C++ API: Move vision ops to new vision files; create transforms namespace

5 years ago · 2aa26d245c
--- a/mindspore/ccsrc/minddata/dataset/api/CMakeLists.txt
+++ b/mindspore/ccsrc/minddata/dataset/api/CMakeLists.txt
@@ -28,4 +28,5 @@ add_library(cpp-API OBJECT
        transforms.cc
        samplers.cc
        text.cc
        vision.cc
        )
--- a/mindspore/ccsrc/minddata/dataset/api/text.cc
+++ b/mindspore/ccsrc/minddata/dataset/api/text.cc
@@ -20,8 +20,13 @@
 namespace mindspore {
 namespace dataset {
 namespace api {
 // Transform operations for text.
 namespace text {
 // FUNCTIONS TO CREATE TEXT OPERATIONS
 // (In alphabetical order)
 std::shared_ptr<LookupOperation> Lookup(const std::shared_ptr<Vocab> &vocab, const std::string &unknown_token,
                                        const DataType &data_type) {
  auto op = std::make_shared<LookupOperation>(vocab, unknown_token, data_type);
@@ -32,6 +37,12 @@ std::shared_ptr<LookupOperation> Lookup(const std::shared_ptr<Vocab> &vocab, con
  return op;
 }
 /* ####################################### Validator Functions ############################################ */
 /* ####################################### Derived TensorOperation classes ################################# */
 // (In alphabetical order)
 // LookupOperation
 LookupOperation::LookupOperation(const std::shared_ptr<Vocab> &vocab, const std::string &unknown_token,
                                 const DataType &data_type)
--- a/mindspore/ccsrc/minddata/dataset/api/transforms.cc
+++ b/mindspore/ccsrc/minddata/dataset/api/transforms.cc
@@ -17,31 +17,8 @@
 #include "minddata/dataset/include/transforms.h"
 #include "minddata/dataset/kernels/image/image_utils.h"
 #include "minddata/dataset/kernels/image/center_crop_op.h"
 #include "minddata/dataset/kernels/image/crop_op.h"
 #include "minddata/dataset/kernels/image/cutmix_batch_op.h"
 #include "minddata/dataset/kernels/image/cut_out_op.h"
 #include "minddata/dataset/kernels/image/decode_op.h"
 #include "minddata/dataset/kernels/image/hwc_to_chw_op.h"
 #include "minddata/dataset/kernels/image/mixup_batch_op.h"
 #include "minddata/dataset/kernels/image/normalize_op.h"
 // Kernel data headers (in alphabetical order)
 #include "minddata/dataset/kernels/data/one_hot_op.h"
 #include "minddata/dataset/kernels/image/pad_op.h"
 #include "minddata/dataset/kernels/image/random_affine_op.h"
 #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_horizontal_flip_op.h"
 #include "minddata/dataset/kernels/image/random_posterize_op.h"
 #include "minddata/dataset/kernels/image/random_rotation_op.h"
 #include "minddata/dataset/kernels/image/random_sharpness_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/rgba_to_bgr_op.h"
 #include "minddata/dataset/kernels/image/rgba_to_rgb_op.h"
 #include "minddata/dataset/kernels/image/swap_red_blue_op.h"
 #include "minddata/dataset/kernels/image/uniform_aug_op.h"
 namespace mindspore {
 namespace dataset {
@@ -49,88 +26,11 @@ namespace api {
 TensorOperation::TensorOperation() {}
 // Transform operations for computer vision.
 namespace vision {
 // Transform operations for data.
 namespace transforms {
 // Function to create CenterCropOperation.
 std::shared_ptr<CenterCropOperation> CenterCrop(std::vector<int32_t> size) {
  auto op = std::make_shared<CenterCropOperation>(size);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create CropOperation.
 std::shared_ptr<CropOperation> Crop(std::vector<int32_t> coordinates, std::vector<int32_t> size) {
  auto op = std::make_shared<CropOperation>(coordinates, size);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create CutMixBatchOperation.
 std::shared_ptr<CutMixBatchOperation> CutMixBatch(ImageBatchFormat image_batch_format, float alpha, float prob) {
  auto op = std::make_shared<CutMixBatchOperation>(image_batch_format, alpha, prob);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create CutOutOp.
 std::shared_ptr<CutOutOperation> CutOut(int32_t length, int32_t num_patches) {
  auto op = std::make_shared<CutOutOperation>(length, num_patches);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create DecodeOperation.
 std::shared_ptr<DecodeOperation> Decode(bool rgb) {
  auto op = std::make_shared<DecodeOperation>(rgb);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create HwcToChwOperation.
 std::shared_ptr<HwcToChwOperation> HWC2CHW() {
  auto op = std::make_shared<HwcToChwOperation>();
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create MixUpBatchOperation.
 std::shared_ptr<MixUpBatchOperation> MixUpBatch(float alpha) {
  auto op = std::make_shared<MixUpBatchOperation>(alpha);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create NormalizeOperation.
 std::shared_ptr<NormalizeOperation> Normalize(std::vector<float> mean, std::vector<float> std) {
  auto op = std::make_shared<NormalizeOperation>(mean, std);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // FUNCTIONS TO CREATE DATA TRANSFORM OPERATIONS
 // (In alphabetical order)
 // Function to create OneHotOperation.
 std::shared_ptr<OneHotOperation> OneHot(int32_t num_classes) {
@@ -142,373 +42,11 @@ std::shared_ptr<OneHotOperation> OneHot(int32_t num_classes) {
  return op;
 }
 // Function to create PadOperation.
 std::shared_ptr<PadOperation> Pad(std::vector<int32_t> padding, std::vector<uint8_t> fill_value,
                                  BorderType padding_mode) {
  auto op = std::make_shared<PadOperation>(padding, fill_value, padding_mode);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RandomColorOperation.
 std::shared_ptr<RandomColorOperation> RandomColor(float t_lb, float t_ub) {
  auto op = std::make_shared<RandomColorOperation>(t_lb, t_ub);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 std::shared_ptr<TensorOp> RandomColorOperation::Build() {
  std::shared_ptr<RandomColorOp> tensor_op = std::make_shared<RandomColorOp>(t_lb_, t_ub_);
  return tensor_op;
 }
 // Function to create RandomColorAdjustOperation.
 std::shared_ptr<RandomColorAdjustOperation> RandomColorAdjust(std::vector<float> brightness,
                                                              std::vector<float> contrast,
                                                              std::vector<float> saturation, std::vector<float> hue) {
  auto op = std::make_shared<RandomColorAdjustOperation>(brightness, contrast, saturation, hue);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RandomAffineOperation.
 std::shared_ptr<RandomAffineOperation> RandomAffine(const std::vector<float_t> &degrees,
                                                    const std::vector<float_t> &translate_range,
                                                    const std::vector<float_t> &scale_range,
                                                    const std::vector<float_t> &shear_ranges,
                                                    InterpolationMode interpolation,
                                                    const std::vector<uint8_t> &fill_value) {
  auto op = std::make_shared<RandomAffineOperation>(degrees, translate_range, scale_range, shear_ranges, interpolation,
                                                    fill_value);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RandomCropOperation.
 std::shared_ptr<RandomCropOperation> RandomCrop(std::vector<int32_t> size, std::vector<int32_t> padding,
                                                bool pad_if_needed, std::vector<uint8_t> fill_value,
                                                BorderType padding_mode) {
  auto op = std::make_shared<RandomCropOperation>(size, padding, pad_if_needed, fill_value, padding_mode);
  // 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);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RandomPosterizeOperation.
 std::shared_ptr<RandomPosterizeOperation> RandomPosterize(const std::vector<uint8_t> &bit_range) {
  auto op = std::make_shared<RandomPosterizeOperation>(bit_range);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RandomRotationOperation.
 std::shared_ptr<RandomRotationOperation> RandomRotation(std::vector<float> degrees, InterpolationMode resample,
                                                        bool expand, std::vector<float> center,
                                                        std::vector<uint8_t> fill_value) {
  auto op = std::make_shared<RandomRotationOperation>(degrees, resample, expand, center, fill_value);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RandomSolarizeOperation.
 std::shared_ptr<RandomSolarizeOperation> RandomSolarize(std::vector<uint8_t> threshold) {
  auto op = std::make_shared<RandomSolarizeOperation>(threshold);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RandomSharpnessOperation.
 std::shared_ptr<RandomSharpnessOperation> RandomSharpness(std::vector<float> degrees) {
  auto op = std::make_shared<RandomSharpnessOperation>(degrees);
  // 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);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create ResizeOperation.
 std::shared_ptr<ResizeOperation> Resize(std::vector<int32_t> size, InterpolationMode interpolation) {
  auto op = std::make_shared<ResizeOperation>(size, interpolation);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RgbaToBgrOperation.
 std::shared_ptr<RgbaToBgrOperation> RGBA2BGR() {
  auto op = std::make_shared<RgbaToBgrOperation>();
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RgbaToRgbOperation.
 std::shared_ptr<RgbaToRgbOperation> RGBA2RGB() {
  auto op = std::make_shared<RgbaToRgbOperation>();
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create SwapRedBlueOperation.
 std::shared_ptr<SwapRedBlueOperation> SwapRedBlue() {
  auto op = std::make_shared<SwapRedBlueOperation>();
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create UniformAugOperation.
 std::shared_ptr<UniformAugOperation> UniformAugment(std::vector<std::shared_ptr<TensorOperation>> transforms,
                                                    int32_t num_ops) {
  auto op = std::make_shared<UniformAugOperation>(transforms, num_ops);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 /* ####################################### Validator Functions ############################################ */
 bool CheckVectorPositive(const std::vector<int32_t> &size) {
  for (int i = 0; i < size.size(); ++i) {
    if (size[i] <= 0) return false;
  }
  return true;
 }
 bool CmpFloat(const float &a, const float &b, float epsilon = 0.0000000001f) { return (std::fabs(a - b) < epsilon); }
 /* ####################################### Derived TensorOperation classes ################################# */
 // CenterCropOperation
 CenterCropOperation::CenterCropOperation(std::vector<int32_t> size) : size_(size) {}
 bool CenterCropOperation::ValidateParams() {
  if (size_.empty() || size_.size() > 2) {
    MS_LOG(ERROR) << "CenterCrop: size vector has incorrect size.";
    return false;
  }
  // We have to limit crop size due to library restrictions, optimized to only iterate over size_ once
  for (int i = 0; i < size_.size(); ++i) {
    if (size_[i] <= 0) {
      MS_LOG(ERROR) << "CenterCrop: invalid size, size must be greater than 0, got: " << size_[i];
      return false;
    }
    if (size_[i] == INT_MAX) {
      MS_LOG(ERROR) << "CenterCrop: invalid size, size too large, got: " << size_[i];
      return false;
    }
  }
  return true;
 }
 std::shared_ptr<TensorOp> CenterCropOperation::Build() {
  int32_t crop_height = size_[0];
  int32_t crop_width = 0;
  // User has specified crop_width.
  if (size_.size() == 2) {
    crop_width = size_[1];
  }
  std::shared_ptr<CenterCropOp> tensor_op = std::make_shared<CenterCropOp>(crop_height, crop_width);
  return tensor_op;
 }
 // CropOperation.
 CropOperation::CropOperation(std::vector<int32_t> coordinates, std::vector<int32_t> size)
    : coordinates_(coordinates), size_(size) {}
 bool CropOperation::ValidateParams() {
  // Do some input validation.
