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!4955 Fixes for Dynamic Augmentation Ops 

Merge pull request !4955 from MahdiRahmaniHanzaki/dynamic-ops-fix
This commit is contained in:
mindspore-ci-bot 2020-08-22 10:27:50 +08:00 committed by Gitee
parent 528fb81093 a5f9b8f92e
commit 9b503e4f38
11 changed files with 285 additions and 56 deletions
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4
mindspore/ccsrc/minddata/dataset/api/transforms.cc
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@ -382,7 +382,7 @@ CutMixBatchOperation::CutMixBatchOperation(ImageBatchFormat image_batch_format,
: image_batch_format_(image_batch_format), alpha_(alpha), prob_(prob) {}
bool CutMixBatchOperation::ValidateParams() {
if (alpha_ < 0) {
if (alpha_ <= 0) {
MS_LOG(ERROR) << "CutMixBatch: alpha cannot be negative.";
return false;
}
@ -434,7 +434,7 @@ std::shared_ptr<TensorOp> HwcToChwOperation::Build() { return std::make_shared<H
MixUpBatchOperation::MixUpBatchOperation(float alpha) : alpha_(alpha) {}
bool MixUpBatchOperation::ValidateParams() {
if (alpha_ < 0) {
if (alpha_ <= 0) {
MS_LOG(ERROR) << "MixUpBatch: alpha must be a positive floating value however it is: " << alpha_;
return false;
}

17
mindspore/ccsrc/minddata/dataset/kernels/image/cutmix_batch_op.cc
View File

@ -59,7 +59,7 @@ Status CutMixBatchOp::Compute(const TensorRow &input, TensorRow *output) {
// Check inputs
if (image_shape.size() != 4 || image_shape[0] != label_shape[0]) {
RETURN_STATUS_UNEXPECTED("You must batch before calling CutMixBatch.");
RETURN_STATUS_UNEXPECTED("You must make sure images are HWC or CHW and batch before calling CutMixBatch.");
}
if (label_shape.size() != 2) {
RETURN_STATUS_UNEXPECTED("CutMixBatch: Label's must be in one-hot format and in a batch");
@ -139,10 +139,17 @@ Status CutMixBatchOp::Compute(const TensorRow &input, TensorRow *output) {
// Compute labels
for (int j = 0; j < label_shape[1]; j++) {
uint64_t first_value, second_value;
RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, {i, j}));
RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, {rand_indx[i] % label_shape[0], j}));
RETURN_IF_NOT_OK(out_labels->SetItemAt({i, j}, label_lam * first_value + (1 - label_lam) * second_value));
if (input.at(1)->type().IsSignedInt()) {
int64_t first_value, second_value;
RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, {i, j}));
RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, {rand_indx[i] % label_shape[0], j}));
RETURN_IF_NOT_OK(out_labels->SetItemAt({i, j}, label_lam * first_value + (1 - label_lam) * second_value));
} else {
uint64_t first_value, second_value;
RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, {i, j}));
RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, {rand_indx[i] % label_shape[0], j}));
RETURN_IF_NOT_OK(out_labels->SetItemAt({i, j}, label_lam * first_value + (1 - label_lam) * second_value));
}
}
}
}

17
mindspore/ccsrc/minddata/dataset/kernels/image/mixup_batch_op.cc
View File

@ -38,7 +38,7 @@ Status MixUpBatchOp::Compute(const TensorRow &input, TensorRow *output) {
// Check inputs
if (image_shape.size() != 4 || image_shape[0] != label_shape[0]) {
RETURN_STATUS_UNEXPECTED("You must batch before calling MixUpBatch");
RETURN_STATUS_UNEXPECTED("You must make sure images are HWC or CHW and batch before calling MixUpBatch");
}
if (label_shape.size() != 2) {
RETURN_STATUS_UNEXPECTED("MixUpBatch: Label's must be in one-hot format and in a batch");
@ -68,10 +68,17 @@ Status MixUpBatchOp::Compute(const TensorRow &input, TensorRow *output) {
RETURN_IF_NOT_OK(TypeCast(std::move(input.at(1)), &out_labels, DataType("float32")));
for (int64_t i = 0; i < label_shape[0]; i++) {
for (int64_t j = 0; j < label_shape[1]; j++) {
uint64_t first_value, second_value;
RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, {i, j}));
RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, {rand_indx[i], j}));
RETURN_IF_NOT_OK(out_labels->SetItemAt({i, j}, lam * first_value + (1 - lam) * second_value));
if (input.at(1)->type().IsSignedInt()) {
int64_t first_value, second_value;
RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, {i, j}));
RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, {rand_indx[i], j}));
RETURN_IF_NOT_OK(out_labels->SetItemAt({i, j}, lam * first_value + (1 - lam) * second_value));
} else {
uint64_t first_value, second_value;
RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&first_value, {i, j}));
RETURN_IF_NOT_OK(input.at(1)->GetItemAt(&second_value, {rand_indx[i], j}));
RETURN_IF_NOT_OK(out_labels->SetItemAt({i, j}, lam * first_value + (1 - lam) * second_value));
}
}
}

