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 = []
# 'selected_data_channels': None,
if params.get("selected_data_channels") is not None:
tr_transforms.append(
DataChannelSelectionTransform(
params.get("selected_data_channels")))
# 'selected_seg_channels': [0],
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))
# 'regions': None
if regions is not None:
tr_transforms.append(
ConvertSegmentationToRegionsTransform(regions, 'target', 'target'))
# 'deep_supervision_scales': [[1, 1, 1],
# [1.0, 0.5, 0.5],
# [1.0, 0.25, 0.25],
# [0.5, 0.125, 0.125],
# [0.5, 0.0625, 0.0625]],
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,
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,
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
def get_no_augmentation(dataloader_train,
dataloader_val,
patch_size,
params=default_3D_augmentation_params,
border_val_seg=-1,
seeds_train=None,
seeds_val=None,
order_seg=1,
order_data=3,
deep_supervision_scales=None,
soft_ds=False,
classes=None,
pin_memory=True):
"""
use this instead of get_default_augmentation (drop in replacement) to turn off all data augmentation
:param dataloader_train:
:param dataloader_val:
:param patch_size:
:param params:
:param border_val_seg:
:return:
"""
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 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,
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=True)
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 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,
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=True)
batchgenerator_val.restart()
return batchgenerator_train, batchgenerator_val
def get_insaneDA_augmentation(dataloader_train,
dataloader_val,
patch_size,
params=default_3D_augmentation_params,
border_val_seg=-1,
seeds_train=None,
seeds_val=None,
order_seg=1,
order_data=3,
deep_supervision_scales=None,
soft_ds=False,
classes=None,
pin_memory=True):
assert params.get(
'mirror') is None, "old version of params, use new keyword do_mirror"
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")))
# don't do color augmentations while in 2d mode with 3d data because the color channel is overloaded!!
if params.get("dummy_2D") is not None and params.get("dummy_2D"):
ignore_axes = (0, )
tr_transforms.append(Convert3DTo2DTransform())
else:
ignore_axes = None
tr_transforms.append(
SpatialTransform(patch_size,
patch_center_dist_from_border=None,
do_elastic_deform=params.get("do_elastic"),
alpha=params.get("elastic_deform_alpha"),
sigma=params.get("elastic_deform_sigma"),
do_rotation=params.get("do_rotation"),
angle_x=params.get("rotation_x"),
angle_y=params.get("rotation_y"),
angle_z=params.get("rotation_z"),
do_scale=params.get("do_scaling"),
scale=params.get("scale_range"),
border_mode_data=params.get("border_mode_data"),
border_cval_data=0,
order_data=order_data,
border_mode_seg="constant",
border_cval_seg=border_val_seg,
order_seg=order_seg,
random_crop=params.get("random_crop"),
p_el_per_sample=params.get("p_eldef"),
p_scale_per_sample=params.get("p_scale"),
p_rot_per_sample=params.get("p_rot"),
independent_scale_for_each_axis=params.get(
"independent_scale_factor_for_each_axis")))
if params.get("dummy_2D"):
tr_transforms.append(Convert2DTo3DTransform())
# we need to put the color augmentations after the dummy 2d part (if applicable). Otherwise the overloaded color
# channel gets in the way
tr_transforms.append(GaussianNoiseTransform(p_per_sample=0.15))
tr_transforms.append(
GaussianBlurTransform((0.5, 1.5),
different_sigma_per_channel=True,
p_per_sample=0.2,
p_per_channel=0.5))
tr_transforms.append(
BrightnessMultiplicativeTransform(multiplier_range=(0.70, 1.3),
p_per_sample=0.15))
tr_transforms.append(
ContrastAugmentationTransform(contrast_range=(0.65, 1.5),
p_per_sample=0.15))
tr_transforms.append(
