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
Exemple #2
0
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
Exemple #3
0
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
Exemple #6
0
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