def test_center_crop_comp(height=375, width=375, plot=False): """ Test CenterCrop between python and c image augmentation """ logger.info("Test CenterCrop") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = vision.Decode() center_crop_op = vision.CenterCrop([height, width]) data1 = data1.map(input_columns=["image"], operations=decode_op) data1 = data1.map(input_columns=["image"], operations=center_crop_op) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms = [ py_vision.Decode(), py_vision.CenterCrop([height, width]), py_vision.ToTensor() ] transform = py_vision.ComposeOp(transforms) data2 = data2.map(input_columns=["image"], operations=transform()) image_c_cropped = [] image_py_cropped = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): c_image = item1["image"] py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) # Note: The images aren't exactly the same due to rounding error assert diff_mse(py_image, c_image) < 0.001 image_c_cropped.append(c_image.copy()) image_py_cropped.append(py_image.copy()) if plot: visualize_list(image_c_cropped, image_py_cropped, visualize_mode=2)
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_vision.RandomApply(transforms_list), py_vision.ToTensor() ] transform = py_vision.ComposeOp(transforms) # Generate dataset data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data = data.map(input_columns=["image"], operations=transform()) # 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_linear_transformation_md5_01(): """ Test LinearTransformation op: valid params (transformation_matrix, mean_vector) Expected to pass """ logger.info("test_linear_transformation_md5_01") # Initialize parameters height = 50 weight = 50 dim = 3 * height * weight transformation_matrix = np.ones([dim, dim]) mean_vector = np.zeros(dim) # Generate dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms = [ py_vision.Decode(), py_vision.CenterCrop([height, weight]), py_vision.ToTensor(), py_vision.LinearTransformation(transformation_matrix, mean_vector) ] transform = py_vision.ComposeOp(transforms) data1 = data1.map(input_columns=["image"], operations=transform()) # Compare with expected md5 from images filename = "linear_transformation_01_result.npz" save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
def test_linear_transformation_md5_05(): """ Test LinearTransformation op: mean_vector does not match dimension of transformation_matrix Expected to raise ValueError """ logger.info("test_linear_transformation_md5_05") # Initialize parameters height = 50 weight = 50 dim = 3 * height * weight transformation_matrix = np.ones([dim, dim]) mean_vector = np.zeros(dim - 1) # Generate dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) try: transforms = [ py_vision.Decode(), py_vision.CenterCrop([height, weight]), py_vision.ToTensor(), py_vision.LinearTransformation(transformation_matrix, mean_vector) ] transform = py_vision.ComposeOp(transforms) data1 = data1.map(input_columns=["image"], operations=transform()) except ValueError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert "should match" in str(e)
def create_dataset_py(dataset_path, do_train, config, device_target, repeat_num=1, batch_size=32): """ create a train or eval dataset Args: dataset_path(string): the path of dataset. do_train(bool): whether dataset is used for train or eval. repeat_num(int): the repeat times of dataset. Default: 1. batch_size(int): the batch size of dataset. Default: 32. Returns: dataset """ if device_target == "Ascend": rank_size = int(os.getenv("RANK_SIZE")) rank_id = int(os.getenv("RANK_ID")) if do_train: if rank_size == 1: ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True) else: ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True, num_shards=rank_size, shard_id=rank_id) else: ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=False) else: raise ValueError("Unsupported device target.") resize_height = config.image_height if do_train: buffer_size = 20480 # apply shuffle operations ds = ds.shuffle(buffer_size=buffer_size) # define map operations decode_op = P.Decode() resize_crop_op = P.RandomResizedCrop(resize_height, scale=(0.08, 1.0), ratio=(0.75, 1.333)) horizontal_flip_op = P.RandomHorizontalFlip(prob=0.5) resize_op = P.Resize(256) center_crop = P.CenterCrop(resize_height) to_tensor = P.ToTensor() normalize_op = P.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) if do_train: trans = [decode_op, resize_crop_op, horizontal_flip_op, to_tensor, normalize_op] else: trans = [decode_op, resize_op, center_crop, to_tensor, normalize_op] compose = P.ComposeOp(trans) ds = ds.map(input_columns="image", operations=compose(), num_parallel_workers=8, python_multiprocessing=True) # apply batch operations ds = ds.batch(batch_size, drop_remainder=True) # apply dataset repeat operation ds = ds.repeat(repeat_num) return ds
