[feat] [assistant] [I40GZE] add new data operator RandomAdjustSharpness

mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/kernels/ir/image/bindings.cc

@@ -36,6 +36,7 @@
 #include "minddata/dataset/kernels/ir/vision/normalize_ir.h"
 #include "minddata/dataset/kernels/ir/vision/normalize_pad_ir.h"
 #include "minddata/dataset/kernels/ir/vision/pad_ir.h"
+#include "minddata/dataset/kernels/ir/vision/random_adjust_sharpness_ir.h"
 #include "minddata/dataset/kernels/ir/vision/random_affine_ir.h"
 #include "minddata/dataset/kernels/ir/vision/random_auto_contrast_ir.h"
 #include "minddata/dataset/kernels/ir/vision/random_color_adjust_ir.h"
@@ -275,6 +276,18 @@ PYBIND_REGISTER(PadOperation, 1, ([](const py::module *m) {
                     }));
                 }));
 
+PYBIND_REGISTER(RandomAdjustSharpnessOperation, 1, ([](const py::module *m) {
+                  (void)py::class_<vision::RandomAdjustSharpnessOperation, TensorOperation,
+                                   std::shared_ptr<vision::RandomAdjustSharpnessOperation>>(
+                    *m, "RandomAdjustSharpnessOperation")
+                    .def(py::init([](float degree, float prob) {
+                      auto random_adjust_sharpness =
+                        std::make_shared<vision::RandomAdjustSharpnessOperation>(degree, prob);
+                      THROW_IF_ERROR(random_adjust_sharpness->ValidateParams());
+                      return random_adjust_sharpness;
+                    }));
+                }));
+
 PYBIND_REGISTER(
   RandomAffineOperation, 1, ([](const py::module *m) {
     (void)py::class_<vision::RandomAffineOperation, TensorOperation, std::shared_ptr<vision::RandomAffineOperation>>(

mindspore/ccsrc/minddata/dataset/api/vision.cc

@@ -40,6 +40,7 @@
 #include "minddata/dataset/kernels/ir/vision/normalize_ir.h"
 #include "minddata/dataset/kernels/ir/vision/normalize_pad_ir.h"
 #include "minddata/dataset/kernels/ir/vision/pad_ir.h"
+#include "minddata/dataset/kernels/ir/vision/random_adjust_sharpness_ir.h"
 #include "minddata/dataset/kernels/ir/vision/random_affine_ir.h"
 #include "minddata/dataset/kernels/ir/vision/random_auto_contrast_ir.h"
 #include "minddata/dataset/kernels/ir/vision/random_color_adjust_ir.h"
@@ -450,6 +451,19 @@ Pad::Pad(std::vector<int32_t> padding, std::vector<uint8_t> fill_value, BorderTy
 std::shared_ptr<TensorOperation> Pad::Parse() {
   return std::make_shared<PadOperation>(data_->padding_, data_->fill_value_, data_->padding_mode_);
 }
+
+// RandomAdjustSharpness Transform Operation.
+struct RandomAdjustSharpness::Data {
+  Data(float degree, float prob) : degree_(degree), probability_(prob) {}
+  float degree_;
+  float probability_;
+};
+
+RandomAdjustSharpness::RandomAdjustSharpness(float degree, float prob) : data_(std::make_shared<Data>(degree, prob)) {}
+
+std::shared_ptr<TensorOperation> RandomAdjustSharpness::Parse() {
+  return std::make_shared<RandomAdjustSharpnessOperation>(data_->degree_, data_->probability_);
+}
 #endif  // not ENABLE_ANDROID
 
 // RandomAffine Transform Operation.

mindspore/ccsrc/minddata/dataset/include/dataset/vision.h

@@ -348,6 +348,28 @@ class RandomAutoContrast final : public TensorTransform {
   std::shared_ptr<Data> data_;
 };
 
+/// \brief Randomly adjust the sharpness of the input image with a given probability.
+class RandomAdjustSharpness final : public TensorTransform {
+ public:
+  /// \brief Constructor.
+  /// \param[in] degree A float representing sharpness adjustment degree, which must be non negative.
+  /// \param[in] prob A float representing the probability of the image being sharpness adjusted, which
+  ///     must in range of [0, 1] (default=0.5).
+  explicit RandomAdjustSharpness(float degree, float prob = 0.5);
+
+  /// \brief Destructor.
+  ~RandomAdjustSharpness() = default;
+
+ protected:
+  /// \brief The function to convert a TensorTransform object into a TensorOperation object.
+  /// \return Shared pointer to TensorOperation object.
+  std::shared_ptr<TensorOperation> Parse() override;
+
+ private:
+  struct Data;
+  std::shared_ptr<Data> data_;
+};
+
 /// \brief Blend an image with its grayscale version with random weights
 ///        t and 1 - t generated from a given range. If the range is trivial
 ///        then the weights are determinate and t equals to the bound of the interval.

