def get_transforms(mode="train", target_size=128): tranform_list = [] if mode == "train": tranform_list = [# CenterCropTransform(crop_size=target_size), ResizeTransform(target_size=target_size, order=1), MirrorTransform(axes=(1,)), SpatialTransform(patch_size=(target_size,target_size), random_crop=False, patch_center_dist_from_border=target_size // 2, do_elastic_deform=True, alpha=(0., 1000.), sigma=(40., 60.), do_rotation=True, p_rot_per_sample=0.5, angle_x=(-0.1, 0.1), angle_y=(0, 1e-8), angle_z=(0, 1e-8), scale=(0.5, 1.9), p_scale_per_sample=0.5, border_mode_data="nearest", border_mode_seg="nearest"), ] elif mode == "val": tranform_list = [CenterCropTransform(crop_size=target_size), ResizeTransform(target_size=target_size, order=1), ] elif mode == "test": tranform_list = [CenterCropTransform(crop_size=target_size), ResizeTransform(target_size=target_size, order=1), ] tranform_list.append(NumpyToTensor()) return Compose(tranform_list)
def get_transforms(mode="train", target_size=128): tranform_list = [] if mode == "train": tranform_list = [ # CenterCropTransform(crop_size=target_size), ResizeTransform(target_size=(target_size, target_size), order=1), MirrorTransform(axes=(1, )), ] elif mode == "val": tranform_list = [ #CenterCropTransform(crop_size=target_size), ResizeTransform(target_size=target_size, order=1), MirrorTransform(axes=(1, )), ] elif mode == "test": tranform_list = [ #CenterCropTransform(crop_size=target_size), ResizeTransform(target_size=target_size, order=1), MirrorTransform(axes=(1, )), ] tranform_list.append(NumpyToTensor()) return Compose(tranform_list)
dataset_test = ConditionalGanDataset(path_test_real, load_sample_cgan_test, ['.PNG', '.png'], ['.PNG', '.png']) ### Transforms applied to data from batchgenerators.transforms import RandomCropTransform, Compose from batchgenerators.transforms.spatial_transforms import ResizeTransform,SpatialTransform transforms = Compose([ #SpatialTransform(patch_size=(1024, 1024), do_rotation=True, patch_center_dist_from_border=1024, border_mode_data='reflect', # border_mode_seg='reflect', angle_x=(args.rot_angle, args.rot_angle), angle_y=(0, 0), angle_z=(0, 0), # do_elastic_deform=False, order_data=1, order_seg=1) ResizeTransform((int(args.resize_size), int(args.resize_size)), order=1), RandomCropTransform((params.nested_get("image_size"), params.nested_get("image_size"))), ]) from delira.data_loading import BaseDataManager, SequentialSampler, RandomSampler manager_test = BaseDataManager(dataset_test, params.nested_get("batch_size"), transforms=transforms, sampler_cls=SequentialSampler, n_process_augmentation=1) import warnings warnings.simplefilter("ignore", UserWarning) # ignore UserWarnings raised by dependency code warnings.simplefilter("ignore", FutureWarning) # ignore FutureWarnings raised by dependency code
path_val.append(os.path.join(root_path, dataset_name + '/Original50mm/')) """ dataset_train = ConditionalGanDataset(path_train, load_sample_cgan, ['.PNG', '.png'], ['.PNG', '.png']) dataset_val = ConditionalGanDataset(path_val, load_sample_cgan, ['.PNG', '.png'], ['.PNG', '.png']) ### Transforms applied to data from batchgenerators.transforms import RandomCropTransform, Compose from batchgenerators.transforms.spatial_transforms import ResizeTransform transforms = Compose([ ResizeTransform((1024, 1024), order=1), RandomCropTransform( (params.nested_get("image_size"), params.nested_get("image_size") )), # Perform Random Crops of Size 256 x 256 pixels, ]) ### Data manager from delira.data_loading import BaseDataManager, SequentialSampler, RandomSampler manager_train = BaseDataManager(dataset_train, params.nested_get("batch_size"), transforms=transforms, sampler_cls=RandomSampler, n_process_augmentation=4)
def get_transforms(mode="train", n_channels=1, target_size=128, add_resize=False, add_noise=False, mask_type="", batch_size=16, rotate=True, elastic_deform=True, rnd_crop=False, color_augment=True): tranform_list = [] noise_list = [] if mode == "train": tranform_list = [ FillupPadTransform(min_size=(n_channels, target_size + 5, target_size + 5)), ResizeTransform(target_size=(target_size + 1, target_size + 1), order=1, concatenate_list=True), # RandomCropTransform(crop_size=(target_size + 5, target_size + 5)), MirrorTransform(axes=(2, )), ReshapeTransform(new_shape=(1, -1, "h", "w")), SpatialTransform(patch_size=(target_size, target_size), random_crop=rnd_crop, patch_center_dist_from_border=target_size // 2, do_elastic_deform=elastic_deform, alpha=(0., 100.), sigma=(10., 13.), do_rotation=rotate, angle_x=(-0.1, 0.1), angle_y=(0, 1e-8), angle_z=(0, 1e-8), scale=(0.9, 1.2), border_mode_data="nearest", border_mode_seg="nearest"), ReshapeTransform(new_shape=(batch_size, -1, "h", "w")) ] if color_augment: tranform_list += [ # BrightnessTransform(mu=0, sigma=0.2), BrightnessMultiplicativeTransform(multiplier_range=(0.95, 1.1)) ] tranform_list += [ GaussianNoiseTransform(noise_variance=(0., 0.05)), ClipValueRange(min=-1.5, max=1.5), ] noise_list = [] if mask_type == "gaussian": noise_list += [GaussianNoiseTransform(noise_variance=(0., 0.2))] elif mode == "val": tranform_list = [ FillupPadTransform(min_size=(n_channels, target_size + 5, target_size + 5)), ResizeTransform(target_size=(target_size + 1, target_size + 1), order=1, concatenate_list=True), CenterCropTransform(crop_size=(target_size, target_size)), ClipValueRange(min=-1.5, max=1.5), # BrightnessTransform(mu=0, sigma=0.2), # BrightnessMultiplicativeTransform(multiplier_range=(0.95, 1.1)), CopyTransform({"data": "data_clean"}, copy=True) ] noise_list += [] if add_noise: tranform_list = tranform_list + noise_list tranform_list.append(NumpyToTensor()) return Compose(tranform_list)