def test_random_crop_and_resize_op_py(plot=False): """ Test RandomCropAndResize op in py transforms """ logger.info("test_random_crop_and_resize_op_py") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) # With these inputs we expect the code to crop the whole image transforms1 = [ py_vision.Decode(), py_vision.RandomResizedCrop((256, 512), (2, 2), (1, 3)), py_vision.ToTensor() ] transform1 = py_vision.ComposeOp(transforms1) data1 = data1.map(input_columns=["image"], operations=transform1()) # Second dataset # Second dataset for comparison data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms2 = [py_vision.Decode(), py_vision.ToTensor()] transform2 = py_vision.ComposeOp(transforms2) data2 = data2.map(input_columns=["image"], operations=transform2()) num_iter = 0 crop_and_resize_images = [] original_images = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): crop_and_resize = (item1["image"].transpose(1, 2, 0) * 255).astype( np.uint8) original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) original = cv2.resize(original, (512, 256)) mse = diff_mse(crop_and_resize, original) # Due to rounding error the mse for Python is not exactly 0 assert mse <= 0.05 logger.info("random_crop_and_resize_op_{}, mse: {}".format( num_iter + 1, mse)) num_iter += 1 crop_and_resize_images.append(crop_and_resize) original_images.append(original) if plot: visualize_list(original_images, crop_and_resize_images)
def test_random_color_adjust_op_brightness(plot=False): """ Test RandomColorAdjust op """ logger.info("test_random_color_adjust_op") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = c_vision.Decode() random_adjust_op = c_vision.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0)) ctrans = [ decode_op, random_adjust_op, ] data1 = data1.map(input_columns=["image"], operations=ctrans) # Second dataset transforms = [ py_vision.Decode(), py_vision.RandomColorAdjust((0.8, 0.8), (1, 1), (1, 1), (0, 0)), py_vision.ToTensor(), ] transform = py_vision.ComposeOp(transforms) data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=transform()) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): num_iter += 1 c_image = item1["image"] py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) logger.info("shape of c_image: {}".format(c_image.shape)) logger.info("shape of py_image: {}".format(py_image.shape)) logger.info("dtype of c_image: {}".format(c_image.dtype)) logger.info("dtype of py_image: {}".format(py_image.dtype)) mse = diff_mse(c_image, py_image) logger.info("mse is {}".format(mse)) logger.info("random_rotation_op_{}, mse: {}".format(num_iter + 1, mse)) assert mse < 0.01 # if mse != 0: # logger.info("mse is: {}".format(mse)) if plot: visualize(c_image, mse, py_image)
def test_random_color_jitter_op_hue(): """ Test RandomColorAdjust op """ logger.info("test_random_color_jitter_op") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = c_vision.Decode() # channel_swap_op = c_vision.ChannelSwap() # change_mode_op = c_vision.ChangeMode() random_jitter_op = c_vision.RandomColorAdjust((1, 1), (1, 1), (1, 1), (0.2, 0.2)) ctrans = [ decode_op, random_jitter_op, ] data1 = data1.map(input_columns=["image"], operations=ctrans) # Second dataset transforms = [ py_vision.Decode(), py_vision.RandomColorAdjust((1, 1), (1, 1), (1, 1), (0.2, 0.2)), py_vision.ToTensor(), ] transform = py_vision.ComposeOp(transforms) data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=transform()) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): num_iter += 1 c_image = item1["image"] py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) # logger.info("shape of img: {}".format(img.shape)) logger.info("shape of c_image: {}".format(c_image.shape)) logger.info("shape of py_image: {}".format(py_image.shape)) logger.info("dtype of c_image: {}".format(c_image.dtype)) logger.info("dtype of py_image: {}".format(py_image.dtype)) # logger.info("dtype of img: {}".format(img.dtype)) diff = c_image - py_image # mse = (np.sum(np.power(diff, 2))) / (c_image.shape[0] * c_image.shape[1]) mse = diff_mse(c_image, py_image) logger.info("mse is {}".format(mse)) assert mse < 0.01