def test_linear_transformation_exception_02(): """ Test LinearTransformation op: mean_vector is not provided Expected to raise ValueError """ logger.info("test_linear_transformation_exception_02") # Initialize parameters height = 50 weight = 50 dim = 3 * height * weight transformation_matrix = np.ones([dim, dim]) # Generate dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) try: transforms = [ py_vision.Decode(), py_vision.CenterCrop([height, weight]), py_vision.ToTensor(), py_vision.LinearTransformation(transformation_matrix, None) ] transform = py_vision.ComposeOp(transforms) data1 = data1.map(input_columns=["image"], operations=transform()) except TypeError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert "Argument mean_vector with value None is not of type (<class 'numpy.ndarray'>,)" in str(e)
def test_random_choice_comp(plot=False): """ Test RandomChoice and compare with single CenterCrop results """ logger.info("test_random_choice_comp") # define map operations transforms_list = [py_vision.CenterCrop(64)] transforms1 = [ py_vision.Decode(), py_vision.RandomChoice(transforms_list), py_vision.ToTensor() ] transform1 = py_vision.ComposeOp(transforms1) transforms2 = [ py_vision.Decode(), py_vision.CenterCrop(64), py_vision.ToTensor() ] transform2 = py_vision.ComposeOp(transforms2) # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data1 = data1.map(input_columns=["image"], operations=transform1()) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=transform2()) image_choice = [] image_original = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image_choice.append(image1) image_original.append(image2) mse = diff_mse(image1, image2) assert mse == 0 if plot: visualize_list(image_original, image_choice)
def test_linear_transformation_op(plot=False): """ Test LinearTransformation op: verify if images transform correctly """ logger.info("test_linear_transformation_01") # Initialize parameters height = 50 weight = 50 dim = 3 * height * weight transformation_matrix = np.eye(dim) mean_vector = np.zeros(dim) # Define operations transforms = [ py_vision.Decode(), py_vision.CenterCrop([height, weight]), py_vision.ToTensor() ] transform = py_vision.ComposeOp(transforms) # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data1 = data1.map(input_columns=["image"], operations=transform()) # Note: if transformation matrix is diagonal matrix with all 1 in diagonal, # the output matrix in expected to be the same as the input matrix. data1 = data1.map(input_columns=["image"], operations=py_vision.LinearTransformation( transformation_matrix, mean_vector)) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=transform()) image_transformed = [] image = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image_transformed.append(image1) image.append(image2) mse = diff_mse(image1, image2) assert mse == 0 if plot: visualize_list(image, image_transformed)
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_vision.RandomApply(transforms_list, prob=0.6), py_vision.ToTensor() ] transform1 = py_vision.ComposeOp(transforms1) transforms2 = [py_vision.Decode(), py_vision.ToTensor()] transform2 = py_vision.ComposeOp(transforms2) # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data1 = data1.map(input_columns=["image"], operations=transform1()) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=transform2()) image_apply = [] image_original = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): 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(image_original, image_apply)
