkx2/Y/h+3Tm9Erq4igVwCW4sLXSlD8LQbGo4ft3JDdgyFcPxDczBcJbjfBpppuEzhsfSJU8zW32MIWY9lM+W+Hspe7uIHYNAB0Ngd+7vfu3gnQ+qg+Xx0hW5ejZ88YoHS3cn3i4MT4fXX+E7dqpytr78roe/wwaw/Rmv+pZ+48IX747B5a8cHi/NneEe2N6mlz5gnBCXz+P13bd5uLzDuQ66G7vszaXzH1/HDBubBcP8o6x7jI1Qh1bGXnYSnF0JbxYCzjlXErcteXvIF9i2Xleq4IkhjYczzLuUTbgol/GJWUnJnBITZlJz4MZlubQm1+398rhgMgZeA6bz5ThfJrzP7B8oB/uO1Ds0OnLvyZ2SW4f2mdQnVmcrUkqsNy1dnzg/XwMDMwAhWKx5VZiBdj2EFXY2eba3V13L0FMFVS2MQJnURQLZlbIZa00goz65LxuIl60IiW1W451u/xXDJUcxb0CaEsOdYfIej043juc0bFpqvRxUEylu0ByJ3Zqce9q2dIZzAUKNrxtbCEIFKeGcM1lSKaGQYghcpgQubXPjOV68pg//bguVDbwvBpCvGktwrGf4qiFlYdNGEi2ht026DhVOoK7sig1Gg3gdP/fC3vsIODq6RQgNJ8/dZ7U8QwS6vmV1fszy/JiUoO0SfVcRo1GuMVY4J3SdnXNbmsXUreWJ+NDZon6pRNKYgWCqdeJRl0ECkNj2MKgpqvxlTF8pgVB3jITy+tAqWr1tsrqHKQxew8keTvaBqoQKrr6B7cJVM8LeDbzvET1FcoS8Ktt16XZQJMMHgS/xAafgQwZnjBxQtitwIzPgcB5c5REvxgL5bWKd2TllXAeGaNcElqE4c2dapEGwyTLfnHh2Qw4lqFZUB0ticSqCYDmVXAHr6pfLdXJhgQ+LVzZ2XgPr9Zqz8zOCe4LQBJxXoGU2c9w42idwk0DH+b3M2csP6LrMS5zjyDT0BElU/bLwRDZ4dV3jPOSuJyU49AsWoeZYzmkz3DxcEA4Ds6Ma5sJZ27Jqlf3geHLuuRZmfPfsOvMqcW3WF0P1GOc884PAzEcOryubTeJr52tUsdwkEZrO42LFcfREcaz7OZE9UhQgc75Ujk8jq82GEDqyCn0WzpbdI4/dWxWTMfCasNuw601wqI+QdkRdtIi8kEAj5KikLtLHSN/1prK16S5Y2rlo3nebaMZAScgZSxLLCjGIFXnTlrH3ZC2Jg0MZo1isX8Teo1tPX17xv8FRH2x288gHb3GMU4rHypJ8+fIrxh6L55rz4G2YamJKVibZlwZEQ7JkyWbEeU/OtmHhdNy0BgdvJGHYLrSjl7z1my++8cJxba/v+DCQGOVfnPOWJ+CH5C2TCJYYUe3xzuNxJvbiMENAdUvRcOk7HhFNMyOmzN7+Ac45Fnv7zOcLunZpnrlSulvaPBhkiHOW0VBMyUIgA6uVUjK53BzJ4tDcm9/rAjkrXixXAwnF2ewZxYN0CBHAVndgHFHYyQsYN7+BmVBsHqkgpeGRMMPJHBkNgV3p4ddpEDiPq2ZIbBBfMU58tvfG9go5TB7YcmScMxnwsm8zKvw5cF7Gn1GcamezHc57y2zJhdd3oRf+bsivKN71joT5cCCiY6bNaLyP82xkgIrJIA6RgLjBcHs0xBjp+57grQbfRD0jwcN8VhNkRiV7rI9r+gSa1bocOsUFY5S63JejL8a1r+xzUo8CtVTUUlHhqFSZ1YE+CH4mSCN0bYaUmGnCB0fjPAehpnE9tbcmSD09TjyzOkFM1DO1zofSDxqwiDhSEiQ7zqPQi1g+AA05BzR7ughtl0k54n0stS+erp+qCSYUWCm0sO4t1nd2lplXGa+CU1if9bTrltj2pD4SUySuTVErx4QD5osaZUcToJTXhODJCnU21bHYld4DcWAKjPbtGJqWlCW3yG1eoPN9LhnPrlBvrtBv5Z8LYzqUJzpnC/dQIJZKN0DNe0CNuEPE7aPSMARHHhklBJJzKu2eM327IufIprXY92p9Qk6RzfoU1cxy1ZJSR6+OREBCjeQMrsUsLsyDKjxj1h2qnMEg2PHCdpfhYd0se9hA/RaWHHFF3NUFqyioaiTURBXamDhbL+mpaJpEqFqCD2he4L1DNJTmOBWiWxnTMcnvCsPXzBZkHE89/Tb6rsUHT11XfOOFr4Aomnty7ul7pWvtG/rePMoYjQFqSzVB26Yi0RsLFduVcIwvLIvgXEC1VBrQIW6QprbEQotLezM+dcgZ2OVh8u4wb5Pf8pBjsAcS8BwBAecOzBiQoTXyRbwes8CFhrA4wukKun0k99CdgmZT90O3jMUQVgumzpk2tpEM13DoBTE2eyw6A8P2i6qpZYqUQosdhqhoV+SSrDjE9vNI/xl9JrkI3gxNtNiGv5QiXlIMYSdmVzlnSb4k+6ykDsWTpQEc89khvlrsGFjfPvrc0qUlGu8j2m2TWX1kXkHfZlbrjjY6InvkDOdry53RZm3dGr1VkyyzOSs36z28d6STJX0fuf+Nu6yrivZkBX0mzISm9mgj0AhrgZhhhkN8TU4b2vUGCYnZLOO948knruF9YG9+yGrWU+/Naek55ZQMzPYXOF+z3szQXLNxB2QCuH3E7aGuA4mkvKCPM7yvLA8ILblEiyvMvrcmJmPg24GY+BA4ui7TbizNySP0rdKtI6mL5GgNjDRZyQ+Fxg6VZxs6V7QvNmuppTelLiUN9DdlkRmYhDTEoYsxkKyefFh2bTMbPJ5dR0V2/l9e160XvX1eQgRJMbp2BlKDs7O8ctwRxrpizaVDY+5JKZJSR0w9fd+RYk/bdahmut6a8iQtPpAr3y9u26eA4ZDLZw/sw/C9qjtHcMlJH/9pV7PfBmcU0hm8PedLHwQhpkwfI9K1iKstfKSZGGyMkrNASs6lk+JVdv9LsFbFFYu9fVLTcLi8xuroBicn92iamfWD73ty0iJNbayVTwNDYK+LUJiXYvSV/BcpTI2dfulZ4EwlXsQ6czoZJIMFSMVwsAZcF8/xouG1axAMBgUUqWFmiAQcNU7qHQ/64Xl2VX5AnMdVjRmTvrIjGsMQeTzAcS7BSOtv50Vhokq9+9YQGLQXGENVo1Ew3HM7zMBuPgmqF1KNBjZqO8ZbOafh/hzJlZ0BH/MICpswrAE6Zi16fLAOm1fJu1CN5Bzpc2siVRLxwbzkEKBvlT4mUrZSRlC6lPCaqZOiTulLz4c+mUMlJQdmYAjb1Qa8OVGkXHIGMMnmYEJnfbamRVbGK6Y0KMkMeg+LWY0PFbNZZXO/CkjIJiykSlUFCBXdJpDUk6hQKhO3chVIAKnIGkipiGsNHdo08ThtkY/Pmb4eKEAAVc7OlNxlDuYVe/WMGJdkTYjPOKel09oBSZWQIjknYrIwQd8lthKwduOap24WtNG55khptoqB3QVXdSu9vwvrfFY4zXLjey94j3VNK+8ZvgOB2o8rGDknugxRHer3QQ5RdwjsMeRM6JXCBNacJmsi6zAWLSlF+n5DjD2bzYoYe86X56gq5+dLco506966Q2pAyWRXQ8jkGEvpZSnP1DxuQdu4/663epFJvfB8DBFIYQaMpq5s4IjZkonWm5bslySt6KMZYk2TzVtyjuwDtbNvcz6AmMb97nW7Cpx4vPMcHl4HVXxw7B3sM1vM2T884O6db3D/7os4l9m0kazK8rwnzjz7B9Z6NRWGoOuyZV+XjnveD/bOVhvAOUElWda6Wg8D1VKaN2yKWHKhMrRG3qW6C6muhZrW2h5lAXi8XEck4OUA8LYgX7Vk9TUgocbNDpF4CO0BkjrYzEAj9HE8bEGQIpubQkBSYigMKqc96igOKY7j+Y4bOWOnwd3yV9mZXjIkeIpt9dk+0jx/zbiyAVlYzI1/vDWqSrkxQ8koY4nyMO6ZgEqNuAXiAnt7R4R6vlXWfATEvKbLZ9DdR/OKhW9o3BwXTHxr0y15+d4x/QY0HJHVergkWs76JV4iPZYrtck2JovlkhA8h8kcpbt3T6nEsdz0tF1i2QgbYP9oj7pqWKN0Wcidp1tlUi84ZuQUWbUtIXnOT+c4Fzhz+6zWiZP105z3iWpxnaxw+23P0sxnvPjVQNc6blx/Fh8a6uaAut7nwalwchbZbDL37q7oDyr25oGE0qXI6nwKE0woGG/JcjP3vbDOwsw7og+lMYiCK9ulCK6ucFnJ2ZNShL6ImRT9AKP3tCy+lORAtQQlAcmlXYkbQgpbr2CQPR48+13t7O3yUbLwh7rdMSvf/l0o6w2F3FXLrLUq8gpcA1KhUrETpXzksbNj3taYaymFMobA5JWtFXQaBZj6IjbUlbCFxWksBqrOo1gi4RgWYDcbfrhWu4+7RDbbkIJirWlLPFukbEslLCQiZDUWN6aEj5E+9rjYE2NPCBERIaVoLWNzNm96YCregJwB8ywddd0gAou0D2SW5yesVmesV2ecnz4gp7aMYabvk1GxyRIGczZPJ5dultmUaUjJ5kAuja1y2Z0kmxCPOB2pa0vlGKjqwhCM43qRzB/niu4mGtYlHNFYHLvkCAhbDYA3HOKQUEOwUI/do9bwZ6TZRgw5A2KNsUpuCpTpV+4R4WLS6kCs2RnvjsPo24/jMuSibMdt5K24MD92qL2tEWvzNO/O3d25LVvNDhMnMhniUNWEquEq829oapVza46JMuYxOOdIKbLedNYbwDWQlVw0Tvpc1hK1jbQ3eoU2Ris6Lce/2XREhE2X6GKmjyanLTiCs/mBejQLqVc0CaLGLMRknxl7y22KGlivHV1cEFNGQoVTmO3dZL7XEJreQg57e1TVjNlsn9lsn3U342zlTd570zOrPZW38GNfJOEfF0zGwGsiD3Y8ADF5coawsSSURXOTvb0FokvQDVmNLiOBdIWKL7HVnCOaS+Lw2MNgsPBLbXHeLtxDMuFA8Q5CMuNqUnqhi4D3Q4tVcE7Lc8W5smWWhcQNq1I5pZQDGSG5a6ifk/0NkAOU2o5h/O/RxTdk6Mw2sO7Ovl+dtQV2mvE+oKrjo7gKUSFqRVZHKjRJlprsIIVoqmyZEnow1cTBy7KvvGggjL5vWVRHW7+oBPpg8X7fzBHv8M0+zgckzMCFkoBWtPyRsfGJY3Di5JLi3BuD4SODN3W7vb0DZrOGqqq4efM2N2/c5onbT/PgwTc5Ob4LJNpuSSZxfLxhNquZz/csTNBYx7wYFedMWdP2Dj9uVN6Z6SautNKVoukggjj7O8SMHtjZYmRQuWtsAZcFgsexBwheDowRcHsYIzBoCQyswLBFvnHjJ65G/D5U16G5jfo1yX8TpKPK58V+L/Mm2nGkzpKCnVQldm9GbLTJhs/gxZQHnbP3WnRj9zyGRzO6GO7lcq46bNzYPB0VBLPVCTgXkZzL1HRlXgspJ5LtyFAMVcp1SEnJGoipQao51ewG4jz1wAxcQXio7Vs23QqnrYWESv6DD0qoAuoybQbVBpghIoRQga6JeopoSx8tMzqVOMq9s5bgHE22eoczlwkIp11iExOrU6XvlMX+hiYJBzGwcIFF6+heOmGd1qziEnyGOhq7pRV9dHz+K8dses9X7z1DJrC/f9MuQe7oe+WpZ/cQAt/7fd/PfDFns+nZbHpO10q+vzH9EjLH99ecH4MLnmavIfaT6NCEEQO9DmALQFJPn5QuwdzPqGYOckQ0EZNRxFkHBcBh0dtaxEM6wUVd+cEQKN+66xWM7xkOZzc2aZvuBc9/pCy3zwcycZu8QLlZbLFRV4Obo1KDqy0xiW39/lXt4+G4LlCmDGyFteYdBFVQy+gWcWQ1ydVUmIGMeT1ZPOoCKrHIxtq2PKRzDQT2cNxjfrua0v6OHYQxKIIOfQZ8sM3RB/spErBj0xy22eG7Ie5XSoAfdBBeL4zhsd4EVVURgmX7V1XFZr2k7zb0/ZrNZkVKHZv1EomZrk14l8lpSBB0ozIhg6ebLeQxhKeMldLCdGhhDoZ9UbeFGTv13TDMJQGK4p+WWCyNXWOZFUNqkBr2OzkClw2BN8ogcBYTdjX4GeRMLgadqXTq9quG+zALOYFJ7G7Pz9gspfQYwg2NC8p5v7IZU4wB2REYYnvfmcG65fKMcbCwlDir7pGh66NSqnAoDsCWodjtT2AzPyC+tiqYUI/Nth4VWUuTqxIWQjLiUmnMZHPB7i0PYnkJ6hpQJecaNJPUj+cHQttnolMiZuj2hfjrktImpesTvVNSm6BN1LlCnSdkIa+7kmcU0ZCL4aSsVybB/eI3O9pYcdYtEN+wd3i7rHv3yLlnsdgjhJqbt6+xt7fH6ek57mxJqECxMKZqoIuJXs3gCbNq1Ih5HDAZA68Bu+13xFPKfdVngd4x6wN1VIKrCK4q1FhvMcKskAVNtllpSeqyhVnHzH7yYCyUjP6BMUiDtKzuGBBa6OOd5VOG9ptDGdCuIAhlQdFC95bFJQ110aGwAHsoVj1gN7jCkHUNoxfyaIO3K87iSqmeLRAhVAhCXTU48XTNHFVlNtu3nIJkJYddodyTODT2Fm+NPckFXI7o0N9hJzFAxjCBbgvgCmNAMc9EBPEBRKhqk0Ku5guc8zTzA5wPNPNDnPPMZvvUzYK6nlNXDXVVUxWJ4BCqHaliX6jUbYncq7XO/fbG7/JwWhe/qrJN9vrRbRBTFmzmc2K/4fhBjZBxrgU867Wp7jWzBu+Fqvbj3BJn5YgCZF+MntH42T7mbNdOsxXaa/ZlFO25yAzU4dyeMQJu3x5lrxilDTJ+pjx8Ym8GxBlj4WdQGUORwxxJQop+vK93j8XmqD2qU3JMDCI0w2Y9Jt7C2KJXsloHUil8kYKWHgPbUJWM9sHWstbtvem0sDFGGw6FwKpW4pmySexK+XvFmK6slCQ+b/kCvqGZ7yPOE6oGH+orjbYWQ9FVAe+qselQHztW63P61KPO4f0c567bOrcxWh+5hg8LFgv7m9PzM7JaX4IsSifWhbT1lo+xybDOsO4zvSbieU/CETrFR0+l4BO4vCHHlhiMvheXcbKm7QOrOKfLM2K4jg8zmvkRIhC7l0mpZV7buC7P7xLTKZtNR9IOXIsPiXldURcFRU2ZUAUWB3Pm80dXXn2rYjIGXhODLznUo1m2rhmvjroBXwf2mpYqZEQ7HBYuGMKsmksGbfKmblUS4MYEoOzKv1uoIBcNAR3DCVt2AAbPejACCjNQlLvkUv3zWMJU7HjJJkKTYlW84hrcHirXwR2RaVCpgIhIHL/vKsbA4Pnb765QiTXZJRrNJG/tiS1fYGBBHCknkEDOibWrUM30oSGnnljNiljRhhx7cozF0NkxBsrjcPWGOGsovxd3G1/ZGIS6Ns97VoyB2QHOe+bzQ7z3NM2cupnTNAuaesZsNqeuZ3gfqEJN8MEMgsIwDImIr1df35iI7WcNfRJCCMCCpplx6/aT3Lhxi5OTu6zX53zj6wv6bs3JgxfJWTk97XDOjIEQhKaxc021x6mUNAAhJdsecwqAR3J5FDuvnIcWxg5cQAe6Xx3i9hDx1mxIAkEO7blbFHasGhmyV9YReBOMA/HFENhHZkeoq0nVvt2/fYUrFL/V7tv3O2fJqna+kLRHtejcqzUwcpgxriIEp3gKq9Bnc3e9mCFdEk5Ge0AYkwzLBMXMkWyByJysD0iKNmFy0T3QjBJIecgLKPe12kYaM8RsZbi4Oa7aZ75vDYCq2QLvr8gMZEdOjjBrqKoZ3tv60fUbum7Jpt9AqPD1IbPmKXJUNr0Ca+CUUCk3bzyFauas/TK5b9l0G5xkNtKTBdY1BIHzBMsM600iukj7YEPcKAtNzAj4lKl7ZZM25LSi83Be2zVebc7oUs1pd0Rkj27+NHXdsHfwFCJwcvpZYlxyuH8EwfPg+GuEJUWS2yNuSVUnjo5mXD88IraR1Cdc8Mz25xwczN+Q6fhWwGQMfFvYpTOxzae81naO5UpxWoE2aHYgHbiMlFruIUxAEfgxes9qjwcZYhh5Ny7v/IOWDWwZWSdakgCHTUJKlrj1IHDelLcGpbMti2A/qaiUqdtD3QFalAbNs9GHl+dXaJjy2qNmY2QUu5138JVR/UHxLpCTEkIq3pSO8VEnjpQzdQhozsR+bm1G+w05RVJsycm0HHSI/e7GUkoOwTBmgx8IgCtdCUOFgGkDiKNq5ohzNDPzrCze7mmaGVXV0NQz6toeq6rBe2+Pzpf6ZGs5bB7mVp1PrmoN7IzkQ49S2BVx7B9cx3tP1x0imun6NXuLOVkjsT8HMZEcROhj6V7ZexPOC7ZBOz+EZyrIDqcNphjYmLdHBaNYUGVGgtRmVLl9RDx+MArcDMHhJJRZMFQd/DkwAgWWG2KaCRJmRnfX++AcaT0jS4+jNRalzJ+sluib2JaqDsaksUuWO+MKRy5FOmCg/UVLhYXk0QAYulkOomFaDHUpLv4og+uw1tF+SLq0f1cdKohKqKyU3Gas+ifhrFzO1YRmj6pZ0Myso6YMkolXGT915OyJCRCl9ludFB2CjuKo6hmL/WtohCo7NK/p+gcEn1GJmKLiHkoomiCJVi2peO0zQWCdIuuYyMF4O+lAJONyh9MOn8FFxWtPyImgbhR+ms08LgfmhwsiC3S+R1XVOG/hjeBtJP//7Z1ZjxtJtt9/JyIyk2QtWrp7enq5wFyvuLbhR39rP/nRn8CAAT8YsA3YsDHA3MZsbqmlWkhmRhw/nIjMrFKppS5J093m+QFSVpEsMplLxD/OOh5vUZ3o90fSpMQuEVJEdY+ETAgTIYx0HXQhELvE2W5gM7hlwKnMbvo2sbXH1cykt3sLekHNvB440oeMxJEQJ0TVTKx12+p1I+aXU62uhLol65I7RJ2842oYXa82ZclCktpRLKZkfviY6gKjrlDaoqIGvE1YupfGp5CeUcLOzIzcMZze2/5UhECcx7ZQrQulFBMCxZrjlJLpuw2qSj9sKaWw25xZLvLhgrYiKWVimg6WojiNlpFQWzvP/R1qQNbdMj93V6FSxUCqgilW037qByQEut4sBH2/MYtB6uhSZ+6BrjdrQOoIEui6wVbrsZ99+4tloH7eow9fmzhYBEV1Fdh+J/peGIYN+uxzpunI8+efczze8vLFbzkcbnnx4o8WfT2+AqyOgwi1lbFU94YQa4xEKRbpr2UASVUMJFjXB5AekZ4YN0AgxioGZGdCSDZ1smuxAffPwadHJVBCQtJAiOeUMRK2zyjjwPTqHNEjkT3WrfEWVJmKlcTO1dJU6nXb0nmtj6Mitd49KsQq3qUaBkKp7gFRmqyw+13rDUtT88CSa1OCuRmi1IJFzZHR9kWtammpFoKswoiQiUwyEOKWfveEYfeEze5ydstJeKwYiKgmxlrcbAyWprqMXWYh2uzOefr8N2gWyqZnmm744YcfiJLRsLf7UZ6hcrAof524mSZGMlfxQApwNU1cTxODCJ0k4h7ipMRpb5N/gU6FTjJDKGSFrt5/52cdRxk4j5dMYlagFAKxy0Ch7wolKPv9a+QohOGG1BW6oaMberJeE2MmxpGUjlabQTpS13FxueF813/glfjrwcXAI7E4glzNiIHjIXGjQgqCDCNaAhIO1mRGcg14MQ92lEDRQtBWabwslkPqinYeO+sqvU4AbSKQIHNjDzO/14Y2Ic4uA1vPmKk/VEGgskUlovESiGjaoWGw1frsQ7XtXZ/qIwfz1WRg6Xtlqc5WrFyJVfqqn19NozFYoNzQdyjKNG0tonqy6o45m4thEQE6/1zemvnQzMGWFRCjCZVYO/fFrkdE6DqLwO568813qbMmKakjxY6Ukv1NCNUi0BoJyRsuAu6Ikseh85l4+By0mIyUejbb8xpTAON4ZBi2KMph/wrVjJY9oMTa1GUY7D1TisQYSLGv4mYghESMFtMRQkcMA0ESIVjBlhBqgGDYmiUg9DRL0N968n8rEkEGJClh8wxCT0lPkHwg6xW1tA2IkvVgwknN2pRrR51W5bKtDCywr9RJsYWwmpaPwR6nVbNs1qmWAhzbtmYCtJV2620QzQ6hwapCFg1kLZZBoyYCcq1RMhLsvu02SLdj2F3Sb86JaaDFL7QGVj8VO+e1w2IB1UQuHapWijuPmXzco7kQdE+Iid3zM6apR7gkT3uuXr+w4xbPiN0GjRMwMo6CkklPD3RB2V1DOBTOiQwEtiUSJysfbPczjNjSYqMmWpEeCYld6kkysNtcMHLGCEQVYk2pjXELciDrLUUzh7EwFuVYRsKY2d+OjIfM1es9ZXzF0A100cRAIXB1ffP4a+9XhouB9+beDSWtW51QNHF7K0x7pUtTbQh0IMSRoEdSHFFVUggUlg5dsebMNrOk5cwv7oE2nyyrep1bDsdY3yNaUFZqFoEQlziC+TaCGCzWocgFKgntPoeQKPESDTtKibMX836O9OMPmczlVYPYjR1bjYVoq/iYOjsGnZlo+36wAWBjAYW51jef8oRqqdulx8FSf6GtoO5bBR46k6tMhtkysLhWQuzr5JoQsQZA1j3Q3ABh5QZo2Q/tvZoJeK5J/yFaYH0aqmXgjiBsDweLko9A1w9A4fLJM3LOjOMB1cLN9UtKyez3r2z1m2+BgkhtcZysLkFKPTEmUjIx0ERBEHtMJBJjXy1RfT0G3c9uCXgIqRkFJj4T6exLcrqm9H+C8ZaRHwg6InIwywAmTMdaA6MUs4u30tc1ZJ8xq2UMoYRSqhgQAkquZYtDDHN8ik3vtah3aZWMWnOhJhdrfEEpKMFeL1INhdZtz3LfrTpnAUYRNCVIZ4Thgu3F5/SDTbz2uY8XZc3a1dyDpfQUvaDk0WJ3jpl8uIEpE7ml73q++qpnGqGPz7m+esV3f7iyoOX4lBhbBdGJw3Egk+m+2DN0ypPvJ85vJp7dJnZT5KJAX6ykOpj1ZBSAwLkKGxLbOEBIDGnDMWy56J5zLFuON2KOrGLXZkrniBbG4/eUsudmv0FiQPcHkMzV1YHjvvByf8UrmdhtBzabgRgTN7cjX798/qjj92vExcCjaaP8UgrUqoNZoxgV63OuLd0KxQqvmD+8iLWBpeZzA8skImIDz9rMLKuUwXBvW/+WebtEPC++fotdUOmwgkJWetOqC36M9euPIctG7z++THKImLVEFaprpRUsihopWqvh12I4IVRrADUau9i3mQXCW/bEYhiYMxtmMbDa0n5nEQ3NCnP/fM1mfBbh9lF4rxNzLyKeZktqwYdV8FQLRpcGVAtzbwqZACUli6SP1TIQQj9bBKQVssFEx1IjoH3ORxKPn4R27YVaXjohYYCYsTr+uXnkZv990earX8WfLE/OKZeyuqwFodR7kSpKYR23srKztZhe1VkMSI15kVpIpNDS95b+G7YLFjxoQqIGZcaOEDsTs2FJ2/zQe7r1+Wipla3OSbljjcvkfLTMJx1RMnXdMR+fRTxugcliASQj4QhiPQZCbU08Fz/XZYEEZnWZhXUNpBDVufR7qcXMQhVtOefVEbDy27laEpulFcH6vxSq20bqYirOYv+DxPyvDBcDj6bexpJAA2PpyRqY8sSYCylFdjsIOtH1tsrtpu9txTHWlW2MlFDI2cyTWUFjRnKGUuqkxFI6NrRAweabrpMRd1ek9njG8u9r1oCeAQnt/g4NA9p9ZpYCUs3hb3XOPyJy7x8Cpe1zfUm76VIdNLUG7BQLvCrztlUxxLalDhR10GAdMFjuDsRv7lYTWDZ6B0L9va3u6/EIMlsR7v+D+wKsfeH76I8893689d3l/vN2oIMISCJG6HtrtHK2e2p7U2zyL7luq+WlDZxhdnNY+uCSJrn8vLgB5EO+1ielme6DBKCHkAibLyEe6J+NlMNrrl59j+Rbor4AhSkXNGf2R+vLoJOZ8rXUAg21Slir+hkj9RqhWvsCKZmLzsqMLS6ruUOhmoUg11lm7vyg5mLSPFnVw7aSDpZrb1kD1oyoaEIlMYaBlM558uxbhu0FF09+S4g9iJWitlIJjztBbYItk6VJxzBwTJCnwjROHA8jmo/sb7/n1Q/KdrvhmO26OL+sxaviRED4+ut/StftYNijZK5f/tViNs7+KxKOfK6XbLeRbTnS1WqtoFZkqZ1MsfiMLgMUioxoLNz89TWHkHklry1LaZPRknn9/Q+AEi6BlLi6ueU4XiPHASTObrHDvjAdC6EbCGnDbnfJk6fn5GwuhJy96JDzTgRaQxrB/IhV6Zfm68vN9N5h4cdxMesiFsVaqp9Za3ogwUyFq8m+VblbxEBN2ZNlYr0zWVGDlWh+SyFKDQiTHuiqCdGa8FhbWftOFhW9fMWPcpjqzSza9st4uDBPzbyoUdCh1kUw5a6zUrdOcyxuFVaiQN4tBoC5ZnsTVOsgPe5N+u8SAh+jwNBDtKIzbVn5tlOzklf39qdZLixLRINNU0I7/u1VLWC1TvitEn8Is8iQO8el/dP1xz7Az6MW1mtjFbvGqUWkQhrQfETSBiUzWc1hcu1FUQsOLoJzbgZQjxl6RwsFsLEg1OBCBOq2NOfbXMTIru/czO/VjTBbBNqKVWqRJ1ubk4v9s5TCKs6qRSB2AzENS42Lesw/irZflQ/POZOLlSlWLEBPydZ8rERytjogqgHV+vx83wREOhSLR7GS2JbqnGtNkXV6MC0QeHYDgrVfqdd3s5ZMEzlMTDKicaSLdm5ybbBVNEPJtbkbhCIQxJqyKbW8e73WxVyYJec71o9TwcXAoxHMzA5QKKGZ+5WsHcexYz8OpFAY+hEoSCqgE2FjN8FQOussF+qkFiytrqRUsw/Mv2i+axMAsfVVnW/4JYApCKQISEAlUYiMeoalAH2LSA/pMyCSm++yThaphFkEqLzL6/6eR6gKk7VhYH1v3Q20q7QcrCYCHmJlBXjoNe/a93aDz5+8MvHbLtx9h3dN9J9UCLA4AmbP0fwCWQ7sHTP9QwKlCssqsiR09dH7k/mPWTl+fG/Xn/VLoM6l2CagYQtpYHgWyMdr9vsXjLcvefHi92YJuCnolCl7u/byqNUkbcpAy4QVBLKJO8ZmhaPG8khNUYvEyYSv1uM9tfThYENuCeaiaul/ZZqsVHGszYy6ZJNWyJQwMWVlLID0qERSv+HiyVcMZ0949sXviGlD7C6q1SbV7/34JjtBbEwZ88GsJUXJYwaOiNxa8GW6JquyP06oDLy62lo8TUnsj1dkrhECecpEUbpkRagutj2aD7z40w+QX1P233MYr4njQEdHwcbLKY8WFwQmQmuNC1GQrEiZuHnxklvZ85LviJtLPvt2AoVQrPHZy8NLxnLL7Q1MU6IPG2tpPO6Zpj1lDEBtoy1we3tDzgcsVqi3bKUTwcXAo7HVhNBWqWYpaKYCGy5qHEFpwWZ2uLX57KOY7ztZe8JQSg0wCosYgCWwLZhvbVn3LKv4ebKtqUsqNV4hbOpKt0dDLf5yz6z9pntgteL7GMdp/duPTZzNcvBjlgl9y+Mfsmf3xMBDn/1+E/7HnwTfOAvaPkfaxbccrrd9/P3juQSUcGef7wmCt2UvPMzHvGY+nEUerUWSPSMhWTZEvyPkI6Qz0MyoHVqKeaiqZQClegTNOqArMaCYgG6ZgCJLA63a2Wi2UpXZvWXBBnPr6NokqVRLgNa/ES1QhEIha2YqgSkrEsXiOEJPN+zoui0x9sTQgjjNMtAsZ49G6xdvcRNtRJv7oTR3SYspKJbhg2UJTZPFD6DKOB5BD/PYFMsEJdcAwWiTe7lbOfROIPViXrSzqWYZoFpgrE50Bs2kZK+Kl1Ya+dULNddLjQOxPg5KzsVcAKW5w1oGVlssLLEap4KLgQ9kGYpze6DOyAHF2uBORzPNhvAMKMR4gUS1XFgtnOXXgNIdbqywTi2k0yYgC8CxVZ2ZAWuEjtbgF5Z6+xNUMWD14WN6YnsZLxBa+uBSBmkZJsudb/NRuPdm739f2c3/YAyD3Nl89PlXAQnrjISfbzB4o1vkPNOwzL3rp6vb5f727nve/dM3tdWHHNBfhkXAaK6f2lMCMFN+gG5HSj2XX/1rDvvXvNpb5sAf/7zneHzF9OoGzRPbWEt8aybohE5HtCwTSzPLhyC1vsfSxCjmZeIEKE0U1EOU71zIsWYUCdplMyBohiCMOjJqYMwbjnng/MkZu91v6c+f8uXf/VtSv+Ps4reAFYya3VY0V8/jyHkij0cESxeNMdN1SkxC6pJZOm5HlKONc7lwdfWCUpTj7ZH9fs84XQPCP373vxG2tTRyYNslUsj8/dkZXUicH16xydd0auNSri3PuxiJUqt6xkDKwFgISrW8KPFwNA9iuiISefKssNtt+Id//g+IwL//D/+D6z9fMR0L4yjcjHuUES1HShnZ9B1D2tB3ib63nhTWybPYWJy9hbHzDu6srebVWh1a64/LQFtN+mqrcq2Fca2Kl2J+fIWQEY2EmKtv0t7LWpI2f20r6xqsa1q4O7DbxwkWH5Bma8R6oH5jodgen33tH4umrJe9W/up7ffl57t7KNwZLz8x97/z+4iNN8XNj5k0PgI/4W3n8IzVbL+4G2rMgLxt7f9TLQK/TOZ4mpXIbfE02nzuaaDbXKJlots+QQscX20oBQ7ZioZFIKjOVcnbYtneSwkq1WNjga2CRbkjQlAzczfXk3UgXyqDzp0H6/5prrEHk82MEzCpjRkhbUj9jmFzQTecE7sNMVkl0VbqWVb3zYfcyOt7UWaLZ62PXlMsFbuWcrZOluM4UYoyjhPTNJm/XgXKiBBrS2wrNy5z1yw7fpHQRsn5upV1bFSLq5IwW0Rl/uatoFkhlwNK5Oy8RwSGPtJ1dkzmzAhkXv23zytqjaDW1taWlXAquBj4aNw3Ryp2Ky8GS1Wrcz2rzToQBM4RlNBbWF+8N8usg8F0bSau5UnX1t8imbYusDtgHWPw9tHhU6x/77/nusOayLssBR/RH/De3LcGfNhRWXecfAz3r6j143d/uPf8A9kNn9Yi8Gn4kOBMs4wsR7BNHCYExERyf8mmO+frf3ZhlriwZX/zmv/2n/4jt1c/8PIP/4s8HhjEijF3ibm8rcDsRxApSLLaAqm3TJ4Qpxq4Wa/5OtI2EVBq++wQJizGx4J5y1grDN5gcQ6xp8Qdl8++4enTb/ny63/BF1/+E7puw/mTb81C2LIH7h2rFiD7GEQse6nkbJ6SAtPYJkzLKtAiHA8FLUdCmBjHF6DKdByZpgP7/Wsg0OkLhB6JJkL3h0CkcJRrkIkhC0/DsFRzjFYhMw09XUz2HWOgGwudZkopSLFMhY0IRYQ+TIje8Nf/+z+R8Jx/+a/+HTEGvv3Pl2h8zfffv+B4zOQyotSGUKK2ZeJwmDgcMiFappYWZZoy11fXjz6GvzZcDHwgb/Wr2mix8vG27Wo41uV3Xa1f5KEAvuZLl6afTQjcrT2uK9NgWO3Hewz7n3QueHPquT9BzqvYn43mo3zcTizi5tMcyMXkX/evXgvarpwHMzPeeJNlZ9cPr37+cEvBL4e1yVx52IWiarnlFIslsFx9q3Knq0l6XkfqvQt1fTs3H7MIi6/ZlrDt2tD5fm9bllX2vX9IcxUFqO2X58qAIbKI/scJph8/dsto1VbRd9slW8G1JnbnugOzAG7X6foblfqdQO80El9dYc0ouP4+K/U711pZiby61KofV+sNVE9qy75qL16HUrwRu9wsHtrchPOZOAlcDHxyqnntDZZLOLNevb/tPdY/11vgjZcL1kjml4GtVFbmWpG5GMuDg9dKM330mgfviQVf3d+ht7y2Cb72l7Iyz8M8eT92nC6q5Fpp0VYrWJlamvh4c4ID5uDTZWKyctR33ATt9atgwiZHfynXz/r7fMz3M8ynHoMg3RZQvvrdv6Hkie3uOeNhz//57/+F8bDn5V/+wPUPf+HFqz9xuPmBabylTEfrWihqjZ86m3hiV6V9bSnetLpUU7WEVjSkZnMkyybINRBUujNEEmeX35DSwLPnX/Pks294/vk3PPvsG3Znz9lun0CwjCHe6ur5MMyRUpiON0yTMrZeB7UA1fE4WStojdacDTjcHi2zQqzlcjveqd8TJbO7uCFEJZaOCDyJPb10bG8D3aF2bUSQGC1eAisNbe4Eq7uwSZGQQXIhqCL7kRBgs1WKjBz2f+F2P3Fz+xdSDHzxmx2Ez/j97/9I0ZEhDqgkYlRCLCQ6okb2hyOH40iZMutYCw8gdGZCLUX7KS+KeQ2xWsncfb488Oo60dx7L3lkkZF3sXThe3+W6nztO9mEI++cbBe/4d+K9TFfJzT8OOup8+7fLz8vUco/H3onUeO+IPhUqZG/XKSa720CDhJBlc32HFXl6edfMx4PXPz5jxz3t9zc3nJzGMnX1xw51HLFVlAoihJEULWW0LnUWiJF59W1/d5836m69qoYUBMHU92XSI9Ih6RzpNvSbZ+xO/+Cs4svOL/8nK4/I/XbajkIn0y8xRBqVcpc+5rYskVnM4c1yrJS1ebysMA7qX1TWgdPoeuEGIR+sAk41jiMIXT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" ] @@ -323,243 +323,22 @@ "execution_count": 8, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating loss on init\n", - "Validating model EfiNet\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\logger_connector\\logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", - " warning_cache.warn(\n", - "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\data_connector.py:442: PossibleUserWarning: The dataloader, val_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", - " rank_zero_warn(\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0d00232f67324dfd9dc475061b48b248", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\torchvision\\transforms\\functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n", - " warnings.warn(\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Error while executing step Check Loss On Init!\n", - "\u001b[33m\u001b[1mTraceback (most recent call last):\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\\u001b[0m\u001b[32m\u001b[1mrunpy.py\u001b[0m\", line \u001b[33m196\u001b[0m, in \u001b[35m_run_module_as_main\u001b[0m\n", - " \u001b[35m\u001b[1mreturn\u001b[0m \u001b[1m_run_code\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode\u001b[0m\u001b[1m,\u001b[0m \u001b[1mmain_globals\u001b[0m\u001b[1m,\u001b[0m \u001b[36m\u001b[1mNone\u001b[0m\u001b[1m,\u001b[0m\n", - " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m{'__name__': '__main__', '__doc__': 'Entry point for launching an IPython kernel.\\n\\nThis is separate from the ipykernel pack...\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m at 0x0000022AB3583050, file \"c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel_launcher.py\"...\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\\u001b[0m\u001b[32m\u001b[1mrunpy.py\u001b[0m\", line \u001b[33m86\u001b[0m, in \u001b[35m_run_code\u001b[0m\n", - " \u001b[1mexec\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode\u001b[0m\u001b[1m,\u001b[0m \u001b[1mrun_globals\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m{'__name__': '__main__', '__doc__': 'Entry point for launching an IPython kernel.\\n\\nThis is separate from the ipykernel pack...\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m at 0x0000022AB3583050, file \"c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel_launcher.py\"...\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\\u001b[0m\u001b[32m\u001b[1mipykernel_launcher.py\u001b[0m\", line \u001b[33m17\u001b[0m, in \u001b[35m\u001b[0m\n", - " \u001b[1mapp\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mlaunch_new_instance\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m>\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\traitlets\\config\\\u001b[0m\u001b[32m\u001b[1mapplication.py\u001b[0m\", line \u001b[33m1053\u001b[0m, in \u001b[35mlaunch_instance\u001b[0m\n", - " \u001b[1mapp\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mstart\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelapp.py\u001b[0m\", line \u001b[33m737\u001b[0m, in \u001b[35mstart\u001b[0m\n", - " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mio_loop\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mstart\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\tornado\\platform\\\u001b[0m\u001b[32m\u001b[1masyncio.py\u001b[0m\", line \u001b[33m215\u001b[0m, in \u001b[35mstart\u001b[0m\n", - " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1masyncio_loop\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_forever\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m<_WindowsSelectorEventLoop running=True closed=False debug=False>\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\asyncio\\\u001b[0m\u001b[32m\u001b[1mbase_events.py\u001b[0m\", line \u001b[33m603\u001b[0m, in \u001b[35mrun_forever\u001b[0m\n", - " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_run_once\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m<_WindowsSelectorEventLoop running=True closed=False debug=False>\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\asyncio\\\u001b[0m\u001b[32m\u001b[1mbase_events.py\u001b[0m\", line \u001b[33m1909\u001b[0m, in \u001b[35m_run_once\u001b[0m\n", - " \u001b[1mhandle\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_run\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m, ...],))>)>\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\asyncio\\\u001b[0m\u001b[32m\u001b[1mevents.py\u001b[0m\", line \u001b[33m80\u001b[0m, in \u001b[35m_run\u001b[0m\n", - " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_context\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun\u001b[0m\u001b[1m(\u001b[0m\u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_callback\u001b[0m\u001b[1m,\u001b[0m \u001b[35m\u001b[1m*\u001b[0m\u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_args\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m│ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m, ...],))>)>\u001b[0m\n", - " \u001b[36m│ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1m, ...],))>)>\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m, ...],))>)>\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelbase.py\u001b[0m\", line \u001b[33m524\u001b[0m, in \u001b[35mdispatch_queue\u001b[0m\n", - " \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mprocess_one\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelbase.py\u001b[0m\", line \u001b[33m513\u001b[0m, in \u001b[35mprocess_one\u001b[0m\n", - " \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mdispatch\u001b[0m\u001b[1m(\u001b[0m\u001b[35m\u001b[1m*\u001b[0m\u001b[1margs\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m([, , >\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelbase.py\u001b[0m\", line \u001b[33m418\u001b[0m, in \u001b[35mdispatch_shell\u001b[0m\n", - " \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mresult\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mkernelbase.py\u001b[0m\", line \u001b[33m758\u001b[0m, in \u001b[35mexecute_request\u001b[0m\n", - " \u001b[1mreply_content\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mreply_content\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mipkernel.py\u001b[0m\", line \u001b[33m426\u001b[0m, in \u001b[35mdo_execute\u001b[0m\n", - " \u001b[1mres\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mshell\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_cell\u001b[0m\u001b[1m(\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\ipykernel\\\u001b[0m\u001b[32m\u001b[1mzmqshell.py\u001b[0m\", line \u001b[33m549\u001b[0m, in \u001b[35mrun_cell\u001b[0m\n", - " \u001b[35m\u001b[1mreturn\u001b[0m \u001b[1msuper\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_cell\u001b[0m\u001b[1m(\u001b[0m\u001b[35m\u001b[1m*\u001b[0m\u001b[1margs\u001b[0m\u001b[1m,\u001b[0m \u001b[35m\u001b[1m**\u001b[0m\u001b[1mkwargs\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m{'store_history': True, 'silent': False, 'cell_id': 'vscode-notebook-cell:/c%3A/Users/matri/Polibuda/diploma/ART-Templates/%7...