AutoSeg4ETICA / nnunet /training /data_augmentation /default_data_augmentation.py

Chris Xiao

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#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
#    Licensed under the Apache License, Version 2.0 (the "License");
#    you may not use this file except in compliance with the License.
#    You may obtain a copy of the License at
#
#        http://www.apache.org/licenses/LICENSE-2.0
#
#    Unless required by applicable law or agreed to in writing, software
#    distributed under the License is distributed on an "AS IS" BASIS,
#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#    See the License for the specific language governing permissions and
#    limitations under the License.

import os
from copy import deepcopy

import numpy as np
from batchgenerators.dataloading.multi_threaded_augmenter import MultiThreadedAugmenter
from batchgenerators.transforms.abstract_transforms import Compose
from batchgenerators.transforms.channel_selection_transforms import DataChannelSelectionTransform, \
    SegChannelSelectionTransform
from batchgenerators.transforms.color_transforms import GammaTransform
from batchgenerators.transforms.spatial_transforms import SpatialTransform, MirrorTransform
from batchgenerators.transforms.utility_transforms import RemoveLabelTransform, RenameTransform, NumpyToTensor

from nnunet.training.data_augmentation.custom_transforms import Convert3DTo2DTransform, Convert2DTo3DTransform, \
    MaskTransform, ConvertSegmentationToRegionsTransform
from nnunet.training.data_augmentation.pyramid_augmentations import MoveSegAsOneHotToData, \
    ApplyRandomBinaryOperatorTransform, \
    RemoveRandomConnectedComponentFromOneHotEncodingTransform

try:
    from batchgenerators.dataloading.nondet_multi_threaded_augmenter import NonDetMultiThreadedAugmenter
except ImportError as ie:
    NonDetMultiThreadedAugmenter = None


default_3D_augmentation_params = {
    "selected_data_channels": None,
    "selected_seg_channels": None,

    "do_elastic": True,
    "elastic_deform_alpha": (0., 900.),
    "elastic_deform_sigma": (9., 13.),
    "p_eldef": 0.2,

    "do_scaling": True,
    "scale_range": (0.85, 1.25),
    "independent_scale_factor_for_each_axis": False,
    "p_independent_scale_per_axis": 1,
    "p_scale": 0.2,

    "do_rotation": True,
    "rotation_x": (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi),
    "rotation_y": (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi),
    "rotation_z": (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi),
    "rotation_p_per_axis": 1,
    "p_rot": 0.2,

    "random_crop": False,
    "random_crop_dist_to_border": None,

    "do_gamma": True,
    "gamma_retain_stats": True,
    "gamma_range": (0.7, 1.5),
    "p_gamma": 0.3,

    "do_mirror": True,
    "mirror_axes": (0, 1, 2),

    "dummy_2D": False,
    "mask_was_used_for_normalization": None,
    "border_mode_data": "constant",

    "all_segmentation_labels": None,  # used for cascade
    "move_last_seg_chanel_to_data": False,  # used for cascade
    "cascade_do_cascade_augmentations": False,  # used for cascade
    "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": False,
    "additive_brightness_p_per_sample": 0.15,
    "additive_brightness_p_per_channel": 0.5,
    "additive_brightness_mu": 0.0,
    "additive_brightness_sigma": 0.1,

    "num_threads": 12 if 'nnUNet_n_proc_DA' not in os.environ else int(os.environ['nnUNet_n_proc_DA']),
    "num_cached_per_thread": 1,
}

default_2D_augmentation_params = deepcopy(default_3D_augmentation_params)

default_2D_augmentation_params["elastic_deform_alpha"] = (0., 200.)
default_2D_augmentation_params["elastic_deform_sigma"] = (9., 13.)
default_2D_augmentation_params["rotation_x"] = (-180. / 360 * 2. * np.pi, 180. / 360 * 2. * np.pi)
default_2D_augmentation_params["rotation_y"] = (-0. / 360 * 2. * np.pi, 0. / 360 * 2. * np.pi)
default_2D_augmentation_params["rotation_z"] = (-0. / 360 * 2. * np.pi, 0. / 360 * 2. * np.pi)

