!4245 [MD] Add SolarizeOp

Merge pull request !4245 from nhussain/solarize

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

@@ -44,6 +44,7 @@
 #include "minddata/dataset/kernels/image/random_resize_with_bbox_op.h"
 #include "minddata/dataset/kernels/image/random_rotation_op.h"
 #include "minddata/dataset/kernels/image/random_select_subpolicy_op.h"
+#include "minddata/dataset/kernels/image/random_solarize_op.h"
 #include "minddata/dataset/kernels/image/random_vertical_flip_op.h"
 #include "minddata/dataset/kernels/image/random_vertical_flip_with_bbox_op.h"
 #include "minddata/dataset/kernels/image/rescale_op.h"
@@ -383,5 +384,11 @@ PYBIND_REGISTER(
              py::arg("maxIter") = RandomCropDecodeResizeOp::kDefMaxIter);
   }));
 
+PYBIND_REGISTER(RandomSolarizeOp, 1, ([](const py::module *m) {
+                  (void)py::class_<RandomSolarizeOp, TensorOp, std::shared_ptr<RandomSolarizeOp>>(*m,
+                                                                                                  "RandomSolarizeOp")
+                    .def(py::init<uint8_t, uint8_t>());
+                }));
+
 }  // namespace dataset
 }  // namespace mindspore

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

@@ -31,6 +31,7 @@
 #include "minddata/dataset/kernels/image/random_crop_op.h"
 #include "minddata/dataset/kernels/image/random_horizontal_flip_op.h"
 #include "minddata/dataset/kernels/image/random_rotation_op.h"
+#include "minddata/dataset/kernels/image/random_solarize_op.h"
 #include "minddata/dataset/kernels/image/random_vertical_flip_op.h"
 #include "minddata/dataset/kernels/image/resize_op.h"
 #include "minddata/dataset/kernels/image/swap_red_blue_op.h"
@@ -198,6 +199,16 @@ std::shared_ptr<RandomRotationOperation> RandomRotation(std::vector<float> degre
   return op;
 }
 
+// Function to create RandomSolarizeOperation.
+std::shared_ptr<RandomSolarizeOperation> RandomSolarize(uint8_t threshold_min, uint8_t threshold_max) {
+  auto op = std::make_shared<RandomSolarizeOperation>(threshold_min, threshold_max);
+  // Input validation
+  if (!op->ValidateParams()) {
+    return nullptr;
+  }
+  return op;
+}
+
 // Function to create RandomVerticalFlipOperation.
 std::shared_ptr<RandomVerticalFlipOperation> RandomVerticalFlip(float prob) {
   auto op = std::make_shared<RandomVerticalFlipOperation>(prob);
@@ -654,6 +665,23 @@ std::shared_ptr<TensorOp> RandomRotationOperation::Build() {
   return tensor_op;
 }
 
+// RandomSolarizeOperation.
+RandomSolarizeOperation::RandomSolarizeOperation(uint8_t threshold_min, uint8_t threshold_max)
+    : threshold_min_(threshold_min), threshold_max_(threshold_max) {}
+
+bool RandomSolarizeOperation::ValidateParams() {
+  if (threshold_max_ < threshold_min_) {
+    MS_LOG(ERROR) << "RandomSolarize: threshold_max must be greater or equal to threshold_min";
+    return false;
+  }
+  return true;
+}
+
+std::shared_ptr<TensorOp> RandomSolarizeOperation::Build() {
+  std::shared_ptr<RandomSolarizeOp> tensor_op = std::make_shared<RandomSolarizeOp>(threshold_min_, threshold_max_);
+  return tensor_op;
+}
+
 // RandomVerticalFlipOperation
 RandomVerticalFlipOperation::RandomVerticalFlipOperation(float probability) : probability_(probability) {}
 