4
mindspore/dataset/transforms/vision/c_transforms.py
View File

@ -231,7 +231,7 @@ class Normalize(cde.NormalizeOp):
class RandomAffine(cde.RandomAffineOp):
"""
Apply Random affine transformation to the input PIL image.
Apply Random affine transformation to the input image.
Args:
degrees (int or float or sequence): Range of the rotation degrees.
@ -681,12 +681,12 @@ class CenterCrop(cde.CenterCropOp):
class RandomColor(cde.RandomColorOp):
"""
Adjust the color of the input image by a fixed or random degree.
This operation works only with 3-channel color images.
Args:
degrees (sequence): Range of random color adjustment degrees.
It should be in (min, max) format. If min=max, then it is a
single fixed magnitude operation (default=(0.1,1.9)).
Works with 3-channel color images.
"""
@check_positive_degrees

35
mindspore/dataset/transforms/vision/py_transforms.py
View File

@ -1169,39 +1169,12 @@ class RandomAffine:
Returns:
img (PIL Image), Randomly affine transformed image.
"""
# rotation
angle = random.uniform(self.degrees[0], self.degrees[1])
# translation
if self.translate is not None:
max_dx = self.translate[0] * img.size[0]
max_dy = self.translate[1] * img.size[1]
translations = (np.round(random.uniform(-max_dx, max_dx)),
np.round(random.uniform(-max_dy, max_dy)))
else:
translations = (0, 0)
# scale
if self.scale_ranges is not None:
scale = random.uniform(self.scale_ranges[0], self.scale_ranges[1])
else:
scale = 1.0
# shear
if self.shear is not None:
if len(self.shear) == 2:
shear = [random.uniform(self.shear[0], self.shear[1]), 0.]
elif len(self.shear) == 4:
shear = [random.uniform(self.shear[0], self.shear[1]),
random.uniform(self.shear[2], self.shear[3])]
else:
shear = 0.0
return util.random_affine(img,
angle,
translations,
scale,
shear,
self.degrees,
self.translate,
self.scale_ranges,
self.shear,
self.resample,
self.fill_value)

34
mindspore/dataset/transforms/vision/py_transforms_util.py
View File

@ -1153,6 +1153,34 @@ def random_affine(img, angle, translations, scale, shear, resample, fill_value=0
if not is_pil(img):
raise ValueError("Input image should be a Pillow image.")
# rotation
angle = random.uniform(angle[0], angle[1])
# translation
if translations is not None:
max_dx = translations[0] * img.size[0]
max_dy = translations[1] * img.size[1]
translations = (np.round(random.uniform(-max_dx, max_dx)),
np.round(random.uniform(-max_dy, max_dy)))
else:
translations = (0, 0)
# scale
if scale is not None:
scale = random.uniform(scale[0], scale[1])
else:
scale = 1.0
# shear
if shear is not None:
if len(shear) == 2:
shear = [random.uniform(shear[0], shear[1]), 0.]
elif len(shear) == 4:
shear = [random.uniform(shear[0], shear[1]),
random.uniform(shear[2], shear[3])]
else:
shear = 0.0
output_size = img.size
center = (img.size[0] * 0.5 + 0.5, img.size[1] * 0.5 + 0.5)
@ -1416,7 +1444,6 @@ def hsv_to_rgbs(np_hsv_imgs, is_hwc):
def random_color(img, degrees):
"""
Adjust the color of the input PIL image by a random degree.
@ -1437,7 +1464,6 @@ def random_color(img, degrees):
def random_sharpness(img, degrees):
"""
Adjust the sharpness of the input PIL image by a random degree.
@ -1458,7 +1484,6 @@ def random_sharpness(img, degrees):
def auto_contrast(img, cutoff, ignore):
"""
Automatically maximize the contrast of the input PIL image.
@ -1479,7 +1504,6 @@ def auto_contrast(img, cutoff, ignore):
def invert_color(img):
"""
Invert colors of input PIL image.
@ -1498,7 +1522,6 @@ def invert_color(img):
def equalize(img):
"""
Equalize the histogram of input PIL image.
@ -1517,7 +1540,6 @@ def equalize(img):
def uniform_augment(img, transforms, num_ops):
"""
Uniformly select and apply a number of transforms sequentially from
a list of transforms. Randomly assigns a probability to each transform for

