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