SimulateLowResolutionTransform(zoom_range=(0.5, 1),
per_channel=True,
p_per_channel=0.5,
order_downsample=0,
order_upsample=3,
p_per_sample=0.25,
ignore_axes=ignore_axes))
tr_transforms.append(
GammaTransform(params.get("gamma_range"),
True,
True,
retain_stats=params.get("gamma_retain_stats"),
p_per_sample=0.15)) # inverted gamma
if params.get("do_gamma"):
tr_transforms.append(
GammaTransform(params.get("gamma_range"),
False,
True,
retain_stats=params.get("gamma_retain_stats"),
p_per_sample=params["p_gamma"]))
if params.get("do_mirror") or params.get("mirror"):
tr_transforms.append(MirrorTransform(params.get("mirror_axes")))
if params.get("mask_was_used_for_normalization") is not None:
mask_was_used_for_normalization = params.get(
"mask_was_used_for_normalization")
tr_transforms.append(
MaskTransform(mask_was_used_for_normalization,
mask_idx_in_seg=0,
set_outside_to=0))
tr_transforms.append(RemoveLabelTransform(-1, 0))
if params.get("move_last_seg_chanel_to_data") is not None and params.get(
"move_last_seg_chanel_to_data"):
tr_transforms.append(
MoveSegAsOneHotToData(1, params.get("all_segmentation_labels"),
'seg', 'data'))
if params.get("cascade_do_cascade_augmentations"
) and not None and params.get(
"cascade_do_cascade_augmentations"):
if params.get("cascade_random_binary_transform_p") > 0:
tr_transforms.append(
ApplyRandomBinaryOperatorTransform(
channel_idx=list(
range(-len(params.get("all_segmentation_labels")),
0)),
p_per_sample=params.get(
"cascade_random_binary_transform_p"),
key="data",
strel_size=params.get(
"cascade_random_binary_transform_size")))
if params.get("cascade_remove_conn_comp_p") > 0:
tr_transforms.append(
RemoveRandomConnectedComponentFromOneHotEncodingTransform(
channel_idx=list(
range(-len(params.get("all_segmentation_labels")),
0)),
key="data",
p_per_sample=params.get("cascade_remove_conn_comp_p"),
fill_with_other_class_p=params.get(
"cascade_remove_conn_comp_max_size_percent_threshold"
),
dont_do_if_covers_more_than_X_percent=params.get(
"cascade_remove_conn_comp_fill_with_other_class_p")
))
tr_transforms.append(RenameTransform('seg', 'target', True))
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,
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=seeds_train,
pin_memory=pin_memory)
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")))
if params.get("move_last_seg_chanel_to_data") is not None and params.get(
"move_last_seg_chanel_to_data"):
val_transforms.append(
MoveSegAsOneHotToData(1, params.get("all_segmentation_labels"),
'seg', 'data'))
val_transforms.append(RenameTransform('seg', 'target', True))
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,
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=seeds_val,
pin_memory=pin_memory)
return batchgenerator_train, batchgenerator_val
def get_insaneDA_augmentation(dataloader_train,
dataloader_val,
patch_size,
params=default_3D_augmentation_params,
border_val_seg=-1,
seeds_train=None,
seeds_val=None,
order_seg=1,
order_data=3,
deep_supervision_scales=None,
soft_ds=False,
classes=None,
pin_memory=True,
regions=None):
assert params.get(
'mirror') is None, "old version of params, use new keyword do_mirror"
tr_transforms = []
# 'patch_size': array([288, 320]),
# 'border_val_seg': -1,
# 'seeds_train': None,
# 'seeds_val': None,
# 'order_seg': 1,
# 'order_data': 3,
# 'deep_supervision_scales': [[1, 1, 1],
# [1.0, 0.5, 0.5],
# [1.0, 0.25, 0.25],
# [0.5, 0.125, 0.125],
# [0.5, 0.0625, 0.0625]],
# 'soft_ds': False,
# 'classes': None,
# 'pin_memory': True,
# 'regions': None
# params
# {'selected_data_channels': None,
# 'selected_seg_channels': [0],
# 'do_elastic': True,
# 'elastic_deform_alpha': (0.0, 300.0),
# 'elastic_deform_sigma': (9.0, 15.0),
# 'p_eldef': 0.1,
# 'do_scaling': True,
# 'scale_range': (0.65, 1.6),
# 'independent_scale_factor_for_each_axis': True,
# 'p_independent_scale_per_axis': 