num_cached_per_queue=3,
seeds=None,
pin_memory=False)
# we need less processes for vlaidation because we dont apply transformations
val_gen = MultiThreadedAugmenter(dataloader_validation,
None,
num_processes=max(
1,
num_threads_for_brats_example // 2),
num_cached_per_queue=1,
seeds=None,
pin_memory=False)
# lets start the MultiThreadedAugmenter. This is not necessary but allows them to start generating training
# batches while other things run in the main thread
tr_gen.restart()
val_gen.restart()
# now if this was a network training you would run epochs like this (remember tr_gen and val_gen generate
# inifinite examples! Don't do "for batch in tr_gen:"!!!):
num_batches_per_epoch = 10
num_validation_batches_per_epoch = 3
num_epochs = 5
# let's run this to get a time on how long it takes
time_per_epoch = []
start = time()
for epoch in range(num_epochs):
start_epoch = time()
for b in range(num_batches_per_epoch):
batch = next(tr_gen)
# do network training here with this batch
def get_no_augmentation(dataloader_train,
dataloader_val,
params=default_3D_augmentation_params,
deep_supervision_scales=None,
soft_ds=False,
classes=None,
pin_memory=True,
regions=None):
"""
use this instead of get_default_augmentation (drop in replacement) to turn off all data augmentation
"""
tr_transforms = []
if params.get("selected_data_channels") is not None:
tr_transforms.append(
DataChannelSelectionTransform(
params.get("selected_data_channels")))
if params.get("selected_seg_channels") is not None:
tr_transforms.append(
SegChannelSelectionTransform(params.get("selected_seg_channels")))
tr_transforms.append(RemoveLabelTransform(-1, 0))
tr_transforms.append(RenameTransform('seg', 'target', True))
if regions is not None:
tr_transforms.append(
ConvertSegmentationToRegionsTransform(regions, 'target', 'target'))
if deep_supervision_scales is not None:
if soft_ds:
assert classes is not None
tr_transforms.append(
DownsampleSegForDSTransform3(deep_supervision_scales, 'target',
'target', classes))
else:
tr_transforms.append(
DownsampleSegForDSTransform2(deep_supervision_scales,
0,
input_key='target',
output_key='target'))
tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
tr_transforms = Compose(tr_transforms)
batchgenerator_train = MultiThreadedAugmenter(
dataloader_train,
tr_transforms,
params.get('num_threads'),
params.get("num_cached_per_thread"),
seeds=range(params.get('num_threads')),
pin_memory=pin_memory)
batchgenerator_train.restart()
val_transforms = []
val_transforms.append(RemoveLabelTransform(-1, 0))
if params.get("selected_data_channels") is not None:
val_transforms.append(
DataChannelSelectionTransform(
params.get("selected_data_channels")))
if params.get("selected_seg_channels") is not None:
val_transforms.append(
SegChannelSelectionTransform(params.get("selected_seg_channels")))
val_transforms.append(RenameTransform('seg', 'target', True))
if regions is not None:
val_transforms.append(
ConvertSegmentationToRegionsTransform(regions, 'target', 'target'))
if deep_supervision_scales is not None:
if soft_ds:
assert classes is not None
val_transforms.append(
DownsampleSegForDSTransform3(deep_supervision_scales, 'target',
'target', classes))
else:
val_transforms.append(
DownsampleSegForDSTransform2(deep_supervision_scales,
0,
input_key='target',
output_key='target'))
val_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
val_transforms = Compose(val_transforms)
batchgenerator_val = MultiThreadedAugmenter(
dataloader_val,
val_transforms,
max(params.get('num_threads') // 2, 1),
params.get("num_cached_per_thread"),
seeds=range(max(params.get('num_threads') // 2, 1)),
pin_memory=pin_memory)
batchgenerator_val.restart()
return batchgenerator_train, batchgenerator_val