[MD] Transform api decouple

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

@@ -32,6 +32,7 @@
 #endif
 #include "minddata/dataset/kernels/image/decode_op.h"
 #ifndef ENABLE_ANDROID
+#include "minddata/dataset/kernels/image/equalize_op.h"
 #include "minddata/dataset/kernels/image/hwc_to_chw_op.h"
 #include "minddata/dataset/kernels/image/invert_op.h"
 #include "minddata/dataset/kernels/image/mixup_batch_op.h"
@@ -49,7 +50,10 @@
 #include "minddata/dataset/kernels/image/random_horizontal_flip_op.h"
 #include "minddata/dataset/kernels/image/random_horizontal_flip_with_bbox_op.h"
 #include "minddata/dataset/kernels/image/random_posterize_op.h"
+#include "minddata/dataset/kernels/image/random_resize_op.h"
+#include "minddata/dataset/kernels/image/random_resize_with_bbox_op.h"
 #include "minddata/dataset/kernels/image/random_rotation_op.h"
+#include "minddata/dataset/kernels/image/random_select_subpolicy_op.h"
 #include "minddata/dataset/kernels/image/random_sharpness_op.h"
 #include "minddata/dataset/kernels/image/random_solarize_op.h"
 #include "minddata/dataset/kernels/image/random_vertical_flip_op.h"
@@ -61,6 +65,8 @@
 #include "minddata/dataset/kernels/image/resize_with_bbox_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/soft_dvpp/soft_dvpp_decode_random_crop_resize_jpeg_op.h"
+#include "minddata/dataset/kernels/image/soft_dvpp/soft_dvpp_decode_resize_jpeg_op.h"
 #include "minddata/dataset/kernels/image/swap_red_blue_op.h"
 #include "minddata/dataset/kernels/image/uniform_aug_op.h"
 #endif
@@ -78,10 +84,7 @@ namespace vision {
 std::shared_ptr<AutoContrastOperation> AutoContrast(float cutoff, std::vector<uint32_t> ignore) {
   auto op = std::make_shared<AutoContrastOperation>(cutoff, ignore);
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create BoundingBoxAugmentOperation.
@@ -89,20 +92,14 @@ std::shared_ptr<BoundingBoxAugmentOperation> BoundingBoxAugment(std::shared_ptr<
                                                                 float ratio) {
   auto op = std::make_shared<BoundingBoxAugmentOperation>(transform, ratio);
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // 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;
+  return op->ValidateParams() ? op : nullptr;
 }
 #endif
 
@@ -110,80 +107,63 @@ std::shared_ptr<CenterCropOperation> CenterCrop(std::vector<int32_t> size) {
 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;
+  return op->ValidateParams() ? op : nullptr;
 }
 #ifndef ENABLE_ANDROID
 // 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;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // 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;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // 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;
+  return op->ValidateParams() ? op : nullptr;
+}
+
+// Function to create EqualizeOperation.
+std::shared_ptr<EqualizeOperation> Equalize() {
+  auto op = std::make_shared<EqualizeOperation>();
+  // Input validation
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create HwcToChwOperation.
 std::shared_ptr<HwcToChwOperation> HWC2CHW() {
   auto op = std::make_shared<HwcToChwOperation>();
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create InvertOperation.
 std::shared_ptr<InvertOperation> Invert() {
   auto op = std::make_shared<InvertOperation>();
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // 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;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // 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;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create PadOperation.
@@ -191,10 +171,7 @@ std::shared_ptr<PadOperation> Pad(std::vector<int32_t> padding, std::vector<uint
                                   BorderType padding_mode) {
   auto op = std::make_shared<PadOperation>(padding, fill_value, padding_mode);
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create RandomAffineOperation.
@@ -207,20 +184,14 @@ std::shared_ptr<RandomAffineOperation> RandomAffine(const std::vector<float_t> &
   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;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // 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;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 std::shared_ptr<TensorOp> RandomColorOperation::Build() {
@@ -234,10 +205,7 @@ std::shared_ptr<RandomColorAdjustOperation> RandomColorAdjust(std::vector<float>
                                                               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;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create RandomCropOperation.
@@ -246,10 +214,7 @@ std::shared_ptr<RandomCropOperation> RandomCrop(std::vector<int32_t> size, std::
                                                 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;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create RandomCropDecodeResizeOperation.
@@ -260,10 +225,7 @@ std::shared_ptr<RandomCropDecodeResizeOperation> RandomCropDecodeResize(std::vec
                                                                         int32_t max_attempts) {
   auto op = std::make_shared<RandomCropDecodeResizeOperation>(size, scale, ratio, interpolation, max_attempts);
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create RandomCropWithBBoxOperation.
@@ -272,40 +234,42 @@ std::shared_ptr<RandomCropWithBBoxOperation> RandomCropWithBBox(std::vector<int3
