!7184 [MD] C++ api add TypeCaseOp & RandomCropDecodeResizeOp

Merge pull request !7184 from luoyang/pylint

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

@@ -19,6 +19,7 @@
 
 // Kernel data headers (in alphabetical order)
 #include "minddata/dataset/kernels/data/one_hot_op.h"
+#include "minddata/dataset/kernels/data/type_cast_op.h"
 
 namespace mindspore {
 namespace dataset {
@@ -42,6 +43,16 @@ std::shared_ptr<OneHotOperation> OneHot(int32_t num_classes) {
   return op;
 }
 
+// Function to create TypeCastOperation.
+std::shared_ptr<TypeCastOperation> TypeCast(std::string data_type) {
+  auto op = std::make_shared<TypeCastOperation>(data_type);
+  // Input validation
+  if (!op->ValidateParams()) {
+    return nullptr;
+  }
+  return op;
+}
+
 /* ####################################### Validator Functions ############################################ */
 
 /* ####################################### Derived TensorOperation classes ################################# */
@@ -62,6 +73,24 @@ bool OneHotOperation::ValidateParams() {
 
 std::shared_ptr<TensorOp> OneHotOperation::Build() { return std::make_shared<OneHotOp>(num_classes_); }
 
+// TypeCastOperation
+TypeCastOperation::TypeCastOperation(std::string data_type) : data_type_(data_type) {}
+
+bool TypeCastOperation::ValidateParams() {
+  std::vector<std::string> predefine_type = {"bool",  "int8",   "uint8",   "int16",   "uint16",  "int32", "uint32",
+                                             "int64", "uint64", "float16", "float32", "float64", "string"};
+  auto itr = std::find(predefine_type.begin(), predefine_type.end(), data_type_);
+  if (itr == predefine_type.end()) {
+    MS_LOG(ERROR) << "TypeCast: Only support type bool, int8, uint8, int16, uint16, int32, uint32, "
+                  << "int64, uint64, float16, float32, float64, string, but got " << data_type_;
+    return false;
+  }
+
+  return true;
+}
+
+std::shared_ptr<TensorOp> TypeCastOperation::Build() { return std::make_shared<TypeCastOp>(data_type_); }
+
 }  // namespace transforms
 }  // namespace api
 }  // namespace dataset

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

@@ -32,6 +32,7 @@
 #include "minddata/dataset/kernels/image/random_color_op.h"
 #include "minddata/dataset/kernels/image/random_color_adjust_op.h"
 #include "minddata/dataset/kernels/image/random_crop_op.h"
+#include "minddata/dataset/kernels/image/random_crop_decode_resize_op.h"
 #include "minddata/dataset/kernels/image/random_horizontal_flip_op.h"
 #include "minddata/dataset/kernels/image/random_posterize_op.h"
 #include "minddata/dataset/kernels/image/random_rotation_op.h"
@@ -200,6 +201,20 @@ std::shared_ptr<RandomCropOperation> RandomCrop(std::vector<int32_t> size, std::
   return op;
 }
 
+// Function to create RandomCropDecodeResizeOperation.
+std::shared_ptr<RandomCropDecodeResizeOperation> RandomCropDecodeResize(std::vector<int32_t> size,
+                                                                        std::vector<float> scale,
+                                                                        std::vector<float> ratio,
+                                                                        InterpolationMode interpolation,
+                                                                        int32_t max_attempts) {
+  auto op = std::make_shared<RandomCropDecodeResizeOperation>(size, scale, ratio, interpolation, max_attempts);
+  // Input validation
+  if (!op->ValidateParams()) {
+    return nullptr;
+  }
+  return op;
+}
+
 // Function to create RandomHorizontalFlipOperation.
 std::shared_ptr<RandomHorizontalFlipOperation> RandomHorizontalFlip(float prob) {
   auto op = std::make_shared<RandomHorizontalFlipOperation>(prob);
@@ -784,6 +799,66 @@ std::shared_ptr<TensorOp> RandomCropOperation::Build() {
   return tensor_op;
 }
 
