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import os, sys |
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currentdir = os.path.dirname(os.path.realpath(__file__)) |
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parentdir = os.path.dirname(currentdir) |
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sys.path.append(parentdir) |
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import argparse |
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from configparser import ConfigParser |
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from datetime import datetime |
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import ddmr.utils.constants as C |
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TRAIN_DATASET = '/mnt/EncryptedData1/Users/javier/ext_datasets/IXI_dataset/T1/training' |
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err = list() |
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if __name__ == '__main__': |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--ini', help='Configuration file') |
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args = parser.parse_args() |
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configFile = ConfigParser() |
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configFile.read(args.ini) |
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print('Loaded configuration file: ' + args.ini) |
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print({section: dict(configFile[section]) for section in configFile.sections()}) |
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print('\n\n') |
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trainConfig = configFile['TRAIN'] |
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lossesConfig = configFile['LOSSES'] |
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datasetConfig = configFile['DATASETS'] |
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othersConfig = configFile['OTHERS'] |
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augmentationConfig = configFile['AUGMENTATION'] |
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simil = lossesConfig['similarity'].split(',') |
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segm = lossesConfig['segmentation'].split(',') |
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if trainConfig['name'].lower() == 'uw': |
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from Brain_study.Train_UncertaintyWeighted import launch_train |
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loss_config = {'simil': simil, 'segm': segm} |
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elif trainConfig['name'].lower() == 'segguided': |
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from Brain_study.Train_SegmentationGuided import launch_train |
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loss_config = {'simil': simil[0], 'segm': segm[0]} |
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else: |
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from Brain_study.Train_Baseline import launch_train |
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loss_config = {'simil': simil[0]} |
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output_folder = os.path.join(othersConfig['outputFolder'], |
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'{}_Lsim_{}__Lseg_{}'.format(trainConfig['name'], '_'.join(simil), '_'.join(segm))) |
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output_folder = output_folder + '_' + datetime.now().strftime("%H%M%S-%d%m%Y") |
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print('TRAIN ' + datasetConfig['train']) |
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if augmentationConfig: |
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C.GAMMA_AUGMENTATION = augmentationConfig['gamma'].lower() == 'true' |
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C.BRIGHTNESS_AUGMENTATION = augmentationConfig['brightness'].lower() == 'true' |
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try: |
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unet = [int(x) for x in trainConfig['unet'].split(',')] |
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except KeyError as e: |
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unet = [16, 32, 64, 128, 256] |
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try: |
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head = [int(x) for x in trainConfig['head'].split(',')] |
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except KeyError as e: |
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head = [16, 16] |
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try: |
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resume_checkpoint = trainConfig['resumeCheckpoint'] |
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except KeyError as e: |
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resume_checkpoint = None |
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launch_train(dataset_folder=datasetConfig['train'], |
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validation_folder=datasetConfig['validation'], |
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output_folder=output_folder, |
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gpu_num=eval(trainConfig['gpu']), |
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lr=eval(trainConfig['learningRate']), |
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rw=eval(trainConfig['regularizationWeight']), |
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acc_gradients=eval(trainConfig['accumulativeGradients']), |
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batch_size=eval(trainConfig['batchSize']), |
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max_epochs=eval(trainConfig['epochs']), |
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image_size=eval(trainConfig['imageSize']), |
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early_stop_patience=eval(trainConfig['earlyStopPatience']), |
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unet=unet, |
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head=head, |
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resume=resume_checkpoint, |
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**loss_config) |
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