# sometimes you have 3d data and a 3d net but cannot augment them properly in 3d due to anisotropy (which is currently
# not supported in batchgenerators). In that case you can 'cheat' and transfer your 3d data into 2d data and
# transform them back after augmentation
default_2D_augmentation_params["dummy_2D"] = False
default_2D_augmentation_params["mirror_axes"] = (0, 1)  # this can be (0, 1, 2) if dummy_2D=True


def get_patch_size(final_patch_size, rot_x, rot_y, rot_z, scale_range):
    if isinstance(rot_x, (tuple, list)):
        rot_x = max(np.abs(rot_x))
    if isinstance(rot_y, (tuple, list)):
        rot_y = max(np.abs(rot_y))
    if isinstance(rot_z, (tuple, list)):
        rot_z = max(np.abs(rot_z))
    rot_x = min(90 / 360 * 2. * np.pi, rot_x)
    rot_y = min(90 / 360 * 2. * np.pi, rot_y)
    rot_z = min(90 / 360 * 2. * np.pi, rot_z)
    from batchgenerators.augmentations.utils import rotate_coords_3d, rotate_coords_2d
    coords = np.array(final_patch_size)
    final_shape = np.copy(coords)
    if len(coords) == 3:
        final_shape = np.max(np.vstack((np.abs(rotate_coords_3d(coords, rot_x, 0, 0)), final_shape)), 0)
        final_shape = np.max(np.vstack((np.abs(rotate_coords_3d(coords, 0, rot_y, 0)), final_shape)), 0)
        final_shape = np.max(np.vstack((np.abs(rotate_coords_3d(coords, 0, 0, rot_z)), final_shape)), 0)
    elif len(coords) == 2:
        final_shape = np.max(np.vstack((np.abs(rotate_coords_2d(coords, rot_x)), final_shape)), 0)
    final_shape /= min(scale_range)
    return final_shape.astype(int)


def get_default_augmentation(dataloader_train, dataloader_val, patch_size, params=default_3D_augmentation_params,
                             border_val_seg=-1, pin_memory=True,
                             seeds_train=None, seeds_val=None, regions=None):
    assert params.get('mirror') is None, "old version of params, use new keyword do_mirror"
    tr_transforms = []

    if params.get("selected_data_channels") is not None:
        tr_transforms.append(DataChannelSelectionTransform(params.get("selected_data_channels")))

    if params.get("selected_seg_channels") is not None:
        tr_transforms.append(SegChannelSelectionTransform(params.get("selected_seg_channels")))

    # don't do color augmentations while in 2d mode with 3d data because the color channel is overloaded!!
    if params.get("dummy_2D") is not None and params.get("dummy_2D"):
        tr_transforms.append(Convert3DTo2DTransform())
        patch_size_spatial = patch_size[1:]
    else:
        patch_size_spatial = patch_size

    tr_transforms.append(SpatialTransform(
        patch_size_spatial, patch_center_dist_from_border=None, do_elastic_deform=params.get("do_elastic"),
        alpha=params.get("elastic_deform_alpha"), sigma=params.get("elastic_deform_sigma"),
        do_rotation=params.get("do_rotation"), angle_x=params.get("rotation_x"), angle_y=params.get("rotation_y"),
        angle_z=params.get("rotation_z"), do_scale=params.get("do_scaling"), scale=params.get("scale_range"),
        border_mode_data=params.get("border_mode_data"), border_cval_data=0, order_data=3, border_mode_seg="constant",
        border_cval_seg=border_val_seg,
        order_seg=1, random_crop=params.get("random_crop"), p_el_per_sample=params.get("p_eldef"),
        p_scale_per_sample=params.get("p_scale"), p_rot_per_sample=params.get("p_rot"),
        independent_scale_for_each_axis=params.get("independent_scale_factor_for_each_axis")
    ))
    if params.get("dummy_2D") is not None and params.get("dummy_2D"):
        tr_transforms.append(Convert2DTo3DTransform())

