def __init__(self, file_dir, shape=(128, 128, 128), is_aug=True):
super(DatasetFromFolder3D, self).__init__()
self.is_aug = is_aug
self.image_filenames = [
x for x in listdir(join(file_dir, "image")) if is_image_file(x)
]
self.file_dir = file_dir
self.shape = shape
if is_aug:
self.random_crop = Crop(shape)
self.mirror_transform = MirrorTransform()
self.spatial_transform = SpatialTransform(
patch_center_dist_from_border=np.array(shape) // 2,
do_elastic_deform=False,
alpha=(0., 1000.),
sigma=(10., 13.),
do_rotation=True,
angle_x=(-np.pi / 9, np.pi / 9),
angle_y=(-np.pi / 9, np.pi / 9),
angle_z=(-np.pi / 9, np.pi / 9),
do_scale=True,
scale=(0.75, 1.25),
border_mode_data='constant',
border_cval_data=0,
order_data=1,
random_crop=True)
self.gamma_transform = GammaTransform(gamma_range=(0.75, 1.25))
else:
self.random_crop = Crop(shape)
def get_augmentation(patch_size,
params=default_3D_augmentation_params,
border_val_seg=-1):
print(f'patch size after augmentation {patch_size}')
tr_transforms = []
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("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")))
tr_transforms = Compose(tr_transforms)
return tr_transforms
def create_data_gen_train(patient_data_train, INPUT_PATCH_SIZE, num_classes, BATCH_SIZE, contrast_range=(0.75, 1.5),
gamma_range = (0.6, 2),
num_workers=5, num_cached_per_worker=3,
do_elastic_transform=False, alpha=(0., 1300.), sigma=(10., 13.),
do_rotation=False, a_x=(0., 2*np.pi), a_y=(0., 2*np.pi), a_z=(0., 2*np.pi),
do_scale=True, scale_range=(0.75, 1.25), seeds=None):
if seeds is None:
seeds = [None]*num_workers
elif seeds == 'range':
seeds = range(num_workers)
else:
assert len(seeds) == num_workers
data_gen_train = BatchGenerator3D_random_sampling(patient_data_train, BATCH_SIZE, num_batches=None, seed=False,
patch_size=(160, 192, 160), convert_labels=True)
tr_transforms = []
tr_transforms.append(DataChannelSelectionTransform([0, 1, 2, 3]))
tr_transforms.append(GenerateBrainMaskTransform())
tr_transforms.append(MirrorTransform())
tr_transforms.append(SpatialTransform(INPUT_PATCH_SIZE, list(np.array(INPUT_PATCH_SIZE)//2.),
do_elastic_deform=do_elastic_transform, alpha=alpha, sigma=sigma,
do_rotation=do_rotation, angle_x=a_x, angle_y=a_y, angle_z=a_z,
do_scale=do_scale, scale=scale_range, border_mode_data='nearest',
border_cval_data=0, order_data=3, border_mode_seg='constant', border_cval_seg=0,
order_seg=0, random_crop=True))
tr_transforms.append(BrainMaskAwareStretchZeroOneTransform((-5, 5), True))
tr_transforms.append(ContrastAugmentationTransform(contrast_range, True))
tr_transforms.append(GammaTransform(gamma_range, False))
tr_transforms.append(BrainMaskAwareStretchZeroOneTransform(per_channel=True))
tr_transforms.append(BrightnessTransform(0.0, 0.1, True))
tr_transforms.append(SegChannelSelectionTransform([0]))
tr_transforms.append(ConvertSegToOnehotTransform(range(num_classes), 0, "seg_onehot"))
gen_train = MultiThreadedAugmenter(data_gen_train, Compose(tr_transforms), num_workers, num_cached_per_worker,
seeds)
gen_train.restart()
return gen_train
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_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())
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"),
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"):
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_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 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 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 create_data_gen_train(patient_data_train,
BATCH_SIZE,
num_classes,
patch_size,
num_workers=5,
num_cached_per_worker=2,
do_elastic_transform=False,
alpha=(0., 1300.),
sigma=(10., 13.),
do_rotation=False,
a_x=(0., 2 * np.pi),
a_y=(0., 2 * np.pi),
a_z=(0., 2 * np.pi),
do_scale=True,
scale_range=(0.75, 1.25),
seeds=None):
if seeds is None:
seeds = [None] * num_workers
elif seeds == 'range':
seeds = range(num_workers)
else:
assert len(seeds) == num_workers