\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m('project.add_step(\\n CheckLossOnInit(EfiNet),\\n [CheckScoreCloseTo(metric=ce_loss,\\n ...\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3024\u001b[0m, in \u001b[35mrun_cell\u001b[0m\n", - " \u001b[1mresult\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m_run_cell\u001b[0m\u001b[1m(\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3079\u001b[0m, in \u001b[35m_run_cell\u001b[0m\n", - " \u001b[1mresult\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[1mrunner\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcoro\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1masync_helpers.py\u001b[0m\", line \u001b[33m129\u001b[0m, in \u001b[35m_pseudo_sync_runner\u001b[0m\n", - " \u001b[1mcoro\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1msend\u001b[0m\u001b[1m(\u001b[0m\u001b[36m\u001b[1mNone\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3284\u001b[0m, in \u001b[35mrun_cell_async\u001b[0m\n", - " \u001b[1mhas_raised\u001b[0m \u001b[35m\u001b[1m=\u001b[0m \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_ast_nodes\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode_ast\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mbody\u001b[0m\u001b[1m,\u001b[0m \u001b[1mcell_name\u001b[0m\u001b[1m,\u001b[0m\n", - " \u001b[36m │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m'C:\\\\Users\\\\matri\\\\AppData\\\\Local\\\\Temp\\\\ipykernel_33072\\\\1791217716.py'\u001b[0m\n", - " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m[, ]\u001b[0m\n", - " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3466\u001b[0m, in \u001b[35mrun_ast_nodes\u001b[0m\n", - " \u001b[35m\u001b[1mif\u001b[0m \u001b[35m\u001b[1mawait\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_code\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode\u001b[0m\u001b[1m,\u001b[0m \u001b[1mresult\u001b[0m\u001b[1m,\u001b[0m \u001b[1masync_\u001b[0m\u001b[35m\u001b[1m=\u001b[0m\u001b[1masy\u001b[0m\u001b[1m)\u001b[0m\u001b[1m:\u001b[0m\n", - " \u001b[36m │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1mFalse\u001b[0m\n", - " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m at 0x0000022AF3C56340, file \"C:\\Users\\matri\\AppData\\Local\\Temp\\ipykernel_33072\\1791217716.py\", line 1>\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mc:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\IPython\\core\\\u001b[0m\u001b[32m\u001b[1minteractiveshell.py\u001b[0m\", line \u001b[33m3526\u001b[0m, in \u001b[35mrun_code\u001b[0m\n", - " \u001b[1mexec\u001b[0m\u001b[1m(\u001b[0m\u001b[1mcode_obj\u001b[0m\u001b[1m,\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1muser_global_ns\u001b[0m\u001b[1m,\u001b[0m \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1muser_ns\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m │ │ │ │ └ \u001b[0m\u001b[36m\u001b[1m{'__name__': '__main__', '__doc__': 'Automatically created module for IPython interactive environment', '__package__': None, ...\u001b[0m\n", - " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1m at 0x0000022AF3C56340, file \"C:\\Users\\matri\\AppData\\Local\\Temp\\ipykernel_33072\\1791217716.py\", line 1>\u001b[0m\n", - "\n", - " File \"\u001b[32mC:\\Users\\matri\\AppData\\Local\\Temp\\ipykernel_33072\\\u001b[0m\u001b[32m\u001b[1m1791217716.py\u001b[0m\", line \u001b[33m7\u001b[0m, in \u001b[35m\u001b[0m\n", - " \u001b[1mproject\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mrun_all\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1mproject.py\u001b[0m\", line \u001b[33m201\u001b[0m, in \u001b[35mrun_all\u001b[0m\n", - " \u001b[1mstep\u001b[0m\u001b[1m(\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1msteps.py\u001b[0m\", line \u001b[33m249\u001b[0m, in \u001b[35m__call__\u001b[0m\n", - " \u001b[1msuper\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1m__call__\u001b[0m\u001b[1m(\u001b[0m\u001b[1mprevious_states\u001b[0m\u001b[1m,\u001b[0m \u001b[1mdatamodule\u001b[0m\u001b[1m,\u001b[0m \u001b[1mmetric_calculator\u001b[0m\u001b[1m,\u001b[0m \u001b[1mrun_id\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m │ │ │ └ \u001b[0m\u001b[36m\u001b[1m'2023-12-01_15-38-42_e12e7153-d0bc-4a18-aef0-ff8409199751'\u001b[0m\n", - " \u001b[36m │ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m └ \u001b[0m\u001b[36m\u001b[1mdefaultdict(. at 0x0000022ACF270430>, {'': defaultdict( File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1msteps.py\u001b[0m\", line \u001b[33m78\u001b[0m, in \u001b[35m__call__\u001b[0m\n", - " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mdo\u001b[0m\u001b[1m(\u001b[0m\u001b[1mprevious_states\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1mdefaultdict(. at 0x0000022ACF270430>, {'': defaultdict(\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1msteps.py\u001b[0m\", line \u001b[33m428\u001b[0m, in \u001b[35mdo\u001b[0m\n", - " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mvalidate\u001b[0m\u001b[1m(\u001b[0m\u001b[1mtrainer_kwargs\u001b[0m\u001b[35m\u001b[1m=\u001b[0m\u001b[1m{\u001b[0m\u001b[36m\"dataloaders\"\u001b[0m\u001b[1m:\u001b[0m \u001b[1mtrain_loader\u001b[0m\u001b[1m}\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", - "\n", - " File \"\u001b[32mC:\\Users\\matri\\Polibuda\\diploma\\art\\art\\\u001b[0m\u001b[32m\u001b[1msteps.py\u001b[0m\", line \u001b[33m304\u001b[0m, in \u001b[35mvalidate\u001b[0m\n", - " \u001b[1mself\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mresults\u001b[0m\u001b[1m[\u001b[0m\u001b[36m\"scores\"\u001b[0m\u001b[1m]\u001b[0m\u001b[35m\u001b[1m.\u001b[0m\u001b[1mupdate\u001b[0m\u001b[1m(\u001b[0m\u001b[1mresult\u001b[0m\u001b[1m[\u001b[0m\u001b[34m\u001b[1m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n", - " \u001b[36m│ │ └ \u001b[0m\u001b[36m\u001b[1mNone\u001b[0m\n", - " \u001b[36m│ └ \u001b[0m\u001b[36m\u001b[1m{'scores': {}, 'parameters': {'lr': 0.001, 'model_name': 'EfficientNet', 'n_parameters': 17727916, 'batch_size': 32, 'train_s...\u001b[0m\n", - " \u001b[36m└ \u001b[0m\u001b[36m\u001b[1m\u001b[0m\n", + "Summary: \n", + "Step: Data analysis, Model: , Passed: True. Results:\n", "\n", - "\u001b[31m\u001b[1mTypeError\u001b[0m:\u001b[1m 'NoneType' object is not subscriptable\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\call.py:53: UserWarning: Detected KeyboardInterrupt, attempting graceful shutdown...\n", - " rank_zero_warn(\"Detected KeyboardInterrupt, attempting graceful shutdown...\")\n" - ] - }, - { - "ename": "TypeError", - "evalue": "'NoneType' object is not subscriptable", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Users\\matri\\Polibuda\\diploma\\ART-Templates\\{{cookiecutter.project_slug}}\\Tutorial.ipynb Cell 20\u001b[0m line \u001b[0;36m7\n\u001b[0;32m 1\u001b[0m project\u001b[39m.\u001b[39madd_step(\n\u001b[0;32m 2\u001b[0m CheckLossOnInit(EfiNet),\n\u001b[0;32m 3\u001b[0m [CheckScoreCloseTo(metric\u001b[39m=\u001b[39mce_loss,\n\u001b[0;32m 4\u001b[0m value\u001b[39m=\u001b[39mEXPECTED_LOSS, rel_tol\u001b[39m=\u001b[39m\u001b[39m0.1\u001b[39m)]\n\u001b[0;32m 5\u001b[0m )\n\u001b[1;32m----> 7\u001b[0m project\u001b[39m.\u001b[39;49mrun_all()\n", - "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\project.py:218\u001b[0m, in \u001b[0;36mArtProject.run_all\u001b[1;34m(self, force_rerun)\u001b[0m\n\u001b[0;32m 216\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprint_summary()\n\u001b[0;32m 217\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m--> 218\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[0;32m 219\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m 220\u001b[0m remove_logger(logger_id)\n", - "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\project.py:201\u001b[0m, in \u001b[0;36mArtProject.run_all\u001b[1;34m(self, force_rerun)\u001b[0m\n\u001b[0;32m 199\u001b[0m \u001b[39mcontinue\u001b[39;00m\n\u001b[0;32m 200\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 201\u001b[0m step(\n\u001b[0;32m 202\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstate\u001b[39m.\u001b[39;49mstep_states,\n\u001b[0;32m 203\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdatamodule,\n\u001b[0;32m 204\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmetric_calculator,\n\u001b[0;32m 205\u001b[0m run_id,\n\u001b[0;32m 206\u001b[0m )\n\u001b[0;32m 207\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcheck_checks(step, checks)\n\u001b[0;32m 208\u001b[0m \u001b[39mexcept\u001b[39;00m CheckFailedException \u001b[39mas\u001b[39;00m e:\n", - "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:249\u001b[0m, in \u001b[0;36mModelStep.__call__\u001b[1;34m(self, previous_states, datamodule, metric_calculator, run_id)\u001b[0m\n\u001b[0;32m 245\u001b[0m curr_device \u001b[39m=\u001b[39m (\n\u001b[0;32m 246\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mcuda\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39maccelerator, CUDAAccelerator) \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mcpu\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 247\u001b[0m )\n\u001b[0;32m 248\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmetric_calculator\u001b[39m.\u001b[39mto(curr_device)\n\u001b[1;32m--> 249\u001b[0m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__call__\u001b[39;49m(previous_states, datamodule, metric_calculator, run_id)\n\u001b[0;32m 250\u001b[0m \u001b[39mdel\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\n\u001b[0;32m 251\u001b[0m gc\u001b[39m.\u001b[39mcollect()\n", - "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:82\u001b[0m, in \u001b[0;36mStep.__call__\u001b[1;34m(self, previous_states, datamodule, metric_calculator, run_id)\u001b[0m\n\u001b[0;32m 80\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m 81\u001b[0m art_logger\u001b[39m.\u001b[39mexception(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mError while executing step \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname\u001b[39m}\u001b[39;00m\u001b[39m!\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m---> 82\u001b[0m \u001b[39mraise\u001b[39;00m e\n\u001b[0;32m 83\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m 84\u001b[0m remove_logger(logger_id)\n", - "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:78\u001b[0m, in \u001b[0;36mStep.__call__\u001b[1;34m(self, previous_states, datamodule, metric_calculator, run_id)\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdatamodule \u001b[39m=\u001b[39m datamodule\n\u001b[0;32m 77\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfill_basic_results()\n\u001b[1;32m---> 78\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdo(previous_states)\n\u001b[0;32m 79\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfinalized \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m 80\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n", - "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:428\u001b[0m, in \u001b[0;36mCheckLossOnInit.do\u001b[1;34m(self, previous_states)\u001b[0m\n\u001b[0;32m 426\u001b[0m train_loader \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdatamodule\u001b[39m.\u001b[39mtrain_dataloader()\n\u001b[0;32m 427\u001b[0m art_logger\u001b[39m.\u001b[39minfo(\u001b[39m\"\u001b[39m\u001b[39mCalculating loss on init\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m--> 428\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mvalidate(trainer_kwargs\u001b[39m=\u001b[39;49m{\u001b[39m\"\u001b[39;49m\u001b[39mdataloaders\u001b[39;49m\u001b[39m\"\u001b[39;49m: train_loader})\n", - "File \u001b[1;32m~\\Polibuda\\diploma\\art\\art\\steps.py:304\u001b[0m, in \u001b[0;36mModelStep.validate\u001b[1;34m(self, trainer_kwargs)\u001b[0m\n\u001b[0;32m 301\u001b[0m art_logger\u001b[39m.