                                                                 BorderType padding_mode) {
   auto op = std::make_shared<RandomCropWithBBoxOperation>(size, padding, pad_if_needed, fill_value, padding_mode);
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // 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;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create RandomHorizontalFlipOperation.
 std::shared_ptr<RandomHorizontalFlipWithBBoxOperation> RandomHorizontalFlipWithBBox(float prob) {
   auto op = std::make_shared<RandomHorizontalFlipWithBBoxOperation>(prob);
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // 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;
+  return op->ValidateParams() ? op : nullptr;
+}
+
+// Function to create RandomResizeOperation.
+std::shared_ptr<RandomResizeOperation> RandomResize(std::vector<int32_t> size) {
+  auto op = std::make_shared<RandomResizeOperation>(size);
+  // Input validation
+  return op->ValidateParams() ? op : nullptr;
+}
+
+// Function to create RandomResizeWithBBoxOperation.
+std::shared_ptr<RandomResizeWithBBoxOperation> RandomResizeWithBBox(std::vector<int32_t> size) {
+  auto op = std::make_shared<RandomResizeWithBBoxOperation>(size);
+  // Input validation
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create RandomResizedCropOperation.
@@ -314,10 +278,7 @@ std::shared_ptr<RandomResizedCropOperation> RandomResizedCrop(std::vector<int32_
                                                               int32_t max_attempts) {
   auto op = std::make_shared<RandomResizedCropOperation>(size, scale, ratio, interpolation, max_attempts);
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create RandomRotationOperation.
@@ -326,60 +287,50 @@ std::shared_ptr<RandomRotationOperation> RandomRotation(std::vector<float> degre
                                                         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;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // 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;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // 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;
+  return op->ValidateParams() ? op : nullptr;
+}
+
+// Function to create RandomSelectSubpolicyOperation.
+std::shared_ptr<RandomSelectSubpolicyOperation> RandomSelectSubpolicy(
+  std::vector<std::vector<std::pair<std::shared_ptr<TensorOperation>, double>>> policy) {
+  auto op = std::make_shared<RandomSelectSubpolicyOperation>(policy);
+  // Input validation
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // 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;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create RandomVerticalFlipWithBBoxOperation.
 std::shared_ptr<RandomVerticalFlipWithBBoxOperation> RandomVerticalFlipWithBBox(float prob) {
   auto op = std::make_shared<RandomVerticalFlipWithBBoxOperation>(prob);
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create RescaleOperation.
 std::shared_ptr<RescaleOperation> Rescale(float rescale, float shift) {
   auto op = std::make_shared<RescaleOperation>(rescale, shift);
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 #endif
@@ -387,10 +338,7 @@ std::shared_ptr<RescaleOperation> Rescale(float rescale, float shift) {
 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;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 #ifndef ENABLE_ANDROID
@@ -398,40 +346,43 @@ std::shared_ptr<ResizeOperation> Resize(std::vector<int32_t> size, Interpolation
 std::shared_ptr<ResizeWithBBoxOperation> ResizeWithBBox(std::vector<int32_t> size, InterpolationMode interpolation) {
   auto op = std::make_shared<ResizeWithBBoxOperation>(size, interpolation);
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create RgbaToBgrOperation.
 std::shared_ptr<RgbaToBgrOperation> RGBA2BGR() {
   auto op = std::make_shared<RgbaToBgrOperation>();
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create RgbaToRgbOperation.
 std::shared_ptr<RgbaToRgbOperation> RGBA2RGB() {
   auto op = std::make_shared<RgbaToRgbOperation>();
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
+}
+
+// Function to create SoftDvppDecodeRandomCropResizeJpegOperation.
+std::shared_ptr<SoftDvppDecodeRandomCropResizeJpegOperation> SoftDvppDecodeRandomCropResizeJpeg(
+  std::vector<int32_t> size, std::vector<float> scale, std::vector<float> ratio, int32_t max_attempts) {
+  auto op = std::make_shared<SoftDvppDecodeRandomCropResizeJpegOperation>(size, scale, ratio, max_attempts);
+  // Input validation
+  return op->ValidateParams() ? op : nullptr;
+}
+
+// Function to create SoftDvppDecodeResizeJpegOperation.
+std::shared_ptr<SoftDvppDecodeResizeJpegOperation> SoftDvppDecodeResizeJpeg(std::vector<int32_t> size) {
+  auto op = std::make_shared<SoftDvppDecodeResizeJpegOperation>(size);
+  // Input validation
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create SwapRedBlueOperation.
 std::shared_ptr<SwapRedBlueOperation> SwapRedBlue() {
   auto op = std::make_shared<SwapRedBlueOperation>();
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create UniformAugOperation.
@@ -439,10 +390,7 @@ std::shared_ptr<UniformAugOperation> UniformAugment(std::vector<std::shared_ptr<
                                                     int32_t num_ops) {
   auto op = std::make_shared<UniformAugOperation>(transforms, num_ops);
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
 }
 