def test_random_grayscale_input_grayscale_images(): """ Test RandomGrayscale Op: valid parameter with grayscale images as input, expect to pass """ logger.info("test_random_grayscale_input_grayscale_images") original_seed = config_get_set_seed(0) original_num_parallel_workers = config_get_set_num_parallel_workers(1) # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms1 = [ py_vision.Decode(), py_vision.Grayscale(1), # Note: If the input images is grayscale image with 1 channel. py_vision.RandomGrayscale(0.5), py_vision.ToTensor() ] transform1 = py_vision.ComposeOp(transforms1) data1 = data1.map(input_columns=["image"], operations=transform1()) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms2 = [ py_vision.Decode(), py_vision.ToTensor() ] transform2 = py_vision.ComposeOp(transforms2) data2 = data2.map(input_columns=["image"], operations=transform2()) image_gray = [] image = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image_gray.append(image1) image.append(image2) assert len(image1.shape) == 3 assert image1.shape[2] == 1 assert len(image2.shape) == 3 assert image2.shape[2] == 3 # Restore config ds.config.set_seed(original_seed) ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_deterministic_python_seed_multi_thread(): """ Test deterministic execution with seed in python, this fails with multi-thread pyfunc run """ logger.info("test_deterministic_python_seed_multi_thread") # Save original configuration values num_parallel_workers_original = ds.config.get_num_parallel_workers() seed_original = ds.config.get_seed() ds.config.set_num_parallel_workers(3) ds.config.set_seed(0) # when we set the seed all operations within our dataset should be deterministic # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms = [ py_vision.Decode(), py_vision.RandomCrop([512, 512], [200, 200, 200, 200]), py_vision.ToTensor(), ] transform = py_vision.ComposeOp(transforms) data1 = data1.map(input_columns=["image"], operations=transform(), python_multiprocessing=True) data1_output = [] # config.set_seed() calls random.seed() for data_one in data1.create_dict_iterator(): data1_output.append(data_one["image"]) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) # If seed is set up on constructor data2 = data2.map(input_columns=["image"], operations=transform(), python_multiprocessing=True) # config.set_seed() calls random.seed() ds.config.set_seed(0) data2_output = [] for data_two in data2.create_dict_iterator(): data2_output.append(data_two["image"]) try: np.testing.assert_equal(data1_output, data2_output) except Exception as e: # expect output to not match during multi-threaded excution logger.info("Got an exception in DE: {}".format(str(e))) assert "Array" in str(e) # Restore original configuration values ds.config.set_num_parallel_workers(num_parallel_workers_original) ds.config.set_seed(seed_original)
def test_random_erasing_op(plot=False): """ Test RandomErasing and Cutout """ logger.info("test_random_erasing") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms_1 = [ vision.Decode(), vision.ToTensor(), vision.RandomErasing(value='random') ] transform_1 = vision.ComposeOp(transforms_1) data1 = data1.map(input_columns=["image"], operations=transform_1()) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms_2 = [vision.Decode(), vision.ToTensor(), vision.Cutout(80)] transform_2 = vision.ComposeOp(transforms_2) data2 = data2.map(input_columns=["image"], operations=transform_2()) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): num_iter += 1 image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image_2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) logger.info("shape of image_1: {}".format(image_1.shape)) logger.info("shape of image_2: {}".format(image_2.shape)) logger.info("dtype of image_1: {}".format(image_1.dtype)) logger.info("dtype of image_2: {}".format(image_2.dtype)) mse = diff_mse(image_1, image_2) if plot: visualize_image(image_1, image_2, mse)
def test_random_affine_op(plot=False): """ Test RandomAffine in python transformations """ logger.info("test_random_affine_op") # define map operations transforms1 = [ py_vision.Decode(), py_vision.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.9, 1.1)), py_vision.ToTensor() ] transform1 = py_vision.ComposeOp(transforms1) transforms2 = [py_vision.Decode(), py_vision.ToTensor()] transform2 = py_vision.ComposeOp(transforms2) # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data1 = data1.map(input_columns=["image"], operations=transform1()) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=transform2()) image_affine = [] image_original = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image_affine.append(image1) image_original.append(image2) if plot: visualize_list(image_original, image_affine)