\u001b[39minfo(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mValidating model \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel_name\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 303\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39mvalidate(model\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39minitialize_model(), \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mtrainer_kwargs)\n\u001b[1;32m--> 304\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mresults[\u001b[39m\"\u001b[39m\u001b[39mscores\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mupdate(result[\u001b[39m0\u001b[39;49m])\n", - "\u001b[1;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable" + "Step: Evaluate Baseline, Model: HeuristicBaseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.012299999594688416\n", + "Step: Evaluate Baseline, Model: MlBaseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.15649999678134918\n", + "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.6883000135421753\n", + "Step: Check Loss On Init, Model: EffiNet, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.00675999978557229\n", + "\tCrossEntropyLoss-validate: 4.893486022949219\n" ] } ], @@ -583,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -599,14 +378,12 @@ "\tMulticlassAccuracy-validate: 0.15649999678134918\n", "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", "\tMulticlassAccuracy-validate: 0.6883000135421753\n", - "Step: Check Loss On Init, Model: ResNet18, Passed: True. Results:\n", - "\tMulticlassAccuracy-validate: 0.015399999916553497\n", - "\tCrossEntropyLoss-validate: 5.087794780731201\n", - "Step: Overfit One Batch, Model: ResNet18, Passed: True. Results:\n", + "Step: Check Loss On Init, Model: EffiNet, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.00675999978557229\n", + "\tCrossEntropyLoss-validate: 4.893486022949219\n", + "Step: Overfit One Batch, Model: EffiNet, Passed: True. Results:\n", "\tMulticlassAccuracy-train: 1.0\n", - "\tCrossEntropyLoss-train: 0.00015361519763246179\n", - "Code of the following steps was changed: Data analysis, ResNet18_Check Loss On Init, ResNet18_Overfit One Batch\n", - " Rerun could be needed.\n" + "\tCrossEntropyLoss-train: 2.902682354033459e-05\n" ] } ], @@ -627,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -640,9 +417,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Summary: \n", + "Step: Data analysis, Model: , Passed: True. Results:\n", + "\n", + "Step: Evaluate Baseline, Model: HeuristicBaseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.012299999594688416\n", + "Step: Evaluate Baseline, Model: MlBaseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.15649999678134918\n", + "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.6883000135421753\n", + "Step: Check Loss On Init, Model: EffiNet, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.00675999978557229\n", + "\tCrossEntropyLoss-validate: 4.893486022949219\n", + "Step: Overfit One Batch, Model: EffiNet, Passed: True. Results:\n", + "\tMulticlassAccuracy-train: 1.0\n", + "\tCrossEntropyLoss-train: 2.902682354033459e-05\n", + "Step: Overfit, Model: EffiNet, Passed: True. Results:\n", + "\tMulticlassAccuracy-train: 0.9455999732017517\n", + "\tCrossEntropyLoss-train: 0.16572201251983643\n", + "\tMulticlassAccuracy-validate: 0.7613999843597412\n", + "\tCrossEntropyLoss-validate: 1.1986972093582153\n" + ] + } + ], "source": [ "project.run_all()" ] @@ -656,31 +460,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\neptune\\common\\warnings.py:62: NeptuneWarning: The following monitoring options are disabled by default in interactive sessions: 'capture_stdout', 'capture_stderr', 'capture_traceback', and 'capture_hardware_metrics'. To enable them, set each parameter to 'True' when initializing the run. The monitoring will continue until you call run.stop() or the kernel stops. Also note: Your source files can only be tracked if you pass the path(s) to the 'source_code' argument. For help, see the Neptune docs: https://docs.neptune.ai/logging/source_code/\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "https://app.neptune.ai/mmaecki/transfer-learning/e/TRAN-5\n" - ] - } - ], + "outputs": [], "source": [ "from art.steps import TransferLearning\n", "from art.loggers import NeptuneLoggerAdapter\n", "from lightning.pytorch.callbacks import EarlyStopping\n", "\n", - "logger = NeptuneLoggerAdapter(project=\"mmaecki/transfer-learning\")\n", "early_stopping = EarlyStopping('CrossEntropyLoss-validate', patience=6)\n", "project.add_step(TransferLearning(EffiNet,\n", " freezed_trainer_kwargs={\"max_epochs\": 4,\n", @@ -690,308 +477,57 @@ " \"check_val_every_n_epoch\": 2,\n", " \"callbacks\": [early_stopping]},\n", " keep_unfrozen=1,\n", - " logger = logger,\n", " ),\n", " [CheckScoreGreaterThan(metric=accuracy_metric, value=0.70)])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "Using cache found in C:\\Users\\matri/.cache\\torch\\hub\\facebookresearch_semi-supervised-ImageNet1K-models_master\n", - "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", - " warnings.warn(\n", - "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", - " warnings.warn(msg)\n", - "\n", - " | Name | Type | Params\n", - "-------------------------------------------\n", - "0 | model | ResNet | 11.2 M\n", - "1 | loss | CrossEntropyLoss | 0 \n", - "-------------------------------------------\n", - "100 Trainable params\n", - "11.2 M Non-trainable params\n", - "11.2 M Total params\n", - "44.911 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8e251ec73820468f94bb4c83398d3c83", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\data_connector.py:442: PossibleUserWarning: The dataloader, val_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", - " rank_zero_warn(\n", - "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\torchvision\\transforms\\functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n", - " warnings.warn(\n", - "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\connectors\\data_connector.py:442: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", - " rank_zero_warn(\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "355a47ccd7b64123aeca48cb419480f3", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f3699618a688453c8a3fb3c6625424a6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "216b851e36f0424daf4303df10a4d7b2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9250ceacdf704b8f83b84719781063f9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2b5ce9bc901d40d4b7c517dc989a358a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5f88c244f2fe4d93948f4fe25c641562", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9672598ad6d54d0397c02d54b040b65c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "name": "stdout", "output_type": "stream", "text": [ - "Experiencing connection interruptions. Will try to reestablish communication with Neptune. Internal exception was: RequestsFutureAdapterTimeout\n", - "Communication with Neptune restored!\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7ff72e8e6e854a999c8f7c36ecc1473a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ec9bac2ba2d94fa69be326fc053b7ada", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8e8186dc69a047109e3cb2ec79823312", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e6e7c248acc04c6182b96ff095616e23", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "18881a8514e64557a5c4b612d318a7a1", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\trainer\\call.py:53: UserWarning: Detected KeyboardInterrupt, attempting graceful shutdown...\n", - " rank_zero_warn(\"Detected KeyboardInterrupt, attempting graceful shutdown...\")\n", - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "Using cache found in C:\\Users\\matri/.cache\\torch\\hub\\facebookresearch_semi-supervised-ImageNet1K-models_master\n", - "c:\\Users\\matri\\anaconda3\\envs\\art\\lib\\site-packages\\lightning\\pytorch\\callbacks\\model_checkpoint.py:617: UserWarning: Checkpoint directory c:\\Users\\matri\\Polibuda\\diploma\\ART-Templates\\{{cookiecutter.project_slug}}\\.neptune\\Untitled\\TRAN-5\\checkpoints exists and is not empty.\n", - " rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n", + "Summary: \n", + "Step: Data analysis, Model: , Passed: True. Results:\n", "\n", - " | Name | Type | Params\n", - "-------------------------------------------\n", - "0 | model | ResNet | 11.2 M\n", - "1 | loss | CrossEntropyLoss | 0 \n", - "-------------------------------------------\n", - "11.2 M Trainable params\n", - "0 Non-trainable params\n", - "11.2 M Total params\n", - "44.911 Total estimated model params size (MB)\n" + "Step: Evaluate Baseline, Model: HeuristicBaseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.012299999594688416\n", + "Step: Evaluate Baseline, Model: MlBaseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.15649999678134918\n", + "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.6883000135421753\n", + "Step: Check Loss On Init, Model: EffiNet, Passed: True. Results:\n", + "\tMulticlassAccuracy-validate: 0.00675999978557229\n", + "\tCrossEntropyLoss-validate: 4.893486022949219\n", + "Step: Overfit One Batch, Model: EffiNet, Passed: True. Results:\n", + "\tMulticlassAccuracy-train: 1.0\n", + "\tCrossEntropyLoss-train: 2.902682354033459e-05\n", + "Step: Overfit, Model: EffiNet, Passed: True. Results:\n", + "\tMulticlassAccuracy-train: 0.9455999732017517\n", + "\tCrossEntropyLoss-train: 0.16572201251983643\n", + "\tMulticlassAccuracy-validate: 0.7613999843597412\n", + "\tCrossEntropyLoss-validate: 1.1986972093582153\n", + "Step: TransferLearning, Model: EffiNet, Passed: True. Results:\n", + "\tMulticlassAccuracy-train: 0.9606000185012817\n", + "\tCrossEntropyLoss-train: 0.12111776322126389\n", + "\tMulticlassAccuracy-validate: 0.7610999941825867\n", + "\tCrossEntropyLoss-validate: 1.254715085029602\n" ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0e51f689e33a44809e224ba265ea4323", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5426b450053746578fa344a35f6cd4db", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Training: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "project.run_all()\n", - "logger.stop()" + "project.run_all()" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "We are all done! We achieved 76,1% of accuracy using a small [EfficientNet](https://huggingface.co/timm/efficientnet_b2.ra_in1k), running only couple of epochs!" + ] } ], "metadata": { diff --git a/{{cookiecutter.project_slug}}/art_checkpoints/AlreadyExistingResNet20Baseline_Evaluate Baseline/results.json b/{{cookiecutter.project_slug}}/art_checkpoints/AlreadyExistingResNet20Baseline_Evaluate Baseline/results.json new file mode 100644 index 0000000..3e31503 --- /dev/null +++ b/{{cookiecutter.project_slug}}/art_checkpoints/AlreadyExistingResNet20Baseline_Evaluate Baseline/results.json @@ -0,0 +1 @@ +{"name": "Evaluate Baseline", "model": "AlreadyExistingResNet20Baseline", "runs": [{"scores": {"MulticlassAccuracy-validate": 0.6883000135421753}, "parameters": {}, "timestamp": "2023-12-02 11:10:00.493226", "successful": true, "hash": "60e2ce2589c01f74052ee4142b3f874a", "commit_id": "1b740ecf84c50de0b79fb155ef306ef622d92f08", "log_file_name": "2023-12-02_11-10-00_e6aac212-98e4-4836-9884-ba7e210a7b2a.log"}]} \ No newline at end of file diff --git a/{{cookiecutter.project_slug}}/art_checkpoints/Data analysis/results.json b/{{cookiecutter.project_slug}}/art_checkpoints/Data analysis/results.json new file mode 100644 index 0000000..1519b8c --- /dev/null +++ b/{{cookiecutter.project_slug}}/art_checkpoints/Data analysis/results.json @@ -0,0 +1 @@ +{"name": "Data analysis", "model": "", "runs": [{"scores": {}, "parameters": {}, "timestamp": "2023-12-02 