!12183 Dvpp New

From: @lizhenglong1992
Reviewed-by: 
Signed-off-by:

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

@@ -16,6 +16,7 @@
 
 #include "minddata/dataset/include/execute.h"
 #include "minddata/dataset/core/de_tensor.h"
+#include "minddata/dataset/core/device_tensor.h"
 #include "minddata/dataset/core/tensor_row.h"
 #include "minddata/dataset/include/tensor.h"
 #include "minddata/dataset/include/type_id.h"
@@ -25,53 +26,194 @@
 #else
 #include "mindspore/lite/src/common/log_adapter.h"
 #endif
+#ifdef ENABLE_ACL
+#include "acl/acl.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/ResourceManager.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/ErrorCode.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/MDAclProcess.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/CommonDataType.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/DvppCommon.h"
+#endif
 
 namespace mindspore {
 namespace dataset {
+#ifdef ENABLE_ACL
+class AscendResource {
+ public:
+  AscendResource();
+  ~AscendResource() = default;
+
+  Status InitChipResource();
+
+  Status FinalizeChipResource();
+
+  Status Sink(const mindspore::MSTensor &host_input, std::shared_ptr<DeviceTensor> *device_input);
+
+  Status Pop(std::shared_ptr<DeviceTensor> device_output, std::shared_ptr<Tensor> *host_output);
+
+  Status DeviceDataRelease();
+
+  std::shared_ptr<MDAclProcess> processor_;
+  std::shared_ptr<ResourceManager> ascend_resource_;
+};
+
+AscendResource::AscendResource() { InitChipResource(); }
+
+Status AscendResource::InitChipResource() {
+  ResourceInfo resource;
+  resource.aclConfigPath = "";
+  resource.deviceIds.insert(mindspore::GlobalContext::GetGlobalDeviceID());
+  ascend_resource_ = ResourceManager::GetInstance();
+  APP_ERROR ret = ascend_resource_->InitResource(resource);
+  if (ret != APP_ERR_OK) {
+    ascend_resource_->Release();
+    std::string err_msg = "Error in Init D-chip:" + std::to_string(ret);
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_UNEXPECTED(err_msg);
+  }
+  int device_id = *(resource.deviceIds.begin());
+  aclrtContext context = ascend_resource_->GetContext(device_id);
+  processor_ = std::make_shared<MDAclProcess>(context, false);
+  ret = processor_->InitResource();
+  if (ret != APP_ERR_OK) {
+    ascend_resource_->Release();
+    std::string err_msg = "Error in Init resource:" + std::to_string(ret);
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_UNEXPECTED(err_msg);
+  }
+  MS_LOG(INFO) << "Ascend resource all initialized!";
+  return Status::OK();
+}
+
+Status AscendResource::FinalizeChipResource() {
+  processor_->Release();
+  return Status::OK();
+}
+
+Status AscendResource::Sink(const mindspore::MSTensor &host_input, std::shared_ptr<DeviceTensor> *device_input) {
+  std::shared_ptr<mindspore::dataset::Tensor> de_input;
+  Status rc = dataset::Tensor::CreateFromMemory(dataset::TensorShape(host_input.Shape()),
+                                                MSTypeToDEType(static_cast<TypeId>(host_input.DataType())),
+                                                (const uchar *)(host_input.Data().get()), &de_input);
+  RETURN_IF_NOT_OK(rc);
+  APP_ERROR ret = processor_->H2D_Sink(de_input, *device_input);
+  if (ret != APP_ERR_OK) {
+    ascend_resource_->Release();
+    std::string err_msg = "Error in data sink process:" + std::to_string(ret);
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_UNEXPECTED(err_msg);
+  }
+  MS_LOG(INFO) << "Process data sink successfully";
+  return Status::OK();
+}
 
-Execute::Execute(std::shared_ptr<TensorOperation> op) { ops_.emplace_back(std::move(op)); }
+Status AscendResource::Pop(std::shared_ptr<DeviceTensor> device_output, std::shared_ptr<Tensor> *host_output) {
+  APP_ERROR ret = processor_->D2H_Pop(device_output, *host_output);
+  if (ret != APP_ERR_OK) {
+    ascend_resource_->Release();
+    std::string err_msg = "Error in data pop processing:" + std::to_string(ret);
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_UNEXPECTED(err_msg);
+  }
+  return Status::OK();
+}
+
+Status AscendResource::DeviceDataRelease() {
+  APP_ERROR ret = processor_->device_memory_release();
+  if (ret != APP_ERR_OK) {
+    ascend_resource_->Release();
+    std::string err_msg = "Error in device data release:" + std::to_string(ret);
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_UNEXPECTED(err_msg);
+  }
+  return Status::OK();
+}
+#endif
+
+Execute::Execute(std::shared_ptr<TensorOperation> op, std::string deviceType) {
+  ops_.emplace_back(std::move(op));
+  device_type_ = deviceType;
+  MS_LOG(INFO) << "Running Device: " << device_type_;
+#ifdef ENABLE_ACL
+  if (device_type_ == "Ascend310") {
+    D_resource_ = std::make_shared<AscendResource>();
+  }
+#endif
+}
+
+Execute::Execute(std::vector<std::shared_ptr<TensorOperation>> ops, std::string deviceType)
+    : ops_(std::move(ops)), device_type_(deviceType) {
+  MS_LOG(INFO) << "Running Device: " << device_type_;
+#ifdef ENABLE_ACL
+  if (device_type_ == "Ascend310") {
+    D_resource_ = std::make_shared<AscendResource>();
+  }
+#endif
+}
 
-Execute::Execute(std::vector<std::shared_ptr<TensorOperation>> ops) : ops_(std::move(ops)) {}
+Execute::~Execute() {
+#ifdef ENABLE_ACL
+  if (device_type_ == "Ascend310") {
+    D_resource_->FinalizeChipResource();
+  }
+#endif
+}
 
 Status Execute::operator()(const mindspore::MSTensor &input, mindspore::MSTensor *output) {
   // Validate input tensor
   CHECK_FAIL_RETURN_UNEXPECTED(input.DataSize() > 0, "Input Tensor has no data");
   CHECK_FAIL_RETURN_UNEXPECTED(!ops_.empty(), "Input TensorOperation should be provided");
-
+  CHECK_FAIL_RETURN_UNEXPECTED(validate_device_(), "Device Type should be 'Ascend310' or 'CPU'");
   // Validate and build runtime ops
-  std::vector<std::shared_ptr<TensorOp>> transforms;
+  std::vector<std::shared_ptr<TensorOp>> transforms;  // record the transformations
   for (int32_t i = 0; i < ops_.size(); i++) {
     CHECK_FAIL_RETURN_UNEXPECTED(ops_[i] != nullptr, "Input TensorOperation[" + std::to_string(i) + "] is null");
     RETURN_IF_NOT_OK(ops_[i]->ValidateParams());
     transforms.emplace_back(ops_[i]->Build());
   }
+  if (device_type_ == "CPU") {
+    // Convert mindspore::Tensor to dataset::Tensor
+    std::shared_ptr<dataset::Tensor> de_tensor;
+    Status rc = dataset::Tensor::CreateFromMemory(dataset::TensorShape(input.Shape()),
+                                                  MSTypeToDEType(static_cast<TypeId>(input.DataType())),
+                                                  (const uchar *)(input.Data().get()), input.DataSize(), &de_tensor);
+    if (rc.IsError()) {
+      MS_LOG(ERROR) << rc;
+      return rc;
+    }
 
