def test_random_apply_md5(): """ Test RandomApply op with md5 check """ logger.info("test_random_apply_md5") original_seed = config_get_set_seed(10) original_num_parallel_workers = config_get_set_num_parallel_workers(1) # define map operations transforms_list = [py_vision.CenterCrop(64), py_vision.RandomRotation(30)] transforms = [ py_vision.Decode(), # Note: using default value "prob=0.5" py_transforms.RandomApply(transforms_list), py_vision.ToTensor() ] transform = py_transforms.Compose(transforms) # Generate dataset data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data = data.map(operations=transform, input_columns=["image"]) # check results with md5 comparison filename = "random_apply_01_result.npz" save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN) # Restore configuration ds.config.set_seed(original_seed) ds.config.set_num_parallel_workers((original_num_parallel_workers))
def test_random_apply_exception_random_crop_badinput(): """ Test RandomApply: test invalid input for one of the transform functions, expected to raise error """ logger.info("test_random_apply_exception_random_crop_badinput") original_seed = config_get_set_seed(200) original_num_parallel_workers = config_get_set_num_parallel_workers(1) # define map operations transforms_list = [ py_vision.Resize([32, 32]), py_vision.RandomCrop(100), # crop size > image size py_vision.RandomRotation(30) ] transforms = [ py_vision.Decode(), py_transforms.RandomApply(transforms_list, prob=0.6), py_vision.ToTensor() ] transform = py_transforms.Compose(transforms) # Generate dataset data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data = data.map(operations=transform, input_columns=["image"]) try: _ = data.create_dict_iterator(num_epochs=1).get_next() except RuntimeError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert "Crop size" in str(e) # Restore configuration ds.config.set_seed(original_seed) ds.config.set_num_parallel_workers(original_num_parallel_workers)
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(operations=ctrans, input_columns=["image"]) # Second dataset transforms = [ py_vision.Decode(), py_vision.RandomRotation((45, 45)), py_vision.ToTensor(), ] transform = mindspore.dataset.transforms.py_transforms.Compose(transforms) data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(operations=transform, input_columns=["image"]) num_iter = 0 image_list_c, image_list_py = [], [] for item1, item2 in zip( data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)): 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 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(operations=decode_op, input_columns=["image"]) data1 = data1.map(operations=resize_op, input_columns=["image"]) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False) transform2 = mindspore.dataset.transforms.py_transforms.Compose([ 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(operations=transform2, input_columns=["image"]) # 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)
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 = mindspore.dataset.transforms.py_transforms.Compose([ py_vision.Decode(), py_vision.RandomRotation((90, 90), expand=True), py_vision.ToTensor() ]) data1 = data1.map(operations=transform1, input_columns=["image"]) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transform2 = mindspore.dataset.transforms.py_transforms.Compose( [py_vision.Decode(), py_vision.ToTensor()]) data2 = data2.map(operations=transform2, input_columns=["image"]) num_iter = 0 for item1, item2 in zip( data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)): 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_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_transforms.RandomApply(transforms_list, prob=0.6), py_vision.ToTensor() ] transform1 = py_transforms.Compose(transforms1) transforms2 = [py_vision.Decode(), py_vision.ToTensor()] transform2 = py_transforms.Compose(transforms2) # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data1 = data1.map(operations=transform1, input_columns=["image"]) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(operations=transform2, input_columns=["image"]) image_apply = [] image_original = [] for item1, item2 in zip( data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)): 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_list(image_original, image_apply)
def test_random_rotation_op_py_ANTIALIAS(): """ Test RandomRotation in python transformations op """ logger.info("test_random_rotation_op_py_ANTIALIAS") # 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 = mindspore.dataset.transforms.py_transforms.Compose([ py_vision.Decode(), py_vision.RandomRotation((90, 90), expand=True, resample=Inter.ANTIALIAS), py_vision.ToTensor() ]) data1 = data1.map(operations=transform1, input_columns=["image"]) num_iter = 0 for _ in data1.create_dict_iterator(num_epochs=1, output_numpy=True): num_iter += 1 logger.info( "use RandomRotation by Inter.ANTIALIAS process {} images.".format( num_iter))
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)