def get_transforms(self):
transforms = Compose(
RandomFlip3D(),
RandomRotate(),
ElasticTransform(alpha=2000., sigma=50.), # Hard coded for now
AsTorchBatch(2))
return transforms
def get_additional_transforms(self, master_config):
transforms = self.transforms if self.transforms is not None else Compose(
)
master_config = {} if master_config is None else master_config
# Replicate and downscale batch:
if master_config.get("downscale_and_crop") is not None:
ds_config = master_config.get("downscale_and_crop")
apply_to = [conf.pop('apply_to') for conf in ds_config]
transforms.add(ReplicateTensorsInBatch(apply_to))
for indx, conf in enumerate(ds_config):
transforms.add(
DownSampleAndCropTensorsInBatch(apply_to=[indx],
order=None,
**conf))
# crop invalid affinity labels and elastic augment reflection padding assymetrically
crop_config = master_config.get('crop_after_target', {})
if crop_config:
# One might need to crop after elastic transform to avoid edge artefacts of affinity
# computation being warped into the FOV.
transforms.add(VolumeAsymmetricCrop(**crop_config))
transforms.add(AsTorchBatch(3, add_channel_axis_if_necessary=True))
return transforms
def get_additional_transforms(self, transform_config):
transforms = self.transforms if self.transforms is not None else Compose(
)
stack_scaling_factors = transform_config["stack_scaling_factors"]
# Replicate and downscale batch:
num_inputs = len(stack_scaling_factors)
input_indices = list(range(num_inputs))
transforms.add(ReplicateBatch(num_inputs))
inv_scaling_facts = deepcopy(stack_scaling_factors)
inv_scaling_facts.reverse()
for in_idx, dws_fact, crop_fact in zip(input_indices,
stack_scaling_factors,
inv_scaling_facts):
transforms.add(
DownsampleAndCrop3D(apply_to=[in_idx],
order=2,
zoom_factor=dws_fact,
crop_factor=crop_fact))
transforms.add(AsTorchBatch(3))
return transforms
def get_transforms(self):
transforms = Compose()
if self.transform_config.get('random_flip', False):
transforms.add(RandomFlip3D())
transforms.add(RandomRotate())
# Elastic transforms can be skipped by
# setting elastic_transform to false in the
# yaml config file.
if self.transform_config.get('elastic_transform'):
elastic_transform_config = self.transform_config.get(
'elastic_transform')
if elastic_transform_config.get('apply', False):
transforms.add(
ElasticTransform(
alpha=elastic_transform_config.get('alpha', 2000.),
sigma=elastic_transform_config.get('sigma', 50.),
order=elastic_transform_config.get('order', 0)))
# Replicate and downscale batch:
nb_inputs = 1
if self.transform_config.get("downscale_and_crop") is not None:
ds_config = self.transform_config.get("downscale_and_crop")
apply_to = [conf.pop('apply_to') for conf in ds_config]
nb_inputs = (np.array(apply_to) == 0).sum()
transforms.add(ReplicateTensorsInBatch(apply_to))
for indx, conf in enumerate(ds_config):
transforms.add(
DownSampleAndCropTensorsInBatch(apply_to=[indx],
order=None,
**conf))
# Check if to compute binary-affinity-targets from GT labels:
if self.transform_config.get("affinity_config") is not None:
affs_config = deepcopy(
self.transform_config.get("affinity_config"))
global_kwargs = affs_config.pop("global", {})
aff_transform = Segmentation2AffinitiesDynamicOffsets if affs_config.pop("use_dynamic_offsets", False) \
else affinity_config_to_transform
for input_index in affs_config:
affs_kwargs = deepcopy(global_kwargs)
affs_kwargs.update(affs_config[input_index])
transforms.add(
aff_transform(apply_to=[input_index + nb_inputs],
**affs_kwargs))
# crop invalid affinity labels and elastic augment reflection padding assymetrically
crop_config = self.transform_config.get('crop_after_target', {})
if crop_config:
# One might need to crop after elastic transform to avoid edge artefacts of affinity
# computation being warped into the FOV.
transforms.add(VolumeAsymmetricCrop(**crop_config))
transforms.add(AsTorchBatch(3))
return transforms
def get_transforms(self):
all_transforms = [RandomRotate()]
if 'elastic_transform' in self.master_config:
all_transforms.append(ElasticTransform(**self.master_config.get('elastic_transform',
{})))
if self.master_config.get('crop_after_elastic_transform', False):
all_transforms\
.append(CenterCrop(**self.master_config.get('crop_after_elastic_transform')))
all_transforms.append(AsTorchBatch(2))
transforms = Compose(*all_transforms)
return transforms
def get_additional_transforms(self, master_config):
transforms = self.transforms if self.transforms is not None else Compose(
)
master_config = {} if master_config is None else master_config
# TODO: somehow merge with the trainer loader...
# Replicate and downscale batch:
if master_config.get("downscale_and_crop") is not None:
ds_config = master_config.get("downscale_and_crop")
apply_to = [conf.pop('apply_to') for conf in ds_config]
transforms.add(ReplicateBatchGeneralized(apply_to))
for indx, conf in enumerate(ds_config):
transforms.add(
DownsampleAndCrop3D(apply_to=[indx], order=None, **conf))
# # # affinity transforms for affinity targets
# # # we apply the affinity target calculation only to the segmentation (1)
# if master_config.get("affinity_config") is not None:
# affs_config = master_config.get("affinity_config")
# global_kwargs = affs_config.pop("global", {})
# # TODO: define computed affs not in this way, but with a variable in config...
# nb_affs = len(affs_config)
# assert nb_affs == num_inputs
# # all_affs_kwargs = [deepcopy(global_kwargs) for _ in range(nb_affs)]
# for input_index in affs_config:
# affs_kwargs = deepcopy(global_kwargs)
# affs_kwargs.update(affs_config[input_index])
# transforms.add(affinity_config_to_transform(apply_to=[input_index+num_inputs], **affs_kwargs))
# crop invalid affinity labels and elastic augment reflection padding assymetrically
crop_config = master_config.get('crop_after_target', {})
if crop_config:
# One might need to crop after elastic transform to avoid edge artefacts of affinity
# computation being warped into the FOV.
transforms.add(VolumeAsymmetricCrop(**crop_config))
transforms.add(AsTorchBatch(3, add_channel_axis_if_necessary=True))
# transforms.add(CheckBatchAndChannelDim(3))
return transforms
def get_transforms(self):
transforms = AsTorchBatch(3)
return transforms
def get_additional_transforms(self):
transforms = self.transforms if self.transforms is not None else Compose(
)
transforms.add(AsTorchBatch(3))
return transforms