 #endif
@@ -660,6 +608,7 @@ std::shared_ptr<TensorOp> CropOperation::Build() {
 
   height = size_[0];
   width = size_[0];
+  // User has specified crop_width.
   if (size_.size() == 2) {
     width = size_[1];
   }
@@ -722,6 +671,11 @@ Status DecodeOperation::ValidateParams() { return Status::OK(); }
 
 std::shared_ptr<TensorOp> DecodeOperation::Build() { return std::make_shared<DecodeOp>(rgb_); }
 
+// EqualizeOperation
+Status EqualizeOperation::ValidateParams() { return Status::OK(); }
+
+std::shared_ptr<TensorOp> EqualizeOperation::Build() { return std::make_shared<EqualizeOp>(); }
+
 // HwcToChwOperation
 Status HwcToChwOperation::ValidateParams() { return Status::OK(); }
 
@@ -1417,11 +1371,80 @@ std::shared_ptr<TensorOp> RandomPosterizeOperation::Build() {
   return tensor_op;
 }
 
+// RandomResizeOperation
+RandomResizeOperation::RandomResizeOperation(std::vector<int32_t> size) : size_(size) {}
+
+Status RandomResizeOperation::ValidateParams() {
+  // size
+  if (size_.size() != 2 && size_.size() != 1) {
+    std::string err_msg =
+      "RandomResize: size must be a vector of one or two values, got: " + std::to_string(size_.size());
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  if (size_[0] <= 0 || (size_.size() == 2 && size_[1] <= 0)) {
+    std::string err_msg = "RandomResize: size must only contain positive integers.";
+    MS_LOG(ERROR) << "RandomResize: size must only contain positive integers, got: " << size_;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  return Status::OK();
+}
+
+std::shared_ptr<TensorOp> RandomResizeOperation::Build() {
+  // If size is a single value, the smaller edge of the image will be
+  // resized to this value with the same image aspect ratio.
+  int32_t height = size_[0];
+  int32_t width = 0;
+
+  // User specified the width value.
+  if (size_.size() == 2) {
+    width = size_[1];
+  }
+
+  std::shared_ptr<RandomResizeOp> tensor_op = std::make_shared<RandomResizeOp>(height, width);
+  return tensor_op;
+}
+
+// RandomResizeWithBBoxOperation
+RandomResizeWithBBoxOperation::RandomResizeWithBBoxOperation(std::vector<int32_t> size) : size_(size) {}
+
+Status RandomResizeWithBBoxOperation::ValidateParams() {
+  // size
+  if (size_.size() != 2 && size_.size() != 1) {
+    std::string err_msg =
+      "RandomResizeWithBBox: size must be a vector of one or two values, got: " + std::to_string(size_.size());
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  if (size_[0] <= 0 || (size_.size() == 2 && size_[1] <= 0)) {
+    std::string err_msg = "RandomResizeWithBBox: size must only contain positive integers.";
+    MS_LOG(ERROR) << "RandomResizeWithBBox: size must only contain positive integers, got: " << size_;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  return Status::OK();
+}
+
+std::shared_ptr<TensorOp> RandomResizeWithBBoxOperation::Build() {
+  // If size is a single value, the smaller edge of the image will be
+  // resized to this value with the same image aspect ratio.
+  int32_t height = size_[0];
+  int32_t width = 0;
+
+  // User specified the width value.
+  if (size_.size() == 2) {
+    width = size_[1];
+  }
+
+  std::shared_ptr<RandomResizeWithBBoxOp> tensor_op = std::make_shared<RandomResizeWithBBoxOp>(height, width);
+  return tensor_op;
+}
+
 // RandomResizedCropOperation
 RandomResizedCropOperation::RandomResizedCropOperation(std::vector<int32_t> size, std::vector<float> scale,
                                                        std::vector<float> ratio, InterpolationMode interpolation,
                                                        int32_t max_attempts)
     : size_(size), scale_(scale), ratio_(ratio), interpolation_(interpolation), max_attempts_(max_attempts) {}
+
 Status RandomResizedCropOperation::ValidateParams() {
   // size
   if (size_.size() != 2 && size_.size() != 1) {
@@ -1455,8 +1478,9 @@ Status RandomResizedCropOperation::ValidateParams() {
   }
   // ratio
   if (ratio_.size() != 2) {
-    std::string err_msg = "RandomResizedCrop: ratio must be in the format of (min, max).";
-    MS_LOG(ERROR) << "RandomResizedCrop: ratio must be in the format of (min, max), but got: " << ratio_;
+    std::string err_msg =
+      "RandomResizedCrop: ratio must be a vector of two values, got: " + std::to_string(ratio_.size());
+    MS_LOG(ERROR) << err_msg;
     RETURN_STATUS_SYNTAX_ERROR(err_msg);
   }
   if (ratio_[0] < 0 || ratio_[1] < 0) {
@@ -1470,12 +1494,23 @@ Status RandomResizedCropOperation::ValidateParams() {
                   << ratio_;
     RETURN_STATUS_SYNTAX_ERROR(err_msg);
   }
+  // max_attempts
+  if (max_attempts_ < 1) {
+    std::string err_msg =
+      "RandomResizedCrop: max_attempts must be greater than or equal to 1, got: " + std::to_string(max_attempts_);
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
   return Status::OK();
 }
 
 std::shared_ptr<TensorOp> RandomResizedCropOperation::Build() {
-  int32_t height = size_[0], width = size_[0];
-  if (size_.size() == 2) width = size_[1];
+  int32_t height = size_[0];
+  int32_t width = size_[0];
+  // User specified the width value.
+  if (size_.size() == 2) {
+    width = size_[1];
+  }
   std::shared_ptr<RandomCropAndResizeOp> tensor_op = std::make_shared<RandomCropAndResizeOp>(
     height, width, scale_[0], scale_[1], ratio_[0], ratio_[1], interpolation_, max_attempts_);
   return tensor_op;
@@ -1549,6 +1584,55 @@ std::shared_ptr<TensorOp> RandomRotationOperation::Build() {
   return tensor_op;
 }
 
+// RandomSelectSubpolicyOperation.
+RandomSelectSubpolicyOperation::RandomSelectSubpolicyOperation(
+  std::vector<std::vector<std::pair<std::shared_ptr<TensorOperation>, double>>> policy)
+    : policy_(policy) {}
+
+Status RandomSelectSubpolicyOperation::ValidateParams() {
+  if (policy_.empty()) {
+    std::string err_msg = "RandomSelectSubpolicy: policy must not be empty";
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  for (int32_t i = 0; i < policy_.size(); i++) {
+    if (policy_[i].empty()) {
+      std::string err_msg = "RandomSelectSubpolicy: policy[" + std::to_string(i) + "] must not be empty";
+      MS_LOG(ERROR) << err_msg;
+      RETURN_STATUS_SYNTAX_ERROR(err_msg);
+    }
+    for (int32_t j = 0; j < policy_[i].size(); j++) {
+      if (policy_[i][j].first == nullptr) {
+        std::string transform_pos = "[" + std::to_string(i) + "]" + "[" + std::to_string(j) + "]";
+        std::string err_msg = "RandomSelectSubpolicy: transform in policy" + transform_pos + " must not be null";
+        MS_LOG(ERROR) << err_msg;
+        RETURN_STATUS_SYNTAX_ERROR(err_msg);
+      }
+      if (policy_[i][j].second < 0.0 || policy_[i][j].second > 1.0) {
+        std::string transform_pos = "[" + std::to_string(i) + "]" + "[" + std::to_string(j) + "]";
+        std::string err_msg = "RandomSelectSubpolicy: probability of transform in policy" + transform_pos +
+                              " must be between 0.0 and 1.0, got: " + std::to_string(policy_[i][j].second);
+        MS_LOG(ERROR) << err_msg;
+        RETURN_STATUS_SYNTAX_ERROR(err_msg);
+      }
+    }
+  }
+  return Status::OK();
+}
+
+std::shared_ptr<TensorOp> RandomSelectSubpolicyOperation::Build() {
+  std::vector<Subpolicy> policy_tensor_ops;
+  for (int32_t i = 0; i < policy_.size(); i++) {
+    Subpolicy sub_policy_tensor_ops;
+    for (int32_t j = 0; j < policy_[i].size(); j++) {
+      sub_policy_tensor_ops.push_back(std::make_pair(policy_[i][j].first->Build(), policy_[i][j].second));
+    }
+    policy_tensor_ops.push_back(sub_policy_tensor_ops);
+  }
+  std::shared_ptr<RandomSelectSubpolicyOp> tensor_op = std::make_shared<RandomSelectSubpolicyOp>(policy_tensor_ops);
+  return tensor_op;
+}
+
 // Function to create RandomSharpness.
 RandomSharpnessOperation::RandomSharpnessOperation(std::vector<float> degrees) : degrees_(degrees) {}
 