-  // Convert mindspore::Tensor to dataset::Tensor
-  std::shared_ptr<dataset::Tensor> de_tensor;
-  Status rc = dataset::Tensor::CreateFromMemory(dataset::TensorShape(input.Shape()),
-                                                MSTypeToDEType(static_cast<TypeId>(input.DataType())),
-                                                (const uchar *)(input.Data().get()), input.DataSize(), &de_tensor);
-  if (rc.IsError()) {
-    MS_LOG(ERROR) << rc;
-    RETURN_IF_NOT_OK(rc);
-  }
+    // Apply transforms on tensor
+    for (auto &t : transforms) {
+      std::shared_ptr<dataset::Tensor> de_output;
+      Status rc_ = t->Compute(de_tensor, &de_output);
+      if (rc_.IsError()) {
+        MS_LOG(ERROR) << rc_;
+        return rc_;
+      }
 
-  // Apply transforms on tensor
-  for (auto &t : transforms) {
-    std::shared_ptr<dataset::Tensor> de_output;
-    Status rc_ = t->Compute(de_tensor, &de_output);
-    if (rc_.IsError()) {
-      MS_LOG(ERROR) << rc_;
-      RETURN_IF_NOT_OK(rc_);
+      // For next transform
+      de_tensor = std::move(de_output);
     }
 
-    // For next transform
-    de_tensor = std::move(de_output);
-  }
+    // Convert dataset::Tensor to mindspore::Tensor
+    CHECK_FAIL_RETURN_UNEXPECTED(de_tensor->HasData(), "Apply transform failed, output tensor has no data");
+    *output = mindspore::MSTensor(std::make_shared<DETensor>(de_tensor));
+  } else {  // Ascend310 case, where we must set Ascend resource on each operators
+#ifdef ENABLE_ACL
+    std::shared_ptr<mindspore::dataset::DeviceTensor> device_input;
+    RETURN_IF_NOT_OK(D_resource_->Sink(input, &device_input));
+    for (auto &t : transforms) {
+      std::shared_ptr<DeviceTensor> device_output;
+      RETURN_IF_NOT_OK(t->SetAscendResource(D_resource_->processor_));
+      RETURN_IF_NOT_OK(t->Compute(device_input, &device_output));
 
-  // Convert dataset::Tensor to mindspore::Tensor
-  CHECK_FAIL_RETURN_UNEXPECTED(de_tensor->HasData(), "Apply transform failed, output tensor has no data");
-  *output = mindspore::MSTensor(std::make_shared<DETensor>(de_tensor));
+      // For next transform
+      device_input = std::move(device_output);
+    }
+    CHECK_FAIL_RETURN_UNEXPECTED(device_input->HasDeviceData(), "Apply transform failed, output tensor has no data");
+    *output = mindspore::MSTensor(std::make_shared<DETensor>(device_input, true));
+#endif
+  }
   return Status::OK();
 }
 
@@ -82,6 +224,7 @@ Status Execute::operator()(const std::vector<MSTensor> &input_tensor_list, std::
     CHECK_FAIL_RETURN_UNEXPECTED(tensor.DataSize() > 0, "Input Tensor has no data");
   }
   CHECK_FAIL_RETURN_UNEXPECTED(!ops_.empty(), "Input TensorOperation should be provided");
+  CHECK_FAIL_RETURN_UNEXPECTED(validate_device_(), "Device Type should be 'Ascend310' or 'CPU'");
 
   // Validate and build runtime ops
   std::vector<std::shared_ptr<TensorOp>> transforms;
@@ -90,40 +233,81 @@ Status Execute::operator()(const std::vector<MSTensor> &input_tensor_list, std::
     RETURN_IF_NOT_OK(ops_[i]->ValidateParams());
     transforms.emplace_back(ops_[i]->Build());
   }
+  if (device_type_ == "CPU") {  // Case CPU
+    TensorRow de_tensor_list;
+    for (auto &tensor : input_tensor_list) {
+      std::shared_ptr<dataset::Tensor> de_tensor;
+      Status rc = dataset::Tensor::CreateFromMemory(
+        dataset::TensorShape(tensor.Shape()), MSTypeToDEType(static_cast<TypeId>(tensor.DataType())),
+        (const uchar *)(tensor.Data().get()), tensor.DataSize(), &de_tensor);
+      if (rc.IsError()) {
+        MS_LOG(ERROR) << rc;
+        RETURN_IF_NOT_OK(rc);
+      }
+      de_tensor_list.emplace_back(std::move(de_tensor));
+    }
+    // Apply transforms on tensor
+    for (auto &t : transforms) {
+      TensorRow de_output_list;
+      RETURN_IF_NOT_OK(t->Compute(de_tensor_list, &de_output_list));
+      // For next transform
+      de_tensor_list = std::move(de_output_list);
+    }
 
-  TensorRow de_tensor_list;
-  for (auto &tensor : input_tensor_list) {
-    std::shared_ptr<dataset::Tensor> de_tensor;
-    Status rc = dataset::Tensor::CreateFromMemory(dataset::TensorShape(tensor.Shape()),
-                                                  MSTypeToDEType(static_cast<TypeId>(tensor.DataType())),
-                                                  (const uchar *)(tensor.Data().get()), tensor.DataSize(), &de_tensor);
-    if (rc.IsError()) {
-      MS_LOG(ERROR) << rc;
-      RETURN_IF_NOT_OK(rc);
+    for (auto &tensor : de_tensor_list) {
+      CHECK_FAIL_RETURN_UNEXPECTED(tensor->HasData(), "Apply transform failed, output tensor has no data");
+      auto ms_tensor = mindspore::MSTensor(std::make_shared<DETensor>(tensor));
+      output_tensor_list->emplace_back(ms_tensor);
     }
-    de_tensor_list.emplace_back(std::move(de_tensor));
-  }
+    CHECK_FAIL_RETURN_UNEXPECTED(!output_tensor_list->empty(), "Output Tensor is not valid");
+  } else {  // Case Ascend310
+#ifdef ENABLE_ACL
+    for (auto &input_tensor : input_tensor_list) {
+      std::shared_ptr<dataset::DeviceTensor> device_input;
+      RETURN_IF_NOT_OK(D_resource_->Sink(input_tensor, &device_input));
+      for (auto &t : transforms) {
+        std::shared_ptr<DeviceTensor> device_output;
+        RETURN_IF_NOT_OK(t->SetAscendResource(D_resource_->processor_));
+        RETURN_IF_NOT_OK(t->Compute(device_input, &device_output));
 
-  // Apply transforms on tensor
-  for (auto &t : transforms) {
-    TensorRow de_output_list;
-    Status rc = t->Compute(de_tensor_list, &de_output_list);
-    if (rc.IsError()) {
-      MS_LOG(ERROR) << rc;
-      RETURN_IF_NOT_OK(rc);
+        // For next transform
+        device_input = std::move(device_output);
+      }
+      CHECK_FAIL_RETURN_UNEXPECTED(device_input->HasDeviceData(), "Apply transform failed, output tensor has no data");
+      // Due to the limitation of Ascend310 memory, we have to pop every data onto host memory
+      // So the speed of this method is slower than solo mode
+      std::shared_ptr<mindspore::dataset::Tensor> host_output;
+      RETURN_IF_NOT_OK(D_resource_->Pop(device_input, &host_output));
+      auto ms_tensor = mindspore::MSTensor(std::make_shared<DETensor>(host_output));
+      output_tensor_list->emplace_back(ms_tensor);
+      RETURN_IF_NOT_OK(D_resource_->DeviceDataRelease());
     }
-    // For next transform
-    de_tensor_list = std::move(de_output_list);
+    CHECK_FAIL_RETURN_UNEXPECTED(!output_tensor_list->empty(), "Output Tensor vector is empty");
+#endif
   }
+  return Status::OK();
+}
 
-  for (auto &tensor : de_tensor_list) {
-    CHECK_FAIL_RETURN_UNEXPECTED(tensor->HasData(), "Apply transform failed, output tensor has no data");
-    auto ms_tensor = mindspore::MSTensor(std::make_shared<DETensor>(tensor));
-    output_tensor_list->emplace_back(ms_tensor);
+Status Execute::validate_device_() {
+  if (device_type_ != "CPU" && device_type_ != "Ascend310") {
+    std::string err_msg = device_type_ + " is not supported. (Option: CPU or Ascend310)";
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_UNEXPECTED(err_msg);
   }
-  CHECK_FAIL_RETURN_UNEXPECTED(!output_tensor_list->empty(), "Output Tensor is not valid");
   return Status::OK();
 }
 