mindspore/ccsrc/minddata/dataset/kernels/image/CMakeLists.txt

@@ -28,6 +28,7 @@ add_library(kernels-image OBJECT
     normalize_pad_op.cc
     pad_op.cc
     posterize_op.cc
+    random_adjust_sharpness_op.cc
     random_affine_op.cc
     random_auto_contrast_op.cc
     random_color_adjust_op.cc

mindspore/ccsrc/minddata/dataset/kernels/image/random_adjust_sharpness_op.cc

@@ -0,0 +1,34 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "minddata/dataset/kernels/image/random_adjust_sharpness_op.h"
+
+namespace mindspore {
+namespace dataset {
+
+const float RandomAdjustSharpnessOp::kDefProbability = 0.5;
+
+Status RandomAdjustSharpnessOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
+  IO_CHECK(input, output);
+
+  if (distribution_(rnd_)) {
+    return SharpnessOp::Compute(input, output);
+  }
+  *output = input;
+  return Status::OK();
+}
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/kernels/image/random_adjust_sharpness_op.h

@@ -0,0 +1,60 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_RANDOM_ADJUST_SHARPNESS_OP_H_
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_RANDOM_ADJUST_SHARPNESS_OP_H_
+
+#include <memory>
+#include <random>
+#include <string>
+
+#include "minddata/dataset/kernels/image/sharpness_op.h"
+#include "minddata/dataset/util/random.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+
+class RandomAdjustSharpnessOp : public SharpnessOp {
+ public:
+  // Default values, also used by python_bindings.cc
+  static const float kDefProbability;
+
+  explicit RandomAdjustSharpnessOp(float degree, float prob = kDefProbability)
+      : SharpnessOp(degree), distribution_(prob) {
+    is_deterministic_ = false;
+    rnd_.seed(GetSeed());
+  }
+
+  ~RandomAdjustSharpnessOp() override = default;
+
+  // Provide stream operator for displaying it
+  friend std::ostream &operator<<(std::ostream &out, const RandomAdjustSharpnessOp &so) {
+    so.Print(out);
+    return out;
+  }
+
+  Status Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) override;
+
+  std::string Name() const override { return kRandomAdjustSharpnessOp; }
+
+ private:
+  std::mt19937 rnd_;
+  std::bernoulli_distribution distribution_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_RANDOM_ADJUST_SHARPNESS_OP_H_

mindspore/ccsrc/minddata/dataset/kernels/ir/vision/CMakeLists.txt

@@ -21,6 +21,7 @@ set(DATASET_KERNELS_IR_VISION_SRC_FILES
         normalize_ir.cc
         normalize_pad_ir.cc
         pad_ir.cc
+        random_adjust_sharpness_ir.cc
         random_affine_ir.cc
         random_auto_contrast_ir.cc
         random_color_adjust_ir.cc

