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_invert_py_c(plot=False): """ Test Invert Cpp op and python op """ logger.info("Test Invert cpp and python op") # Invert 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_invert = data_set.map(operations=C.Invert(), input_columns="image") ds_c_invert = ds_c_invert.batch(512) for idx, (image, _) in enumerate(ds_c_invert): if idx == 0: images_c_invert = image.asnumpy() else: images_c_invert = np.append(images_c_invert, image.asnumpy(), axis=0) # invert 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_invert = mindspore.dataset.transforms.py_transforms.Compose( [lambda img: img.astype(np.uint8), F.ToPIL(), F.Invert(), np.array]) ds_p_invert = data_set.map(operations=transforms_p_invert, input_columns="image") ds_p_invert = ds_p_invert.batch(512) for idx, (image, _) in enumerate(ds_p_invert): if idx == 0: images_p_invert = image.asnumpy() else: images_p_invert = np.append(images_p_invert, image.asnumpy(), axis=0) num_samples = images_c_invert.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_p_invert[i], images_c_invert[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize_list(images_c_invert, images_p_invert, visualize_mode=2)
def test_invert_callable(): """ Test Invert is callable """ logger.info("Test Invert callable") img = np.fromfile("../data/dataset/apple.jpg", dtype=np.uint8) logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape)) img = C.Decode()(img) img = C.Invert()(img) logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape)) assert img.shape == (2268, 4032, 3)
def test_invert_c(plot=False): """ Test Invert Cpp op """ logger.info("Test Invert 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) # Invert Images data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transform_invert = [C.Decode(), C.Resize(size=[224, 224]), C.Invert()] ds_invert = data_set.map(operations=transform_invert, input_columns="image") ds_invert = ds_invert.batch(512) for idx, (image, _) in enumerate(ds_invert): if idx == 0: images_invert = image.asnumpy() else: images_invert = np.append(images_invert, image.asnumpy(), axis=0) if plot: visualize_list(images_original, images_invert) num_samples = images_original.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_invert[i], images_original[i]) logger.info("MSE= {}".format(str(np.mean(mse))))
def test_invert_md5_c(): """ Test Invert cpp op with md5 check """ logger.info("Test Invert cpp op with md5 check") # Generate dataset data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms_invert = [C.Decode(), C.Resize(size=[224, 224]), C.Invert(), F.ToTensor()] data = data_set.map(operations=transforms_invert, input_columns="image") # Compare with expected md5 from images filename = "invert_01_result_c.npz" save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
def test_invert_one_channel(): """ Test Invert cpp op with one channel image """ logger.info("Test Invert C Op With One Channel Images") c_op = C.Invert() 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"]) data_set.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)