+#ifdef ENABLE_ACL
+Status Execute::DeviceMemoryRelease() {
+  Status rc = D_resource_->DeviceDataRelease();
+  if (rc.IsError()) {
+    D_resource_->ascend_resource_->Release();
+    std::string err_msg = "Error in device data release";
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_UNEXPECTED(err_msg);
+  }
+  return Status::OK();
+}
+#endif
 }  // namespace dataset
 }  // namespace mindspore

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

@@ -37,7 +37,13 @@
 #endif
 #include "minddata/dataset/kernels/image/decode_op.h"
 #ifdef ENABLE_ACL
+#include "minddata/dataset/include/vision_ascend.h"
+#include "minddata/dataset/kernels/image/dvpp/dvpp_crop_jpeg_op.h"
+#include "minddata/dataset/kernels/image/dvpp/dvpp_decode_resize_jpeg_op.h"
 #include "minddata/dataset/kernels/image/dvpp/dvpp_decode_resize_crop_jpeg_op.h"
+#include "minddata/dataset/kernels/image/dvpp/dvpp_decode_jpeg_op.h"
+#include "minddata/dataset/kernels/image/dvpp/dvpp_decode_png_op.h"
+#include "minddata/dataset/kernels/image/dvpp/dvpp_resize_jpeg_op.h"
 #endif
 #ifndef ENABLE_ANDROID
 #include "minddata/dataset/kernels/image/equalize_op.h"
@@ -143,6 +149,20 @@ std::shared_ptr<DecodeOperation> Decode(bool rgb) {
 }
 
 #ifdef ENABLE_ACL
+// Function to create DvppResizeOperation.
+std::shared_ptr<DvppCropJpegOperation> DvppCropJpeg(std::vector<uint32_t> crop) {
+  auto op = std::make_shared<DvppCropJpegOperation>(crop);
+  // Input validation
+  return op->ValidateParams() ? op : nullptr;
+}
+
+// Function to create DvppDecodeResizeOperation.
+std::shared_ptr<DvppDecodeResizeOperation> DvppDecodeResizeJpeg(std::vector<uint32_t> resize) {
+  auto op = std::make_shared<DvppDecodeResizeOperation>(resize);
+  // Input validation
+  return op->ValidateParams() ? op : nullptr;
+}
+
 // Function to create DvppDecodeResizeCropOperation.
 std::shared_ptr<DvppDecodeResizeCropOperation> DvppDecodeResizeCropJpeg(std::vector<uint32_t> crop,
                                                                         std::vector<uint32_t> resize) {
@@ -150,6 +170,27 @@ std::shared_ptr<DvppDecodeResizeCropOperation> DvppDecodeResizeCropJpeg(std::vec
   // Input validation
   return op->ValidateParams() ? op : nullptr;
 }
+
+// Function to create DvppDecodeJpegOperation.
+std::shared_ptr<DvppDecodeJpegOperation> DvppDecodeJpeg() {
+  auto op = std::make_shared<DvppDecodeJpegOperation>();
+  // Input validation
+  return op->ValidateParams() ? op : nullptr;
+}
+
+// Function to create DvppDecodePngOperation.
+std::shared_ptr<DvppDecodePngOperation> DvppDecodePng() {
+  auto op = std::make_shared<DvppDecodePngOperation>();
+  // Input validation
+  return op->ValidateParams() ? op : nullptr;
+}
+
+// Function to create DvppResizeOperation.
+std::shared_ptr<DvppResizeJpegOperation> DvppResizeJpeg(std::vector<uint32_t> resize) {
+  auto op = std::make_shared<DvppResizeJpegOperation>(resize);
+  // Input validation
+  return op->ValidateParams() ? op : nullptr;
+}
 #endif
 
 // Function to create EqualizeOperation.
@@ -445,6 +486,232 @@ std::shared_ptr<UniformAugOperation> UniformAugment(std::vector<std::shared_ptr<
 }
 #endif
 