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

@@ -61,6 +61,7 @@ class RandomColorAdjustOperation;
 class RandomCropOperation;
 class RandomHorizontalFlipOperation;
 class RandomRotationOperation;
+class RandomSolarizeOperation;
 class RandomVerticalFlipOperation;
 class ResizeOperation;
 class SwapRedBlueOperation;
@@ -208,6 +209,13 @@ std::shared_ptr<RandomRotationOperation> RandomRotation(
   std::vector<float> degrees, InterpolationMode resample = InterpolationMode::kNearestNeighbour, bool expand = false,
   std::vector<float> center = {-1, -1}, std::vector<uint8_t> fill_value = {0, 0, 0});
 
+/// \brief Function to create a RandomSolarize TensorOperation.
+/// \notes Invert pixels within specified range. If min=max, then it inverts all pixel above that threshold
+/// \param[in] threshold_min - lower limit
+/// \param[in] threshold_max - upper limit
+/// \return Shared pointer to the current TensorOperation.
+std::shared_ptr<RandomSolarizeOperation> RandomSolarize(uint8_t threshold_min = 0, uint8_t threshold_max = 255);
+
 /// \brief Function to create a RandomVerticalFlip TensorOperation.
 /// \notes Tensor operation to perform random vertical flip.
 /// \param[in] prob - float representing the probability of flip.
@@ -515,6 +523,21 @@ class SwapRedBlueOperation : public TensorOperation {
 
   bool ValidateParams() override;
 };
+
+class RandomSolarizeOperation : public TensorOperation {
+ public:
+  explicit RandomSolarizeOperation(uint8_t threshold_min, uint8_t threshold_max);
+
+  ~RandomSolarizeOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  bool ValidateParams() override;
+
+ private:
+  uint8_t threshold_min_;
+  uint8_t threshold_max_;
+};
 }  // namespace vision
 }  // namespace api
 }  // namespace dataset

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

@@ -29,11 +29,13 @@ add_library(kernels-image OBJECT
     random_resize_op.cc
     random_rotation_op.cc
     random_select_subpolicy_op.cc
+    random_solarize_op.cc
     random_vertical_flip_op.cc
     random_vertical_flip_with_bbox_op.cc
     rescale_op.cc
     resize_bilinear_op.cc
     resize_op.cc
+    solarize_op.cc
     swap_red_blue_op.cc
     uniform_aug_op.cc
     resize_with_bbox_op.cc

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

@@ -0,0 +1,42 @@
+/**
+ * Copyright 2020 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_solarize_op.h"
+#include "minddata/dataset/kernels/image/solarize_op.h"
+#include "minddata/dataset/kernels/image/image_utils.h"
+#include "minddata/dataset/core/cv_tensor.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+
+Status RandomSolarizeOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
+  IO_CHECK(input, output);
+  CHECK_FAIL_RETURN_UNEXPECTED(threshold_min_ <= threshold_max_,
+                               "threshold_min must be smaller or equal to threshold_max.");
+
+  uint8_t threshold_min = std::uniform_int_distribution(threshold_min_, threshold_max_)(rnd_);
+  uint8_t threshold_max = std::uniform_int_distribution(threshold_min_, threshold_max_)(rnd_);
+
+  if (threshold_max < threshold_min) {
+    uint8_t temp = threshold_min;
+    threshold_min = threshold_max;
+    threshold_max = temp;
+  }
+  std::unique_ptr<SolarizeOp> op(new SolarizeOp(threshold_min, threshold_max));
+  return op->Compute(input, output);
+}
+}  // namespace dataset
+}  // namespace mindspore

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

@@ -0,0 +1,53 @@
+/**
+ * Copyright 2020 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_SOLARIZE_OP_H
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_RANDOM_SOLARIZE_OP_H
+
+#include <memory>
+#include <string>
+
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/status.h"
+#include "minddata/dataset/kernels/image/solarize_op.h"
+#include "minddata/dataset/util/random.h"
+
+namespace mindspore {
+namespace dataset {
+class RandomSolarizeOp : public SolarizeOp {
+ public:
+  // Pick a random threshold value to solarize the image with
+  explicit RandomSolarizeOp(uint8_t threshold_min = 0, uint8_t threshold_max = 255)
+      : threshold_min_(threshold_min), threshold_max_(threshold_max) {
+    rnd_.seed(GetSeed());
+  }
+
+  ~RandomSolarizeOp() = default;
+
+  Status Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) override;
+
+  std::string Name() const override { return kRandomSolarizeOp; }
+
+ private:
+  uint8_t threshold_min_;
+  uint8_t threshold_max_;
+  std::mt19937 rnd_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_RANDOM_SOLARIZE_OP_H