data_gen_train = BatchGenerator(patient_data_train,
BATCH_SIZE,
num_batches=None,
seed=False,
PATCH_SIZE=(10, 352, 352))
# train transforms
tr_transforms = []
tr_transforms.append(MotionAugmentationTransform(0.1, 0, 20))
tr_transforms.append(MirrorTransform((3, 4)))
tr_transforms.append(Convert3DTo2DTransform())
tr_transforms.append(
RndTransform(SpatialTransform(patch_size[1:],
112,
do_elastic_transform,
alpha,
sigma,
do_rotation,
a_x,
a_y,
a_z,
do_scale,
scale_range,
'constant',
0,
3,
'constant',
0,
0,
random_crop=False),
prob=0.67,
alternative_transform=RandomCropTransform(
patch_size[1:])))
tr_transforms.append(Convert2DTo3DTransform(patch_size))
tr_transforms.append(
RndTransform(GammaTransform((0.85, 1.3), False), prob=0.5))
tr_transforms.append(
RndTransform(GammaTransform((0.85, 1.3), True), prob=0.5))
tr_transforms.append(CutOffOutliersTransform(0.3, 99.7, True))
tr_transforms.append(ZeroMeanUnitVarianceTransform(True))
tr_transforms.append(
ConvertSegToOnehotTransform(range(num_classes), 0, 'seg_onehot'))
tr_composed = Compose(tr_transforms)
tr_mt_gen = MultiThreadedAugmenter(data_gen_train, tr_composed,
num_workers, num_cached_per_worker,
seeds)
tr_mt_gen.restart()
return tr_mt_gen
def get_default_augmentation(dataloader_train, dataloader_val=None, params=None,
patch_size=None, 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 = []
assert params is not None, "augmentation params expect to be not None"
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"),
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")))
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'))
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.restart()
if dataloader_val is None:
return batchgenerator_train, None
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'))
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)
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,
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
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
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,
anisotropy=False,
extra_label_keys=None,
val_mode=False,
use_conf=False,
):
'''
Work as Dataloader with augmentation
:return: train_loader, val_loader
for each iterator, return {'data': (B, D, H, W), 'target': (B, D, H, W)}
'''
if not val_mode:
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")))
# anistropic setting
if anisotropy or params.get("dummy_2D"):
ignore_axes = (0, )
tr_transforms.append(
Convert3DTo2DTransform(extra_label_keys=extra_label_keys))
patch_size = patch_size[1:] # 2D patch size
print('Using dummy2d data augmentation')
params["elastic_deform_alpha"] = (0., 200.)
params["elastic_deform_sigma"] = (9., 13.)
params["rotation_x"] = (-180. / 360 * 2. * np.pi,
180. / 360 * 2. * np.pi)
params["rotation_y"] = (-0. / 360 * 2. * np.pi,
0. / 360 * 2. * np.pi)
params["rotation_z"] = (-0. / 360 * 2. * np.pi,
0. / 360 * 2. * np.pi)
else:
ignore_axes = None
# 1. Spatial Transform: rotation, scaling
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"),
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"),
extra_label_keys=extra_label_keys))
if anisotropy or params.get("dummy_2D"):
tr_transforms.append(
Convert2DTo3DTransform(extra_label_keys=extra_label_keys))
# 2. Noise Augmentation: gaussian noise, gaussian blur
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))
# 3. Color Augmentation: brightness, constrast, low resolution, gamma_transform
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"]))
# 4. Mirror Transform
if params.get("do_mirror") or params.get("mirror"):
tr_transforms.append(
MirrorTransform(params.get("mirror_axes"),
extra_label_keys=extra_label_keys))
# 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, extra_label_keys=extra_label_keys))
tr_transforms.append(RenameTransform('data', 'image', True))
tr_transforms.append(RenameTransform('seg', 'gt', True))
if deep_supervision_scales is not None:
if soft_ds:
assert classes is not None
tr_transforms.append(
DownsampleSegForDSTransform3(deep_supervision_scales, 'gt',
'gt', classes))
else:
tr_transforms.append(
DownsampleSegForDSTransform2(
deep_supervision_scales,
0,
0,
input_key='gt',
output_key='gt',
extra_label_keys=extra_label_keys))
toTensorKeys = [
'image', 'gt'
] + extra_label_keys if extra_label_keys is not None else [
'image', 'gt'
]
tr_transforms.append(NumpyToTensor(toTensorKeys, 'float'))
tr_transforms = Compose(tr_transforms)
if seeds_train is not None:
seeds_train = [seeds_train] * params.get('num_threads')
if use_conf:
num_threads = 1
num_cached_per_thread = 1
else:
num_threads, num_cached_per_thread = params.get(
'num_threads'), params.get("num_cached_per_thread")
batchgenerator_train = MultiThreadedAugmenter(dataloader_train,
tr_transforms,
num_threads,
num_cached_per_thread,
seeds=seeds_train,
pin_memory=pin_memory)
val_transforms = []
val_transforms.append(
RemoveLabelTransform(-1, 0, extra_label_keys=extra_label_keys))
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('data', 'image', True))
val_transforms.append(RenameTransform('seg', 'gt', True))
if deep_supervision_scales is not None:
if soft_ds:
assert classes is not None
val_transforms.append(
DownsampleSegForDSTransform3(deep_supervision_scales, 'gt',
'gt', classes))
else:
val_transforms.append(
DownsampleSegForDSTransform2(
deep_supervision_scales,
0,
0,
input_key='gt',
output_key='gt',
extra_label_keys=extra_label_keys))
val_transforms.append(NumpyToTensor(toTensorKeys, '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)
if seeds_val is not None:
seeds_val = [seeds_val] * 1
# batchgenerator_val = MultiThreadedAugmenter(dataloader_val, val_transforms, 1,
# params.get("num_cached_per_thread"),
# seeds=seeds_val, pin_memory=False)
batchgenerator_val = SingleThreadedAugmenter(dataloader_val,
val_transforms)
else:
val_transforms = []
val_transforms.append(
RemoveLabelTransform(-1, 0, extra_label_keys=extra_label_keys))
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('data', 'image', True))
val_transforms.append(RenameTransform('seg', 'gt', True))
if deep_supervision_scales is not None:
if soft_ds:
assert classes is not None
val_transforms.append(
DownsampleSegForDSTransform3(deep_supervision_scales, 'gt',
'gt', classes))
else:
val_transforms.append(
DownsampleSegForDSTransform2(
deep_supervision_scales,
0,
0,
input_key='gt',
output_key='gt',
extra_label_keys=extra_label_keys))
toTensorKeys = [
'image', 'gt'
] + extra_label_keys if extra_label_keys is not None else [
'image', 'gt'
]
val_transforms.append(NumpyToTensor(toTensorKeys, 'float'))
val_transforms = Compose(val_transforms)
batchgenerator_val = SingleThreadedAugmenter(dataloader_val,
val_transforms)
if dataloader_train is not None:
batchgenerator_train = SingleThreadedAugmenter(
dataloader_train, val_transforms)
else:
batchgenerator_train = None
return batchgenerator_train, batchgenerator_val
do_rotation=True,
angle_z=(0, 2 * np.pi), # 旋转
do_scale=True,
scale=(0.3, 3.), # 缩放
border_mode_data='constant',
border_cval_data=0,
order_data=1,
random_crop=False)
my_transforms.append(spatial_transform)
GaussianNoise = GaussianNoiseTransform() # 高斯噪声
my_transforms.append(GaussianNoise)
GaussianBlur = GaussianBlurTransform() # 高斯模糊
my_transforms.append(GaussianBlur)
Brightness = BrightnessTransform(0, 0.2) # 亮度
my_transforms.append(Brightness)
brightness_transform = ContrastAugmentationTransform(
(0.3, 3.), preserve_range=True) # 对比度
my_transforms.append(brightness_transform)
SimulateLowResolution = SimulateLowResolutionTransform() # 低分辨率
my_transforms.append(SimulateLowResolution)
Gamma = GammaTransform() # 伽马增强
my_transforms.append(Gamma)
mirror_transform = MirrorTransform(axes=(0, 1)) # 镜像
my_transforms.append(mirror_transform)
all_transforms = Compose(my_transforms)
multithreaded_generator = MultiThreadedAugmenter(batchgen, all_transforms, 1,
2)
t = multithreaded_generator.next()
plot_batch(t)