+#ifdef ENABLE_ACL
+// DvppCropOperation
+DvppCropJpegOperation::DvppCropJpegOperation(const std::vector<uint32_t> &crop) : crop_(crop) {}
+
+Status DvppCropJpegOperation::ValidateParams() {
+  // size
+  if (crop_.empty() || crop_.size() > 2) {
+    std::string err_msg =
+      "DvppCropJpeg: Crop resolution must be a vector of one or two elements, got: " + std::to_string(crop_.size());
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  if (*min_element(crop_.begin(), crop_.end()) < 32 || *max_element(crop_.begin(), crop_.end()) > 2048) {
+    std::string err_msg = "Dvpp module supports crop image with resolution in range [32, 2048], got crop Parameters: ";
+    if (crop_.size() == 2) {
+      MS_LOG(ERROR) << err_msg << "[" << crop_[0] << ", " << crop_[1] << "]";
+    } else {
+      MS_LOG(ERROR) << err_msg << "[" << crop_[0] << ", " << crop_[0] << "]";
+    }
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  return Status::OK();
+}
+
+std::shared_ptr<TensorOp> DvppCropJpegOperation::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.
+  uint32_t cropHeight, cropWidth;
+  // User specified the width value.
+  if (crop_.size() == 1) {
+    cropHeight = crop_[0];
+    cropWidth = crop_[0];
+  } else {
+    cropHeight = crop_[0];
+    cropWidth = crop_[1];
+  }
+  std::shared_ptr<DvppCropJpegOp> tensor_op = std::make_shared<DvppCropJpegOp>(cropHeight, cropWidth);
+  return tensor_op;
+}
+
+// DvppDecodeResizeOperation
+DvppDecodeResizeOperation::DvppDecodeResizeOperation(const std::vector<uint32_t> &resize) : resize_(resize) {}
+
+Status DvppDecodeResizeOperation::ValidateParams() {
+  // size
+  if (resize_.empty() || resize_.size() > 2) {
+    std::string err_msg = "DvppDecodeResizeJpeg: resize resolution must be a vector of one or two elements, got: " +
+                          std::to_string(resize_.size());
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  if (*min_element(resize_.begin(), resize_.end()) < 32 || *max_element(resize_.begin(), resize_.end()) > 2048) {
+    std::string err_msg =
+      "Dvpp module supports resize image with resolution in range [32, 2048], got resize Parameters: ";
+    if (resize_.size() == 2) {
+      MS_LOG(ERROR) << err_msg << "[" << resize_[0] << ", " << resize_[1] << "]";
+    } else {
+      MS_LOG(ERROR) << err_msg << "[" << resize_[0] << ", " << resize_[0] << "]";
+    }
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  return Status::OK();
+}
+
+std::shared_ptr<TensorOp> DvppDecodeResizeOperation::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.
+  uint32_t resizeHeight, resizeWidth;
+  // User specified the width value.
+  if (resize_.size() == 1) {
+    resizeHeight = resize_[0];
+    resizeWidth = 0;
+  } else {
+    resizeHeight = resize_[0];
+    resizeWidth = resize_[1];
+  }
+  std::shared_ptr<DvppDecodeResizeJpegOp> tensor_op =
+    std::make_shared<DvppDecodeResizeJpegOp>(resizeHeight, resizeWidth);
+  return tensor_op;
+}
+
+// DvppDecodeResizeCropOperation
+DvppDecodeResizeCropOperation::DvppDecodeResizeCropOperation(const std::vector<uint32_t> &crop,
+                                                             const std::vector<uint32_t> &resize)
+    : crop_(crop), resize_(resize) {}
+
+Status DvppDecodeResizeCropOperation::ValidateParams() {
+  // size
+  if (crop_.empty() || crop_.size() > 2) {
+    std::string err_msg = "DvppDecodeResizeCropJpeg: crop resolution must be a vector of one or two elements, got: " +
+                          std::to_string(crop_.size());
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  if (resize_.empty() || resize_.size() > 2) {
+    std::string err_msg = "DvppDecodeResizeCropJpeg: resize resolution must be a vector of one or two elements, got: " +
+                          std::to_string(resize_.size());
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  if (*min_element(crop_.begin(), crop_.end()) < 32 || *max_element(crop_.begin(), crop_.end()) > 2048) {
+    std::string err_msg = "Dvpp module supports crop image with resolution in range [32, 2048], got Crop Parameters: ";
+    if (crop_.size() == 2) {
+      MS_LOG(ERROR) << err_msg << "[" << crop_[0] << ", " << crop_[1] << "]";
+    } else {
+      MS_LOG(ERROR) << err_msg << "[" << crop_[0] << ", " << crop_[0] << "]";
+    }
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  if (*min_element(resize_.begin(), resize_.end()) < 32 || *max_element(resize_.begin(), resize_.end()) > 2048) {
+    std::string err_msg =
+      "Dvpp module supports resize image with resolution in range [32, 2048], got Crop Parameters: ";
+    if (resize_.size() == 2) {
+      MS_LOG(ERROR) << err_msg << "[" << resize_[0] << ", " << resize_[1] << "]";
+    } else {
+      MS_LOG(ERROR) << err_msg << "[" << resize_[0] << "]";
+    }
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  if (crop_.size() < resize_.size()) {
+    if (crop_[0] > MIN(resize_[0], resize_[1])) {
+      std::string err_msg =
+        "Each value of crop parameter must be smaller than corresponding resize parameter, for example: x[0] <= "
+        "y[0],  and x[1] <= y[1], please verify your input parameters.";
+      MS_LOG(ERROR) << err_msg;
+      RETURN_STATUS_SYNTAX_ERROR(err_msg);
+    }
+  }
+  if (crop_.size() > resize_.size()) {
+    if (MAX(crop_[0], crop_[1]) > resize_[0]) {
+      std::string err_msg =
+        "Each value of crop parameter must be smaller than corresponding resize parameter, for example: x[0] <= "
+        "y[0],  and x[1] <= y[1], please verify your input parameters.";
+      MS_LOG(ERROR) << err_msg;
+      RETURN_STATUS_SYNTAX_ERROR(err_msg);
+    }
+  }
+  if (crop_.size() == resize_.size()) {
+    for (int32_t i = 0; i < crop_.size(); ++i) {
+      if (crop_[i] > resize_[i]) {
+        std::string err_msg =
+          "Each value of crop parameter must be smaller than corresponding resize parameter, for example: x[0] <= "
+          "y[0],  and x[1] <= y[1], please verify your input parameters.";
+        MS_LOG(ERROR) << err_msg;
+        RETURN_STATUS_SYNTAX_ERROR(err_msg);
+      }
+    }
+  }
+  return Status::OK();
+}
+
+std::shared_ptr<TensorOp> DvppDecodeResizeCropOperation::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.
+  uint32_t cropHeight, cropWidth, resizeHeight, resizeWidth;
+  if (crop_.size() == 1) {
+    cropHeight = crop_[0];
+    cropWidth = crop_[0];
+  } else {
+    cropHeight = crop_[0];
+    cropWidth = crop_[1];
+  }
+  // User specified the width value.
+  if (resize_.size() == 1) {
+    resizeHeight = resize_[0];
+    resizeWidth = 0;
+  } else {
+    resizeHeight = resize_[0];
+    resizeWidth = resize_[1];
+  }
+  std::shared_ptr<DvppDecodeResizeCropJpegOp> tensor_op =
+    std::make_shared<DvppDecodeResizeCropJpegOp>(cropHeight, cropWidth, resizeHeight, resizeWidth);
+  return tensor_op;
+}
+
+// DvppDecodeJPEG
+Status DvppDecodeJpegOperation::ValidateParams() { return Status::OK(); }
+
+std::shared_ptr<TensorOp> DvppDecodeJpegOperation::Build() { return std::make_shared<DvppDecodeJpegOp>(); }
+
+// DvppDecodePNG
+Status DvppDecodePngOperation::ValidateParams() { return Status::OK(); }
+
+std::shared_ptr<TensorOp> DvppDecodePngOperation::Build() { return std::make_shared<DvppDecodePngOp>(); }
+
+// DvppResizeOperation
+DvppResizeJpegOperation::DvppResizeJpegOperation(const std::vector<uint32_t> &resize) : resize_(resize) {}
+
+Status DvppResizeJpegOperation::ValidateParams() {
+  // size
+  if (resize_.empty() || resize_.size() > 2) {
+    std::string err_msg = "DvppResizeJpeg: resize resolution must be a vector of one or two elements, got: " +
+                          std::to_string(resize_.size());
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  if (*min_element(resize_.begin(), resize_.end()) < 32 || *max_element(resize_.begin(), resize_.end()) > 2048) {
+    std::string err_msg =
+      "Dvpp module supports resize image with resolution in range [32, 2048], got resize Parameters: ";
+    if (resize_.size() == 2) {
+      MS_LOG(ERROR) << err_msg << "[" << resize_[0] << ", " << resize_[1] << "]";
+    } else {
+      MS_LOG(ERROR) << err_msg << "[" << resize_[0] << ", " << resize_[0] << "]";
+    }
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  return Status::OK();
+}
+
+std::shared_ptr<TensorOp> DvppResizeJpegOperation::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.
+  uint32_t resizeHeight, resizeWidth;
+  // User specified the width value.
+  if (resize_.size() == 1) {
+    resizeHeight = resize_[0];
+    resizeWidth = 0;
+  } else {
+    resizeHeight = resize_[0];
+    resizeWidth = resize_[1];
+  }
+  std::shared_ptr<DvppResizeJpegOp> tensor_op = std::make_shared<DvppResizeJpegOp>(resizeHeight, resizeWidth);
+  return tensor_op;
+}
+#endif
+
 }  // namespace vision
 }  // namespace dataset
 }  // namespace mindspore

mindspore/ccsrc/minddata/dataset/core/CMakeLists.txt

@@ -1,17 +1,18 @@
 file(GLOB_RECURSE _CURRENT_SRC_FILES RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "*.cc")
 set_property(SOURCE ${_CURRENT_SRC_FILES} PROPERTY COMPILE_DEFINITIONS SUBMODULE_ID=mindspore::SubModuleId::SM_MD)
 set(DATASET_CORE_SRC_FILES
-  client.cc
-  config_manager.cc
-  cv_tensor.cc
-  data_type.cc
-  de_tensor.cc
-  global_context.cc
-  tensor.cc
-  tensor_helpers.cc
-  tensor_row.cc
-  tensor_shape.cc
-)
+        client.cc
+        config_manager.cc
+        cv_tensor.cc
+        data_type.cc
+        device_tensor.cc
+        de_tensor.cc
+        global_context.cc
+        tensor.cc
+        tensor_helpers.cc
+        tensor_row.cc
+        tensor_shape.cc
+        )
 
 ms_protobuf_generate(EXAMPLE_SRCS EXAMPLE_HDRS example.proto)
 ms_protobuf_generate(FEATURE_SRCS FEATURE_HDRS feature.proto)