11:08:56.045512", "successful": true, "hash": "795c9dee0922778709cfff57b8a49dd6", "commit_id": "1b740ecf84c50de0b79fb155ef306ef622d92f08", "number_of_classes": 100, "class_names": ["apple", "aquarium_fish", "baby", "bear", "beaver", "bed", "bee", "beetle", "bicycle", "bottle", "bowl", "boy", "bridge", "bus", "butterfly", "camel", "can", "castle", "caterpillar", "cattle", "chair", "chimpanzee", "clock", "cloud", "cockroach", "couch", "cra", "crocodile", "cup", "dinosaur", "dolphin", "elephant", "flatfish", "forest", "fox", "girl", "hamster", "house", "kangaroo", "keyboard", "lamp", "lawn_mower", "leopard", "lion", "lizard", "lobster", "man", "maple_tree", "motorcycle", "mountain", "mouse", "mushroom", "oak_tree", "orange", "orchid", "otter", "palm_tree", "pear", "pickup_truck", "pine_tree", "plain", "plate", "poppy", "porcupine", "possum", "rabbit", "raccoon", "ray", "road", "rocket", "rose", "sea", "seal", "shark", "shrew", "skunk", "skyscraper", "snail", "snake", "spider", "squirrel", "streetcar", "sunflower", "sweet_pepper", "table", "tank", "telephone", "television", "tiger", "tractor", "train", "trout", "tulip", "turtle", "wardrobe", "whale", "willow_tree", "wolf", "woman", "worm"], "number_of_examples_in_each_class": {"cattle": 500, "dinosaur": 500, "apple": 500, "boy": 500, "aquarium_fish": 500, "telephone": 500, "train": 500, "cup": 500, "cloud": 500, "elephant": 500, "keyboard": 500, "willow_tree": 500, "sunflower": 500, "castle": 500, "sea": 500, "bicycle": 500, "wolf": 500, "squirrel": 500, "shrew": 500, 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"TransferLearning", "model": "EffiNet", "runs": [{"scores": {"MulticlassAccuracy-train": 0.9606000185012817, "CrossEntropyLoss-train": 0.12111776322126389, "MulticlassAccuracy-validate": 0.7610999941825867, "CrossEntropyLoss-validate": 1.254715085029602}, "parameters": {"max_epochs": 2, "check_val_every_n_epoch": 2, "lr": 0.001, "model_name": "EfficientNet", "n_parameters": 7841894, "batch_size": 32, "train_samples": 50000, "val_samples": 10000}, "timestamp": "2023-12-02 10:02:36.630585", "successful": true, "hash": "d28f003c3fbfd1c95b4623d835865483", "commit_id": "1b740ecf84c50de0b79fb155ef306ef622d92f08", "model_path": "/content/ART-Templates/{{cookiecutter.project_slug}}/.neptune/Untitled/TRAN-21/checkpoints/epoch=13-step=21882.ckpt", "log_file_name": "2023-12-02_10-02-40_686443d1-130e-496b-ae10-7619cf9ea9dc.log"}]} \ No newline at end of file diff --git a/{{cookiecutter.project_slug}}/art_checkpoints/HeuristicBaseline_Evaluate Baseline/results.json b/{{cookiecutter.project_slug}}/art_checkpoints/HeuristicBaseline_Evaluate Baseline/results.json new file mode 100644 index 0000000..d4e25b6 --- /dev/null +++ b/{{cookiecutter.project_slug}}/art_checkpoints/HeuristicBaseline_Evaluate Baseline/results.json @@ -0,0 +1 @@ +{"name": "Evaluate Baseline", "model": "HeuristicBaseline", "runs": [{"scores": {"MulticlassAccuracy-validate": 0.012299999594688416}, "parameters": {}, "timestamp": "2023-12-02 11:10:00.493179", "successful": true, "hash": "846d1021c04cb6bd229da3ba4ee335eb", "commit_id": "1b740ecf84c50de0b79fb155ef306ef622d92f08", "log_file_name": "2023-12-02_11-10-00_e6aac212-98e4-4836-9884-ba7e210a7b2a.log"}]} \ No newline at end of file diff --git a/{{cookiecutter.project_slug}}/art_checkpoints/MlBaseline_Evaluate Baseline/results.json b/{{cookiecutter.project_slug}}/art_checkpoints/MlBaseline_Evaluate Baseline/results.json new file mode 100644 index 0000000..2979541 --- /dev/null +++ b/{{cookiecutter.project_slug}}/art_checkpoints/MlBaseline_Evaluate Baseline/results.json @@ -0,0 +1 @@ +{"name": "Evaluate Baseline", "model": "MlBaseline", "runs": [{"scores": {"MulticlassAccuracy-validate": 0.15649999678134918}, "parameters": {}, "timestamp": "2023-12-02 11:10:00.493217", "successful": true, "hash": "8f7a22a47a3c1f8f5de8db21910be7bd", "commit_id": "1b740ecf84c50de0b79fb155ef306ef622d92f08", "log_file_name": "2023-12-02_11-10-00_e6aac212-98e4-4836-9884-ba7e210a7b2a.log"}]} \ No newline at end of file From be883fa4c36b93b66aca3c197849ee09f0bcd00a Mon Sep 17 00:00:00 2001 From: mmaecki Date: Wed, 6 Dec 2023 12:51:49 +0100 Subject: [PATCH 10/10] Final version --- {{cookiecutter.project_slug}}/Tutorial.ipynb | 126 ++++++++---------- .../results.json | 2 +- .../Data analysis/results.json | 2 +- .../EffiNet_Check Loss On Init/results.json | 1 - .../EffiNet_Overfit One Batch/results.json | 1 - .../EffiNet_Overfit/results.json | 1 - .../EffiNet_TransferLearning/results.json | 1 - .../results.json | 1 + .../results.json | 1 + .../EfficientNet_Overfit/results.json | 1 + .../results.json | 1 + .../results.json | 2 +- .../MlBaseline_Evaluate Baseline/results.json | 2 +- {{cookiecutter.project_slug}}/checks.py | 2 +- .../{MyDataset.py => dataset.py} | 23 +++- .../lightning_logs/version_0/hparams.yaml | 1 - .../lightning_logs/version_1/hparams.yaml | 1 - .../lightning_logs/version_2/hparams.yaml | 1 - .../models/EffiNet.py | 59 -------- .../models/EfficientNet.py | 78 +++++++++++ .../models/ResNet.py | 60 --------- .../models/baselines.py | 29 ++-- {{cookiecutter.project_slug}}/run.py | 0 {{cookiecutter.project_slug}}/steps.py | 19 ++- 24 files changed, 185 insertions(+), 230 deletions(-) delete mode 100644 {{cookiecutter.project_slug}}/art_checkpoints/EffiNet_Check Loss On Init/results.json delete mode 100644 {{cookiecutter.project_slug}}/art_checkpoints/EffiNet_Overfit One Batch/results.json delete mode 100644 {{cookiecutter.project_slug}}/art_checkpoints/EffiNet_Overfit/results.json delete mode 100644 {{cookiecutter.project_slug}}/art_checkpoints/EffiNet_TransferLearning/results.json create mode 100644 {{cookiecutter.project_slug}}/art_checkpoints/EfficientNet_Check Loss On Init/results.json create mode 100644 {{cookiecutter.project_slug}}/art_checkpoints/EfficientNet_Overfit One Batch/results.json create mode 100644 {{cookiecutter.project_slug}}/art_checkpoints/EfficientNet_Overfit/results.json create mode 100644 {{cookiecutter.project_slug}}/art_checkpoints/EfficientNet_TransferLearning/results.json rename {{cookiecutter.project_slug}}/{MyDataset.py => dataset.py} (63%) delete mode 100644 {{cookiecutter.project_slug}}/lightning_logs/version_0/hparams.yaml delete mode 100644 {{cookiecutter.project_slug}}/lightning_logs/version_1/hparams.yaml delete mode 100644 {{cookiecutter.project_slug}}/lightning_logs/version_2/hparams.yaml delete mode 100644 {{cookiecutter.project_slug}}/models/EffiNet.py create mode 100644 {{cookiecutter.project_slug}}/models/EfficientNet.py delete mode 100644 {{cookiecutter.project_slug}}/models/ResNet.py delete mode 100644 {{cookiecutter.project_slug}}/run.py diff --git a/{{cookiecutter.project_slug}}/Tutorial.ipynb b/{{cookiecutter.project_slug}}/Tutorial.ipynb index 2da6d56..0baa4a9 100644 --- a/{{cookiecutter.project_slug}}/Tutorial.ipynb +++ b/{{cookiecutter.project_slug}}/Tutorial.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## This is a showcase of usage of ART - a package inspired by Andrej Karpathy's blogpost.\n", + "## This is a showcase of the usage of ART - a package inspired by Andrej Karpathy's blog post.\n", "\n", - "### In this tutorial, you will be presented with how to use ART to perform very popular form of model training - the transfer learning, while following steps recommended by Andrej Karpathy." + "### In this tutorial, you will be presented with how to use ART to perform a very popular form of model training - the transfer learning, while following steps recommended by Andrej Karpathy." ] }, { @@ -14,7 +14,7 @@ "metadata": {}, "source": [ "Let's start with the dataset. Since ART is based on Pytorch-Lightning, we need to create LightningDataModule.\\\n", - "For this tutorial, I decided to use very well known dataset called Cifar100 and I wrapped it into LightningDataModule creating [CifarDataModule](https://github.com/SebChw/ART-Templates/blob/cv_transfer_learning_tutorial/%7B%7Bcookiecutter.project_slug%7D%7D/MyDataset.py)" + "For this tutorial, I decided to use a very well known dataset called Cifar100 and I wrapped it into LightningDataModule creating [CifarDataModule](https://github.com/SebChw/ART-Templates/blob/cv_transfer_learning_tutorial/%7B%7Bcookiecutter.project_slug%7D%7D/dataset.py)" ] }, { @@ -22,7 +22,7 @@ "metadata": {}, "source": [ "Okay, we have datamodule, but how do we use it?\\\n", - "Let me introduce you, to the core of ART, the ArtProject." + "Let us introduce you, to the core of ART, the ArtProject." ] }, { @@ -31,24 +31,26 @@ "metadata": {}, "outputs": [], "source": [ - "from MyDataset import CifarDataModule\n", + "from dataset import CifarDataModule\n", "from art.project import ArtProject\n", "project_name = \"Cifar100\"\n", "dataset = CifarDataModule(batch_size=32)\n", - "project = ArtProject(project_name, dataset)" + "project = ArtProject(project_name, dataset)\n", + "force_rerun = False # whole process is saved in checkpoints, for fast rerun. \n", + " # If you want to use it set to False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now, that we have a Datamodule and ArtProject let's perform a first step: \\\n", - "`Become one with the data`\n", + "Now, that we have a Datamodule and ArtProject let's perform the first step: \\\n", + "**Become one with the data**\n", "\n", "\n", "To achieve this, we'll perform some data analysis.\\\n", "Firstly, create a new step for our project - Data analysis\\\n", - "Here is how it could look like: [Data analysis step](https://github.com/SebChw/ART-Templates/blob/4c10a65fa8f7356933105ce78b694cbcbed3b6d0/%7B%7Bcookiecutter.project_slug%7D%7D/steps.py#L9)" + "Here is what it could look like: [Data analysis step](https://github.com/SebChw/ART-Templates/blob/4c10a65fa8f7356933105ce78b694cbcbed3b6d0/%7B%7Bcookiecutter.project_slug%7D%7D/steps.py#L9)" ] }, { @@ -56,11 +58,11 @@ "metadata": {}, "source": [ "In this class, we check multiple characteristics of the data:\n", - "* Number of classes\n", - "* Names of calsses\n", - "* Whether dataset is balanced - number of examples in each class\n", - "* The dimentions of images\n", - "* We get to know the actual visaluzations of the images (10 visualizations per class)" + "* The number of classes\n", + "* Names of classes\n", + "* Whether the dataset is balanced - number of examples in each class\n", + "* The dimensions of the images\n", + "* We get to know the actual visualizations of the images (10 visualizations per class)" ] }, { @@ -96,17 +98,17 @@ " CheckResultExists(\"img_dimensions\"),\n", " CheckClassImagesExist(),\n", " CheckLenClassNamesEqualToNumClasses()])\n", - "project.run_all()\n" + "project.run_all(force_rerun=force_rerun)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You can see, that except for the data analysis step, there are some checks.\\\n", - "Those checks, are run after the step, so that we can be sure, that everything went as planned.\\\n", - "`You`, as the user of ART, are responsible for creating checks that will suit your experiments.\\\n", - "We used only two `custom` [checks](https://github.com/SebChw/ART-Templates/blob/cv_transfer_learning_tutorial/%7B%7Bcookiecutter.project_slug%7D%7D/checks.py) and one universal check implemented into art [CheckResultExists](https://github.com/SebChw/Actually-Robust-Training/blob/main/art/checks.py#L82).\n", + "You can see that there are some checks except for the data analysis step.\\\n", + "Those checks are run after the step so that we can be sure that everything went as planned.\\\n", + "**You**, as the user of ART, are responsible for creating checks that will suit your experiments.\\\n", + "We used only two **custom** [checks](https://github.com/SebChw/ART-Templates/blob/cv_transfer_learning_tutorial/%7B%7Bcookiecutter.project_slug%7D%7D/checks.py) and one universal check implemented into art: [CheckResultExists](https://github.com/SebChw/Actually-Robust-Training/blob/main/art/checks.py#L82).