def test_cpp_uniform_augment(plot=False, num_ops=2): """ Test UniformAugment """ logger.info("Test CPP UniformAugment") # Original Images data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms_original = [C.Decode(), C.Resize(size=[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) transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]), C.RandomHorizontalFlip(), C.RandomVerticalFlip(), C.RandomColorAdjust(), C.RandomRotation(degrees=45)] uni_aug = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops) transforms_all = [C.Decode(), C.Resize(size=[224, 224]), uni_aug, F.ToTensor()] ds_ua = data_set.map(operations=transforms_all, input_columns="image", num_parallel_workers=1) 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) if plot: visualize_list(images_original, images_ua) 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))))
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_bounding_box_augment_with_rotation_op(plot_vis=False): """ Test BoundingBoxAugment op (passing rotation op as transform) Prints images side by side with and without Aug applied + bboxes to compare and test """ logger.info("test_bounding_box_augment_with_rotation_op") original_seed = config_get_set_seed(0) original_num_parallel_workers = config_get_set_num_parallel_workers(1) dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", usage="train", shuffle=False, decode=True) dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", usage="train", shuffle=False, decode=True) # Ratio is set to 1 to apply rotation on all bounding boxes. test_op = c_vision.BoundingBoxAugment(c_vision.RandomRotation(90), 1) # map to apply ops dataVoc2 = dataVoc2.map(operations=[test_op], input_columns=["image", "bbox"], output_columns=["image", "bbox"], column_order=["image", "bbox"]) filename = "bounding_box_augment_rotation_c_result.npz" save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN) unaugSamp, augSamp = [], [] for unAug, Aug in zip( dataVoc1.create_dict_iterator(num_epochs=1, output_numpy=True), dataVoc2.create_dict_iterator(num_epochs=1, output_numpy=True)): unaugSamp.append(unAug) augSamp.append(Aug) if plot_vis: visualize_with_bounding_boxes(unaugSamp, augSamp) # Restore config setting ds.config.set_seed(original_seed) ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_cpp_uniform_augment_exception_float_numops(num_ops=2.5): """ Test UniformAugment invalid float number of ops """ logger.info("Test CPP UniformAugment invalid float num_ops exception") transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]), C.RandomHorizontalFlip(), C.RandomVerticalFlip(), C.RandomColorAdjust(), C.RandomRotation(degrees=45)] try: _ = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops) except Exception as e: logger.info("Got an exception in DE: {}".format(str(e))) assert "Argument num_ops with value 2.5 is not of type (<class 'int'>,)" in str(e)
def test_cpp_uniform_augment_exception_nonpositive_numops(num_ops=0): """ Test UniformAugment invalid non-positive number of ops """ logger.info("Test CPP UniformAugment invalid non-positive num_ops exception") transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]), C.RandomHorizontalFlip(), C.RandomVerticalFlip(), C.RandomColorAdjust(), C.RandomRotation(degrees=45)] try: _ = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops) except Exception as e: logger.info("Got an exception in DE: {}".format(str(e))) assert "Input num_ops must be greater than 0" in str(e)
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_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_serdes_voc_dataset(remove_json_files=True): """ Test serdes on VOC dataset pipeline. """ data_dir = "../data/dataset/testVOC2012" original_seed = config_get_set_seed(1) original_num_parallel_workers = config_get_set_num_parallel_workers(1) # define map operations random_color_adjust_op = vision.RandomColorAdjust(brightness=(0.5, 0.5)) random_rotation_op = vision.RandomRotation((0, 90), expand=True, resample=Inter.BILINEAR, center=(50, 50), fill_value=150) data1 = ds.VOCDataset(data_dir, task="Detection", usage="train", decode=True) data1 = data1.map(operations=random_color_adjust_op, input_columns=["image"]) data1 = data1.map(operations=random_rotation_op, input_columns=["image"]) data1 = data1.skip(2) data2 = util_check_serialize_deserialize_file(data1, "voc_dataset_pipeline", remove_json_files) num_samples = 0 # Iterate and compare the data in the original pipeline (data1) against the deserialized pipeline (data2) 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)): np.testing.assert_array_equal(item1['image'], item2['image']) num_samples += 1 assert num_samples == 7 # Restore configuration num_parallel_workers ds.config.set_seed(original_seed) ds.config.set_num_parallel_workers(original_num_parallel_workers)
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_random_rotation_expand(): """ 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() # expand is set to be True to match output size random_rotation_op = c_vision.RandomRotation((0, 90), expand=True) data1 = data1.map(operations=decode_op, input_columns=["image"]) data1 = data1.map(operations=random_rotation_op, input_columns=["image"]) num_iter = 0 for item in data1.create_dict_iterator(num_epochs=1): rotation = item["image"] logger.info("shape after rotate: {}".format(rotation.shape)) num_iter += 1
def test_random_rotation_op_c(plot=False): """ Test RandomRotation in c++ transformations op """ logger.info("test_random_rotation_op_c") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False) decode_op = c_vision.Decode() # use [90, 90] to force rotate 90 degrees, expand is set to be True to match output size random_rotation_op = c_vision.RandomRotation((90, 90), expand=True) data1 = data1.map(operations=decode_op, input_columns=["image"]) data1 = data1.map(operations=random_rotation_op, input_columns=["image"]) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(operations=decode_op, 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"] original = item2["image"] 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)