p47068915
/
mindspore
forked from mindspore-Ecosystem/mindspore
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

!3180 Update for python AutoContrast API, cpp UnifromAugment API and UT 

Merge pull request !3180 from alashkari/update-aug-ops-2
This commit is contained in:
mindspore-ci-bot 2020-07-22 16:09:12 +08:00 committed by Gitee
parent 174acbec13 6f186aafa0
commit c84d4bbd36
11 changed files with 151 additions and 36 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
mindspore/ccsrc/minddata/dataset/api/python_bindings.cc
View File

@ -425,7 +425,7 @@ void bindTensorOps1(py::module *m) {
(void)py::class_<UniformAugOp, TensorOp, std::shared_ptr<UniformAugOp>>(
*m, "UniformAugOp", "Tensor operation to apply random augmentation(s).")
.def(py::init<std::vector<std::shared_ptr<TensorOp>>, int32_t>(), py::arg("operations"),
.def(py::init<std::vector<std::shared_ptr<TensorOp>>, int32_t>(), py::arg("transforms"),
py::arg("NumOps") = UniformAugOp::kDefNumOps);
(void)py::class_<BoundingBoxAugmentOp, TensorOp, std::shared_ptr<BoundingBoxAugmentOp>>(

10
mindspore/ccsrc/minddata/dataset/api/transforms.cc
View File

@ -90,9 +90,9 @@ std::shared_ptr<CenterCropOperation> CenterCrop(std::vector<int32_t> size) {
}
// Function to create UniformAugOperation.
std::shared_ptr<UniformAugOperation> UniformAugment(std::vector<std::shared_ptr<TensorOperation>> operations,
std::shared_ptr<UniformAugOperation> UniformAugment(std::vector<std::shared_ptr<TensorOperation>> transforms,
int32_t num_ops) {
auto op = std::make_shared<UniformAugOperation>(operations, num_ops);
auto op = std::make_shared<UniformAugOperation>(transforms, num_ops);
// Input validation
if (!op->ValidateParams()) {
return nullptr;
@ -290,14 +290,14 @@ std::shared_ptr<TensorOp> CenterCropOperation::Build() {
}
// UniformAugOperation
UniformAugOperation::UniformAugOperation(std::vector<std::shared_ptr<TensorOperation>> operations, int32_t num_ops)
: operations_(operations), num_ops_(num_ops) {}
UniformAugOperation::UniformAugOperation(std::vector<std::shared_ptr<TensorOperation>> transforms, int32_t num_ops)
: transforms_(transforms), num_ops_(num_ops) {}
bool UniformAugOperation::ValidateParams() { return true; }
std::shared_ptr<TensorOp> UniformAugOperation::Build() {
std::vector<std::shared_ptr<TensorOp>> tensor_ops;
(void)std::transform(operations_.begin(), operations_.end(), std::back_inserter(tensor_ops),
(void)std::transform(transforms_.begin(), transforms_.end(), std::back_inserter(tensor_ops),
[](std::shared_ptr<TensorOperation> op) -> std::shared_ptr<TensorOp> { return op->Build(); });
std::shared_ptr<UniformAugOp> tensor_op = std::make_shared<UniformAugOp>(tensor_ops, num_ops_);
return tensor_op;

8
mindspore/ccsrc/minddata/dataset/include/transforms.h
View File

@ -108,10 +108,10 @@ std::shared_ptr<CenterCropOperation> CenterCrop(std::vector<int32_t> size);
/// \brief Function to create a UniformAugment TensorOperation.
/// \notes Tensor operation to perform randomly selected augmentation.
/// \param[in] operations - a vector of TensorOperation operations.
/// \param[in] transforms - a vector of TensorOperation transforms.
/// \param[in] num_ops - integer representing the number of OPs to be selected and applied.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<UniformAugOperation> UniformAugment(std::vector<std::shared_ptr<TensorOperation>> operations,
std::shared_ptr<UniformAugOperation> UniformAugment(std::vector<std::shared_ptr<TensorOperation>> transforms,
int32_t num_ops = 2);
/// \brief Function to create a RandomHorizontalFlip TensorOperation.
@ -264,7 +264,7 @@ class CenterCropOperation : public TensorOperation {
class UniformAugOperation : public TensorOperation {
public:
explicit UniformAugOperation(std::vector<std::shared_ptr<TensorOperation>> operations, int32_t num_ops = 2);
explicit UniformAugOperation(std::vector<std::shared_ptr<TensorOperation>> transforms, int32_t num_ops = 2);
~UniformAugOperation() = default;
@ -273,7 +273,7 @@ class UniformAugOperation : public TensorOperation {
bool ValidateParams() override;
private:
std::vector<std::shared_ptr<TensorOperation>> operations_;
std::vector<std::shared_ptr<TensorOperation>> transforms_;
int32_t num_ops_;
};

