def get_train_transform(patch_size):
# we now create a list of transforms. These are not necessarily the best transforms to use for BraTS, this is just
# to showcase some things
tr_transforms = []
# the first thing we want to run is the SpatialTransform. It reduces the size of our data to patch_size and thus
# also reduces the computational cost of all subsequent operations. All subsequent operations do not modify the
# shape and do not transform spatially, so no border artifacts will be introduced
# Here we use the new SpatialTransform_2 which uses a new way of parameterizing elastic_deform
# We use all spatial transformations with a probability of 0.2 per sample. This means that 1 - (1 - 0.1) ** 3 = 27%
# of samples will be augmented, the rest will just be cropped
tr_transforms.append(
SpatialTransform_2(
patch_size, [i // 2 for i in patch_size],
do_elastic_deform=True,
deformation_scale=(0, 0.25),
do_rotation=True,
angle_x=(-15 / 360. * 2 * np.pi, 15 / 360. * 2 * np.pi),
angle_y=(-15 / 360. * 2 * np.pi, 15 / 360. * 2 * np.pi),
angle_z=(-15 / 360. * 2 * np.pi, 15 / 360. * 2 * np.pi),
do_scale=True,
scale=(0.75, 1.25),
border_mode_data='constant',
border_cval_data=0,
border_mode_seg='constant',
border_cval_seg=0,
order_seg=1,
order_data=3,
random_crop=True,
p_el_per_sample=0.1,
p_rot_per_sample=0.1,
p_scale_per_sample=0.1))
# now we mirror along all axes
tr_transforms.append(MirrorTransform(axes=(0, 1, 2)))
# gamma transform. This is a nonlinear transformation of intensity values
# (https://en.wikipedia.org/wiki/Gamma_correction)
tr_transforms.append(
GammaTransform(gamma_range=(0.5, 2),
invert_image=False,
per_channel=True,
p_per_sample=0.15))
# we can also invert the image, apply the transform and then invert back
tr_transforms.append(
GammaTransform(gamma_range=(0.5, 2),
invert_image=True,
per_channel=True,
p_per_sample=0.15))
# Gaussian Noise
tr_transforms.append(
GaussianNoiseTransform(noise_variance=(0, 0.05), p_per_sample=0.15))
# now we compose these transforms together
tr_transforms = Compose(tr_transforms)
return tr_transforms
def get_train_transform(patch_size):
tr_transforms = []
tr_transforms.append(
SpatialTransform_2(
patch_size, [i // 2 for i in patch_size],
do_elastic_deform=True,
deformation_scale=(0, 0.05),
do_rotation=True,
angle_x=(-5 / 360. * 2 * np.pi, 5 / 360. * 2 * np.pi),
angle_y=(-5 / 360. * 2 * np.pi, 5 / 360. * 2 * np.pi),
angle_z=(-5 / 360. * 2 * np.pi, 5 / 360. * 2 * np.pi),
do_scale=True,
scale=(0.75, 1.25),
border_mode_data='constant',
border_cval_data=-2.34,
border_mode_seg='constant',
border_cval_seg=0))
tr_transforms.append(MirrorTransform(axes=(0, 1, 2)))
tr_transforms.append(
BrightnessMultiplicativeTransform((0.7, 1.5),
per_channel=True,
p_per_sample=0.15))
tr_transforms.append(
GammaTransform(gamma_range=(0.5, 2),
invert_image=True,
per_channel=True,
p_per_sample=0.15))
tr_transforms.append(
GaussianNoiseTransform(noise_variance=(0, 0.15), p_per_sample=0.15))
tr_transforms = Compose(tr_transforms)
return tr_transforms
def get_train_transform(patch_size):
"""
data augmentation for training data, inspired by:
https://github.com/MIC-DKFZ/batchgenerators/blob/master/batchgenerators/examples/brats2017/brats2017_dataloader_3D.py
:param patch_size: shape of network's input
:return list of transformations
"""
train_transforms = []
def rad(deg):
return (-deg / 360 * 2 * np.pi, deg / 360 * 2 * np.pi)
train_transforms.append(
SpatialTransform_2(
patch_size,
(10, 10, 10),
do_elastic_deform=True,
deformation_scale=(0, 0.25),
do_rotation=True,
angle_z=rad(15),
angle_x=(0, 0),
angle_y=(0, 0),
do_scale=True,
scale=(0.75, 1.25),
border_mode_data='constant',
border_cval_data=0,
border_mode_seg='constant',
border_cval_seg=0,
order_seg=1,
random_crop=False,
p_el_per_sample=0.2,
p_rot_per_sample=0.2,
p_scale_per_sample=0.2,
))
train_transforms.append(MirrorTransform(axes=(0, 1)))
train_transforms.append(
BrightnessMultiplicativeTransform((0.7, 1.5),
per_channel=True,
p_per_sample=0.2))
train_transforms.append(
GammaTransform(gamma_range=(0.2, 1.0),
invert_image=False,
per_channel=False,
p_per_sample=0.2))
train_transforms.append(
GaussianNoiseTransform(noise_variance=(0, 0.05), p_per_sample=0.2))
train_transforms.append(
GaussianBlurTransform(blur_sigma=(0.2, 1.0),
different_sigma_per_channel=False,
p_per_channel=0.0,
p_per_sample=0.2))
return Compose(train_transforms)
def _make_training_transforms(self):
if self.no_data_augmentation:
print("No data augmentation will be performed during training!")