\n", "\n", "\n", "Let's see the results:" @@ -123,14 +125,14 @@ "text": [ "Number of classes: 100\n", "All classes names: ['apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle', 'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel', 'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock', 'cloud', 'cockroach', 'couch', 'cra', 'crocodile', 'cup', 'dinosaur', 'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster', 'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion', 'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain', 'mouse', 'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree', 'pear', 'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy', 'porcupine', 'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose', 'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake', 'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', 'table', 'tank', 'telephone', 'television', 'tiger', 'tractor', 'train', 'trout', 'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf', 'woman', 'worm']\n", - "Number of examples in each class: {'cattle': 500, 'dinosaur': 500, 'apple': 500, 'boy': 500, 'aquarium_fish': 500, 'telephone': 500, 'train': 500, 'cup': 500, 'cloud': 500, 'elephant': 500, 'keyboard': 500, 'willow_tree': 500, 'sunflower': 500, 'castle': 500, 'sea': 500, 'bicycle': 500, 'wolf': 500, 'squirrel': 500, 'shrew': 500, 'pine_tree': 500, 'rose': 500, 'television': 500, 'table': 500, 'possum': 500, 'oak_tree': 500, 'leopard': 500, 'maple_tree': 500, 'rabbit': 500, 'chimpanzee': 500, 'clock': 500, 'streetcar': 500, 'cockroach': 500, 'snake': 500, 'lobster': 500, 'mountain': 500, 'palm_tree': 500, 'skyscraper': 500, 'tractor': 500, 'shark': 500, 'butterfly': 500, 'bottle': 500, 'bee': 500, 'chair': 500, 'woman': 500, 'hamster': 500, 'otter': 500, 'seal': 500, 'lion': 500, 'mushroom': 500, 'girl': 500, 'sweet_pepper': 500, 'forest': 500, 'crocodile': 500, 'orange': 500, 'tulip': 500, 'mouse': 500, 'camel': 500, 'caterpillar': 500, 'man': 500, 'skunk': 500, 'kangaroo': 500, 'raccoon': 500, 'snail': 500, 'rocket': 500, 'whale': 500, 'worm': 500, 'turtle': 500, 'beaver': 500, 'plate': 500, 'wardrobe': 500, 'road': 500, 'fox': 500, 'flatfish': 500, 'tiger': 500, 'ray': 500, 'dolphin': 500, 'poppy': 500, 'porcupine': 500, 'lamp': 500, 'cra': 500, 'motorcycle': 500, 'spider': 500, 'tank': 500, 'orchid': 500, 'lizard': 500, 'beetle': 500, 'bridge': 500, 'baby': 500, 'lawn_mower': 500, 'house': 500, 'bus': 500, 'couch': 500, 'bowl': 500, 'pear': 500, 'bed': 500, 'plain': 500, 'trout': 500, 'bear': 500, 'pickup_truck': 500, 'can': 500}\n", - "Demansions of images: [32, 32, 3]\n", - "art_checkpoints\\Data analysis\\class_images\\class_train.png\n" + "Number of examples in each class: [('cattle', 500), ('dinosaur', 500), ('apple', 500), ('boy', 500), ('aquarium_fish', 500)] ...\n", + "All classes have the same number of examples: True.\n", + "Dimentions of images: [32, 32, 3]\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -138,16 +140,9 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "art_checkpoints\\Data analysis\\class_images\\class_bus.png\n" - ] - }, { "data": { - "image/png": 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", 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", 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" ] @@ -155,16 +150,9 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "art_checkpoints\\Data analysis\\class_images\\class_apple.png\n" - ] - }, { "data": { - "image/png": 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", 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" ] @@ -181,12 +169,12 @@ "results = project.get_step(0).get_latest_run()\n", "print(\"Number of classes: \", results[\"number_of_classes\"])\n", "print(\"All classes names: \", results[\"class_names\"])\n", - "print(\"Number of examples in each class: \", results[\"number_of_examples_in_each_class\"])\n", - "print(\"Demansions of images: \", results[\"img_dimensions\"])\n", + "print(\"Number of examples in each class: \", [(x, y) for x, y in results[\"number_of_examples_in_each_class\"].items()][:5], \"...\")\n", + "print(f\"All classes have the same number of examples: {all(i == [x for x in results['number_of_examples_in_each_class'].values()][0] for i in [x for x in results['number_of_examples_in_each_class'].values()])}.\")\n", + "print(\"Dimentions of images: \", results[\"img_dimensions\"])\n", "for i in range(3):\n", " img_class = results[\"class_names\"][random.randint(0, 100)]\n", " img_path = normpath(f\"art_checkpoints/Data analysis/{project.get_step(0).get_class_image_path(img_class)}\")\n", - " print(img_path)\n", " image = Image.open(img_path)\n", " plt.imshow(image)\n", " plt.axis('off')\n", @@ -199,9 +187,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can see, that the dataset is perfectly balanced, and indeed has 100 classes as it's name suggests.\n", + "We can see, that the dataset is perfectly balanced, and indeed has 100 classes as its name suggests.\n", "\n", - "Now that we know something about our dataset, we can proceed to the next steps. It is time to decide, what `metrics` will we use in our project." + "Now that we know something about our dataset, we can proceed to the next steps. It is time to decide, what **metrics** will we use in our project." ] }, { @@ -224,8 +212,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Having registered the metrics, we can proceed to the next step which is `Add baselines`. We want to make sure, that everything we did until this moment, works as it sholud, and there are no bugs in our DataLoader.\\\n", - "This we used three [baselines](https://github.com/SebChw/ART-Templates/blob/cv_transfer_learning_tutorial/%7B%7Bcookiecutter.project_slug%7D%7D/models/baselines.py).\\\n", + "Having registered the metrics, we can proceed to the next step which is to **Add baselines**. We want to make sure, that everything we have done until this moment, works as it should, and there are no bugs in our DataLoader.\\\n", + "For this purpose, we used three [baselines](https://github.com/SebChw/ART-Templates/blob/cv_transfer_learning_tutorial/%7B%7Bcookiecutter.project_slug%7D%7D/models/baselines.py).\\\n", "Feel free to implement yours however you like, but remember to follow the structure of ArtModule." ] }, @@ -269,7 +257,7 @@ } ], "source": [ - "project.run_all()" + "project.run_all(force_rerun=force_rerun)" ] }, { @@ -284,7 +272,7 @@ "metadata": {}, "source": [ "Now, that we are sure, that our pipeline works as intended, we can start implementing our model.\\\n", - "We will start with yet another good pracitce which is `check loss on init` - we want to make sure, that our model, which didn't see any of the Cifar100 data, will give us `loss` equal to `-log(1/100)`." + "We will start with yet another good pracitce which is **check loss on init** - we want to make sure, that our model, which didn't see any of the Cifar100 data, will give us **loss** equal to `-log(1/100)`." ] }, { @@ -302,7 +290,7 @@ ], "source": [ "# from models.ResNet import ResNet\n", - "from models.EffiNet import EffiNet\n", + "from models.EfficientNet import EfficientNet\n", "from art.steps import CheckLossOnInit\n", "from art.checks import CheckScoreCloseTo\n", "import math\n", @@ -336,7 +324,7 @@ "\tMulticlassAccuracy-validate: 0.15649999678134918\n", "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", "\tMulticlassAccuracy-validate: 0.6883000135421753\n", - "Step: Check Loss On Init, Model: EffiNet, Passed: True. Results:\n", + "Step: Check Loss On Init, Model: EfficientNet, Passed: True. Results:\n", "\tMulticlassAccuracy-validate: 0.00675999978557229\n", "\tCrossEntropyLoss-validate: 4.893486022949219\n" ] @@ -344,12 +332,12 @@ ], "source": [ "project.add_step(\n", - " CheckLossOnInit(EffiNet),\n", + " CheckLossOnInit(EfficientNet),\n", " [CheckScoreCloseTo(metric=ce_loss,\n", " value=EXPECTED_LOSS, rel_tol=0.1)]\n", " )\n", "\n", - "project.run_all()" + "project.run_all(force_rerun=force_rerun)" ] }, { @@ -357,7 +345,7 @@ "metadata": {}, "source": [ "Great, the loss looks good!\\\n", - "The next step we want to perform, is to `overfit one batch` of our data. Thanks to this, we can be sure that our model is properly implemented." + "The next step we want to perform, is to **overfit one batch** of our data. Thanks to this, we can be sure that our model is properly implemented." ] }, { @@ -378,10 +366,10 @@ "\tMulticlassAccuracy-validate: 0.15649999678134918\n", "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", "\tMulticlassAccuracy-validate: 0.6883000135421753\n", - "Step: Check Loss On Init, Model: EffiNet, Passed: True. Results:\n", + "Step: Check Loss On Init, Model: EfficientNet, Passed: True. Results:\n", "\tMulticlassAccuracy-validate: 0.00675999978557229\n", "\tCrossEntropyLoss-validate: 4.893486022949219\n", - "Step: Overfit One Batch, Model: EffiNet, Passed: True. Results:\n", + "Step: Overfit One Batch, Model: EfficientNet, Passed: True. Results:\n", "\tMulticlassAccuracy-train: 1.0\n", "\tCrossEntropyLoss-train: 2.902682354033459e-05\n" ] @@ -390,9 +378,9 @@ "source": [ "from art.steps import OverfitOneBatch\n", "from art.checks import CheckScoreLessThan\n", - "project.add_step(OverfitOneBatch(EffiNet, number_of_steps=40),\n", + "project.add_step(OverfitOneBatch(EfficientNet, number_of_steps=40),\n", " [CheckScoreLessThan(metric=ce_loss, value=0.05)])\n", - "project.run_all()" + "project.run_all(force_rerun=force_rerun)" ] }, { @@ -411,7 +399,7 @@ "from art.steps import Overfit\n", "from art.checks import CheckScoreGreaterThan\n", "\n", - "project.add_step(Overfit(EffiNet, max_epochs=10),\n", + "project.add_step(Overfit(EfficientNet, max_epochs=10),\n", " [CheckScoreGreaterThan(metric=accuracy_metric, value=0.8)])" ] }, @@ -433,13 +421,13 @@ "\tMulticlassAccuracy-validate: 0.15649999678134918\n", "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", "\tMulticlassAccuracy-validate: 0.6883000135421753\n", - "Step: Check Loss On Init, Model: EffiNet, Passed: True. Results:\n", + "Step: Check Loss On Init, Model: EfficientNet, Passed: True. Results:\n", "\tMulticlassAccuracy-validate: 0.00675999978557229\n", "\tCrossEntropyLoss-validate: 4.893486022949219\n", - "Step: Overfit One Batch, Model: EffiNet, Passed: True. Results:\n", + "Step: Overfit One Batch, Model: EfficientNet, Passed: True. Results:\n", "\tMulticlassAccuracy-train: 1.0\n", "\tCrossEntropyLoss-train: 2.902682354033459e-05\n", - "Step: Overfit, Model: EffiNet, Passed: True. Results:\n", + "Step: Overfit, Model: EfficientNet, Passed: True. Results:\n", "\tMulticlassAccuracy-train: 0.9455999732017517\n", "\tCrossEntropyLoss-train: 0.16572201251983643\n", "\tMulticlassAccuracy-validate: 0.7613999843597412\n", @@ -448,14 +436,14 @@ } ], "source": [ - "project.run_all()" + "project.run_all(force_rerun=force_rerun)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now, that the model passed all steps and checks we can finaly proceed with the `transfer learning`!" + "Now, that the model passed all steps and checks we can finaly proceed with the **transfer learning**!" ] }, { @@ -469,7 +457,7 @@ "from lightning.pytorch.callbacks import EarlyStopping\n", "\n", "early_stopping = EarlyStopping('CrossEntropyLoss-validate', patience=6)\n", - "project.add_step(TransferLearning(EffiNet,\n", + "project.add_step(TransferLearning(EfficientNet,\n", " freezed_trainer_kwargs={\"max_epochs\": 4,\n", " \"check_val_every_n_epoch\": 2,\n", " \"callbacks\": [early_stopping]},\n", @@ -499,18 +487,18 @@ "\tMulticlassAccuracy-validate: 0.15649999678134918\n", "Step: Evaluate Baseline, Model: AlreadyExistingResNet20Baseline, Passed: True. Results:\n", "\tMulticlassAccuracy-validate: 0.6883000135421753\n", - "Step: Check Loss On Init, Model: EffiNet, Passed: True. Results:\n", + "Step: Check Loss On Init, Model: EfficientNet, Passed: True. Results:\n", "\tMulticlassAccuracy-validate: 0.00675999978557229\n", "\tCrossEntropyLoss-validate: 4.893486022949219\n", - "Step: Overfit One Batch, Model: EffiNet, Passed: True. Results:\n", + "Step: Overfit One Batch, Model: EfficientNet, Passed: True. Results:\n", "\tMulticlassAccuracy-train: 1.0\n", "\tCrossEntropyLoss-train: 2.902682354033459e-05\n", - "Step: Overfit, Model: EffiNet, Passed: True. Results:\n", + "Step: Overfit, Model: EfficientNet, Passed: True. Results:\n", "\tMulticlassAccuracy-train: 0.9455999732017517\n", "\tCrossEntropyLoss-train: 0.16572201251983643\n", "\tMulticlassAccuracy-validate: 0.7613999843597412\n", "\tCrossEntropyLoss-validate: 1.1986972093582153\n", - "Step: TransferLearning, Model: EffiNet, Passed: True. Results:\n", + "Step: TransferLearning, Model: EfficientNet, Passed: True. Results:\n", "\tMulticlassAccuracy-train: 0.9606000185012817\n", "\tCrossEntropyLoss-train: 0.12111776322126389\n", "\tMulticlassAccuracy-validate: 0.7610999941825867\n", @@ -519,7 +507,7 @@ } ], "source": [ - "project.run_all()" + "project.run_all(force_rerun=force_rerun)" ] }, { diff --git a/{{cookiecutter.project_slug}}/art_checkpoints/AlreadyExistingResNet20Baseline_Evaluate Baseline/results.json b/{{cookiecutter.project_slug}}/art_checkpoints/AlreadyExistingResNet20Baseline_Evaluate Baseline/results.json index 3e31503..0a7acf7 100644 --- a/{{cookiecutter.project_slug}}/art_checkpoints/AlreadyExistingResNet20Baseline_Evaluate Baseline/results.json +++ b/{{cookiecutter.project_slug}}/art_checkpoints/AlreadyExistingResNet20Baseline_Evaluate Baseline/results.json @@ -1 +1 @@ -{"name": "Evaluate Baseline", "model": "AlreadyExistingResNet20Baseline", "runs": [{"scores": {"MulticlassAccuracy-validate": 0.6883000135421753}, "parameters": {}, "timestamp": "2023-12-02 11:10:00.493226", 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b/{{cookiecutter.project_slug}}/art_checkpoints/EfficientNet_Check Loss On Init/results.json @@ -0,0 +1 @@ +{"name": "Check Loss On Init", "model": "EfficientNet", "runs": [{"scores": {"MulticlassAccuracy-validate": 0.00675999978557229, "CrossEntropyLoss-validate": 4.893486022949219}, "parameters": {"lr": 0.001, "model_name": "EfficientNet", "n_parameters": 7841894, "batch_size": 32, "train_samples": 50000, "val_samples": 10000}, "timestamp": "2023-12-02 11:14:53.975501", "successful": true, "hash": "eef024742b33fd04192b7c9fdc0c1124", "commit_id": "1b740ecf84c50de0b79fb155ef306ef622d92f08", "log_file_name": "2023-12-02_11-14-53_43361818-6469-4547-8a82-02be2670bd52.log"}]} \ No newline at end of file diff --git a/{{cookiecutter.project_slug}}/art_checkpoints/EfficientNet_Overfit One Batch/results.json b/{{cookiecutter.project_slug}}/art_checkpoints/EfficientNet_Overfit One Batch/results.json new file mode 100644 index 0000000..56745b1 --- /dev/null +++ b/{{cookiecutter.project_slug}}/art_checkpoints/EfficientNet_Overfit One Batch/results.json @@ -0,0 +1 @@ +{"name": "Overfit One Batch", "model": "EfficientNet", "runs": [{"scores": {"MulticlassAccuracy-train": 1.0, "CrossEntropyLoss-train": 2.902682354033459e-05}, "parameters": {"number_of_steps": 40, "lr": 0.001, "model_name": "EfficientNet", "n_parameters": 7841894, "batch_size": 32, "train_samples": 50000, "val_samples": 10000}, "timestamp": "2023-12-02 11:16:49.769266", "successful": true, "hash": "eef024742b33fd04192b7c9fdc0c1124", "commit_id": "1b740ecf84c50de0b79fb155ef306ef622d92f08", "model_path": "/content/ART-Templates/{{cookiecutter.project_slug}}/lightning_logs/version_7/checkpoints/epoch=39-step=40.ckpt", "MulticlassAccuracy-train": 1.0, "CrossEntropyLoss-train": 2.902682354033459e-05, "log_file_name": "2023-12-02_11-16-49_0704e137-3c69-4515-a943-bffe0f645f44.log"}]} \ No newline at end of file diff --git a/{{cookiecutter.project_slug}}/art_checkpoints/EfficientNet_Overfit/results.json b/{{cookiecutter.project_slug}}/art_checkpoints/EfficientNet_Overfit/results.json new file mode 100644 index 0000000..9bbe549 --- /dev/null +++ b/{{cookiecutter.project_slug}}/art_checkpoints/EfficientNet_Overfit/results.json @@ -0,0 +1 @@ +{"name": "Overfit", "model": "EfficientNet", "runs": [{"scores": {"MulticlassAccuracy-train": 0.9455999732017517, "CrossEntropyLoss-train": 0.16572201251983643, "MulticlassAccuracy-validate": 0.7613999843597412, "CrossEntropyLoss-validate": 1.1986972093582153}, "parameters": {"max_epochs": 10, "lr": 0.001, "model_name": "EfficientNet", "n_parameters": 7841894, "batch_size": 32, "train_samples": 50000, "val_samples": 10000}, "timestamp": "2023-12-02 11:17:52.515484", "successful": true, "hash": "eef024742b33fd04192b7c9fdc0c1124", "commit_id": "1b740ecf84c50de0b79fb155ef306ef622d92f08", "model_path": "/content/ART-Templates/{{cookiecutter.project_slug}}/lightning_logs/version_8/checkpoints/epoch=9-step=15630.ckpt", "log_file_name": "2023-12-02_11-17-52_fb7fe9c4-02bb-4a5b-8d37-bd257c6d9f85.log"}]} \ No newline at end of file diff --git a/{{cookiecutter.project_slug}}/art_checkpoints/EfficientNet_TransferLearning/results.json b/{{cookiecutter.project_slug}}/art_checkpoints/EfficientNet_TransferLearning/results.json new file mode 100644 index 0000000..d0652e7 --- /dev/null +++ b/{{cookiecutter.project_slug}}/art_checkpoints/EfficientNet_TransferLearning/results.json @@ -0,0 +1 @@ +{"name": "TransferLearning", "model": "EfficientNet", "runs": [{"scores": {"MulticlassAccuracy-train": 0.9606000185012817, "CrossEntropyLoss-train": 0.12111776322126389, "MulticlassAccuracy-validate": 0.7610999941825867, "CrossEntropyLoss-validate": 1.254715085029602}, "parameters": {"max_epochs": 2, "check_val_every_n_epoch": 2, "lr": 0.001, "model_name": "EfficientNet", "n_parameters": 7841894, "batch_size": 32, "train_samples": 50000, "val_samples": 10000}, "timestamp": "2023-12-02 10:02:36.630585", "successful": true, "hash": "eef024742b33fd04192b7c9fdc0c1124", "commit_id": "1b740ecf84c50de0b79fb155ef306ef622d92f08", "model_path": "/content/ART-Templates/{{cookiecutter.project_slug}}/.neptune/Untitled/TRAN-21/checkpoints/epoch=13-step=21882.ckpt", "log_file_name": "2023-12-02_10-02-40_686443d1-130e-496b-ae10-7619cf9ea9dc.log"}]} \ No newline at end of file diff --git a/{{cookiecutter.project_slug}}/art_checkpoints/HeuristicBaseline_Evaluate Baseline/results.json b/{{cookiecutter.project_slug}}/art_checkpoints/HeuristicBaseline_Evaluate Baseline/results.json index d4e25b6..646ddac 100644 --- a/{{cookiecutter.project_slug}}/art_checkpoints/HeuristicBaseline_Evaluate Baseline/results.json +++ b/{{cookiecutter.project_slug}}/art_checkpoints/HeuristicBaseline_Evaluate Baseline/results.json @@ -1 +1 @@ -{"name": "Evaluate Baseline", "model": "HeuristicBaseline", "runs": [{"scores": {"MulticlassAccuracy-validate": 0.012299999594688416}, "parameters": {}, "timestamp": "2023-12-02 11:10:00.493179", "successful": true, "hash": "846d1021c04cb6bd229da3ba4ee335eb", "commit_id": "1b740ecf84c50de0b79fb155ef306ef622d92f08", "log_file_name": "2023-12-02_11-10-00_e6aac212-98e4-4836-9884-ba7e210a7b2a.log"}]} \ No newline at end of file +{"name": "Evaluate Baseline", "model": "HeuristicBaseline", "runs": [{"scores": {"MulticlassAccuracy-validate": 0.012299999594688416}, "parameters": {}, "timestamp": "2023-12-02 11:10:00.493179", "successful": true, "hash": "06dbb6cfec9272cc2e03dc1e9f8f1846", "commit_id": "1b740ecf84c50de0b79fb155ef306ef622d92f08", "log_file_name": "2023-12-02_11-10-00_e6aac212-98e4-4836-9884-ba7e210a7b2a.log"}]} \ No newline at end of file diff --git a/{{cookiecutter.project_slug}}/art_checkpoints/MlBaseline_Evaluate Baseline/results.json b/{{cookiecutter.project_slug}}/art_checkpoints/MlBaseline_Evaluate Baseline/results.json index 2979541..4420aa1 100644 --- a/{{cookiecutter.project_slug}}/art_checkpoints/MlBaseline_Evaluate Baseline/results.json +++ b/{{cookiecutter.project_slug}}/art_checkpoints/MlBaseline_Evaluate Baseline/results.json @@ -1 +1 @@ -{"name": "Evaluate Baseline", "model": "MlBaseline", "runs": [{"scores": {"MulticlassAccuracy-validate": 0.15649999678134918}, "parameters": {}, "timestamp": "2023-12-02 11:10:00.493217", "successful": true, "hash": "8f7a22a47a3c1f8f5de8db21910be7bd", "commit_id": "1b740ecf84c50de0b79fb155ef306ef622d92f08", "log_file_name": "2023-12-02_11-10-00_e6aac212-98e4-4836-9884-ba7e210a7b2a.log"}]} \ No newline at end of file +{"name": "Evaluate Baseline", "model": "MlBaseline", "runs": [{"scores": {"MulticlassAccuracy-validate": 0.15649999678134918}, "parameters": {}, "timestamp": "2023-12-02 11:10:00.493217", "successful": true, "hash": "49b251eb0a6cbddbbd5b185281399fa3", "commit_id": "1b740ecf84c50de0b79fb155ef306ef622d92f08", "log_file_name": "2023-12-02_11-10-00_e6aac212-98e4-4836-9884-ba7e210a7b2a.log"}]} \ No newline at end of file diff --git a/{{cookiecutter.project_slug}}/checks.py b/{{cookiecutter.project_slug}}/checks.py index 134c06e..b34f406 100644 --- a/{{cookiecutter.project_slug}}/checks.py +++ b/{{cookiecutter.project_slug}}/checks.py @@ -22,4 +22,4 @@ def _check_method(self, result) -> ResultOfCheck: is_positive=False, error="Number of class names is different than number of classes", ) - return ResultOfCheck(is_positive=True) \ No newline at end of file + return ResultOfCheck(is_positive=True) diff --git a/{{cookiecutter.project_slug}}/MyDataset.py b/{{cookiecutter.project_slug}}/dataset.py similarity index 63% rename from {{cookiecutter.project_slug}}/MyDataset.py rename to {{cookiecutter.project_slug}}/dataset.py index 7902f36..61fcf93 100644 --- a/{{cookiecutter.project_slug}}/MyDataset.py +++ b/{{cookiecutter.project_slug}}/dataset.py @@ -2,15 +2,16 @@ from datasets import load_dataset from torch.utils.data import DataLoader + class CifarDataModule(pl.LightningDataModule): def __init__(self, batch_size: int = 32, num_workers: int = 4): super().__init__() self.batch_size = batch_size self.dataset = load_dataset("cifar100").with_format("torch") - self.dataset = self.dataset.rename_columns({"img": "input", "fine_label": "target"}) - # self.dataset = self.dataset.rename_columns({"img": "input", "coarse_label": "target"}) + self.dataset = self.dataset.rename_columns( + {"img": "input", "fine_label": "target"} + ) self.dataset = self.dataset.remove_columns(["coarse_label"]) - # self.dataset = self.dataset.remove_columns(["fine_label"]) self.num_workers = num_workers def setup(self, stage: str): @@ -18,14 +19,22 @@ def setup(self, stage: str): self.test = self.dataset["test"] def train_dataloader(self): - return DataLoader(self.dataset["train"], batch_size=self.batch_size, num_workers=self.num_workers) + return DataLoader( + self.dataset["train"], + batch_size=self.batch_size, + num_workers=self.num_workers, + ) def val_dataloader(self): - return DataLoader(self.dataset["test"], batch_size=self.batch_size, num_workers=self.num_workers) - + return DataLoader( + self.dataset["test"], + batch_size=self.batch_size, + num_workers=self.num_workers, + ) + def log_params(self): return { "batch_size": self.batch_size, "train_samples": len(self.dataset["train"]), "val_samples": len(self.dataset["test"]), - } \ No newline at end of file + } diff --git a/{{cookiecutter.project_slug}}/lightning_logs/version_0/hparams.yaml b/{{cookiecutter.project_slug}}/lightning_logs/version_0/hparams.yaml deleted file mode 100644 index 0967ef4..0000000 --- a/{{cookiecutter.project_slug}}/lightning_logs/version_0/hparams.yaml +++ /dev/null @@ -1 +0,0 @@ -{} diff --git a/{{cookiecutter.project_slug}}/lightning_logs/version_1/hparams.yaml b/{{cookiecutter.project_slug}}/lightning_logs/version_1/hparams.yaml deleted file mode 100644 index 0967ef4..0000000 --- a/{{cookiecutter.project_slug}}/lightning_logs/version_1/hparams.yaml +++ /dev/null @@ -1 +0,0 @@ -{} diff --git a/{{cookiecutter.project_slug}}/lightning_logs/version_2/hparams.yaml b/{{cookiecutter.project_slug}}/lightning_logs/version_2/hparams.yaml deleted file mode 100644 index 0967ef4..0000000 --- a/{{cookiecutter.project_slug}}/lightning_logs/version_2/hparams.yaml +++ /dev/null @@ -1 +0,0 @@ -{} diff --git a/{{cookiecutter.project_slug}}/models/EffiNet.py b/{{cookiecutter.project_slug}}/models/EffiNet.py deleted file mode 100644 index 106845b..0000000 --- a/{{cookiecutter.project_slug}}/models/EffiNet.py +++ /dev/null @@ -1,59 +0,0 @@ -from typing import Dict -from art.core import ArtModule -import torch -import timm -import torch.nn as nn -from torchvision import transforms -import numpy as np -from einops import rearrange -from art.utils.enums import ( - BATCH, - INPUT, - LOSS, - PREDICTION, - TARGET, - TRAIN_LOSS, - VALIDATION_LOSS, -) - -class EffiNet(ArtModule): - def __init__(self, num_classes=100, lr=1e-3): - super().