+// RandomCropDecodeResizeOperation
+RandomCropDecodeResizeOperation::RandomCropDecodeResizeOperation(std::vector<int32_t> size, std::vector<float> scale,
+                                                                 std::vector<float> ratio,
+                                                                 InterpolationMode interpolation, int32_t max_attempts)
+    : size_(size), scale_(scale), ratio_(ratio), interpolation_(interpolation), max_attempts_(max_attempts) {}
+
+bool RandomCropDecodeResizeOperation::ValidateParams() {
+  if (size_.empty() || size_.size() > 2) {
+    MS_LOG(ERROR) << "RandomCropDecodeResize: size vector has incorrect size: " << size_.size();
+    return false;
+  }
+
+  if (scale_.empty() || scale_.size() != 2) {
+    MS_LOG(ERROR) << "RandomCropDecodeResize: scale vector has incorrect size: " << scale_.size();
+    return false;
+  }
+
+  if (scale_[0] > scale_[1]) {
+    MS_LOG(ERROR) << "RandomCropDecodeResize: scale should be in (min,max) format. Got (max,min).";
+    return false;
+  }
+
+  if (ratio_.empty() || ratio_.size() != 2) {
+    MS_LOG(ERROR) << "RandomCropDecodeResize: ratio vector has incorrect size: " << ratio_.size();
+    return false;
+  }
+
+  if (ratio_[0] > ratio_[1]) {
+    MS_LOG(ERROR) << "RandomCropDecodeResize: ratio should be in (min,max) format. Got (max,min).";
+    return false;
+  }
+
+  if (max_attempts_ < 1) {
+    MS_LOG(ERROR) << "RandomCropDecodeResize: max_attempts must be greater than or equal to 1.";
+    return false;
+  }
+  return true;
+}
+
+std::shared_ptr<TensorOp> RandomCropDecodeResizeOperation::Build() {
+  int32_t crop_height = size_[0];
+  int32_t crop_width = size_[0];
+
+  // User has specified the crop_width value.
+  if (size_.size() == 2) {
+    crop_width = size_[1];
+  }
+
+  float scale_lower_bound = scale_[0];
+  float scale_upper_bound = scale_[1];
+
+  float aspect_lower_bound = ratio_[0];
+  float aspect_upper_bound = ratio_[1];
+
+  auto tensor_op =
+    std::make_shared<RandomCropDecodeResizeOp>(crop_height, crop_width, scale_lower_bound, scale_upper_bound,
+                                               aspect_lower_bound, aspect_upper_bound, interpolation_, max_attempts_);
+  return tensor_op;
+}
+
 // RandomHorizontalFlipOperation
 RandomHorizontalFlipOperation::RandomHorizontalFlipOperation(float probability) : probability_(probability) {}
 

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

@@ -19,6 +19,7 @@
 
 #include <vector>
 #include <memory>
+#include <string>
 #include "minddata/dataset/core/constants.h"
 
 namespace mindspore {
@@ -48,6 +49,7 @@ namespace transforms {
 
 // Transform Op classes (in alphabetical order)
 class OneHotOperation;
+class TypeCastOperation;
 
 /// \brief Function to create a OneHot TensorOperation.
 /// \notes Convert the labels into OneHot format.
@@ -55,6 +57,12 @@ class OneHotOperation;
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<OneHotOperation> OneHot(int32_t num_classes);
 
+/// \brief Function to create a TypeCast TensorOperation.
+/// \notes Tensor operation to cast to a given MindSpore data type.
+/// \param[in] data_type mindspore.dtype to be cast to.
+/// \return Shared pointer to the current TensorOperation.
+std::shared_ptr<TypeCastOperation> TypeCast(std::string data_type);
+
 /* ####################################### Derived TensorOperation classes ################################# */
 
 class OneHotOperation : public TensorOperation {
@@ -70,6 +78,20 @@ class OneHotOperation : public TensorOperation {
  private:
   float num_classes_;
 };
+
+class TypeCastOperation : public TensorOperation {
+ public:
+  explicit TypeCastOperation(std::string data_type);
+
+  ~TypeCastOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  bool ValidateParams() override;
+
+ private:
+  std::string data_type_;
+};
 }  // namespace transforms
 }  // namespace api
 }  // namespace dataset

