def setup_DA_params(self):
if self.threeD:
self.data_aug_params = default_3D_augmentation_params
if self.do_dummy_2D_aug:
self.data_aug_params["dummy_2D"] = True
self.print_to_log_file("Using dummy2d data augmentation")
self.data_aug_params["elastic_deform_alpha"] = \
default_2D_augmentation_params["elastic_deform_alpha"]
self.data_aug_params["elastic_deform_sigma"] = \
default_2D_augmentation_params["elastic_deform_sigma"]
self.data_aug_params["rotation_x"] = default_2D_augmentation_params["rotation_x"]
else:
self.do_dummy_2D_aug = False
if max(self.patch_size) / min(self.patch_size) > 1.5:
default_2D_augmentation_params['rotation_x'] = (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi)
self.data_aug_params = default_2D_augmentation_params
self.data_aug_params["mask_was_used_for_normalization"] = self.use_mask_for_norm
if self.do_dummy_2D_aug:
self.basic_generator_patch_size = get_patch_size(self.patch_size[1:],
self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
self.basic_generator_patch_size = np.array([self.patch_size[0]] + list(self.basic_generator_patch_size))
patch_size_for_spatialtransform = self.patch_size[1:]
else:
self.basic_generator_patch_size = get_patch_size(self.patch_size, self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
patch_size_for_spatialtransform = self.patch_size
self.data_aug_params['selected_seg_channels'] = [0]
self.data_aug_params['patch_size_for_spatialtransform'] = patch_size_for_spatialtransform
def setup_DA_params(self):
"""
- we increase roation angle from [-15, 15] to [-30, 30]
- scale range is now (0.7, 1.4), was (0.85, 1.25)
- we don't do elastic deformation anymore
:return:
"""
self.deep_supervision_scales = [[1, 1, 1]] + list(list(i) for i in 1 / np.cumprod(
np.vstack(self.net_num_pool_op_kernel_sizes), axis=0))[:-1]
if self.threeD:
self.data_aug_params = default_3D_augmentation_params
self.data_aug_params['rotation_x'] = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi)
self.data_aug_params['rotation_y'] = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi)
self.data_aug_params['rotation_z'] = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi)
if self.do_dummy_2D_aug:
self.data_aug_params["dummy_2D"] = True
self.print_to_log_file("Using dummy2d data augmentation")
self.data_aug_params["elastic_deform_alpha"] = \
default_2D_augmentation_params["elastic_deform_alpha"]
self.data_aug_params["elastic_deform_sigma"] = \
default_2D_augmentation_params["elastic_deform_sigma"]
self.data_aug_params["rotation_x"] = default_2D_augmentation_params["rotation_x"]
else:
self.do_dummy_2D_aug = False
if max(self.patch_size) / min(self.patch_size) > 1.5:
default_2D_augmentation_params['rotation_x'] = (-15. / 360 * 2. * np.pi, 15. / 360 * 2. * np.pi)
self.data_aug_params = default_2D_augmentation_params
self.data_aug_params["mask_was_used_for_normalization"] = self.use_mask_for_norm
if self.do_dummy_2D_aug:
self.basic_generator_patch_size = get_patch_size(self.patch_size[1:],
self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
self.basic_generator_patch_size = np.array([self.patch_size[0]] + list(self.basic_generator_patch_size))
patch_size_for_spatialtransform = self.patch_size[1:]
else:
self.basic_generator_patch_size = get_patch_size(self.patch_size, self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
patch_size_for_spatialtransform = self.patch_size
self.data_aug_params["scale_range"] = (0.7, 1.4)
self.data_aug_params["do_elastic"] = False
self.data_aug_params['patch_size_for_spatialtransform'] = patch_size_for_spatialtransform
self.data_aug_params['selected_seg_channels'] = [0, 1] # New
self.data_aug_params['move_last_seg_chanel_to_data'] = True # New
self.data_aug_params['all_segmentation_labels'] = list(range(1, self.num_classes)) # New
self.data_aug_params["num_cached_per_thread"] = 2
def setup_DA_params(self):
"""
we leave out the creation of self.deep_supervision_scales, so it remains None
:return:
"""
if self.threeD:
self.data_aug_params = default_3D_augmentation_params
self.data_aug_params['rotation_x'] = (-30. / 360 * 2. * np.pi,
30. / 360 * 2. * np.pi)
self.data_aug_params['rotation_y'] = (-30. / 360 * 2. * np.pi,
30. / 360 * 2. * np.pi)
self.data_aug_params['rotation_z'] = (-30. / 360 * 2. * np.pi,
30. / 360 * 2. * np.pi)
if self.do_dummy_2D_aug:
self.data_aug_params["dummy_2D"] = True
self.print_to_log_file("Using dummy2d data augmentation")
self.data_aug_params["elastic_deform_alpha"] = \
default_2D_augmentation_params["elastic_deform_alpha"]
self.data_aug_params["elastic_deform_sigma"] = \
default_2D_augmentation_params["elastic_deform_sigma"]
self.data_aug_params[
"rotation_x"] = default_2D_augmentation_params[
"rotation_x"]
else:
self.do_dummy_2D_aug = False
if max(self.patch_size) / min(self.patch_size) > 1.5:
default_2D_augmentation_params['rotation_x'] = (-15. / 360 *
2. * np.pi,
15. / 360 *
2. * np.pi)
self.data_aug_params = default_2D_augmentation_params
self.data_aug_params[
"mask_was_used_for_normalization"] = self.use_mask_for_norm
if self.do_dummy_2D_aug:
self.basic_generator_patch_size = get_patch_size(
self.patch_size[1:], self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
self.basic_generator_patch_size = np.array(
[self.patch_size[0]] + list(self.basic_generator_patch_size))
else:
self.basic_generator_patch_size = get_patch_size(
self.patch_size, self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
self.data_aug_params["scale_range"] = (0.7, 1.4)
self.data_aug_params["do_elastic"] = False
self.data_aug_params['selected_seg_channels'] = [0]
self.data_aug_params[
'patch_size_for_spatialtransform'] = self.patch_size
def setup_DA_params(self):
# self.deep_supervision_scales = [[1, 1, 1]] + list(list(i) for i in 1 / np.cumprod(
# np.vstack(self.net_num_pool_op_kernel_sizes), axis=0))[:-1]
self.deep_supervision_scales = None
if self.threeD:
# Focus on 3D
self.data_aug_params = default_3D_augmentation_params
self.data_aug_params['rotation_x'] = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi)
self.data_aug_params['rotation_y'] = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi)
self.data_aug_params['rotation_z'] = (-30. / 360 * 2. * np.pi, 30. / 360 * 2. * np.pi)
if self.do_dummy_2D_aug:
self.data_aug_params["dummy_2D"] = True
self.print_to_log_file("Using dummy2d data augmentation")
self.data_aug_params["elastic_deform_alpha"] = \
default_2D_augmentation_params["elastic_deform_alpha"]
self.data_aug_params["elastic_deform_sigma"] = \
default_2D_augmentation_params["elastic_deform_sigma"]
self.data_aug_params["rotation_x"] = default_2D_augmentation_params["rotation_x"]
else:
self.do_dummy_2D_aug = False
if max(self.patch_size) / min(self.patch_size) > 1.5:
default_2D_augmentation_params['rotation_x'] = (-180. / 360 * 2. * np.pi, 180. / 360 * 2. * np.pi)
self.data_aug_params = default_2D_augmentation_params
self.data_aug_params["mask_was_used_for_normalization"] = self.use_mask_for_norm
if self.do_dummy_2D_aug:
self.basic_generator_patch_size = get_patch_size(self.patch_size[1:],
self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
self.basic_generator_patch_size = np.array([self.patch_size[0]] + list(self.basic_generator_patch_size))
patch_size_for_spatialtransform = self.patch_size[1:]
else:
self.basic_generator_patch_size = get_patch_size(self.patch_size, self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
patch_size_for_spatialtransform = self.patch_size
self.data_aug_params["scale_range"] = (0.65, 1.6)
self.data_aug_params["do_elastic"] = True
self.data_aug_params["elastic_deform_alpha"] = (0., 1300.)
self.data_aug_params["elastic_deform_sigma"] = (9., 15.)
self.data_aug_params["p_eldef"] = 0.2
self.data_aug_params['selected_seg_channels'] = [0]
self.data_aug_params['gamma_range'] = (0.6, 2)
self.data_aug_params['patch_size_for_spatialtransform'] = patch_size_for_spatialtransform
def setup_DA_params(self):
nnUNetTrainerV2.setup_DA_params(self)
self.deep_supervision_scales = [[1, 1, 1]] + list(
list(i) for i in 1 / np.cumprod(
np.vstack(self.net_num_pool_op_kernel_sizes), axis=0))[:-1]
if self.threeD:
self.data_aug_params = default_3D_augmentation_params
self.data_aug_params['rotation_x'] = (-90. / 360 * 2. * np.pi,
90. / 360 * 2. * np.pi)
self.data_aug_params['rotation_y'] = (-90. / 360 * 2. * np.pi,
90. / 360 * 2. * np.pi)
self.data_aug_params['rotation_z'] = (-90. / 360 * 2. * np.pi,
90. / 360 * 2. * np.pi)
if self.do_dummy_2D_aug:
self.data_aug_params["dummy_2D"] = True
self.print_to_log_file("Using dummy2d data augmentation")
self.data_aug_params["elastic_deform_alpha"] = \
default_2D_augmentation_params["elastic_deform_alpha"]