2
mindspore/dataset/transforms/vision/validators.py
View File

@ -45,6 +45,7 @@ def check_cut_mix_batch_c(method):
[image_batch_format, alpha, prob], _ = parse_user_args(method, *args, **kwargs)
type_check(image_batch_format, (ImageBatchFormat,), "image_batch_format")
check_pos_float32(alpha)
check_positive(alpha, "alpha")
check_value(prob, [0, 1], "prob")
return method(self, *args, **kwargs)
@ -68,6 +69,7 @@ def check_mix_up_batch_c(method):
@wraps(method)
def new_method(self, *args, **kwargs):
[alpha], _ = parse_user_args(method, *args, **kwargs)
check_positive(alpha, "alpha")
check_pos_float32(alpha)
return method(self, *args, **kwargs)

54
tests/ut/cpp/dataset/c_api_transforms_test.cc
View File

@ -191,11 +191,37 @@ TEST_F(MindDataTestPipeline, TestCutMixBatchFail2) {
ds = ds->Map({one_hot_op},{"label"});
EXPECT_NE(ds, nullptr);
std::shared_ptr<TensorOperation> cutmix_batch_op = vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNHWC, 1, -0.5);
std::shared_ptr<TensorOperation> cutmix_batch_op = vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNHWC,
1, -0.5);
EXPECT_EQ(cutmix_batch_op, nullptr);
}
TEST_F(MindDataTestPipeline, TestCutMixBatchFail3) {
// Must fail because alpha can't be zero
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 5;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> one_hot_op = vision::OneHot(10);
EXPECT_NE(one_hot_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({one_hot_op},{"label"});
EXPECT_NE(ds, nullptr);
std::shared_ptr<TensorOperation> cutmix_batch_op = vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNHWC,
0.0, 0.5);
EXPECT_EQ(cutmix_batch_op, nullptr);
}
TEST_F(MindDataTestPipeline, TestCutOut) {
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
@ -365,6 +391,30 @@ TEST_F(MindDataTestPipeline, TestMixUpBatchFail1) {
EXPECT_EQ(mixup_batch_op, nullptr);
}
TEST_F(MindDataTestPipeline, TestMixUpBatchFail2) {
// This should fail because alpha can't be zero
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Batch operation on ds
int32_t batch_size = 5;
ds = ds->Batch(batch_size);
EXPECT_NE(ds, nullptr);
// Create objects for the tensor ops
std::shared_ptr<TensorOperation> one_hot_op = vision::OneHot(10);
EXPECT_NE(one_hot_op, nullptr);
// Create a Map operation on ds
ds = ds->Map({one_hot_op}, {"label"});
EXPECT_NE(ds, nullptr);
std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch(0.0);
EXPECT_EQ(mixup_batch_op, nullptr);
}
TEST_F(MindDataTestPipeline, TestMixUpBatchSuccess1) {
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
@ -384,7 +434,7 @@ TEST_F(MindDataTestPipeline, TestMixUpBatchSuccess1) {
ds = ds->Map({one_hot_op}, {"label"});
EXPECT_NE(ds, nullptr);
std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch(0.5);
std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch(2.0);
EXPECT_NE(mixup_batch_op, nullptr);
// Create a Map operation on ds