  if (coordinates_.size() != 2) {
    MS_LOG(ERROR) << "Crop: coordinates must be a vector of two values";
    return false;
  }
  // we don't check the coordinates here because we don't have access to image dimensions
  if (size_.empty() || size_.size() > 2) {
    MS_LOG(ERROR) << "Crop: size must be a vector of one or two values";
    return false;
  }
  // We have to limit crop size due to library restrictions, optimized to only iterate over size_ once
  for (int i = 0; i < size_.size(); ++i) {
    if (size_[i] <= 0) {
      MS_LOG(ERROR) << "Crop: invalid size, size must be greater than 0, got: " << size_[i];
      return false;
    }
    if (size_[i] == INT_MAX) {
      MS_LOG(ERROR) << "Crop: invalid size, size too large, got: " << size_[i];
      return false;
    }
  }
  return true;
 }
 std::shared_ptr<TensorOp> CropOperation::Build() {
  int32_t x, y, height, width;
  x = coordinates_[0];
  y = coordinates_[1];
  height = size_[0];
  width = size_[1];
  std::shared_ptr<CropOp> tensor_op = std::make_shared<CropOp>(x, y, height, width);
  return tensor_op;
 }
 // CutMixBatchOperation
 CutMixBatchOperation::CutMixBatchOperation(ImageBatchFormat image_batch_format, float alpha, float prob)
    : image_batch_format_(image_batch_format), alpha_(alpha), prob_(prob) {}
 bool CutMixBatchOperation::ValidateParams() {
  if (alpha_ <= 0) {
    MS_LOG(ERROR) << "CutMixBatch: alpha must be a positive floating value however it is: " << alpha_;
    return false;
  }
  if (prob_ < 0 || prob_ > 1) {
    MS_LOG(ERROR) << "CutMixBatch: Probability has to be between 0 and 1.";
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> CutMixBatchOperation::Build() {
  std::shared_ptr<CutMixBatchOp> tensor_op = std::make_shared<CutMixBatchOp>(image_batch_format_, alpha_, prob_);
  return tensor_op;
 }
 // CutOutOperation
 CutOutOperation::CutOutOperation(int32_t length, int32_t num_patches) : length_(length), num_patches_(num_patches) {}
 bool CutOutOperation::ValidateParams() {
  if (length_ < 0) {
    MS_LOG(ERROR) << "CutOut: length cannot be negative";
    return false;
  }
  if (num_patches_ < 0) {
    MS_LOG(ERROR) << "CutOut: number of patches cannot be negative";
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> CutOutOperation::Build() {
  std::shared_ptr<CutOutOp> tensor_op = std::make_shared<CutOutOp>(length_, length_, num_patches_, false, 0, 0, 0);
  return tensor_op;
 }
 // DecodeOperation
 DecodeOperation::DecodeOperation(bool rgb) : rgb_(rgb) {}
 bool DecodeOperation::ValidateParams() { return true; }
 std::shared_ptr<TensorOp> DecodeOperation::Build() { return std::make_shared<DecodeOp>(rgb_); }
 // HwcToChwOperation
 bool HwcToChwOperation::ValidateParams() { return true; }
 std::shared_ptr<TensorOp> HwcToChwOperation::Build() { return std::make_shared<HwcToChwOp>(); }
 // MixUpOperation
 MixUpBatchOperation::MixUpBatchOperation(float alpha) : alpha_(alpha) {}
 bool MixUpBatchOperation::ValidateParams() {
  if (alpha_ <= 0) {
    MS_LOG(ERROR) << "MixUpBatch: alpha must be a positive floating value however it is: " << alpha_;
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> MixUpBatchOperation::Build() { return std::make_shared<MixUpBatchOp>(alpha_); }
 // NormalizeOperation
 NormalizeOperation::NormalizeOperation(std::vector<float> mean, std::vector<float> std) : mean_(mean), std_(std) {}
 bool NormalizeOperation::ValidateParams() {
  if (mean_.size() != 3) {
    MS_LOG(ERROR) << "Normalize: mean vector has incorrect size: " << mean_.size();
    return false;
  }
  // check mean value
  for (int i = 0; i < mean_.size(); ++i) {
    if (mean_[i] < 0.0f || mean_[i] > 255.0f || CmpFloat(mean_[i], 0.0f)) {
      MS_LOG(ERROR) << "Normalize: mean vector has incorrect value: " << mean_[i];
      return false;
    }
  }
  if (std_.size() != 3) {
    MS_LOG(ERROR) << "Normalize: std vector has incorrect size: " << std_.size();
    return false;
  }
  // check std value
  for (int i = 0; i < std_.size(); ++i) {
    if (std_[i] < 0.0f || std_[i] > 255.0f || CmpFloat(std_[i], 0.0f)) {
      MS_LOG(ERROR) << "Normalize: std vector has incorrect value: " << std_[i];
      return false;
    }
  }
  return true;
 }
 std::shared_ptr<TensorOp> NormalizeOperation::Build() {
  return std::make_shared<NormalizeOp>(mean_[0], mean_[1], mean_[2], std_[0], std_[1], std_[2]);
 }
 // (In alphabetical order)
 // OneHotOperation
 OneHotOperation::OneHotOperation(int32_t num_classes) : num_classes_(num_classes) {}
@@ -524,476 +62,7 @@ bool OneHotOperation::ValidateParams() {
 std::shared_ptr<TensorOp> OneHotOperation::Build() { return std::make_shared<OneHotOp>(num_classes_); }
 // PadOperation
 PadOperation::PadOperation(std::vector<int32_t> padding, std::vector<uint8_t> fill_value, BorderType padding_mode)
    : padding_(padding), fill_value_(fill_value), padding_mode_(padding_mode) {}
 bool PadOperation::ValidateParams() {
  if (padding_.empty() || padding_.size() == 3 || padding_.size() > 4) {
    MS_LOG(ERROR) << "Pad: padding vector has incorrect size: padding.size()";
    return false;
  }
  if (fill_value_.empty() || (fill_value_.size() != 1 && fill_value_.size() != 3)) {
    MS_LOG(ERROR) << "Pad: fill_value vector has incorrect size: fill_value.size()";
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> PadOperation::Build() {
  int32_t pad_top, pad_bottom, pad_left, pad_right;
  switch (padding_.size()) {
    case 1:
      pad_left = padding_[0];
      pad_top = padding_[0];
      pad_right = padding_[0];
      pad_bottom = padding_[0];
      break;
    case 2:
      pad_left = padding_[0];
      pad_top = padding_[1];
      pad_right = padding_[0];
      pad_bottom = padding_[1];
      break;
    default:
      pad_left = padding_[0];
      pad_top = padding_[1];
      pad_right = padding_[2];
      pad_bottom = padding_[3];
  }
  uint8_t fill_r, fill_g, fill_b;
  fill_r = fill_value_[0];
  fill_g = fill_value_[0];
  fill_b = fill_value_[0];
  if (fill_value_.size() == 3) {
    fill_r = fill_value_[0];
    fill_g = fill_value_[1];
    fill_b = fill_value_[2];
  }
  std::shared_ptr<PadOp> tensor_op =
    std::make_shared<PadOp>(pad_top, pad_bottom, pad_left, pad_right, padding_mode_, fill_r, fill_g, fill_b);
  return tensor_op;
 }
 // RandomColorOperation.
 RandomColorOperation::RandomColorOperation(float t_lb, float t_ub) : t_lb_(t_lb), t_ub_(t_ub) {}
 bool RandomColorOperation::ValidateParams() {
  // Do some input validation.
  if (t_lb_ > t_ub_) {
    MS_LOG(ERROR) << "RandomColor: lower bound must be less or equal to upper bound";
    return false;
  }
  return true;
 }
 // RandomColorAdjustOperation.
 RandomColorAdjustOperation::RandomColorAdjustOperation(std::vector<float> brightness, std::vector<float> contrast,
                                                       std::vector<float> saturation, std::vector<float> hue)
    : brightness_(brightness), contrast_(contrast), saturation_(saturation), hue_(hue) {}
 bool RandomColorAdjustOperation::ValidateParams() {
  // Do some input validation.
  if (brightness_.empty() || brightness_.size() > 2) {
    MS_LOG(ERROR) << "RandomColorAdjust: brightness must be a vector of one or two values";
    return false;
  }
  if (contrast_.empty() || contrast_.size() > 2) {
    MS_LOG(ERROR) << "RandomColorAdjust: contrast must be a vector of one or two values";
    return false;
  }
  if (saturation_.empty() || saturation_.size() > 2) {
    MS_LOG(ERROR) << "RandomColorAdjust: saturation must be a vector of one or two values";
    return false;
  }
  if (hue_.empty() || hue_.size() > 2) {
    MS_LOG(ERROR) << "RandomColorAdjust: hue must be a vector of one or two values";
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> RandomColorAdjustOperation::Build() {
  float brightness_lb, brightness_ub, contrast_lb, contrast_ub, saturation_lb, saturation_ub, hue_lb, hue_ub;
  brightness_lb = brightness_[0];
  brightness_ub = brightness_[0];
  if (brightness_.size() == 2) brightness_ub = brightness_[1];
  contrast_lb = contrast_[0];
  contrast_ub = contrast_[0];
  if (contrast_.size() == 2) contrast_ub = contrast_[1];
  saturation_lb = saturation_[0];
  saturation_ub = saturation_[0];
  if (saturation_.size() == 2) saturation_ub = saturation_[1];
  hue_lb = hue_[0];
  hue_ub = hue_[0];
  if (hue_.size() == 2) hue_ub = hue_[1];
  std::shared_ptr<RandomColorAdjustOp> tensor_op = std::make_shared<RandomColorAdjustOp>(
    brightness_lb, brightness_ub, contrast_lb, contrast_ub, saturation_lb, saturation_ub, hue_lb, hue_ub);
  return tensor_op;
 }
 // RandomAffineOperation
 RandomAffineOperation::RandomAffineOperation(const std::vector<float_t> &degrees,
                                             const std::vector<float_t> &translate_range,
                                             const std::vector<float_t> &scale_range,
                                             const std::vector<float_t> &shear_ranges, InterpolationMode interpolation,
                                             const std::vector<uint8_t> &fill_value)
    : degrees_(degrees),
      translate_range_(translate_range),
      scale_range_(scale_range),
      shear_ranges_(shear_ranges),
      interpolation_(interpolation),
      fill_value_(fill_value) {}
 bool RandomAffineOperation::ValidateParams() {
  // Degrees
  if (degrees_.size() != 2) {
    MS_LOG(ERROR) << "RandomAffine: degrees expecting size 2, got: degrees.size() = " << degrees_.size();
    return false;
  }
  if (degrees_[0] > degrees_[1]) {
    MS_LOG(ERROR) << "RandomAffine: minimum of degrees range is greater than maximum: min = " << degrees_[0]
                  << ", max = " << degrees_[1];
    return false;
  }
  // Translate
  if (translate_range_.size() != 2 && translate_range_.size() != 4) {
    MS_LOG(ERROR) << "RandomAffine: translate_range expecting size 2 or 4, got: translate_range.size() = "
                  << translate_range_.size();
    return false;
  }
  if (translate_range_[0] > translate_range_[1]) {
    MS_LOG(ERROR) << "RandomAffine: minimum of translate range on x is greater than maximum: min = "
                  << translate_range_[0] << ", max = " << translate_range_[1];
    return false;
  }
  if (translate_range_[0] < -1 || translate_range_[0] > 1) {
    MS_LOG(ERROR) << "RandomAffine: minimum of translate range on x is out of range of [-1, 1], value = "
                  << translate_range_[0];
    return false;
  }
  if (translate_range_[1] < -1 || translate_range_[1] > 1) {
    MS_LOG(ERROR) << "RandomAffine: maximum of translate range on x is out of range of [-1, 1], value = "
                  << translate_range_[1];
    return false;
  }
  if (translate_range_.size() == 4) {
    if (translate_range_[2] > translate_range_[3]) {
      MS_LOG(ERROR) << "RandomAffine: minimum of translate range on y is greater than maximum: min = "
                    << translate_range_[2] << ", max = " << translate_range_[3];
      return false;
    }
    if (translate_range_[2] < -1 || translate_range_[2] > 1) {
      MS_LOG(ERROR) << "RandomAffine: minimum of translate range on y is out of range of [-1, 1], value = "
                    << translate_range_[2];
      return false;
    }
    if (translate_range_[3] < -1 || translate_range_[3] > 1) {
      MS_LOG(ERROR) << "RandomAffine: maximum of translate range on y is out of range of [-1, 1], value = "
                    << translate_range_[3];
      return false;
    }
  }
  // Scale
  if (scale_range_.size() != 2) {
    MS_LOG(ERROR) << "RandomAffine: scale_range vector has incorrect size: scale_range.size() = "
                  << scale_range_.size();
    return false;
  }
  if (scale_range_[0] > scale_range_[1]) {
    MS_LOG(ERROR) << "RandomAffine: minimum of scale range is greater than maximum: min = " << scale_range_[0]
                  << ", max = " << scale_range_[1];
    return false;
  }
  // Shear
  if (shear_ranges_.size() != 2 && shear_ranges_.size() != 4) {
    MS_LOG(ERROR) << "RandomAffine: shear_ranges expecting size 2 or 4, got: shear_ranges.size() = "
                  << shear_ranges_.size();
    return false;
  }
  if (shear_ranges_[0] > shear_ranges_[1]) {
    MS_LOG(ERROR) << "RandomAffine: minimum of horizontal shear range is greater than maximum: min = "
                  << shear_ranges_[0] << ", max = " << shear_ranges_[1];
    return false;
  }
  if (shear_ranges_.size() == 4 && shear_ranges_[2] > shear_ranges_[3]) {
    MS_LOG(ERROR) << "RandomAffine: minimum of vertical shear range is greater than maximum: min = " << shear_ranges_[2]
                  << ", max = " << scale_range_[3];
    return false;
  }
  // Fill Value
  if (fill_value_.size() != 3) {
    MS_LOG(ERROR) << "RandomAffine: fill_value vector has incorrect size: fill_value.size() = " << fill_value_.size();
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> RandomAffineOperation::Build() {
  if (shear_ranges_.size() == 2) {
    shear_ranges_.resize(4);
  }
  if (translate_range_.size() == 2) {
    translate_range_.resize(4);
  }
  auto tensor_op = std::make_shared<RandomAffineOp>(degrees_, translate_range_, scale_range_, shear_ranges_,
                                                    interpolation_, fill_value_);
  return tensor_op;
 }
 // RandomCropOperation
 RandomCropOperation::RandomCropOperation(std::vector<int32_t> size, std::vector<int32_t> padding, bool pad_if_needed,
                                         std::vector<uint8_t> fill_value, BorderType padding_mode)
    : size_(size),
      padding_(padding),
      pad_if_needed_(pad_if_needed),
      fill_value_(fill_value),
      padding_mode_(padding_mode) {}
 bool RandomCropOperation::ValidateParams() {
  if (size_.empty() || size_.size() > 2) {
    MS_LOG(ERROR) << "RandomCrop: size vector has incorrect size: " << size_.size();
    return false;
  }
  if (padding_.empty() || padding_.size() != 4) {
    MS_LOG(ERROR) << "RandomCrop: padding vector has incorrect size: padding.size()";
    return false;
  }
  if (fill_value_.empty() || fill_value_.size() != 3) {
    MS_LOG(ERROR) << "RandomCrop: fill_value vector has incorrect size: fill_value.size()";
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> RandomCropOperation::Build() {
  int32_t crop_height = size_[0];
  int32_t crop_width = 0;
  int32_t pad_top = padding_[0];
  int32_t pad_bottom = padding_[1];
  int32_t pad_left = padding_[2];
  int32_t pad_right = padding_[3];
  uint8_t fill_r = fill_value_[0];
  uint8_t fill_g = fill_value_[1];
  uint8_t fill_b = fill_value_[2];
  // User has specified the crop_width value.
  if (size_.size() == 2) {
    crop_width = size_[1];
  }
  auto tensor_op = std::make_shared<RandomCropOp>(crop_height, crop_width, pad_top, pad_bottom, pad_left, pad_right,
                                                  padding_mode_, pad_if_needed_, fill_r, fill_g, fill_b);
  return tensor_op;
 }
 // RandomHorizontalFlipOperation
 RandomHorizontalFlipOperation::RandomHorizontalFlipOperation(float probability) : probability_(probability) {}
 bool RandomHorizontalFlipOperation::ValidateParams() { return true; }
 std::shared_ptr<TensorOp> RandomHorizontalFlipOperation::Build() {
  std::shared_ptr<RandomHorizontalFlipOp> tensor_op = std::make_shared<RandomHorizontalFlipOp>(probability_);
  return tensor_op;
 }
 // RandomPosterizeOperation
 RandomPosterizeOperation::RandomPosterizeOperation(const std::vector<uint8_t> &bit_range) : bit_range_(bit_range) {}
 bool RandomPosterizeOperation::ValidateParams() {
  if (bit_range_.size() != 2) {
    MS_LOG(ERROR) << "RandomPosterize: bit_range needs to be of size 2 but is of size: " << bit_range_.size();
    return false;
  }
  if (bit_range_[0] < 1 || bit_range_[0] > 8) {
    MS_LOG(ERROR) << "RandomPosterize: min_bit value is out of range [1-8]: " << bit_range_[0];
    return false;
  }
  if (bit_range_[1] < 1 || bit_range_[1] > 8) {
    MS_LOG(ERROR) << "RandomPosterize: max_bit value is out of range [1-8]: " << bit_range_[1];
    return false;
  }
  if (bit_range_[1] < bit_range_[0]) {
    MS_LOG(ERROR) << "RandomPosterize: max_bit value is less than min_bit: max =" << bit_range_[1]
                  << ", min = " << bit_range_[0];
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> RandomPosterizeOperation::Build() {
  std::shared_ptr<RandomPosterizeOp> tensor_op = std::make_shared<RandomPosterizeOp>(bit_range_);
  return tensor_op;
 }
 // Function to create RandomRotationOperation.