    if params.get("do_gamma"):
        tr_transforms.append(
            GammaTransform(params.get("gamma_range"), False, True, retain_stats=params.get("gamma_retain_stats"),
                           p_per_sample=params["p_gamma"]))

    if params.get("do_mirror"):
        tr_transforms.append(MirrorTransform(params.get("mirror_axes")))

    if params.get("mask_was_used_for_normalization") is not None:
        mask_was_used_for_normalization = params.get("mask_was_used_for_normalization")
        tr_transforms.append(MaskTransform(mask_was_used_for_normalization, mask_idx_in_seg=0, set_outside_to=0))

    tr_transforms.append(RemoveLabelTransform(-1, 0))

    if params.get("move_last_seg_chanel_to_data") is not None and params.get("move_last_seg_chanel_to_data"):
        tr_transforms.append(MoveSegAsOneHotToData(1, params.get("all_segmentation_labels"), 'seg', 'data'))
        if params.get("cascade_do_cascade_augmentations") and not None and params.get(
                "cascade_do_cascade_augmentations"):
            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


if __name__ == "__main__":
    from nnunet.training.dataloading.dataset_loading import DataLoader3D, load_dataset
    from nnunet.paths import preprocessing_output_dir
    import os
    import pickle

    t = "Task002_Heart"
    p = os.path.join(preprocessing_output_dir, t)
    dataset = load_dataset(p, 0)
    with open(os.path.join(p, "plans.pkl"), 'rb') as f:
        plans = pickle.load(f)

    basic_patch_size = get_patch_size(np.array(plans['stage_properties'][0].patch_size),
                                      default_3D_augmentation_params['rotation_x'],
                                      default_3D_augmentation_params['rotation_y'],
                                      default_3D_augmentation_params['rotation_z'],
                                      default_3D_augmentation_params['scale_range'])

    dl = DataLoader3D(dataset, basic_patch_size, np.array(plans['stage_properties'][0].patch_size).astype(int), 1)
    tr, val = get_default_augmentation(dl, dl, np.array(plans['stage_properties'][0].patch_size).astype(int))

	# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	import os
	from copy import deepcopy

	import numpy as np
	from batchgenerators.dataloading.multi_threaded_augmenter import MultiThreadedAugmenter
	from batchgenerators.transforms.abstract_transforms import Compose
	from batchgenerators.transforms.channel_selection_transforms import DataChannelSelectionTransform, \
	SegChannelSelectionTransform
	from batchgenerators.transforms.color_transforms import GammaTransform
	from batchgenerators.transforms.spatial_transforms import SpatialTransform, MirrorTransform
	from batchgenerators.transforms.utility_transforms import RemoveLabelTransform, RenameTransform, NumpyToTensor

	from nnunet.training.data_augmentation.custom_transforms import Convert3DTo2DTransform, Convert2DTo3DTransform, \
	MaskTransform, ConvertSegmentationToRegionsTransform
	from nnunet.training.data_augmentation.pyramid_augmentations import MoveSegAsOneHotToData, \
	ApplyRandomBinaryOperatorTransform, \
	RemoveRandomConnectedComponentFromOneHotEncodingTransform

	try:
	from batchgenerators.dataloading.nondet_multi_threaded_augmenter import NonDetMultiThreadedAugmenter
	except ImportError as ie:
	NonDetMultiThreadedAugmenter = None


	default_3D_augmentation_params = {
	"selected_data_channels": None,
	"selected_seg_channels": None,

	"do_elastic": True,
	"elastic_deform_alpha": (0., 900.),
	"elastic_deform_sigma": (9., 13.),
	"p_eldef": 0.2,

	"do_scaling": True,
	"scale_range": (0.85, 1.25),
	"independent_scale_factor_for_each_axis": False,
	"p_independent_scale_per_axis": 1,
	"p_scale": 0.2,

