forked from mindspore-Ecosystem/mindspore
Adding new UT cases and missing TCs in PY Ops.
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@ -21,7 +21,8 @@ import mindspore.dataset.transforms.vision.py_transforms as py_vision
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import mindspore.dataset.transforms.vision.utils as mode
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import mindspore.dataset as ds
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from mindspore import log as logger
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from util import save_and_check_md5, visualize
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from util import save_and_check_md5, visualize, config_get_set_seed, \
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config_get_set_num_parallel_workers
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GENERATE_GOLDEN = False
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@ -30,11 +31,11 @@ DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
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SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
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def test_random_crop_op(plot=False):
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def test_random_crop_op_c(plot=False):
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"""
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Test RandomCrop Op
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Test RandomCrop Op in c transforms
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"""
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logger.info("test_random_crop_op")
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logger.info("test_random_crop_op_c")
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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@ -58,13 +59,47 @@ def test_random_crop_op(plot=False):
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if plot:
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visualize(image, image_cropped)
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def test_random_crop_op_py(plot=False):
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"""
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Test RandomCrop op in py transforms
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"""
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logger.info("test_random_crop_op_py")
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms1 = [
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py_vision.Decode(),
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py_vision.RandomCrop([512, 512], [200, 200, 200, 200]),
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py_vision.ToTensor()
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]
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transform1 = py_vision.ComposeOp(transforms1)
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data1 = data1.map(input_columns=["image"], operations=transform1())
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# Second dataset
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# Second dataset for comparison
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms2 = [
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py_vision.Decode(),
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py_vision.ToTensor()
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]
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transform2 = py_vision.ComposeOp(transforms2)
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data2 = data2.map(input_columns=["image"], operations=transform2())
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crop_images = []
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original_images = []
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for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
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crop = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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crop_images.append(crop)
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original_images.append(original)
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if plot:
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visualize(original_images, crop_images)
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def test_random_crop_01_c():
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"""
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Test RandomCrop op with c_transforms: size is a single integer, expected to pass
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"""
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logger.info("test_random_crop_01_c")
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ds.config.set_seed(0)
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ds.config.set_num_parallel_workers(1)
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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@ -77,13 +112,17 @@ def test_random_crop_01_c():
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filename = "random_crop_01_c_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_random_crop_01_py():
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"""
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Test RandomCrop op with py_transforms: size is a single integer, expected to pass
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"""
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logger.info("test_random_crop_01_py")
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ds.config.set_seed(0)
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ds.config.set_num_parallel_workers(1)
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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@ -99,13 +138,17 @@ def test_random_crop_01_py():
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filename = "random_crop_01_py_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_random_crop_02_c():
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"""
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Test RandomCrop op with c_transforms: size is a list/tuple with length 2, expected to pass
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"""
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logger.info("test_random_crop_02_c")
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ds.config.set_seed(0)
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ds.config.set_num_parallel_workers(1)
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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@ -118,13 +161,17 @@ def test_random_crop_02_c():
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filename = "random_crop_02_c_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_random_crop_02_py():
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"""
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Test RandomCrop op with py_transforms: size is a list/tuple with length 2, expected to pass
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"""
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logger.info("test_random_crop_02_py")
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ds.config.set_seed(0)
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ds.config.set_num_parallel_workers(1)
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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@ -140,13 +187,17 @@ def test_random_crop_02_py():
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filename = "random_crop_02_py_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_random_crop_03_c():
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"""
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Test RandomCrop op with c_transforms: input image size == crop size, expected to pass
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"""
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logger.info("test_random_crop_03_c")
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ds.config.set_seed(0)
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ds.config.set_num_parallel_workers(1)
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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@ -159,13 +210,17 @@ def test_random_crop_03_c():
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filename = "random_crop_03_c_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_random_crop_03_py():
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"""
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Test RandomCrop op with py_transforms: input image size == crop size, expected to pass
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"""
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logger.info("test_random_crop_03_py")
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ds.config.set_seed(0)
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ds.config.set_num_parallel_workers(1)
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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@ -181,27 +236,28 @@ def test_random_crop_03_py():
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filename = "random_crop_03_py_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_random_crop_04_c():
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"""
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Test RandomCrop op with c_transforms: input image size < crop size, expected to fail
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"""
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logger.info("test_random_crop_04_c")
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ds.config.set_seed(0)
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ds.config.set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: The size of the image is 4032*2268
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random_crop_op = c_vision.RandomCrop([2268, 4033])
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decode_op = c_vision.Decode()
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data = data.map(input_columns=["image"], operations=decode_op)
