def test_auto_contrast_invalid_cutoff_param_py(): """ Test AutoContrast python Op with invalid cutoff parameter """ logger.info("Test AutoContrast python Op with invalid cutoff parameter") try: data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data_set = data_set.map(operations=[ mindspore.dataset.transforms.py_transforms.Compose([ F.Decode(), F.Resize((224, 224)), F.AutoContrast(cutoff=-10.0), F.ToTensor() ]) ], 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, 50)." in str( error) try: data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data_set = data_set.map(operations=[ mindspore.dataset.transforms.py_transforms.Compose([ F.Decode(), F.Resize((224, 224)), F.AutoContrast(cutoff=120.0), F.ToTensor() ]) ], 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, 50)." in str( error)
def test_auto_contrast_invalid_ignore_param_py(): """ Test AutoContrast python Op with invalid ignore parameter """ logger.info("Test AutoContrast python Op with invalid ignore parameter") try: data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data_set = data_set.map(operations=[ mindspore.dataset.transforms.py_transforms.Compose([ F.Decode(), F.Resize((224, 224)), F.AutoContrast(ignore=255.5), F.ToTensor() ]) ], 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=[ mindspore.dataset.transforms.py_transforms.Compose([ F.Decode(), F.Resize((224, 224)), F.AutoContrast(ignore=(10, 100)), F.ToTensor() ]) ], 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_auto_contrast_py(plot=False): """ Test AutoContrast """ logger.info("Test AutoContrast Python Op") # Original Images data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms_original = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(), F.Resize((224, 224)), F.ToTensor()]) 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 = np.transpose(image.asnumpy(), (0, 2, 3, 1)) else: images_original = np.append(images_original, np.transpose(image.asnumpy(), (0, 2, 3, 1)), axis=0) # AutoContrast Images data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms_auto_contrast = \ mindspore.dataset.transforms.py_transforms.Compose([F.Decode(), F.Resize((224, 224)), F.AutoContrast(cutoff=10.0, ignore=[10, 20]), F.ToTensor()]) ds_auto_contrast = data_set.map(operations=transforms_auto_contrast, input_columns="image") ds_auto_contrast = ds_auto_contrast.batch(512) for idx, (image, _) in enumerate(ds_auto_contrast): if idx == 0: images_auto_contrast = np.transpose(image.asnumpy(), (0, 2, 3, 1)) else: images_auto_contrast = np.append(images_auto_contrast, np.transpose(image.asnumpy(), (0, 2, 3, 1)), axis=0) num_samples = images_original.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_auto_contrast[i], images_original[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) # Compare with expected md5 from images filename = "autocontrast_01_result_py.npz" save_and_check_md5(ds_auto_contrast, filename, generate_golden=GENERATE_GOLDEN) if plot: visualize_list(images_original, images_auto_contrast)
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)
def test_uniform_augment(plot=False, num_ops=2): """ Test UniformAugment """ logger.info("Test UniformAugment") # Original Images data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms_original = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(), F.Resize((224, 224)), F.ToTensor()]) 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 = np.transpose(image.asnumpy(), (0, 2, 3, 1)) else: images_original = np.append(images_original, np.transpose(image.asnumpy(), (0, 2, 3, 1)), axis=0) # UniformAugment Images data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transform_list = [F.RandomRotation(45), F.RandomColor(), F.RandomSharpness(), F.Invert(), F.AutoContrast(), F.Equalize()] transforms_ua = \ mindspore.dataset.transforms.py_transforms.Compose([F.Decode(), F.Resize((224, 224)), F.UniformAugment(transforms=transform_list, num_ops=num_ops), F.ToTensor()]) ds_ua = data_set.map(operations=transforms_ua, input_columns="image") ds_ua = ds_ua.batch(512) for idx, (image, _) in enumerate(ds_ua): if idx == 0: images_ua = np.transpose(image.asnumpy(), (0, 2, 3, 1)) else: images_ua = np.append(images_ua, np.transpose(image.asnumpy(), (0, 2, 3, 1)), axis=0) num_samples = images_original.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_ua[i], images_original[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize_list(images_original, images_ua)