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

@@ -0,0 +1,81 @@
+/**
+ * Copyright 2020 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/solarize_op.h"
+#include "minddata/dataset/kernels/image/image_utils.h"
+#include "minddata/dataset/core/cv_tensor.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+
+// only supports RGB images
+const uint8_t kPixelValue = 255;
+
+Status SolarizeOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
+  IO_CHECK(input, output);
+
+  CHECK_FAIL_RETURN_UNEXPECTED(threshold_min_ <= threshold_max_,
+                               "threshold_min must be smaller or equal to threshold_max.");
+
+  try {
+    std::shared_ptr<CVTensor> input_cv = CVTensor::AsCVTensor(input);
+    cv::Mat input_img = input_cv->mat();
+    if (!input_cv->mat().data) {
+      RETURN_STATUS_UNEXPECTED("Could not convert to CV Tensor");
+    }
+
+    if (input_cv->Rank() != 2 && input_cv->Rank() != 3) {
+      RETURN_STATUS_UNEXPECTED("Shape not of either <H,W,C> or <H,W> format.");
+    }
+    if (input_cv->Rank() == 3) {
+      int num_channels = input_cv->shape()[2];
+      if (num_channels != 3 && num_channels != 1) {
+        RETURN_STATUS_UNEXPECTED("Number of channels is not 1 or 3.");
+      }
+    }
+
+    std::shared_ptr<CVTensor> mask_mat_tensor;
+    std::shared_ptr<CVTensor> output_cv_tensor;
+    RETURN_IF_NOT_OK(CVTensor::CreateFromMat(input_cv->mat(), &mask_mat_tensor));
+
+    RETURN_IF_NOT_OK(CVTensor::CreateEmpty(input_cv->shape(), input_cv->type(), &output_cv_tensor));
+    RETURN_UNEXPECTED_IF_NULL(mask_mat_tensor);
+    RETURN_UNEXPECTED_IF_NULL(output_cv_tensor);
+
+    if (threshold_min_ == threshold_max_) {
+      mask_mat_tensor->mat().setTo(0, ~(input_cv->mat() >= threshold_min_));
+    } else {
+      mask_mat_tensor->mat().setTo(0, ~((input_cv->mat() >= threshold_min_) & (input_cv->mat() <= threshold_max_)));
+    }
+
+    // solarize desired portion
+    output_cv_tensor->mat() = cv::Scalar::all(255) - mask_mat_tensor->mat();
+    input_cv->mat().copyTo(output_cv_tensor->mat(), mask_mat_tensor->mat() == 0);
+    input_cv->mat().copyTo(output_cv_tensor->mat(), input_cv->mat() < threshold_min_);
+
+    *output = std::static_pointer_cast<Tensor>(output_cv_tensor);
+  }
+
+  catch (const cv::Exception &e) {
+    const char *cv_err_msg = e.what();
+    std::string err_message = "Error in SolarizeOp: ";
+    err_message += cv_err_msg;
+    RETURN_STATUS_UNEXPECTED(err_message);
+  }
+  return Status::OK();
+}
+}  // namespace dataset
+}  // namespace mindspore

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

@@ -0,0 +1,47 @@
+/**
+ * Copyright 2020 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_SOLARIZE_OP_H
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_SOLARIZE_OP_H
+
+#include <memory>
+#include <string>
+
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+class SolarizeOp : public TensorOp {
+ public:
+  explicit SolarizeOp(uint8_t threshold_min = 0, uint8_t threshold_max = 255)
+      : threshold_min_(threshold_min), threshold_max_(threshold_max) {}
+
+  ~SolarizeOp() = default;
+
+  Status Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) override;
+
+  std::string Name() const override { return kSolarizeOp; }
+
+ private:
+  uint8_t threshold_min_;
+  uint8_t threshold_max_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_SOLARIZE_OP_H