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data = data.map(input_columns=["image"], operations=random_crop_op)
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try:
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: The size of the image is 4032*2268
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random_crop_op = c_vision.RandomCrop([2268, 4033])
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decode_op = c_vision.Decode()
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data = data.map(input_columns=["image"], operations=decode_op)
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data = data.map(input_columns=["image"], operations=random_crop_op)
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image_list = []
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for item in data.create_dict_iterator():
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image = item["image"]
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image_list.append(image.shape)
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except Exception as e:
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data.create_dict_iterator().get_next()
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except RuntimeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Crop size is greater than the image dim" in str(e)
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def test_random_crop_04_py():
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"""
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@ -209,25 +265,20 @@ def test_random_crop_04_py():
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input image size < crop size, expected to fail
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"""
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logger.info("test_random_crop_04_py")
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ds.config.set_seed(0)
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ds.config.set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: The size of the image is 4032*2268
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transforms = [
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py_vision.Decode(),
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py_vision.RandomCrop([2268, 4033]),
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py_vision.ToTensor()
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]
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transform = py_vision.ComposeOp(transforms)
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data = data.map(input_columns=["image"], operations=transform())
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try:
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: The size of the image is 4032*2268
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transforms = [
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py_vision.Decode(),
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py_vision.RandomCrop([2268, 4033]),
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py_vision.ToTensor()
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]
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transform = py_vision.ComposeOp(transforms)
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data = data.map(input_columns=["image"], operations=transform())
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image_list = []
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for item in data.create_dict_iterator():
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image = (item["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image_list.append(image.shape)
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except Exception as e:
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data.create_dict_iterator().get_next()
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except RuntimeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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def test_random_crop_05_c():
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@ -237,8 +288,8 @@ def test_random_crop_05_c():
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expected to pass
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"""
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logger.info("test_random_crop_05_c")
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ds.config.set_seed(0)
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ds.config.set_num_parallel_workers(1)
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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@ -251,6 +302,10 @@ def test_random_crop_05_c():
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filename = "random_crop_05_c_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_random_crop_05_py():
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"""
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Test RandomCrop op with py_transforms:
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@ -258,8 +313,8 @@ def test_random_crop_05_py():
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expected to pass
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"""
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logger.info("test_random_crop_05_py")
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ds.config.set_seed(0)
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ds.config.set_num_parallel_workers(1)
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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@ -275,30 +330,28 @@ def test_random_crop_05_py():
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filename = "random_crop_05_py_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_random_crop_06_c():
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"""
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Test RandomCrop op with c_transforms:
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invalid size, expected to raise TypeError
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"""
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logger.info("test_random_crop_06_c")
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ds.config.set_seed(0)
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ds.config.set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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try:
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: if size is neither an int nor a list of length 2, an exception will raise
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random_crop_op = c_vision.RandomCrop([512, 512, 375])
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decode_op = c_vision.Decode()
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data = data.map(input_columns=["image"], operations=decode_op)
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data = data.map(input_columns=["image"], operations=random_crop_op)
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image_list = []
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for item in data.create_dict_iterator():
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image = item["image"]
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image_list.append(image.shape)
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except TypeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Size" in str(e)
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assert "Size should be a single integer" in str(e)
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def test_random_crop_06_py():
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"""
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@ -306,12 +359,10 @@ def test_random_crop_06_py():
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invalid size, expected to raise TypeError
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"""
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logger.info("test_random_crop_06_py")
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ds.config.set_seed(0)
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ds.config.set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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try:
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Note: if size is neither an int nor a list of length 2, an exception will raise
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transforms = [
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py_vision.Decode(),
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@ -320,13 +371,9 @@ def test_random_crop_06_py():
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]
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transform = py_vision.ComposeOp(transforms)
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data = data.map(input_columns=["image"], operations=transform())
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image_list = []
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for item in data.create_dict_iterator():
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image = (item["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image_list.append(image.shape)
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except TypeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Size" in str(e)
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assert "Size should be a single integer" in str(e)
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def test_random_crop_07_c():
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"""
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@ -335,8 +382,8 @@ def test_random_crop_07_c():
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expected to pass
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"""
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logger.info("test_random_crop_07_c")
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ds.config.set_seed(0)
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ds.config.set_num_parallel_workers(1)
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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@ -349,6 +396,10 @@ def test_random_crop_07_c():