@@ -1669,6 +1753,8 @@ Status ResizeOperation::ValidateParams() {
 }
 
 std::shared_ptr<TensorOp> ResizeOperation::Build() {
+  // If size is a single value, the smaller edge of the image will be
+  // resized to this value with the same image aspect ratio.
   int32_t height = size_[0];
   int32_t width = 0;
 
@@ -1730,6 +1816,115 @@ std::shared_ptr<TensorOp> RgbaToRgbOperation::Build() {
   return tensor_op;
 }
 
+// SoftDvppDecodeRandomCropResizeJpegOperation
+SoftDvppDecodeRandomCropResizeJpegOperation::SoftDvppDecodeRandomCropResizeJpegOperation(std::vector<int32_t> size,
+                                                                                         std::vector<float> scale,
+                                                                                         std::vector<float> ratio,
+                                                                                         int32_t max_attempts)
+    : size_(size), scale_(scale), ratio_(ratio), max_attempts_(max_attempts) {}
+
+Status SoftDvppDecodeRandomCropResizeJpegOperation::ValidateParams() {
+  // size
+  if (size_.size() != 2 && size_.size() != 1) {
+    std::string err_msg = "SoftDvppDecodeRandomCropResizeJpeg: size must be a vector of one or two values, got: " +
+                          std::to_string(size_.size());
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  if (size_[0] <= 0 || (size_.size() == 2 && size_[1] <= 0)) {
+    std::string err_msg = "SoftDvppDecodeRandomCropResizeJpeg: size must only contain positive integers.";
+    MS_LOG(ERROR) << "SoftDvppDecodeRandomCropResizeJpeg: size must only contain positive integers, got: " << size_;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  // scale
+  if (scale_.size() != 2) {
+    std::string err_msg =
+      "SoftDvppDecodeRandomCropResizeJpeg: scale must be a vector of two values, got: " + std::to_string(scale_.size());
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  if (scale_[0] < 0 || scale_[1] < 0) {
+    std::string err_msg = "SoftDvppDecodeRandomCropResizeJpeg: scale must be greater than or equal to 0.";
+    MS_LOG(ERROR) << "SoftDvppDecodeRandomCropResizeJpeg: scale must be greater than or equal to 0, got: " << scale_;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  if (scale_[1] < scale_[0]) {
+    std::string err_msg = "SoftDvppDecodeRandomCropResizeJpeg: scale must be in the format of (min, max).";
+    MS_LOG(ERROR) << "SoftDvppDecodeRandomCropResizeJpeg: scale must be in the format of (min, max), but got: "
+                  << scale_;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  // ratio
+  if (ratio_.size() != 2) {
+    std::string err_msg =
+      "SoftDvppDecodeRandomCropResizeJpeg: ratio must be a vector of two values, got: " + std::to_string(ratio_.size());
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  if (ratio_[0] < 0 || ratio_[1] < 0) {
+    std::string err_msg = "SoftDvppDecodeRandomCropResizeJpeg: ratio must be greater than or equal to 0.";
+    MS_LOG(ERROR) << "SoftDvppDecodeRandomCropResizeJpeg: ratio must be greater than or equal to 0, got: " << ratio_;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  if (ratio_[1] < ratio_[0]) {
+    std::string err_msg = "SoftDvppDecodeRandomCropResizeJpeg: ratio must be in the format of (min, max).";
+    MS_LOG(ERROR) << "SoftDvppDecodeRandomCropResizeJpeg: ratio must be in the format of (min, max), but got: "
+                  << ratio_;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  // max_attempts
+  if (max_attempts_ < 1) {
+    std::string err_msg = "SoftDvppDecodeRandomCropResizeJpeg: max_attempts must be greater than or equal to 1, got: " +
+                          std::to_string(max_attempts_);
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  return Status::OK();
+}
+
+std::shared_ptr<TensorOp> SoftDvppDecodeRandomCropResizeJpegOperation::Build() {
+  int32_t height = size_[0];
+  int32_t width = size_[0];
+  // User specified the width value.
+  if (size_.size() == 2) {
+    width = size_[1];
+  }
+
+  auto tensor_op = std::make_shared<SoftDvppDecodeRandomCropResizeJpegOp>(height, width, scale_[0], scale_[1],
+                                                                          ratio_[0], ratio_[1], max_attempts_);
+  return tensor_op;
+}
+
+// SoftDvppDecodeResizeJpegOperation
+SoftDvppDecodeResizeJpegOperation::SoftDvppDecodeResizeJpegOperation(std::vector<int32_t> size) : size_(size) {}
+
+Status SoftDvppDecodeResizeJpegOperation::ValidateParams() {
+  // size
+  if (size_.empty() || size_.size() > 2) {
+    std::string err_msg =
+      "SoftDvppDecodeResizeJpeg: size must be a vector of one or two values, got: " + std::to_string(size_.size());
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  RETURN_IF_NOT_OK(ValidateVectorPositive("SoftDvppDecodeResizeJpeg", size_));
+
+  return Status::OK();
+}
+
+std::shared_ptr<TensorOp> SoftDvppDecodeResizeJpegOperation::Build() {
+  // If size is a single value, the smaller edge of the image will be
+  // resized to this value with the same image aspect ratio.
+  int32_t height = size_[0];
+  int32_t width = 0;
+
+  // User specified the width value.
+  if (size_.size() == 2) {
+    width = size_[1];
+  }
+  std::shared_ptr<SoftDvppDecodeResizeJpegOp> tensor_op = std::make_shared<SoftDvppDecodeResizeJpegOp>(height, width);
+  return tensor_op;
+}
+
 // SwapRedBlueOperation.
 SwapRedBlueOperation::SwapRedBlueOperation() {}
 

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

@@ -17,7 +17,9 @@
 #ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_VISION_H_
 #define MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_VISION_H_
 
