'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