10
mindspore/dataset/transforms/vision/c_transforms.py
View File

@ -722,7 +722,7 @@ class UniformAugment(cde.UniformAugOp):
Tensor operation to perform randomly selected augmentation.
Args:
operations: list of C++ operations (python OPs are not accepted).
transforms: list of C++ operations (python OPs are not accepted).
num_ops (int, optional): number of OPs to be selected and applied (default=2).
Examples:
@ -730,7 +730,7 @@ class UniformAugment(cde.UniformAugOp):
>>> c_transforms.RandomVerticalFlip(),
>>> c_transforms.RandomColorAdjust(),
>>> c_transforms.RandomRotation(degrees=45)]
>>> uni_aug = c_transforms.UniformAugment(operations=transforms_list, num_ops=2)
>>> uni_aug = c_transforms.UniformAugment(transforms=transforms_list, num_ops=2)
>>> transforms_all = [c_transforms.Decode(), c_transforms.Resize(size=[224, 224]),
>>> uni_aug, F.ToTensor()]
>>> ds_ua = ds.map(input_columns="image",
@ -738,10 +738,10 @@ class UniformAugment(cde.UniformAugOp):
"""
@check_uniform_augment_cpp
def __init__(self, operations, num_ops=2):
self.operations = operations
def __init__(self, transforms, num_ops=2):
self.transforms = transforms
self.num_ops = num_ops
super().__init__(operations, num_ops)
super().__init__(transforms, num_ops)
class RandomSelectSubpolicy(cde.RandomSelectSubpolicyOp):

13
mindspore/dataset/transforms/vision/py_transforms.py
View File

@ -33,7 +33,7 @@ from .validators import check_prob, check_crop, check_resize_interpolation, chec
check_normalize_py, check_random_crop, check_random_color_adjust, check_random_rotation, \
check_transforms_list, check_random_apply, check_ten_crop, check_num_channels, check_pad, \
check_random_perspective, check_random_erasing, check_cutout, check_linear_transform, check_random_affine, \
check_mix_up, check_positive_degrees, check_uniform_augment_py, check_compose_list
check_mix_up, check_positive_degrees, check_uniform_augment_py, check_compose_list, check_auto_contrast
from .utils import Inter, Border
DE_PY_INTER_MODE = {Inter.NEAREST: Image.NEAREST,
@ -1361,6 +1361,10 @@ class AutoContrast:
"""
Automatically maximize the contrast of the input PIL image.
Args:
cutoff (float, optional): Percent of pixels to cut off from the histogram (default=0.0).
ignore (int or sequence, optional): Pixel values to ignore (default=None).
Examples:
>>> py_transforms.ComposeOp([py_transforms.Decode(),
>>> py_transforms.AutoContrast(),
@ -1368,6 +1372,11 @@ class AutoContrast:
"""
@check_auto_contrast
def __init__(self, cutoff=0.0, ignore=None):
self.cutoff = cutoff
self.ignore = ignore
def __call__(self, img):
"""
Call method.
@ -1379,7 +1388,7 @@ class AutoContrast:
img (PIL Image), Augmented image.
"""
return util.auto_contrast(img)
return util.auto_contrast(img, self.cutoff, self.ignore)
class Invert:

6
mindspore/dataset/transforms/vision/py_transforms_util.py
View File

@ -1457,13 +1457,15 @@ def random_sharpness(img, degrees):
return ImageEnhance.Sharpness(img).enhance(v)
def auto_contrast(img):
def auto_contrast(img, cutoff, ignore):
"""
Automatically maximize the contrast of the input PIL image.
Args:
img (PIL Image): Image to be augmented with AutoContrast.
cutoff (float, optional): Percent of pixels to cut off from the histogram (default=0.0).
ignore (int or sequence, optional): Pixel values to ignore (default=None).
Returns:
img (PIL Image), Augmented image.
@ -1473,7 +1475,7 @@ def auto_contrast(img):
if not is_pil(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
return ImageOps.autocontrast(img)
return ImageOps.autocontrast(img, cutoff, ignore)
def invert_color(img):