+#include <map>
 #include <memory>
+#include <utility>
 #include <vector>
 #include "minddata/dataset/core/constants.h"
 #include "minddata/dataset/include/transforms.h"
@@ -42,6 +44,7 @@ class CutOutOperation;
 #endif
 class DecodeOperation;
 #ifndef ENABLE_ANDROID
+class EqualizeOperation;
 class HwcToChwOperation;
 class InvertOperation;
 class MixUpBatchOperation;
@@ -58,8 +61,11 @@ class RandomCropWithBBoxOperation;
 class RandomHorizontalFlipOperation;
 class RandomHorizontalFlipWithBBoxOperation;
 class RandomPosterizeOperation;
+class RandomResizeOperation;
+class RandomResizeWithBBoxOperation;
 class RandomResizedCropOperation;
 class RandomRotationOperation;
+class RandomSelectSubpolicyOperation;
 class RandomSharpnessOperation;
 class RandomSolarizeOperation;
 class RandomVerticalFlipOperation;
@@ -71,6 +77,8 @@ class ResizeOperation;
 class ResizeWithBBoxOperation;
 class RgbaToBgrOperation;
 class RgbaToRgbOperation;
+class SoftDvppDecodeRandomCropResizeJpegOperation;
+class SoftDvppDecodeResizeJpegOperation;
 class SwapRedBlueOperation;
 class UniformAugOperation;
 
@@ -129,6 +137,12 @@ std::shared_ptr<CutOutOperation> CutOut(int32_t length, int32_t num_patches = 1)
 std::shared_ptr<DecodeOperation> Decode(bool rgb = true);
 
 #ifndef ENABLE_ANDROID
+
+/// \brief Function to create a Equalize TensorOperation.
+/// \notes Apply histogram equalization on input image.
+/// \return Shared pointer to the current TensorOperation.
+std::shared_ptr<EqualizeOperation> Equalize();
+
 /// \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.
@@ -296,6 +310,21 @@ std::shared_ptr<RandomHorizontalFlipWithBBoxOperation> RandomHorizontalFlipWithB
 /// \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 RandomResize TensorOperation.
+/// \notes Resize the input image using a randomly selected interpolation mode.
+/// \param[in] size A vector representing the output size of the resized image.
+///     If size is a single value, the smaller edge of the image will be resized to this value with
+//      the same image aspect ratio. If size has 2 values, it should be (height, width).
+std::shared_ptr<RandomResizeOperation> RandomResize(std::vector<int32_t> size);
+
+/// \brief Function to create a RandomResizeWithBBox TensorOperation.
+/// \notes Resize the input image using a randomly selected interpolation mode and adjust
+///     bounding boxes accordingly.
+/// \param[in] size A vector representing the output size of the resized image.
+///     If size is a single value, the smaller edge of the image will be resized to this value with
+//      the same image aspect ratio. If size has 2 values, it should be (height, width).
+std::shared_ptr<RandomResizeWithBBoxOperation> RandomResizeWithBBox(std::vector<int32_t> size);
+
 /// \brief Function to create a RandomResizedCrop TensorOperation.
 /// \notes Crop the input image to a random size and aspect ratio.
 /// \param[in] size A vector representing the output size of the cropped image.
@@ -325,6 +354,15 @@ 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 RandomSelectSubpolicy TensorOperation.
+/// \notes Choose a random sub-policy from a list to be applied on the input image. A sub-policy is a list of tuples
+///     (op, prob), where op is a TensorOp operation and prob is the probability that this op will be applied. Once
+///     a sub-policy is selected, each op within the subpolicy with be applied in sequence according to its probability.
+/// \param[in] policy Vector of sub-policies to choose from.
+/// \return Shared pointer to the current TensorOperation.
+std::shared_ptr<RandomSelectSubpolicyOperation> RandomSelectSubpolicy(
+  std::vector<std::vector<std::pair<std::shared_ptr<TensorOperation>, double>>> policy);
+
 /// \brief Function to create a RandomSharpness TensorOperation.
 /// \notes Tensor operation to perform random sharpness.
 /// \param[in] degrees A float vector of size 2, representing the starting and ending degree to uniformly
@@ -390,6 +428,35 @@ std::shared_ptr<RgbaToBgrOperation> RGBA2BGR();
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RgbaToRgbOperation> RGBA2RGB();
 
+/// \brief Function to create a SoftDvppDecodeRandomCropResizeJpeg TensorOperation.
+/// \notes Tensor operation to decode, random crop and resize JPEG image using the simulation algorithm of
+///     Ascend series chip DVPP module. The usage scenario is consistent with SoftDvppDecodeResizeJpeg.
+///     The input image size should be in range [32*32, 8192*8192].
+///     The zoom-out and zoom-in multiples of the image length and width should in the range [1/32, 16].
+///     Only images with an even resolution can be output. The output of odd resolution is not supported.
+/// \param[in] size A vector representing the output size of the resized image.
+///     If size is a single value, smaller edge of the image will be resized to this value with
+///     the same image aspect ratio. If size has 2 values, it should be (height, width).
+/// \return Shared pointer to the current TensorOperation.
+std::shared_ptr<SoftDvppDecodeRandomCropResizeJpegOperation> SoftDvppDecodeRandomCropResizeJpeg(
+  std::vector<int32_t> size, std::vector<float> scale = {0.08, 1.0}, std::vector<float> ratio = {3. / 4., 4. / 3.},
+  int32_t max_attempts = 10);
+
+/// \brief Function to create a SoftDvppDecodeResizeJpeg TensorOperation.
+/// \notes Tensor operation to decode and resize JPEG image using the simulation algorithm of Ascend series
+///     chip DVPP module. It is recommended to use this algorithm in the following scenarios:
+///     When training, the DVPP of the Ascend chip is not used,
+///     and the DVPP of the Ascend chip is used during inference,
+///     and the accuracy of inference is lower than the accuracy of training;
+///     and the input image size should be in range [32*32, 8192*8192].
+///     The zoom-out and zoom-in multiples of the image length and width should in the range [1/32, 16].
+///     Only images with an even resolution can be output. The output of odd resolution is not supported.
+/// \param[in] size A vector representing the output size of the resized image.
+///     If size is a single value, smaller edge of the image will be resized to this value with
+///     the same image aspect ratio. If size has 2 values, it should be (height, width).
+/// \return Shared pointer to the current TensorOperation.
+std::shared_ptr<SoftDvppDecodeResizeJpegOperation> SoftDvppDecodeResizeJpeg(std::vector<int32_t> size);
+
 /// \brief Function to create a SwapRedBlue TensorOp
 /// \notes Swaps the red and blue channels in image
 /// \return Shared pointer to the current TensorOp
@@ -512,6 +579,15 @@ class DecodeOperation : public TensorOperation {
 };
 