return []
patch_size = self.patch_size[::-1] # (x, y, z) order
rot_angle_x = self.training_augmentation_args.get('angle_x', 15)
rot_angle_y = self.training_augmentation_args.get('angle_y', 15)
rot_angle_z = self.training_augmentation_args.get('angle_z', 15)
p_per_sample = self.training_augmentation_args.get(
'p_per_sample', 0.15)
train_transforms = [
SpatialTransform_2(
patch_size,
patch_size // 2,
do_elastic_deform=self.training_augmentation_args.get(
'do_elastic_deform', True),
deformation_scale=self.training_augmentation_args.get(
'deformation_scale', (0, 0.25)),
do_rotation=self.training_augmentation_args.get(
'do_rotation', True),
angle_x=(-rot_angle_x / 360. * 2 * np.pi,
rot_angle_x / 360. * 2 * np.pi),
angle_y=(-rot_angle_y / 360. * 2 * np.pi,
rot_angle_y / 360. * 2 * np.pi),
angle_z=(-rot_angle_z / 360. * 2 * np.pi,
rot_angle_z / 360. * 2 * np.pi),
do_scale=self.training_augmentation_args.get('do_scale', True),
scale=self.training_augmentation_args.get(
'scale', (0.75, 1.25)),
border_mode_data='nearest',
border_cval_data=0,
order_data=3,
# border_mode_seg='nearest', border_cval_seg=0,
# order_seg=0,
random_crop=False,
p_el_per_sample=self.training_augmentation_args.get(
'p_el_per_sample', 0.5),
p_rot_per_sample=self.training_augmentation_args.get(
'p_rot_per_sample', 0.5),
p_scale_per_sample=self.training_augmentation_args.get(
'p_scale_per_sample', 0.5))
]
if self.training_augmentation_args.get("do_mirror", False):
train_transforms.append(MirrorTransform(axes=(0, 1, 2)))
train_transforms.append(
BrightnessMultiplicativeTransform(
self.training_augmentation_args.get('brightness_range',
(0.7, 1.5)),
per_channel=True,
p_per_sample=p_per_sample))
train_transforms.append(
GaussianNoiseTransform(
noise_variance=self.training_augmentation_args.get(
'gaussian_noise_variance', (0, 0.05)),
p_per_sample=p_per_sample))
train_transforms.append(
GammaTransform(gamma_range=self.training_augmentation_args.get(
'gamma_range', (0.5, 2)),
invert_image=False,
per_channel=True,
p_per_sample=p_per_sample))
print("train_transforms\n", train_transforms)
return train_transforms
def get_default_augmentation(dataloader_train,
dataloader_val,
patch_size,
params=default_3D_augmentation_params,
border_val_seg=-1,
pin_memory=True,
seeds_train=None,
seeds_val=None,
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"):
tr_transforms.append(Convert3DTo2DTransform())
patch_size_spatial = patch_size[1:]
else:
patch_size_spatial = patch_size
# Set order_data=0 and order_seg=0 for some more speed for cascade???
tr_transforms.append(
SpatialTransform(patch_size_spatial,
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"),
independent_scale_for_each_axis=params.get(
"independent_scale_factor_for_each_axis")))
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"]))
if params.get("do_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"):
