def test_equalize_mnist_c(plot=False): """ Test Equalize C op with MNIST dataset (Grayscale images) """ logger.info("Test Equalize C Op With MNIST Images") data_set = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False) ds_equalize_c = data_set.map(operations=C.Equalize(), 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_equalize_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 = "equalize_mnist_result_c.npz" save_and_check_md5(ds_equalize_c, filename, generate_golden=GENERATE_GOLDEN) if plot: visualize_one_channel_dataset(images, images_trans, labels)
def test_random_solarize_mnist(plot=False, run_golden=True): """ Test RandomSolarize op with MNIST dataset (Grayscale images) """ mnist_1 = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False) mnist_2 = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False) mnist_2 = mnist_2.map(operations=vision.RandomSolarize((0, 255)), input_columns="image") 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.asnumpy()) labels.append(label_orig.asnumpy()) images_trans.append(image_trans.asnumpy()) 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_sharpness_one_channel_c(degrees=(1.4, 1.4), plot=False): """ Test Random Sharpness cpp op with one channel """ logger.info( "Test RandomSharpness C Op With MNIST Dataset (Grayscale images)") c_op = C.RandomSharpness() if degrees is not None: c_op = C.RandomSharpness(degrees) # RandomSharpness Images data = de.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False) ds_random_sharpness_c = data.map(operations=c_op, input_columns="image") # Original images data = de.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False) images = [] images_trans = [] labels = [] for _, (data_orig, data_trans) in enumerate(zip(data, ds_random_sharpness_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()) if plot: visualize_one_channel_dataset(images, images_trans, labels)
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)