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filename = "random_crop_07_c_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_random_crop_07_py():
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"""
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Test RandomCrop op with py_transforms:
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@ -356,8 +407,8 @@ def test_random_crop_07_py():
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expected to pass
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"""
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logger.info("test_random_crop_07_py")
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ds.config.set_seed(0)
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ds.config.set_num_parallel_workers(1)
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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|
|
@ -373,14 +424,18 @@ def test_random_crop_07_py():
|
|||
filename = "random_crop_07_py_result.npz"
|
||||
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
# Restore config setting
|
||||
ds.config.set_seed(original_seed)
|
||||
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
||||
|
||||
def test_random_crop_08_c():
|
||||
"""
|
||||
Test RandomCrop op with c_transforms: padding_mode is Border.EDGE,
|
||||
expected to pass
|
||||
"""
|
||||
logger.info("test_random_crop_08_c")
|
||||
ds.config.set_seed(0)
|
||||
ds.config.set_num_parallel_workers(1)
|
||||
original_seed = config_get_set_seed(0)
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
|
|
@ -393,14 +448,18 @@ def test_random_crop_08_c():
|
|||
filename = "random_crop_08_c_result.npz"
|
||||
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
# Restore config setting
|
||||
ds.config.set_seed(original_seed)
|
||||
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
||||
|
||||
def test_random_crop_08_py():
|
||||
"""
|
||||
Test RandomCrop op with py_transforms: padding_mode is Border.EDGE,
|
||||
expected to pass
|
||||
"""
|
||||
logger.info("test_random_crop_08_py")
|
||||
ds.config.set_seed(0)
|
||||
ds.config.set_num_parallel_workers(1)
|
||||
original_seed = config_get_set_seed(0)
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
|
|
@ -416,13 +475,15 @@ def test_random_crop_08_py():
|
|||
filename = "random_crop_08_py_result.npz"
|
||||
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
# Restore config setting
|
||||
ds.config.set_seed(original_seed)
|
||||
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
||||
|
||||
def test_random_crop_09():
|
||||
"""
|
||||
Test RandomCrop op: invalid type of input image (not PIL), expected to raise TypeError
|
||||
"""
|
||||
logger.info("test_random_crop_09")
|
||||
ds.config.set_seed(0)
|
||||
ds.config.set_num_parallel_workers(1)
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
|
|
@ -433,13 +494,10 @@ def test_random_crop_09():
|
|||
py_vision.RandomCrop(512)
|
||||
]
|
||||
transform = py_vision.ComposeOp(transforms)
|
||||
data = data.map(input_columns=["image"], operations=transform())
|
||||
try:
|
||||
data = data.map(input_columns=["image"], operations=transform())
|
||||
image_list = []
|
||||
for item in data.create_dict_iterator():
|
||||
image = item["image"]
|
||||
image_list.append(image.shape)
|
||||
except Exception as e:
|
||||
data.create_dict_iterator().get_next()
|
||||
except RuntimeError as e:
|
||||
logger.info("Got an exception in DE: {}".format(str(e)))
|
||||
assert "should be PIL Image" in str(e)
|
||||
|
||||
|
|
@ -448,8 +506,6 @@ def test_random_crop_comp(plot=False):
|
|||
Test RandomCrop and compare between python and c image augmentation
|
||||
"""
|
||||
logger.info("Test RandomCrop with c_transform and py_transform comparison")
|
||||
ds.config.set_seed(0)
|
||||
ds.config.set_num_parallel_workers(1)
|
||||
cropped_size = 512
|
||||
|
||||
# First dataset
|
||||
|
|
@ -479,6 +535,7 @@ def test_random_crop_comp(plot=False):
|
|||
if plot:
|
||||
visualize(image_c_cropped, image_py_cropped)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_random_crop_01_c()
|
||||
test_random_crop_02_c()
|
||||
|
|
@ -497,5 +554,6 @@ if __name__ == "__main__":
|
|||
test_random_crop_07_py()
|
||||
test_random_crop_08_py()
|
||||
test_random_crop_09()
|
||||
test_random_crop_op(True)
|
||||
test_random_crop_op_c(True)
|
||||
test_random_crop_op_py(True)
|
||||
test_random_crop_comp(True)
|
||||
|
|
|
|||
|
|
@ -23,7 +23,8 @@ import mindspore.dataset.transforms.vision.py_transforms as py_vision
|
|||
import mindspore.dataset.transforms.vision.utils as mode
|
||||
import mindspore.dataset as ds
|
||||
from mindspore import log as logger
|
||||
from util import diff_mse, save_and_check_md5, visualize
|
||||
from util import diff_mse, save_and_check_md5, visualize, \
|
||||
config_get_set_seed, config_get_set_num_parallel_workers
|
||||
|
||||
DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
|
||||
SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
|
||||
|
|
@ -31,11 +32,11 @@ SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
|
|||
GENERATE_GOLDEN = False
|
||||
|
||||
|
||||
def test_random_crop_and_resize_op(plot=False):
|
||||
def test_random_crop_and_resize_op_c(plot=False):
|
||||
"""
|
||||
Test RandomCropAndResize op
|
||||
Test RandomCropAndResize op in c transforms
|
||||
"""
|
||||
logger.info("test_random_crop_and_resize_op")
|
||||
logger.info("test_random_crop_and_resize_op_c")
|
||||
|
||||
# First dataset
|
||||
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
|
|
@ -64,13 +65,51 @@ def test_random_crop_and_resize_op(plot=False):
|
|||
if plot:
|
||||
visualize(original_images, crop_and_resize_images)
|
||||
|
||||
def test_random_crop_and_resize_op_py(plot=False):
|
||||
"""
|
||||
Test RandomCropAndResize op in py transforms
|
||||
"""
|
||||
logger.info("test_random_crop_and_resize_op_py")
|
||||
# First dataset
|
||||
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
transforms1 = [
|
||||
py_vision.Decode(),
|
||||
py_vision.RandomResizedCrop((256, 512), (1, 1), (0.5, 0.5)),
|
||||
py_vision.ToTensor()
|
||||
]
|
||||
transform1 = py_vision.ComposeOp(transforms1)
|
||||
data1 = data1.map(input_columns=["image"], operations=transform1())
|
||||
# Second dataset
|
||||
# Second dataset for comparison
|
||||
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
transforms2 = [
|
||||
py_vision.Decode(),
|
||||
py_vision.ToTensor()
|
||||
]
|
||||
transform2 = py_vision.ComposeOp(transforms2)
|
||||
data2 = data2.map(input_columns=["image"], operations=transform2())
|
||||
num_iter = 0
|
||||
crop_and_resize_images = []
|
||||
original_images = []
|
||||
for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
|
||||
crop_and_resize = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
|
||||
original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
|
||||
original = cv2.resize(original, (512,256))
|
||||
mse = diff_mse(crop_and_resize, original)
|
||||
logger.info("random_crop_and_resize_op_{}, mse: {}".format(num_iter + 1, mse))
|
||||
num_iter += 1
|
||||
crop_and_resize_images.append(crop_and_resize)
|
||||
original_images.append(original)
|
||||
if plot:
|
||||
visualize(original_images, crop_and_resize_images)
|
||||
|
||||
def test_random_crop_and_resize_01():
|
||||
"""
|
||||
Test RandomCropAndResize with md5 check, expected to pass
|
||||
"""
|
||||
logger.info("test_random_crop_and_resize_01")
|
||||
ds.config.set_seed(0)
|
||||
ds.config.set_num_parallel_workers(1)
|
||||
original_seed = config_get_set_seed(0)
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
# First dataset
|
||||
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
|
|
@ -94,14 +133,18 @@ def test_random_crop_and_resize_01():
|
|||
save_and_check_md5(data1, filename1, generate_golden=GENERATE_GOLDEN)
|
||||
save_and_check_md5(data2, filename2, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
# Restore config setting
|
||||
ds.config.set_seed(original_seed)
|
||||
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
||||
|
||||
def test_random_crop_and_resize_02():
|
||||
"""
|
||||
Test RandomCropAndResize with md5 check:Image interpolation mode is Inter.NEAREST,
|
||||
expected to pass
|
||||
"""
|
||||
logger.info("test_random_crop_and_resize_02")
|
||||
ds.config.set_seed(0)
|
||||
ds.config.set_num_parallel_workers(1)
|
||||
original_seed = config_get_set_seed(0)
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
# First dataset
|
||||
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
|
|
@ -125,13 +168,17 @@ def test_random_crop_and_resize_02():
|
|||
save_and_check_md5(data1, filename1, generate_golden=GENERATE_GOLDEN)
|
||||
save_and_check_md5(data2, filename2, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
# Restore config setting
|
||||
ds.config.set_seed(original_seed)
|
||||
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
||||
|
||||
def test_random_crop_and_resize_03():
|
||||
"""
|
||||
Test RandomCropAndResize with md5 check: max_attempts is 1, expected to pass
|
||||
"""
|
||||
logger.info("test_random_crop_and_resize_03")
|
||||
ds.config.set_seed(0)
|
||||
ds.config.set_num_parallel_workers(1)
|
||||
original_seed = config_get_set_seed(0)
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
# First dataset
|
||||
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
|
|
@ -155,27 +202,25 @@ def test_random_crop_and_resize_03():
|
|||
save_and_check_md5(data1, filename1, generate_golden=GENERATE_GOLDEN)
|
||||
save_and_check_md5(data2, filename2, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
# Restore config setting
|
||||
ds.config.set_seed(original_seed)
|
||||
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
||||
|
||||
def test_random_crop_and_resize_04_c():
|
||||
"""
|
||||
Test RandomCropAndResize with c_tranforms: invalid range of scale (max<min),
|
||||
expected to raise ValueError
|
||||
"""
|
||||
logger.info("test_random_crop_and_resize_04_c")
|
||||
ds.config.set_seed(0)
|
||||
ds.config.set_num_parallel_workers(1)
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
decode_op = c_vision.Decode()
|
||||
try:
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
decode_op = c_vision.Decode()