__init__() - self.model = timm.create_model('efficientnet_b2.ra_in1k', pretrained=True, num_classes=100) - self.loss = torch.nn.CrossEntropyLoss() - self.lr = lr - self.preprocess = transforms.Compose([ - transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), - transforms.Resize(256), - ]) - - def parse_data(self, data): - """This is first step of your pipeline it always has batch keys inside""" - X = data[BATCH][INPUT] - X = X / 255 - X = rearrange(X, "b h w c -> b c h w") - X = self.preprocess(X) - target = data[BATCH][TARGET].long() - return {INPUT: X, TARGET: target} - - - - def predict(self, data: Dict): - return {PREDICTION: self.model(data[INPUT]), TARGET: data[TARGET]} - - def compute_loss(self, data): - # Notice that the loss calculation is done in MetricsCalculator! - # We only need to specify which loss (metric) we want to use - loss = data["CrossEntropyLoss"] - return {LOSS: loss} - - def configure_optimizers(self): - return torch.optim.Adam(self.parameters(), lr=self.lr) - - def log_params(self): - # Log relevant parameters - return { - "lr": self.lr, - "model_name": self.model.__class__.__name__, - "n_parameters": sum(p.numel() for p in self.parameters() if p.requires_grad), - } \ No newline at end of file diff --git a/{{cookiecutter.project_slug}}/models/EfficientNet.py b/{{cookiecutter.project_slug}}/models/EfficientNet.py new file mode 100644 index 0000000..de8d7a7 --- /dev/null +++ b/{{cookiecutter.project_slug}}/models/EfficientNet.py @@ -0,0 +1,78 @@ +from typing import Dict, Any +from art.core import ArtModule +import torch +import timm +from torchvision import transforms +from einops import rearrange +from art.utils.enums import ( + BATCH, + INPUT, + LOSS, + PREDICTION, + TARGET, +) + + +class EfficientNet(ArtModule): + def __init__(self, num_classes: int = 100, lr: float = 1e-3): + super().__init__() + self.model = timm.create_model( + "efficientnet_b2.ra_in1k", pretrained=True, num_classes=num_classes + ) + self.lr = lr + self.preprocess = transforms.Compose( + [ + transforms.Normalize( + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225], # statistics of ImageNet dataset + ), + transforms.Resize(256), # Size desired by this particular model + ] + ) + + def parse_data(self, data: Dict[str, Any]) -> Dict[str, Any]: + """ + This is first step of your pipeline it always has batch keys inside + The result of this step is passed to the next step in the pipeline which is predict + """ + X = data[BATCH][INPUT] + X = X / 255 + X = rearrange(X, "b h w c -> b c h w") + X = self.preprocess(X) + target = data[BATCH][TARGET].long() + return {INPUT: X, TARGET: target} + + def predict(self, data: Dict[str, Any]) -> Dict[str, Any]: + """ + This is the second step of your pipeline. The input of this step is the output of the previous step. + You should return a dictionary with PREDICTION and TARGET keys. + """ + return {PREDICTION: self.model(data[INPUT]), TARGET: data[TARGET]} + + def compute_loss(self, data: Dict[str, Any]) -> Dict[str, Any]: + """ + This is the last step of your pipeline. The input of this step is the output of the previous step. + You should return a dictionary with LOSS key. + You only need to specify which loss (metric) we want to use. + """ + loss = data["CrossEntropyLoss"] + return {LOSS: loss} + + def configure_optimizers(self) -> torch.optim.Optimizer: + """ + Set up your optimizer. + """ + return torch.optim.Adam(self.parameters(), lr=self.lr) + + def log_params(self) -> Dict[str, Any]: + """ + This is a method for logging relevant parameters. + It has to be implemented, however, it can be empty. + """ + return { + "lr": self.lr, + "model_name": self.model.__class__.__name__, + "n_parameters": sum( + p.numel() for p in self.parameters() if p.requires_grad + ), + } diff --git a/{{cookiecutter.project_slug}}/models/ResNet.py b/{{cookiecutter.project_slug}}/models/ResNet.py deleted file mode 100644 index 0e07ee3..0000000 --- a/{{cookiecutter.project_slug}}/models/ResNet.py +++ /dev/null @@ -1,60 +0,0 @@ -from typing import Dict -from art.core import ArtModule -import torch -import torch.nn as nn -from torchvision import transforms -import numpy as np -from einops import rearrange -from art.utils.enums import ( - BATCH, - INPUT, - LOSS, - PREDICTION, - TARGET, - TRAIN_LOSS, - VALIDATION_LOSS, -) - -class ResNet(ArtModule): - def __init__(self, num_classes=100, lr=1e-3): - super().__init__() - self.model = torch.hub.load('facebookresearch/semi-supervised-ImageNet1K-models', 'resnet18_swsl') - self.loss = torch.nn.CrossEntropyLoss() - self.lr = lr - self.model.fc = nn.Linear(self.model.fc.in_features, num_classes) - self.preprocess = transforms.Compose([ - transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), - transforms.Resize(256), - transforms.CenterCrop(224), - ]) - - def parse_data(self, data): - """This is first step of your pipeline it always has batch keys inside""" - X = data[BATCH][INPUT] - X = X / 255 - X = rearrange(X, "b h w c -> b c h w") - X = self.preprocess(X) - target = data[BATCH][TARGET].long() - return {INPUT: X, TARGET: target} - - - - def predict(self, data: Dict): - return {PREDICTION: self.model(data[INPUT]), TARGET: data[TARGET]} - - def compute_loss(self, data): - # Notice that the loss calculation is done in MetricsCalculator! - # We only need to specify which loss (metric) we want to use - loss = data["CrossEntropyLoss"] - return {LOSS: loss} - - def configure_optimizers(self): - return torch.optim.Adam(self.parameters(), lr=self.lr) - - def log_params(self): - # Log relevant parameters - return { - "lr": self.lr, - "model_name": self.model.__class__.__name__, - "n_parameters": sum(p.numel() for p in self.parameters() if p.requires_grad), - } \ No newline at end of file diff --git a/{{cookiecutter.project_slug}}/models/baselines.py b/{{cookiecutter.project_slug}}/models/baselines.py index 8f94c8a..2888fe5 100644 --- a/{{cookiecutter.project_slug}}/models/baselines.py +++ b/{{cookiecutter.project_slug}}/models/baselines.py @@ -1,4 +1,4 @@ -from typing import Dict +from typing import Dict, Any import numpy as np from einops import rearrange @@ -11,11 +11,11 @@ class MlBaseline(ArtModule): name = "ML Baseline" - def __init__(self, model=LogisticRegression()): + def __init__(self, model: Any = LogisticRegression()): super().__init__() self.model = model - def ml_parse_data(self, data): + def ml_parse_data(self, data: Dict): X = [] y = [] for batch in data["dataloader"]: @@ -24,11 +24,11 @@ def ml_parse_data(self, data): return {INPUT: np.concatenate(X), TARGET: np.concatenate(y)} - def baseline_train(self, data): + def baseline_train(self, data: Dict): self.model = self.model.fit(data[INPUT], data[TARGET]) return {"model": self.model} - def parse_data(self, data): + def parse_data(self, data: Dict): """This is first step of your pipeline it always has batch keys inside""" batch = data[BATCH] return { @@ -36,7 +36,7 @@ def parse_data(self, data): TARGET: batch[TARGET].numpy(), } - def predict(self, data): + def predict(self, data: Dict): return {PREDICTION: self.model.predict(data[INPUT]), TARGET: data[TARGET]} def log_params(self): @@ -51,7 +51,7 @@ class HeuristicBaseline(ArtModule): def __init__(self): super().__init__() - def parse_data(self, data): + def parse_data(self, data: Dict): """This is first step of your pipeline it always has batch keys inside""" batch = data[BATCH] return { @@ -59,7 +59,7 @@ def parse_data(self, data): TARGET: batch[TARGET].numpy(), } - def baseline_train(self, data): + def baseline_train(self, data: Dict): self.prototypes = np.zeros( (self.n_classes, self.img_shape[0] * self.img_shape[1] * self.img_shape[2]) ) @@ -71,7 +71,7 @@ def baseline_train(self, data): self.prototypes = self.prototypes / self.counts[:, None] - def predict(self, data): + def predict(self, data: Dict): y_hat = np.argmax((data[INPUT] @ self.prototypes.T), axis=1) return {PREDICTION: y_hat, TARGET: data[TARGET]} @@ -81,13 +81,16 @@ def log_params(self): class AlreadyExistingResNet20Baseline(ArtModule): name = "Already Existing ResNet20 Baseline" - + def __init__(self): super().__init__() import torch - self.model = torch.hub.load("chenyaofo/pytorch-cifar-models", "cifar100_resnet20", pretrained=True) - def parse_data(self, data): + self.model = torch.hub.load( + "chenyaofo/pytorch-cifar-models", "cifar100_resnet20", pretrained=True + ) + + def parse_data(self, data: Dict): mean = np.asarray([0.5071, 0.4867, 0.4408], dtype=np.float32) std = np.asarray([0.2675, 0.2565, 0.2761], dtype=np.float32) X = data[BATCH][INPUT] @@ -100,4 +103,4 @@ def predict(self, data: Dict): return {PREDICTION: preds, TARGET: data[TARGET]} def log_params(self): - return {"model": self.model.__class__.__name__} \ No newline at end of file + return {"model": self.model.__class__.__name__} diff --git a/{{cookiecutter.project_slug}}/run.py b/{{cookiecutter.project_slug}}/run.py deleted file mode 100644 index e69de29..0000000 diff --git a/{{cookiecutter.project_slug}}/steps.py b/{{cookiecutter.project_slug}}/steps.py index 460d833..4e49855 100644 --- a/{{cookiecutter.project_slug}}/steps.py +++ b/{{cookiecutter.project_slug}}/steps.py @@ -3,14 +3,15 @@ import matplotlib.pyplot as plt from art.utils.savers import MatplotLibSaver from art.steps import ExploreData -from random import sample -from art.utils.enums import BATCH, INPUT, PREDICTION, TARGET +from art.utils.enums import INPUT, TARGET + class DataAnalysis(ExploreData): def do(self, previous_states): targets = [] - index2label = lambda x: self.datamodule.dataset["train"].features[TARGET].int2str(x) - label2index = lambda x: self.datamodule.dataset["train"].features[TARGET].str2int(x) + index2label = ( + lambda x: self.datamodule.dataset["train"].features[TARGET].int2str(x) + ) # Loop through batches in the cifar_datamodule train dataloader for batch in self.datamodule.train_dataloader(): targets.extend(batch[TARGET]) @@ -31,10 +32,7 @@ def do(self, previous_states): fig, axes = plt.subplots(1, 5, figsize=(15, 5)) for i, sample_idx in enumerate(class_samples): - img = ( - self.datamodule.train_dataloader() - .dataset[sample_idx][INPUT] - ) + img = self.datamodule.train_dataloader().dataset[sample_idx][INPUT] axes[i].imshow(img, cmap="gray") axes[i].set_title(f"Class: {cls}") axes[i].axis("off") @@ -51,8 +49,9 @@ def do(self, previous_states): "img_dimensions": img_dimensions, } ) + def log_params(self): return {} - + def get_class_image_path(self, class_name: str): - return f"class_images/class_{class_name}.png" \ No newline at end of file + return f"class_images/class_{class_name}.png"