def get_no_augmentation(dataloader_train, dataloader_val, patch_size, params=default_3D_augmentation_params, border_val_seg=-1):
"""
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))
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))
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_validation_transforms(self):
val_transforms = []
if self.params.get("selected_data_channels"):
val_transforms.append(
DataChannelSelectionTransform(
self.params.get("selected_data_channels")))
if self.params.get("selected_seg_channels"):
val_transforms.append(
SegChannelSelectionTransform(
self.params.get("selected_seg_channels")))
val_transforms.append(CenterCropTransform(self.patch_size))
val_transforms.append(RemoveLabelTransform(-1, 0))
val_transforms.append(RenameTransform('seg', 'target', True))
val_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
return Compose(val_transforms)
def get_default_augmentation_withEDT(dataloader_train,
dataloader_val,
patch_size,
idx_of_edts,
params=default_3D_augmentation_params,
border_val_seg=-1,
pin_memory=True,
seeds_train=None,
seeds_val=None):
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"):
tr_transforms.append(Convert3DTo2DTransform())
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=3,
border_mode_seg="constant",
border_cval_seg=border_val_seg,
order_seg=1,
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")))
if params.get("dummy_2D") is not None and params.get("dummy_2D"):
tr_transforms.append(Convert2DTo3DTransform())
"""
##############################################################
##############################################################
Here we insert moving the EDT to a different key so that it does not get intensity transformed
##############################################################
##############################################################
"""
tr_transforms.append(
AppendChannelsTransform("data",
"bound",
idx_of_edts,
remove_from_input=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"]))
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(
"advanced_pyramid_augmentations") and not None and params.get(
"advanced_pyramid_augmentations"):
tr_transforms.append(
ApplyRandomBinaryOperatorTransform(channel_idx=list(
range(-len(params.get("all_segmentation_labels")), 0)),
p_per_sample=0.4,
key="data",
strel_size=(1, 8)))
tr_transforms.append(
RemoveRandomConnectedComponentFromOneHotEncodingTransform(
channel_idx=list(
range(-len(params.get("all_segmentation_labels")), 0)),
key="data",
p_per_sample=0.2,
fill_with_other_class_p=0.0,
dont_do_if_covers_more_than_X_percent=0.15))
tr_transforms.append(RenameTransform('seg', 'target', True))
tr_transforms.append(NumpyToTensor(['data', 'target', 'bound'], '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")))
"""
##############################################################
##############################################################
Here we insert moving the EDT to a different key
##############################################################
##############################################################
"""
val_transforms.append(
AppendChannelsTransform("data",
"bound",
idx_of_edts,
remove_from_input=True))
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))
val_transforms.append(NumpyToTensor(['data', 'target', 'bound'], '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_training_transforms(self):
assert self.params.get(
'mirror'
) is None, "old version of params, use new keyword do_mirror"
tr_transforms = []
if self.params.get("selected_data_channels"):
tr_transforms.append(
DataChannelSelectionTransform(
self.params.get("selected_data_channels")))
if self.params.get("selected_seg_channels"):
tr_transforms.append(
SegChannelSelectionTransform(
self.params.get("selected_seg_channels")))
# don't do color augmentations while in 2d mode with 3d data because the color channel is overloaded!!
if self.params.get("dummy_2D", False):
ignore_axes = (0, )
tr_transforms.append(Convert3DTo2DTransform())
else:
ignore_axes = None
tr_transforms.append(
SpatialTransform(
self._spatial_transform_patch_size,
patch_center_dist_from_border=None,
do_elastic_deform=self.params.get("do_elastic"),
alpha=self.params.get("elastic_deform_alpha"),
sigma=self.params.get("elastic_deform_sigma"),
do_rotation=self.params.get("do_rotation"),
angle_x=self.params.get("rotation_x"),
angle_y=self.params.get("rotation_y"),
angle_z=self.params.get("rotation_z"),
do_scale=self.params.get("do_scaling"),
scale=self.params.get("scale_range"),
order_data=self.params.get("order_data"),
border_mode_data=self.params.get("border_mode_data"),
border_cval_data=self.params.get("border_cval_data"),
order_seg=self.params.get("order_seg"),
border_mode_seg=self.params.get("border_mode_seg"),
border_cval_seg=self.params.get("border_cval_seg"),
random_crop=self.params.get("random_crop"),
p_el_per_sample=self.params.get("p_eldef"),
p_scale_per_sample=self.params.get("p_scale"),
p_rot_per_sample=self.params.get("p_rot"),
independent_scale_for_each_axis=self.params.get(
"independent_scale_factor_for_each_axis"),
))
if self.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=(0.75, 1.3),
p_per_sample=0.15))
if self.params.get("do_additive_brightness"):
tr_transforms.append(
BrightnessTransform(
self.params.get("additive_brightness_mu"),
self.params.get("additive_brightness_sigma"),
True,
p_per_sample=self.params.get(
"additive_brightness_p_per_sample"),
p_per_channel=self.params.get(
"additive_brightness_p_per_channel")))
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(self.params.get("gamma_range"),
True,
True,
retain_stats=self.params.get("gamma_retain_stats"),
p_per_sample=0.15)) # inverted gamma
if self.params.get("do_gamma"):
tr_transforms.append(
GammaTransform(
self.params.get("gamma_range"),
False,
True,
retain_stats=self.params.get("gamma_retain_stats"),
p_per_sample=self.params["p_gamma"]))
if self.params.get("do_mirror") or self.params.get("mirror"):
tr_transforms.append(
MirrorTransform(self.params.get("mirror_axes")))
if self.params.get("use_mask_for_norm"):
use_mask_for_norm = self.params.get("use_mask_for_norm")
tr_transforms.append(
MaskTransform(use_mask_for_norm,
mask_idx_in_seg=0,
set_outside_to=0))
tr_transforms.append(RemoveLabelTransform(-1, 0))
tr_transforms.append(RenameTransform('seg', 'target', True))
tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
return Compose(tr_transforms)
def get_training_transforms(self):
assert self.params.get(
'mirror'
) is None, "old version of params, use new keyword do_mirror"
tr_transforms = []
if self.params.get("selected_data_channels"):
tr_transforms.append(
DataChannelSelectionTransform(
self.params.get("selected_data_channels")))
if self.params.get("selected_seg_channels"):
tr_transforms.append(
SegChannelSelectionTransform(
self.params.get("selected_seg_channels")))
if self.params.get("dummy_2D", False):