|
||||
# If input range of scale is not in the order of (min, max), ValueError will be raised.
|
||||
random_crop_and_resize_op = c_vision.RandomResizedCrop((256, 512), (1, 0.5), (0.5, 0.5))
|
||||
data = data.map(input_columns=["image"], operations=decode_op)
|
||||
data = data.map(input_columns=["image"], operations=random_crop_and_resize_op)
|
||||
image_list = []
|
||||
for item in data.create_dict_iterator():
|
||||
image = item["image"]
|
||||
image_list.append(image.shape)
|
||||
except ValueError as e:
|
||||
logger.info("Got an exception in DE: {}".format(str(e)))
|
||||
assert "Input range is not valid" in str(e)
|
||||
|
|
@ -186,12 +231,10 @@ def test_random_crop_and_resize_04_py():
|
|||
expected to raise ValueError
|
||||
"""
|
||||
logger.info("test_random_crop_and_resize_04_py")
|
||||
ds.config.set_seed(0)
|
||||
ds.config.set_num_parallel_workers(1)
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
try:
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
transforms = [
|
||||
py_vision.Decode(),
|
||||
# If input range of scale is not in the order of (min, max), ValueError will be raised.
|
||||
|
|
@ -200,10 +243,6 @@ def test_random_crop_and_resize_04_py():
|
|||
]
|
||||
transform = py_vision.ComposeOp(transforms)
|
||||
data = data.map(input_columns=["image"], operations=transform())
|
||||
image_list = []
|
||||
for item in data.create_dict_iterator():
|
||||
image = item["image"]
|
||||
image_list.append(image.shape)
|
||||
except ValueError as e:
|
||||
logger.info("Got an exception in DE: {}".format(str(e)))
|
||||
assert "Input range is not valid" in str(e)
|
||||
|
|
@ -214,21 +253,15 @@ def test_random_crop_and_resize_05_c():
|
|||
expected to raise ValueError
|
||||
"""
|
||||
logger.info("test_random_crop_and_resize_05_c")
|
||||
ds.config.set_seed(0)
|
||||
ds.config.set_num_parallel_workers(1)
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
decode_op = c_vision.Decode()
|
||||
try:
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
decode_op = c_vision.Decode()
|
||||
random_crop_and_resize_op = c_vision.RandomResizedCrop((256, 512), (1, 1), (1, 0.5))
|
||||
# If input range of ratio is not in the order of (min, max), ValueError will be raised.
|
||||
data = data.map(input_columns=["image"], operations=decode_op)
|
||||
data = data.map(input_columns=["image"], operations=random_crop_and_resize_op)
|
||||
image_list = []
|
||||
for item in data.create_dict_iterator():
|
||||
image = item["image"]
|
||||
image_list.append(image.shape)
|
||||
except ValueError as e:
|
||||
logger.info("Got an exception in DE: {}".format(str(e)))
|
||||
assert "Input range is not valid" in str(e)
|
||||
|
|
@ -239,12 +272,10 @@ def test_random_crop_and_resize_05_py():
|
|||
expected to raise ValueError
|
||||
"""
|
||||
logger.info("test_random_crop_and_resize_05_py")
|
||||
ds.config.set_seed(0)
|
||||
ds.config.set_num_parallel_workers(1)
|
||||
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
try:
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
transforms = [
|
||||
py_vision.Decode(),
|
||||
# If input range of ratio is not in the order of (min, max), ValueError will be raised.
|
||||
|
|
@ -253,10 +284,6 @@ def test_random_crop_and_resize_05_py():
|
|||
]
|
||||
transform = py_vision.ComposeOp(transforms)
|
||||
data = data.map(input_columns=["image"], operations=transform())
|
||||
image_list = []
|
||||
for item in data.create_dict_iterator():
|
||||
image = item["image"]
|
||||
image_list.append(image.shape)
|
||||
except ValueError as e:
|
||||
logger.info("Got an exception in DE: {}".format(str(e)))
|
||||
assert "Input range is not valid" in str(e)
|
||||
|
|
@ -295,7 +322,8 @@ def test_random_crop_and_resize_comp(plot=False):
|
|||
visualize(image_c_cropped, image_py_cropped)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_random_crop_and_resize_op(True)
|
||||
test_random_crop_and_resize_op_c(True)
|
||||
test_random_crop_and_resize_op_py(True)
|
||||
test_random_crop_and_resize_01()
|
||||
test_random_crop_and_resize_02()
|
||||
test_random_crop_and_resize_03()
|
||||
|
|
|
|||
|
|
@ -0,0 +1,189 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
# ==============================================================================
|
||||
"""
|
||||
Testing RandomGrayscale op in DE
|
||||
"""
|
||||
import numpy as np
|
||||
import mindspore.dataset.transforms.vision.py_transforms as py_vision
|
||||
import mindspore.dataset as ds
|
||||
from mindspore import log as logger
|
||||
from util import save_and_check_md5, visualize, \
|
||||
config_get_set_seed, config_get_set_num_parallel_workers
|
||||
|
||||
GENERATE_GOLDEN = False
|
||||
|
||||
DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
|
||||
SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
|
||||
|
||||
def test_random_grayscale_valid_prob(plot=False):
|
||||
"""
|
||||
Test RandomGrayscale Op: valid input, expect to pass
|
||||
"""
|
||||
logger.info("test_random_grayscale_valid_prob")
|
||||
|
||||
# First dataset
|
||||
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
transforms1 = [
|
||||
py_vision.Decode(),
|
||||
# Note: prob is 1 so the output should always be grayscale images
|
||||
py_vision.RandomGrayscale(1),
|
||||
py_vision.ToTensor()
|
||||
]
|
||||
transform1 = py_vision.ComposeOp(transforms1)
|
||||
data1 = data1.map(input_columns=["image"], operations=transform1())
|
||||
|
||||
# Second dataset
|
||||
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
transforms2 = [
|
||||
py_vision.Decode(),
|
||||
py_vision.ToTensor()
|
||||
]
|
||||
transform2 = py_vision.ComposeOp(transforms2)
|
||||
data2 = data2.map(input_columns=["image"], operations=transform2())
|
||||
|
||||
image_gray = []
|
||||
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_gray.append(image1)
|
||||
image.append(image2)
|
||||
if plot:
|
||||
visualize(image, image_gray)
|
||||
|
||||
def test_random_grayscale_input_grayscale_images():
|
||||
"""
|
||||
Test RandomGrayscale Op: valid parameter with grayscale images as input, expect to pass
|
||||
"""
|
||||
logger.info("test_random_grayscale_input_grayscale_images")
|
||||
original_seed = config_get_set_seed(0)
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
# First dataset
|
||||
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
transforms1 = [
|
||||
py_vision.Decode(),
|
||||
py_vision.Grayscale(1),
|
||||
# Note: If the input images is grayscale image with 1 channel.
|
||||
py_vision.RandomGrayscale(0.5),
|
||||
py_vision.ToTensor()
|
||||
]
|
||||
transform1 = py_vision.ComposeOp(transforms1)
|
||||
data1 = data1.map(input_columns=["image"], operations=transform1())
|
||||
|
||||
# Second dataset
|
||||
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
transforms2 = [
|
||||
py_vision.Decode(),
|
||||
py_vision.ToTensor()
|
||||
]
|
||||
transform2 = py_vision.ComposeOp(transforms2)
|
||||
data2 = data2.map(input_columns=["image"], operations=transform2())
|
||||
|
||||
image_gray = []
|
||||
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_gray.append(image1)
|
||||
image.append(image2)
|
||||
|
||||
assert len(image1.shape) == 3
|
||||
assert image1.shape[2] == 1
|
||||
assert len(image2.shape) == 3
|
||||
assert image2.shape[2] == 3
|
||||
|
||||
# Restore config
|
||||
ds.config.set_seed(original_seed)
|
||||
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
||||
|
||||
def test_random_grayscale_md5_valid_input():
|
||||
"""
|
||||
Test RandomGrayscale with md5 comparison: valid parameter, expect to pass
|
||||
"""
|
||||
logger.info("test_random_grayscale_md5_valid_input")
|
||||
original_seed = config_get_set_seed(0)
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
transforms = [
|
||||
py_vision.Decode(),
|
||||
py_vision.RandomGrayscale(0.8),
|
||||
py_vision.ToTensor()
|
||||
]
|
||||
transform = py_vision.ComposeOp(transforms)
|
||||
data = data.map(input_columns=["image"], operations=transform())
|
||||
|
||||
# Check output images with md5 comparison
|
||||
filename = "random_grayscale_01_result.npz"
|
||||
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
# Restore config
|
||||
ds.config.set_seed(original_seed)
|
||||
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
||||
|
||||
def test_random_grayscale_md5_no_param():
|
||||
"""
|
||||
Test RandomGrayscale with md5 comparison: no parameter given, expect to pass
|
||||
"""
|
||||
logger.info("test_random_grayscale_md5_no_param")
|
||||
original_seed = config_get_set_seed(0)
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
transforms = [
|
||||
py_vision.Decode(),
|
||||
py_vision.RandomGrayscale(),
|
||||
py_vision.ToTensor()
