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: data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0])], input_columns=["image"]) # invalid ignore data_set = data_set.map(operations=C.AutoContrast(cutoff=-10.0), input_columns="image") 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: data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0])], input_columns=["image"]) # invalid ignore data_set = data_set.map(operations=C.AutoContrast(cutoff=120.0), input_columns="image") 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_invalid_ignore_param_c(): """ Test AutoContrast C Op with invalid ignore parameter """ logger.info("Test AutoContrast C Op with invalid ignore parameter") try: data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data_set = data_set.map(operations=[ C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0]) ], input_columns=["image"]) # invalid ignore data_set = data_set.map(operations=C.AutoContrast(ignore=255.5), input_columns="image") 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: data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data_set = data_set.map(operations=[ C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0]) ], input_columns=["image"]) # invalid ignore data_set = data_set.map(operations=C.AutoContrast(ignore=(10, 100)), input_columns="image") 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_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_auto_contrast_mnist_c(plot=False): """ Test AutoContrast C op with MNIST dataset (Grayscale images) """ logger.info("Test AutoContrast C Op With MNIST Images") data_set = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False) ds_auto_contrast_c = data_set.map(operations=C.AutoContrast(cutoff=1, ignore=(0, 255)), 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_auto_contrast_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 = "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 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"]) python_op = F.AutoContrast(cutoff=10.0, ignore=[10, 20]) c_op = C.AutoContrast(cutoff=10.0, ignore=[10, 20]) transforms_op = mindspore.dataset.transforms.py_transforms.Compose([lambda img: F.ToPIL()(img.astype(np.uint8)), python_op, np.array]) ds_auto_contrast_py = data_set.map(operations=transforms_op, input_columns="image") 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.asnumpy() else: images_auto_contrast_py = np.append(images_auto_contrast_py, image.asnumpy(), axis=0) 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_auto_contrast_c = data_set.map(operations=c_op, input_columns="image") 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.asnumpy() else: images_auto_contrast_c = np.append(images_auto_contrast_c, image.asnumpy(), 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)
def test_auto_contrast_one_channel_c(plot=False): """ Test AutoContrast C op with one channel """ logger.info("Test AutoContrast C Op With One Channel Images") # AutoContrast Images 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"]) python_op = F.AutoContrast() c_op = C.AutoContrast() # not using F.ToTensor() since it converts to floats transforms_op = mindspore.dataset.transforms.py_transforms.Compose( [lambda img: (np.array(img)[:, :, 0]).astype(np.uint8), F.ToPIL(), python_op, np.array]) ds_auto_contrast_py = data_set.map(operations=transforms_op, input_columns="image") 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.asnumpy() else: images_auto_contrast_py = np.append(images_auto_contrast_py, image.asnumpy(), axis=0) data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0])], input_columns=["image"]) ds_auto_contrast_c = data_set.map(operations=c_op, input_columns="image") 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.asnumpy() else: images_auto_contrast_c = np.append(images_auto_contrast_c, image.asnumpy(), 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)