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

@@ -113,12 +113,14 @@ constexpr char kRandomHorizontalFlipOp[] = "RandomHorizontalFlipOp";
 constexpr char kRandomResizeOp[] = "RandomResizeOp";
 constexpr char kRandomResizeWithBBoxOp[] = "RandomResizeWithBBoxOp";
 constexpr char kRandomRotationOp[] = "RandomRotationOp";
+constexpr char kRandomSolarizeOp[] = "RandomSolarizeOp";
 constexpr char kRandomVerticalFlipOp[] = "RandomVerticalFlipOp";
 constexpr char kRandomVerticalFlipWithBBoxOp[] = "RandomVerticalFlipWithBBoxOp";
 constexpr char kRescaleOp[] = "RescaleOp";
 constexpr char kResizeBilinearOp[] = "ResizeBilinearOp";
 constexpr char kResizeOp[] = "ResizeOp";
 constexpr char kResizeWithBBoxOp[] = "ResizeWithBBoxOp";
+constexpr char kSolarizeOp[] = "SolarizeOp";
 constexpr char kSwapRedBlueOp[] = "SwapRedBlueOp";
 constexpr char kUniformAugOp[] = "UniformAugOp";
 constexpr char kSoftDvppDecodeRandomCropResizeJpegOp[] = "SoftDvppDecodeRandomCropResizeJpegOp";

mindspore/dataset/transforms/vision/c_transforms.py

@@ -48,7 +48,7 @@ from .validators import check_prob, check_crop, check_resize_interpolation, chec
     check_mix_up_batch_c, check_normalize_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_bounding_box_augment_cpp, check_random_select_subpolicy_op, check_auto_contrast, check_random_affine, \
-    check_soft_dvpp_decode_random_crop_resize_jpeg, FLOAT_MAX_INTEGER
+    check_random_solarize, check_soft_dvpp_decode_random_crop_resize_jpeg, FLOAT_MAX_INTEGER
 
 DE_C_INTER_MODE = {Inter.NEAREST: cde.InterpolationMode.DE_INTER_NEAREST_NEIGHBOUR,
                    Inter.LINEAR: cde.InterpolationMode.DE_INTER_LINEAR,
@@ -932,3 +932,20 @@ class SoftDvppDecodeRandomCropResizeJpeg(cde.SoftDvppDecodeRandomCropResizeJpegO
         self.ratio = ratio
         self.max_attempts = max_attempts
         super().__init__(*size, *scale, *ratio, max_attempts)
+
+
+class RandomSolarize(cde.RandomSolarizeOp):
+    """
+    Invert all pixel values above a threshold.
+
+    Args:
+        threshold (sequence): Range of random solarize threshold.
+        Threshold values should always be in range of [0, 255], and
+        include at least one integer value in the given range and
+        be in (min, max) format. If min=max, then it is a single
+        fixed magnitude operation (default=(0, 255)).
+    """
+
+    @check_random_solarize
+    def __init__(self, threshold=(0, 255)):
+        super().__init__(*threshold)

mindspore/dataset/transforms/vision/validators.py

@@ -21,7 +21,8 @@ from mindspore._c_dataengine import TensorOp
 
 from .utils import Inter, Border
 from ...core.validator_helpers import check_value, check_uint8, FLOAT_MAX_INTEGER, check_pos_float32, \
-    check_2tuple, check_range, check_positive, INT32_MAX, parse_user_args, type_check, type_check_list, check_tensor_op
+    check_2tuple, check_range, check_positive, INT32_MAX, parse_user_args, type_check, type_check_list, \
+    check_tensor_op, UINT8_MAX
 
 
 def check_crop_size(size):
@@ -674,4 +675,25 @@ def check_soft_dvpp_decode_random_crop_resize_jpeg(method):
         check_size_scale_ration_max_attempts_paras(size, scale, ratio, max_attempts)
 