 RandomRotationOperation::RandomRotationOperation(std::vector<float> degrees, InterpolationMode interpolation_mode,
                                                 bool expand, std::vector<float> center,
                                                 std::vector<uint8_t> fill_value)
    : degrees_(degrees),
      interpolation_mode_(interpolation_mode),
      expand_(expand),
      center_(center),
      fill_value_(fill_value) {}
 bool RandomRotationOperation::ValidateParams() {
  if (degrees_.empty() || degrees_.size() != 2) {
    MS_LOG(ERROR) << "RandomRotation: degrees vector has incorrect size: degrees.size()";
    return false;
  }
  if (center_.empty() || center_.size() != 2) {
    MS_LOG(ERROR) << "RandomRotation: center vector has incorrect size: center.size()";
    return false;
  }
  if (fill_value_.empty() || fill_value_.size() != 3) {
    MS_LOG(ERROR) << "RandomRotation: fill_value vector has incorrect size: fill_value.size()";
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> RandomRotationOperation::Build() {
  std::shared_ptr<RandomRotationOp> tensor_op =
    std::make_shared<RandomRotationOp>(degrees_[0], degrees_[1], center_[0], center_[1], interpolation_mode_, expand_,
                                       fill_value_[0], fill_value_[1], fill_value_[2]);
  return tensor_op;
 }
 // Function to create RandomSharpness.
 RandomSharpnessOperation::RandomSharpnessOperation(std::vector<float> degrees) : degrees_(degrees) {}
 bool RandomSharpnessOperation::ValidateParams() {
  if (degrees_.empty() || degrees_.size() != 2) {
    MS_LOG(ERROR) << "RandomSharpness: degrees vector has incorrect size: degrees.size()";
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> RandomSharpnessOperation::Build() {
  std::shared_ptr<RandomSharpnessOp> tensor_op = std::make_shared<RandomSharpnessOp>(degrees_[0], degrees_[1]);
  return tensor_op;
 }
 // RandomSolarizeOperation.
 RandomSolarizeOperation::RandomSolarizeOperation(std::vector<uint8_t> threshold) : threshold_(threshold) {}
 bool RandomSolarizeOperation::ValidateParams() {
  if (threshold_.size() != 2) {
    MS_LOG(ERROR) << "RandomSolarize: threshold vector has incorrect size: " << threshold_.size();
    return false;
  }
  if (threshold_.at(0) > threshold_.at(1)) {
    MS_LOG(ERROR) << "RandomSolarize: threshold must be passed in a min, max format";
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> RandomSolarizeOperation::Build() {
  std::shared_ptr<RandomSolarizeOp> tensor_op = std::make_shared<RandomSolarizeOp>(threshold_);
  return tensor_op;
 }
 // RandomVerticalFlipOperation
 RandomVerticalFlipOperation::RandomVerticalFlipOperation(float probability) : probability_(probability) {}
 bool RandomVerticalFlipOperation::ValidateParams() { return true; }
 std::shared_ptr<TensorOp> RandomVerticalFlipOperation::Build() {
  std::shared_ptr<RandomVerticalFlipOp> tensor_op = std::make_shared<RandomVerticalFlipOp>(probability_);
  return tensor_op;
 }
 // ResizeOperation
 ResizeOperation::ResizeOperation(std::vector<int32_t> size, InterpolationMode interpolation)
    : size_(size), interpolation_(interpolation) {}
 bool ResizeOperation::ValidateParams() {
  if (size_.empty() || size_.size() > 2) {
    MS_LOG(ERROR) << "Resize: size vector has incorrect size: " << size_.size();
    return false;
  }
  if (!CheckVectorPositive(size_)) {
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> ResizeOperation::Build() {
  int32_t height = size_[0];
  int32_t width = 0;
  // User specified the width value.
  if (size_.size() == 2) {
    width = size_[1];
  }
  return std::make_shared<ResizeOp>(height, width, interpolation_);
 }
 // RgbaToBgrOperation.
 RgbaToBgrOperation::RgbaToBgrOperation() {}
 bool RgbaToBgrOperation::ValidateParams() { return true; }
 std::shared_ptr<TensorOp> RgbaToBgrOperation::Build() {
  std::shared_ptr<RgbaToBgrOp> tensor_op = std::make_shared<RgbaToBgrOp>();
  return tensor_op;
 }
 // RgbaToRgbOperation.
 RgbaToRgbOperation::RgbaToRgbOperation() {}
 bool RgbaToRgbOperation::ValidateParams() { return true; }
 std::shared_ptr<TensorOp> RgbaToRgbOperation::Build() {
  std::shared_ptr<RgbaToRgbOp> tensor_op = std::make_shared<RgbaToRgbOp>();
  return tensor_op;
 }
 // SwapRedBlueOperation.
 SwapRedBlueOperation::SwapRedBlueOperation() {}
 bool SwapRedBlueOperation::ValidateParams() { return true; }
 std::shared_ptr<TensorOp> SwapRedBlueOperation::Build() {
  std::shared_ptr<SwapRedBlueOp> tensor_op = std::make_shared<SwapRedBlueOp>();
  return tensor_op;
 }
 // UniformAugOperation
 UniformAugOperation::UniformAugOperation(std::vector<std::shared_ptr<TensorOperation>> transforms, int32_t num_ops)
    : transforms_(transforms), num_ops_(num_ops) {}
 bool UniformAugOperation::ValidateParams() { return true; }
 std::shared_ptr<TensorOp> UniformAugOperation::Build() {
  std::vector<std::shared_ptr<TensorOp>> tensor_ops;
  (void)std::transform(transforms_.begin(), transforms_.end(), std::back_inserter(tensor_ops),
                       [](std::shared_ptr<TensorOperation> op) -> std::shared_ptr<TensorOp> { return op->Build(); });
  std::shared_ptr<UniformAugOp> tensor_op = std::make_shared<UniformAugOp>(tensor_ops, num_ops_);
  return tensor_op;
 }
 }  // namespace vision
 }  // namespace transforms
 }  // namespace api
 }  // namespace dataset
 }  // namespace mindspore
--- a/mindspore/ccsrc/minddata/dataset/api/vision.cc
+++ b/mindspore/ccsrc/minddata/dataset/api/vision.cc
@@ -0,0 +1,979 @@
 /**
 * 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/include/transforms.h"
 #include "minddata/dataset/include/vision.h"
 #include "minddata/dataset/kernels/image/image_utils.h"
 // Kernel image headers (in alphabetical order)
 #include "minddata/dataset/kernels/image/center_crop_op.h"
 #include "minddata/dataset/kernels/image/crop_op.h"
 #include "minddata/dataset/kernels/image/cutmix_batch_op.h"
 #include "minddata/dataset/kernels/image/cut_out_op.h"
 #include "minddata/dataset/kernels/image/decode_op.h"
 #include "minddata/dataset/kernels/image/hwc_to_chw_op.h"
 #include "minddata/dataset/kernels/image/mixup_batch_op.h"
 #include "minddata/dataset/kernels/image/normalize_op.h"
 #include "minddata/dataset/kernels/image/pad_op.h"
 #include "minddata/dataset/kernels/image/random_affine_op.h"
 #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_horizontal_flip_op.h"
 #include "minddata/dataset/kernels/image/random_posterize_op.h"
 #include "minddata/dataset/kernels/image/random_rotation_op.h"
 #include "minddata/dataset/kernels/image/random_sharpness_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/rgba_to_bgr_op.h"
 #include "minddata/dataset/kernels/image/rgba_to_rgb_op.h"
 #include "minddata/dataset/kernels/image/swap_red_blue_op.h"
 #include "minddata/dataset/kernels/image/uniform_aug_op.h"
 namespace mindspore {
 namespace dataset {
 namespace api {
 // Transform operations for computer vision.
 namespace vision {
 // FUNCTIONS TO CREATE VISION TRANSFORM OPERATIONS
 // (In alphabetical order)
 // Function to create CenterCropOperation.
 std::shared_ptr<CenterCropOperation> CenterCrop(std::vector<int32_t> size) {
  auto op = std::make_shared<CenterCropOperation>(size);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create CropOperation.
 std::shared_ptr<CropOperation> Crop(std::vector<int32_t> coordinates, std::vector<int32_t> size) {
  auto op = std::make_shared<CropOperation>(coordinates, size);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create CutMixBatchOperation.
 std::shared_ptr<CutMixBatchOperation> CutMixBatch(ImageBatchFormat image_batch_format, float alpha, float prob) {
  auto op = std::make_shared<CutMixBatchOperation>(image_batch_format, alpha, prob);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create CutOutOp.
 std::shared_ptr<CutOutOperation> CutOut(int32_t length, int32_t num_patches) {
  auto op = std::make_shared<CutOutOperation>(length, num_patches);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create DecodeOperation.
 std::shared_ptr<DecodeOperation> Decode(bool rgb) {
  auto op = std::make_shared<DecodeOperation>(rgb);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create HwcToChwOperation.
 std::shared_ptr<HwcToChwOperation> HWC2CHW() {
  auto op = std::make_shared<HwcToChwOperation>();
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create MixUpBatchOperation.
 std::shared_ptr<MixUpBatchOperation> MixUpBatch(float alpha) {
  auto op = std::make_shared<MixUpBatchOperation>(alpha);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create NormalizeOperation.
 std::shared_ptr<NormalizeOperation> Normalize(std::vector<float> mean, std::vector<float> std) {
  auto op = std::make_shared<NormalizeOperation>(mean, std);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create PadOperation.
 std::shared_ptr<PadOperation> Pad(std::vector<int32_t> padding, std::vector<uint8_t> fill_value,
                                  BorderType padding_mode) {
  auto op = std::make_shared<PadOperation>(padding, fill_value, padding_mode);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RandomColorOperation.
 std::shared_ptr<RandomColorOperation> RandomColor(float t_lb, float t_ub) {
  auto op = std::make_shared<RandomColorOperation>(t_lb, t_ub);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 std::shared_ptr<TensorOp> RandomColorOperation::Build() {
  std::shared_ptr<RandomColorOp> tensor_op = std::make_shared<RandomColorOp>(t_lb_, t_ub_);
  return tensor_op;
 }
 // Function to create RandomColorAdjustOperation.
 std::shared_ptr<RandomColorAdjustOperation> RandomColorAdjust(std::vector<float> brightness,
                                                              std::vector<float> contrast,
                                                              std::vector<float> saturation, std::vector<float> hue) {
  auto op = std::make_shared<RandomColorAdjustOperation>(brightness, contrast, saturation, hue);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RandomAffineOperation.
 std::shared_ptr<RandomAffineOperation> RandomAffine(const std::vector<float_t> &degrees,
                                                    const std::vector<float_t> &translate_range,
                                                    const std::vector<float_t> &scale_range,
                                                    const std::vector<float_t> &shear_ranges,
                                                    InterpolationMode interpolation,
                                                    const std::vector<uint8_t> &fill_value) {
  auto op = std::make_shared<RandomAffineOperation>(degrees, translate_range, scale_range, shear_ranges, interpolation,
                                                    fill_value);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RandomCropOperation.
 std::shared_ptr<RandomCropOperation> RandomCrop(std::vector<int32_t> size, std::vector<int32_t> padding,
                                                bool pad_if_needed, std::vector<uint8_t> fill_value,
                                                BorderType padding_mode) {
  auto op = std::make_shared<RandomCropOperation>(size, padding, pad_if_needed, fill_value, padding_mode);
  // 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);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RandomPosterizeOperation.
 std::shared_ptr<RandomPosterizeOperation> RandomPosterize(const std::vector<uint8_t> &bit_range) {
  auto op = std::make_shared<RandomPosterizeOperation>(bit_range);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RandomRotationOperation.
 std::shared_ptr<RandomRotationOperation> RandomRotation(std::vector<float> degrees, InterpolationMode resample,
                                                        bool expand, std::vector<float> center,
                                                        std::vector<uint8_t> fill_value) {
  auto op = std::make_shared<RandomRotationOperation>(degrees, resample, expand, center, fill_value);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RandomSolarizeOperation.
 std::shared_ptr<RandomSolarizeOperation> RandomSolarize(std::vector<uint8_t> threshold) {
  auto op = std::make_shared<RandomSolarizeOperation>(threshold);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RandomSharpnessOperation.
 std::shared_ptr<RandomSharpnessOperation> RandomSharpness(std::vector<float> degrees) {
  auto op = std::make_shared<RandomSharpnessOperation>(degrees);
  // 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);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create ResizeOperation.
 std::shared_ptr<ResizeOperation> Resize(std::vector<int32_t> size, InterpolationMode interpolation) {
  auto op = std::make_shared<ResizeOperation>(size, interpolation);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RgbaToBgrOperation.
 std::shared_ptr<RgbaToBgrOperation> RGBA2BGR() {
  auto op = std::make_shared<RgbaToBgrOperation>();
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create RgbaToRgbOperation.
