'batch_size': PARAMS.batch_size_inference, 'shuffle': False, 'num_workers': PARAMS.num_workers, 'pin_memory': PARAMS.pin_memory }, }, 'augmentation_params': { 'image_augment_train': aug.intensity_seq, 'image_augment_with_target_train': aug.resize_pad_seq( resize_target_size=PARAMS.resize_target_size, pad_method=PARAMS.pad_method, pad_size=PARAMS.pad_size), 'image_augment_inference': aug.pad_to_fit_net(64, PARAMS.pad_method), 'image_augment_with_target_inference': aug.pad_to_fit_net(64, PARAMS.pad_method) }, }, 'pad_tta': { 'dataset_params': { 'h': PARAMS.image_h, 'w': PARAMS.image_w, 'image_source': PARAMS.image_source, 'target_format': PARAMS.target_format, 'MEAN': MEAN, 'STD': STD }, 'loader_params': { 'training': {
'num_workers': PARAMS.num_workers, 'pin_memory': PARAMS.pin_memory }, 'inference': {'batch_size': PARAMS.batch_size_inference, 'shuffle': False, 'num_workers': PARAMS.num_workers, 'pin_memory': PARAMS.pin_memory }, }, 'augmentation_params': {'image_augment_train': aug.intensity_seq, 'image_augment_with_target_train': aug.resize_pad_seq( resize_target_size=PARAMS.resize_target_size, pad_method=PARAMS.pad_method, pad_size=PARAMS.pad_size), 'image_augment_inference': aug.pad_to_fit_net(64, PARAMS.pad_method), 'image_augment_with_target_inference': aug.pad_to_fit_net(64, PARAMS.pad_method) }, }, 'pad_tta': {'dataset_params': {'h': PARAMS.image_h, 'w': PARAMS.image_w, 'image_source': PARAMS.image_source, 'use_depth': USE_DEPTH, 'MEAN': MEAN, 'STD': STD }, 'loader_params': {'training': {'batch_size': PARAMS.batch_size_train, 'shuffle': True, 'num_workers': PARAMS.num_workers, 'pin_memory': PARAMS.pin_memory