mindspore/ccsrc/minddata/dataset/kernels/ir/vision/random_adjust_sharpness_ir.cc

@@ -0,0 +1,68 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "minddata/dataset/kernels/ir/vision/random_adjust_sharpness_ir.h"
+
+#ifndef ENABLE_ANDROID
+#include "minddata/dataset/kernels/image/random_adjust_sharpness_op.h"
+#endif
+
+#include "minddata/dataset/kernels/ir/validators.h"
+
+namespace mindspore {
+namespace dataset {
+namespace vision {
+#ifndef ENABLE_ANDROID
+// RandomAdjustSharpnessOperation
+RandomAdjustSharpnessOperation::RandomAdjustSharpnessOperation(float degree, float prob)
+    : degree_(degree), probability_(prob) {}
+
+RandomAdjustSharpnessOperation::~RandomAdjustSharpnessOperation() = default;
+
+std::string RandomAdjustSharpnessOperation::Name() const { return kRandomAdjustSharpnessOperation; }
+
+Status RandomAdjustSharpnessOperation::ValidateParams() {
+  RETURN_IF_NOT_OK(ValidateFloatScalarNonNegative("RandomAdjustSharpness", "degree", degree_));
+  RETURN_IF_NOT_OK(ValidateProbability("RandomAdjustSharpness", probability_));
+  return Status::OK();
+}
+
+std::shared_ptr<TensorOp> RandomAdjustSharpnessOperation::Build() {
+  std::shared_ptr<RandomAdjustSharpnessOp> tensor_op = std::make_shared<RandomAdjustSharpnessOp>(degree_, probability_);
+  return tensor_op;
+}
+
+Status RandomAdjustSharpnessOperation::to_json(nlohmann::json *out_json) {
+  nlohmann::json args;
+  args["degree"] = degree_;
+  args["prob"] = probability_;
+  *out_json = args;
+  return Status::OK();
+}
+
+Status RandomAdjustSharpnessOperation::from_json(nlohmann::json op_params,
+                                                 std::shared_ptr<TensorOperation> *operation) {
+  CHECK_FAIL_RETURN_UNEXPECTED(op_params.find("degree") != op_params.end(), "Failed to find degree");
+  CHECK_FAIL_RETURN_UNEXPECTED(op_params.find("prob") != op_params.end(), "Failed to find prob");
+  float degree = op_params["degree"];
+  float prob = op_params["prob"];
+  *operation = std::make_shared<vision::RandomAdjustSharpnessOperation>(degree, prob);
+  return Status::OK();
+}
+
+#endif
+}  // namespace vision
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/kernels/ir/vision/random_adjust_sharpness_ir.h

@@ -0,0 +1,63 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IR_VISION_RANDOM_ADJUST_SHARPNESS_IR_H_
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IR_VISION_RANDOM_ADJUST_SHARPNESS_IR_H_
+
+#include <map>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "include/api/status.h"
+#include "minddata/dataset/include/dataset/constants.h"
+#include "minddata/dataset/include/dataset/transforms.h"
+#include "minddata/dataset/kernels/ir/tensor_operation.h"
+
+namespace mindspore {
+namespace dataset {
+
+namespace vision {
+
+constexpr char kRandomAdjustSharpnessOperation[] = "RandomAdjustSharpness";
+
+class RandomAdjustSharpnessOperation : public TensorOperation {
+ public:
+  RandomAdjustSharpnessOperation(float degree, float prob);
+
+  ~RandomAdjustSharpnessOperation();
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+  std::string Name() const override;
+
+  Status to_json(nlohmann::json *out_json) override;
+
+  static Status from_json(nlohmann::json op_params, std::shared_ptr<TensorOperation> *operation);
+
+ private:
+  float degree_;
+  float probability_;
+};
+
+}  // namespace vision
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IR_VISION_RANDOM_ADJUST_SHARPNESS_IR_H_

mindspore/ccsrc/minddata/dataset/kernels/tensor_op.h

@@ -79,6 +79,7 @@ constexpr char kMixUpBatchOp[] = "MixUpBatchOp";
 constexpr char kNormalizeOp[] = "NormalizeOp";
 constexpr char kNormalizePadOp[] = "NormalizePadOp";
 constexpr char kPadOp[] = "PadOp";
+constexpr char kRandomAdjustSharpnessOp[] = "RandomAdjustSharpnessOp";
 constexpr char kRandomAffineOp[] = "RandomAffineOp";
 constexpr char kRandomAutoContrastOp[] = "RandomAutoContrastOp";
 constexpr char kRandomColorAdjustOp[] = "RandomColorAdjustOp";

mindspore/dataset/vision/c_transforms.py

@@ -52,6 +52,7 @@ from .validators import check_prob, check_crop, check_center_crop, check_resize_
     check_mix_up_batch_c, check_normalize_c, check_normalizepad_c, check_random_crop, check_random_color_adjust, \
     check_random_rotation, check_range, check_resize, check_rescale, check_pad, check_cutout, \
     check_uniform_augment_cpp, check_convert_color, check_random_resize_crop, check_random_auto_contrast, \
+    check_random_adjust_sharpness, \
     check_bounding_box_augment_cpp, check_random_select_subpolicy_op, check_auto_contrast, check_random_affine, \
     check_random_solarize, check_soft_dvpp_decode_random_crop_resize_jpeg, check_positive_degrees, FLOAT_MAX_INTEGER, \
     check_cut_mix_batch_c, check_posterize, check_gaussian_blur, check_rotate, check_slice_patches, check_adjust_gamma
@@ -671,6 +672,32 @@ class Pad(ImageTensorOperation):
         return cde.PadOperation(self.padding, self.fill_value, DE_C_BORDER_TYPE[self.padding_mode])
 