	"do_rotation": True,
	"rotation_x": (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi),
	"rotation_y": (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi),
	"rotation_z": (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi),
	"rotation_p_per_axis": 1,
	"p_rot": 0.2,

	"random_crop": False,
	"random_crop_dist_to_border": None,

	"do_gamma": True,
	"gamma_retain_stats": True,
	"gamma_range": (0.7, 1.5),
	"p_gamma": 0.3,

	"do_mirror": True,
	"mirror_axes": (0, 1, 2),

	"dummy_2D": False,
	"mask_was_used_for_normalization": None,
	"border_mode_data": "constant",

	"all_segmentation_labels": None, # used for cascade
	"move_last_seg_chanel_to_data": False, # used for cascade
	"cascade_do_cascade_augmentations": False, # used for cascade
	"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": False,
	"additive_brightness_p_per_sample": 0.15,
	"additive_brightness_p_per_channel": 0.5,
	"additive_brightness_mu": 0.0,
	"additive_brightness_sigma": 0.1,

	"num_threads": 12 if 'nnUNet_n_proc_DA' not in os.environ else int(os.environ['nnUNet_n_proc_DA']),
	"num_cached_per_thread": 1,
	}

	default_2D_augmentation_params = deepcopy(default_3D_augmentation_params)

	default_2D_augmentation_params["elastic_deform_alpha"] = (0., 200.)
	default_2D_augmentation_params["elastic_deform_sigma"] = (9., 13.)
	default_2D_augmentation_params["rotation_x"] = (-180. / 360 * 2. * np.pi, 180. / 360 * 2. * np.pi)
	default_2D_augmentation_params["rotation_y"] = (-0. / 360 * 2. * np.pi, 0. / 360 * 2. * np.pi)
	default_2D_augmentation_params["rotation_z"] = (-0. / 360 * 2. * np.pi, 0. / 360 * 2. * np.pi)

	# sometimes you have 3d data and a 3d net but cannot augment them properly in 3d due to anisotropy (which is currently
	# not supported in batchgenerators). In that case you can 'cheat' and transfer your 3d data into 2d data and
	# transform them back after augmentation
	default_2D_augmentation_params["dummy_2D"] = False
	default_2D_augmentation_params["mirror_axes"] = (0, 1) # this can be (0, 1, 2) if dummy_2D=True


	def get_patch_size(final_patch_size, rot_x, rot_y, rot_z, scale_range):
	if isinstance(rot_x, (tuple, list)):
	rot_x = max(np.abs(rot_x))
	if isinstance(rot_y, (tuple, list)):
	rot_y = max(np.abs(rot_y))
	if isinstance(rot_z, (tuple, list)):
	rot_z = max(np.abs(rot_z))
	rot_x = min(90 / 360 * 2. * np.pi, rot_x)
	rot_y = min(90 / 360 * 2. * np.pi, rot_y)
	rot_z = min(90 / 360 * 2. * np.pi, rot_z)
	from batchgenerators.augmentations.utils import rotate_coords_3d, rotate_coords_2d
	coords = np.array(final_patch_size)
	final_shape = np.copy(coords)
	if len(coords) == 3:
	final_shape = np.max(np.vstack((np.abs(rotate_coords_3d(coords, rot_x, 0, 0)), final_shape)), 0)
	final_shape = np.max(np.vstack((np.abs(rotate_coords_3d(coords, 0, rot_y, 0)), final_shape)), 0)
	final_shape = np.max(np.vstack((np.abs(rotate_coords_3d(coords, 0, 0, rot_z)), final_shape)), 0)
	elif len(coords) == 2:
	final_shape = np.max(np.vstack((np.abs(rotate_coords_2d(coords, rot_x)), final_shape)), 0)
	final_shape /= min(scale_range)
	return final_shape.astype(int)


	def get_default_augmentation(dataloader_train, dataloader_val, patch_size, params=default_3D_augmentation_params,
	border_val_seg=-1, pin_memory=True,
	seeds_train=None, seeds_val=None, regions=None):
	assert params.get('mirror') is None, "old version of params, use new keyword do_mirror"
	tr_transforms = []

	if params.get("selected_data_channels") is not None:
	tr_transforms.append(DataChannelSelectionTransform(params.get("selected_data_channels")))

	if params.get("selected_seg_channels") is not None:
	tr_transforms.append(SegChannelSelectionTransform(params.get("selected_seg_channels")))