 #ifndef ENABLE_ANDROID
+class EqualizeOperation : public TensorOperation {
+ public:
+  ~EqualizeOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+};
+
 class HwcToChwOperation : public TensorOperation {
  public:
   ~HwcToChwOperation() = default;
@@ -735,6 +811,34 @@ class RandomPosterizeOperation : public TensorOperation {
   std::vector<uint8_t> bit_range_;
 };
 
+class RandomResizeOperation : public TensorOperation {
+ public:
+  explicit RandomResizeOperation(std::vector<int32_t> size);
+
+  ~RandomResizeOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+ private:
+  std::vector<int32_t> size_;
+};
+
+class RandomResizeWithBBoxOperation : public TensorOperation {
+ public:
+  explicit RandomResizeWithBBoxOperation(std::vector<int32_t> size);
+
+  ~RandomResizeWithBBoxOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+ private:
+  std::vector<int32_t> size_;
+};
+
 class RandomResizedCropOperation : public TensorOperation {
  public:
   explicit RandomResizedCropOperation(std::vector<int32_t> size, std::vector<float> scale = {0.08, 1.0},
@@ -775,6 +879,21 @@ class RandomRotationOperation : public TensorOperation {
   std::vector<uint8_t> fill_value_;
 };
 
+class RandomSelectSubpolicyOperation : public TensorOperation {
+ public:
+  explicit RandomSelectSubpolicyOperation(
+    std::vector<std::vector<std::pair<std::shared_ptr<TensorOperation>, double>>> policy);
+
+  ~RandomSelectSubpolicyOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+ private:
+  std::vector<std::vector<std::pair<std::shared_ptr<TensorOperation>, double>>> policy_;
+};
+
 class RandomSharpnessOperation : public TensorOperation {
  public:
   explicit RandomSharpnessOperation(std::vector<float> degrees = {0.1, 1.9});
@@ -902,6 +1021,38 @@ class RgbaToRgbOperation : public TensorOperation {
   Status ValidateParams() override;
 };
 
+class SoftDvppDecodeRandomCropResizeJpegOperation : public TensorOperation {
+ public:
+  explicit SoftDvppDecodeRandomCropResizeJpegOperation(std::vector<int32_t> size, std::vector<float> scale,
+                                                       std::vector<float> ratio, int32_t max_attempts);
+
+  ~SoftDvppDecodeRandomCropResizeJpegOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+ private:
+  std::vector<int32_t> size_;
+  std::vector<float> scale_;
+  std::vector<float> ratio_;
+  int32_t max_attempts_;
+};
+
+class SoftDvppDecodeResizeJpegOperation : public TensorOperation {
+ public:
+  explicit SoftDvppDecodeResizeJpegOperation(std::vector<int32_t> size);
+
+  ~SoftDvppDecodeResizeJpegOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+ private:
+  std::vector<int32_t> size_;
+};
+
 class SwapRedBlueOperation : public TensorOperation {
  public:
   SwapRedBlueOperation();

mindspore/dataset/vision/c_transforms.py

@@ -1260,7 +1260,7 @@ class SoftDvppDecodeRandomCropResizeJpeg(cde.SoftDvppDecodeRandomCropResizeJpegO
     Tensor operation to decode, random crop and resize JPEG image using the simulation algorithm of
     Ascend series chip DVPP module.
 
-    The usage scenario is consistent with SoftDvppDecodeReiszeJpeg.
+    The usage scenario is consistent with SoftDvppDecodeResizeJpeg.
     The input image size should be in range [32*32, 8192*8192].
     The zoom-out and zoom-in multiples of the image length and width should in the range [1/32, 16].
     Only images with an even resolution can be output. The output of odd resolution is not supported.

tests/ut/cpp/dataset/c_api_vision_test.cc

@@ -1640,6 +1640,208 @@ TEST_F(MindDataTestPipeline, TestRandomPosterizeSuccess2) {
   iter->Stop();
 }
 
+TEST_F(MindDataTestPipeline, TestRandomResizeSuccess1) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomResizeSuccess1 with single integer input.";
+
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 5));
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> random_resize = vision::RandomResize({66});
+  EXPECT_NE(random_resize, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({random_resize}, {"image"});
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    EXPECT_EQ(image->shape()[0] == 66, true);
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 5);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestRandomResizeSuccess2) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomResizeSuccess2 with (height, width) input.";
+
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 3));
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Repeat operation on ds
+  int32_t repeat_num = 2;
+  ds = ds->Repeat(repeat_num);
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> random_resize = vision::RandomResize({66, 77});
+  EXPECT_NE(random_resize, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({random_resize}, {"image"});
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    EXPECT_EQ(image->shape()[0] == 66 && image->shape()[1] == 77, true);
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 6);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestRandomResizeFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomResizeFail incorrect size.";
+
+  // RandomResize : size must only contain positive integers
+  std::shared_ptr<TensorOperation> random_resize1 = vision::RandomResize({-66, 77});
+  EXPECT_EQ(random_resize1, nullptr);
+
+  // RandomResize : size must be a vector of one or two values
+  std::shared_ptr<TensorOperation> random_resize2 = vision::RandomResize({1, 2, 3});
+  EXPECT_EQ(random_resize2, nullptr);
+
+  // RandomResize : size must be a vector of one or two values
+  std::shared_ptr<TensorOperation> random_resize3 = vision::RandomResize({});
+  EXPECT_EQ(random_resize3, nullptr);
+}
+
+TEST_F(MindDataTestPipeline, TestRandomResizeWithBBoxSuccess1) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomResizeWithBBoxSuccess1 with single integer input.";
+
+  // Create an VOC Dataset
+  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
+  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> random_resize = vision::RandomResizeWithBBox({88});
+  EXPECT_NE(random_resize, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({random_resize}, {"image", "bbox"});
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    EXPECT_EQ(image->shape()[0] == 88, true);
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 3);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestRandomResizeWithBBoxSuccess2) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomResizeWithBBoxSuccess2 with (height, width) input.";
+
+  // Create an VOC Dataset
+  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
+  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 4));
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Repeat operation on ds
+  int32_t repeat_num = 2;
+  ds = ds->Repeat(repeat_num);
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> random_resize = vision::RandomResizeWithBBox({88, 99});
+  EXPECT_NE(random_resize, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({random_resize}, {"image", "bbox"});
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    EXPECT_EQ(image->shape()[0] == 88 && image->shape()[1] == 99, true);
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 8);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestRandomResizeWithBBoxFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomResizeWithBBoxFail incorrect size.";
+
+  // RandomResizeWithBBox : size must only contain positive integers
+  std::shared_ptr<TensorOperation> random_resize_with_bbox1 = vision::RandomResizeWithBBox({-66, 77});
+  EXPECT_EQ(random_resize_with_bbox1, nullptr);
+
+  // RandomResizeWithBBox : size must be a vector of one or two values
+  std::shared_ptr<TensorOperation> random_resize_with_bbox2 = vision::RandomResizeWithBBox({1, 2, 3});
+  EXPECT_EQ(random_resize_with_bbox2, nullptr);
+
+  // RandomResizeWithBBox : size must be a vector of one or two values
+  std::shared_ptr<TensorOperation> random_resize_with_bbox3 = vision::RandomResizeWithBBox({});
+  EXPECT_EQ(random_resize_with_bbox3, nullptr);
+}
+
 TEST_F(MindDataTestPipeline, TestRandomResizedCropSuccess1) {
   // Testing RandomResizedCrop with default values
   // Create a Cifar10 Dataset
@@ -1864,6 +2066,75 @@ TEST_F(MindDataTestPipeline, TestRandomRotationFail) {
   EXPECT_EQ(random_rotation_op6, nullptr);
 }
 
