class NumpyDataSet(object):
"""
TODO
"""
def __init__(self, base_dir, mode="train", batch_size=16, num_batches=10000000, seed=None, num_processes=8, num_cached_per_queue=8 * 4, target_size=128,
file_pattern='*.npy', label_slice=1, input_slice=(0,), do_reshuffle=True, keys=None):
data_loader = NumpyDataLoader(base_dir=base_dir, mode=mode, batch_size=batch_size, num_batches=num_batches, seed=seed, file_pattern=file_pattern,
input_slice=input_slice, label_slice=label_slice, keys=keys)
self.data_loader = data_loader
self.batch_size = batch_size
self.do_reshuffle = do_reshuffle
self.number_of_slices = 1
self.transforms = get_transforms(mode=mode, target_size=target_size)
self.augmenter = MultiThreadedDataLoader(data_loader, self.transforms, num_processes=num_processes,
num_cached_per_queue=num_cached_per_queue, seeds=seed,
shuffle=do_reshuffle)
self.augmenter.restart()
def __len__(self):
return len(self.data_loader)
def __iter__(self):
if self.do_reshuffle:
self.data_loader.reshuffle()
self.augmenter.renew()
return self.augmenter
def __next__(self):
return next(self.augmenter)
def __init__(self,
base_dir,
mode="train",
batch_size=16,
num_batches=10000000,
seed=None,
num_processes=8,
num_cached_per_queue=8 * 4,
target_size=128,
file_pattern='*.npy',
label_slice=1,
input_slice=(0, ),
do_reshuffle=True,
keys=None):
data_loader = NumpyDataLoader(base_dir=base_dir,
mode=mode,
batch_size=batch_size,
num_batches=num_batches,
seed=seed,
file_pattern=file_pattern,
input_slice=input_slice,
label_slice=label_slice,
keys=keys)
#data_loader = BraTS2017DataLoader2D(train, batch_size, max_shape, 1)
self.data_loader = data_loader
self.batch_size = batch_size
self.do_reshuffle = do_reshuffle
self.number_of_slices = 1
self.transforms = get_transforms(mode=mode, target_size=target_size)
self.augmenter = MultiThreadedDataLoader(
data_loader,
self.transforms,
num_processes=num_processes,
num_cached_per_queue=num_cached_per_queue,
seeds=seed,
shuffle=do_reshuffle)
self.augmenter.restart()