0.3,
# 'p_scale': 0.3,
# 'do_rotation': True,
# 'rotation_x': (-3.141592653589793, 3.141592653589793),
# 'rotation_y': (-0.5235987755982988, 0.5235987755982988),
# 'rotation_z': (-0.5235987755982988, 0.5235987755982988),
# 'rotation_p_per_axis': 1,
# 'p_rot': 0.7,
# 'random_crop': False,
# 'random_crop_dist_to_border': None,
# 'do_gamma': True,
# 'gamma_retain_stats': True,
# 'gamma_range': (0.5, 1.6),
# 'p_gamma': 0.3,
# 'do_mirror': True,
# 'mirror_axes': (0, 1, 2),
# 'dummy_2D': True,
# 'mask_was_used_for_normalization': OrderedDict([(0, False)]),
# 'border_mode_data': 'constant',
# 'all_segmentation_labels': None,
# 'move_last_seg_chanel_to_data': False,
# 'cascade_do_cascade_augmentations': False,
# 'cascade_random_binary_transform_p': 0.4,
# 'cascade_random_binary_transform_p_per_label': 1,
# 'cascade_random_binary_transform_size': (1, 8),
# 'cascade_remove_conn_comp_p': 0.2,
# 'cascade_remove_conn_comp_max_size_percent_threshold': 0.15,
# 'cascade_remove_conn_comp_fill_with_other_class_p': 0.0,
# 'do_additive_brightness': True,
# 'additive_brightness_p_per_sample': 0.3,
# 'additive_brightness_p_per_channel': 1,
# 'additive_brightness_mu': 0,
# 'additive_brightness_sigma': 0.2,
# 'num_threads': 12,
# 'num_cached_per_thread': 1,
# 'patch_size_for_spatialtransform': array([288, 320])}
# selected_data_channels is None
if params.get("selected_data_channels") is not None:
tr_transforms.append(
DataChannelSelectionTransform(
params.get("selected_data_channels")))
# selected_seg_channels is [0]
if params.get("selected_seg_channels") is not None:
tr_transforms.append(
SegChannelSelectionTransform(params.get("selected_seg_channels")))
# don't do color augmentations while in 2d mode with 3d data because the color channel is overloaded!!
# dummy_2D is True
if params.get("dummy_2D") is not None and params.get("dummy_2D"):
ignore_axes = (0, )
tr_transforms.append(Convert3DTo2DTransform())
else:
ignore_axes = None
tr_transforms.append(
SpatialTransform(patch_size,
patch_center_dist_from_border=None,
do_elastic_deform=params.get("do_elastic"),
alpha=params.get("elastic_deform_alpha"),
sigma=params.get("elastic_deform_sigma"),
do_rotation=params.get("do_rotation"),
angle_x=params.get("rotation_x"),
angle_y=params.get("rotation_y"),
angle_z=params.get("rotation_z"),
do_scale=params.get("do_scaling"),
scale=params.get("scale_range"),
border_mode_data=params.get("border_mode_data"),
border_cval_data=0,
order_data=order_data,
border_mode_seg="constant",
border_cval_seg=border_val_seg,
order_seg=order_seg,
random_crop=params.get("random_crop"),
p_el_per_sample=params.get("p_eldef"),
p_scale_per_sample=params.get("p_scale"),
p_rot_per_sample=params.get("p_rot"),
independent_scale_for_each_axis=params.get(
"independent_scale_factor_for_each_axis"),
p_independent_scale_per_axis=params.get(
"p_independent_scale_per_axis")))
if params.get("dummy_2D"):
tr_transforms.append(Convert2DTo3DTransform())
# we need to put the color augmentations after the dummy 2d part (if applicable). Otherwise the overloaded color
# channel gets in the way
tr_transforms.append(GaussianNoiseTransform(p_per_sample=0.15))
tr_transforms.append(
GaussianBlurTransform((0.5, 1.5),
different_sigma_per_channel=True,
p_per_sample=0.2,
p_per_channel=0.5))
tr_transforms.append(
BrightnessMultiplicativeTransform(multiplier_range=(0.70, 1.3),
p_per_sample=0.15))
tr_transforms.append(
ContrastAugmentationTransform(contrast_range=(0.65, 1.5),
p_per_sample=0.15))
tr_transforms.append(
SimulateLowResolutionTransform(zoom_range=(0.5, 1),
per_channel=True,
p_per_channel=0.5,
order_downsample=0,
order_upsample=3,
p_per_sample=0.25,
ignore_axes=ignore_axes))
tr_transforms.append(
GammaTransform(params.get("gamma_range"),
True,
True,
retain_stats=params.get("gamma_retain_stats"),
p_per_sample=0.15)) # inverted gamma