 std::shared_ptr<RgbaToRgbOperation> RGBA2RGB() {
  auto op = std::make_shared<RgbaToRgbOperation>();
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create SwapRedBlueOperation.
 std::shared_ptr<SwapRedBlueOperation> SwapRedBlue() {
  auto op = std::make_shared<SwapRedBlueOperation>();
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 // Function to create UniformAugOperation.
 std::shared_ptr<UniformAugOperation> UniformAugment(std::vector<std::shared_ptr<TensorOperation>> transforms,
                                                    int32_t num_ops) {
  auto op = std::make_shared<UniformAugOperation>(transforms, num_ops);
  // Input validation
  if (!op->ValidateParams()) {
    return nullptr;
  }
  return op;
 }
 /* ####################################### Validator Functions ############################################ */
 bool CheckVectorPositive(const std::vector<int32_t> &size) {
  for (int i = 0; i < size.size(); ++i) {
    if (size[i] <= 0) return false;
  }
  return true;
 }
 bool CmpFloat(const float &a, const float &b, float epsilon = 0.0000000001f) { return (std::fabs(a - b) < epsilon); }
 /* ####################################### Derived TensorOperation classes ################################# */
 // (In alphabetical order)
 // CenterCropOperation
 CenterCropOperation::CenterCropOperation(std::vector<int32_t> size) : size_(size) {}
 bool CenterCropOperation::ValidateParams() {
  if (size_.empty() || size_.size() > 2) {
    MS_LOG(ERROR) << "CenterCrop: size vector has incorrect size.";
    return false;
  }
  // We have to limit crop size due to library restrictions, optimized to only iterate over size_ once
  for (int i = 0; i < size_.size(); ++i) {
    if (size_[i] <= 0) {
      MS_LOG(ERROR) << "CenterCrop: invalid size, size must be greater than 0, got: " << size_[i];
      return false;
    }
    if (size_[i] == INT_MAX) {
      MS_LOG(ERROR) << "CenterCrop: invalid size, size too large, got: " << size_[i];
      return false;
    }
  }
  return true;
 }
 std::shared_ptr<TensorOp> CenterCropOperation::Build() {
  int32_t crop_height = size_[0];
  int32_t crop_width = 0;
  // User has specified crop_width.
  if (size_.size() == 2) {
    crop_width = size_[1];
  }
  std::shared_ptr<CenterCropOp> tensor_op = std::make_shared<CenterCropOp>(crop_height, crop_width);
  return tensor_op;
 }
 // CropOperation.
 CropOperation::CropOperation(std::vector<int32_t> coordinates, std::vector<int32_t> size)
    : coordinates_(coordinates), size_(size) {}
 bool CropOperation::ValidateParams() {
  // Do some input validation.
  if (coordinates_.size() != 2) {
    MS_LOG(ERROR) << "Crop: coordinates must be a vector of two values";
    return false;
  }
  // we don't check the coordinates here because we don't have access to image dimensions
  if (size_.empty() || size_.size() > 2) {
    MS_LOG(ERROR) << "Crop: size must be a vector of one or two values";
    return false;
  }
  // We have to limit crop size due to library restrictions, optimized to only iterate over size_ once
  for (int i = 0; i < size_.size(); ++i) {
    if (size_[i] <= 0) {
      MS_LOG(ERROR) << "Crop: invalid size, size must be greater than 0, got: " << size_[i];
      return false;
    }
    if (size_[i] == INT_MAX) {
      MS_LOG(ERROR) << "Crop: invalid size, size too large, got: " << size_[i];
      return false;
    }
  }
  return true;
 }
 std::shared_ptr<TensorOp> CropOperation::Build() {
  int32_t x, y, height, width;
  x = coordinates_[0];
  y = coordinates_[1];
  height = size_[0];
  width = size_[1];
  std::shared_ptr<CropOp> tensor_op = std::make_shared<CropOp>(x, y, height, width);
  return tensor_op;
 }
 // CutMixBatchOperation
 CutMixBatchOperation::CutMixBatchOperation(ImageBatchFormat image_batch_format, float alpha, float prob)
    : image_batch_format_(image_batch_format), alpha_(alpha), prob_(prob) {}
 bool CutMixBatchOperation::ValidateParams() {
  if (alpha_ <= 0) {
    MS_LOG(ERROR) << "CutMixBatch: alpha must be a positive floating value however it is: " << alpha_;
    return false;
  }
  if (prob_ < 0 || prob_ > 1) {
    MS_LOG(ERROR) << "CutMixBatch: Probability has to be between 0 and 1.";
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> CutMixBatchOperation::Build() {
  std::shared_ptr<CutMixBatchOp> tensor_op = std::make_shared<CutMixBatchOp>(image_batch_format_, alpha_, prob_);
  return tensor_op;
 }
 // CutOutOperation
 CutOutOperation::CutOutOperation(int32_t length, int32_t num_patches) : length_(length), num_patches_(num_patches) {}
 bool CutOutOperation::ValidateParams() {
  if (length_ < 0) {
    MS_LOG(ERROR) << "CutOut: length cannot be negative";
    return false;
  }
  if (num_patches_ < 0) {
    MS_LOG(ERROR) << "CutOut: number of patches cannot be negative";
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> CutOutOperation::Build() {
  std::shared_ptr<CutOutOp> tensor_op = std::make_shared<CutOutOp>(length_, length_, num_patches_, false, 0, 0, 0);
  return tensor_op;
 }
 // DecodeOperation
 DecodeOperation::DecodeOperation(bool rgb) : rgb_(rgb) {}
 bool DecodeOperation::ValidateParams() { return true; }
 std::shared_ptr<TensorOp> DecodeOperation::Build() { return std::make_shared<DecodeOp>(rgb_); }
 // HwcToChwOperation
 bool HwcToChwOperation::ValidateParams() { return true; }
 std::shared_ptr<TensorOp> HwcToChwOperation::Build() { return std::make_shared<HwcToChwOp>(); }
 // MixUpOperation
 MixUpBatchOperation::MixUpBatchOperation(float alpha) : alpha_(alpha) {}
 bool MixUpBatchOperation::ValidateParams() {
  if (alpha_ <= 0) {
    MS_LOG(ERROR) << "MixUpBatch: alpha must be a positive floating value however it is: " << alpha_;
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> MixUpBatchOperation::Build() { return std::make_shared<MixUpBatchOp>(alpha_); }
 // NormalizeOperation
 NormalizeOperation::NormalizeOperation(std::vector<float> mean, std::vector<float> std) : mean_(mean), std_(std) {}
 bool NormalizeOperation::ValidateParams() {
  if (mean_.size() != 3) {
    MS_LOG(ERROR) << "Normalize: mean vector has incorrect size: " << mean_.size();
    return false;
  }
  // check mean value
  for (int i = 0; i < mean_.size(); ++i) {
    if (mean_[i] < 0.0f || mean_[i] > 255.0f || CmpFloat(mean_[i], 0.0f)) {
      MS_LOG(ERROR) << "Normalize: mean vector has incorrect value: " << mean_[i];
      return false;
    }
  }
  if (std_.size() != 3) {
    MS_LOG(ERROR) << "Normalize: std vector has incorrect size: " << std_.size();
    return false;
  }
  // check std value
  for (int i = 0; i < std_.size(); ++i) {
    if (std_[i] < 0.0f || std_[i] > 255.0f || CmpFloat(std_[i], 0.0f)) {
      MS_LOG(ERROR) << "Normalize: std vector has incorrect value: " << std_[i];
      return false;
    }
  }
  return true;
 }
 std::shared_ptr<TensorOp> NormalizeOperation::Build() {
  return std::make_shared<NormalizeOp>(mean_[0], mean_[1], mean_[2], std_[0], std_[1], std_[2]);
 }
 // PadOperation
 PadOperation::PadOperation(std::vector<int32_t> padding, std::vector<uint8_t> fill_value, BorderType padding_mode)
    : padding_(padding), fill_value_(fill_value), padding_mode_(padding_mode) {}
 bool PadOperation::ValidateParams() {
  if (padding_.empty() || padding_.size() == 3 || padding_.size() > 4) {
    MS_LOG(ERROR) << "Pad: padding vector has incorrect size: padding.size()";
    return false;
  }
  if (fill_value_.empty() || (fill_value_.size() != 1 && fill_value_.size() != 3)) {
    MS_LOG(ERROR) << "Pad: fill_value vector has incorrect size: fill_value.size()";
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> PadOperation::Build() {
  int32_t pad_top, pad_bottom, pad_left, pad_right;
  switch (padding_.size()) {
    case 1:
      pad_left = padding_[0];
      pad_top = padding_[0];
      pad_right = padding_[0];
      pad_bottom = padding_[0];
      break;
    case 2:
      pad_left = padding_[0];
      pad_top = padding_[1];
      pad_right = padding_[0];
      pad_bottom = padding_[1];
      break;
    default:
      pad_left = padding_[0];
      pad_top = padding_[1];
      pad_right = padding_[2];
      pad_bottom = padding_[3];
  }
  uint8_t fill_r, fill_g, fill_b;
  fill_r = fill_value_[0];
  fill_g = fill_value_[0];
  fill_b = fill_value_[0];
  if (fill_value_.size() == 3) {
    fill_r = fill_value_[0];
    fill_g = fill_value_[1];
    fill_b = fill_value_[2];
  }
  std::shared_ptr<PadOp> tensor_op =
    std::make_shared<PadOp>(pad_top, pad_bottom, pad_left, pad_right, padding_mode_, fill_r, fill_g, fill_b);
  return tensor_op;
 }
 // RandomColorOperation.
 RandomColorOperation::RandomColorOperation(float t_lb, float t_ub) : t_lb_(t_lb), t_ub_(t_ub) {}
 bool RandomColorOperation::ValidateParams() {
  // Do some input validation.
  if (t_lb_ > t_ub_) {
    MS_LOG(ERROR) << "RandomColor: lower bound must be less or equal to upper bound";
    return false;
  }
  return true;
 }
 // RandomColorAdjustOperation.
 RandomColorAdjustOperation::RandomColorAdjustOperation(std::vector<float> brightness, std::vector<float> contrast,
                                                       std::vector<float> saturation, std::vector<float> hue)
    : brightness_(brightness), contrast_(contrast), saturation_(saturation), hue_(hue) {}
 bool RandomColorAdjustOperation::ValidateParams() {
  // Do some input validation.
