def test_random_sharpness_one_channel_c(degrees=(1.4, 1.4), plot=False): """ Test Random Sharpness cpp op with one channel """ logger.info( "Test RandomSharpness C Op With MNIST Dataset (Grayscale images)") c_op = C.RandomSharpness() if degrees is not None: c_op = C.RandomSharpness(degrees) # RandomSharpness Images data = de.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False) ds_random_sharpness_c = data.map(operations=c_op, input_columns="image") # Original images data = de.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False) images = [] images_trans = [] labels = [] for _, (data_orig, data_trans) in enumerate(zip(data, ds_random_sharpness_c)): image_orig, label_orig = data_orig image_trans, _ = data_trans images.append(image_orig.asnumpy()) labels.append(label_orig.asnumpy()) images_trans.append(image_trans.asnumpy()) if plot: visualize_one_channel_dataset(images, images_trans, labels)
def test_random_sharpness_c(degrees=(1.6, 1.6), plot=False): """ Test RandomSharpness cpp op """ print(degrees) logger.info("Test RandomSharpness cpp op") # Original Images data = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms_original = [C.Decode(), C.Resize((224, 224))] ds_original = data.map(operations=transforms_original, input_columns="image") ds_original = ds_original.batch(512) for idx, (image, _) in enumerate( ds_original.create_tuple_iterator(output_numpy=True)): if idx == 0: images_original = image else: images_original = np.append(images_original, image, axis=0) # Random Sharpness Adjusted Images data = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) c_op = C.RandomSharpness() if degrees is not None: c_op = C.RandomSharpness(degrees) transforms_random_sharpness = [C.Decode(), C.Resize((224, 224)), c_op] ds_random_sharpness = data.map(operations=transforms_random_sharpness, input_columns="image") ds_random_sharpness = ds_random_sharpness.batch(512) for idx, (image, _) in enumerate( ds_random_sharpness.create_tuple_iterator(output_numpy=True)): if idx == 0: images_random_sharpness = image else: images_random_sharpness = np.append(images_random_sharpness, image, axis=0) num_samples = images_original.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_random_sharpness[i], images_original[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize_list(images_original, images_random_sharpness)
def test_serdes_uniform_augment(remove_json_files=True): """ Test serdes on uniform augment. """ data_dir = "../data/dataset/testPK/data" data = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False) ds.config.set_seed(1) transforms_ua = [ vision.RandomHorizontalFlip(), vision.RandomVerticalFlip(), vision.RandomColor(), vision.RandomSharpness(), vision.Invert(), vision.AutoContrast(), vision.Equalize() ] transforms_all = [ vision.Decode(), vision.Resize(size=[224, 224]), vision.UniformAugment(transforms=transforms_ua, num_ops=5) ] data = data.map(operations=transforms_all, input_columns="image", num_parallel_workers=1) util_check_serialize_deserialize_file(data, "uniform_augment_pipeline", remove_json_files)
def test_random_sharpness_invalid_params(): """ Test RandomSharpness with invalid input parameters. """ logger.info("Test RandomSharpness with invalid input parameters.") try: data = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data = data.map(operations=[ C.Decode(), C.Resize((224, 224)), C.RandomSharpness(10) ], input_columns=["image"]) except TypeError as error: logger.info("Got an exception in DE: {}".format(str(error))) assert "tuple" in str(error) try: data = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data = data.map(operations=[ C.Decode(), C.Resize((224, 224)), C.RandomSharpness((-10, 10)) ], input_columns=["image"]) except ValueError as error: logger.info("Got an exception in DE: {}".format(str(error))) assert "interval" in str(error) try: data = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data = data.map(operations=[ C.Decode(), C.Resize((224, 224)), C.RandomSharpness((10, 5)) ], input_columns=["image"]) except ValueError as error: logger.info("Got an exception in DE: {}".format(str(error))) assert "(min,max)" in str(error)
def test_random_sharpness_c_md5(): """ Test RandomSharpness cpp op with md5 comparison """ logger.info("Test RandomSharpness cpp op with md5 comparison") original_seed = config_get_set_seed(200) original_num_parallel_workers = config_get_set_num_parallel_workers(1) # define map operations transforms = [C.Decode(), C.RandomSharpness((10.0, 15.0))] # Generate dataset data = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data = data.map(operations=transforms, input_columns=["image"]) # check results with md5 comparison filename = "random_sharpness_cpp_01_result.npz" save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN) # Restore configuration ds.config.set_seed(original_seed) ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_sharpness_c_py(degrees=(1.0, 1.0), plot=False): """ Test Random Sharpness C and python Op """ logger.info("Test RandomSharpness C and python Op") # RandomSharpness Images data = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data = data.map(operations=[C.Decode(), C.Resize((200, 300))], input_columns=["image"]) python_op = F.RandomSharpness(degrees) c_op = C.RandomSharpness(degrees) transforms_op = mindspore.dataset.transforms.py_transforms.Compose( [lambda img: F.ToPIL()(img.astype(np.uint8)), python_op, np.array]) ds_random_sharpness_py = data.map(operations=transforms_op, input_columns="image") ds_random_sharpness_py = ds_random_sharpness_py.batch(512) for idx, (image, _) in enumerate( ds_random_sharpness_py.create_tuple_iterator(output_numpy=True)): if idx == 0: images_random_sharpness_py = image else: images_random_sharpness_py = np.append(images_random_sharpness_py, image, axis=0) data = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data = data.map(operations=[C.Decode(), C.Resize((200, 300))], input_columns=["image"]) ds_images_random_sharpness_c = data.map(operations=c_op, input_columns="image") ds_images_random_sharpness_c = ds_images_random_sharpness_c.batch(512) for idx, (image, _) in enumerate( ds_images_random_sharpness_c.create_tuple_iterator( output_numpy=True)): if idx == 0: images_random_sharpness_c = image else: images_random_sharpness_c = np.append(images_random_sharpness_c, image, axis=0) num_samples = images_random_sharpness_c.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_random_sharpness_c[i], images_random_sharpness_py[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize_list(images_random_sharpness_c, images_random_sharpness_py, visualize_mode=2)