How to use the batchgenerators.transforms.spatial_transforms.SpatialTransform function in batchgenerators

create mutli-threaded train/val/test batch generation and augmentation pipeline.
    :param patient_data: dictionary containing one dictionary per patient in the train/test subset.
    :param is_training: (optional) whether to perform data augmentation (training) or not (validation/testing)
    :return: multithreaded_generator
    """

    # create instance of batch generator as first element in pipeline.
    data_gen = BatchGenerator(cf, patient_data, **kwargs)

    my_transforms = []
    if do_aug:
        if cf.da_kwargs["mirror"]:
            mirror_transform = Mirror(axes=cf.da_kwargs['mirror_axes'])
            my_transforms.append(mirror_transform)

        spatial_transform = SpatialTransform(patch_size=cf.patch_size[:cf.dim],
                                             patch_center_dist_from_border=cf.da_kwargs['rand_crop_dist'],
                                             do_elastic_deform=cf.da_kwargs['do_elastic_deform'],
                                             alpha=cf.da_kwargs['alpha'], sigma=cf.da_kwargs['sigma'],
                                             do_rotation=cf.da_kwargs['do_rotation'], angle_x=cf.da_kwargs['angle_x'],
                                             angle_y=cf.da_kwargs['angle_y'], angle_z=cf.da_kwargs['angle_z'],
                                             do_scale=cf.da_kwargs['do_scale'], scale=cf.da_kwargs['scale'],
                                             random_crop=cf.da_kwargs['random_crop'])

        my_transforms.append(spatial_transform)
    else:
        my_transforms.append(CenterCropTransform(crop_size=cf.patch_size[:cf.dim]))

    my_transforms.append(ConvertSegToBoundingBoxCoordinates(cf.dim, cf.roi_items, False, cf.class_specific_seg))
    all_transforms = Compose(my_transforms)
    # multithreaded_generator = SingleThreadedAugmenter(data_gen, all_transforms)
    multithreaded_generator = MultiThreadedAugmenter(data_gen, all_transforms, num_processes=data_gen.n_filled_threads,

per_channel=False, retain_stats=True)
            my_transforms.append(gamma_transform)
        if cf.dim == 3:
            # augmentations with desired effect on z-dimension
            spatial_transform = SpatialTransform(patch_size=cf.patch_size,
                                             patch_center_dist_from_border=cf.da_kwargs['rand_crop_dist'],
                                             do_elastic_deform=False,
                                             do_rotation=cf.da_kwargs['do_rotation'], angle_x=cf.da_kwargs['angle_x'],
                                             angle_y=cf.da_kwargs['angle_y'], angle_z=cf.da_kwargs['angle_z'],
                                             do_scale=cf.da_kwargs['do_scale'], scale=cf.da_kwargs['scale'],
                                             random_crop=cf.da_kwargs['random_crop'],
                                             border_mode_data=cf.da_kwargs['border_mode_data'])
            my_transforms.append(spatial_transform)
            # augmentations that are only meant to affect x-y
            my_transforms.append(Convert3DTo2DTransform())
            spatial_transform = SpatialTransform(patch_size=cf.patch_size[:2],
                                             patch_center_dist_from_border=cf.da_kwargs['rand_crop_dist'][:2],
                                             do_elastic_deform=cf.da_kwargs['do_elastic_deform'],
                                             alpha=cf.da_kwargs['alpha'], sigma=cf.da_kwargs['sigma'],
                                             do_rotation=False,
                                             do_scale=False,
                                             random_crop=False,
                                             border_mode_data=cf.da_kwargs['border_mode_data'])
            my_transforms.append(spatial_transform)
            my_transforms.append(Convert2DTo3DTransform())

        else:
            spatial_transform = SpatialTransform(patch_size=cf.patch_size[:cf.dim],
                                             patch_center_dist_from_border=cf.da_kwargs['rand_crop_dist'][:2],
                                             do_elastic_deform=cf.da_kwargs['do_elastic_deform'],
                                             alpha=cf.da_kwargs['alpha'], sigma=cf.da_kwargs['sigma'],
                                             do_rotation=cf.da_kwargs['do_rotation'], angle_x=cf.da_kwargs['angle_x'],