mindspore/ccsrc/minddata/dataset/core/de_tensor.cc

@@ -15,6 +15,7 @@
  */
 
 #include "minddata/dataset/core/de_tensor.h"
+#include "minddata/dataset/core/device_tensor.h"
 #include "minddata/dataset/core/constants.h"
 #include "minddata/dataset/core/data_type.h"
 #include "minddata/dataset/include/type_id.h"
@@ -35,7 +36,27 @@ DETensor::DETensor(std::shared_ptr<dataset::Tensor> tensor_impl)
     : tensor_impl_(tensor_impl),
       name_("MindDataTensor"),
       type_(static_cast<mindspore::DataType>(DETypeToMSType(tensor_impl_->type()))),
-      shape_(tensor_impl_->shape().AsVector()) {}
+      shape_(tensor_impl_->shape().AsVector()),
+      is_device_(false) {}
+
+#ifndef ENABLE_ANDROID
+DETensor::DETensor(std::shared_ptr<dataset::DeviceTensor> device_tensor_impl, bool is_device)
+    : device_tensor_impl_(device_tensor_impl), name_("MindDataDeviceTensor"), is_device_(is_device) {
+  // The sequence of shape_ is (width, widthStride, height, heightStride) in Dvpp module
+  // We need to add [1]widthStride and [3]heightStride, which are actual YUV image shape, into shape_ attribute
+  uint8_t flag = 0;
+  for (auto &i : device_tensor_impl->GetYuvStrideShape()) {
+    if (flag % 2 == 1) {
+      int64_t j = static_cast<int64_t>(i);
+      shape_.emplace_back(j);
+    }
+    ++flag;
+  }
+  std::reverse(shape_.begin(), shape_.end());
+  MS_LOG(INFO) << "This is a YUV420 format image, one pixel takes 1.5 bytes. Therefore, the shape of"
+               << " image is in (H, W) format. You can search for more information about YUV420 format";
+}
+#endif
 
 const std::string &DETensor::Name() const { return name_; }
 
@@ -45,6 +66,12 @@ enum mindspore::DataType DETensor::DataType() const {
 }
 
 size_t DETensor::DataSize() const {
+#ifndef ENABLE_ANDROID
+  if (is_device_) {
+    ASSERT_NULL(device_tensor_impl_);
+    return device_tensor_impl_->DeviceDataSize();
+  }
+#endif
   ASSERT_NULL(tensor_impl_);
   return tensor_impl_->SizeInBytes();
 }
@@ -52,6 +79,11 @@ size_t DETensor::DataSize() const {
 const std::vector<int64_t> &DETensor::Shape() const { return shape_; }
 
 std::shared_ptr<const void> DETensor::Data() const {
+#ifndef ENABLE_ANDROID
+  if (is_device_) {
+    return std::shared_ptr<const void>(device_tensor_impl_->GetDeviceBuffer(), [](const void *) {});
+  }
+#endif
   return std::shared_ptr<const void>(tensor_impl_->GetBuffer(), [](const void *) {});
 }
 
@@ -60,7 +92,7 @@ void *DETensor::MutableData() {
   return tensor_impl_->GetMutableBuffer();
 }
 
-bool DETensor::IsDevice() const { return false; }
+bool DETensor::IsDevice() const { return is_device_; }
 
 std::shared_ptr<mindspore::MSTensor::Impl> DETensor::Clone() const { return std::make_shared<DETensor>(tensor_impl_); }
 }  // namespace dataset

mindspore/ccsrc/minddata/dataset/core/de_tensor.h

@@ -31,7 +31,9 @@ class DETensor : public mindspore::MSTensor::Impl {
   DETensor() = default;
   ~DETensor() override = default;
   explicit DETensor(std::shared_ptr<dataset::Tensor> tensor_impl);
-
+#ifndef ENABLE_ANDROID
+  explicit DETensor(std::shared_ptr<dataset::DeviceTensor> device_tensor_impl, bool is_device);
+#endif
   const std::string &Name() const override;
 
   enum mindspore::DataType DataType() const override;
@@ -50,6 +52,10 @@ class DETensor : public mindspore::MSTensor::Impl {
 
  private:
   std::shared_ptr<dataset::Tensor> tensor_impl_;
+#ifndef ENABLE_ANDROID
+  std::shared_ptr<dataset::DeviceTensor> device_tensor_impl_;
+#endif
+  bool is_device_;
   std::string name_;
   enum mindspore::DataType type_;
   std::vector<int64_t> shape_;

mindspore/ccsrc/minddata/dataset/core/device_tensor.cc

@@ -0,0 +1,77 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "minddata/dataset/core/global_context.h"
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/core/device_tensor.h"
+
+namespace mindspore {
+namespace dataset {
+Status DeviceTensor::SetYuvStrideShape_(const uint32_t &width, const uint32_t &widthStride, const uint32_t &height,
+                                        const uint32_t &heightStride) {
+  YUV_shape_ = {width, widthStride, height, heightStride};
+  return Status::OK();
+}
+
+std::vector<uint32_t> DeviceTensor::GetYuvStrideShape() { return YUV_shape_; }
+
+#ifdef ENABLE_ACL
+Status DeviceTensor::SetAttributes(const std::shared_ptr<DvppDataInfo> &data_ptr) {
+  device_data_ = data_ptr->data;
+  CHECK_FAIL_RETURN_UNEXPECTED(device_data_ != nullptr, "Fail to get the device data.");
+  SetSize_(data_ptr->dataSize);
+  SetYuvStrideShape_(data_ptr->width, data_ptr->widthStride, data_ptr->height, data_ptr->heightStride);
+  return Status::OK();
+}
+#endif
+
+DeviceTensor::DeviceTensor(const TensorShape &shape, const DataType &type) : Tensor(shape, type) {
+  // grab the mem pool from global context and create the allocator for char data area
+  std::shared_ptr<MemoryPool> global_pool = GlobalContext::Instance()->mem_pool();
+  data_allocator_ = std::make_unique<Allocator<unsigned char>>(global_pool);
+}
+
+Status DeviceTensor::CreateEmpty(const TensorShape &shape, const DataType &type, std::shared_ptr<DeviceTensor> *out) {
+  CHECK_FAIL_RETURN_UNEXPECTED(shape.known(), "Invalid shape.");
+  CHECK_FAIL_RETURN_UNEXPECTED(type != DataType::DE_UNKNOWN, "Invalid data type.");
+  const DeviceTensorAlloc *alloc = GlobalContext::Instance()->device_tensor_allocator();
+  *out = std::allocate_shared<DeviceTensor>(*alloc, shape, type);
+  // if it's a string tensor and it has no elements, Just initialize the shape and type.
+  if (!type.IsNumeric() && shape.NumOfElements() == 0) {
+    return Status::OK();
+  }
+
+  CHECK_FAIL_RETURN_UNEXPECTED(type.IsNumeric(), "Number of elements is not 0. The type should be numeric.");
+
+  int64_t byte_size = (*out)->SizeInBytes();
+
+  // Don't allocate if we have a tensor with no elements.
+  if (byte_size != 0) {
+    RETURN_IF_NOT_OK((*out)->AllocateBuffer(byte_size));
+  }
+  return Status::OK();
+}
+
+uint8_t *DeviceTensor::GetDeviceBuffer() { return device_data_; }
+
+uint32_t DeviceTensor::DeviceDataSize() { return size_; }
+
+Status DeviceTensor::SetSize_(const uint32_t &new_size) {
+  size_ = new_size;
+  return Status::OK();
+}
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/core/device_tensor.h