  if (brightness_.empty() || brightness_.size() > 2) {
    MS_LOG(ERROR) << "RandomColorAdjust: brightness must be a vector of one or two values";
    return false;
  }
  if (contrast_.empty() || contrast_.size() > 2) {
    MS_LOG(ERROR) << "RandomColorAdjust: contrast must be a vector of one or two values";
    return false;
  }
  if (saturation_.empty() || saturation_.size() > 2) {
    MS_LOG(ERROR) << "RandomColorAdjust: saturation must be a vector of one or two values";
    return false;
  }
  if (hue_.empty() || hue_.size() > 2) {
    MS_LOG(ERROR) << "RandomColorAdjust: hue must be a vector of one or two values";
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> RandomColorAdjustOperation::Build() {
  float brightness_lb, brightness_ub, contrast_lb, contrast_ub, saturation_lb, saturation_ub, hue_lb, hue_ub;
  brightness_lb = brightness_[0];
  brightness_ub = brightness_[0];
  if (brightness_.size() == 2) brightness_ub = brightness_[1];
  contrast_lb = contrast_[0];
  contrast_ub = contrast_[0];
  if (contrast_.size() == 2) contrast_ub = contrast_[1];
  saturation_lb = saturation_[0];
  saturation_ub = saturation_[0];
  if (saturation_.size() == 2) saturation_ub = saturation_[1];
  hue_lb = hue_[0];
  hue_ub = hue_[0];
  if (hue_.size() == 2) hue_ub = hue_[1];
  std::shared_ptr<RandomColorAdjustOp> tensor_op = std::make_shared<RandomColorAdjustOp>(
    brightness_lb, brightness_ub, contrast_lb, contrast_ub, saturation_lb, saturation_ub, hue_lb, hue_ub);
  return tensor_op;
 }
 // RandomAffineOperation
 RandomAffineOperation::RandomAffineOperation(const std::vector<float_t> &degrees,
                                             const std::vector<float_t> &translate_range,
                                             const std::vector<float_t> &scale_range,
                                             const std::vector<float_t> &shear_ranges, InterpolationMode interpolation,
                                             const std::vector<uint8_t> &fill_value)
    : degrees_(degrees),
      translate_range_(translate_range),
      scale_range_(scale_range),
      shear_ranges_(shear_ranges),
      interpolation_(interpolation),
      fill_value_(fill_value) {}
 bool RandomAffineOperation::ValidateParams() {
  // Degrees
  if (degrees_.size() != 2) {
    MS_LOG(ERROR) << "RandomAffine: degrees expecting size 2, got: degrees.size() = " << degrees_.size();
    return false;
  }
  if (degrees_[0] > degrees_[1]) {
    MS_LOG(ERROR) << "RandomAffine: minimum of degrees range is greater than maximum: min = " << degrees_[0]
                  << ", max = " << degrees_[1];
    return false;
  }
  // Translate
  if (translate_range_.size() != 2 && translate_range_.size() != 4) {
    MS_LOG(ERROR) << "RandomAffine: translate_range expecting size 2 or 4, got: translate_range.size() = "
                  << translate_range_.size();
    return false;
  }
  if (translate_range_[0] > translate_range_[1]) {
    MS_LOG(ERROR) << "RandomAffine: minimum of translate range on x is greater than maximum: min = "
                  << translate_range_[0] << ", max = " << translate_range_[1];
    return false;
  }
  if (translate_range_[0] < -1 || translate_range_[0] > 1) {
    MS_LOG(ERROR) << "RandomAffine: minimum of translate range on x is out of range of [-1, 1], value = "
                  << translate_range_[0];
    return false;
  }
  if (translate_range_[1] < -1 || translate_range_[1] > 1) {
    MS_LOG(ERROR) << "RandomAffine: maximum of translate range on x is out of range of [-1, 1], value = "
                  << translate_range_[1];
    return false;
  }
  if (translate_range_.size() == 4) {
    if (translate_range_[2] > translate_range_[3]) {
      MS_LOG(ERROR) << "RandomAffine: minimum of translate range on y is greater than maximum: min = "
                    << translate_range_[2] << ", max = " << translate_range_[3];
      return false;
    }
    if (translate_range_[2] < -1 || translate_range_[2] > 1) {
      MS_LOG(ERROR) << "RandomAffine: minimum of translate range on y is out of range of [-1, 1], value = "
                    << translate_range_[2];
      return false;
    }
    if (translate_range_[3] < -1 || translate_range_[3] > 1) {
      MS_LOG(ERROR) << "RandomAffine: maximum of translate range on y is out of range of [-1, 1], value = "
                    << translate_range_[3];
      return false;
    }
  }
  // Scale
  if (scale_range_.size() != 2) {
    MS_LOG(ERROR) << "RandomAffine: scale_range vector has incorrect size: scale_range.size() = "
                  << scale_range_.size();
    return false;
  }
  if (scale_range_[0] > scale_range_[1]) {
    MS_LOG(ERROR) << "RandomAffine: minimum of scale range is greater than maximum: min = " << scale_range_[0]
                  << ", max = " << scale_range_[1];
    return false;
  }
  // Shear
  if (shear_ranges_.size() != 2 && shear_ranges_.size() != 4) {
    MS_LOG(ERROR) << "RandomAffine: shear_ranges expecting size 2 or 4, got: shear_ranges.size() = "
                  << shear_ranges_.size();
    return false;
  }
  if (shear_ranges_[0] > shear_ranges_[1]) {
    MS_LOG(ERROR) << "RandomAffine: minimum of horizontal shear range is greater than maximum: min = "
                  << shear_ranges_[0] << ", max = " << shear_ranges_[1];
    return false;
  }
  if (shear_ranges_.size() == 4 && shear_ranges_[2] > shear_ranges_[3]) {
    MS_LOG(ERROR) << "RandomAffine: minimum of vertical shear range is greater than maximum: min = " << shear_ranges_[2]
                  << ", max = " << scale_range_[3];
    return false;
  }
  // Fill Value
  if (fill_value_.size() != 3) {
    MS_LOG(ERROR) << "RandomAffine: fill_value vector has incorrect size: fill_value.size() = " << fill_value_.size();
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> RandomAffineOperation::Build() {
  if (shear_ranges_.size() == 2) {
    shear_ranges_.resize(4);
  }
  if (translate_range_.size() == 2) {
    translate_range_.resize(4);
  }
  auto tensor_op = std::make_shared<RandomAffineOp>(degrees_, translate_range_, scale_range_, shear_ranges_,
                                                    interpolation_, fill_value_);
  return tensor_op;
 }
 // RandomCropOperation
 RandomCropOperation::RandomCropOperation(std::vector<int32_t> size, std::vector<int32_t> padding, bool pad_if_needed,
                                         std::vector<uint8_t> fill_value, BorderType padding_mode)
    : size_(size),
      padding_(padding),
      pad_if_needed_(pad_if_needed),
      fill_value_(fill_value),
      padding_mode_(padding_mode) {}
 bool RandomCropOperation::ValidateParams() {
  if (size_.empty() || size_.size() > 2) {
    MS_LOG(ERROR) << "RandomCrop: size vector has incorrect size: " << size_.size();
    return false;
  }
  if (padding_.empty() || padding_.size() != 4) {
    MS_LOG(ERROR) << "RandomCrop: padding vector has incorrect size: padding.size()";
    return false;
  }
  if (fill_value_.empty() || fill_value_.size() != 3) {
    MS_LOG(ERROR) << "RandomCrop: fill_value vector has incorrect size: fill_value.size()";
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> RandomCropOperation::Build() {
  int32_t crop_height = size_[0];
  int32_t crop_width = 0;
  int32_t pad_top = padding_[0];
  int32_t pad_bottom = padding_[1];
  int32_t pad_left = padding_[2];
  int32_t pad_right = padding_[3];
  uint8_t fill_r = fill_value_[0];
  uint8_t fill_g = fill_value_[1];
  uint8_t fill_b = fill_value_[2];
  // User has specified the crop_width value.
  if (size_.size() == 2) {
    crop_width = size_[1];
  }
  auto tensor_op = std::make_shared<RandomCropOp>(crop_height, crop_width, pad_top, pad_bottom, pad_left, pad_right,
                                                  padding_mode_, pad_if_needed_, fill_r, fill_g, fill_b);
  return tensor_op;
 }
 // RandomHorizontalFlipOperation
 RandomHorizontalFlipOperation::RandomHorizontalFlipOperation(float probability) : probability_(probability) {}
 bool RandomHorizontalFlipOperation::ValidateParams() { return true; }
 std::shared_ptr<TensorOp> RandomHorizontalFlipOperation::Build() {
  std::shared_ptr<RandomHorizontalFlipOp> tensor_op = std::make_shared<RandomHorizontalFlipOp>(probability_);
  return tensor_op;
 }
 // RandomPosterizeOperation
 RandomPosterizeOperation::RandomPosterizeOperation(const std::vector<uint8_t> &bit_range) : bit_range_(bit_range) {}
 bool RandomPosterizeOperation::ValidateParams() {
  if (bit_range_.size() != 2) {
    MS_LOG(ERROR) << "RandomPosterize: bit_range needs to be of size 2 but is of size: " << bit_range_.size();
    return false;
  }
  if (bit_range_[0] < 1 || bit_range_[0] > 8) {
    MS_LOG(ERROR) << "RandomPosterize: min_bit value is out of range [1-8]: " << bit_range_[0];
    return false;
  }
  if (bit_range_[1] < 1 || bit_range_[1] > 8) {
    MS_LOG(ERROR) << "RandomPosterize: max_bit value is out of range [1-8]: " << bit_range_[1];
    return false;
  }
  if (bit_range_[1] < bit_range_[0]) {
    MS_LOG(ERROR) << "RandomPosterize: max_bit value is less than min_bit: max =" << bit_range_[1]
                  << ", min = " << bit_range_[0];
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> RandomPosterizeOperation::Build() {
  std::shared_ptr<RandomPosterizeOp> tensor_op = std::make_shared<RandomPosterizeOp>(bit_range_);
  return tensor_op;
 }
 // Function to create RandomRotationOperation.
 RandomRotationOperation::RandomRotationOperation(std::vector<float> degrees, InterpolationMode interpolation_mode,
                                                 bool expand, std::vector<float> center,
                                                 std::vector<uint8_t> fill_value)
    : degrees_(degrees),
      interpolation_mode_(interpolation_mode),
      expand_(expand),
      center_(center),
      fill_value_(fill_value) {}
 bool RandomRotationOperation::ValidateParams() {
  if (degrees_.empty() || degrees_.size() != 2) {
    MS_LOG(ERROR) << "RandomRotation: degrees vector has incorrect size: degrees.size()";
    return false;
  }
  if (center_.empty() || center_.size() != 2) {
    MS_LOG(ERROR) << "RandomRotation: center vector has incorrect size: center.size()";
    return false;
  }
  if (fill_value_.empty() || fill_value_.size() != 3) {
    MS_LOG(ERROR) << "RandomRotation: fill_value vector has incorrect size: fill_value.size()";
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> RandomRotationOperation::Build() {
  std::shared_ptr<RandomRotationOp> tensor_op =
    std::make_shared<RandomRotationOp>(degrees_[0], degrees_[1], center_[0], center_[1], interpolation_mode_, expand_,
                                       fill_value_[0], fill_value_[1], fill_value_[2]);
  return tensor_op;
 }
 // Function to create RandomSharpness.
 RandomSharpnessOperation::RandomSharpnessOperation(std::vector<float> degrees) : degrees_(degrees) {}
 bool RandomSharpnessOperation::ValidateParams() {
  if (degrees_.empty() || degrees_.size() != 2) {
    MS_LOG(ERROR) << "RandomSharpness: degrees vector has incorrect size: degrees.size()";
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> RandomSharpnessOperation::Build() {
  std::shared_ptr<RandomSharpnessOp> tensor_op = std::make_shared<RandomSharpnessOp>(degrees_[0], degrees_[1]);
  return tensor_op;
 }
 // RandomSolarizeOperation.
 RandomSolarizeOperation::RandomSolarizeOperation(std::vector<uint8_t> threshold) : threshold_(threshold) {}
 bool RandomSolarizeOperation::ValidateParams() {
  if (threshold_.size() != 2) {
    MS_LOG(ERROR) << "RandomSolarize: threshold vector has incorrect size: " << threshold_.size();
    return false;
  }
  if (threshold_.at(0) > threshold_.at(1)) {
    MS_LOG(ERROR) << "RandomSolarize: threshold must be passed in a min, max format";
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> RandomSolarizeOperation::Build() {
  std::shared_ptr<RandomSolarizeOp> tensor_op = std::make_shared<RandomSolarizeOp>(threshold_);
  return tensor_op;
 }
 // RandomVerticalFlipOperation
 RandomVerticalFlipOperation::RandomVerticalFlipOperation(float probability) : probability_(probability) {}
 bool RandomVerticalFlipOperation::ValidateParams() { return true; }
 std::shared_ptr<TensorOp> RandomVerticalFlipOperation::Build() {
  std::shared_ptr<RandomVerticalFlipOp> tensor_op = std::make_shared<RandomVerticalFlipOp>(probability_);
  return tensor_op;
 }
 // ResizeOperation
 ResizeOperation::ResizeOperation(std::vector<int32_t> size, InterpolationMode interpolation)
    : size_(size), interpolation_(interpolation) {}
 bool ResizeOperation::ValidateParams() {
  if (size_.empty() || size_.size() > 2) {
    MS_LOG(ERROR) << "Resize: size vector has incorrect size: " << size_.size();
    return false;
  }
  if (!CheckVectorPositive(size_)) {
    return false;
  }
  return true;
 }
 std::shared_ptr<TensorOp> ResizeOperation::Build() {
  int32_t height = size_[0];
  int32_t width = 0;
  // User specified the width value.
  if (size_.size() == 2) {
    width = size_[1];
  }
  return std::make_shared<ResizeOp>(height, width, interpolation_);
 }
 // RgbaToBgrOperation.
 RgbaToBgrOperation::RgbaToBgrOperation() {}
 bool RgbaToBgrOperation::ValidateParams() { return true; }
 std::shared_ptr<TensorOp> RgbaToBgrOperation::Build() {
  std::shared_ptr<RgbaToBgrOp> tensor_op = std::make_shared<RgbaToBgrOp>();
  return tensor_op;
 }
 // RgbaToRgbOperation.
 RgbaToRgbOperation::RgbaToRgbOperation() {}
 bool RgbaToRgbOperation::ValidateParams() { return true; }
 std::shared_ptr<TensorOp> RgbaToRgbOperation::Build() {
  std::shared_ptr<RgbaToRgbOp> tensor_op = std::make_shared<RgbaToRgbOp>();
  return tensor_op;
 }
 // SwapRedBlueOperation.
 SwapRedBlueOperation::SwapRedBlueOperation() {}
 bool SwapRedBlueOperation::ValidateParams() { return true; }
 std::shared_ptr<TensorOp> SwapRedBlueOperation::Build() {
  std::shared_ptr<SwapRedBlueOp> tensor_op = std::make_shared<SwapRedBlueOp>();
  return tensor_op;
 }
 // UniformAugOperation
 UniformAugOperation::UniformAugOperation(std::vector<std::shared_ptr<TensorOperation>> transforms, int32_t num_ops)
    : transforms_(transforms), num_ops_(num_ops) {}
 bool UniformAugOperation::ValidateParams() { return true; }
 std::shared_ptr<TensorOp> UniformAugOperation::Build() {
  std::vector<std::shared_ptr<TensorOp>> tensor_ops;
  (void)std::transform(transforms_.begin(), transforms_.end(), std::back_inserter(tensor_ops),
                       [](std::shared_ptr<TensorOperation> op) -> std::shared_ptr<TensorOp> { return op->Build(); });
  std::shared_ptr<UniformAugOp> tensor_op = std::make_shared<UniformAugOp>(tensor_ops, num_ops_);
  return tensor_op;
 }
 }  // namespace vision
 }  // namespace api
 }  // namespace dataset
 }  // namespace mindspore
--- a/mindspore/ccsrc/minddata/dataset/include/text.h
+++ b/mindspore/ccsrc/minddata/dataset/include/text.h
@@ -14,8 +14,8 @@
 * limitations under the License.
 */
 #ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_API_TEXT_H_
 #define MINDSPORE_CCSRC_MINDDATA_DATASET_API_TEXT_H_
 #ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_TEXT_H_
 #define MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_TEXT_H_
 #include <vector>
 #include <memory>
@@ -68,4 +68,4 @@ class LookupOperation : public TensorOperation {
 }  // namespace api
 }  // namespace dataset
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_API_TEXT_H_
 #endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_TEXT_H_
--- a/mindspore/ccsrc/minddata/dataset/include/transforms.h
+++ b/mindspore/ccsrc/minddata/dataset/include/transforms.h
@@ -14,8 +14,8 @@
 * limitations under the License.
 */
 #ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_API_TRANSFORMS_H_
 #define MINDSPORE_CCSRC_MINDDATA_DATASET_API_TRANSFORMS_H_
 #ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_TRANSFORMS_H_
 #define MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_TRANSFORMS_H_
 #include <vector>
 #include <memory>
@@ -43,92 +43,11 @@ class TensorOperation : public std::enable_shared_from_this<TensorOperation> {
  virtual bool ValidateParams() = 0;
 };
 // Transform operations for performing computer vision.
 namespace vision {
 // Transform operations for performing data transformation.
 namespace transforms {
 // Transform Op classes (in alphabetical order)
 class CenterCropOperation;
 class CropOperation;
 class CutMixBatchOperation;
 class CutOutOperation;
 class DecodeOperation;
 class HwcToChwOperation;
 class MixUpBatchOperation;
 class NormalizeOperation;
 class OneHotOperation;
 class PadOperation;
 class RandomAffineOperation;
 class RandomColorOperation;
 class RandomColorAdjustOperation;
 class RandomCropOperation;
 class RandomHorizontalFlipOperation;
 class RandomPosterizeOperation;
 class RandomRotationOperation;
 class RandomSharpnessOperation;
 class RandomSolarizeOperation;
 class RandomVerticalFlipOperation;
 class ResizeOperation;
 class RgbaToBgrOperation;
 class RgbaToRgbOperation;
 class SwapRedBlueOperation;
 class UniformAugOperation;
 /// \brief Function to create a CenterCrop TensorOperation.