         return method(self, *args, **kwargs)
+
+    return new_method
+
+
+def check_random_solarize(method):
+    """Wrapper method to check the parameters of RandomSolarizeOp."""
+
+    @wraps(method)
+    def new_method(self, *args, **kwargs):
+        [threshold], _ = parse_user_args(method, *args, **kwargs)
+
+        type_check(threshold, (tuple,), "threshold")
+        type_check_list(threshold, (int,), "threshold")
+        if len(threshold) != 2:
+            raise ValueError("threshold must be a sequence of two numbers")
+        for element in threshold:
+            check_value(element, (0, UINT8_MAX))
+        if threshold[1] < threshold[0]:
+            raise ValueError("threshold must be in min max format numbers")
+
+        return method(self, *args, **kwargs)
     return new_method

tests/ut/cpp/dataset/CMakeLists.txt

@@ -50,6 +50,7 @@ SET(DE_UT_SRCS
         random_resize_op_test.cc
         random_resize_with_bbox_op_test.cc
         random_rotation_op_test.cc
+        random_solarize_op_test.cc
         random_vertical_flip_op_test.cc
         random_vertical_flip_with_bbox_op_test.cc
         rename_op_test.cc
@@ -104,8 +105,9 @@ SET(DE_UT_SRCS
         sliding_window_op_test.cc
         epoch_ctrl_op_test.cc
         sentence_piece_vocab_op_test.cc
-	swap_red_blue_test.cc
-	distributed_sampler_test.cc
+        solarize_op_test.cc
+        swap_red_blue_test.cc
+        distributed_sampler_test.cc
         )
 
 if (ENABLE_PYTHON)

tests/ut/cpp/dataset/c_api_transforms_test.cc

@@ -729,3 +729,50 @@ TEST_F(MindDataTestPipeline, TestRandomRotation) {
   // Manually terminate the pipeline
   iter->Stop();
 }
+
+TEST_F(MindDataTestPipeline, TestRandomSolarize) {
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Repeat operation on ds
+  int32_t repeat_num = 2;
+  ds = ds->Repeat(repeat_num);
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> random_solarize = mindspore::dataset::api::vision::RandomSolarize(23, 23); //vision::RandomSolarize();
+  EXPECT_NE(random_solarize, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({random_solarize});
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Batch operation on ds
+  int32_t batch_size = 1;
+  ds = ds->Batch(batch_size);
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 20);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}

tests/ut/cpp/dataset/common/cvop_common.cc

@@ -142,6 +142,10 @@ void CVOpCommon::CheckImageShapeAndData(const std::shared_ptr<Tensor> &output_te
       expect_image_path = dir_path + "imagefolder/apple_expect_equalize.jpg";
       actual_image_path = dir_path + "imagefolder/apple_actual_equalize.jpg";
       break;
+    case kRandomSolarize:
+      expect_image_path = dir_path + "imagefolder/apple_expect_random_solarize.jpg";
+      actual_image_path = dir_path + "imagefolder/apple_actual_random_solarize.jpg";
+      break;
     default:
       MS_LOG(INFO) << "Not pass verification! Operation type does not exists.";
       EXPECT_EQ(0, 1);

tests/ut/cpp/dataset/common/cvop_common.h

@@ -36,6 +36,7 @@ class CVOpCommon : public Common {
     kDecode,
     kChannelSwap,
     kChangeMode,
+    kRandomSolarize,
     kTemplate,
     kCrop,
     kRandomAffine,

tests/ut/cpp/dataset/random_solarize_op_test.cc

@@ -0,0 +1,49 @@
+/**
+ * Copyright 2019 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 "common/common.h"
+#include "common/cvop_common.h"
+#include "minddata/dataset/kernels/image/random_solarize_op.h"
+#include "minddata/dataset/core/cv_tensor.h"
+#include "minddata/dataset/util/status.h"
+#include "utils/log_adapter.h"
+
+using namespace mindspore::dataset;
+using mindspore::LogStream;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::MsLogLevel::INFO;
+
+class MindDataTestRandomSolarizeOp : public UT::CVOP::CVOpCommon {
+ protected:
+  MindDataTestRandomSolarizeOp() : CVOpCommon() {}
+
+  std::shared_ptr<Tensor> output_tensor_;
+};
+
+TEST_F(MindDataTestRandomSolarizeOp, TestOp1) {
+  MS_LOG(INFO) << "Doing testRandomSolarizeOp1.";
+  // setting seed here
+  uint32_t curr_seed = GlobalContext::config_manager()->seed();
+  GlobalContext::config_manager()->set_seed(0);
+
+  std::unique_ptr<RandomSolarizeOp> op(new RandomSolarizeOp(100, 100));
+  EXPECT_TRUE(op->OneToOne());
+  Status s = op->Compute(input_tensor_, &output_tensor_);
+
+  EXPECT_TRUE(s.IsOk());
+  CheckImageShapeAndData(output_tensor_, kRandomSolarize);
+  // restoring the seed
+  GlobalContext::config_manager()->set_seed(curr_seed);
+}