def test_to_pil_01(): """ Test ToPIL Op with md5 comparison: input is already PIL image Expected to pass """ logger.info("test_to_pil_01") # Generate dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms = [ py_vision.Decode(), # If input is already PIL image. py_vision.ToPIL(), py_vision.CenterCrop(375), py_vision.ToTensor() ] transform = py_vision.ComposeOp(transforms) data1 = data1.map(input_columns=["image"], operations=transform()) # Compare with expected md5 from images filename = "to_pil_01_result.npz" save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
def create_dataset(dataset_path, do_train, config, platform, repeat_num=1, batch_size=100): """ create a train or eval dataset Args: dataset_path(string): the path of dataset. do_train(bool): whether dataset is used for train or eval. repeat_num(int): the repeat times of dataset. Default: 1 batch_size(int): the batch size of dataset. Default: 32 Returns: dataset """ if platform == "Ascend": rank_size = int(os.getenv("RANK_SIZE")) rank_id = int(os.getenv("RANK_ID")) if rank_size == 1: ds = de.MindDataset(dataset_path, num_parallel_workers=8, shuffle=True) else: ds = de.MindDataset(dataset_path, num_parallel_workers=8, shuffle=True, num_shards=rank_size, shard_id=rank_id) elif platform == "GPU": if do_train: from mindspore.communication.management import get_rank, get_group_size ds = de.MindDataset(dataset_path, num_parallel_workers=8, shuffle=True, num_shards=get_group_size(), shard_id=get_rank()) else: ds = de.MindDataset(dataset_path, num_parallel_workers=8, shuffle=False) else: raise ValueError("Unsupport platform.") resize_height = config.image_height buffer_size = 1000 # define map operations resize_crop_op = C.RandomCropDecodeResize(resize_height, scale=(0.08, 1.0), ratio=(0.75, 1.333)) horizontal_flip_op = C.RandomHorizontalFlip(prob=0.5) color_op = C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4) rescale_op = C.Rescale(1 / 255.0, 0) normalize_op = C.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) change_swap_op = C.HWC2CHW() # define python operations decode_p = P.Decode() resize_p = P.Resize(256, interpolation=Inter.BILINEAR) center_crop_p = P.CenterCrop(224) totensor = P.ToTensor() normalize_p = P.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) composeop = P.ComposeOp( [decode_p, resize_p, center_crop_p, totensor, normalize_p]) if do_train: trans = [ resize_crop_op, horizontal_flip_op, color_op, rescale_op, normalize_op, change_swap_op ] else: trans = composeop() type_cast_op = C2.TypeCast(mstype.int32) ds = ds.map(input_columns="image", operations=trans, num_parallel_workers=8) ds = ds.map(input_columns="label_list", operations=type_cast_op, num_parallel_workers=8) # apply shuffle operations ds = ds.shuffle(buffer_size=buffer_size) # apply batch operations ds = ds.batch(batch_size, drop_remainder=True) # apply dataset repeat operation ds = ds.repeat(repeat_num) return ds
def create_dataset_py(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"): """ create a train or eval dataset Args: dataset_path(string): the path of dataset. do_train(bool): whether dataset is used for train or eval. repeat_num(int): the repeat times of dataset. Default: 1 batch_size(int): the batch size of dataset. Default: 32 target(str): the device target. Default: Ascend Returns: dataset """ if target == "Ascend": device_num = int(os.getenv("RANK_SIZE")) rank_id = int(os.getenv("RANK_ID")) else: init("nccl") rank_id = get_rank() device_num = get_group_size() if do_train: if device_num == 1: ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True) else: ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True, num_shards=device_num, shard_id=rank_id) else: ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=False) image_size = 224 # define map operations decode_op = P.Decode() resize_crop_op = P.RandomResizedCrop(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)) horizontal_flip_op = P.RandomHorizontalFlip(prob=0.5) resize_op = P.Resize(256) center_crop = P.CenterCrop(image_size) to_tensor = P.ToTensor() normalize_op = P.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # define map operations if do_train: trans = [ decode_op, resize_crop_op, horizontal_flip_op, to_tensor, normalize_op ] else: trans = [decode_op, resize_op, center_crop, to_tensor, normalize_op] compose = P.ComposeOp(trans) ds = ds.map(input_columns="image", operations=compose(), num_parallel_workers=8, python_multiprocessing=True) # apply batch operations ds = ds.batch(batch_size, drop_remainder=True) # apply dataset repeat operation ds = ds.repeat(repeat_num) return ds