 /// \notes Crops the input image at the center to the given size.
 /// \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).
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<CenterCropOperation> CenterCrop(std::vector<int32_t> size);
 /// \brief Function to create a Crop TensorOp
 /// \notes Crop an image based on location and crop size
 /// \param[in] coordinates Starting location of crop. Must be a vector of two values, in the form of {x_coor, y_coor}
 /// \param[in] size Size of the cropped area. Must be a vector of two values, in the form of {height, width}
 /// \return Shared pointer to the current TensorOp
 std::shared_ptr<CropOperation> Crop(std::vector<int32_t> coordinates, std::vector<int32_t> size);
 /// \brief Function to apply CutMix on a batch of images
 /// \notes Masks a random section of each image with the corresponding part of another randomly selected image in
 ///     that batch
 /// \param[in] image_batch_format The format of the batch
 /// \param[in] alpha The hyperparameter of beta distribution (default = 1.0)
 /// \param[in] prob The probability by which CutMix is applied to each image (default = 1.0)
 /// \return Shared pointer to the current TensorOp
 std::shared_ptr<CutMixBatchOperation> CutMixBatch(ImageBatchFormat image_batch_format, float alpha = 1.0,
                                                  float prob = 1.0);
 /// \brief Function to create a CutOut TensorOp
 /// \notes Randomly cut (mask) out a given number of square patches from the input image
 /// \param[in] length Integer representing the side length of each square patch
 /// \param[in] num_patches Integer representing the number of patches to be cut out of an image
 /// \return Shared pointer to the current TensorOp
 std::shared_ptr<CutOutOperation> CutOut(int32_t length, int32_t num_patches = 1);
 /// \brief Function to create a Decode TensorOperation.
 /// \notes Decode the input image in RGB mode.
 /// \param[in] rgb - a boolean of whether to decode in RGB mode or not.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<DecodeOperation> Decode(bool rgb = true);
 /// \brief Function to create a HwcToChw TensorOperation.
 /// \notes Transpose the input image; shape (H, W, C) to shape (C, H, W).
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<HwcToChwOperation> HWC2CHW();
 /// \brief Function to create a MixUpBatch TensorOperation.
 /// \notes Apply MixUp transformation on an input batch of images and labels. The labels must be in one-hot format and
 ///     Batch must be called before calling this function.
 /// \param[in] alpha hyperparameter of beta distribution (default = 1.0)
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<MixUpBatchOperation> MixUpBatch(float alpha = 1);
 /// \brief Function to create a Normalize TensorOperation.
 /// \notes Normalize the input image with respect to mean and standard deviation.
 /// \param[in] mean A vector of mean values for each channel, w.r.t channel order.
 /// \param[in] std A vector of standard deviations for each channel, w.r.t. channel order.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<NormalizeOperation> Normalize(std::vector<float> mean, std::vector<float> std);
 /// \brief Function to create a OneHot TensorOperation.
 /// \notes Convert the labels into OneHot format.
@@ -136,281 +55,8 @@ std::shared_ptr<NormalizeOperation> Normalize(std::vector<float> mean, std::vect
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<OneHotOperation> OneHot(int32_t num_classes);
 /// \brief Function to create a Pad TensorOp
 /// \notes Pads the image according to padding parameters
 /// \param[in] padding A vector representing the number of pixels to pad the image
 ///    If vector has one value, it pads all sides of the image with that value
 ///    If vector has two values, it pads left and right with the first and
 ///    top and bottom with the second value
 ///    If vector has four values, it pads left, top, right, and bottom with
 ///    those values respectively
 /// \param[in] fill_value A vector representing the pixel intensity of the borders if the padding_mode is
 ///    BorderType.kConstant. If 3 values are provided,
 ///    it is used to fill R, G, B channels respectively
 /// \param[in] padding_mode The method of padding (default=BorderType.kConstant)
 ///    Can be any of
 ///    [BorderType.kConstant, BorderType.kEdge, BorderType.kReflect, BorderType.kSymmetric]
 ///    - BorderType.kConstant, means it fills the border with constant values
 ///    - BorderType.kEdge, means it pads with the last value on the edge
 ///    - BorderType.kReflect, means it reflects the values on the edge omitting the last value of edge
 ///    - BorderType.kSymmetric, means it reflects the values on the edge repeating the last value of edge
 /// \return Shared pointer to the current TensorOp
 std::shared_ptr<PadOperation> Pad(std::vector<int32_t> padding, std::vector<uint8_t> fill_value = {0},
                                  BorderType padding_mode = BorderType::kConstant);
 /// \brief Function to create a RandomAffine TensorOperation.
 /// \notes Applies a Random Affine transformation on input image in RGB or Greyscale mode.
 /// \param[in] degrees A float vector size 2, representing the starting and ending degree
 /// \param[in] translate_range A float vector size 2 or 4, representing percentages of translation on x and y axes.
 ///    if size is 2, (min_dx, max_dx, 0, 0)
 ///    if size is 4, (min_dx, max_dx, min_dy, max_dy)
 ///    all values are in range [-1, 1]
 /// \param[in] scale_range A float vector size 2, representing the starting and ending scales in the range.
 /// \param[in] shear_ranges A float vector size 2 or 4, representing the starting and ending shear degrees vertically
 ///    and horizontally.
 ///    if size is 2, (min_shear_x, max_shear_x, 0, 0)
 ///    if size is 4, (min_shear_x, max_shear_x, min_shear_y, max_shear_y)
 /// \param[in] interpolation An enum for the mode of interpolation
 /// \param[in] fill_value A uint8_t vector size 3, representing the pixel intensity of the borders, it is used to
 ///    fill R, G, B channels respectively.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RandomAffineOperation> RandomAffine(
  const std::vector<float_t> &degrees, const std::vector<float_t> &translate_range = {0.0, 0.0, 0.0, 0.0},
  const std::vector<float_t> &scale_range = {1.0, 1.0}, const std::vector<float_t> &shear_ranges = {0.0, 0.0, 0.0, 0.0},
  InterpolationMode interpolation = InterpolationMode::kNearestNeighbour,
  const std::vector<uint8_t> &fill_value = {0, 0, 0});
 /// \brief Blends 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 the bound of the interval
 /// \param[in] t_lb lower bound on the range of random weights
 /// \param[in] t_lb upper bound on the range of random weights
 /// \return Shared pointer to the current TensorOp
 std::shared_ptr<RandomColorOperation> RandomColor(float t_lb, float t_ub);
 /// \brief Randomly adjust the brightness, contrast, saturation, and hue of the input image
 /// \param[in] brightness Brightness adjustment factor. Must be a vector of one or two values
 ///     if it's a vector of two values it needs to be in the form of [min, max]. Default value is {1, 1}
 /// \param[in] contrast Contrast adjustment factor. Must be a vector of one or two values
 ///     if it's a vector of two values it needs to be in the form of [min, max]. Default value is {1, 1}
 /// \param[in] saturation Saturation adjustment factor. Must be a vector of one or two values
 ///     if it's a vector of two values it needs to be in the form of [min, max]. Default value is {1, 1}
 /// \param[in] hue Brightness adjustment factor. Must be a vector of one or two values
 ///     if it's a vector of two values it must be in the form of [min, max] where -0.5 <= min <= max <= 0.5
 ///     Default value is {0, 0}
 /// \return Shared pointer to the current TensorOp
 std::shared_ptr<RandomColorAdjustOperation> RandomColorAdjust(std::vector<float> brightness = {1.0, 1.0},
                                                              std::vector<float> contrast = {1.0, 1.0},
                                                              std::vector<float> saturation = {1.0, 1.0},
                                                              std::vector<float> hue = {0.0, 0.0});
 /// \brief Function to create a RandomCrop TensorOperation.
 /// \notes Crop the input image at a random location.
 /// \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] padding - a vector with the value of pixels to pad the image. If 4 values are provided,
 ///                  it pads the left, top, right and bottom respectively.
 /// \param[in] pad_if_needed - a boolean whether to pad the image if either side is smaller than
 ///                        the given output size.
 /// \param[in] fill_value - a vector representing the pixel intensity of the borders, it is used to
 ///                     fill R, G, B channels respectively.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RandomCropOperation> RandomCrop(std::vector<int32_t> size, std::vector<int32_t> padding = {0, 0, 0, 0},
                                                bool pad_if_needed = false, std::vector<uint8_t> fill_value = {0, 0, 0},
                                                BorderType padding_mode = BorderType::kConstant);
 /// \brief Function to create a RandomHorizontalFlip TensorOperation.
 /// \notes Tensor operation to perform random horizontal flip.
 /// \param[in] prob - float representing the probability of flip.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RandomHorizontalFlipOperation> RandomHorizontalFlip(float prob = 0.5);
 /// \brief Function to create a RandomPosterize TensorOperation.
 /// \notes Tensor operation to perform random posterize.
 /// \param[in] bit_range - uint8_t vector representing the minimum and maximum bit in range. (Default={4, 8})
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RandomPosterizeOperation> RandomPosterize(const std::vector<uint8_t> &bit_range = {4, 8});
 /// \brief Function to create a RandomRotation TensorOp
 /// \notes Rotates the image according to parameters
 /// \param[in] degrees A float vector size 2, representing the starting and ending degree
 /// \param[in] resample An enum for the mode of interpolation
 /// \param[in] expand A boolean representing whether the image is expanded after rotation
 /// \param[in] center A float vector size 2, representing the x and y center of rotation.
 /// \param[in] fill_value A uint8_t vector size 3, representing the rgb value of the fill color
 /// \return Shared pointer to the current TensorOp
 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 RandomSharpness TensorOperation.
 /// \notes Tensor operation to perform random sharpness.
 /// \param[in] start_degree - float representing the start of the range to uniformly sample the factor from it.
 /// \param[in] end_degree - float representing the end of the range.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RandomSharpnessOperation> RandomSharpness(std::vector<float> degrees = {0.1, 1.9});
 /// \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 - a vector with two elements specifying the pixel range to invert.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RandomSolarizeOperation> RandomSolarize(std::vector<uint8_t> threshold = {0, 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.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RandomVerticalFlipOperation> RandomVerticalFlip(float prob = 0.5);
 /// \brief Function to create a RgbaToBgr TensorOperation.
 /// \notes Changes the input 4 channel RGBA tensor to 3 channel BGR.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RgbaToBgrOperation> RGBA2BGR();
 /// \brief Function to create a RgbaToRgb TensorOperation.
 /// \notes Changes the input 4 channel RGBA tensor to 3 channel RGB.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RgbaToRgbOperation> RGBA2RGB();
 /// \brief Function to create a Resize TensorOperation.
 /// \notes Resize the input image to the given size.
 /// \param[in] size - a vector representing the output size of the resized image.
 ///               If size is a single value, the image will be resized to this value with
 ///               the same image aspect ratio. If size has 2 values, it should be (height, width).
 /// \param[in] interpolation An enum for the mode of interpolation
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<ResizeOperation> Resize(std::vector<int32_t> size,
                                        InterpolationMode interpolation = InterpolationMode::kLinear);
 /// \brief Function to create a SwapRedBlue TensorOp
 /// \notes Swaps the red and blue channels in image
 /// \return Shared pointer to the current TensorOp
 std::shared_ptr<SwapRedBlueOperation> SwapRedBlue();
 /// \brief Function to create a UniformAugment TensorOperation.
 /// \notes Tensor operation to perform randomly selected augmentation.
 /// \param[in] transforms - a vector of TensorOperation transforms.