tests/ut/cpp/dataset/solarize_op_test.cc

@@ -0,0 +1,164 @@
+/**
+ * Copyright 2019 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 "common/common.h"
+#include "common/cvop_common.h"
+#include "minddata/dataset/kernels/image/solarize_op.h"
+#include "minddata/dataset/core/cv_tensor.h"
+#include "minddata/dataset/util/status.h"
+#include "utils/log_adapter.h"
+#include "gtest/gtest.h"
+
+using namespace mindspore::dataset;
+using mindspore::LogStream;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::MsLogLevel::INFO;
+
+class MindDataTestSolarizeOp : public UT::CVOP::CVOpCommon {
+ protected:
+  MindDataTestSolarizeOp() : CVOpCommon() {}
+
+  std::shared_ptr<Tensor> output_tensor_;
+};
+
+TEST_F(MindDataTestSolarizeOp, TestOp1) {
+  MS_LOG(INFO) << "Doing testSolarizeOp1.";
+
+  std::unique_ptr<SolarizeOp> op(new SolarizeOp());
+  EXPECT_TRUE(op->OneToOne());
+  Status s = op->Compute(input_tensor_, &output_tensor_);
+
+  EXPECT_TRUE(s.IsOk());
+}
+
+TEST_F(MindDataTestSolarizeOp, TestOp2) {
+  MS_LOG(INFO) << "Doing testSolarizeOp2 - test default values";
+
+  //  unsigned int threshold = 128;
+  std::unique_ptr<SolarizeOp> op(new SolarizeOp());
+
+  std::vector<uint8_t> test_vector = {3, 4, 59, 210, 255};
+  std::vector<uint8_t> expected_output_vector = {252, 251, 196, 45, 0};
+  std::shared_ptr<Tensor> test_input_tensor;
+  std::shared_ptr<Tensor> expected_output_tensor;
+  Tensor::CreateFromVector(test_vector, TensorShape({1, (long int)test_vector.size(), 1}), &test_input_tensor);
+  Tensor::CreateFromVector(expected_output_vector, TensorShape({1, (long int)test_vector.size(), 1}),
+                           &expected_output_tensor);
+
+  std::shared_ptr<Tensor> test_output_tensor;
+  Status s = op->Compute(test_input_tensor, &test_output_tensor);
+
+  EXPECT_TRUE(s.IsOk());
+
+  ASSERT_TRUE(test_output_tensor->shape() == expected_output_tensor->shape());
+  ASSERT_TRUE(test_output_tensor->type() == expected_output_tensor->type());
+  MS_LOG(DEBUG) << *test_output_tensor << std::endl;
+  MS_LOG(DEBUG) << *expected_output_tensor << std::endl;
+
+  ASSERT_TRUE(*test_output_tensor == *expected_output_tensor);
+}
+
+TEST_F(MindDataTestSolarizeOp, TestOp3) {
+  MS_LOG(INFO) << "Doing testSolarizeOp3 - Pass in only threshold_min parameter";
+
+  //  unsigned int threshold = 128;
+  std::unique_ptr<SolarizeOp> op(new SolarizeOp(1));
+
+  std::vector<uint8_t> test_vector = {3, 4, 59, 210, 255};
+  std::vector<uint8_t> expected_output_vector = {252, 251, 196, 45, 0};
+  std::shared_ptr<Tensor> test_input_tensor;
+  std::shared_ptr<Tensor> expected_output_tensor;
+  Tensor::CreateFromVector(test_vector, TensorShape({1, (long int)test_vector.size(), 1}), &test_input_tensor);
+  Tensor::CreateFromVector(expected_output_vector, TensorShape({1, (long int)test_vector.size(), 1}),
+                           &expected_output_tensor);
+
+  std::shared_ptr<Tensor> test_output_tensor;
+  Status s = op->Compute(test_input_tensor, &test_output_tensor);
+
+  EXPECT_TRUE(s.IsOk());
+  ASSERT_TRUE(test_output_tensor->shape() == expected_output_tensor->shape());
+  ASSERT_TRUE(test_output_tensor->type() == expected_output_tensor->type());
+  MS_LOG(DEBUG) << *test_output_tensor << std::endl;
