def test_serdes_uniform_augment(remove_json_files=True):
"""
Test serdes on uniform augment.
"""
data_dir = "../data/dataset/testPK/data"
data = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
ds.config.set_seed(1)
transforms_ua = [
vision.RandomHorizontalFlip(),
vision.RandomVerticalFlip(),
vision.RandomColor(),
vision.RandomSharpness(),
vision.Invert(),
vision.AutoContrast(),
vision.Equalize()
]
transforms_all = [
vision.Decode(),
vision.Resize(size=[224, 224]),
vision.UniformAugment(transforms=transforms_ua, num_ops=5)
]
data = data.map(operations=transforms_all,
input_columns="image",
num_parallel_workers=1)
util_check_serialize_deserialize_file(data, "uniform_augment_pipeline",
remove_json_files)
def test_cpp_uniform_augment(plot=False, num_ops=2):
"""
Test UniformAugment
"""
logger.info("Test CPP UniformAugment")
# Original Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = [C.Decode(), C.Resize(size=[224, 224]),
F.ToTensor()]
ds_original = data_set.map(operations=transforms_original, input_columns="image")
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image.asnumpy(), (0, 2, 3, 1)),
axis=0)
# UniformAugment Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
C.RandomHorizontalFlip(),
C.RandomVerticalFlip(),
C.RandomColorAdjust(),
C.RandomRotation(degrees=45)]
uni_aug = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
transforms_all = [C.Decode(), C.Resize(size=[224, 224]),
uni_aug,
F.ToTensor()]
ds_ua = data_set.map(operations=transforms_all, input_columns="image", num_parallel_workers=1)
ds_ua = ds_ua.batch(512)
for idx, (image, _) in enumerate(ds_ua):
if idx == 0:
images_ua = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_ua = np.append(images_ua,
np.transpose(image.asnumpy(), (0, 2, 3, 1)),
axis=0)
if plot:
visualize_list(images_original, images_ua)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_ua[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
def test_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(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=random_horizontal_op, input_columns=["image"])
# 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 = mindspore.dataset.transforms.py_transforms.Compose(transforms)
data2 = data2.map(operations=transform, input_columns=["image"])
images_list_c = []
images_list_py = []
for item1, item2 in zip(
data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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_list(images_list_c, images_list_py, visualize_mode=2)
def test_cpp_uniform_augment_exception_float_numops(num_ops=2.5):
"""
Test UniformAugment invalid float number of ops
"""
logger.info("Test CPP UniformAugment invalid float num_ops exception")
transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
C.RandomHorizontalFlip(),
C.RandomVerticalFlip(),
C.RandomColorAdjust(),
C.RandomRotation(degrees=45)]
try:
_ = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
except Exception as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Argument num_ops with value 2.5 is not of type (<class 'int'>,)" in str(e)
def test_cpp_uniform_augment_exception_nonpositive_numops(num_ops=0):
"""
Test UniformAugment invalid non-positive number of ops
"""
logger.info("Test CPP UniformAugment invalid non-positive num_ops exception")
transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
C.RandomHorizontalFlip(),
C.RandomVerticalFlip(),
C.RandomColorAdjust(),
C.RandomRotation(degrees=45)]
try:
_ = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
except Exception as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Input num_ops must be greater than 0" in str(e)
def test_cpp_uniform_augment_exception_pyops(num_ops=2):
"""
Test UniformAugment invalid op in operations
"""
logger.info("Test CPP UniformAugment invalid OP exception")
transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
C.RandomHorizontalFlip(),
C.RandomVerticalFlip(),
C.RandomColorAdjust(),
C.RandomRotation(degrees=45),
F.Invert()]
with pytest.raises(TypeError) as e:
C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Type of Transforms[5] must be c_transform" in str(e.value)
def test_random_vertical_op(plot=False):
"""
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 = c_vision.Decode()
random_vertical_op = c_vision.RandomVerticalFlip(1.0)
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=random_vertical_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data2 = data2.map(operations=decode_op, input_columns=["image"])
num_iter = 0
for item1, item2 in zip(
data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
# with the seed value, we can only guarantee the first number generated
if num_iter > 0:
break
image_v_flipped = item1["image"]
image = item2["image"]
image_v_flipped_2 = v_flip(image)
mse = diff_mse(image_v_flipped, image_v_flipped_2)
assert mse == 0
logger.info("image_{}, mse: {}".format(num_iter + 1, mse))
num_iter += 1
if plot:
visualize_image(image, image_v_flipped, mse, image_v_flipped_2)
def test_cpp_uniform_augment_exception_pyops(num_ops=2):
"""
Test UniformAugment invalid op in operations
"""
logger.info("Test CPP UniformAugment invalid OP exception")
transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
C.RandomHorizontalFlip(),
C.RandomVerticalFlip(),
C.RandomColorAdjust(),
C.RandomRotation(degrees=45),
F.Invert()]
with pytest.raises(TypeError) as e:
C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Argument transforms[5] with value" \
" <mindspore.dataset.vision.py_transforms.Invert" in str(e.value)
assert "is not of type (<class 'mindspore._c_dataengine.TensorOp'>,"\
" <class 'mindspore._c_dataengine.TensorOperation'>)" in str(e.value)
def test_random_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(operations=decode_op, input_columns=["image"])
data = data.map(operations=random_horizontal_op,
input_columns=["image"])
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert 'Input prob is not within the required interval of (0.0 to 1.0).' in str(
e)
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(operations=decode_op, input_columns=["image"])
data = data.map(operations=random_horizontal_op, input_columns=["image"])
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)
