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_equalize_mnist_c(plot=False): """ Test Equalize C op with MNIST dataset (Grayscale images) """ logger.info("Test Equalize C Op With MNIST Images") data_set = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False) ds_equalize_c = data_set.map(operations=C.Equalize(), input_columns="image") ds_orig = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False) images = [] images_trans = [] labels = [] for _, (data_orig, data_trans) in enumerate(zip(ds_orig, ds_equalize_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()) # Compare with expected md5 from images filename = "equalize_mnist_result_c.npz" save_and_check_md5(ds_equalize_c, filename, generate_golden=GENERATE_GOLDEN) if plot: visualize_one_channel_dataset(images, images_trans, labels)
def test_equalize_py_c(plot=False): """ Test Equalize Cpp op and python op """ logger.info("Test Equalize cpp and python op") # equalize Images in cpp data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224))], input_columns=["image"]) ds_c_equalize = data_set.map(operations=C.Equalize(), input_columns="image") ds_c_equalize = ds_c_equalize.batch(512) for idx, (image, _) in enumerate(ds_c_equalize): if idx == 0: images_c_equalize = image.asnumpy() else: images_c_equalize = np.append(images_c_equalize, image.asnumpy(), axis=0) # Equalize images in python data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224))], input_columns=["image"]) transforms_p_equalize = mindspore.dataset.transforms.py_transforms.Compose( [lambda img: img.astype(np.uint8), F.ToPIL(), F.Equalize(), np.array]) ds_p_equalize = data_set.map(operations=transforms_p_equalize, input_columns="image") ds_p_equalize = ds_p_equalize.batch(512) for idx, (image, _) in enumerate(ds_p_equalize): if idx == 0: images_p_equalize = image.asnumpy() else: images_p_equalize = np.append(images_p_equalize, image.asnumpy(), axis=0) num_samples = images_c_equalize.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_p_equalize[i], images_c_equalize[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize_list(images_c_equalize, images_p_equalize, visualize_mode=2)
def test_equalize_c(plot=False): """ Test Equalize Cpp op """ logger.info("Test Equalize cpp op") # Original Images data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms_original = [C.Decode(), C.Resize(size=[224, 224])] ds_original = data_set.map(operations=transforms_original, input_columns="image") ds_original = ds_original.batch(512) for idx, (image, _) in enumerate(ds_original): if idx == 0: images_original = image.asnumpy() else: images_original = np.append(images_original, image.asnumpy(), axis=0) # Equalize Images data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transform_equalize = [C.Decode(), C.Resize(size=[224, 224]), C.Equalize()] ds_equalize = data_set.map(operations=transform_equalize, input_columns="image") ds_equalize = ds_equalize.batch(512) for idx, (image, _) in enumerate(ds_equalize): if idx == 0: images_equalize = image.asnumpy() else: images_equalize = np.append(images_equalize, image.asnumpy(), axis=0) if plot: visualize_list(images_original, images_equalize) num_samples = images_original.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_equalize[i], images_original[i]) logger.info("MSE= {}".format(str(np.mean(mse))))
def test_equalize_md5_c(): """ Test Equalize cpp op with md5 check """ logger.info("Test Equalize cpp op with md5 check") # Generate dataset ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms_equalize = [C.Decode(), C.Resize(size=[224, 224]), C.Equalize(), F.ToTensor()] data = ds.map(operations=transforms_equalize, input_columns="image") # Compare with expected md5 from images filename = "equalize_01_result_c.npz" save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
def test_equalize_one_channel(): """ Test Equalize cpp op with one channel image """ logger.info("Test Equalize C Op With One Channel Images") c_op = C.Equalize() try: ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(operations=[C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0])], input_columns=["image"]) ds.map(operations=c_op, input_columns="image") except RuntimeError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert "The shape" in str(e)