70
tests/ut/python/dataset/test_cutmix_batch_op.py
View File

@ -26,6 +26,7 @@ from util import save_and_check_md5, diff_mse, visualize_list, config_get_set_se
config_get_set_num_parallel_workers
DATA_DIR = "../data/dataset/testCifar10Data"
DATA_DIR2 = "../data/dataset/testImageNetData2/train/"
GENERATE_GOLDEN = False
@ -114,6 +115,53 @@ def test_cutmix_batch_success2(plot=False):
logger.info("MSE= {}".format(str(np.mean(mse))))
def test_cutmix_batch_success3(plot=False):
"""
Test CutMixBatch op with default values for alpha and prob on a batch of HWC images on ImageFolderDatasetV2
"""
logger.info("test_cutmix_batch_success3")
ds_original = ds.ImageFolderDatasetV2(dataset_dir=DATA_DIR2, shuffle=False)
decode_op = vision.Decode()
ds_original = ds_original.map(input_columns=["image"], operations=[decode_op])
ds_original = ds_original.batch(4, pad_info={}, drop_remainder=True)
images_original = None
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = image
else:
images_original = np.append(images_original, image, axis=0)
# CutMix Images
data1 = ds.ImageFolderDatasetV2(dataset_dir=DATA_DIR2, shuffle=False)
decode_op = vision.Decode()
data1 = data1.map(input_columns=["image"], operations=[decode_op])
one_hot_op = data_trans.OneHot(num_classes=10)
data1 = data1.map(input_columns=["label"], operations=one_hot_op)
cutmix_batch_op = vision.CutMixBatch(mode.ImageBatchFormat.NHWC)
data1 = data1.batch(4, pad_info={}, drop_remainder=True)
data1 = data1.map(input_columns=["image", "label"], operations=cutmix_batch_op)
images_cutmix = None
for idx, (image, _) in enumerate(data1):
if idx == 0:
images_cutmix = image
else:
images_cutmix = np.append(images_cutmix, image, axis=0)
if plot:
visualize_list(images_original, images_cutmix)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_cutmix[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
def test_cutmix_batch_nhwc_md5():
"""
Test CutMixBatch on a batch of HWC images with MD5:
@ -185,7 +233,7 @@ def test_cutmix_batch_fail1():
images_cutmix = image
else:
images_cutmix = np.append(images_cutmix, image, axis=0)
error_message = "You must batch before calling CutMixBatch"
error_message = "You must make sure images are HWC or CHW and batch "
assert error_message in str(error.value)
@ -322,9 +370,28 @@ def test_cutmix_batch_fail7():
assert error_message in str(error.value)
def test_cutmix_batch_fail8():
"""
Test CutMixBatch Fail 8
We expect this to fail because alpha is zero
"""
logger.info("test_cutmix_batch_fail8")
# CutMixBatch Images
data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
one_hot_op = data_trans.OneHot(num_classes=10)
data1 = data1.map(input_columns=["label"], operations=one_hot_op)
with pytest.raises(ValueError) as error:
vision.CutMixBatch(mode.ImageBatchFormat.NHWC, 0.0)
error_message = "Input is not within the required interval"
assert error_message in str(error.value)
if __name__ == "__main__":
test_cutmix_batch_success1(plot=True)
test_cutmix_batch_success2(plot=True)
test_cutmix_batch_success3(plot=True)
test_cutmix_batch_nchw_md5()
test_cutmix_batch_nhwc_md5()
test_cutmix_batch_fail1()
@ -334,3 +401,4 @@ if __name__ == "__main__":
test_cutmix_batch_fail5()
test_cutmix_batch_fail6()
test_cutmix_batch_fail7()
test_cutmix_batch_fail8()

