Refactor the model utils functions (download weight files, get weight files path, and load models)
dc4b749
| import os | |
| import requests | |
| from datetime import datetime | |
| from email.utils import parsedate_to_datetime, formatdate | |
| from DeepDeformationMapRegistration.utils.constants import ANATOMIES, MODEL_TYPES, ENCODER_FILTERS, DECODER_FILTERS, IMG_SHAPE | |
| import voxelmorph as vxm | |
| # taken from: https://lenon.dev/blog/downloading-and-caching-large-files-using-python/ | |
| def download(url, destination_file): | |
| headers = {} | |
| if os.path.exists(destination_file): | |
| mtime = os.path.getmtime(destination_file) | |
| headers["if-modified-since"] = formatdate(mtime, usegmt=True) | |
| response = requests.get(url, headers=headers, stream=True) | |
| response.raise_for_status() | |
| if response.status_code == requests.codes.not_modified: | |
| return | |
| if response.status_code == requests.codes.ok: | |
| with open(destination_file, "wb") as f: | |
| for chunk in response.iter_content(chunk_size=1048576): | |
| f.write(chunk) | |
| last_modified = response.headers.get("last-modified") | |
| if last_modified: | |
| new_mtime = parsedate_to_datetime(last_modified).timestamp() | |
| os.utime(destination_file, times=(datetime.now().timestamp(), new_mtime)) | |
| def get_models_path(anatomy: str, model_type: str, output_root_dir: str): | |
| assert anatomy in ANATOMIES.keys(), 'Invalid anatomy' | |
| assert model_type in MODEL_TYPES.keys(), 'Invalid model type' | |
| anatomy = ANATOMIES[anatomy] | |
| model_type = MODEL_TYPES[model_type] | |
| url = 'https://github.com/jpdefrutos/DDMR/releases/download/trained_models_v0/' + anatomy + '_' + model_type + '.h5' | |
| file_path = os.path.join(output_root_dir, 'models', anatomy, model_type + '.h5') | |
| if not os.path.exists(file_path): | |
| os.makedirs(os.path.split(file_path)[0], exist_ok=True) | |
| download(url, file_path) | |
| return file_path | |
| def load_model(weights_file_path: str, trainable: bool = False, return_registration_model: bool=True): | |
| assert os.path.exists(weights_file_path), f'File {weights_file_path} not found' | |
| assert weights_file_path.endswith('h5'), 'Invalid file extension. Expected .h5' | |
| ret_val = vxm.networks.VxmDense(inshape=IMG_SHAPE[:-1], | |
| nb_unet_features=[ENCODER_FILTERS, DECODER_FILTERS], | |
| int_steps=0) | |
| ret_val.load_weights(weights_file_path, by_name=True) | |
| ret_val.trainable = trainable | |
| if return_registration_model: | |
| ret_val = (ret_val, ret_val.get_registration_model()) | |
| return ret_val | |