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

@@ -43,6 +43,7 @@ class RandomAffineOperation;
 class RandomColorOperation;
 class RandomColorAdjustOperation;
 class RandomCropOperation;
+class RandomCropDecodeResizeOperation;
 class RandomHorizontalFlipOperation;
 class RandomPosterizeOperation;
 class RandomRotationOperation;
@@ -196,6 +197,23 @@ std::shared_ptr<RandomCropOperation> RandomCrop(std::vector<int32_t> size, std::
                                                 bool pad_if_needed = false, std::vector<uint8_t> fill_value = {0, 0, 0},
                                                 BorderType padding_mode = BorderType::kConstant);
 
+/// \brief Function to create a RandomCropDecodeResize TensorOperation.
+/// \notes Equivalent to RandomResizedCrop, but crops before decodes.
+/// \param[in] size - a vector representing the output size of the cropped image.
+///               If size is a single value, a square crop of size (size, size) is returned.
+///               If size has 2 values, it should be (height, width).
+/// \param[in] scale - range [min, max) of respective size of the
+///               original size to be cropped (default=(0.08, 1.0))
+/// \param[in] ratio - range [min, max) of aspect ratio to be
+///               cropped (default=(3. / 4., 4. / 3.))
+/// \param[in] interpolation - an enum for the mode of interpolation
+/// \param[in] The maximum number of attempts to propose a valid crop_area (default=10).
+///               If exceeded, fall back to use center_crop instead.
+/// \return Shared pointer to the current TensorOperation.
+std::shared_ptr<RandomCropDecodeResizeOperation> RandomCropDecodeResize(
+  std::vector<int32_t> size, std::vector<float> scale = {0.08, 1.0}, std::vector<float> ratio = {3. / 4, 4. / 3},
+  InterpolationMode interpolation = InterpolationMode::kLinear, int32_t max_attempts = 10);
+
 /// \brief Function to create a RandomHorizontalFlip TensorOperation.
 /// \notes Tensor operation to perform random horizontal flip.
 /// \param[in] prob - float representing the probability of flip.
@@ -480,6 +498,25 @@ class RandomCropOperation : public TensorOperation {
   BorderType padding_mode_;
 };
 
+class RandomCropDecodeResizeOperation : public TensorOperation {
+ public:
+  RandomCropDecodeResizeOperation(std::vector<int32_t> size, std::vector<float> scale, std::vector<float> ratio,
+                                  InterpolationMode interpolation, int32_t max_attempts);
+
+  ~RandomCropDecodeResizeOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  bool ValidateParams() override;
+
+ private:
+  std::vector<int32_t> size_;
+  std::vector<float> scale_;
+  std::vector<float> ratio_;
+  InterpolationMode interpolation_;
+  int32_t max_attempts_;
+};
+
 class RandomHorizontalFlipOperation : public TensorOperation {
  public:
   explicit RandomHorizontalFlipOperation(float probability = 0.5);

tests/ut/cpp/dataset/c_api_transforms_test.cc

@@ -142,3 +142,61 @@ TEST_F(MindDataTestPipeline, TestOneHotSuccess2) {
   // Manually terminate the pipeline
   iter->Stop();
 }
+
+TEST_F(MindDataTestPipeline, TestTypeCastSuccess) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestTypeCastSuccess.";
+
+  // Create a Cifar10 Dataset
+  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
+  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 1));
+  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);
+
+  // Check original data type of dataset
+  auto image = row["image"];
+  std::string ori_type = image->type().ToString();
+  MS_LOG(INFO) << "Original data type: " << ori_type;
+  EXPECT_NE(ori_type.c_str(), "uint8");
+
+  // Manually terminate the pipeline
+  iter->Stop();
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> type_cast = transforms::TypeCast("uint16");
+  EXPECT_NE(type_cast, nullptr);
+
+  // Create a Map operation on ds
+  std::shared_ptr<Dataset> ds2 = ds->Map({type_cast}, {"image"});
+  EXPECT_NE(ds2, 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> iter2 = ds2->CreateIterator();
+  EXPECT_NE(iter2, nullptr);
+
+  // Check current data type of dataset
+  iter2->GetNextRow(&row);
+  auto image2 = row["image"];
+  std::string cur_type = image2->type().ToString();
+  MS_LOG(INFO) << "Current data type: " << cur_type;
+  EXPECT_NE(cur_type.c_str(), "uint16");
+
+  // Manually terminate the pipeline
+  iter2->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestTypeCastFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestTypeCastFail with invalid params.";
+
+  // incorrect data type
+  std::shared_ptr<TensorOperation> type_cast = transforms::TypeCast("char");
+  EXPECT_EQ(type_cast, nullptr);
+}