+TEST_F(MindDataTestPipeline, TestRandomSelectSubpolicySuccess) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomSelectSubpolicySuccess.";
+
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 7));
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  // Valid case: TensorOperation is not null and probability is between (0,1)
+  std::shared_ptr<TensorOperation> random_select_subpolicy = vision::RandomSelectSubpolicy(
+    {{{vision::Invert(), 0.5}, {vision::Equalize(), 0.5}}, {{vision::Resize({15, 15}), 1}}});
+  EXPECT_NE(random_select_subpolicy, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({random_select_subpolicy});
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 7);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestRandomSelectSubpolicyFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomSelectSubpolicyFail.";
+
+  // RandomSelectSubpolicy : probability of transform must be between 0.0 and 1.0
+  std::shared_ptr<TensorOperation> random_select_subpolicy1 = vision::RandomSelectSubpolicy(
+    {{{vision::Invert(), 1.5}, {vision::Equalize(), 0.5}}, {{vision::Resize({15, 15}), 1}}});
+  EXPECT_EQ(random_select_subpolicy1, nullptr);
+
+  // RandomSelectSubpolicy: policy must not be empty
+  std::shared_ptr<TensorOperation> random_select_subpolicy2 = vision::RandomSelectSubpolicy(
+    {{{vision::Invert(), 0.5}, {vision::Equalize(), 0.5}}, {{nullptr, 1}}});
+  EXPECT_EQ(random_select_subpolicy2, nullptr);
+
+  // RandomSelectSubpolicy: policy must not be empty
+  std::shared_ptr<TensorOperation> random_select_subpolicy3 = vision::RandomSelectSubpolicy({});
+  EXPECT_EQ(random_select_subpolicy3, nullptr);
+
+  // RandomSelectSubpolicy: policy must not be empty
+  std::shared_ptr<TensorOperation> random_select_subpolicy4 = vision::RandomSelectSubpolicy(
+    {{{vision::Invert(), 0.5}, {vision::Equalize(), 0.5}}, {}});
+  EXPECT_EQ(random_select_subpolicy4, nullptr);
+
+  // RandomSelectSubpolicy: policy must not be empty
+  std::shared_ptr<TensorOperation> random_select_subpolicy5 = vision::RandomSelectSubpolicy(
+    {{{}, {vision::Equalize(), 0.5}}, {{vision::Resize({15, 15}), 1}}});
+  EXPECT_EQ(random_select_subpolicy5, nullptr);
+}
+
 TEST_F(MindDataTestPipeline, TestRandomSharpness) {
   MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomSharpness.";
 
@@ -2297,6 +2568,237 @@ TEST_F(MindDataTestPipeline, TestRescaleFail) {
   EXPECT_EQ(rescale, nullptr);
 }
 