# do_additive_brightness is True
if params.get("do_additive_brightness"):
tr_transforms.append(
BrightnessTransform(
params.get("additive_brightness_mu"),
params.get("additive_brightness_sigma"),
True,
p_per_sample=params.get("additive_brightness_p_per_sample"),
p_per_channel=params.get("additive_brightness_p_per_channel")))
# do_gamma is True
if params.get("do_gamma"):
tr_transforms.append(
GammaTransform(params.get("gamma_range"),
False,
True,
retain_stats=params.get("gamma_retain_stats"),
p_per_sample=params["p_gamma"]))
# do_mirror is True
if params.get("do_mirror") or params.get("mirror"):
tr_transforms.append(MirrorTransform(params.get("mirror_axes")))
# mask_was_used_for_normalization is OrderedDict([(0, False)]),
if params.get("mask_was_used_for_normalization") is not None:
mask_was_used_for_normalization = params.get(
"mask_was_used_for_normalization")
tr_transforms.append(
MaskTransform(mask_was_used_for_normalization,
mask_idx_in_seg=0,
set_outside_to=0))
tr_transforms.append(RemoveLabelTransform(-1, 0))
# move_last_seg_chanel_to_data is False
if params.get("move_last_seg_chanel_to_data") is not None and params.get(
"move_last_seg_chanel_to_data"):
tr_transforms.append(
MoveSegAsOneHotToData(1, params.get("all_segmentation_labels"),
'seg', 'data'))
if params.get("cascade_do_cascade_augmentations"
) and not None and params.get(
"cascade_do_cascade_augmentations"):
if params.get("cascade_random_binary_transform_p") > 0:
tr_transforms.append(
ApplyRandomBinaryOperatorTransform(
channel_idx=list(
range(-len(params.get("all_segmentation_labels")),
0)),
p_per_sample=params.get(
"cascade_random_binary_transform_p"),
key="data",
strel_size=params.get(
"cascade_random_binary_transform_size")))
if params.get("cascade_remove_conn_comp_p") > 0:
tr_transforms.append(
RemoveRandomConnectedComponentFromOneHotEncodingTransform(
channel_idx=list(
range(-len(params.get("all_segmentation_labels")),
0)),
key="data",
p_per_sample=params.get("cascade_remove_conn_comp_p"),
fill_with_other_class_p=params.get(
"cascade_remove_conn_comp_max_size_percent_threshold"
),
dont_do_if_covers_more_than_X_percent=params.get(
"cascade_remove_conn_comp_fill_with_other_class_p")
))
tr_transforms.append(RenameTransform('seg', 'target', True))
# regions is None
if regions is not None:
tr_transforms.append(
ConvertSegmentationToRegionsTransform(regions, 'target', 'target'))
# deep_supervision_scales is a not None
if deep_supervision_scales is not None:
# soft_ds is False
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,
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=seeds_train,
pin_memory=pin_memory)
# ========================================================
val_transforms = []
val_transforms.append(RemoveLabelTransform(-1, 0))
# selected_data_channels is None
if params.get("selected_data_channels") is not None:
val_transforms.append(
DataChannelSelectionTransform(
params.get("selected_data_channels")))
# selected_seg_channels is [0]
if params.get("selected_seg_channels") is not None:
val_transforms.append(
SegChannelSelectionTransform(params.get("selected_seg_channels")))
# move_last_seg_chanel_to_data is False
if params.get("move_last_seg_chanel_to_data") is not None and params.get(
"move_last_seg_chanel_to_data"):
val_transforms.append(
MoveSegAsOneHotToData(1, params.get("all_segmentation_labels"),
'seg', 'data'))
val_transforms.append(RenameTransform('seg', 'target', True))
# regions is None
if regions is not None:
val_transforms.append(
ConvertSegmentationToRegionsTransform(regions, 'target', 'target'))
# deep_supervision_scales is not None
if deep_supervision_scales is not None:
# soft_ds is False
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,
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=seeds_val,
pin_memory=pin_memory)
return batchgenerator_train, batchgenerator_val