"""
    create mutli-threaded train/val/test batch generation and augmentation pipeline.
    :param patient_data: dictionary containing one dictionary per patient in the train/test subset.
    :param is_training: (optional) whether to perform data augmentation (training) or not (validation/testing)
    :return: multithreaded_generator
    """

    # create instance of batch generator as first element in pipeline.
    data_gen = BatchGenerator(patient_data, batch_size=cf.batch_size, cf=cf)

    # add transformations to pipeline.
    my_transforms = []
    if is_training:
        mirror_transform = Mirror(axes=np.arange(cf.dim))
        my_transforms.append(mirror_transform)
        spatial_transform = SpatialTransform(patch_size=cf.patch_size[:cf.dim],
                                             patch_center_dist_from_border=cf.da_kwargs['rand_crop_dist'],
                                             do_elastic_deform=cf.da_kwargs['do_elastic_deform'],
                                             alpha=cf.da_kwargs['alpha'], sigma=cf.da_kwargs['sigma'],
                                             do_rotation=cf.da_kwargs['do_rotation'], angle_x=cf.da_kwargs['angle_x'],
                                             angle_y=cf.da_kwargs['angle_y'], angle_z=cf.da_kwargs['angle_z'],
                                             do_scale=cf.da_kwargs['do_scale'], scale=cf.da_kwargs['scale'],
                                             random_crop=cf.da_kwargs['random_crop'])

        my_transforms.append(spatial_transform)
    else:
        my_transforms.append(CenterCropTransform(crop_size=cf.patch_size[:cf.dim]))

    my_transforms.append(ConvertSegToBoundingBoxCoordinates(cf.dim, get_rois_from_seg_flag=False, class_specific_seg_flag=cf.class_specific_seg_flag))
    all_transforms = Compose(my_transforms)
    # multithreaded_generator = SingleThreadedAugmenter(data_gen, all_transforms)
    multithreaded_generator = MultiThreadedAugmenter(data_gen, all_transforms, num_processes=cf.n_workers, seeds=range(cf.n_workers))

"""
    create mutli-threaded train/val/test batch generation and augmentation pipeline.
    :param patient_data: dictionary containing one dictionary per patient in the train/test subset.
    :param is_training: (optional) whether to perform data augmentation (training) or not (validation/testing)
    :return: multithreaded_generator
    """

    # create instance of batch generator as first element in pipeline.
    data_gen = BatchGenerator(patient_data, batch_size=cf.batch_size, cf=cf)

    # add transformations to pipeline.
    my_transforms = []
    if do_aug:
        mirror_transform = Mirror(axes=np.arange(2, cf.dim+2, 1))
        my_transforms.append(mirror_transform)
        spatial_transform = SpatialTransform(patch_size=cf.patch_size[:cf.dim],
                                             patch_center_dist_from_border=cf.da_kwargs['rand_crop_dist'],
                                             do_elastic_deform=cf.da_kwargs['do_elastic_deform'],
                                             alpha=cf.da_kwargs['alpha'], sigma=cf.da_kwargs['sigma'],
                                             do_rotation=cf.da_kwargs['do_rotation'], angle_x=cf.da_kwargs['angle_x'],
                                             angle_y=cf.da_kwargs['angle_y'], angle_z=cf.da_kwargs['angle_z'],
                                             do_scale=cf.da_kwargs['do_scale'], scale=cf.da_kwargs['scale'],
                                             random_crop=cf.da_kwargs['random_crop'])

        my_transforms.append(spatial_transform)
    else:
        my_transforms.append(CenterCropTransform(crop_size=cf.patch_size[:cf.dim]))

