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_insaneDA_augmentation2(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 = []
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_2(
patch_size,
patch_center_dist_from_border=None,
do_elastic_deform=params.get("do_elastic"),
deformation_scale=params.get("eldef_deformation_scale"),
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
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")))
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 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,
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)
#batchgenerator_train = SingleThreadedAugmenter(dataloader_train, tr_transforms)
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 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=seeds_val,
pin_memory=pin_memory)
return batchgenerator_train, batchgenerator_val
def run(self, img_data, seg_data):
# Create a parser for the batchgenerators module
data_generator = DataParser(img_data, seg_data)
# Initialize empty transform list
transforms = []
# Add mirror augmentation
if self.mirror:
aug_mirror = MirrorTransform(axes=self.config_mirror_axes)
transforms.append(aug_mirror)
# Add contrast augmentation
if self.contrast:
aug_contrast = ContrastAugmentationTransform(
self.config_contrast_range,
preserve_range=True,
per_channel=True,
p_per_sample=self.config_p_per_sample)
transforms.append(aug_contrast)
# Add brightness augmentation
if self.brightness:
aug_brightness = BrightnessMultiplicativeTransform(
self.config_brightness_range,
per_channel=True,
p_per_sample=self.config_p_per_sample)
transforms.append(aug_brightness)
# Add gamma augmentation
if self.gamma:
aug_gamma = GammaTransform(self.config_gamma_range,
invert_image=False,
per_channel=True,
retain_stats=True,
p_per_sample=self.config_p_per_sample)
transforms.append(aug_gamma)
# Add gaussian noise augmentation
if self.gaussian_noise:
aug_gaussian_noise = GaussianNoiseTransform(
self.config_gaussian_noise_range,
p_per_sample=self.config_p_per_sample)
transforms.append(aug_gaussian_noise)
# Add spatial transformations as augmentation
# (rotation, scaling, elastic deformation)
if self.rotations or self.scaling or self.elastic_deform or \
self.cropping:
# Identify patch shape (full image or cropping)
if self.cropping: patch_shape = self.cropping_patch_shape
else: patch_shape = img_data[0].shape[0:-1]
# Assembling the spatial transformation
aug_spatial_transform = SpatialTransform(
patch_shape, [i // 2 for i in patch_shape],
do_elastic_deform=self.elastic_deform,
alpha=self.config_elastic_deform_alpha,
sigma=self.config_elastic_deform_sigma,
do_rotation=self.rotations,
angle_x=self.config_rotations_angleX,
angle_y=self.config_rotations_angleY,
angle_z=self.config_rotations_angleZ,
do_scale=self.scaling,
scale=self.config_scaling_range,
border_mode_data='constant',
border_cval_data=0,
border_mode_seg='constant',
border_cval_seg=0,
order_data=3,
order_seg=0,
p_el_per_sample=self.config_p_per_sample,
p_rot_per_sample=self.config_p_per_sample,
p_scale_per_sample=self.config_p_per_sample,
random_crop=self.cropping)
# Append spatial transformation to transformation list
transforms.append(aug_spatial_transform)
# Compose the batchgenerators transforms
all_transforms = Compose(transforms)
# Assemble transforms into a augmentation generator
augmentation_generator = SingleThreadedAugmenter(
data_generator, all_transforms)
# Perform the data augmentation x times (x = cycles)
aug_img_data = None
aug_seg_data = None
for i in range(0, self.cycles):
# Run the computation process for the data augmentations
augmentation = next(augmentation_generator)
# Access augmentated data from the batchgenerators data structure
if aug_img_data is None and aug_seg_data is None:
aug_img_data = augmentation["data"]
aug_seg_data = augmentation["seg"]
# Concatenate the new data augmentated data with the cached data
else:
aug_img_data = np.concatenate(
(augmentation["data"], aug_img_data), axis=0)
aug_seg_data = np.concatenate(
(augmentation["seg"], aug_seg_data), axis=0)
# Transform data from channel-first back to channel-last structure
# Data structure channel-first 3D: (batch, channel, x, y, z)
# Data structure channel-last 3D: (batch, x, y, z, channel)
aug_img_data = np.moveaxis(aug_img_data, 1, -1)
aug_seg_data = np.moveaxis(aug_seg_data, 1, -1)
# Return augmentated image and segmentation data
return aug_img_data, aug_seg_data
def get_transforms(mode="train",
n_channels=1,
target_size=128,
add_resize=False,
add_noise=False,
mask_type="",
batch_size=16,
rotate=True,
elastic_deform=True,
rnd_crop=False,
color_augment=True):
tranform_list = []
noise_list = []
if mode == "train":
tranform_list = [
FillupPadTransform(min_size=(n_channels, target_size + 5,
target_size + 5)),
ResizeTransform(target_size=(target_size + 1, target_size + 1),
order=1,
concatenate_list=True),
# RandomCropTransform(crop_size=(target_size + 5, target_size + 5)),
MirrorTransform(axes=(2, )),
ReshapeTransform(new_shape=(1, -1, "h", "w")),
SpatialTransform(patch_size=(target_size, target_size),
random_crop=rnd_crop,
patch_center_dist_from_border=target_size // 2,
do_elastic_deform=elastic_deform,
alpha=(0., 100.),
sigma=(10., 13.),
do_rotation=rotate,
angle_x=(-0.1, 0.1),
angle_y=(0, 1e-8),
angle_z=(0, 1e-8),
scale=(0.9, 1.2),
border_mode_data="nearest",
border_mode_seg="nearest"),
ReshapeTransform(new_shape=(batch_size, -1, "h", "w"))
]
if color_augment:
tranform_list += [ # BrightnessTransform(mu=0, sigma=0.2),
BrightnessMultiplicativeTransform(multiplier_range=(0.95, 1.1))
]
tranform_list += [
GaussianNoiseTransform(noise_variance=(0., 0.05)),
ClipValueRange(min=-1.5, max=1.5),
]
noise_list = []
if mask_type == "gaussian":
noise_list += [GaussianNoiseTransform(noise_variance=(0., 0.2))]
elif mode == "val":
tranform_list = [
FillupPadTransform(min_size=(n_channels, target_size + 5,
target_size + 5)),
ResizeTransform(target_size=(target_size + 1, target_size + 1),
order=1,
concatenate_list=True),
CenterCropTransform(crop_size=(target_size, target_size)),
ClipValueRange(min=-1.5, max=1.5),
# BrightnessTransform(mu=0, sigma=0.2),
# BrightnessMultiplicativeTransform(multiplier_range=(0.95, 1.1)),
CopyTransform({"data": "data_clean"}, copy=True)
]
noise_list += []
if add_noise:
tranform_list = tranform_list + noise_list
tranform_list.append(NumpyToTensor())
return Compose(tranform_list)