def test_invert_py(plot=False): """ Test Invert python op """ logger.info("Test Invert 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) # Color Inverted Images data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms_invert = mindspore.dataset.transforms.py_transforms.Compose( [F.Decode(), F.Resize((224, 224)), F.Invert(), F.ToTensor()]) ds_invert = data_set.map(operations=transforms_invert, input_columns="image") ds_invert = ds_invert.batch(512) for idx, (image, _) in enumerate(ds_invert): if idx == 0: images_invert = np.transpose(image.asnumpy(), (0, 2, 3, 1)) else: images_invert = np.append(images_invert, 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] = np.mean((images_invert[i] - images_original[i])**2) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize_list(images_original, images_invert)
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_md5_py(): """ Test Invert python op with md5 check """ logger.info("Test Invert python op with md5 check") # Generate dataset data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms_invert = mindspore.dataset.transforms.py_transforms.Compose( [F.Decode(), F.Invert(), F.ToTensor()]) data = data_set.map(operations=transforms_invert, input_columns="image") # Compare with expected md5 from images filename = "invert_01_result_py.npz" save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
def test_cpp_uniform_augment_exception_pyops(num_ops=2): """ Test UniformAugment invalid op in operations """ logger.info("Test CPP UniformAugment invalid OP exception") transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]), C.RandomHorizontalFlip(), C.RandomVerticalFlip(), C.RandomColorAdjust(), C.RandomRotation(degrees=45), F.Invert()] with pytest.raises(TypeError) as e: C.UniformAugment(transforms=transforms_ua, num_ops=num_ops) logger.info("Got an exception in DE: {}".format(str(e))) assert "Type of Transforms[5] must be c_transform" in str(e.value)
def test_cpp_uniform_augment_exception_pyops(num_ops=2): """ Test UniformAugment invalid op in operations """ logger.info("Test CPP UniformAugment invalid OP exception") transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]), C.RandomHorizontalFlip(), C.RandomVerticalFlip(), C.RandomColorAdjust(), C.RandomRotation(degrees=45), F.Invert()] with pytest.raises(TypeError) as e: C.UniformAugment(transforms=transforms_ua, num_ops=num_ops) logger.info("Got an exception in DE: {}".format(str(e))) assert "Argument transforms[5] with value" \ " <mindspore.dataset.vision.py_transforms.Invert" in str(e.value) assert "is not of type (<class 'mindspore._c_dataengine.TensorOp'>,"\ " <class 'mindspore._c_dataengine.TensorOperation'>)" in str(e.value)
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)