+  MS_LOG(DEBUG) << *expected_output_tensor << std::endl;
+  ASSERT_TRUE(*test_output_tensor == *expected_output_tensor);
+}
+
+TEST_F(MindDataTestSolarizeOp, TestOp4) {
+  MS_LOG(INFO) << "Doing testSolarizeOp4 - Pass in both threshold parameters.";
+
+  //  unsigned int threshold = 128;
+  std::unique_ptr<SolarizeOp> op(new SolarizeOp(1, 230));
+
+  std::vector<uint8_t> test_vector = {3, 4, 59, 210, 255};
+  std::vector<uint8_t> expected_output_vector = {252, 251, 196, 45, 255};
+  std::shared_ptr<Tensor> test_input_tensor;
+  std::shared_ptr<Tensor> expected_output_tensor;
+  Tensor::CreateFromVector(test_vector, TensorShape({1, (long int)test_vector.size(), 1}), &test_input_tensor);
+  Tensor::CreateFromVector(expected_output_vector, TensorShape({1, (long int)test_vector.size(), 1}),
+                           &expected_output_tensor);
+
+  std::shared_ptr<Tensor> test_output_tensor;
+  Status s = op->Compute(test_input_tensor, &test_output_tensor);
+
+  EXPECT_TRUE(s.IsOk());
+  ASSERT_TRUE(test_output_tensor->shape() == expected_output_tensor->shape());
+  ASSERT_TRUE(test_output_tensor->type() == expected_output_tensor->type());
+  MS_LOG(DEBUG) << *test_output_tensor << std::endl;
+  MS_LOG(DEBUG) << *expected_output_tensor << std::endl;
+  ASSERT_TRUE(*test_output_tensor == *expected_output_tensor);
+}
+
+TEST_F(MindDataTestSolarizeOp, TestOp5) {
+  MS_LOG(INFO) << "Doing testSolarizeOp5 - Rank 2 input tensor.";
+
+  //  unsigned int threshold = 128;
+  std::unique_ptr<SolarizeOp> op(new SolarizeOp(1, 230));
+
+  std::vector<uint8_t> test_vector = {3, 4, 59, 210, 255};
+  std::vector<uint8_t> expected_output_vector = {252, 251, 196, 45, 255};
+  std::shared_ptr<Tensor> test_input_tensor;
+  std::shared_ptr<Tensor> expected_output_tensor;
+  Tensor::CreateFromVector(test_vector, TensorShape({1, (long int)test_vector.size()}), &test_input_tensor);
+  Tensor::CreateFromVector(expected_output_vector, TensorShape({1, (long int)test_vector.size()}),
+                           &expected_output_tensor);
+
+  std::shared_ptr<Tensor> test_output_tensor;
+  Status s = op->Compute(test_input_tensor, &test_output_tensor);
+
+  EXPECT_TRUE(s.IsOk());
+  ASSERT_TRUE(test_output_tensor->shape() == expected_output_tensor->shape());
+  ASSERT_TRUE(test_output_tensor->type() == expected_output_tensor->type());
+  MS_LOG(DEBUG) << *test_output_tensor << std::endl;
+  MS_LOG(DEBUG) << *expected_output_tensor << std::endl;
+
+  ASSERT_TRUE(*test_output_tensor == *expected_output_tensor);
+}
+
+TEST_F(MindDataTestSolarizeOp, TestOp6) {
+  MS_LOG(INFO) << "Doing testSolarizeOp6 - Bad Input.";
+
+  std::unique_ptr<SolarizeOp> op(new SolarizeOp(10, 1));
+
+  std::vector<uint8_t> test_vector = {3, 4, 59, 210, 255};
+  std::shared_ptr<Tensor> test_input_tensor;
+  std::shared_ptr<Tensor> test_output_tensor;
+  Tensor::CreateFromVector(test_vector, TensorShape({1, (long int)test_vector.size(), 1}), &test_input_tensor);
+
+  Status s = op->Compute(test_input_tensor, &test_output_tensor);
+
+  EXPECT_TRUE(s.IsError());
+  EXPECT_NE(s.ToString().find("threshold_min must be smaller or equal to threshold_max."), std::string::npos);
+  ASSERT_TRUE(s.get_code() == StatusCode::kUnexpectedError);
+}

