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 get_train_transform(patch_size, prob=0.1):
# 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=prob, p_rot_per_sample=prob, p_scale_per_sample=prob
)
)
# now we mirror along all axes
tr_transforms.append(MirrorTransform(axes=(0, 1, 2)))
# brightness transform for 15% of samples
tr_transforms.append(BrightnessMultiplicativeTransform(
(0.7, 1.5), per_channel=True, p_per_sample=0.05+prob))
# 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=0.05+prob))
# 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=0.05+prob))
# Gaussian Noise
tr_transforms.append(GaussianNoiseTransform(
noise_variance=(0, 0.05), p_per_sample=0.05+prob))
# blurring. Some BraTS cases have very blurry modalities. This can simulate more patients with this problem and
# thus make the model more robust to it
tr_transforms.append(GaussianBlurTransform(blur_sigma=(0.5, 1.5), different_sigma_per_channel=True,
p_per_channel=prob, p_per_sample=0.05+prob))
# now we compose these transforms together
tr_transforms = Compose(tr_transforms)
return tr_transforms
def _augment_data(self, batch_generator, type=None):
if self.Config.DATA_AUGMENTATION:
num_processes = 15 # 15 is a bit faster than 8 on cluster
# num_processes = multiprocessing.cpu_count() # on cluster: gives all cores, not only assigned cores
else:
num_processes = 6
tfs = []
if self.Config.NORMALIZE_DATA:
tfs.append(ZeroMeanUnitVarianceTransform(per_channel=self.Config.NORMALIZE_PER_CHANNEL))
if self.Config.SPATIAL_TRANSFORM == "SpatialTransformPeaks":
SpatialTransformUsed = SpatialTransformPeaks
elif self.Config.SPATIAL_TRANSFORM == "SpatialTransformCustom":
SpatialTransformUsed = SpatialTransformCustom
else:
SpatialTransformUsed = SpatialTransform
if self.Config.DATA_AUGMENTATION:
if type == "train":
# patch_center_dist_from_border:
# if 144/2=72 -> always exactly centered; otherwise a bit off center
# (brain can get off image and will be cut then)
if self.Config.DAUG_SCALE:
if self.Config.INPUT_RESCALING:
source_mm = 2 # for bb
target_mm = float(self.Config.RESOLUTION[:-2])
scale_factor = target_mm / source_mm
scale = (scale_factor, scale_factor)
else:
scale = (0.9, 1.5)
if self.Config.PAD_TO_SQUARE:
patch_size = self.Config.INPUT_DIM
else:
patch_size = None # keeps dimensions of the data
# spatial transform automatically crops/pads to correct size
center_dist_from_border = int(self.Config.INPUT_DIM[0] / 2.) - 10 # (144,144) -> 62
tfs.append(SpatialTransformUsed(patch_size,
patch_center_dist_from_border=center_dist_from_border,
do_elastic_deform=self.Config.DAUG_ELASTIC_DEFORM,
alpha=self.Config.DAUG_ALPHA, sigma=self.Config.DAUG_SIGMA,
do_rotation=self.Config.DAUG_ROTATE,
angle_x=self.Config.DAUG_ROTATE_ANGLE,
angle_y=self.Config.DAUG_ROTATE_ANGLE,
angle_z=self.Config.DAUG_ROTATE_ANGLE,
do_scale=True, scale=scale, border_mode_data='constant',
border_cval_data=0,
order_data=3,
border_mode_seg='constant', border_cval_seg=0,
order_seg=0, random_crop=True,
p_el_per_sample=self.Config.P_SAMP,
p_rot_per_sample=self.Config.P_SAMP,
p_scale_per_sample=self.Config.P_SAMP))
if self.Config.DAUG_RESAMPLE:
tfs.append(SimulateLowResolutionTransform(zoom_range=(0.5, 1), p_per_sample=0.2, per_channel=False))
if self.Config.DAUG_RESAMPLE_LEGACY:
tfs.append(ResampleTransformLegacy(zoom_range=(0.5, 1)))
if self.Config.DAUG_GAUSSIAN_BLUR:
tfs.append(GaussianBlurTransform(blur_sigma=self.Config.DAUG_BLUR_SIGMA,
different_sigma_per_channel=False,
p_per_sample=self.Config.P_SAMP))
if self.Config.DAUG_NOISE:
tfs.append(GaussianNoiseTransform(noise_variance=self.Config.DAUG_NOISE_VARIANCE,