def test_random_rotation_op_py(plot=False): """ Test RandomRotation in python transformations op """ logger.info("test_random_rotation_op_py") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False) # use [90, 90] to force rotate 90 degrees, expand is set to be True to match output size transform1 = py_vision.ComposeOp([ py_vision.Decode(), py_vision.RandomRotation((90, 90), expand=True), py_vision.ToTensor() ]) data1 = data1.map(input_columns=["image"], operations=transform1()) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transform2 = py_vision.ComposeOp( [py_vision.Decode(), py_vision.ToTensor()]) data2 = data2.map(input_columns=["image"], operations=transform2()) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): if num_iter > 0: break rotation_de = (item1["image"].transpose(1, 2, 0) * 255).astype( np.uint8) original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) logger.info("shape before rotate: {}".format(original.shape)) rotation_cv = cv2.rotate(original, cv2.ROTATE_90_COUNTERCLOCKWISE) mse = diff_mse(rotation_de, rotation_cv) logger.info("random_rotation_op_{}, mse: {}".format(num_iter + 1, mse)) assert mse == 0 num_iter += 1 if plot: visualize_image(original, rotation_de, mse, rotation_cv)
def test_linear_transformation_op(plot=False): """ Test LinearTransformation op: verify if images transform correctly """ logger.info("test_linear_transformation_01") # Initialize parameters height = 50 weight = 50 dim = 3 * height * weight transformation_matrix = np.eye(dim) mean_vector = np.zeros(dim) # Define operations transforms = [ py_vision.Decode(), py_vision.CenterCrop([height, weight]), py_vision.ToTensor() ] transform = py_vision.ComposeOp(transforms) # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data1 = data1.map(input_columns=["image"], operations=transform()) # Note: if transformation matrix is diagonal matrix with all 1 in diagonal, # the output matrix in expected to be the same as the input matrix. data1 = data1.map(input_columns=["image"], operations=py_vision.LinearTransformation( transformation_matrix, mean_vector)) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=transform()) image_transformed = [] image = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image_transformed.append(image1) image.append(image2) mse = diff_mse(image1, image2) assert mse == 0 if plot: visualize_list(image, image_transformed)
def test_random_apply_op(plot=False): """ Test RandomApply in python transformations """ logger.info("test_random_apply_op") # define map operations transforms_list = [py_vision.CenterCrop(64), py_vision.RandomRotation(30)] transforms1 = [ py_vision.Decode(), py_vision.RandomApply(transforms_list, prob=0.6), py_vision.ToTensor() ] transform1 = py_vision.ComposeOp(transforms1) transforms2 = [py_vision.Decode(), py_vision.ToTensor()] transform2 = py_vision.ComposeOp(transforms2) # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data1 = data1.map(input_columns=["image"], operations=transform1()) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=transform2()) image_apply = [] image_original = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image_apply.append(image1) image_original.append(image2) if plot: visualize(image_original, image_apply)
def test_pad_grayscale(): """ Tests that the pad works for grayscale images """ def channel_swap(image): """ Py func hack for our pytransforms to work with c transforms """ return (image.transpose(1, 2, 0) * 255).astype(np.uint8) transforms = [ py_vision.Decode(), py_vision.Grayscale(1), py_vision.ToTensor(), (lambda image: channel_swap(image)) ] transform = py_vision.ComposeOp(transforms) data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data1 = data1.map(input_columns=["image"], operations=transform()) # if input is grayscale, the output dimensions should be single channel pad_gray = c_vision.Pad(100, fill_value=(20, 20, 20)) data1 = data1.map(input_columns=["image"], operations=pad_gray) dataset_shape_1 = [] for item1 in data1.create_dict_iterator(): c_image = item1["image"] dataset_shape_1.append(c_image.shape) # Dataset for comparison data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = c_vision.Decode() # we use the same padding logic ctrans = [decode_op, pad_gray] dataset_shape_2 = [] data2 = data2.map(input_columns=["image"], operations=ctrans) for item2 in data2.create_dict_iterator(): c_image = item2["image"] dataset_shape_2.append(c_image.shape) for shape1, shape2 in zip(dataset_shape_1, dataset_shape_2): # validate that the first two dimensions are the same # we have a little inconsistency here because the third dimension is 1 after py_vision.Grayscale assert (shape1[0:1] == shape2[0:1])