 
+class RandomAdjustSharpness(ImageTensorOperation):
+    """
+    Randomly adjust the sharpness of the input image with a given probability.
+
+    Args:
+        degree (float): Sharpness adjustment degree, which must be non negative.
+            Degree of 0.0 gives a blurred image, degree of 1.0 gives the original image,
+            and degree of 2.0 increases the sharpness by a factor of 2.
+        prob (float, optional): Probability of the image being sharpness adjusted, which
+            must be in range of [0, 1] (default=0.5).
+
+    Examples:
+        >>> transforms_list = [c_vision.Decode(), c_vision.RandomAdjustSharpness(2.0, 0.5)]
+        >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
+        ...                                                 input_columns=["image"])
+    """
+
+    @check_random_adjust_sharpness
+    def __init__(self, degree, prob=0.5):
+        self.prob = prob
+        self.degree = degree
+
+    def parse(self):
+        return cde.RandomAdjustSharpnessOperation(self.degree, self.prob)
+
+
 class RandomAffine(ImageTensorOperation):
     """
     Apply Random affine transformation to the input image.

mindspore/dataset/vision/validators.py

@@ -296,6 +296,22 @@ def check_size_scale_ration_max_attempts_paras(size, scale, ratio, max_attempts)
         check_value(max_attempts, (1, FLOAT_MAX_INTEGER))
 
 
+def check_random_adjust_sharpness(method):
+    """Wrapper method to check the parameters of RandomAdjustSharpness."""
+
+    @wraps(method)
+    def new_method(self, *args, **kwargs):
+        [degree, prob], _ = parse_user_args(method, *args, **kwargs)
+        type_check(degree, (float, int), "degree")
+        check_non_negative_float32(degree, "degree")
+        type_check(prob, (float, int), "prob")
+        check_value(prob, [0., 1.], "prob")
+
+        return method(self, *args, **kwargs)
+
+    return new_method
+
+
 def check_random_resize_crop(method):
     """A wrapper that wraps a parameter checker around the original function(random resize crop operation)."""
 

