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: ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[ F.ComposeOp([ F.Decode(), F.Resize((224, 224)), F.AutoContrast(cutoff=-10.0), F.ToTensor() ]) ]) 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 to 100)." in str( error) try: ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[ F.ComposeOp([ F.Decode(), F.Resize((224, 224)), F.AutoContrast(cutoff=120.0), F.ToTensor() ]) ]) 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 to 100)." in str( error)
def test_random_sharpness_py(degrees=(0.7, 0.7), plot=False): """ Test RandomSharpness python op """ logger.info("Test RandomSharpness python op") # Original Images data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) transforms_original = F.ComposeOp( [F.Decode(), F.Resize((224, 224)), F.ToTensor()]) ds_original = data.map(input_columns="image", operations=transforms_original()) ds_original = ds_original.batch(512) for idx, (image, _) in enumerate(ds_original): if idx == 0: images_original = np.transpose(image, (0, 2, 3, 1)) else: images_original = np.append(images_original, np.transpose(image, (0, 2, 3, 1)), axis=0) # Random Sharpness Adjusted Images data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) py_op = F.RandomSharpness() if degrees is not None: py_op = F.RandomSharpness(degrees) transforms_random_sharpness = F.ComposeOp( [F.Decode(), F.Resize((224, 224)), py_op, F.ToTensor()]) ds_random_sharpness = data.map(input_columns="image", operations=transforms_random_sharpness()) ds_random_sharpness = ds_random_sharpness.batch(512) for idx, (image, _) in enumerate(ds_random_sharpness): if idx == 0: images_random_sharpness = np.transpose(image, (0, 2, 3, 1)) else: images_random_sharpness = np.append(images_random_sharpness, np.transpose( image, (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_random_sharpness[i], images_original[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize_list(images_original, images_random_sharpness)
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: ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[ F.ComposeOp([ F.Decode(), F.Resize((224, 224)), F.AutoContrast(ignore=255.5), F.ToTensor() ]) ]) 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: ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[ F.ComposeOp([ F.Decode(), F.Resize((224, 224)), F.AutoContrast(ignore=(10, 100)), F.ToTensor() ]) ]) 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_random_color(degrees=(0.1, 1.9), plot=False): """ Test RandomColor """ logger.info("Test RandomColor") # Original Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) transforms_original = F.ComposeOp( [F.Decode(), F.Resize((224, 224)), F.ToTensor()]) ds_original = ds.map(input_columns="image", operations=transforms_original()) ds_original = ds_original.batch(512) for idx, (image, _) in enumerate(ds_original): if idx == 0: images_original = np.transpose(image, (0, 2, 3, 1)) else: images_original = np.append(images_original, np.transpose(image, (0, 2, 3, 1)), axis=0) # Random Color Adjusted Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) transforms_random_color = F.ComposeOp([ F.Decode(), F.Resize((224, 224)), F.RandomColor(degrees=degrees), F.ToTensor() ]) ds_random_color = ds.map(input_columns="image", operations=transforms_random_color()) ds_random_color = ds_random_color.batch(512) for idx, (image, _) in enumerate(ds_random_color): if idx == 0: images_random_color = np.transpose(image, (0, 2, 3, 1)) else: images_random_color = np.append(images_random_color, np.transpose(image, (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_random_color[i] - images_original[i])**2) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize(images_original, images_random_color)
def test_equalize_py(plot=False): """ Test Equalize py op """ logger.info("Test Equalize") # Original Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) transforms_original = F.ComposeOp( [F.Decode(), F.Resize((224, 224)), F.ToTensor()]) ds_original = ds.map(input_columns="image", operations=transforms_original()) ds_original = ds_original.batch(512) for idx, (image, _) in enumerate(ds_original): if idx == 0: images_original = np.transpose(image, (0, 2, 3, 1)) else: images_original = np.append(images_original, np.transpose(image, (0, 2, 3, 1)), axis=0) # Color Equalized Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) transforms_equalize = F.ComposeOp( [F.Decode(), F.Resize((224, 224)), F.Equalize(), F.ToTensor()]) ds_equalize = ds.map(input_columns="image", operations=transforms_equalize()) ds_equalize = ds_equalize.batch(512) for idx, (image, _) in enumerate(ds_equalize): if idx == 0: images_equalize = np.transpose(image, (0, 2, 3, 1)) else: images_equalize = np.append(images_equalize, np.transpose(image, (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_equalize[i], images_original[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize_list(images_original, images_equalize)
def test_auto_contrast(plot=False): """ Test AutoContrast """ logger.info("Test AutoContrast") # Original Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) transforms_original = F.ComposeOp( [F.Decode(), F.Resize((224, 224)), F.ToTensor()]) ds_original = ds.map(input_columns="image", operations=transforms_original()) ds_original = ds_original.batch(512) for idx, (image, _) in enumerate(ds_original): if idx == 0: images_original = np.transpose(image, (0, 2, 3, 1)) else: images_original = np.append(images_original, np.transpose(image, (0, 2, 3, 1)), axis=0) # AutoContrast Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) transforms_auto_contrast = F.ComposeOp( [F.Decode(), F.Resize((224, 224)), F.AutoContrast(), F.ToTensor()]) ds_auto_contrast = ds.map(input_columns="image", operations=transforms_auto_contrast()) 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, (0, 2, 3, 1)) else: images_auto_contrast = np.append(images_auto_contrast, np.transpose(image, (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_auto_contrast[i] - images_original[i])**2) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize(images_original, images_auto_contrast)
