def test_invert_py(plot=False):
"""
Test Invert python op
"""
logger.info("Test Invert Python op")
# Original Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = mindspore.dataset.transforms.py_transforms.Compose(
[F.Decode(), F.Resize((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)
# Color Inverted Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_invert = mindspore.dataset.transforms.py_transforms.Compose(
[F.Decode(),
F.Resize((224, 224)),
F.Invert(),
F.ToTensor()])
ds_invert = data_set.map(operations=transforms_invert,
input_columns="image")
ds_invert = ds_invert.batch(512)
for idx, (image, _) in enumerate(ds_invert):
if idx == 0:
images_invert = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_invert = np.append(images_invert,
np.transpose(image.asnumpy(),
(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_invert[i] - images_original[i])**2)
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_original, images_invert)
def test_invert_py_c(plot=False):
"""
Test Invert Cpp op and python op
"""
logger.info("Test Invert cpp and python op")
# Invert Images in cpp
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data_set = data_set.map(operations=[C.Decode(),
C.Resize((224, 224))],
input_columns=["image"])
ds_c_invert = data_set.map(operations=C.Invert(), input_columns="image")
ds_c_invert = ds_c_invert.batch(512)
for idx, (image, _) in enumerate(ds_c_invert):
if idx == 0:
images_c_invert = image.asnumpy()
else:
images_c_invert = np.append(images_c_invert,
image.asnumpy(),
axis=0)
# invert images in python
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data_set = data_set.map(operations=[C.Decode(),
C.Resize((224, 224))],
input_columns=["image"])
transforms_p_invert = mindspore.dataset.transforms.py_transforms.Compose(
[lambda img: img.astype(np.uint8),
F.ToPIL(),
F.Invert(), np.array])
ds_p_invert = data_set.map(operations=transforms_p_invert,
input_columns="image")
ds_p_invert = ds_p_invert.batch(512)
for idx, (image, _) in enumerate(ds_p_invert):
if idx == 0:
images_p_invert = image.asnumpy()
else:
images_p_invert = np.append(images_p_invert,
image.asnumpy(),
axis=0)
num_samples = images_c_invert.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_p_invert[i], images_c_invert[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_c_invert, images_p_invert, visualize_mode=2)
def test_invert_md5_py():
"""
Test Invert python op with md5 check
"""
logger.info("Test Invert python op with md5 check")
# Generate dataset
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_invert = mindspore.dataset.transforms.py_transforms.Compose(
[F.Decode(), F.Invert(), F.ToTensor()])
data = data_set.map(operations=transforms_invert, input_columns="image")
# Compare with expected md5 from images
filename = "invert_01_result_py.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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_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_uniform_augment(plot=False, num_ops=2):
"""
Test UniformAugment
"""
logger.info("Test UniformAugment")
# Original Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
F.Resize((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)
transform_list = [F.RandomRotation(45),
F.RandomColor(),
F.RandomSharpness(),
F.Invert(),
F.AutoContrast(),
F.Equalize()]
transforms_ua = \
mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
F.Resize((224, 224)),
F.UniformAugment(transforms=transform_list,
num_ops=num_ops),
F.ToTensor()])
ds_ua = data_set.map(operations=transforms_ua, input_columns="image")
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)
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))))
if plot:
visualize_list(images_original, images_ua)