	# don't do color augmentations while in 2d mode with 3d data because the color channel is overloaded!!
	if params.get("dummy_2D") is not None and params.get("dummy_2D"):
	tr_transforms.append(Convert3DTo2DTransform())
	patch_size_spatial = patch_size[1:]
	else:
	patch_size_spatial = patch_size

	tr_transforms.append(SpatialTransform(
	patch_size_spatial, patch_center_dist_from_border=None, do_elastic_deform=params.get("do_elastic"),
	alpha=params.get("elastic_deform_alpha"), sigma=params.get("elastic_deform_sigma"),
	do_rotation=params.get("do_rotation"), angle_x=params.get("rotation_x"), angle_y=params.get("rotation_y"),
	angle_z=params.get("rotation_z"), do_scale=params.get("do_scaling"), scale=params.get("scale_range"),
	border_mode_data=params.get("border_mode_data"), border_cval_data=0, order_data=3, border_mode_seg="constant",
	border_cval_seg=border_val_seg,
	order_seg=1, random_crop=params.get("random_crop"), p_el_per_sample=params.get("p_eldef"),
	p_scale_per_sample=params.get("p_scale"), p_rot_per_sample=params.get("p_rot"),
	independent_scale_for_each_axis=params.get("independent_scale_factor_for_each_axis")
	))
	if params.get("dummy_2D") is not None and params.get("dummy_2D"):
	tr_transforms.append(Convert2DTo3DTransform())

	if params.get("do_gamma"):
	tr_transforms.append(
	GammaTransform(params.get("gamma_range"), False, True, retain_stats=params.get("gamma_retain_stats"),
	p_per_sample=params["p_gamma"]))

	if params.get("do_mirror"):
	tr_transforms.append(MirrorTransform(params.get("mirror_axes")))

	if params.get("mask_was_used_for_normalization") is not None:
	mask_was_used_for_normalization = params.get("mask_was_used_for_normalization")
	tr_transforms.append(MaskTransform(mask_was_used_for_normalization, mask_idx_in_seg=0, set_outside_to=0))

	tr_transforms.append(RemoveLabelTransform(-1, 0))

	if params.get("move_last_seg_chanel_to_data") is not None and params.get("move_last_seg_chanel_to_data"):
	tr_transforms.append(MoveSegAsOneHotToData(1, params.get("all_segmentation_labels"), 'seg', 'data'))
	if params.get("cascade_do_cascade_augmentations") and not None and params.get(
	"cascade_do_cascade_augmentations"):
	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


	if __name__ == "__main__":
	from nnunet.training.dataloading.dataset_loading import DataLoader3D, load_dataset
	from nnunet.paths import preprocessing_output_dir
	import os
	import pickle

	t = "Task002_Heart"
	p = os.path.join(preprocessing_output_dir, t)
	dataset = load_dataset(p, 0)
	with open(os.path.join(p, "plans.pkl"), 'rb') as f:
	plans = pickle.load(f)

	basic_patch_size = get_patch_size(np.array(plans['stage_properties'][0].patch_size),
	default_3D_augmentation_params['rotation_x'],
	default_3D_augmentation_params['rotation_y'],
	default_3D_augmentation_params['rotation_z'],
	default_3D_augmentation_params['scale_range'])

	dl = DataLoader3D(dataset, basic_patch_size, np.array(plans['stage_properties'][0].patch_size).astype(int), 1)
	tr, val = get_default_augmentation(dl, dl, np.array(plans['stage_properties'][0].patch_size).astype(int))