 /// \param[in] num_ops - integer representing the number of OPs to be selected and applied.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<UniformAugOperation> UniformAugment(std::vector<std::shared_ptr<TensorOperation>> transforms,
                                                    int32_t num_ops = 2);
 /* ####################################### Derived TensorOperation classes ################################# */
 class CenterCropOperation : public TensorOperation {
 public:
  explicit CenterCropOperation(std::vector<int32_t> size);
  ~CenterCropOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<int32_t> size_;
 };
 class CropOperation : public TensorOperation {
 public:
  CropOperation(std::vector<int32_t> coordinates, std::vector<int32_t> size);
  ~CropOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<int32_t> coordinates_;
  std::vector<int32_t> size_;
 };
 class CutMixBatchOperation : public TensorOperation {
 public:
  explicit CutMixBatchOperation(ImageBatchFormat image_batch_format, float alpha = 1.0, float prob = 1.0);
  ~CutMixBatchOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  float alpha_;
  float prob_;
  ImageBatchFormat image_batch_format_;
 };
 class CutOutOperation : public TensorOperation {
 public:
  explicit CutOutOperation(int32_t length, int32_t num_patches = 1);
  ~CutOutOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  int32_t length_;
  int32_t num_patches_;
  ImageBatchFormat image_batch_format_;
 };
 class DecodeOperation : public TensorOperation {
 public:
  explicit DecodeOperation(bool rgb = true);
  ~DecodeOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  bool rgb_;
 };
 class HwcToChwOperation : public TensorOperation {
 public:
  ~HwcToChwOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 };
 class MixUpBatchOperation : public TensorOperation {
 public:
  explicit MixUpBatchOperation(float alpha = 1);
  ~MixUpBatchOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  float alpha_;
 };
 class NormalizeOperation : public TensorOperation {
 public:
  NormalizeOperation(std::vector<float> mean, std::vector<float> std);
  ~NormalizeOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<float> mean_;
  std::vector<float> std_;
 };
 class OneHotOperation : public TensorOperation {
 public:
  explicit OneHotOperation(int32_t num_classes_);
@@ -424,254 +70,8 @@ class OneHotOperation : public TensorOperation {
 private:
  float num_classes_;
 };
 class PadOperation : public TensorOperation {
 public:
  PadOperation(std::vector<int32_t> padding, std::vector<uint8_t> fill_value = {0},
               BorderType padding_mode = BorderType::kConstant);
  ~PadOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<int32_t> padding_;
  std::vector<uint8_t> fill_value_;
  BorderType padding_mode_;
 };
 class RandomAffineOperation : public TensorOperation {
 public:
  RandomAffineOperation(const std::vector<float_t> &degrees, const std::vector<float_t> &translate_range = {0.0, 0.0},
                        const std::vector<float_t> &scale_range = {1.0, 1.0},
                        const std::vector<float_t> &shear_ranges = {0.0, 0.0, 0.0, 0.0},
                        InterpolationMode interpolation = InterpolationMode::kNearestNeighbour,
                        const std::vector<uint8_t> &fill_value = {0, 0, 0});
  ~RandomAffineOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<float_t> degrees_;          // min_degree, max_degree
  std::vector<float_t> translate_range_;  // maximum x translation percentage, maximum y translation percentage
  std::vector<float_t> scale_range_;      // min_scale, max_scale
  std::vector<float_t> shear_ranges_;     // min_x_shear, max_x_shear, min_y_shear, max_y_shear
  InterpolationMode interpolation_;
  std::vector<uint8_t> fill_value_;
 };
 class RandomColorOperation : public TensorOperation {
 public:
  RandomColorOperation(float t_lb, float t_ub);
  ~RandomColorOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  float t_lb_;
  float t_ub_;
 };
 class RandomColorAdjustOperation : public TensorOperation {
 public:
  RandomColorAdjustOperation(std::vector<float> brightness = {1.0, 1.0}, std::vector<float> contrast = {1.0, 1.0},
                             std::vector<float> saturation = {1.0, 1.0}, std::vector<float> hue = {0.0, 0.0});
  ~RandomColorAdjustOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<float> brightness_;
  std::vector<float> contrast_;
  std::vector<float> saturation_;
  std::vector<float> hue_;
 };
 class RandomCropOperation : public TensorOperation {
 public:
  RandomCropOperation(std::vector<int32_t> size, std::vector<int32_t> padding = {0, 0, 0, 0},
                      bool pad_if_needed = false, std::vector<uint8_t> fill_value = {0, 0, 0},
                      BorderType padding_mode = BorderType::kConstant);
  ~RandomCropOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<int32_t> size_;
  std::vector<int32_t> padding_;
  bool pad_if_needed_;
  std::vector<uint8_t> fill_value_;
  BorderType padding_mode_;
 };
 class RandomHorizontalFlipOperation : public TensorOperation {
 public:
  explicit RandomHorizontalFlipOperation(float probability = 0.5);
  ~RandomHorizontalFlipOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  float probability_;
 };
 class RandomPosterizeOperation : public TensorOperation {
 public:
  explicit RandomPosterizeOperation(const std::vector<uint8_t> &bit_range = {4, 8});
  ~RandomPosterizeOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<uint8_t> bit_range_;
 };
 class RandomRotationOperation : public TensorOperation {
 public:
  RandomRotationOperation(std::vector<float> degrees, InterpolationMode interpolation_mode, bool expand,
                          std::vector<float> center, std::vector<uint8_t> fill_value);
  ~RandomRotationOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<float> degrees_;
  InterpolationMode interpolation_mode_;
  std::vector<float> center_;
  bool expand_;
  std::vector<uint8_t> fill_value_;
 };
 class RandomSharpnessOperation : public TensorOperation {
 public:
  explicit RandomSharpnessOperation(std::vector<float> degrees = {0.1, 1.9});
  ~RandomSharpnessOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<float> degrees_;
 };
 class RandomVerticalFlipOperation : public TensorOperation {
 public:
  explicit RandomVerticalFlipOperation(float probability = 0.5);
  ~RandomVerticalFlipOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  float probability_;
 };
 class ResizeOperation : public TensorOperation {
 public:
  explicit ResizeOperation(std::vector<int32_t> size,
                           InterpolationMode interpolation_mode = InterpolationMode::kLinear);
  ~ResizeOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<int32_t> size_;
  InterpolationMode interpolation_;
 };
 class RgbaToBgrOperation : public TensorOperation {
 public:
  RgbaToBgrOperation();
  ~RgbaToBgrOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 };
 class RgbaToRgbOperation : public TensorOperation {
 public:
  RgbaToRgbOperation();
  ~RgbaToRgbOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 };
 class UniformAugOperation : public TensorOperation {
 public:
  explicit UniformAugOperation(std::vector<std::shared_ptr<TensorOperation>> transforms, int32_t num_ops = 2);
  ~UniformAugOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<std::shared_ptr<TensorOperation>> transforms_;
  int32_t num_ops_;
 };
 class SwapRedBlueOperation : public TensorOperation {
 public:
  SwapRedBlueOperation();
  ~SwapRedBlueOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 };
 class RandomSolarizeOperation : public TensorOperation {
 public:
  explicit RandomSolarizeOperation(std::vector<uint8_t> threshold);
  ~RandomSolarizeOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<uint8_t> threshold_;
 };
 }  // namespace vision
 }  // namespace transforms
 }  // namespace api
 }  // namespace dataset
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_API_TRANSFORMS_H_
 #endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_TRANSFORMS_H_
--- a/mindspore/ccsrc/minddata/dataset/include/vision.h
+++ b/mindspore/ccsrc/minddata/dataset/include/vision.h
@@ -0,0 +1,639 @@
 /**
 * 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_INCLUDE_VISION_H_
 #define MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_VISION_H_
 #include <vector>
 #include <memory>
 #include "minddata/dataset/core/constants.h"
 #include "minddata/dataset/include/transforms.h"
 namespace mindspore {
 namespace dataset {
 namespace api {
 // Transform operations for performing computer vision.
 namespace vision {
 // Transform Op classes (in alphabetical order)
 class CenterCropOperation;
 class CropOperation;
 class CutMixBatchOperation;
 class CutOutOperation;
 class DecodeOperation;
 class HwcToChwOperation;
 class MixUpBatchOperation;
 class NormalizeOperation;
 class PadOperation;
 class RandomAffineOperation;
 class RandomColorOperation;
 class RandomColorAdjustOperation;
 class RandomCropOperation;
 class RandomHorizontalFlipOperation;
 class RandomPosterizeOperation;
 class RandomRotationOperation;
 class RandomSharpnessOperation;
 class RandomSolarizeOperation;
 class RandomVerticalFlipOperation;
 class ResizeOperation;
 class RgbaToBgrOperation;
 class RgbaToRgbOperation;
 class SwapRedBlueOperation;
 class UniformAugOperation;
 /// \brief Function to create a CenterCrop TensorOperation.
 /// \notes Crops the input image at the center to the given size.
 /// \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).
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<CenterCropOperation> CenterCrop(std::vector<int32_t> size);
 /// \brief Function to create a Crop TensorOp
 /// \notes Crop an image based on location and crop size
 /// \param[in] coordinates Starting location of crop. Must be a vector of two values, in the form of {x_coor, y_coor}
 /// \param[in] size Size of the cropped area. Must be a vector of two values, in the form of {height, width}
 /// \return Shared pointer to the current TensorOp
 std::shared_ptr<CropOperation> Crop(std::vector<int32_t> coordinates, std::vector<int32_t> size);
 /// \brief Function to apply CutMix on a batch of images
 /// \notes Masks a random section of each image with the corresponding part of another randomly selected image in
 ///     that batch
 /// \param[in] image_batch_format The format of the batch
 /// \param[in] alpha The hyperparameter of beta distribution (default = 1.0)
 /// \param[in] prob The probability by which CutMix is applied to each image (default = 1.0)
 /// \return Shared pointer to the current TensorOp
 std::shared_ptr<CutMixBatchOperation> CutMixBatch(ImageBatchFormat image_batch_format, float alpha = 1.0,
                                                  float prob = 1.0);
 /// \brief Function to create a CutOut TensorOp
 /// \notes Randomly cut (mask) out a given number of square patches from the input image
 /// \param[in] length Integer representing the side length of each square patch
 /// \param[in] num_patches Integer representing the number of patches to be cut out of an image
 /// \return Shared pointer to the current TensorOp
 std::shared_ptr<CutOutOperation> CutOut(int32_t length, int32_t num_patches = 1);
 /// \brief Function to create a Decode TensorOperation.
 /// \notes Decode the input image in RGB mode.
 /// \param[in] rgb - a boolean of whether to decode in RGB mode or not.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<DecodeOperation> Decode(bool rgb = true);
 /// \brief Function to create a HwcToChw TensorOperation.
 /// \notes Transpose the input image; shape (H, W, C) to shape (C, H, W).
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<HwcToChwOperation> HWC2CHW();
 /// \brief Function to create a MixUpBatch TensorOperation.
 /// \notes Apply MixUp transformation on an input batch of images and labels. The labels must be in one-hot format and
 ///     Batch must be called before calling this function.
 /// \param[in] alpha hyperparameter of beta distribution (default = 1.0)
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<MixUpBatchOperation> MixUpBatch(float alpha = 1);
 /// \brief Function to create a Normalize TensorOperation.
 /// \notes Normalize the input image with respect to mean and standard deviation.
 /// \param[in] mean A vector of mean values for each channel, w.r.t channel order.
 /// \param[in] std A vector of standard deviations for each channel, w.r.t. channel order.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<NormalizeOperation> Normalize(std::vector<float> mean, std::vector<float> std);
 /// \brief Function to create a Pad TensorOp
 /// \notes Pads the image according to padding parameters
 /// \param[in] padding A vector representing the number of pixels to pad the image
 ///    If vector has one value, it pads all sides of the image with that value
 ///    If vector has two values, it pads left and right with the first and
 ///    top and bottom with the second value
 ///    If vector has four values, it pads left, top, right, and bottom with
 ///    those values respectively
 /// \param[in] fill_value A vector representing the pixel intensity of the borders if the padding_mode is
 ///    BorderType.kConstant. If 3 values are provided,
 ///    it is used to fill R, G, B channels respectively
 /// \param[in] padding_mode The method of padding (default=BorderType.kConstant)
 ///    Can be any of
 ///    [BorderType.kConstant, BorderType.kEdge, BorderType.kReflect, BorderType.kSymmetric]
 ///    - BorderType.kConstant, means it fills the border with constant values
 ///    - BorderType.kEdge, means it pads with the last value on the edge
 ///    - BorderType.kReflect, means it reflects the values on the edge omitting the last value of edge
 ///    - BorderType.kSymmetric, means it reflects the values on the edge repeating the last value of edge
 /// \return Shared pointer to the current TensorOp
 std::shared_ptr<PadOperation> Pad(std::vector<int32_t> padding, std::vector<uint8_t> fill_value = {0},
                                  BorderType padding_mode = BorderType::kConstant);
 /// \brief Function to create a RandomAffine TensorOperation.
 /// \notes Applies a Random Affine transformation on input image in RGB or Greyscale mode.
 /// \param[in] degrees A float vector size 2, representing the starting and ending degree
 /// \param[in] translate_range A float vector size 2 or 4, representing percentages of translation on x and y axes.
 ///    if size is 2, (min_dx, max_dx, 0, 0)
 ///    if size is 4, (min_dx, max_dx, min_dy, max_dy)
 ///    all values are in range [-1, 1]
 /// \param[in] scale_range A float vector size 2, representing the starting and ending scales in the range.
 /// \param[in] shear_ranges A float vector size 2 or 4, representing the starting and ending shear degrees vertically
 ///    and horizontally.
 ///    if size is 2, (min_shear_x, max_shear_x, 0, 0)
 ///    if size is 4, (min_shear_x, max_shear_x, min_shear_y, max_shear_y)
 /// \param[in] interpolation An enum for the mode of interpolation
 /// \param[in] fill_value A uint8_t vector size 3, representing the pixel intensity of the borders, it is used to
 ///    fill R, G, B channels respectively.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RandomAffineOperation> RandomAffine(
  const std::vector<float_t> &degrees, const std::vector<float_t> &translate_range = {0.0, 0.0, 0.0, 0.0},
  const std::vector<float_t> &scale_range = {1.0, 1.0}, const std::vector<float_t> &shear_ranges = {0.0, 0.0, 0.0, 0.0},
  InterpolationMode interpolation = InterpolationMode::kNearestNeighbour,
  const std::vector<uint8_t> &fill_value = {0, 0, 0});
 /// \brief Blends 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 the bound of the interval
 /// \param[in] t_lb lower bound on the range of random weights
 /// \param[in] t_lb upper bound on the range of random weights
 /// \return Shared pointer to the current TensorOp
 std::shared_ptr<RandomColorOperation> RandomColor(float t_lb, float t_ub);
 /// \brief Randomly adjust the brightness, contrast, saturation, and hue of the input image
 /// \param[in] brightness Brightness adjustment factor. Must be a vector of one or two values
 ///     if it's a vector of two values it needs to be in the form of [min, max]. Default value is {1, 1}
 /// \param[in] contrast Contrast adjustment factor. Must be a vector of one or two values
 ///     if it's a vector of two values it needs to be in the form of [min, max]. Default value is {1, 1}
 /// \param[in] saturation Saturation adjustment factor. Must be a vector of one or two values
 ///     if it's a vector of two values it needs to be in the form of [min, max]. Default value is {1, 1}
 /// \param[in] hue Brightness adjustment factor. Must be a vector of one or two values
 ///     if it's a vector of two values it must be in the form of [min, max] where -0.5 <= min <= max <= 0.5
 ///     Default value is {0, 0}
 /// \return Shared pointer to the current TensorOp
 std::shared_ptr<RandomColorAdjustOperation> RandomColorAdjust(std::vector<float> brightness = {1.0, 1.0},
                                                              std::vector<float> contrast = {1.0, 1.0},
                                                              std::vector<float> saturation = {1.0, 1.0},
                                                              std::vector<float> hue = {0.0, 0.0});
 /// \brief Function to create a RandomCrop TensorOperation.
 /// \notes Crop the input image at a random location.
 /// \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] padding - a vector with the value of pixels to pad the image. If 4 values are provided,
 ///                  it pads the left, top, right and bottom respectively.