|
||||
]
|
||||
transform = py_vision.ComposeOp(transforms)
|
||||
data = data.map(input_columns=["image"], operations=transform())
|
||||
|
||||
# Check output images with md5 comparison
|
||||
filename = "random_grayscale_02_result.npz"
|
||||
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
# Restore config
|
||||
ds.config.set_seed(original_seed)
|
||||
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
||||
|
||||
def test_random_grayscale_invalid_param():
|
||||
"""
|
||||
Test RandomGrayscale: invalid parameter given, expect to raise error
|
||||
"""
|
||||
logger.info("test_random_grayscale_invalid_param")
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
try:
|
||||
transforms = [
|
||||
py_vision.Decode(),
|
||||
py_vision.RandomGrayscale(1.5),
|
||||
py_vision.ToTensor()
|
||||
]
|
||||
transform = py_vision.ComposeOp(transforms)
|
||||
data = data.map(input_columns=["image"], operations=transform())
|
||||
except ValueError as e:
|
||||
logger.info("Got an exception in DE: {}".format(str(e)))
|
||||
assert "Input is not within the required range" in str(e)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_random_grayscale_valid_prob(True)
|
||||
test_random_grayscale_input_grayscale_images()
|
||||
test_random_grayscale_md5_valid_input()
|
||||
test_random_grayscale_md5_no_param()
|
||||
test_random_grayscale_invalid_param()
|
||||
|
|
@ -17,10 +17,14 @@ Testing the random horizontal flip op in DE
|
|||
"""
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
import mindspore.dataset as ds
|
||||
import mindspore.dataset.transforms.vision.c_transforms as vision
|
||||
import mindspore.dataset.transforms.vision.c_transforms as c_vision
|
||||
import mindspore.dataset.transforms.vision.py_transforms as py_vision
|
||||
from mindspore import log as logger
|
||||
from util import save_and_check_md5, visualize, diff_mse, \
|
||||
config_get_set_seed, config_get_set_num_parallel_workers
|
||||
|
||||
GENERATE_GOLDEN = False
|
||||
|
||||
DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
|
||||
SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
|
||||
|
|
@ -37,7 +41,7 @@ def h_flip(image):
|
|||
return image
|
||||
|
||||
|
||||
def visualize(image_de_random_horizontal, image_pil_random_horizontal, mse, image_original):
|
||||
def visualize_mse(image_de_random_horizontal, image_pil_random_horizontal, mse, image_original):
|
||||
"""
|
||||
visualizes the image using DE op and Numpy op
|
||||
"""
|
||||
|
|
@ -61,14 +65,14 @@ def visualize(image_de_random_horizontal, image_pil_random_horizontal, mse, imag
|
|||
|
||||
def test_random_horizontal_op():
|
||||
"""
|
||||
Test random_horizontal
|
||||
Test RandomHorizontalFlip op
|
||||
"""
|
||||
logger.info("Test random_horizontal")
|
||||
logger.info("test_random_horizontal_op")
|
||||
|
||||
# First dataset
|
||||
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
decode_op = vision.Decode()
|
||||
random_horizontal_op = vision.RandomHorizontalFlip()
|
||||
decode_op = c_vision.Decode()
|
||||
random_horizontal_op = c_vision.RandomHorizontalFlip()
|
||||
data1 = data1.map(input_columns=["image"], operations=decode_op)
|
||||
data1 = data1.map(input_columns=["image"], operations=random_horizontal_op)
|
||||
|
||||
|
|
@ -84,17 +88,144 @@ def test_random_horizontal_op():
|
|||
break
|
||||
|
||||
image_h_flipped = item1["image"]
|
||||
|
||||
image = item2["image"]
|
||||
image_h_flipped_2 = h_flip(image)
|
||||
|
||||
diff = image_h_flipped - image_h_flipped_2
|
||||
mse = np.sum(np.power(diff, 2))
|
||||
mse = diff_mse(image_h_flipped, image_h_flipped_2)
|
||||
logger.info("image_{}, mse: {}".format(num_iter + 1, mse))
|
||||
# Uncomment below line if you want to visualize images
|
||||
# visualize(image_h_flipped, image_h_flipped_2, mse, image)
|
||||
# visualize_mse(image_h_flipped, image_h_flipped_2, mse, image)
|
||||
num_iter += 1
|
||||
|
||||
def test_random_horizontal_valid_prob_c():
|
||||
"""
|
||||
Test RandomHorizontalFlip op with c_transforms: valid non-default input, expect to pass
|
||||
"""
|
||||
logger.info("test_random_horizontal_valid_prob_c")
|
||||
original_seed = config_get_set_seed(0)
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
decode_op = c_vision.Decode()
|
||||
random_horizontal_op = c_vision.RandomHorizontalFlip(0.8)
|
||||
data = data.map(input_columns=["image"], operations=decode_op)
|
||||
data = data.map(input_columns=["image"], operations=random_horizontal_op)
|
||||
|
||||
filename = "random_horizontal_01_c_result.npz"
|
||||
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
# Restore config setting
|
||||
ds.config.set_seed(original_seed)
|
||||
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
||||
|
||||
def test_random_horizontal_valid_prob_py():
|
||||
"""
|
||||
Test RandomHorizontalFlip op with py_transforms: valid non-default input, expect to pass
|
||||
"""
|
||||
logger.info("test_random_horizontal_valid_prob_py")
|
||||
original_seed = config_get_set_seed(0)
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
transforms = [
|
||||
py_vision.Decode(),
|
||||
py_vision.RandomHorizontalFlip(0.8),
|
||||
py_vision.ToTensor()
|
||||
]
|
||||
transform = py_vision.ComposeOp(transforms)
|
||||
data = data.map(input_columns=["image"], operations=transform())
|
||||
|
||||
filename = "random_horizontal_01_py_result.npz"
|
||||
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
# Restore config setting
|
||||
ds.config.set_seed(original_seed)
|
||||
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
||||
|
||||
def test_random_horizontal_invalid_prob_c():
|
||||
"""
|
||||
Test RandomHorizontalFlip op in c_transforms: invalid input, expect to raise error
