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make EchoPrime usable in other python projects #18

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26 changes: 13 additions & 13 deletions echo_prime/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,16 +16,14 @@
from tqdm import tqdm
import cv2
import pydicom
import sklearn
import sklearn.metrics
import transformers


# Local module imports
import utils

class EchoPrime:
def __init__(self, device=None, lang='en'):
def __init__(self, device=None, lang='en', weights_root_dir=None, assets_root_dir=None):
"""
Initialize EchoPrime

Expand All @@ -35,10 +33,12 @@ def __init__(self, device=None, lang='en'):
"""

# load language specific files
utils.initialize_language(lang)
utils.initialize_language(lang, assets_root_dir=assets_root_dir)
weights_root_dir = os.path.join(weights_root_dir, "model_data") if weights_root_dir is not None else "model_data"
assets_root_dir = os.path.join(assets_root_dir, "assets") if assets_root_dir is not None else "assets"

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
checkpoint = torch.load("model_data/weights/echo_prime_encoder.pt",map_location=device)
checkpoint = torch.load(os.path.join(weights_root_dir, "weights", "echo_prime_encoder.pt"), map_location=device)
echo_encoder = torchvision.models.video.mvit_v2_s()
echo_encoder.head[-1] = torch.nn.Linear(echo_encoder.head[-1].in_features, 512)
echo_encoder.load_state_dict(checkpoint)
Expand All @@ -47,7 +47,7 @@ def __init__(self, device=None, lang='en'):
for param in echo_encoder.parameters():
param.requires_grad = False

vc_state_dict = torch.load("model_data/weights/view_classifier.pt")
vc_state_dict = torch.load(os.path.join(weights_root_dir, "weights", "view_classifier.pt"))
view_classifier = torchvision.models.convnext_base()
view_classifier.classifier[-1] = torch.nn.Linear(
view_classifier.classifier[-1].in_features, 11
Expand All @@ -70,19 +70,19 @@ def __init__(self, device=None, lang='en'):
self.lang = lang

# load MIL weights per section
self.MIL_weights = pd.read_csv("assets/MIL_weights.csv")
self.MIL_weights = pd.read_csv(os.path.join(assets_root_dir, "MIL_weights.csv"))
self.non_empty_sections=self.MIL_weights['Section']
self.section_weights=self.MIL_weights.iloc[:,1:].to_numpy()

# Load candidate reports
self.candidate_studies=list(pd.read_csv("model_data/candidates_data/candidate_studies.csv")['Study'])
candidate_embeddings_p1=torch.load("model_data/candidates_data/candidate_embeddings_p1.pt")
candidate_embeddings_p2=torch.load("model_data/candidates_data/candidate_embeddings_p2.pt")
self.candidate_studies=list(pd.read_csv(os.path.join(weights_root_dir, "candidates_data", "candidate_studies.csv"))['Study'])
candidate_embeddings_p1=torch.load(os.path.join(weights_root_dir, "candidates_data", "candidate_embeddings_p1.pt"))
candidate_embeddings_p2=torch.load(os.path.join(weights_root_dir, "candidates_data", "candidate_embeddings_p2.pt"))
self.candidate_embeddings=torch.cat((candidate_embeddings_p1,candidate_embeddings_p2),dim=0)
candidate_reports=pd.read_pickle("model_data/candidates_data/candidate_reports.pkl")
candidate_reports=pd.read_pickle(os.path.join(weights_root_dir, "candidates_data", "candidate_reports.pkl"))
self.candidate_reports = [utils.phrase_decode(vec_phr) for vec_phr in tqdm(candidate_reports)]
self.candidate_labels = pd.read_pickle("model_data/candidates_data/candidate_labels.pkl")
self.section_to_phenotypes = pd.read_pickle("assets/section_to_phenotypes.pkl")
self.candidate_labels = pd.read_pickle(os.path.join(weights_root_dir, "candidates_data", "candidate_labels.pkl"))
self.section_to_phenotypes = pd.read_pickle(os.path.join(assets_root_dir, "section_to_phenotypes.pkl"))

def process_dicoms(self,INPUT):
"""
Expand Down
5 changes: 4 additions & 1 deletion requirements.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -6,4 +6,7 @@ pydicom==2.3.1
pytorch-lightning==2.2.0.post0
PyWavelets==1.4.1
wandb==0.16.3
transformers==4.57.0
transformers==4.57.0
matplotlib
torch
numpy<2.0.0
6 changes: 6 additions & 0 deletions setup.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,6 @@
from setuptools import setup

setup(
name="echo_prime",
packages=["echo_prime", "utils"]
)
21 changes: 12 additions & 9 deletions utils/utils.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
import os
import re
import json
import numpy as np
Expand All @@ -9,9 +10,6 @@
import pydicom as dicom
import torch

with open("assets/per_section.json", encoding="utf-8") as f:
json_data = json.load(f)

_ybr_to_rgb_lut = None

COARSE_VIEWS=['A2C',
Expand Down Expand Up @@ -53,7 +51,7 @@
numerical_pattern = r'(\\d+(\\.\\d+)?)' # Escaped backslashes for integers or floats
string_pattern = r'\\b\\w+.*?(?=\\.)'

def initialize_language(lang='en'):
def initialize_language(lang='en', assets_root_dir: str = None):
"""
Initialize language-specific variables.

Expand All @@ -62,13 +60,14 @@ def initialize_language(lang='en'):
"""
all_phrases, t_list = None, None
global phrases_per_section_list, phrases_per_section_list_org, regex_per_section

assets_dir = os.path.join(assets_root_dir, "assets") if assets_root_dir is not None else "assets"

if lang == 'en':
phrases_file = "assets/all_phr.json"
phrases_file = os.path.join(assets_dir, "all_phr.json")
elif lang == 'it':
phrases_file = "assets/all_phr_it.json"
phrases_file = os.path.join(assets_dir, "all_phr_it.json")
elif lang == 'bs':
phrases_file = "assets/all_phr_bs.json"
phrases_file = os.path.join(assets_dir, "all_phr_bs.json")
# add your translated file here
#elif lang == 'your_language_code':
# phrases_file = "assets/all_phr_{your_language_code}.json"
Expand Down Expand Up @@ -106,11 +105,15 @@ def extract_section(report, section_header):
else:
return "Section not found."

def extract_features(report: str) -> list:
def extract_features(report: str, assets_root_dir: str = None) -> list:
"""
Returns a list of 21 different features
see json_data for a list of features
"""
assets_dir = os.path.join(assets_root_dir, "assets") if assets_root_dir is not None else "assets"
with open(os.path.join(assets_dir, "per_section.json"), encoding="utf-8") as f:
json_data = json.load(f)

sorted_features=['impella',
'ejection_fraction',
'pacemaker',
Expand Down