 /// \param[in] pad_if_needed - a boolean whether to pad the image if either side is smaller than
 ///                        the given output size.
 /// \param[in] fill_value - a vector representing the pixel intensity of the borders, it is used to
 ///                     fill R, G, B channels respectively.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RandomCropOperation> RandomCrop(std::vector<int32_t> size, std::vector<int32_t> padding = {0, 0, 0, 0},
                                                bool pad_if_needed = false, std::vector<uint8_t> fill_value = {0, 0, 0},
                                                BorderType padding_mode = BorderType::kConstant);
 /// \brief Function to create a RandomHorizontalFlip TensorOperation.
 /// \notes Tensor operation to perform random horizontal flip.
 /// \param[in] prob - float representing the probability of flip.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RandomHorizontalFlipOperation> RandomHorizontalFlip(float prob = 0.5);
 /// \brief Function to create a RandomPosterize TensorOperation.
 /// \notes Tensor operation to perform random posterize.
 /// \param[in] bit_range - uint8_t vector representing the minimum and maximum bit in range. (Default={4, 8})
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RandomPosterizeOperation> RandomPosterize(const std::vector<uint8_t> &bit_range = {4, 8});
 /// \brief Function to create a RandomRotation TensorOp
 /// \notes Rotates the image according to parameters
 /// \param[in] degrees A float vector size 2, representing the starting and ending degree
 /// \param[in] resample An enum for the mode of interpolation
 /// \param[in] expand A boolean representing whether the image is expanded after rotation
 /// \param[in] center A float vector size 2, representing the x and y center of rotation.
 /// \param[in] fill_value A uint8_t vector size 3, representing the rgb value of the fill color
 /// \return Shared pointer to the current TensorOp
 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 RandomSharpness TensorOperation.
 /// \notes Tensor operation to perform random sharpness.
 /// \param[in] start_degree - float representing the start of the range to uniformly sample the factor from it.
 /// \param[in] end_degree - float representing the end of the range.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RandomSharpnessOperation> RandomSharpness(std::vector<float> degrees = {0.1, 1.9});
 /// \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 - a vector with two elements specifying the pixel range to invert.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RandomSolarizeOperation> RandomSolarize(std::vector<uint8_t> threshold = {0, 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.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RandomVerticalFlipOperation> RandomVerticalFlip(float prob = 0.5);
 /// \brief Function to create a Resize TensorOperation.
 /// \notes Resize the input image to the given size.
 /// \param[in] size - a vector representing the output size of the resized image.
 ///               If size is a single value, the image will be resized to this value with
 ///               the same image aspect ratio. If size has 2 values, it should be (height, width).
 /// \param[in] interpolation An enum for the mode of interpolation
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<ResizeOperation> Resize(std::vector<int32_t> size,
                                        InterpolationMode interpolation = InterpolationMode::kLinear);
 /// \brief Function to create a RgbaToBgr TensorOperation.
 /// \notes Changes the input 4 channel RGBA tensor to 3 channel BGR.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RgbaToBgrOperation> RGBA2BGR();
 /// \brief Function to create a RgbaToRgb TensorOperation.
 /// \notes Changes the input 4 channel RGBA tensor to 3 channel RGB.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RgbaToRgbOperation> RGBA2RGB();
 /// \brief Function to create a SwapRedBlue TensorOp
 /// \notes Swaps the red and blue channels in image
 /// \return Shared pointer to the current TensorOp
 std::shared_ptr<SwapRedBlueOperation> SwapRedBlue();
 /// \brief Function to create a UniformAugment TensorOperation.
 /// \notes Tensor operation to perform randomly selected augmentation.
 /// \param[in] transforms - a vector of TensorOperation transforms.
 /// \param[in] num_ops - integer representing the number of OPs to be selected and applied.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<UniformAugOperation> UniformAugment(std::vector<std::shared_ptr<TensorOperation>> transforms,
                                                    int32_t num_ops = 2);
 /* ####################################### Derived TensorOperation classes ################################# */
 class CenterCropOperation : public TensorOperation {
 public:
  explicit CenterCropOperation(std::vector<int32_t> size);
  ~CenterCropOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<int32_t> size_;
 };
 class CropOperation : public TensorOperation {
 public:
  CropOperation(std::vector<int32_t> coordinates, std::vector<int32_t> size);
  ~CropOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<int32_t> coordinates_;
  std::vector<int32_t> size_;
 };
 class CutMixBatchOperation : public TensorOperation {
 public:
  explicit CutMixBatchOperation(ImageBatchFormat image_batch_format, float alpha = 1.0, float prob = 1.0);
  ~CutMixBatchOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  float alpha_;
  float prob_;
  ImageBatchFormat image_batch_format_;
 };
 class CutOutOperation : public TensorOperation {
 public:
  explicit CutOutOperation(int32_t length, int32_t num_patches = 1);
  ~CutOutOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  int32_t length_;
  int32_t num_patches_;
  ImageBatchFormat image_batch_format_;
 };
 class DecodeOperation : public TensorOperation {
 public:
  explicit DecodeOperation(bool rgb = true);
  ~DecodeOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  bool rgb_;
 };
 class HwcToChwOperation : public TensorOperation {
 public:
  ~HwcToChwOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 };
 class MixUpBatchOperation : public TensorOperation {
 public:
  explicit MixUpBatchOperation(float alpha = 1);
  ~MixUpBatchOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  float alpha_;
 };
 class NormalizeOperation : public TensorOperation {
 public:
  NormalizeOperation(std::vector<float> mean, std::vector<float> std);
  ~NormalizeOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<float> mean_;
  std::vector<float> std_;
 };
 class PadOperation : public TensorOperation {
 public:
  PadOperation(std::vector<int32_t> padding, std::vector<uint8_t> fill_value = {0},
               BorderType padding_mode = BorderType::kConstant);
  ~PadOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<int32_t> padding_;
  std::vector<uint8_t> fill_value_;
  BorderType padding_mode_;
 };
 class RandomAffineOperation : public TensorOperation {
 public:
  RandomAffineOperation(const std::vector<float_t> &degrees, const std::vector<float_t> &translate_range = {0.0, 0.0},
                        const std::vector<float_t> &scale_range = {1.0, 1.0},
                        const std::vector<float_t> &shear_ranges = {0.0, 0.0, 0.0, 0.0},
                        InterpolationMode interpolation = InterpolationMode::kNearestNeighbour,
                        const std::vector<uint8_t> &fill_value = {0, 0, 0});
  ~RandomAffineOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<float_t> degrees_;          // min_degree, max_degree
  std::vector<float_t> translate_range_;  // maximum x translation percentage, maximum y translation percentage
  std::vector<float_t> scale_range_;      // min_scale, max_scale
  std::vector<float_t> shear_ranges_;     // min_x_shear, max_x_shear, min_y_shear, max_y_shear
  InterpolationMode interpolation_;
  std::vector<uint8_t> fill_value_;
 };
 class RandomColorOperation : public TensorOperation {
 public:
  RandomColorOperation(float t_lb, float t_ub);
  ~RandomColorOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  float t_lb_;
  float t_ub_;
 };
 class RandomColorAdjustOperation : public TensorOperation {
 public:
  RandomColorAdjustOperation(std::vector<float> brightness = {1.0, 1.0}, std::vector<float> contrast = {1.0, 1.0},
                             std::vector<float> saturation = {1.0, 1.0}, std::vector<float> hue = {0.0, 0.0});
  ~RandomColorAdjustOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<float> brightness_;
  std::vector<float> contrast_;
  std::vector<float> saturation_;
  std::vector<float> hue_;
 };
 class RandomCropOperation : public TensorOperation {
 public:
  RandomCropOperation(std::vector<int32_t> size, std::vector<int32_t> padding = {0, 0, 0, 0},
                      bool pad_if_needed = false, std::vector<uint8_t> fill_value = {0, 0, 0},
                      BorderType padding_mode = BorderType::kConstant);
  ~RandomCropOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<int32_t> size_;
  std::vector<int32_t> padding_;
  bool pad_if_needed_;
  std::vector<uint8_t> fill_value_;
  BorderType padding_mode_;
 };
 class RandomHorizontalFlipOperation : public TensorOperation {
 public:
  explicit RandomHorizontalFlipOperation(float probability = 0.5);
  ~RandomHorizontalFlipOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  float probability_;
 };
 class RandomPosterizeOperation : public TensorOperation {
 public:
  explicit RandomPosterizeOperation(const std::vector<uint8_t> &bit_range = {4, 8});
  ~RandomPosterizeOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<uint8_t> bit_range_;
 };
 class RandomRotationOperation : public TensorOperation {
 public:
  RandomRotationOperation(std::vector<float> degrees, InterpolationMode interpolation_mode, bool expand,
                          std::vector<float> center, std::vector<uint8_t> fill_value);
  ~RandomRotationOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<float> degrees_;
  InterpolationMode interpolation_mode_;
  std::vector<float> center_;
  bool expand_;
  std::vector<uint8_t> fill_value_;
 };
 class RandomSharpnessOperation : public TensorOperation {
 public:
  explicit RandomSharpnessOperation(std::vector<float> degrees = {0.1, 1.9});
  ~RandomSharpnessOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<float> degrees_;
 };
 class RandomSolarizeOperation : public TensorOperation {
 public:
  explicit RandomSolarizeOperation(std::vector<uint8_t> threshold);
  ~RandomSolarizeOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<uint8_t> threshold_;
 };
 class RandomVerticalFlipOperation : public TensorOperation {
 public:
  explicit RandomVerticalFlipOperation(float probability = 0.5);
  ~RandomVerticalFlipOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  float probability_;
 };
 class ResizeOperation : public TensorOperation {
 public:
  explicit ResizeOperation(std::vector<int32_t> size,
                           InterpolationMode interpolation_mode = InterpolationMode::kLinear);
  ~ResizeOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<int32_t> size_;
  InterpolationMode interpolation_;
 };
 class RgbaToBgrOperation : public TensorOperation {
 public:
  RgbaToBgrOperation();
  ~RgbaToBgrOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 };
 class RgbaToRgbOperation : public TensorOperation {
 public:
  RgbaToRgbOperation();
  ~RgbaToRgbOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 };
 class SwapRedBlueOperation : public TensorOperation {
 public:
  SwapRedBlueOperation();
  ~SwapRedBlueOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 };
 class UniformAugOperation : public TensorOperation {
 public:
  explicit UniformAugOperation(std::vector<std::shared_ptr<TensorOperation>> transforms, int32_t num_ops = 2);
  ~UniformAugOperation() = default;
  std::shared_ptr<TensorOp> Build() override;
  bool ValidateParams() override;
 private:
  std::vector<std::shared_ptr<TensorOperation>> transforms_;
  int32_t num_ops_;
 };
 }  // namespace vision
 }  // namespace api
 }  // namespace dataset
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_VISION_H_
--- a/tests/ut/cpp/dataset/CMakeLists.txt
+++ b/tests/ut/cpp/dataset/CMakeLists.txt
@@ -102,6 +102,7 @@ SET(DE_UT_SRCS
        build_vocab_test.cc
        c_api_samplers_test.cc
        c_api_transforms_test.cc
        c_api_vision_test.cc
        c_api_dataset_ops_test.cc
        c_api_dataset_album_test.cc
        c_api_dataset_cifar_test.cc
@@ -124,8 +125,8 @@ SET(DE_UT_SRCS
        swap_red_blue_test.cc
        distributed_sampler_test.cc
        data_helper_test.cc
 				image_process_test.cc
 				slice_op_test.cc
        image_process_test.cc
        slice_op_test.cc
        )
 if (ENABLE_PYTHON)
--- a/tests/ut/cpp/dataset/c_api_dataset_ops_test.cc
+++ b/tests/ut/cpp/dataset/c_api_dataset_ops_test.cc
@@ -16,7 +16,7 @@
 #include "common/common.h"
 #include "minddata/dataset/core/tensor_row.h"
 #include "minddata/dataset/include/datasets.h"
 #include "minddata/dataset/include/transforms.h"
 #include "minddata/dataset/include/vision.h"
 using namespace mindspore::dataset::api;
 using mindspore::dataset::Tensor;
--- a/tests/ut/cpp/dataset/c_api_dataset_tfrecord_test.cc
+++ b/tests/ut/cpp/dataset/c_api_dataset_tfrecord_test.cc
@@ -15,7 +15,7 @@
 */
 #include "common/common.h"
 #include "minddata/dataset/include/datasets.h"
 #include "minddata/dataset/include/transforms.h"
 #include "minddata/dataset/include/vision.h"
 #include "minddata/dataset/core/config_manager.h"
 #include "minddata/dataset/core/global_context.h"
--- a/tests/ut/cpp/dataset/c_api_transforms_test.cc
+++ b/tests/ut/cpp/dataset/c_api_transforms_test.cc
--- a/tests/ut/cpp/dataset/c_api_vision_test.cc
+++ b/tests/ut/cpp/dataset/c_api_vision_test.cc
--- a/tests/ut/cpp/dataset/center_crop_op_test.cc
+++ b/tests/ut/cpp/dataset/center_crop_op_test.cc
@@ -55,3 +55,17 @@ TEST_F(MindDataTestCenterCropOp, TestOp2) {
  EXPECT_TRUE(s.IsError());
  ASSERT_TRUE(s.get_code() == StatusCode::kUnexpectedError);
 }
 TEST_F(MindDataTestCenterCropOp, TestOp3) {
  MS_LOG(INFO) << "Doing MindDataTestCenterCropOp::TestOp3. Test single integer input for square crop.";
  std::shared_ptr<Tensor> output_tensor;
  int side = 128;
  std::unique_ptr<CenterCropOp> op(new CenterCropOp(side));
  EXPECT_TRUE(op->OneToOne());
  Status s = op->Compute(input_tensor_, &output_tensor);
  EXPECT_TRUE(s.IsOk());
  // Confirm both height and width are of size <side>.
  EXPECT_EQ(side, output_tensor->shape()[0]);
  EXPECT_EQ(side, output_tensor->shape()[1]);
  std::shared_ptr<CVTensor> p = CVTensor::AsCVTensor(output_tensor);
 }