tests/ut/cpp/dataset/c_api_vision_a_to_q_test.cc

@@ -1270,129 +1270,3 @@ TEST_F(MindDataTestPipeline, TestConvertColorFail) {
     std::shared_ptr<Iterator> iter = ds->CreateIterator();
     EXPECT_EQ(iter, nullptr);
 }
-
-TEST_F(MindDataTestPipeline, TestRandomInvert) {
-  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomInvert.";
-
-  std::string MindDataPath = "data/dataset";
-  std::string folder_path = MindDataPath + "/testImageNetData/train/";
-  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, std::make_shared<RandomSampler>(false, 2));
-  EXPECT_NE(ds, nullptr);
-
-  auto random_invert_op = vision::RandomInvert(0.5);
-
-  ds = ds->Map({random_invert_op});
-  EXPECT_NE(ds, nullptr);
-
-  std::shared_ptr<Iterator> iter = ds->CreateIterator();
-  EXPECT_NE(iter, nullptr);
-  std::unordered_map<std::string, mindspore::MSTensor> row;
-  ASSERT_OK(iter->GetNextRow(&row));
-
-  uint64_t i = 0;
-  while (row.size() != 0) {
-    i++;
-    auto image = row["image"];
-    iter->GetNextRow(&row);
-  }
-  EXPECT_EQ(i, 2);
-
-  iter->Stop();
-}
-
-TEST_F(MindDataTestPipeline, TestRandomInvertInvalidProb) {
-  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomInvertInvalidProb.";
-
-  std::string MindDataPath = "data/dataset";
-  std::string folder_path = MindDataPath + "/testImageNetData/train/";
-  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, std::make_shared<RandomSampler>(false, 2));
-  EXPECT_NE(ds, nullptr);
-
-  auto random_invert_op = vision::RandomInvert(1.5);
-
-  ds = ds->Map({random_invert_op});
-  EXPECT_NE(ds, nullptr);
-
-  std::shared_ptr<Iterator> iter = ds->CreateIterator();
-  EXPECT_EQ(iter, nullptr);
-}
-
-TEST_F(MindDataTestPipeline, TestRandomAutoContrast) {
-  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomAutoContrast.";
-
-  std::string MindDataPath = "data/dataset";
-  std::string folder_path = MindDataPath + "/testImageNetData/train/";
-  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, std::make_shared<RandomSampler>(false, 2));
-  EXPECT_NE(ds, nullptr);
-
-  auto random_auto_contrast_op = vision::RandomAutoContrast(1.0, {0, 255}, 0.5);
-
-  ds = ds->Map({random_auto_contrast_op});
-  EXPECT_NE(ds, nullptr);
-
-  std::shared_ptr<Iterator> iter = ds->CreateIterator();
-  EXPECT_NE(iter, nullptr);
-  std::unordered_map<std::string, mindspore::MSTensor> row;
-  ASSERT_OK(iter->GetNextRow(&row));
-
-  uint64_t i = 0;
-  while (row.size() != 0) {
-    i++;
-    auto image = row["image"];
-    iter->GetNextRow(&row);
-  }
-  EXPECT_EQ(i, 2);
-
-  iter->Stop();
-}
-
-TEST_F(MindDataTestPipeline, TestRandomAutoContrastInvalidProb) {
-  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomAutoContrastInvalidProb.";
-
-  std::string MindDataPath = "data/dataset";
-  std::string folder_path = MindDataPath + "/testImageNetData/train/";
-  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, std::make_shared<RandomSampler>(false, 2));
-  EXPECT_NE(ds, nullptr);
-
-  auto random_auto_contrast_op = vision::RandomAutoContrast(0.0, {}, 1.5);
-
-  ds = ds->Map({random_auto_contrast_op});
-  EXPECT_NE(ds, nullptr);
-
-  std::shared_ptr<Iterator> iter = ds->CreateIterator();
-  EXPECT_EQ(iter, nullptr);
-}
-
-TEST_F(MindDataTestPipeline, TestRandomAutoContrastInvalidCutoff) {
-  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomAutoContrastInvalidCutoff.";
-
-  std::string MindDataPath = "data/dataset";
-  std::string folder_path = MindDataPath + "/testImageNetData/train/";
-  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, std::make_shared<RandomSampler>(false, 2));
-  EXPECT_NE(ds, nullptr);
-
-  auto random_auto_contrast_op = vision::RandomAutoContrast(-2.0, {}, 0.5);
-
-  ds = ds->Map({random_auto_contrast_op});
-  EXPECT_NE(ds, nullptr);
-
-  std::shared_ptr<Iterator> iter = ds->CreateIterator();
-  EXPECT_EQ(iter, nullptr);
-}
-
-TEST_F(MindDataTestPipeline, TestRandomAutoContrastInvalidIgnore) {
-  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomAutoContrastInvalidCutoff.";
-
-  std::string MindDataPath = "data/dataset";
-  std::string folder_path = MindDataPath + "/testImageNetData/train/";
-  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, std::make_shared<RandomSampler>(false, 2));
-  EXPECT_NE(ds, nullptr);
-
-  auto random_auto_contrast_op = vision::RandomAutoContrast(1.0, {10, 256}, 0.5);
-
-  ds = ds->Map({random_auto_contrast_op});
-  EXPECT_NE(ds, nullptr);
-
-  std::shared_ptr<Iterator> iter = ds->CreateIterator();
-  EXPECT_EQ(iter, nullptr);
-}

