def get_train_dataflow(add_mask=True):
"""
"""
if config.CROSS_VALIDATION:
imgs = BRATS_SEG.load_from_file(config.BASEDIR, config.TRAIN_DATASET)
else:
imgs = BRATS_SEG.load_many(config.BASEDIR,
config.TRAIN_DATASET,
add_gt=False,
add_mask=add_mask)
# no filter for training
imgs = list(imgs)
ds = DataFromList(imgs, shuffle=True)
def preprocess(data):
if config.NO_CACHE:
fname, gt, im = data['file_name'], data['gt'], data['image_data']
volume_list, label, weight, _, _ = crop_brain_region(im, gt)
batch = sampler3d(volume_list, label, weight)
else:
volume_list, label, weight, _, _ = data['preprocessed']
batch = sampler3d(volume_list, label, weight)
return [batch['images'], batch['weights'], batch['labels']]
ds = BatchData(MapData(ds, preprocess), config.BATCH_SIZE)
ds = PrefetchDataZMQ(ds, 6)
return ds
def get_test_dataflow():
imgs = BRATS_SEG.load_many(config.BASEDIR, config.TEST_DATASET, add_gt=False)
# no filter for training
ds = DataFromListOfDict(imgs, ['file_name', 'id', 'preprocessed'])
def f(data):
volume_list, label, weight, original_shape, bbox = data
batch = sampler3d_whole(volume_list, label, weight, original_shape, bbox)
return batch
ds = MapDataComponent(ds, f, 2)
ds = PrefetchDataZMQ(ds, 1)
return ds
def get_eval_dataflow():
#if config.CROSS_VALIDATION:
imgs = BRATS_SEG.load_from_file(config.BASEDIR, config.VAL_DATASET)
# no filter for training
ds = DataFromListOfDict(imgs, ['file_name', 'id', 'preprocessed'])
def f(data):
volume_list, label, weight, original_shape, bbox = data
batch = sampler3d_whole(volume_list, label, weight, original_shape, bbox)
return batch
ds = MapDataComponent(ds, f, 2)
ds = PrefetchDataZMQ(ds, 1)
return ds