tests/ut/cpp/dataset/c_api_vision_test.cc

@@ -1313,3 +1313,117 @@ TEST_F(MindDataTestPipeline, TestNormalizeFail) {
   normalize = mindspore::dataset::api::vision::Normalize({300.0, 115.0, 100.0}, {70.0, 68.0, 71.0});
   EXPECT_EQ(normalize, nullptr);
 }
+
+TEST_F(MindDataTestPipeline, TestRandomCropDecodeResizeSucess1) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomCropDecodeResize with default params.";
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, SequentialSampler(0, 2));
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> random_crop_decode_resize =
+    mindspore::dataset::api::vision::RandomCropDecodeResize({50, 60});
+  EXPECT_NE(random_crop_decode_resize, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({random_crop_decode_resize});
+  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(image->shape()[0], 50);
+    EXPECT_EQ(image->shape()[1], 60);
+  }
+
+  EXPECT_EQ(i, 2);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestRandomCropDecodeResizeSucess2) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomCropDecodeResize with single size.";
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, RandomSampler(false, 3));
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> random_crop_decode_resize =
+    mindspore::dataset::api::vision::RandomCropDecodeResize({100});
+  EXPECT_NE(random_crop_decode_resize, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({random_crop_decode_resize});
+  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(image->shape()[0], 100);
+    EXPECT_EQ(image->shape()[1], 100);
+  }
+
+  EXPECT_EQ(i, 3);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestRandomCropDecodeResizeFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomCropDecodeResize with invalid params.";
+  // size of size vector is not 1 or 2
+  std::shared_ptr<TensorOperation> random_crop_decode_resize_1 =
+    mindspore::dataset::api::vision::RandomCropDecodeResize({50, 100, 150});
+  EXPECT_EQ(random_crop_decode_resize_1, nullptr);
+
+  // incorrect scale vector
+  std::shared_ptr<TensorOperation> random_crop_decode_resize_2 =
+    mindspore::dataset::api::vision::RandomCropDecodeResize({50, 50}, {0.5});
+  EXPECT_EQ(random_crop_decode_resize_2, nullptr);
+
+  std::shared_ptr<TensorOperation> random_crop_decode_resize_3 =
+    mindspore::dataset::api::vision::RandomCropDecodeResize({50, 50}, {0.5, 0.1});
+  EXPECT_EQ(random_crop_decode_resize_3, nullptr);
+
+  // incorrect ratio vector
+  std::shared_ptr<TensorOperation> random_crop_decode_resize_4 =
+    mindspore::dataset::api::vision::RandomCropDecodeResize({50, 50}, {0.5, 0.6}, {0.9});
+  EXPECT_EQ(random_crop_decode_resize_4, nullptr);
+
+  std::shared_ptr<TensorOperation> random_crop_decode_resize_5 =
+    mindspore::dataset::api::vision::RandomCropDecodeResize({50, 50}, {0.5, 0.6}, {0.9, 0.1});
+  EXPECT_EQ(random_crop_decode_resize_5, nullptr);
+
+  // incorrect max_attempts range
+  std::shared_ptr<TensorOperation> random_crop_decode_resize_6 =
+    mindspore::dataset::api::vision::RandomCropDecodeResize({50, 50}, {0.5, 0.6}, {0.9, 0.9},
+                                                            mindspore::dataset::InterpolationMode::kLinear, 0);
+  EXPECT_EQ(random_crop_decode_resize_6, nullptr);
+}