# don't do color augmentations while in 2d mode with 3d data because the color channel is overloaded!!
tr_transforms.append(Convert3DTo2DTransform())
tr_transforms.append(
SpatialTransform(
self._spatial_transform_patch_size,
patch_center_dist_from_border=None,
do_elastic_deform=self.params.get("do_elastic"),
alpha=self.params.get("elastic_deform_alpha"),
sigma=self.params.get("elastic_deform_sigma"),
do_rotation=self.params.get("do_rotation"),
angle_x=self.params.get("rotation_x"),
angle_y=self.params.get("rotation_y"),
angle_z=self.params.get("rotation_z"),
do_scale=self.params.get("do_scaling"),
scale=self.params.get("scale_range"),
order_data=self.params.get("order_data"),
border_mode_data=self.params.get("border_mode_data"),
border_cval_data=self.params.get("border_cval_data"),
order_seg=self.params.get("order_seg"),
border_mode_seg=self.params.get("border_mode_seg"),
border_cval_seg=self.params.get("border_cval_seg"),
random_crop=self.params.get("random_crop"),
p_el_per_sample=self.params.get("p_eldef"),
p_scale_per_sample=self.params.get("p_scale"),
p_rot_per_sample=self.params.get("p_rot"),
independent_scale_for_each_axis=self.params.get(
"independent_scale_factor_for_each_axis"),
))
if self.params.get("dummy_2D", False):
tr_transforms.append(Convert2DTo3DTransform())
if self.params.get("do_gamma", False):
tr_transforms.append(
GammaTransform(
self.params.get("gamma_range"),
False,
True,
retain_stats=self.params.get("gamma_retain_stats"),
p_per_sample=self.params["p_gamma"]))
if self.params.get("do_mirror", False):
tr_transforms.append(
MirrorTransform(self.params.get("mirror_axes")))
if self.params.get("use_mask_for_norm"):
use_mask_for_norm = self.params.get("use_mask_for_norm")
tr_transforms.append(
MaskTransform(use_mask_for_norm,
mask_idx_in_seg=0,
set_outside_to=0))
tr_transforms.append(RemoveLabelTransform(-1, 0))
tr_transforms.append(RenameTransform('seg', 'target', True))
tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
return Compose(tr_transforms)
def Transforms(patch_size,
params=default_3D_augmentation_params,
border_val_seg=-1):
tr_transforms = []
if params.get("selected_data_channels") is not None:
tr_transforms.append(
DataChannelSelectionTransform(params.get("selected_data_channels"),
data_key="data"))
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"):
tr_transforms.append(Convert3DTo2DTransform())
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=3,
border_mode_seg="constant",
border_cval_seg=border_val_seg,
order_seg=1,
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")))
if params.get("dummy_2D") is not None and params.get("dummy_2D"):
tr_transforms.append(Convert2DTo3DTransform())
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"]))
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(
"advanced_pyramid_augmentations") and not None and params.get(
"advanced_pyramid_augmentations"):
tr_transforms.append(
ApplyRandomBinaryOperatorTransform(channel_idx=list(
range(-len(params.get("all_segmentation_labels")), 0)),
p_per_sample=0.4,
key="data",
strel_size=(1, 8)))
tr_transforms.append(
RemoveRandomConnectedComponentFromOneHotEncodingTransform(
channel_idx=list(
range(-len(params.get("all_segmentation_labels")), 0)),
key="data",
p_per_sample=0.2,
fill_with_other_class_p=0.0,
dont_do_if_covers_more_than_X_percent=0.15))
tr_transforms.append(RenameTransform('seg', 'target', True))
tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
tr_transforms = Compose(tr_transforms)
return tr_transforms