    my_transforms.append(ConvertSegToBoundingBoxCoordinates(cf.dim, get_rois_from_seg_flag=False, class_specific_seg_flag=cf.class_specific_seg_flag))
    all_transforms = Compose(my_transforms)
    # multithreaded_generator = SingleThreadedAugmenter(data_gen, all_transforms)
    multithreaded_generator = MultiThreadedAugmenter(data_gen, all_transforms, num_processes=cf.n_workers, seeds=range(cf.n_workers))

def create_data_gen_pipeline(cf, patient_data, do_aug=True, sample_pids_w_replace=True):
    """
    create mutli-threaded train/val/test batch generation and augmentation pipeline.
    :param patient_data: dictionary containing one dictionary per patient in the train/test subset
    :param test_pids: (optional) list of test patient ids, calls the test generator.
    :param do_aug: (optional) whether to perform data augmentation (training) or not (validation/testing)
    :return: multithreaded_generator
    """
    data_gen = BatchGenerator(cf, patient_data, sample_pids_w_replace=sample_pids_w_replace)

    my_transforms = []
    if do_aug:
        if cf.da_kwargs["mirror"]:
            mirror_transform = Mirror(axes=cf.da_kwargs['mirror_axes'])
            my_transforms.append(mirror_transform)
        spatial_transform = SpatialTransform(patch_size=cf.patch_size[:cf.dim],
                                         patch_center_dist_from_border=cf.da_kwargs['rand_crop_dist'][:2],
                                         do_elastic_deform=cf.da_kwargs['do_elastic_deform'],
                                         alpha=cf.da_kwargs['alpha'], sigma=cf.da_kwargs['sigma'],
                                         do_rotation=cf.da_kwargs['do_rotation'], angle_x=cf.da_kwargs['angle_x'],
                                         angle_y=cf.da_kwargs['angle_y'], angle_z=cf.da_kwargs['angle_z'],
                                         do_scale=cf.da_kwargs['do_scale'], scale=cf.da_kwargs['scale'],
                                         random_crop=cf.da_kwargs['random_crop'],
                                         border_mode_data=cf.da_kwargs['border_mode_data'])
        my_transforms.append(spatial_transform)
        gamma_transform = GammaTransform(gamma_range=cf.da_kwargs["gamma_range"], invert_image=False,
                                         per_channel=False, retain_stats=False)
        my_transforms.append(gamma_transform)
    
    else:
        my_transforms.append(CenterCropTransform(crop_size=cf.patch_size[:cf.dim]))

def _augment_data(self, batch_generator, type=None):
        tfs = []  # transforms

        if self.Config.NORMALIZE_DATA:
            tfs.append(ZeroMeanUnitVarianceTransform(per_channel=False))

        if self.Config.TEST_TIME_DAUG:
            center_dist_from_border = int(self.Config.INPUT_DIM[0] / 2.) - 10  # (144,144) -> 62
            tfs.append(SpatialTransform(self.Config.INPUT_DIM,
                                        patch_center_dist_from_border=center_dist_from_border,
                                        do_elastic_deform=True, alpha=(90., 120.), sigma=(9., 11.),
                                        do_rotation=True, angle_x=(-0.8, 0.8), angle_y=(-0.8, 0.8),
                                        angle_z=(-0.8, 0.8),
                                        do_scale=True, scale=(0.9, 1.5), border_mode_data='constant',
                                        border_cval_data=0,
                                        order_data=3,
                                        border_mode_seg='constant', border_cval_seg=0, order_seg=0, random_crop=True))
            # tfs.append(ResampleTransform(zoom_range=(0.5, 1)))
            # tfs.append(GaussianNoiseTransform(noise_variance=(0, 0.05)))
            tfs.append(ContrastAugmentationTransform(contrast_range=(0.7, 1.3), preserve_range=True, per_channel=False))
            tfs.append(BrightnessMultiplicativeTransform(multiplier_range=(0.7, 1.3), per_channel=False))

        tfs.append(ReorderSegTransform())
        batch_gen = SingleThreadedAugmenter(batch_generator, Compose(tfs))
        return batch_gen