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_vision.RandomApply(transforms_list, prob=0.6), 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()) try: _ = data.create_dict_iterator().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 skip_test_random_perspective_md5(): """ Test RandomPerspective with md5 comparison """ logger.info("test_random_perspective_md5") original_seed = config_get_set_seed(5) original_num_parallel_workers = config_get_set_num_parallel_workers(1) # define map operations transforms = [ py_vision.Decode(), py_vision.RandomPerspective(distortion_scale=0.3, prob=0.7, interpolation=Inter.BILINEAR), py_vision.Resize( 1450), # resize to a smaller size to prevent round-off error 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_perspective_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_concat_14(): """ Test concat: create dataset with different dataset folder, and do diffrent operation then concat """ logger.info("test_concat_14") DATA_DIR = "../data/dataset/testPK/data" DATA_DIR2 = "../data/dataset/testImageNetData/train/" data1 = ds.ImageFolderDatasetV2(DATA_DIR, num_samples=3) data2 = ds.ImageFolderDatasetV2(DATA_DIR2, num_samples=2) transforms1 = F.ComposeOp([F.Decode(), F.Resize((224, 224)), F.ToTensor()]) data1 = data1.map(input_columns=["image"], operations=transforms1()) data2 = data2.map(input_columns=["image"], operations=transforms1()) data3 = data1 + data2 expected, output = [], [] for d in data1: expected.append(d[0]) for d in data2: expected.append(d[0]) for d in data3: output.append(d[0]) assert len(expected) == len(output) np.array_equal(np.array(output), np.array(expected)) assert sum([1 for _ in data3]) == 5 assert data3.get_dataset_size() == 5
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_rgb_hsv_pipeline(): # First dataset transforms1 = [vision.Decode(), vision.Resize([64, 64]), vision.ToTensor()] transforms1 = vision.ComposeOp(transforms1) ds1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) ds1 = ds1.map(input_columns=["image"], operations=transforms1()) # Second dataset transforms2 = [ vision.Decode(), vision.Resize([64, 64]), vision.ToTensor(), vision.RgbToHsv(), vision.HsvToRgb() ] transform2 = vision.ComposeOp(transforms2) ds2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) ds2 = ds2.map(input_columns=["image"], operations=transform2()) num_iter = 0 for data1, data2 in zip(ds1.create_dict_iterator(), ds2.create_dict_iterator()): num_iter += 1 ori_img = data1["image"] cvt_img = data2["image"] assert_allclose(ori_img.flatten(), cvt_img.flatten(), rtol=1e-5, atol=0) assert (ori_img.shape == cvt_img.shape)
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 test_uniform_augment(plot=False, num_ops=2): """ Test UniformAugment """ logger.info("Test UniformAugment") # Original Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) transforms_original = F.ComposeOp( [F.Decode(), F.Resize((224, 224)), F.ToTensor()]) ds_original = ds.map(input_columns="image", operations=transforms_original()) ds_original = ds_original.batch(512) for idx, (image, label) in enumerate(ds_original): if idx == 0: images_original = np.transpose(image, (0, 2, 3, 1)) else: images_original = np.append(images_original, np.transpose(image, (0, 2, 3, 1)), axis=0) # UniformAugment Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) transform_list = [ F.RandomRotation(45), F.RandomColor(), F.RandomSharpness(), F.Invert(), F.AutoContrast(), F.Equalize() ] transforms_ua = F.ComposeOp([ F.Decode(), F.Resize((224, 224)), F.UniformAugment(transforms=transform_list, num_ops=num_ops), F.ToTensor() ]) ds_ua = ds.map(input_columns="image", operations=transforms_ua()) ds_ua = ds_ua.batch(512) for idx, (image, label) in enumerate(ds_ua): if idx == 0: images_ua = np.transpose(image, (0, 2, 3, 1)) else: images_ua = np.append(images_ua, np.transpose(image, (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_ua[i] - images_original[i])**2) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize(images_original, images_ua)
def test_auto_contrast_py(plot=False): """ Test AutoContrast """ logger.info("Test AutoContrast Python Op") # Original Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) transforms_original = F.ComposeOp( [F.Decode(), F.Resize((224, 224)), F.ToTensor()]) ds_original = ds.map(input_columns="image", operations=transforms_original()) ds_original = ds_original.batch(512) for idx, (image, _) in enumerate(ds_original): if idx == 0: images_original = np.transpose(image, (0, 2, 3, 1)) else: images_original = np.append(images_original, np.transpose(image, (0, 2, 3, 1)), axis=0) # AutoContrast Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) transforms_auto_contrast = F.ComposeOp( [F.Decode(), F.Resize((224, 224)), F.AutoContrast(), F.ToTensor()]) ds_auto_contrast = ds.map(input_columns="image", operations=transforms_auto_contrast()) 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, (0, 2, 3, 1)) else: images_auto_contrast = np.append(images_auto_contrast, np.transpose(image, (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 = "autcontrast_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 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