def test_to_type_op(): """ Test ToType Op """ logger.info("test_to_type_op") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms1 = [ py_vision.Decode(), py_vision.ToTensor(), # Note: Convert the datatype from float32 to int16 py_vision.ToType(np.int16) ] transform1 = py_vision.ComposeOp(transforms1) data1 = data1.map(input_columns=["image"], operations=transform1()) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms2 = [py_vision.Decode(), py_vision.ToTensor()] transform2 = py_vision.ComposeOp(transforms2) data2 = data2.map(input_columns=["image"], operations=transform2()) for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): image1 = item1["image"] image2 = item2["image"] assert isinstance(image1, np.ndarray) assert isinstance(image2, np.ndarray) assert image1.dtype == np.int16 assert image2.dtype == np.float32 assert image1.shape == image2.shape
def test_pad_op(): """ Test Pad op """ logger.info("test_random_color_jitter_op") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = c_vision.Decode() pad_op = c_vision.Pad((100, 100, 100, 100)) ctrans = [ decode_op, pad_op, ] data1 = data1.map(input_columns=["image"], operations=ctrans) # Second dataset transforms = [ py_vision.Decode(), py_vision.Pad(100), py_vision.ToTensor(), ] transform = py_vision.ComposeOp(transforms) data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=transform()) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): num_iter += 1 c_image = item1["image"] py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) logger.info("shape of c_image: {}".format(c_image.shape)) logger.info("shape of py_image: {}".format(py_image.shape)) logger.info("dtype of c_image: {}".format(c_image.dtype)) logger.info("dtype of py_image: {}".format(py_image.dtype)) diff = c_image - py_image mse = diff_mse(c_image, py_image) logger.info("mse is {}".format(mse)) assert mse < 0.01
def test_rotation_diff(plot=False): """ Test RandomRotation op """ logger.info("test_random_rotation_op") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = c_vision.Decode() rotation_op = c_vision.RandomRotation((45, 45)) ctrans = [decode_op, rotation_op] data1 = data1.map(input_columns=["image"], operations=ctrans) # Second dataset transforms = [ py_vision.Decode(), py_vision.RandomRotation((45, 45)), py_vision.ToTensor(), ] transform = py_vision.ComposeOp(transforms) data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=transform()) num_iter = 0 image_list_c, image_list_py = [], [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): num_iter += 1 c_image = item1["image"] py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image_list_c.append(c_image) image_list_py.append(py_image) logger.info("shape of c_image: {}".format(c_image.shape)) logger.info("shape of py_image: {}".format(py_image.shape)) logger.info("dtype of c_image: {}".format(c_image.dtype)) logger.info("dtype of py_image: {}".format(py_image.dtype)) mse = diff_mse(c_image, py_image) assert mse < 0.001 # Rounding error if plot: visualize_list(image_list_c, image_list_py, visualize_mode=2)
def create_imagenet_dataset(imagenet_dir): ds = de.ImageFolderDatasetV2(imagenet_dir) transform = F.ComposeOp([ F.Decode(), F.RandomHorizontalFlip(0.5), F.ToTensor(), F.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)), F.RandomErasing() ]) ds = ds.map(input_columns="image", operations=transform()) ds = ds.shuffle(buffer_size=5) ds = ds.repeat(3) return ds
def test_equalize_md5_py(): """ Test Equalize py op with md5 check """ logger.info("Test Equalize") # First dataset data1 = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) transforms = F.ComposeOp([F.Decode(), F.Equalize(), F.ToTensor()]) data1 = data1.map(input_columns="image", operations=transforms()) # Compare with expected md5 from images filename = "equalize_01_result.npz" save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