# Remove the following transforms to remove cascade DA ??
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")))
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'))
tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
tr_transforms = Compose(tr_transforms)
# from batchgenerators.dataloading import SingleThreadedAugmenter
# batchgenerator_train = SingleThreadedAugmenter(dataloader_train, tr_transforms)
# import IPython;IPython.embed()
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 regions is not None:
val_transforms.append(
ConvertSegmentationToRegionsTransform(regions, 'target', 'target'))
val_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
val_transforms = Compose(val_transforms)
# batchgenerator_val = SingleThreadedAugmenter(dataloader_val, 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_moreDA_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,
use_nondetMultiThreadedAugmenter: bool = False):
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())
patch_size_spatial = patch_size[1:]
else:
patch_size_spatial = patch_size
ignore_axes = None
tr_transforms.append(
SpatialTransform(patch_size_spatial,
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"),
p_rot_per_axis=params.get("rotation_p_per_axis"),
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=0.1))
tr_transforms.append(
GaussianBlurTransform((0.5, 1.),
different_sigma_per_channel=True,
p_per_sample=0.2,
p_per_channel=0.5))
tr_transforms.append(
BrightnessMultiplicativeTransform(multiplier_range=(0.75, 1.25),
p_per_sample=0.15))
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")))
tr_transforms.append(ContrastAugmentationTransform(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=0.1)) # 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") is 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"),
p_per_label=params.get(
"cascade_random_binary_transform_p_per_label")))
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,
input_key='target',
output_key='target'))
tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
tr_transforms = Compose(tr_transforms)
if use_nondetMultiThreadedAugmenter:
if NonDetMultiThreadedAugmenter is None:
raise RuntimeError(
'NonDetMultiThreadedAugmenter is not yet available')
batchgenerator_train = NonDetMultiThreadedAugmenter(
dataloader_train,
tr_transforms,
params.get('num_threads'),
params.get("num_cached_per_thread"),
seeds=seeds_train,
pin_memory=pin_memory)
else:
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)
# import IPython;IPython.embed()
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,
input_key='target',
output_key='target'))
val_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
val_transforms = Compose(val_transforms)
if use_nondetMultiThreadedAugmenter:
if NonDetMultiThreadedAugmenter is None:
raise RuntimeError(
'NonDetMultiThreadedAugmenter is not yet available')
batchgenerator_val = NonDetMultiThreadedAugmenter(
dataloader_val,
val_transforms,
max(params.get('num_threads') // 2, 1),
params.get("num_cached_per_thread"),
seeds=seeds_val,
pin_memory=pin_memory)
else:
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)
# batchgenerator_val = SingleThreadedAugmenter(dataloader_val, val_transforms)
return batchgenerator_train, batchgenerator_val
def get_train_transforms(self) -> List[AbstractTransform]:
# used for transpost and rot90
matching_axes = np.array(
[sum([i == j for j in self.patch_size]) for i in self.patch_size])
valid_axes = list(np.where(matching_axes == np.max(matching_axes))[0])
tr_transforms = []
if self.data_aug_params['selected_seg_channels'] is not None:
tr_transforms.append(
SegChannelSelectionTransform(
self.data_aug_params['selected_seg_channels']))
if self.do_dummy_2D_aug:
ignore_axes = (0, )
tr_transforms.append(Convert3DTo2DTransform())
patch_size_spatial = self.patch_size[1:]
else:
patch_size_spatial = self.patch_size
ignore_axes = None
tr_transforms.append(
SpatialTransform(
patch_size_spatial,
patch_center_dist_from_border=None,
do_elastic_deform=False,
do_rotation=True,
angle_x=self.data_aug_params["rotation_x"],
angle_y=self.data_aug_params["rotation_y"],
angle_z=self.data_aug_params["rotation_z"],