p_per_sample=self.Config.P_SAMP))
if self.Config.DAUG_MIRROR:
tfs.append(MirrorTransform())
if self.Config.DAUG_FLIP_PEAKS:
tfs.append(FlipVectorAxisTransform())
tfs.append(NumpyToTensor(keys=["data", "seg"], cast_to="float"))
#num_cached_per_queue 1 or 2 does not really make a difference
batch_gen = MultiThreadedAugmenter(batch_generator, Compose(tfs), num_processes=num_processes,
num_cached_per_queue=1, seeds=None, pin_memory=True)
return batch_gen # data: (batch_size, channels, x, y), seg: (batch_size, channels, x, y)
def get_insaneDA_augmentation(dataloader_train,
dataloader_val,
patch_size,
params=default_3D_augmentation_params,
border_val_seg=-1,
seeds_train=None,
seeds_val=None,
order_seg=1,
order_data=3,
deep_supervision_scales=None,
soft_ds=False,
classes=None,
pin_memory=True):
assert params.get(
'mirror') is None, "old version of params, use new keyword do_mirror"
tr_transforms = []
if params.get("selected_data_channels") is not None:
tr_transforms.append(
DataChannelSelectionTransform(
params.get("selected_data_channels")))
if params.get("selected_seg_channels") is not None:
tr_transforms.append(
SegChannelSelectionTransform(params.get("selected_seg_channels")))
# don't do color augmentations while in 2d mode with 3d data because the color channel is overloaded!!
if params.get("dummy_2D") is not None and params.get("dummy_2D"):
ignore_axes = (0, )
tr_transforms.append(Convert3DTo2DTransform())
else:
ignore_axes = None
tr_transforms.append(
SpatialTransform(patch_size,
patch_center_dist_from_border=None,
do_elastic_deform=params.get("do_elastic"),
alpha=params.get("elastic_deform_alpha"),
sigma=params.get("elastic_deform_sigma"),
do_rotation=params.get("do_rotation"),
angle_x=params.get("rotation_x"),
angle_y=params.get("rotation_y"),
angle_z=params.get("rotation_z"),
do_scale=params.get("do_scaling"),
scale=params.get("scale_range"),
border_mode_data=params.get("border_mode_data"),
border_cval_data=0,
order_data=order_data,
border_mode_seg="constant",
border_cval_seg=border_val_seg,
order_seg=order_seg,
random_crop=params.get("random_crop"),
p_el_per_sample=params.get("p_eldef"),
p_scale_per_sample=params.get("p_scale"),
p_rot_per_sample=params.get("p_rot"),
independent_scale_for_each_axis=params.get(
"independent_scale_factor_for_each_axis")))
if params.get("dummy_2D"):
tr_transforms.append(Convert2DTo3DTransform())
# we need to put the color augmentations after the dummy 2d part (if applicable). Otherwise the overloaded color
# channel gets in the way
tr_transforms.append(GaussianNoiseTransform(p_per_sample=0.15))
tr_transforms.append(
GaussianBlurTransform((0.5, 1.5),
different_sigma_per_channel=True,
p_per_sample=0.2,
p_per_channel=0.5))
tr_transforms.append(
BrightnessMultiplicativeTransform(multiplier_range=(0.70, 1.3),
p_per_sample=0.15))
tr_transforms.append(
ContrastAugmentationTransform(contrast_range=(0.65, 1.5),
p_per_sample=0.15))
tr_transforms.append(
SimulateLowResolutionTransform(zoom_range=(0.5, 1),
per_channel=True,
p_per_channel=0.5,
order_downsample=0,
order_upsample=3,
p_per_sample=0.25,
ignore_axes=ignore_axes))
tr_transforms.append(
GammaTransform(params.get("gamma_range"),
True,
True,
retain_stats=params.get("gamma_retain_stats"),
p_per_sample=0.15)) # inverted gamma
if params.get("do_gamma"):
tr_transforms.append(
GammaTransform(params.get("gamma_range"),
False,
True,
retain_stats=params.get("gamma_retain_stats"),
p_per_sample=params["p_gamma"]))
if params.get("do_mirror") or params.get("mirror"):
tr_transforms.append(MirrorTransform(params.get("mirror_axes")))
if params.get("mask_was_used_for_normalization") is not None:
mask_was_used_for_normalization = params.get(
"mask_was_used_for_normalization")
tr_transforms.append(
MaskTransform(mask_was_used_for_normalization,
mask_idx_in_seg=0,
set_outside_to=0))
tr_transforms.append(RemoveLabelTransform(-1, 0))
if params.get("move_last_seg_chanel_to_data") is not None and params.get(
"move_last_seg_chanel_to_data"):
tr_transforms.append(
MoveSegAsOneHotToData(1, params.get("all_segmentation_labels"),
'seg', 'data'))
if params.get("cascade_do_cascade_augmentations"
) and not None and params.get(
"cascade_do_cascade_augmentations"):
if params.get("cascade_random_binary_transform_p") > 0:
tr_transforms.append(
ApplyRandomBinaryOperatorTransform(
channel_idx=list(
range(-len(params.get("all_segmentation_labels")),
0)),
p_per_sample=params.get(
"cascade_random_binary_transform_p"),
key="data",
strel_size=params.get(
"cascade_random_binary_transform_size")))
if params.get("cascade_remove_conn_comp_p") > 0:
tr_transforms.append(
RemoveRandomConnectedComponentFromOneHotEncodingTransform(
channel_idx=list(
range(-len(params.get("all_segmentation_labels")),
0)),
key="data",
p_per_sample=params.get("cascade_remove_conn_comp_p"),
fill_with_other_class_p=params.get(
"cascade_remove_conn_comp_max_size_percent_threshold"
),
dont_do_if_covers_more_than_X_percent=params.get(
"cascade_remove_conn_comp_fill_with_other_class_p")
))
tr_transforms.append(RenameTransform('seg', 'target', True))
if deep_supervision_scales is not None:
if soft_ds:
assert classes is not None
tr_transforms.append(
DownsampleSegForDSTransform3(deep_supervision_scales, 'target',
'target', classes))
else:
tr_transforms.append(
DownsampleSegForDSTransform2(deep_supervision_scales,
0,
0,
input_key='target',
output_key='target'))
tr_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
tr_transforms = Compose(tr_transforms)
batchgenerator_train = MultiThreadedAugmenter(
dataloader_train,
tr_transforms,
params.get('num_threads'),
params.get("num_cached_per_thread"),
seeds=seeds_train,
pin_memory=pin_memory)
val_transforms = []
val_transforms.append(RemoveLabelTransform(-1, 0))
if params.get("selected_data_channels") is not None:
val_transforms.append(
DataChannelSelectionTransform(
params.get("selected_data_channels")))
if params.get("selected_seg_channels") is not None:
val_transforms.append(
SegChannelSelectionTransform(params.get("selected_seg_channels")))
if params.get("move_last_seg_chanel_to_data") is not None and params.get(
"move_last_seg_chanel_to_data"):
val_transforms.append(
MoveSegAsOneHotToData(1, params.get("all_segmentation_labels"),
'seg', 'data'))
val_transforms.append(RenameTransform('seg', 'target', True))
if deep_supervision_scales is not None:
if soft_ds:
assert classes is not None
val_transforms.append(
DownsampleSegForDSTransform3(deep_supervision_scales, 'target',
'target', classes))
else:
val_transforms.append(
DownsampleSegForDSTransform2(deep_supervision_scales,
0,
0,
input_key='target',
output_key='target'))
val_transforms.append(NumpyToTensor(['data', 'target'], 'float'))
val_transforms = Compose(val_transforms)
batchgenerator_val = MultiThreadedAugmenter(
dataloader_val,
val_transforms,
max(params.get('num_threads') // 2, 1),
params.get("num_cached_per_thread"),
seeds=seeds_val,
pin_memory=pin_memory)
return batchgenerator_train, batchgenerator_val
def get_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
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 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
spatial_transform = SpatialTransform(
img.shape,
np.array(img.shape) // 2,
do_elastic_deform=False,
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)