|
||||
"""
|
||||
logger.info("test_random_horizontal_invalid_prob_c")
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
decode_op = c_vision.Decode()
|
||||
try:
|
||||
# Note: Valid range of prob should be [0.0, 1.0]
|
||||
random_horizontal_op = c_vision.RandomHorizontalFlip(1.5)
|
||||
data = data.map(input_columns=["image"], operations=decode_op)
|
||||
data = data.map(input_columns=["image"], operations=random_horizontal_op)
|
||||
except ValueError as e:
|
||||
logger.info("Got an exception in DE: {}".format(str(e)))
|
||||
assert "Input is not" in str(e)
|
||||
|
||||
def test_random_horizontal_invalid_prob_py():
|
||||
"""
|
||||
Test RandomHorizontalFlip op in py_transforms: invalid input, expect to raise error
|
||||
"""
|
||||
logger.info("test_random_horizontal_invalid_prob_py")
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
|
||||
try:
|
||||
transforms = [
|
||||
py_vision.Decode(),
|
||||
# Note: Valid range of prob should be [0.0, 1.0]
|
||||
py_vision.RandomHorizontalFlip(1.5),
|
||||
py_vision.ToTensor()
|
||||
]
|
||||
transform = py_vision.ComposeOp(transforms)
|
||||
data = data.map(input_columns=["image"], operations=transform())
|
||||
except ValueError as e:
|
||||
logger.info("Got an exception in DE: {}".format(str(e)))
|
||||
assert "Input is not" in str(e)
|
||||
|
||||
def test_random_horizontal_comp(plot=False):
|
||||
"""
|
||||
Test test_random_horizontal_flip and compare between python and c image augmentation ops
|
||||
"""
|
||||
logger.info("test_random_horizontal_comp")
|
||||
# First dataset
|
||||
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
decode_op = c_vision.Decode()
|
||||
# Note: The image must be flipped if prob is set to be 1
|
||||
random_horizontal_op = c_vision.RandomHorizontalFlip(1)
|
||||
data1 = data1.map(input_columns=["image"], operations=decode_op)
|
||||
data1 = data1.map(input_columns=["image"], operations=random_horizontal_op)
|
||||
|
||||
# Second dataset
|
||||
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
transforms = [
|
||||
py_vision.Decode(),
|
||||
# Note: The image must be flipped if prob is set to be 1
|
||||
py_vision.RandomHorizontalFlip(1),
|
||||
py_vision.ToTensor()
|
||||
]
|
||||
transform = py_vision.ComposeOp(transforms)
|
||||
data2 = data2.map(input_columns=["image"], operations=transform())
|
||||
|
||||
images_list_c = []
|
||||
images_list_py = []
|
||||
for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
|
||||
image_c = item1["image"]
|
||||
image_py = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
|
||||
images_list_c.append(image_c)
|
||||
images_list_py.append(image_py)
|
||||
|
||||
# Check if the output images are the same
|
||||
mse = diff_mse(image_c, image_py)
|
||||
assert mse < 0.001
|
||||
if plot:
|
||||
visualize(images_list_c, images_list_py)
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_random_horizontal_op()
|
||||
test_random_horizontal_valid_prob_c()
|
||||
test_random_horizontal_valid_prob_py()
|
||||
test_random_horizontal_invalid_prob_c()
|
||||
test_random_horizontal_invalid_prob_py()
|
||||
test_random_horizontal_comp(True)
|
||||
|
|
|
|||
|
|
@ -19,8 +19,13 @@ import matplotlib.pyplot as plt
|
|||
import numpy as np
|
||||
|
||||
import mindspore.dataset as ds
|
||||
import mindspore.dataset.transforms.vision.c_transforms as vision
|
||||
import mindspore.dataset.transforms.vision.c_transforms as c_vision
|
||||
import mindspore.dataset.transforms.vision.py_transforms as py_vision
|
||||
from mindspore import log as logger
|
||||
from util import save_and_check_md5, visualize, diff_mse, \
|
||||
config_get_set_seed, config_get_set_num_parallel_workers
|
||||
|
||||
GENERATE_GOLDEN = False
|
||||
|
||||
DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
|
||||
SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
|
||||
|
|
@ -37,7 +42,7 @@ def v_flip(image):
|
|||
return image
|
||||
|
||||
|
||||
def visualize(image_de_random_vertical, image_pil_random_vertical, mse, image_original):
|
||||
def visualize_with_mse(image_de_random_vertical, image_pil_random_vertical, mse, image_original):
|
||||
"""
|
||||
visualizes the image using DE op and Numpy op
|
||||
"""
|
||||
|
|
@ -61,14 +66,14 @@ def visualize(image_de_random_vertical, image_pil_random_vertical, mse, image_or
|
|||
|
||||
def test_random_vertical_op():
|
||||
"""
|
||||
Test random_vertical
|
||||
Test random_vertical with default probability
|
||||
"""
|
||||
logger.info("Test random_vertical")
|
||||
|
||||
# First dataset
|
||||
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
decode_op = vision.Decode()
|
||||
random_vertical_op = vision.RandomVerticalFlip()
|
||||
decode_op = c_vision.Decode()
|
||||
random_vertical_op = c_vision.RandomVerticalFlip()
|
||||
data1 = data1.map(input_columns=["image"], operations=decode_op)
|
||||
data1 = data1.map(input_columns=["image"], operations=random_vertical_op)
|
||||
|
||||
|
|
@ -92,9 +97,139 @@ def test_random_vertical_op():
|
|||
mse = np.sum(np.power(diff, 2))
|
||||
logger.info("image_{}, mse: {}".format(num_iter + 1, mse))
|
||||
# Uncomment below line if you want to visualize images
|
||||
# visualize(image_v_flipped, image_v_flipped_2, mse, image)
|
||||
# visualize_with_mse(image_v_flipped, image_v_flipped_2, mse, image)
|
||||
num_iter += 1
|
||||
|
||||
def test_random_vertical_valid_prob_c():
|
||||
"""
|
||||
Test RandomVerticalFlip op with c_transforms: valid non-default input, expect to pass
|
||||
"""
|
||||
logger.info("test_random_vertical_valid_prob_c")
|
||||
original_seed = config_get_set_seed(0)
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