tests/ut/cpp/dataset/c_api_vision_r_to_z_test.cc

@@ -396,3 +396,193 @@ TEST_F(MindDataTestPipeline, TestRandomEqualizeInvalidProb) {
   std::shared_ptr<Iterator> iter = ds->CreateIterator();
   EXPECT_EQ(iter, nullptr);
 }
+
+TEST_F(MindDataTestPipeline, TestRandomInvert) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomInvert.";
+
+  std::string MindDataPath = "data/dataset";
+  std::string folder_path = MindDataPath + "/testImageNetData/train/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, std::make_shared<RandomSampler>(false, 2));
+  EXPECT_NE(ds, nullptr);
+
+  auto random_invert_op = vision::RandomInvert(0.5);
+
+  ds = ds->Map({random_invert_op});
+  EXPECT_NE(ds, nullptr);
+
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+  std::unordered_map<std::string, mindspore::MSTensor> row;
+  ASSERT_OK(iter->GetNextRow(&row));
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    iter->GetNextRow(&row);
+  }
+  EXPECT_EQ(i, 2);
+
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestRandomInvertInvalidProb) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomInvertInvalidProb.";
+
+  std::string MindDataPath = "data/dataset";
+  std::string folder_path = MindDataPath + "/testImageNetData/train/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, std::make_shared<RandomSampler>(false, 2));
+  EXPECT_NE(ds, nullptr);
+
+  auto random_invert_op = vision::RandomInvert(1.5);
+
+  ds = ds->Map({random_invert_op});
+  EXPECT_NE(ds, nullptr);
+
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_EQ(iter, nullptr);
+}
+
+TEST_F(MindDataTestPipeline, TestRandomAutoContrast) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomAutoContrast.";
+
+  std::string MindDataPath = "data/dataset";
+  std::string folder_path = MindDataPath + "/testImageNetData/train/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, std::make_shared<RandomSampler>(false, 2));
+  EXPECT_NE(ds, nullptr);
+
+  auto random_auto_contrast_op = vision::RandomAutoContrast(1.0, {0, 255}, 0.5);
+
+  ds = ds->Map({random_auto_contrast_op});
+  EXPECT_NE(ds, nullptr);
+
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+  std::unordered_map<std::string, mindspore::MSTensor> row;
+  ASSERT_OK(iter->GetNextRow(&row));
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    iter->GetNextRow(&row);
+  }
+  EXPECT_EQ(i, 2);
+
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestRandomAutoContrastInvalidProb) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomAutoContrastInvalidProb.";
+
+  std::string MindDataPath = "data/dataset";
+  std::string folder_path = MindDataPath + "/testImageNetData/train/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, std::make_shared<RandomSampler>(false, 2));
+  EXPECT_NE(ds, nullptr);
+
+  auto random_auto_contrast_op = vision::RandomAutoContrast(0.0, {}, 1.5);
+
+  ds = ds->Map({random_auto_contrast_op});
+  EXPECT_NE(ds, nullptr);
+
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_EQ(iter, nullptr);
+}
+
+TEST_F(MindDataTestPipeline, TestRandomAutoContrastInvalidCutoff) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomAutoContrastInvalidCutoff.";
+
+  std::string MindDataPath = "data/dataset";
+  std::string folder_path = MindDataPath + "/testImageNetData/train/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, std::make_shared<RandomSampler>(false, 2));
+  EXPECT_NE(ds, nullptr);
+
+  auto random_auto_contrast_op = vision::RandomAutoContrast(-2.0, {}, 0.5);
+
+  ds = ds->Map({random_auto_contrast_op});
+  EXPECT_NE(ds, nullptr);
+
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_EQ(iter, nullptr);
+}
+
+TEST_F(MindDataTestPipeline, TestRandomAutoContrastInvalidIgnore) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomAutoContrastInvalidCutoff.";
+
+  std::string MindDataPath = "data/dataset";
+  std::string folder_path = MindDataPath + "/testImageNetData/train/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, std::make_shared<RandomSampler>(false, 2));
+  EXPECT_NE(ds, nullptr);
+
+  auto random_auto_contrast_op = vision::RandomAutoContrast(1.0, {10, 256}, 0.5);
+
+  ds = ds->Map({random_auto_contrast_op});
+  EXPECT_NE(ds, nullptr);
+
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_EQ(iter, nullptr);
+}
+
+TEST_F(MindDataTestPipeline, TestRandomAdjustSharpness) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomAdjustSharpness.";
+
+  std::string MindDataPath = "data/dataset";
+  std::string folder_path = MindDataPath + "/testImageNetData/train/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, std::make_shared<RandomSampler>(false, 2));
+
+  EXPECT_NE(ds, nullptr);
+
+  auto random_adjust_sharpness_op = vision::RandomAdjustSharpness(2.0, 0.5);
+
+  ds = ds->Map({random_adjust_sharpness_op});
+  EXPECT_NE(ds, nullptr);
+
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+  std::unordered_map<std::string, mindspore::MSTensor> row;
+  ASSERT_OK(iter->GetNextRow(&row));
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    iter->GetNextRow(&row);
+  }
+  EXPECT_EQ(i, 2);
+
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestRandomAdjustSharpnessInvalidProb) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomAdjustSharpnessInvalidProb.";
+
+  std::string MindDataPath = "data/dataset";
+  std::string folder_path = MindDataPath + "/testImageNetData/train/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, std::make_shared<RandomSampler>(false, 2));
+  EXPECT_NE(ds, nullptr);
+
+  auto random_adjust_sharpness_op = vision::RandomAdjustSharpness(2.0, 1.5);
+
+  ds = ds->Map({random_adjust_sharpness_op});
+  EXPECT_NE(ds, nullptr);
+
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_EQ(iter, nullptr);
+}
+
+TEST_F(MindDataTestPipeline, TestRandomAdjustSharpnessInvalidDegree) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomAdjustSharpnessInvalidProb.";
+
+  std::string MindDataPath = "data/dataset";
+  std::string folder_path = MindDataPath + "/testImageNetData/train/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, std::make_shared<RandomSampler>(false, 2));
+  EXPECT_NE(ds, nullptr);
+
+  auto random_adjust_sharpness_op = vision::RandomAdjustSharpness(-2.0, 0.3);
+
+  ds = ds->Map({random_adjust_sharpness_op});
+  EXPECT_NE(ds, nullptr);
+
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_EQ(iter, nullptr);
+}