self.data_aug_params["elastic_deform_sigma"] = \
default_2D_augmentation_params["elastic_deform_sigma"]
self.data_aug_params[
"rotation_x"] = default_2D_augmentation_params[
"rotation_x"]
else:
self.do_dummy_2D_aug = False
if max(self.patch_size) / min(self.patch_size) > 1.5:
default_2D_augmentation_params['rotation_x'] = (-180. / 360 *
2. * np.pi,
180. / 360 *
2. * np.pi)
self.data_aug_params = default_2D_augmentation_params
self.data_aug_params[
"mask_was_used_for_normalization"] = self.use_mask_for_norm
if self.do_dummy_2D_aug:
self.basic_generator_patch_size = get_patch_size(
self.patch_size[1:], self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
self.basic_generator_patch_size = np.array(
[self.patch_size[0]] + list(self.basic_generator_patch_size))
patch_size_for_spatialtransform = self.patch_size[1:]
else:
self.basic_generator_patch_size = get_patch_size(
self.patch_size, self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
patch_size_for_spatialtransform = self.patch_size
self.data_aug_params['selected_seg_channels'] = [0]
self.data_aug_params[
'patch_size_for_spatialtransform'] = patch_size_for_spatialtransform
self.data_aug_params["p_rot"] = 0.3
self.data_aug_params["scale_range"] = (0.65, 1.6)
self.data_aug_params["p_scale"] = 0.3
self.data_aug_params["independent_scale_factor_for_each_axis"] = True
self.data_aug_params["p_independent_scale_per_axis"] = 0.3
self.data_aug_params["do_elastic"] = True
self.data_aug_params["p_eldef"] = 0.2
self.data_aug_params["eldef_deformation_scale"] = (0, 0.25)
self.data_aug_params["do_additive_brightness"] = True
self.data_aug_params["additive_brightness_mu"] = 0
self.data_aug_params["additive_brightness_sigma"] = 0.2
self.data_aug_params["additive_brightness_p_per_sample"] = 0.3
self.data_aug_params["additive_brightness_p_per_channel"] = 0.5
self.data_aug_params['gamma_range'] = (0.5, 1.6)
self.data_aug_params['num_cached_per_thread'] = 4
def setup_DA_params(self):
self.deep_supervision_scales = [[1, 1, 1]] + list(
list(i) for i in 1 / np.cumprod(
np.vstack(self.net_num_pool_op_kernel_sizes), axis=0))[:-1]
self.data_aug_params = dict()
self.data_aug_params['scale_range'] = (0.7, 1.43)
# we need this because this is adapted in the cascade
self.data_aug_params['selected_seg_channels'] = None
self.data_aug_params["move_last_seg_chanel_to_data"] = False
if self.threeD:
if self.do_mirroring:
self.mirror_axes = (0, 1, 2)
self.data_aug_params[
'do_mirror'] = True # needed for inference
self.data_aug_params['mirror_axes'] = (
0, 1, 2) # needed for inference
else:
self.data_aug_params['mirror_axes'] = tuple()
self.data_aug_params['do_mirror'] = False
self.data_aug_params['rotation_x'] = (-30. / 360 * 2. * np.pi,
30. / 360 * 2. * np.pi)
self.data_aug_params['rotation_y'] = (-30. / 360 * 2. * np.pi,
30. / 360 * 2. * np.pi)
self.data_aug_params['rotation_z'] = (-30. / 360 * 2. * np.pi,
30. / 360 * 2. * np.pi)
if self.do_dummy_2D_aug:
self.print_to_log_file("Using dummy2d data augmentation")
self.data_aug_params["dummy_2D"] = True
self.data_aug_params["rotation_x"] = (-180. / 360 * 2. * np.pi,
180. / 360 * 2. * np.pi)
else:
if self.do_mirroring:
self.mirror_axes = (0, 1)
self.data_aug_params['mirror_axes'] = (
0, 1) # needed for inference
self.data_aug_params[
'do_mirror'] = True # needed for inference
else:
self.data_aug_params['mirror_axes'] = tuple()
self.data_aug_params[
'do_mirror'] = False # needed for inference
self.do_dummy_2D_aug = False
self.data_aug_params['rotation_x'] = (-180. / 360 * 2. * np.pi,
180. / 360 * 2. * np.pi)
self.data_aug_params['rotation_y'] = (-0. / 360 * 2. * np.pi,
0. / 360 * 2. * np.pi)
self.data_aug_params['rotation_z'] = (-0. / 360 * 2. * np.pi,
0. / 360 * 2. * np.pi)
self.data_aug_params[
"mask_was_used_for_normalization"] = self.use_mask_for_norm
if self.do_dummy_2D_aug:
self.basic_generator_patch_size = get_patch_size(
self.patch_size[1:], self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
self.basic_generator_patch_size = np.array(
[self.patch_size[0]] + list(self.basic_generator_patch_size))
else:
self.basic_generator_patch_size = get_patch_size(
self.patch_size, self.data_aug_params['rotation_x'],
self.data_aug_params['rotation_y'],
self.data_aug_params['rotation_z'],
self.data_aug_params['scale_range'])