+TEST_F(MindDataTestPipeline, TestSoftDvppDecodeRandomCropResizeJpegSuccess1) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSoftDvppDecodeRandomCropResizeJpegSuccess1 with single integer input.";
+
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, RandomSampler(false, 4));
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> soft_dvpp_decode_random_crop_resize_jpeg =
+    vision::SoftDvppDecodeRandomCropResizeJpeg({500});
+  EXPECT_NE(soft_dvpp_decode_random_crop_resize_jpeg, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({soft_dvpp_decode_random_crop_resize_jpeg}, {"image"});
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    EXPECT_EQ(image->shape()[0] == 500 && image->shape()[1] == 500, true);
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 4);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestSoftDvppDecodeRandomCropResizeJpegSuccess2) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSoftDvppDecodeRandomCropResizeJpegSuccess2 with (height, width) input.";
+
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, RandomSampler(false, 6));
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> soft_dvpp_decode_random_crop_resize_jpeg =
+    vision::SoftDvppDecodeRandomCropResizeJpeg({500, 600}, {0.25, 0.75}, {0.5, 1.25}, 20);
+  EXPECT_NE(soft_dvpp_decode_random_crop_resize_jpeg, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({soft_dvpp_decode_random_crop_resize_jpeg}, {"image"});
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    EXPECT_EQ(image->shape()[0] == 500 && image->shape()[1] == 600, true);
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 6);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestSoftDvppDecodeRandomCropResizeJpegFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSoftDvppDecodeRandomCropResizeJpegFail with incorrect parameters.";
+
+  // SoftDvppDecodeRandomCropResizeJpeg: size must only contain positive integers
+  auto soft_dvpp_decode_random_crop_resize_jpeg1 = vision::SoftDvppDecodeRandomCropResizeJpeg({-500, 600});
+  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg1, nullptr);
+
+  // SoftDvppDecodeRandomCropResizeJpeg: size must only contain positive integers
+  auto soft_dvpp_decode_random_crop_resize_jpeg2 = vision::SoftDvppDecodeRandomCropResizeJpeg({-500});
+  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg2, nullptr);
+
+  // SoftDvppDecodeRandomCropResizeJpeg: size must be a vector of one or two values
+  auto soft_dvpp_decode_random_crop_resize_jpeg3 = vision::SoftDvppDecodeRandomCropResizeJpeg({500, 600, 700});
+  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg3, nullptr);
+
+  // SoftDvppDecodeRandomCropResizeJpeg: scale must be greater than or equal to 0
+  auto soft_dvpp_decode_random_crop_resize_jpeg4 = vision::SoftDvppDecodeRandomCropResizeJpeg({500}, {-0.1, 0.9});
+  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg4, nullptr);
+
+  // SoftDvppDecodeRandomCropResizeJpeg: scale must be in the format of (min, max)
+  auto soft_dvpp_decode_random_crop_resize_jpeg5 = vision::SoftDvppDecodeRandomCropResizeJpeg({500}, {0.6, 0.2});
+  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg5, nullptr);
+
+  // SoftDvppDecodeRandomCropResizeJpeg: scale must be a vector of two values
+  auto soft_dvpp_decode_random_crop_resize_jpeg6 = vision::SoftDvppDecodeRandomCropResizeJpeg({500}, {0.5, 0.6, 0.7});
+  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg6, nullptr);
+
+  // SoftDvppDecodeRandomCropResizeJpeg: ratio must be greater than or equal to 0
+  auto soft_dvpp_decode_random_crop_resize_jpeg7 =
+    vision::SoftDvppDecodeRandomCropResizeJpeg({500}, {0.5, 0.9}, {-0.2, 0.4});
+  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg7, nullptr);
+
+  // SoftDvppDecodeRandomCropResizeJpeg: ratio must be in the format of (min, max)
+  auto soft_dvpp_decode_random_crop_resize_jpeg8 =
+    vision::SoftDvppDecodeRandomCropResizeJpeg({500}, {0.5, 0.9}, {0.4, 0.2});
+  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg8, nullptr);
+
+  // SoftDvppDecodeRandomCropResizeJpeg: ratio must be a vector of two values
+  auto soft_dvpp_decode_random_crop_resize_jpeg9 =
+    vision::SoftDvppDecodeRandomCropResizeJpeg({500}, {0.5, 0.9}, {0.1, 0.2, 0.3});
+  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg9, nullptr);
+
+  // SoftDvppDecodeRandomCropResizeJpeg: max_attempts must be greater than or equal to 1
+  auto soft_dvpp_decode_random_crop_resize_jpeg10 =
+    vision::SoftDvppDecodeRandomCropResizeJpeg({500}, {0.5, 0.9}, {0.1, 0.2}, 0);
+  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg10, nullptr);
+}
+
+TEST_F(MindDataTestPipeline, TestSoftDvppDecodeResizeJpegSuccess1) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSoftDvppDecodeResizeJpegSuccess1 with single integer input.";
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, RandomSampler(false, 4));
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Repeat operation on ds
+  int32_t repeat_num = 3;
+  ds = ds->Repeat(repeat_num);
+  EXPECT_NE(ds, nullptr);
+
+  // Create SoftDvppDecodeResizeJpeg object with single integer input
+  std::shared_ptr<TensorOperation> soft_dvpp_decode_resize_jpeg_op = vision::SoftDvppDecodeResizeJpeg({1134});
+  EXPECT_NE(soft_dvpp_decode_resize_jpeg_op, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({soft_dvpp_decode_resize_jpeg_op});
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 12);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestSoftDvppDecodeResizeJpegSuccess2) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSoftDvppDecodeResizeJpegSuccess2 with (height, width) input.";
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, RandomSampler(false, 2));
+  EXPECT_NE(ds, nullptr);
+
+  // Create SoftDvppDecodeResizeJpeg object with single integer input
+  std::shared_ptr<TensorOperation> soft_dvpp_decode_resize_jpeg_op = vision::SoftDvppDecodeResizeJpeg({100, 200});
+  EXPECT_NE(soft_dvpp_decode_resize_jpeg_op, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({soft_dvpp_decode_resize_jpeg_op});
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 2);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestSoftDvppDecodeResizeJpegFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSoftDvppDecodeResizeJpegFail with incorrect size.";
+
+  // CSoftDvppDecodeResizeJpeg: size must be a vector of one or two values
+  std::shared_ptr<TensorOperation> soft_dvpp_decode_resize_jpeg_op1 = vision::SoftDvppDecodeResizeJpeg({});
+  EXPECT_EQ(soft_dvpp_decode_resize_jpeg_op1, nullptr);
+
+  // SoftDvppDecodeResizeJpeg: size must be a vector of one or two values
+  std::shared_ptr<TensorOperation> soft_dvpp_decode_resize_jpeg_op2 = vision::SoftDvppDecodeResizeJpeg({1, 2, 3});
+  EXPECT_EQ(soft_dvpp_decode_resize_jpeg_op2, nullptr);
+
+  // SoftDvppDecodeResizeJpeg: size must only contain positive integers
+  std::shared_ptr<TensorOperation> soft_dvpp_decode_resize_jpeg_op3 = vision::SoftDvppDecodeResizeJpeg({20, -20});
+  EXPECT_EQ(soft_dvpp_decode_resize_jpeg_op3, nullptr);
+
+  // SoftDvppDecodeResizeJpeg: size must only contain positive integers
+  std::shared_ptr<TensorOperation> soft_dvpp_decode_resize_jpeg_op4 = vision::SoftDvppDecodeResizeJpeg({0});
+  EXPECT_EQ(soft_dvpp_decode_resize_jpeg_op4, nullptr);
+}
+
 TEST_F(MindDataTestPipeline, DISABLED_TestUniformAugmentFail1) {
   MS_LOG(INFO) << "Doing MindDataTestPipeline-TestUniformAugmentFail1 with invalid zero num_ops parameter.";