tests/ut/cpp/dataset/execute_test.cc

@@ -1140,3 +1140,17 @@ TEST_F(MindDataTestExecute, TestRandomEqualizeEager) {
   Status rc = transform(image, &image);
   EXPECT_EQ(rc, Status::OK());
 }
+
+TEST_F(MindDataTestExecute, TestRandomAdjustSharpnessEager) {
+  MS_LOG(INFO) << "Doing MindDataTestExecute-TestRandomAdjustSharpnessEager.";
+  // Read images
+  auto image = ReadFileToTensor("data/dataset/apple.jpg");
+
+  // Transform params
+  auto decode = vision::Decode();
+  auto random_adjust_sharpness_op = vision::RandomAdjustSharpness(2.0, 0.6);
+
+  auto transform = Execute({decode, random_adjust_sharpness_op});
+  Status rc = transform(image, &image);
+  EXPECT_EQ(rc, Status::OK());
+}

tests/ut/python/dataset/test_random_adjust_sharpness.py

@@ -0,0 +1,147 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+Testing RandomAdjustSharpness in DE
+"""
+import numpy as np
+
+import mindspore.dataset as ds
+import mindspore.dataset.vision.c_transforms as c_vision
+from mindspore import log as logger
+from util import visualize_list, visualize_image, diff_mse
+
+image_file = "../data/dataset/testImageNetData/train/class1/1_1.jpg"
+data_dir = "../data/dataset/testImageNetData/train/"
+
+
+def test_random_adjust_sharpness_pipeline(plot=False):
+    """
+    Test RandomAdjustSharpness pipeline
+    """
+    logger.info("Test RandomAdjustSharpness pipeline")
+
+    # Original Images
+    data_set = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
+    transforms_original = [c_vision.Decode(), c_vision.Resize(size=[224, 224])]
+    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 = image.asnumpy()
+        else:
+            images_original = np.append(images_original,
+                                        image.asnumpy(),
+                                        axis=0)
+
+    # Randomly Sharpness Adjusted Images
+    data_set1 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
+    transform_random_adjust_sharpness = [c_vision.Decode(),
+                                         c_vision.Resize(size=[224, 224]),
+                                         c_vision.RandomAdjustSharpness(2.0, 0.6)]
+    ds_random_adjust_sharpness = data_set1.map(operations=transform_random_adjust_sharpness, input_columns="image")
+    ds_random_adjust_sharpness = ds_random_adjust_sharpness.batch(512)
+    for idx, (image, _) in enumerate(ds_random_adjust_sharpness):
+        if idx == 0:
+            images_random_adjust_sharpness = image.asnumpy()
+        else:
+            images_random_adjust_sharpness = np.append(images_random_adjust_sharpness,
+                                                       image.asnumpy(),
+                                                       axis=0)
+    if plot:
+        visualize_list(images_original, images_random_adjust_sharpness)
+
+    num_samples = images_original.shape[0]
+    mse = np.zeros(num_samples)
+    for i in range(num_samples):
+        mse[i] = diff_mse(images_random_adjust_sharpness[i], images_original[i])
+    logger.info("MSE= {}".format(str(np.mean(mse))))
+
+
+def test_random_adjust_sharpness_eager():
+    """
+    Test RandomAdjustSharpness eager.
+    """
+    img = np.fromfile(image_file, dtype=np.uint8)
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
+
+    img = c_vision.Decode()(img)
+    img_sharped = c_vision.RandomSharpness((2.0, 2.0))(img)
+    img_random_sharped = c_vision.RandomAdjustSharpness(2.0, 1.0)(img)
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img_random_sharped), img_random_sharped.shape))
+
+    assert img_random_sharped.all() == img_sharped.all()
+
+
+def test_random_adjust_sharpness_comp(plot=False):
+    """
+    Test RandomAdjustSharpness op compared with Sharpness op.
+    """
+    random_adjust_sharpness_op = c_vision.RandomAdjustSharpness(degree=2.0, prob=1.0)
+    sharpness_op = c_vision.RandomSharpness((2.0, 2.0))
+
+    dataset1 = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
+    for item in dataset1.create_dict_iterator(num_epochs=1, output_numpy=True):
+        image = item['image']
+    dataset1.map(operations=random_adjust_sharpness_op, input_columns=['image'])
+    dataset2 = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
+    dataset2.map(operations=sharpness_op, input_columns=['image'])
+
+    for item1, item2 in zip(dataset1.create_dict_iterator(num_epochs=1, output_numpy=True),
+                            dataset2.create_dict_iterator(num_epochs=1, output_numpy=True)):
+        image_random_sharpness = item1['image']
+        image_sharpness = item2['image']
+
+    mse = diff_mse(image_sharpness, image_random_sharpness)
+    assert mse == 0
+    logger.info("mse: {}".format(mse))
+    if plot:
+        visualize_image(image, image_random_sharpness, mse, image_sharpness)
+
+
+def test_random_adjust_sharpness_invalid_prob():
+    """
+    Test invalid prob. prob out of range.
+    """
+    logger.info("test_random_adjust_sharpness_invalid_prob")
+    dataset = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
+    try:
+        random_adjust_sharpness_op = c_vision.RandomAdjustSharpness(2.0, 1.5)
+        dataset = dataset.map(operations=random_adjust_sharpness_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, 1.0]." in str(e)
+
+
+def test_random_adjust_sharpness_invalid_degree():
+    """
+    Test invalid prob. prob out of range.
+    """
+    logger.info("test_random_adjust_sharpness_invalid_prob")
+    dataset = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
+    try:
+        random_adjust_sharpness_op = c_vision.RandomAdjustSharpness(-1.0, 1.5)
+        dataset = dataset.map(operations=random_adjust_sharpness_op, input_columns=['image'])
+    except ValueError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert "interval" in str(e)
+
+
+if __name__ == "__main__":
+    test_random_adjust_sharpness_pipeline(plot=True)
+    test_random_adjust_sharpness_eager()
+    test_random_adjust_sharpness_comp(plot=True)
+    test_random_adjust_sharpness_invalid_prob()
+    test_random_adjust_sharpness_invalid_degree()

