def test_random_solarize_errors(): """ Test that RandomSolarize errors with bad input """ with pytest.raises(ValueError) as error_info: vision.RandomSolarize((12, 1)) assert "threshold must be in min max format numbers" in str( error_info.value) with pytest.raises(ValueError) as error_info: vision.RandomSolarize((12, 1000)) assert "Input is not within the required interval of (0 to 255)." in str( error_info.value) with pytest.raises(TypeError) as error_info: vision.RandomSolarize((122.1, 140)) assert "Argument threshold[0] with value 122.1 is not of type (<class 'int'>,)." in str( error_info.value) with pytest.raises(ValueError) as error_info: vision.RandomSolarize((122, 100, 30)) assert "threshold must be a sequence of two numbers" in str( error_info.value) with pytest.raises(ValueError) as error_info: vision.RandomSolarize((120, )) assert "threshold must be a sequence of two numbers" in str( error_info.value)
def test_random_solarize_op(threshold=None, plot=False): """ Test RandomSolarize """ logger.info("Test RandomSolarize") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"]) decode_op = vision.Decode() if threshold is None: solarize_op = vision.RandomSolarize() else: solarize_op = vision.RandomSolarize(threshold) data1 = data1.map(input_columns=["image"], operations=decode_op) data1 = data1.map(input_columns=["image"], operations=solarize_op) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"]) data2 = data2.map(input_columns=["image"], operations=decode_op) image_solarized = [] image = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): image_solarized.append(item1["image"].copy()) image.append(item2["image"].copy()) if plot: visualize_list(image, image_solarized)
def test_random_solarize_mnist(plot=False, run_golden=True): """ Test RandomSolarize op with MNIST dataset (Grayscale images) """ mnist_1 = de.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False) mnist_2 = de.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False) mnist_2 = mnist_2.map(input_columns="image", operations=vision.RandomSolarize((0, 255))) images = [] images_trans = [] labels = [] for _, (data_orig, data_trans) in enumerate(zip(mnist_1, mnist_2)): image_orig, label_orig = data_orig image_trans, _ = data_trans images.append(image_orig) labels.append(label_orig) images_trans.append(image_trans) if plot: visualize_one_channel_dataset(images, images_trans, labels) if run_golden: filename = "random_solarize_02_result.npz" save_and_check_md5(mnist_2, filename, generate_golden=GENERATE_GOLDEN)
def test_random_solarize_op(threshold=(10, 150), plot=False, run_golden=True): """ Test RandomSolarize """ logger.info("Test RandomSolarize") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = vision.Decode() original_seed = config_get_set_seed(0) original_num_parallel_workers = config_get_set_num_parallel_workers(1) if threshold is None: solarize_op = vision.RandomSolarize() else: solarize_op = vision.RandomSolarize(threshold) data1 = data1.map(input_columns=["image"], operations=decode_op) data1 = data1.map(input_columns=["image"], operations=solarize_op) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=decode_op) if run_golden: filename = "random_solarize_01_result.npz" save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN) image_solarized = [] image = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): image_solarized.append(item1["image"].copy()) image.append(item2["image"].copy()) if plot: visualize_list(image, image_solarized) ds.config.set_seed(original_seed) ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_solarize_md5(): """ Test RandomSolarize """ logger.info("Test RandomSolarize") original_seed = config_get_set_seed(0) original_num_parallel_workers = config_get_set_num_parallel_workers(1) data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = vision.Decode() random_solarize_op = vision.RandomSolarize((10, 150)) data1 = data1.map(input_columns=["image"], operations=decode_op) data1 = data1.map(input_columns=["image"], operations=random_solarize_op) # Compare with expected md5 from images filename = "random_solarize_01_result.npz" save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN) # Restore config setting ds.config.set_seed(original_seed) ds.config.set_num_parallel_workers(original_num_parallel_workers)