tests/ut/python/dataset/test_random_solarize_op.py

@@ -0,0 +1,112 @@
+# Copyright 2019 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 RandomSolarizeOp op in DE
+"""
+import pytest
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.c_transforms as vision
+from mindspore import log as logger
+from util import visualize_list, save_and_check_md5, config_get_set_seed, config_get_set_num_parallel_workers
+
+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"
+
+
+def test_random_solarize_op(threshold=None, plot=False):
+    """
+    Test RandomSolarize
+    """
+    logger.info("Test RandomSolarize")
+
+    # First dataset
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
+    decode_op = vision.Decode()
+
+    if threshold is None:
+        solarize_op = vision.RandomSolarize()
+    else:
+        solarize_op = vision.RandomSolarize(threshold)
+    data1 = data1.map(input_columns=["image"], operations=decode_op)
+    data1 = data1.map(input_columns=["image"], operations=solarize_op)
+
+    # Second dataset
+    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
+    data2 = data2.map(input_columns=["image"], operations=decode_op)
+
+    image_solarized = []
+    image = []
+    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        image_solarized.append(item1["image"].copy())
+        image.append(item2["image"].copy())
+    if plot:
+        visualize_list(image, image_solarized)
+
+
+def test_random_solarize_md5():
+    """
+    Test RandomSolarize
+    """
+    logger.info("Test RandomSolarize")
+    original_seed = config_get_set_seed(0)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
+
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    decode_op = vision.Decode()
+    random_solarize_op = vision.RandomSolarize((10, 150))
+    data1 = data1.map(input_columns=["image"], operations=decode_op)
+    data1 = data1.map(input_columns=["image"], operations=random_solarize_op)
+    # Compare with expected md5 from images
+    filename = "random_solarize_01_result.npz"
+    save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
+
+    # Restore config setting
+    ds.config.set_seed(original_seed)
+    ds.config.set_num_parallel_workers(original_num_parallel_workers)
+
+
+def test_random_solarize_errors():
+    """
+    Test that RandomSolarize errors with bad input
+    """
+    with pytest.raises(ValueError) as error_info:
+        vision.RandomSolarize((12, 1))
+    assert "threshold must be in min max format numbers" in str(error_info.value)
+
+    with pytest.raises(ValueError) as error_info:
+        vision.RandomSolarize((12, 1000))
+    assert "Input is not within the required interval of (0 to 255)." in str(error_info.value)
+
+    with pytest.raises(TypeError) as error_info:
+        vision.RandomSolarize((122.1, 140))
+    assert "Argument threshold[0] with value 122.1 is not of type (<class 'int'>,)." in str(error_info.value)
+
+    with pytest.raises(ValueError) as error_info:
+        vision.RandomSolarize((122, 100, 30))
+    assert "threshold must be a sequence of two numbers" in str(error_info.value)
+
+    with pytest.raises(ValueError) as error_info:
+        vision.RandomSolarize((120,))
+    assert "threshold must be a sequence of two numbers" in str(error_info.value)
+
+
+if __name__ == "__main__":
+    test_random_solarize_op((100, 100), plot=True)
+    test_random_solarize_op((12, 120), plot=True)
+    test_random_solarize_op(plot=True)
+    test_random_solarize_errors()
+    test_random_solarize_md5()