@@ -0,0 +1,65 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_DEVICE_TENSOR_H
+#define MINDSPORE_DEVICE_TENSOR_H
+#include <memory>
+#include <utility>
+#include <vector>
+#include "include/api/status.h"
+#include "minddata/dataset/core/tensor.h"
+#ifdef ENABLE_ACL
+#include "minddata/dataset/kernels/image/dvpp/utils/DvppCommon.h"
+#endif
+#include "minddata/dataset/core/constants.h"
+#include "minddata/dataset/core/data_type.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+class Tensor;
+class DeviceTensor : public Tensor {
+ public:
+  DeviceTensor(const TensorShape &shape, const DataType &type);
+
+  ~DeviceTensor() {}
+#ifdef ENABLE_ACL
+  Status SetAttributes(const std::shared_ptr<DvppDataInfo> &data);
+#endif
+  static Status CreateEmpty(const TensorShape &shape, const DataType &type, std::shared_ptr<DeviceTensor> *out);
+
+  uint8_t *GetDeviceBuffer();
+
+  std::vector<uint32_t> GetYuvStrideShape();
+
+  uint32_t DeviceDataSize();
+
+  bool HasDeviceData() { return device_data_ != nullptr; }
+
+ private:
+  Status SetSize_(const uint32_t &new_size);
+
+  Status SetYuvStrideShape_(const uint32_t &width, const uint32_t &widthStride, const uint32_t &height,
+                            const uint32_t &heightStride);
+
+  std::vector<uint32_t> YUV_shape_;
+  uint8_t *device_data_;
+  uint32_t size_;
+};
+
+}  // namespace dataset
+}  // namespace mindspore
+#endif  // MINDSPORE_DEVICE_TENSOR_H

mindspore/ccsrc/minddata/dataset/core/global_context.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2019-2021 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -22,6 +22,7 @@
 #ifndef ENABLE_ANDROID
 #include "minddata/dataset/core/cv_tensor.h"
 #endif
+#include "minddata/dataset/core/device_tensor.h"
 #include "minddata/dataset/core/tensor.h"
 #include "minddata/dataset/util/allocator.h"
 #include "minddata/dataset/util/circular_pool.h"
@@ -61,6 +62,7 @@ Status GlobalContext::Init() {
 #ifndef ENABLE_ANDROID
   cv_tensor_allocator_ = std::make_unique<Allocator<CVTensor>>(mem_pool_);
 #endif
+  device_tensor_allocator_ = std::make_unique<Allocator<DeviceTensor>>(mem_pool_);
   int_allocator_ = std::make_unique<IntAlloc>(mem_pool_);
   return Status::OK();
 }

mindspore/ccsrc/minddata/dataset/core/global_context.h

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * Copyright 2019-2021 Huawei Technologies Co., Ltd
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -19,10 +19,10 @@
 #include <memory>
 #include <mutex>
 
+#include "include/api/status.h"
 #include "minddata/dataset/core/config_manager.h"
 #include "minddata/dataset/core/constants.h"
 #include "minddata/dataset/util/allocator.h"
-#include "minddata/dataset/util/status.h"
 
 namespace mindspore {
 namespace dataset {
@@ -30,9 +30,11 @@ namespace dataset {
 class MemoryPool;
 class Tensor;
 class CVTensor;
+class DeviceTensor;
 
-using TensorAlloc = Allocator<Tensor>;      // An allocator for Tensors
-using CVTensorAlloc = Allocator<CVTensor>;  // An allocator CVTensors
+using TensorAlloc = Allocator<Tensor>;              // An allocator for Tensors
+using CVTensorAlloc = Allocator<CVTensor>;          // An allocator CVTensors
+using DeviceTensorAlloc = Allocator<DeviceTensor>;  // An allocator for Device_Tensors
 using IntAlloc = Allocator<dsize_t>;
 
 class GlobalContext {
@@ -82,6 +84,10 @@ class GlobalContext {
   // @return the CVTensor allocator as raw pointer
   const CVTensorAlloc *cv_tensor_allocator() const { return cv_tensor_allocator_.get(); }
 
+  // Getter method
+  // @return the DeviceTensor allocator as raw pointer
+  const DeviceTensorAlloc *device_tensor_allocator() const { return device_tensor_allocator_.get(); }
+
   // Getter method
   // @return the integer allocator as raw pointer
   const IntAlloc *int_allocator() const { return int_allocator_.get(); }
@@ -95,12 +101,13 @@ class GlobalContext {
   Status Init();
 
   static std::once_flag init_instance_flag_;
-  static std::unique_ptr<GlobalContext> global_context_;  // The instance of the singleton (global)
-  std::shared_ptr<MemoryPool> mem_pool_;                  // A global memory pool
-  std::shared_ptr<ConfigManager> config_manager_;         // The configs
-  std::unique_ptr<TensorAlloc> tensor_allocator_;         // An allocator for Tensors
-  std::unique_ptr<CVTensorAlloc> cv_tensor_allocator_;    // An allocator for CV Tensors
-  std::unique_ptr<IntAlloc> int_allocator_;               // An allocator for ints
+  static std::unique_ptr<GlobalContext> global_context_;        // The instance of the singleton (global)
+  std::shared_ptr<MemoryPool> mem_pool_;                        // A global memory pool
+  std::shared_ptr<ConfigManager> config_manager_;               // The configs
+  std::unique_ptr<TensorAlloc> tensor_allocator_;               // An allocator for Tensors
+  std::unique_ptr<CVTensorAlloc> cv_tensor_allocator_;          // An allocator for CV Tensors
+  std::unique_ptr<DeviceTensorAlloc> device_tensor_allocator_;  // An allocator for Device Tensors
+  std::unique_ptr<IntAlloc> int_allocator_;                     // An allocator for ints
 };
 }  // namespace dataset
 }  // namespace mindspore

mindspore/ccsrc/minddata/dataset/core/tensor.h

@@ -423,6 +423,17 @@ class Tensor {
   static Status GetBufferInfo(Tensor *t, py::buffer_info *out);
 #endif
 
+#ifdef ENABLE_ACL
+  Status SetYuvShape(const uint32_t &width, const uint32_t &widthStride, const uint32_t &height,
+                     const uint32_t &heightStride) {
+    std::vector<uint32_t> tmp{width, widthStride, height, heightStride};
+    yuv_shape_ = tmp;
+    return Status::OK();
+  }
+
+  std::vector<uint32_t> GetYuvShape() { return yuv_shape_; }
+#endif
+
   /// TensorIterator is a linear iterator that can be used to iterate over the elements of the Tensor
   /// The order  elements  is as the memory layout (i.e., row-major) [[1,2,3],[4,5,6] --> 1,2,3,4,5,6
   /// \tparam T type of values in the Tensor Iterator
@@ -686,6 +697,11 @@ class Tensor {
   /// pointer to the end of the physical data
   unsigned char *data_end_ = nullptr;
 
+#ifdef ENABLE_ACL
+  /// shape for interpretation of YUV image
+  std::vector<uint32_t> yuv_shape_;
+#endif
+
  private:
   friend class DETensor;
 

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

@@ -17,25 +17,28 @@
 #ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_EXECUTE_H_
 #define MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_EXECUTE_H_
 
+#include <string>
 #include <vector>
 #include <memory>
+#include "include/api/context.h"
 #include "include/api/types.h"
 #include "minddata/dataset/include/constants.h"
 #include "minddata/dataset/include/transforms.h"
 
 namespace mindspore {
 namespace dataset {
+class AscendResource;  // Class to manage the resource of Ascend310
 
 // class to run tensor operations in eager mode
 class Execute {
  public:
   /// \brief Constructor
-  explicit Execute(std::shared_ptr<TensorOperation> op);
+  explicit Execute(std::shared_ptr<TensorOperation> op, std::string deviceType = "CPU");
 
-  explicit Execute(std::vector<std::shared_ptr<TensorOperation>> ops);
+  explicit Execute(std::vector<std::shared_ptr<TensorOperation>> ops, std::string deviceType = "CPU");
 
   /// \brief Destructor
-  ~Execute() = default;
+  ~Execute();
 
   /// \brief callable function to execute the TensorOperation in eager mode
   /// \param[in] input Tensor to be transformed
@@ -48,9 +51,19 @@ class Execute {
   /// \param[out] out Result tensor after transform
   /// \return - Status
   Status operator()(const std::vector<mindspore::MSTensor> &input_tensor_list, std::vector<mindspore::MSTensor> *out);
+#ifdef ENABLE_ACL
+  Status DeviceMemoryRelease();
+#endif
 
  private:
+  Status validate_device_();
+
   std::vector<std::shared_ptr<TensorOperation>> ops_;
+
+  std::string device_type_;
+#ifdef ENABLE_ACL
+  std::shared_ptr<AscendResource> D_resource_;
+#endif
 };
 
 }  // namespace dataset

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

@@ -39,7 +39,6 @@ class AutoContrastOperation;
 class BoundingBoxAugmentOperation;
 class CutMixBatchOperation;
 class CutOutOperation;
-class DvppDecodeResizeCropOperation;
 class EqualizeOperation;
 class HwcToChwOperation;
 class InvertOperation;
@@ -106,22 +105,6 @@ std::shared_ptr<CutMixBatchOperation> CutMixBatch(ImageBatchFormat image_batch_f
 /// \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 DvppDecodeResizeCropJpeg 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 [16*16, 4096*4096].
-///     Only images with an even resolution can be output. The output of odd resolution is not supported.
-/// \param[in] crop vector representing the output size of the final crop image.
-/// \param[in] size A vector representing the output size of the intermediate 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<DvppDecodeResizeCropOperation> DvppDecodeResizeCropJpeg(std::vector<uint32_t> crop = {224, 224},
-                                                                        std::vector<uint32_t> resize = {256, 256});
-
 /// \brief Function to create a Equalize TensorOperation.
 /// \notes Apply histogram equalization on input image.
 /// \return Shared pointer to the current TensorOperation.