def test_random_crop_op_py(plot=False): """ Test RandomCrop op in py transforms """ logger.info("test_random_crop_op_py") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms1 = [ py_vision.Decode(), py_vision.RandomCrop([512, 512], [200, 200, 200, 200]), py_vision.ToTensor() ] transform1 = py_vision.ComposeOp(transforms1) data1 = data1.map(input_columns=["image"], operations=transform1()) # Second dataset # Second dataset for comparison data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms2 = [py_vision.Decode(), py_vision.ToTensor()] transform2 = py_vision.ComposeOp(transforms2) data2 = data2.map(input_columns=["image"], operations=transform2()) crop_images = [] original_images = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): crop = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8) original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) crop_images.append(crop) original_images.append(original) if plot: visualize(original_images, crop_images)
def test_deterministic_python_seed(): """ Test deterministic execution with seed in python """ logger.info("test_deterministic_python_seed") # Save original configuration values num_parallel_workers_original = ds.config.get_num_parallel_workers() seed_original = ds.config.get_seed() ds.config.set_seed(0) ds.config.set_num_parallel_workers(1) # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms = [ py_vision.Decode(), py_vision.RandomCrop([512, 512], [200, 200, 200, 200]), py_vision.ToTensor(), ] transform = py_vision.ComposeOp(transforms) data1 = data1.map(input_columns=["image"], operations=transform()) data1_output = [] # config.set_seed() calls random.seed() for data_one in data1.create_dict_iterator(): data1_output.append(data_one["image"]) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=transform()) # config.set_seed() calls random.seed(), resets seed for next dataset iterator ds.config.set_seed(0) data2_output = [] for data_two in data2.create_dict_iterator(): data2_output.append(data_two["image"]) np.testing.assert_equal(data1_output, data2_output) # Restore original configuration values ds.config.set_num_parallel_workers(num_parallel_workers_original) ds.config.set_seed(seed_original)
def test_cut_out_op_multicut(plot=False): """ Test Cutout """ logger.info("test_cut_out") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms_1 = [ f.Decode(), f.ToTensor(), ] transform_1 = f.ComposeOp(transforms_1) data1 = data1.map(input_columns=["image"], operations=transform_1()) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = c.Decode() cut_out_op = c.CutOut(80, num_patches=10) transforms_2 = [decode_op, cut_out_op] data2 = data2.map(input_columns=["image"], operations=transforms_2) num_iter = 0 image_list_1, image_list_2 = [], [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): num_iter += 1 image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8) # C image doesn't require transpose image_2 = item2["image"] image_list_1.append(image_1) image_list_2.append(image_2) logger.info("shape of image_1: {}".format(image_1.shape)) logger.info("shape of image_2: {}".format(image_2.shape)) logger.info("dtype of image_1: {}".format(image_1.dtype)) logger.info("dtype of image_2: {}".format(image_2.dtype)) if plot: visualize_list(image_list_1, image_list_2)
def util_test_normalize_grayscale(num_output_channels, mean, std): """ Utility function for testing Normalize. Input arguments are given by other tests """ transforms = [ py_vision.Decode(), py_vision.Grayscale(num_output_channels), py_vision.ToTensor(), py_vision.Normalize(mean, std) ] transform = py_vision.ComposeOp(transforms) # Generate dataset data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data = data.map(input_columns=["image"], operations=transform()) return data
def test_rgb_hsv_pipeline(): # First dataset transforms1 = [vision.Decode(), vision.Resize([64, 64]), vision.ToTensor()] transforms1 = vision.ComposeOp(transforms1) ds1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) ds1 = ds1.map(input_columns=["image"], operations=transforms1()) # Second dataset transforms2 = [ vision.Decode(), vision.Resize([64, 64]), vision.ToTensor(), vision.RgbToHsv(), vision.HsvToRgb() ] transform2 = vision.ComposeOp(transforms2) ds2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) ds2 = ds2.map(input_columns=["image"], operations=transform2()) num_iter = 0 for data1, data2 in zip(ds1.create_dict_iterator(), ds2.create_dict_iterator()): num_iter += 1 ori_img = data1["image"] cvt_img = data2["image"] assert_allclose(ori_img.flatten(), cvt_img.flatten(), rtol=1e-5, atol=0) assert (ori_img.shape == cvt_img.shape)