decode_op = c_vision.Decode()
|
||||
random_horizontal_op = c_vision.RandomVerticalFlip(0.8)
|
||||
data = data.map(input_columns=["image"], operations=decode_op)
|
||||
data = data.map(input_columns=["image"], operations=random_horizontal_op)
|
||||
|
||||
filename = "random_vertical_01_c_result.npz"
|
||||
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
# Restore config setting
|
||||
ds.config.set_seed(original_seed)
|
||||
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
||||
|
||||
def test_random_vertical_valid_prob_py():
|
||||
"""
|
||||
Test RandomVerticalFlip op with py_transforms: valid non-default input, expect to pass
|
||||
"""
|
||||
logger.info("test_random_vertical_valid_prob_py")
|
||||
original_seed = config_get_set_seed(0)
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
transforms = [
|
||||
py_vision.Decode(),
|
||||
py_vision.RandomVerticalFlip(0.8),
|
||||
py_vision.ToTensor()
|
||||
]
|
||||
transform = py_vision.ComposeOp(transforms)
|
||||
data = data.map(input_columns=["image"], operations=transform())
|
||||
|
||||
filename = "random_vertical_01_py_result.npz"
|
||||
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
# Restore config setting
|
||||
ds.config.set_seed(original_seed)
|
||||
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
||||
|
||||
def test_random_vertical_invalid_prob_c():
|
||||
"""
|
||||
Test RandomVerticalFlip op in c_transforms: invalid input, expect to raise error
|
||||
"""
|
||||
logger.info("test_random_vertical_invalid_prob_c")
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
decode_op = c_vision.Decode()
|
||||
try:
|
||||
# Note: Valid range of prob should be [0.0, 1.0]
|
||||
random_horizontal_op = c_vision.RandomVerticalFlip(1.5)
|
||||
data = data.map(input_columns=["image"], operations=decode_op)
|
||||
data = data.map(input_columns=["image"], operations=random_horizontal_op)
|
||||
except ValueError as e:
|
||||
logger.info("Got an exception in DE: {}".format(str(e)))
|
||||
assert "Input is not" in str(e)
|
||||
|
||||
def test_random_vertical_invalid_prob_py():
|
||||
"""
|
||||
Test RandomVerticalFlip op in py_transforms: invalid input, expect to raise error
|
||||
"""
|
||||
logger.info("test_random_vertical_invalid_prob_py")
|
||||
|
||||
# Generate dataset
|
||||
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
try:
|
||||
transforms = [
|
||||
py_vision.Decode(),
|
||||
# Note: Valid range of prob should be [0.0, 1.0]
|
||||
py_vision.RandomVerticalFlip(1.5),
|
||||
py_vision.ToTensor()
|
||||
]
|
||||
transform = py_vision.ComposeOp(transforms)
|
||||
data = data.map(input_columns=["image"], operations=transform())
|
||||
except ValueError as e:
|
||||
logger.info("Got an exception in DE: {}".format(str(e)))
|
||||
assert "Input is not" in str(e)
|
||||
|
||||
def test_random_vertical_comp(plot=False):
|
||||
"""
|
||||
Test test_random_vertical_flip and compare between python and c image augmentation ops
|
||||
"""
|
||||
logger.info("test_random_vertical_comp")
|
||||
|
||||
# First dataset
|
||||
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
decode_op = c_vision.Decode()
|
||||
# Note: The image must be flipped if prob is set to be 1
|
||||
random_horizontal_op = c_vision.RandomVerticalFlip(1)
|
||||
data1 = data1.map(input_columns=["image"], operations=decode_op)
|
||||
data1 = data1.map(input_columns=["image"], operations=random_horizontal_op)
|
||||
|
||||
# Second dataset
|
||||
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
|
||||
transforms = [
|
||||
py_vision.Decode(),
|
||||
# Note: The image must be flipped if prob is set to be 1
|
||||
py_vision.RandomVerticalFlip(1),
|
||||
py_vision.ToTensor()
|
||||
]
|
||||
transform = py_vision.ComposeOp(transforms)
|
||||
data2 = data2.map(input_columns=["image"], operations=transform())
|
||||
|
||||
images_list_c = []
|
||||
images_list_py = []
|
||||
for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
|
||||
image_c = item1["image"]
|
||||
image_py = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
|
||||
images_list_c.append(image_c)
|
||||
images_list_py.append(image_py)
|
||||
|
||||
# Check if the output images are the same
|
||||
mse = diff_mse(image_c, image_py)
|
||||
assert mse < 0.001
|
||||
if plot:
|
||||
visualize(images_list_c, images_list_py)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_random_vertical_op()
|
||||
test_random_vertical_valid_prob_c()
|
||||
test_random_vertical_valid_prob_py()
|
||||
test_random_vertical_invalid_prob_c()
|
||||
test_random_vertical_invalid_prob_py()
|
||||
test_random_vertical_comp(True)
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@ from mindspore import log as logger
|
|||
from mindspore.dataset.transforms.vision import Inter
|
||||
|
||||
from test_minddataset_sampler import add_and_remove_cv_file, get_data, CV_DIR_NAME, CV_FILE_NAME
|
||||
|
||||
from util import config_get_set_num_parallel_workers
|
||||
|
||||
def test_imagefolder(remove_json_files=True):
|
||||
"""
|
||||
|
|
@ -176,6 +176,7 @@ def test_random_crop():
|
|||
logger.info("test_random_crop")
|
||||
DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
|
||||
SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
# First dataset
|
||||
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
|
||||
|
|
@ -201,6 +202,9 @@ def test_random_crop():
|
|||
assert np.array_equal(item1['image'], item1_1['image'])
|
||||
_ = item2["image"]
|
||||
|
||||
# Restore configuration num_parallel_workers
|
||||
ds.config.set_num_parallel_workers(original_num_parallel_workers)
|
||||
|
||||
|
||||
def validate_jsonfile(filepath):
|
||||
try:
|
||||
|
|
|
|||
Loading…
Reference in New Issue