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

@@ -0,0 +1,219 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_VISION_ASCEND_H_
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_VISION_ASCEND_H_
+
+#include <map>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+#include "include/api/status.h"
+#include "minddata/dataset/include/constants.h"
+#include "minddata/dataset/include/transforms.h"
+
+namespace mindspore {
+namespace dataset {
+
+// Transform operations for performing computer vision.
+namespace vision {
+
+// Char arrays storing name of corresponding classes (in alphabetical order)
+constexpr char kDvppCropJpegOperation[] = "DvppCropJpeg";
+constexpr char kDvppDecodeResizeOperation[] = "DvppDecodeResize";
+constexpr char kDvppDecodeResizeCropOperation[] = "DvppDecodeResizeCrop";
+constexpr char kDvppDecodeJpegOperation[] = "DvppDecodeJpeg";
+constexpr char kDvppDecodePngOperation[] = "DvppDecodePng";
+constexpr char kDvppResizeJpegOperation[] = "DvppResizeJpeg";
+
+class DvppCropJpegOperation;
+class DvppDecodeResizeOperation;
+class DvppDecodeResizeCropOperation;
+class DvppDecodeJpegOperation;
+class DvppDecodePngOperation;
+class DvppResizeJpegOperation;
+
+/// \brief Function to create a DvppCropJpeg TensorOperation.
+/// \notes Tensor operation to crop 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, 2048*2048].
+///     Only images with an even resolution can be output. The output of odd resolution is not supported.
+/// \param[in] crop vector representing the output size of the final crop image.
+/// \param[in] size A vector representing the output size of the intermediate resized image.
+///     If size is a single value, the shape will be a square. If size has 2 values, it should be (height, width).
+/// \return Shared pointer to the current TensorOperation.
+std::shared_ptr<DvppCropJpegOperation> DvppCropJpeg(std::vector<uint32_t> crop = {256, 256});
+
+/// \brief Function to create a DvppDecodeResizeJpeg 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, 2048*2048].
+///     Only images with an even resolution can be output. The output of odd resolution is not supported.
+/// \param[in] crop vector representing the output size of the final crop image.
+/// \param[in] size A vector representing the output size of the intermediate 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<DvppDecodeResizeOperation> DvppDecodeResizeJpeg(std::vector<uint32_t> resize = {256, 256});
+
+/// \brief Function to create a DvppDecodeResizeCropJpeg 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, 2048*2048].
+///     Only images with an even resolution can be output. The output of odd resolution is not supported.
+/// \param[in] crop vector representing the output size of the final crop image.
+/// \param[in] Resize vector representing the output size of the intermediate resized image.
+///     If size is a single value, smaller edge of the image will be resized to the 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<DvppDecodeResizeCropOperation> DvppDecodeResizeCropJpeg(std::vector<uint32_t> crop = {224, 224},
+                                                                        std::vector<uint32_t> resize = {256, 256});
+
+/// \brief Function to create a DvppDecodeJpeg TensorOperation.
+/// \notes Tensor operation to decode 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, 2048*2048].
+///     Only images with an even resolution can be output. The output of odd resolution is not supported.
+/// \return Shared pointer to the current TensorOperation.
+std::shared_ptr<DvppDecodeJpegOperation> DvppDecodeJpeg();
+
+/// \brief Function to create a DvppDecodePng TensorOperation.
+/// \notes Tensor operation to decode PNG 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, 2048*2048].
+///     Only images with an even resolution can be output. The output of odd resolution is not supported.
+/// \return Shared pointer to the current TensorOperation.
+std::shared_ptr<DvppDecodePngOperation> DvppDecodePng();
+
+/// \brief Function to create a DvppResizeJpeg TensorOperation.
+/// \notes Tensor operation to resize JPEG image using 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, 2048*2048].
+///     Only images with an even resolution can be output. The output of odd resolution is not supported.
+/// \param[in] resize vector represents the shape of image after resize.
+/// \return Shared pointer to the current TensorOperation.
+std::shared_ptr<DvppResizeJpegOperation> DvppResizeJpeg(std::vector<uint32_t> resize = {256, 256});
+
+class DvppCropJpegOperation : public TensorOperation {
+ public:
+  explicit DvppCropJpegOperation(const std::vector<uint32_t> &resize);
+
+  ~DvppCropJpegOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+  std::string Name() const override { return kDvppCropJpegOperation; }
+
+ private:
+  std::vector<uint32_t> crop_;
+};
+
+class DvppDecodeResizeOperation : public TensorOperation {
+ public:
+  explicit DvppDecodeResizeOperation(const std::vector<uint32_t> &resize);
+
+  ~DvppDecodeResizeOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+  std::string Name() const override { return kDvppDecodeResizeOperation; }
+
+ private:
+  std::vector<uint32_t> resize_;
+};
+
+class DvppDecodeResizeCropOperation : public TensorOperation {
+ public:
+  explicit DvppDecodeResizeCropOperation(const std::vector<uint32_t> &crop, const std::vector<uint32_t> &resize);
+
+  ~DvppDecodeResizeCropOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+  std::string Name() const override { return kDvppDecodeResizeCropOperation; }
+
+ private:
+  std::vector<uint32_t> crop_;
+  std::vector<uint32_t> resize_;
+};
+
+class DvppDecodeJpegOperation : public TensorOperation {
+ public:
+  ~DvppDecodeJpegOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+  std::string Name() const override { return kDvppDecodeJpegOperation; }
+};
+
+class DvppDecodePngOperation : public TensorOperation {
+ public:
+  ~DvppDecodePngOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+  std::string Name() const override { return kDvppDecodePngOperation; }
+};
+
+class DvppResizeJpegOperation : public TensorOperation {
+ public:
+  explicit DvppResizeJpegOperation(const std::vector<uint32_t> &resize);
+
+  ~DvppResizeJpegOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+  std::string Name() const override { return kDvppResizeJpegOperation; }
+
+ private:
+  std::vector<uint32_t> resize_;
+};
+}  // namespace vision
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_VISION_ASCEND_H_

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

@@ -2,5 +2,10 @@ file(GLOB_RECURSE _CURRENT_SRC_FILES RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "*.cc"
 set_property(SOURCE ${_CURRENT_SRC_FILES} PROPERTY COMPILE_DEFINITIONS SUBMODULE_ID=mindspore::SubModuleId::SM_MD)
 add_subdirectory(utils)
 add_library(kernels-dvpp-image OBJECT
-        dvpp_decode_resize_crop_jpeg_op.cc)
+        dvpp_crop_jpeg_op.cc
+        dvpp_decode_resize_crop_jpeg_op.cc
+        dvpp_decode_resize_jpeg_op.cc
+        dvpp_decode_jpeg_op.cc
+        dvpp_resize_jpeg_op.cc
+        dvpp_decode_png_op.cc)
 add_dependencies(kernels-dvpp-image dvpp-utils)