def test_invert_md5_py(): """ Test Invert python op with md5 check """ logger.info("Test Invert python op with md5 check") # Generate dataset ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) transforms_invert = F.ComposeOp([F.Decode(), F.Invert(), F.ToTensor()]) data = ds.map(input_columns="image", operations=transforms_invert()) # Compare with expected md5 from images filename = "invert_01_result_py.npz" save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
def test_random_color_adjust_op_contrast(): """ Test RandomColorAdjust op """ logger.info("test_random_color_adjust_op") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = c_vision.Decode() random_adjust_op = c_vision.RandomColorAdjust((1, 1), (0.5, 0.5), (1, 1), (0, 0)) ctrans = [decode_op, random_adjust_op ] data1 = data1.map(input_columns=["image"], operations=ctrans) # Second dataset transforms = [ py_vision.Decode(), py_vision.RandomColorAdjust((1, 1), (0.5, 0.5), (1, 1), (0, 0)), py_vision.ToTensor(), ] transform = py_vision.ComposeOp(transforms) data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=transform()) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): num_iter += 1 c_image = item1["image"] py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) logger.info("shape of c_image: {}".format(c_image.shape)) logger.info("shape of py_image: {}".format(py_image.shape)) logger.info("dtype of c_image: {}".format(c_image.dtype)) logger.info("dtype of py_image: {}".format(py_image.dtype)) diff = c_image - py_image logger.info("contrast difference c is : {}".format(c_image[0][0])) logger.info("contrast difference py is : {}".format(py_image[0][0])) logger.info("contrast difference is : {}".format(diff[0][0])) # mse = (np.sum(np.power(diff, 2))) / (c_image.shape[0] * c_image.shape[1]) mse = diff_mse(c_image, py_image) logger.info("mse is {}".format(mse))
def test_ten_crop_list_size_error_msg(): """ Tests TenCrop error message when the size arg has more than 2 elements """ logger.info("test_ten_crop_list_size_error_msg") with pytest.raises(TypeError) as info: transforms = [ vision.Decode(), vision.TenCrop([200, 200, 200]), lambda images: np.stack( [vision.ToTensor()(image) for image in images]) # 4D stack of 10 images ] error_msg = "Size should be a single integer or a list/tuple (h, w) of length 2." assert error_msg == str(info.value)
def test_five_crop_error_msg(): """ Test FiveCrop error message. """ logger.info("test_five_crop_error_msg") data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms = [vision.Decode(), vision.FiveCrop(200), vision.ToTensor()] transform = vision.ComposeOp(transforms) data = data.map(input_columns=["image"], operations=transform()) with pytest.raises(RuntimeError): data.create_tuple_iterator().__next__()
def test_HWC2CHW_comp(plot=False): """ Test HWC2CHW between python and c image augmentation """ logger.info("Test HWC2CHW with c_transform and py_transform comparison") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = c_vision.Decode() hwc2chw_op = c_vision.HWC2CHW() data1 = data1.map(input_columns=["image"], operations=decode_op) data1 = data1.map(input_columns=["image"], operations=hwc2chw_op) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms = [ py_vision.Decode(), py_vision.ToTensor(), py_vision.HWC2CHW() ] transform = py_vision.ComposeOp(transforms) data2 = data2.map(input_columns=["image"], operations=transform()) image_c_transposed = [] image_py_transposed = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): c_image = item1["image"] py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) # compare images between that applying c_transform and py_transform mse = diff_mse(py_image, c_image) # the images aren't exactly the same due to rounding error assert mse < 0.001 image_c_transposed.append(item1["image"].copy()) image_py_transposed.append(item2["image"].copy()) if plot: visualize(image_c_transposed, image_py_transposed)