p_rot_per_axis=0.5,
do_scale=True,
scale=self.data_aug_params['scale_range'],
border_mode_data="constant",
border_cval_data=0,
order_data=3,
border_mode_seg="constant",
border_cval_seg=-1,
order_seg=1,
random_crop=False,
p_el_per_sample=0.2,
p_scale_per_sample=0.2,
p_rot_per_sample=0.4,
independent_scale_for_each_axis=True,
))
if self.do_dummy_2D_aug:
tr_transforms.append(Convert2DTo3DTransform())
if np.any(matching_axes > 1):
tr_transforms.append(
Rot90Transform((0, 1, 2, 3),
axes=valid_axes,
data_key='data',
label_key='seg',
p_per_sample=0.5), )
if np.any(matching_axes > 1):
tr_transforms.append(
TransposeAxesTransform(valid_axes,
data_key='data',
label_key='seg',
p_per_sample=0.5))
tr_transforms.append(
OneOfTransform([
MedianFilterTransform((2, 8),
same_for_each_channel=False,
p_per_sample=0.2,
p_per_channel=0.5),
GaussianBlurTransform((0.3, 1.5),
different_sigma_per_channel=True,
p_per_sample=0.2,
p_per_channel=0.5)
]))
tr_transforms.append(GaussianNoiseTransform(p_per_sample=0.1))
tr_transforms.append(
BrightnessTransform(0,
0.5,
per_channel=True,
p_per_sample=0.1,
p_per_channel=0.5))
tr_transforms.append(
OneOfTransform([
ContrastAugmentationTransform(contrast_range=(0.5, 2),
preserve_range=True,
per_channel=True,
data_key='data',
p_per_sample=0.2,
p_per_channel=0.5),
ContrastAugmentationTransform(contrast_range=(0.5, 2),
preserve_range=False,
per_channel=True,
data_key='data',
p_per_sample=0.2,
p_per_channel=0.5),
]))
tr_transforms.append(
SimulateLowResolutionTransform(zoom_range=(0.25, 1),
per_channel=True,
p_per_channel=0.5,
order_downsample=0,
order_upsample=3,
p_per_sample=0.15,
ignore_axes=ignore_axes))
tr_transforms.append(
GammaTransform((0.7, 1.5),
invert_image=True,
per_channel=True,
retain_stats=True,
p_per_sample=0.1))
tr_transforms.append(
GammaTransform((0.7, 1.5),
invert_image=True,
per_channel=True,
retain_stats=True,
p_per_sample=0.1))
if self.do_mirroring:
tr_transforms.append(MirrorTransform(self.mirror_axes))
tr_transforms.append(
BlankRectangleTransform([[max(1, p // 10), p // 3]
for p in self.patch_size],
rectangle_value=np.mean,
num_rectangles=(1, 5),
force_square=False,
p_per_sample=0.4,
p_per_channel=0.5))
tr_transforms.append(
BrightnessGradientAdditiveTransform(
lambda x, y: np.exp(
np.random.uniform(np.log(x[y] // 6), np.log(x[y]))),
(-0.5, 1.5),
max_strength=lambda x, y: np.random.uniform(-5, -1)
if np.random.uniform() < 0.5 else np.random.uniform(1, 5),
mean_centered=False,
same_for_all_channels=False,
p_per_sample=0.3,
p_per_channel=0.5))
tr_transforms.append(
LocalGammaTransform(
lambda x, y: np.exp(
np.random.uniform(np.log(x[y] // 6), np.log(x[y]))),
(-0.5, 1.5),
lambda: np.random.uniform(0.01, 0.8)
if np.random.uniform() < 0.5 else np.random.uniform(1.5, 4),
same_for_all_channels=False,
p_per_sample=0.3,
p_per_channel=0.5))
tr_transforms.append(
SharpeningTransform(strength=(0.1, 1),
same_for_each_channel=False,
p_per_sample=0.2,
p_per_channel=0.5))
if any(self.use_mask_for_norm.values()):
tr_transforms.append(
MaskTransform(self.use_mask_for_norm,
mask_idx_in_seg=0,
set_outside_to=0))
tr_transforms.append(RemoveLabelTransform(-1, 0))
if self.data_aug_params["move_last_seg_chanel_to_data"]:
all_class_labels = np.arange(1, self.num_classes)
tr_transforms.append(
MoveSegAsOneHotToData(1, all_class_labels, 'seg', 'data'))
if self.data_aug_params["cascade_do_cascade_augmentations"]:
tr_transforms.append(
ApplyRandomBinaryOperatorTransform(channel_idx=list(
range(-len(all_class_labels), 0)),
p_per_sample=0.4,
key="data",
strel_size=(1, 8),
p_per_label=1))
tr_transforms.append(
RemoveRandomConnectedComponentFromOneHotEncodingTransform(
channel_idx=list(range(-len(all_class_labels), 0)),
key="data",
p_per_sample=0.2,
fill_with_other_class_p=0.15,
dont_do_if_covers_more_than_X_percent=0))
tr_transforms.append(RenameTransform('seg', 'target', True))
if self.regions is not None:
tr_transforms.append(
ConvertSegmentationToRegionsTransform(self.regions, 'target',
'target'))
if self.deep_supervision_scales is not None:
tr_transforms.append(
DownsampleSegForDSTransform2(self.deep_supervision_scales,
0,
input_key='target',
output_key='target'))
tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
return tr_transforms
# in order to use neptune logging:
# export NEPTUNE_API_TOKEN = '...' !!!
logging.getLogger().setLevel('INFO')
source_files = [__file__]
if hparams.config:
source_files.append(hparams.config)
neptune_logger = NeptuneLogger(project_name=hparams.neptune_project,
params=vars(hparams),
experiment_name=hparams.experiment_name,
tags=[hparams.experiment_name],
upload_source_files=source_files)
tb_logger = loggers.TensorBoardLogger(hparams.log_dir)
transform = Compose([
BrightnessTransform(mu=0.0, sigma=0.3, data_key='data'),
GammaTransform(gamma_range=(0.7, 1.3), data_key='data'),
ContrastAugmentationTransform(contrast_range=(0.3, 1.7), data_key='data')
])
with open(hparams.train_set, 'r') as keyfile:
train_keys = [l.strip() for l in keyfile.readlines()]
print(train_keys)
with open(hparams.val_set, 'r') as keyfile:
val_keys = [l.strip() for l in keyfile.readlines()]
print(val_keys)
train_ds = MedDataset(hparams.data_path,
train_keys,
hparams.patches_per_subject,
hparams.patch_size,
border_mode_data='constant',
border_cval_data=0,
border_mode_seg='constant',
border_cval_seg=0,
order_seg=1,
order_data=3,
random_crop=False,
p_el_per_sample=0.1,
p_rot_per_sample=0.1,
p_scale_per_sample=0.1)
my_transforms.append(spatial_transform)
my_transforms.append(MirrorTransform(axes=(0, 1, 2)))
my_transforms.append(
GammaTransform(gamma_range=(0.7, 1.),
invert_image=False,
per_channel=True,
p_per_sample=0.1))
all_transforms = Compose(my_transforms)
train_loader = SingleThreadedAugmenter(
batchgen, all_transforms
) #data loader for training, applying on the fly transformation
# add other data loaders
test_loader = torch.utils.data.DataLoader(
dataset_test,
batch_size=1,
shuffle=False,
num_workers=0,
)
},
up_kwargs={
'attention': True
},
encode_block=ResBlockStack,
encode_kwargs_fn=encode_kwargs_fn,
decode_block=ResBlock).cuda()
patch_size = (160, 160, 80)
optimizer = optim.Adam(model.parameters(), lr=1e-4)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
factor=0.2,
patience=30)
tr_transform = Compose([
GammaTransform((0.9, 1.1)),
ContrastAugmentationTransform((0.9, 1.1)),
BrightnessMultiplicativeTransform((0.9, 1.1)),
MirrorTransform(axes=[0]),
SpatialTransform_2(
patch_size,
(90, 90, 50),
random_crop=True,
do_elastic_deform=True,
deformation_scale=(0, 0.05),
do_rotation=True,
angle_x=(-0.1 * np.pi, 0.1 * np.pi),
angle_y=(0, 0),
angle_z=(0, 0),
do_scale=True,
scale=(0.9, 1.1),
from torchvision import transforms
train_transform = transforms.Compose([
# mt_transforms.CenterCrop2D((200, 200)),
mt_transforms.ElasticTransform(alpha_range=(28.0, 30.0),
sigma_range=(3.5, 4.0),
p=0.3),
mt_transforms.RandomAffine(degrees=4.6,
scale=(0.98, 1.02),
translate=(0.03, 0.03)),
mt_transforms.RandomTensorChannelShift((-0.10, 0.10)),
mt_transforms.ToTensor()
# mt_transforms.NormalizeInstance(),
])
gamma_t = GammaTransform(data_key="img", gamma_range=(0.1, 10))
mirror_t = MirrorTransform(data_key="img", label_key="seg")
spatial_t = SpatialTransform(patch_size=(8, 8, 8),
data_key="img",
label_key="seg")
gauss_noise_t = GaussianNoiseTransform(data_key="img", noise_variance=(0, 1))
zoom_t = ZoomTransform(zoom_factors=2, data_key="img")
def show_basic(x, gt, info=None):
if info is not None:
print("Test for " + info)
def run(self, img_data, seg_data):
# Define label for segmentation for segmentation augmentation
if self.seg_augmentation: seg_label = "seg"
else: seg_label = "class"
# Create a parser for the batchgenerators module
data_generator = DataParser(img_data, seg_data, seg_label)
# 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=self.config_contrast_preserverange,
per_channel=self.coloraug_per_channel,
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=self.coloraug_per_channel,
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=self.coloraug_per_channel,
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_label]
# 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_label], 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