84
tests/ut/python/dataset/test_mixup_op.py
View File

@ -25,6 +25,7 @@ from util import save_and_check_md5, diff_mse, visualize_list, config_get_set_se
config_get_set_num_parallel_workers
DATA_DIR = "../data/dataset/testCifar10Data"
DATA_DIR2 = "../data/dataset/testImageNetData2/train/"
GENERATE_GOLDEN = False
@ -71,11 +72,59 @@ def test_mixup_batch_success1(plot=False):
def test_mixup_batch_success2(plot=False):
"""
Test MixUpBatch op with specified alpha parameter on ImageFolderDatasetV2
"""
logger.info("test_mixup_batch_success2")
# Original Images
ds_original = ds.ImageFolderDatasetV2(dataset_dir=DATA_DIR2, shuffle=False)
decode_op = vision.Decode()
ds_original = ds_original.map(input_columns=["image"], operations=[decode_op])
ds_original = ds_original.batch(4, pad_info={}, drop_remainder=True)
images_original = None
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = image
else:
images_original = np.append(images_original, image, axis=0)
# MixUp Images
data1 = ds.ImageFolderDatasetV2(dataset_dir=DATA_DIR2, shuffle=False)
decode_op = vision.Decode()
data1 = data1.map(input_columns=["image"], operations=[decode_op])
one_hot_op = data_trans.OneHot(num_classes=10)
data1 = data1.map(input_columns=["label"], operations=one_hot_op)
mixup_batch_op = vision.MixUpBatch(2.0)
data1 = data1.batch(4, pad_info={}, drop_remainder=True)
data1 = data1.map(input_columns=["image", "label"], operations=mixup_batch_op)
images_mixup = None
for idx, (image, _) in enumerate(data1):
if idx == 0:
images_mixup = image
else:
images_mixup = np.append(images_mixup, image, axis=0)
if plot:
visualize_list(images_original, images_mixup)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_mixup[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
def test_mixup_batch_success3(plot=False):
"""
Test MixUpBatch op without specified alpha parameter.
Alpha parameter will be selected by default in this case
"""
logger.info("test_mixup_batch_success2")
logger.info("test_mixup_batch_success3")
# Original Images
ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
@ -169,7 +218,7 @@ def test_mixup_batch_fail1():
images_mixup = image
else:
images_mixup = np.append(images_mixup, image, axis=0)
error_message = "You must batch before calling MixUp"
error_message = "You must make sure images are HWC or CHW and batch"
assert error_message in str(error.value)
@ -207,6 +256,7 @@ def test_mixup_batch_fail3():
Test MixUpBatch op
We expect this to fail because label column is not passed to mixup_batch
"""
logger.info("test_mixup_batch_fail3")
# Original Images
ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
ds_original = ds_original.batch(5, drop_remainder=True)
@ -237,11 +287,41 @@ def test_mixup_batch_fail3():
error_message = "Both images and labels columns are required"
assert error_message in str(error.value)
def test_mixup_batch_fail4():
"""
Test MixUpBatch Fail 2
We expect this to fail because alpha is zero
"""
logger.info("test_mixup_batch_fail4")
# Original Images
ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
ds_original = ds_original.batch(5)
images_original = np.array([])
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = image
else:
images_original = np.append(images_original, image, axis=0)
# MixUp Images
data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
one_hot_op = data_trans.OneHot(num_classes=10)
data1 = data1.map(input_columns=["label"], operations=one_hot_op)
with pytest.raises(ValueError) as error:
vision.MixUpBatch(0.0)
error_message = "Input is not within the required interval"
assert error_message in str(error.value)
if __name__ == "__main__":
test_mixup_batch_success1(plot=True)
test_mixup_batch_success2(plot=True)
test_mixup_batch_success3(plot=True)
test_mixup_batch_md5()
test_mixup_batch_fail1()
test_mixup_batch_fail2()
test_mixup_batch_fail3()
test_mixup_batch_fail4()

20
tests/ut/python/dataset/test_random_affine.py
View File

@ -27,6 +27,7 @@ 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"
MNIST_DATA_DIR = "../data/dataset/testMnistData"
def test_random_affine_op(plot=False):
@ -155,6 +156,24 @@ def test_random_affine_c_md5():
ds.config.set_num_parallel_workers((original_num_parallel_workers))
def test_random_affine_py_exception_non_pil_images():
"""
Test RandomAffine: input img is ndarray and not PIL, expected to raise TypeError
"""
logger.info("test_random_affine_exception_negative_degrees")
dataset = ds.MnistDataset(MNIST_DATA_DIR, num_parallel_workers=3)
try:
transform = py_vision.ComposeOp([py_vision.ToTensor(),
py_vision.RandomAffine(degrees=(15, 15))])
dataset = dataset.map(input_columns=["image"], operations=transform(), num_parallel_workers=3,
python_multiprocessing=True)
for _ in dataset.create_dict_iterator():
break
except RuntimeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Pillow image" in str(e)
def test_random_affine_exception_negative_degrees():
"""
Test RandomAffine: input degrees in negative, expected to raise ValueError
@ -289,6 +308,7 @@ if __name__ == "__main__":
test_random_affine_op_c(plot=True)
test_random_affine_md5()
test_random_affine_c_md5()
test_random_affine_py_exception_non_pil_images()
test_random_affine_exception_negative_degrees()
test_random_affine_exception_translation_range()
test_random_affine_exception_scale_value()