def test_random_vertical_comp(plot=False): """ Test test_random_vertical_flip and compare between python and c image augmentation ops """ logger.info("test_random_vertical_comp") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = c_vision.Decode() # Note: The image must be flipped if prob is set to be 1 random_horizontal_op = c_vision.RandomVerticalFlip(1) data1 = data1.map(input_columns=["image"], operations=decode_op) data1 = data1.map(input_columns=["image"], operations=random_horizontal_op) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms = [ py_vision.Decode(), # Note: The image must be flipped if prob is set to be 1 py_vision.RandomVerticalFlip(1), py_vision.ToTensor() ] transform = py_vision.ComposeOp(transforms) data2 = data2.map(input_columns=["image"], operations=transform()) images_list_c = [] images_list_py = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): image_c = item1["image"] image_py = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) images_list_c.append(image_c) images_list_py.append(image_py) # Check if the output images are the same mse = diff_mse(image_c, image_py) assert mse < 0.001 if plot: visualize_list(images_list_c, images_list_py, visualize_mode=2)
def test_normalize_op_py(plot=False): """ Test Normalize in python transformations """ logger.info("Test Normalize in python") mean = [0.475, 0.45, 0.392] std = [0.275, 0.267, 0.278] # define map operations transforms = [py_vision.Decode(), py_vision.ToTensor()] transform = py_vision.ComposeOp(transforms) normalize_op = py_vision.Normalize(mean, std) # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data1 = data1.map(input_columns=["image"], operations=transform()) data1 = data1.map(input_columns=["image"], operations=normalize_op) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=transform()) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): image_de_normalized = (item1["image"].transpose(1, 2, 0) * 255).astype( np.uint8) image_np_normalized = ( normalize_np(item2["image"].transpose(1, 2, 0), mean, std) * 255).astype(np.uint8) image_original = (item2["image"].transpose(1, 2, 0) * 255).astype( np.uint8) mse = diff_mse(image_de_normalized, image_np_normalized) logger.info("image_{}, mse: {}".format(num_iter + 1, mse)) assert mse < 0.01 if plot: visualize_image(image_original, image_de_normalized, mse, image_np_normalized) num_iter += 1
def test_type_cast(): """ Test TypeCast op """ logger.info("test_type_cast") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = c_vision.Decode() type_cast_op = data_util.TypeCast(mstype.float32) ctrans = [ decode_op, type_cast_op, ] data1 = data1.map(input_columns=["image"], operations=ctrans) # Second dataset transforms = [py_vision.Decode(), py_vision.ToTensor()] transform = py_vision.ComposeOp(transforms) data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=transform()) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): num_iter += 1 c_image = item1["image"] py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) logger.info("shape of c_image: {}".format(c_image.shape)) logger.info("shape of py_image: {}".format(py_image.shape)) logger.info("dtype of c_image: {}".format(c_image.dtype)) logger.info("dtype of py_image: {}".format(py_image.dtype)) assert c_image.dtype == "float32"
def test_random_rotation_md5(): """ Test RandomRotation with md5 check """ logger.info("Test RandomRotation with md5 check") original_seed = config_get_set_seed(5) original_num_parallel_workers = config_get_set_num_parallel_workers(1) # Fisrt dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = c_vision.Decode() resize_op = c_vision.RandomRotation((0, 90), expand=True, resample=Inter.BILINEAR, center=(50, 50), fill_value=150) data1 = data1.map(input_columns=["image"], operations=decode_op) data1 = data1.map(input_columns=["image"], operations=resize_op) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False) transform2 = py_vision.ComposeOp([ py_vision.Decode(), py_vision.RandomRotation((0, 90), expand=True, resample=Inter.BILINEAR, center=(50, 50), fill_value=150), py_vision.ToTensor() ]) data2 = data2.map(input_columns=["image"], operations=transform2()) # Compare with expected md5 from images filename1 = "random_rotation_01_c_result.npz" save_and_check_md5(data1, filename1, generate_golden=GENERATE_GOLDEN) filename2 = "random_rotation_01_py_result.npz" save_and_check_md5(data2, filename2, generate_golden=GENERATE_GOLDEN) # Restore configuration ds.config.set_seed(original_seed) ds.config.set_num_parallel_workers(original_num_parallel_workers)