def get_no_augmentation(dataloader_train, dataloader_val, params=default_3D_augmentation_params, deep_supervision_scales=None, soft_ds=False, classes=None, pin_memory=True, regions=None): """ use this instead of get_default_augmentation (drop in replacement) to turn off all data augmentation """ tr_transforms = [] # 'selected_data_channels': None, if params.get("selected_data_channels") is not None: tr_transforms.append( DataChannelSelectionTransform( params.get("selected_data_channels"))) # 'selected_seg_channels': [0], if params.get("selected_seg_channels") is not None: tr_transforms.append( SegChannelSelectionTransform(params.get("selected_seg_channels"))) tr_transforms.append(RemoveLabelTransform(-1, 0)) tr_transforms.append(RenameTransform('seg', 'target', True)) # 'regions': None if regions is not None: tr_transforms.append( ConvertSegmentationToRegionsTransform(regions, 'target', 'target')) # '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]], 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=range(params.get('num_threads')), pin_memory=pin_memory) batchgenerator_train.restart() 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"))) val_transforms.append(RenameTransform('seg', 'target', True)) if regions is not None: val_transforms.append( ConvertSegmentationToRegionsTransform(regions, 'target', 'target')) 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=range(max(params.get('num_threads') // 2, 1)), pin_memory=pin_memory) batchgenerator_val.restart() return batchgenerator_train, batchgenerator_val
def get_no_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): """ use this instead of get_default_augmentation (drop in replacement) to turn off all data augmentation :param dataloader_train: :param dataloader_val: :param patch_size: :param params: :param border_val_seg: :return: """ 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"))) tr_transforms.append(RemoveLabelTransform(-1, 0)) 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=range(params.get('num_threads')), pin_memory=True) batchgenerator_train.restart() 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"))) 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=range(max(params.get('num_threads') // 2, 1)), pin_memory=True) batchgenerator_val.restart() return batchgenerator_train, batchgenerator_val
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_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