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_random_crop_badinput(num_ops=1):
"""
Test UniformAugment with greater crop size
"""
logger.info("Test CPP UniformAugment with random_crop bad input")
batch_size = 2
cifar10_dir = "../data/dataset/testCifar10Data"
ds1 = ds.Cifar10Dataset(cifar10_dir, shuffle=False) # shape = [32,32,3]
transforms_ua = [
# Note: crop size [224, 224] > image size [32, 32]
C.RandomCrop(size=[224, 224]),
C.RandomHorizontalFlip()
]
uni_aug = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
ds1 = ds1.map(operations=uni_aug, input_columns="image")
# apply DatasetOps
ds1 = ds1.batch(batch_size, drop_remainder=True, num_parallel_workers=1)
num_batches = 0
try:
for _ in ds1.create_dict_iterator(num_epochs=1, output_numpy=True):
num_batches += 1
except Exception as e:
assert "Crop size" in str(e)
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_uniform_augment_callable(num_ops=2):
"""
Test UniformAugment is callable
"""
logger.info("test_uniform_augment_callable")
img = np.fromfile("../data/dataset/apple.jpg", dtype=np.uint8)
logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
decode_op = C.Decode()
img = decode_op(img)
assert img.shape == (2268, 4032, 3)
transforms_ua = [C.RandomCrop(size=[400, 400], padding=[32, 32, 32, 32]),
C.RandomCrop(size=[400, 400], padding=[32, 32, 32, 32])]
uni_aug = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
img = uni_aug(img)
assert img.shape == (2268, 4032, 3) or img.shape == (400, 400, 3)
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_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)