8
mindspore/dataset/transforms/vision/validators.py
View File

@ -506,13 +506,13 @@ def check_uniform_augment_cpp(method):
@wraps(method)
def new_method(self, *args, **kwargs):
[operations, num_ops], _ = parse_user_args(method, *args, **kwargs)
[transforms, num_ops], _ = parse_user_args(method, *args, **kwargs)
type_check(num_ops, (int,), "num_ops")
check_positive(num_ops, "num_ops")
if num_ops > len(operations):
raise ValueError("num_ops is greater than operations list size")
type_check_list(operations, (TensorOp,), "tensor_ops")
if num_ops > len(transforms):
raise ValueError("num_ops is greater than transforms list size")
type_check_list(transforms, (TensorOp,), "tensor_ops")
return method(self, *args, **kwargs)

BIN
tests/ut/data/dataset/golden/autcontrast_01_result_c.npz Normal file
View File

Binary file not shown.

BIN
tests/ut/data/dataset/golden/autcontrast_01_result_py.npz
View File

Binary file not shown.

118
tests/ut/python/dataset/test_autocontrast.py
View File

@ -58,7 +58,7 @@ def test_auto_contrast_py(plot=False):
transforms_auto_contrast = F.ComposeOp([F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(),
F.AutoContrast(cutoff=10.0, ignore=[10, 20]),
F.ToTensor()])
ds_auto_contrast = ds.map(input_columns="image",
@ -99,8 +99,8 @@ def test_auto_contrast_c(plot=False):
ds = ds.map(input_columns=["image"],
operations=[C.Decode(),
C.Resize((224, 224))])
python_op = F.AutoContrast()
c_op = C.AutoContrast()
python_op = F.AutoContrast(cutoff=10.0, ignore=[10, 20])
c_op = C.AutoContrast(cutoff=10.0, ignore=[10, 20])
transforms_op = F.ComposeOp([lambda img: F.ToPIL()(img.astype(np.uint8)),
python_op,
np.array])()
@ -143,6 +143,10 @@ def test_auto_contrast_c(plot=False):
logger.info("MSE= {}".format(str(np.mean(mse))))
np.testing.assert_equal(np.mean(mse), 0.0)
# Compare with expected md5 from images
filename = "autcontrast_01_result_c.npz"
save_and_check_md5(ds_auto_contrast_c, filename, generate_golden=GENERATE_GOLDEN)
if plot:
visualize_list(images_auto_contrast_c, images_auto_contrast_py, visualize_mode=2)
@ -209,11 +213,11 @@ def test_auto_contrast_one_channel_c(plot=False):
visualize_list(images_auto_contrast_c, images_auto_contrast_py, visualize_mode=2)
def test_auto_contrast_invalid_input_c():
def test_auto_contrast_invalid_ignore_param_c():
"""
Test AutoContrast C Op with invalid params
Test AutoContrast C Op with invalid ignore parameter
"""
logger.info("Test AutoContrast C Op with invalid params")
logger.info("Test AutoContrast C Op with invalid ignore parameter")
try:
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
@ -226,10 +230,110 @@ def test_auto_contrast_invalid_input_c():
except TypeError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Argument ignore with value 255.5 is not of type" in str(error)
try:
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[C.Decode(),
C.Resize((224, 224)),
lambda img: np.array(img[:, :, 0])])
# invalid ignore
ds = ds.map(input_columns="image",
operations=C.AutoContrast(ignore=(10, 100)))
except TypeError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Argument ignore with value (10,100) is not of type" in str(error)
def test_auto_contrast_invalid_cutoff_param_c():
"""
Test AutoContrast C Op with invalid cutoff parameter
"""
logger.info("Test AutoContrast C Op with invalid cutoff parameter")
try:
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[C.Decode(),
C.Resize((224, 224)),
lambda img: np.array(img[:, :, 0])])
# invalid ignore
ds = ds.map(input_columns="image",
operations=C.AutoContrast(cutoff=-10.0))
except ValueError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Input cutoff is not within the required interval of (0 to 100)." in str(error)
try:
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[C.Decode(),
C.Resize((224, 224)),
lambda img: np.array(img[:, :, 0])])
# invalid ignore
ds = ds.map(input_columns="image",
operations=C.AutoContrast(cutoff=120.0))
except ValueError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Input cutoff is not within the required interval of (0 to 100)." in str(error)
def test_auto_contrast_invalid_ignore_param_py():
"""
Test AutoContrast python Op with invalid ignore parameter
"""
logger.info("Test AutoContrast python Op with invalid ignore parameter")
try:
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[F.ComposeOp([F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(ignore=255.5),
F.ToTensor()])])
except TypeError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Argument ignore with value 255.5 is not of type" in str(error)
try:
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[F.ComposeOp([F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(ignore=(10, 100)),
F.ToTensor()])])
except TypeError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Argument ignore with value (10,100) is not of type" in str(error)
def test_auto_contrast_invalid_cutoff_param_py():
"""
Test AutoContrast python Op with invalid cutoff parameter
"""
logger.info("Test AutoContrast python Op with invalid cutoff parameter")
try:
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[F.ComposeOp([F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(cutoff=-10.0),
F.ToTensor()])])
except ValueError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Input cutoff is not within the required interval of (0 to 100)." in str(error)
try:
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[F.ComposeOp([F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(cutoff=120.0),
F.ToTensor()])])
except ValueError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Input cutoff is not within the required interval of (0 to 100)." in str(error)
if __name__ == "__main__":
test_auto_contrast_py(plot=True)
test_auto_contrast_c(plot=True)
test_auto_contrast_one_channel_c(plot=True)
test_auto_contrast_invalid_input_c()
test_auto_contrast_invalid_ignore_param_c()
test_auto_contrast_invalid_ignore_param_py()
test_auto_contrast_invalid_cutoff_param_c()
test_auto_contrast_invalid_cutoff_param_py()

