def test_auto_contrast_invalid_ignore_param_c(): """ Test AutoContrast C Op with invalid ignore parameter """ logger.info("Test AutoContrast C Op with invalid ignore parameter") try: ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[ C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0]) ]) # invalid ignore ds = ds.map(input_columns="image", operations=C.AutoContrast(ignore=255.5)) except TypeError as error: logger.info("Got an exception in DE: {}".format(str(error))) assert "Argument ignore with value 255.5 is not of type" in str(error) try: ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[ C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0]) ]) # invalid ignore ds = ds.map(input_columns="image", operations=C.AutoContrast(ignore=(10, 100))) except TypeError as error: logger.info("Got an exception in DE: {}".format(str(error))) assert "Argument ignore with value (10,100) is not of type" in str( error)
def test_auto_contrast_invalid_cutoff_param_c(): """ Test AutoContrast C Op with invalid cutoff parameter """ logger.info("Test AutoContrast C Op with invalid cutoff parameter") try: ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[ C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0]) ]) # invalid ignore ds = ds.map(input_columns="image", operations=C.AutoContrast(cutoff=-10.0)) except ValueError as error: logger.info("Got an exception in DE: {}".format(str(error))) assert "Input cutoff is not within the required interval of (0 to 100)." in str( error) try: ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[ C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0]) ]) # invalid ignore ds = ds.map(input_columns="image", operations=C.AutoContrast(cutoff=120.0)) except ValueError as error: logger.info("Got an exception in DE: {}".format(str(error))) assert "Input cutoff is not within the required interval of (0 to 100)." in str( error)
def test_auto_contrast_mnist_c(plot=False): """ Test AutoContrast C op with MNIST dataset (Grayscale images) """ logger.info("Test AutoContrast C Op With MNIST Images") ds = de.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False) ds_auto_contrast_c = ds.map(input_columns="image", operations=C.AutoContrast(cutoff=1, ignore=(0, 255))) ds_orig = de.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_auto_contrast_c)): image_orig, label_orig = data_orig image_trans, _ = data_trans images.append(image_orig) labels.append(label_orig) images_trans.append(image_trans) # Compare with expected md5 from images filename = "autocontrast_mnist_result_c.npz" save_and_check_md5(ds_auto_contrast_c, filename, generate_golden=GENERATE_GOLDEN) if plot: visualize_one_channel_dataset(images, images_trans, labels)
def test_auto_contrast_c(plot=False): """ Test AutoContrast C Op """ logger.info("Test AutoContrast C Op") # AutoContrast Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224))]) python_op = F.AutoContrast() c_op = C.AutoContrast() transforms_op = F.ComposeOp( [lambda img: F.ToPIL()(img.astype(np.uint8)), python_op, np.array])() ds_auto_contrast_py = ds.map(input_columns="image", operations=transforms_op) ds_auto_contrast_py = ds_auto_contrast_py.batch(512) for idx, (image, _) in enumerate(ds_auto_contrast_py): if idx == 0: images_auto_contrast_py = image else: images_auto_contrast_py = np.append(images_auto_contrast_py, image, axis=0) ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224))]) ds_auto_contrast_c = ds.map(input_columns="image", operations=c_op) ds_auto_contrast_c = ds_auto_contrast_c.batch(512) for idx, (image, _) in enumerate(ds_auto_contrast_c): if idx == 0: images_auto_contrast_c = image else: images_auto_contrast_c = np.append(images_auto_contrast_c, image, axis=0) num_samples = images_auto_contrast_c.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_auto_contrast_c[i], images_auto_contrast_py[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) np.testing.assert_equal(np.mean(mse), 0.0) if plot: visualize_list(images_auto_contrast_c, images_auto_contrast_py, visualize_mode=2)
def test_auto_contrast_c(plot=False): """ Test AutoContrast C Op """ logger.info("Test AutoContrast C Op") # AutoContrast Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224))]) python_op = F.AutoContrast(cutoff=10.0, ignore=[10, 20]) c_op = C.AutoContrast(cutoff=10.0, ignore=[10, 20]) transforms_op = F.ComposeOp( [lambda img: F.ToPIL()(img.astype(np.uint8)), python_op, np.array])() ds_auto_contrast_py = ds.map(input_columns="image", operations=transforms_op) ds_auto_contrast_py = ds_auto_contrast_py.batch(512) for idx, (image, _) in enumerate(ds_auto_contrast_py): if idx == 0: images_auto_contrast_py = image else: images_auto_contrast_py = np.append(images_auto_contrast_py, image, axis=0) ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224))]) ds_auto_contrast_c = ds.map(input_columns="image", operations=c_op) ds_auto_contrast_c = ds_auto_contrast_c.batch(512) for idx, (image, _) in enumerate(ds_auto_contrast_c): if idx == 0: images_auto_contrast_c = image else: images_auto_contrast_c = np.append(images_auto_contrast_c, image, axis=0) num_samples = images_auto_contrast_c.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_auto_contrast_c[i], images_auto_contrast_py[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) np.testing.assert_equal(np.mean(mse), 0.0) # Compare with expected md5 from images filename = "autocontrast_01_result_c.npz" save_and_check_md5(ds_auto_contrast_c, filename, generate_golden=GENERATE_GOLDEN) if plot: visualize_list(images_auto_contrast_c, images_auto_contrast_py, visualize_mode=2)