tests/ut/python/dataset/test_random_auto_contrast.py

@@ -93,13 +93,13 @@ def test_random_auto_contrast_comp(plot=False):
     auto_contrast_op = c_vision.AutoContrast()
 
     dataset1 = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
-    for item in dataset1.create_dict_iterator(output_numpy=True):
+    for item in dataset1.create_dict_iterator(num_epochs=1, output_numpy=True):
         image = item['image']
     dataset1.map(operations=random_auto_contrast_op, input_columns=['image'])
     dataset2 = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
     dataset2.map(operations=auto_contrast_op, input_columns=['image'])
-    for item1, item2 in zip(dataset1.create_dict_iterator(output_numpy=True),
-                            dataset2.create_dict_iterator(output_numpy=True)):
+    for item1, item2 in zip(dataset1.create_dict_iterator(num_epochs=1, output_numpy=True),
+                            dataset2.create_dict_iterator(num_epochs=1, output_numpy=True)):
         image_random_auto_contrast = item1['image']
         image_auto_contrast = item2['image']
 

tests/ut/python/dataset/test_random_invert.py

@@ -91,13 +91,13 @@ def test_random_invert_comp(plot=False):
     invert_op = Invert()
 
     dataset1 = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
-    for item in dataset1.create_dict_iterator(output_numpy=True):
+    for item in dataset1.create_dict_iterator(num_epochs=1, output_numpy=True):
         image = item['image']
     dataset1.map(operations=random_invert_op, input_columns=['image'])
     dataset2 = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
     dataset2.map(operations=invert_op, input_columns=['image'])
-    for item1, item2 in zip(dataset1.create_dict_iterator(output_numpy=True),
-                            dataset2.create_dict_iterator(output_numpy=True)):
+    for item1, item2 in zip(dataset1.create_dict_iterator(num_epochs=1, output_numpy=True),
+                            dataset2.create_dict_iterator(num_epochs=1, output_numpy=True)):
         image_random_inverted = item1['image']
         image_inverted = item2['image']