12
tests/ut/python/dataset/test_uniform_augment.py
View File

@ -124,7 +124,7 @@ def test_cpp_uniform_augment(plot=False, num_ops=2):
C.RandomColorAdjust(),
C.RandomRotation(degrees=45)]
uni_aug = C.UniformAugment(operations=transforms_ua, num_ops=num_ops)
uni_aug = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
transforms_all = [C.Decode(), C.Resize(size=[224, 224]),
uni_aug,
@ -166,7 +166,7 @@ def test_cpp_uniform_augment_exception_pyops(num_ops=2):
F.Invert()]
with pytest.raises(TypeError) as e:
C.UniformAugment(operations=transforms_ua, num_ops=num_ops)
C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Argument tensor_ops[5] with value" \
@ -187,7 +187,7 @@ def test_cpp_uniform_augment_exception_large_numops(num_ops=6):
C.RandomRotation(degrees=45)]
try:
_ = C.UniformAugment(operations=transforms_ua, num_ops=num_ops)
_ = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
except Exception as e:
logger.info("Got an exception in DE: {}".format(str(e)))
@ -207,7 +207,7 @@ def test_cpp_uniform_augment_exception_nonpositive_numops(num_ops=0):
C.RandomRotation(degrees=45)]
try:
_ = C.UniformAugment(operations=transforms_ua, num_ops=num_ops)
_ = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
except Exception as e:
logger.info("Got an exception in DE: {}".format(str(e)))
@ -227,7 +227,7 @@ def test_cpp_uniform_augment_exception_float_numops(num_ops=2.5):
C.RandomRotation(degrees=45)]
try:
_ = C.UniformAugment(operations=transforms_ua, num_ops=num_ops)
_ = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
except Exception as e:
logger.info("Got an exception in DE: {}".format(str(e)))
@ -248,7 +248,7 @@ def test_cpp_uniform_augment_random_crop_badinput(num_ops=1):
C.RandomCrop(size=[224, 224]),
C.RandomHorizontalFlip()
]
uni_aug = C.UniformAugment(operations=transforms_ua, num_ops=num_ops)
uni_aug = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
ds1 = ds1.map(input_columns="image", operations=uni_aug)
# apply DatasetOps