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/dvpp_crop_jpeg_op.cc

@@ -0,0 +1,145 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <string>
+#include <vector>
+#include <iostream>
+#include "include/api/context.h"
+#include "minddata/dataset/core/data_type.h"
+#include "minddata/dataset/core/device_tensor.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/MDAclProcess.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/CommonDataType.h"
+#include "minddata/dataset/kernels/image/dvpp/dvpp_crop_jpeg_op.h"
+#include "minddata/dataset/kernels/image/image_utils.h"
+
+namespace mindspore {
+namespace dataset {
+Status DvppCropJpegOp::Compute(const std::shared_ptr<DeviceTensor> &input, std::shared_ptr<DeviceTensor> *output) {
+  IO_CHECK(input, output);
+  try {
+    CHECK_FAIL_RETURN_UNEXPECTED(input->GetDeviceBuffer() != nullptr, "The input image buffer is empty.");
+    std::string last_step = "Resize";
+    std::shared_ptr<DvppDataInfo> imageinfo(processor_->Get_Resized_DeviceData());
+    if (!imageinfo->data) {
+      last_step = "Decode";
+    }
+    APP_ERROR ret = processor_->JPEG_C(last_step);
+    if (ret != APP_ERR_OK) {
+      processor_->Release();
+      std::string error = "Error in dvpp crop processing:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    std::shared_ptr<DvppDataInfo> CropOut(processor_->Get_Croped_DeviceData());
+    const TensorShape dvpp_shape({1, 1, 1});
+    const DataType dvpp_data_type(DataType::DE_UINT8);
+    mindspore::dataset::DeviceTensor::CreateEmpty(dvpp_shape, dvpp_data_type, output);
+    (*output)->SetAttributes(CropOut);
+    if (!((*output)->HasDeviceData())) {
+      std::string error = "[ERROR] Fail to get the Output result from device memory!";
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+  } catch (const cv::Exception &e) {
+    std::string error = "[ERROR] Fail in DvppCropJpegOp:" + std::string(e.what());
+    RETURN_STATUS_UNEXPECTED(error);
+  }
+  return Status::OK();
+}
+
+Status DvppCropJpegOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
+  IO_CHECK(input, output);
+  try {
+    CHECK_FAIL_RETURN_UNEXPECTED(input->GetBuffer() != nullptr, "The input image buffer is empty.");
+    unsigned char *buffer = const_cast<unsigned char *>(input->GetBuffer());
+    DvppDataInfo imageinfo;
+    imageinfo.dataSize = input->SizeInBytes();
+    imageinfo.data = static_cast<uint8_t *>(buffer);
+    std::vector<uint32_t> yuv_shape_ = input->GetYuvShape();
+    imageinfo.width = yuv_shape_[0];
+    imageinfo.widthStride = yuv_shape_[1];
+    imageinfo.height = yuv_shape_[2];
+    imageinfo.heightStride = yuv_shape_[3];
+    imageinfo.format = PIXEL_FORMAT_YUV_SEMIPLANAR_420;
+    ResourceInfo resource;
+    resource.aclConfigPath = "";
+    resource.deviceIds.insert(mindspore::GlobalContext::GetGlobalDeviceID());
+    std::shared_ptr<ResourceManager> instance = ResourceManager::GetInstance();
+    APP_ERROR ret = instance->InitResource(resource);
+    if (ret != APP_ERR_OK) {
+      instance->Release();
+      std::string error = "Error in Init D-chip:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    int deviceId = *(resource.deviceIds.begin());
+    aclrtContext context = instance->GetContext(deviceId);
+    // Second part end where we initialize the resource of D-chip and set up all configures
+    MDAclProcess process(crop_width_, crop_height_, context, true);
+    ret = process.InitResource();
+    if (ret != APP_ERR_OK) {
+      instance->Release();
+      std::string error = "Error in Init resource:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+
+    ret = process.JPEG_C(imageinfo);
+    if (ret != APP_ERR_OK) {
+      instance->Release();
+      std::string error = "Error in dvpp crop processing:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+
+    // Third part end where we execute the core function of dvpp
+    auto data = std::static_pointer_cast<unsigned char>(process.Get_Memory_Data());
+    unsigned char *ret_ptr = data.get();
+    std::shared_ptr<DvppDataInfo> CropOut(process.Get_Croped_DeviceData());
+    dsize_t dvpp_length = CropOut->dataSize;
+    const TensorShape dvpp_shape({dvpp_length, 1, 1});
+    uint32_t crop_height = CropOut->height;
+    uint32_t crop_heightStride = CropOut->heightStride;
+    uint32_t crop_width = CropOut->width;
+    uint32_t crop_widthStride = CropOut->widthStride;
+    const DataType dvpp_data_type(DataType::DE_UINT8);
+    mindspore::dataset::Tensor::CreateFromMemory(dvpp_shape, dvpp_data_type, ret_ptr, output);
+    (*output)->SetYuvShape(crop_width, crop_widthStride, crop_height, crop_heightStride);
+    if (!((*output)->HasData())) {
+      std::string error = "[ERROR] Fail to get the Output result from memory!";
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    process.device_memory_release();
+    process.Release();
+    // Last part end where we transform the processed data into a tensor which can be applied in later units.
+  } catch (const cv::Exception &e) {
+    std::string error = "[ERROR] Fail in DvppCropJpegOp:" + std::string(e.what());
+    RETURN_STATUS_UNEXPECTED(error);
+  }
+  return Status::OK();
+}
+
+Status DvppCropJpegOp::OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) {
+  RETURN_IF_NOT_OK(TensorOp::OutputShape(inputs, outputs));
+  outputs.clear();
+  TensorShape out({-1, 1, 1});  // we don't know what is output image size, but we know it should be 1 channels
+  if (inputs[0].Rank() == 1) outputs.emplace_back(out);
+  if (!outputs.empty()) return Status::OK();
+  return Status(StatusCode::kMDUnexpectedError, "Input has a wrong shape");
+}
+
+Status DvppCropJpegOp::SetAscendResource(const std::shared_ptr<MDAclProcess> &processor) {
+  processor_ = processor;
+  processor_->SetCropParas(crop_width_, crop_height_);
+  return Status::OK();
+}
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/dvpp_crop_jpeg_op.h

@@ -0,0 +1,62 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_CROP_JPEG_OP_H
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_CROP_JPEG_OP_H
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "acl/acl.h"
+#include "mindspore/core/utils/log_adapter.h"
+#include "minddata/dataset/core/data_type.h"
+#include "minddata/dataset/core/device_tensor.h"
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/ErrorCode.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/ResourceManager.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+class DvppCropJpegOp : public TensorOp {
+ public:
+  DvppCropJpegOp(int32_t crop_height, int32_t crop_width) : crop_height_(crop_height), crop_width_(crop_width) {}
+
+  /// \brief Destructor
+  ~DvppCropJpegOp() = default;
+
+  Status Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) override;
+
+  Status Compute(const std::shared_ptr<DeviceTensor> &input, std::shared_ptr<DeviceTensor> *output) override;
+
+  Status OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) override;
+
+  std::string Name() const override { return kDvppCropJpegOp; }
+
+  Status SetAscendResource(const std::shared_ptr<MDAclProcess> &processor) override;
+
+ private:
+  uint32_t crop_height_;
+  uint32_t crop_width_;
+
+  std::shared_ptr<MDAclProcess> processor_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_CROP_JPEG_OP_H

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/dvpp_decode_jpeg_op.cc

@@ -0,0 +1,139 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <string>
+#include <vector>
+#include <iostream>
+#include "include/api/context.h"
+#include "minddata/dataset/core/cv_tensor.h"
+#include "minddata/dataset/core/data_type.h"
+#include "minddata/dataset/core/device_tensor.h"
+#include "minddata/dataset/kernels/image/dvpp/dvpp_decode_resize_crop_jpeg_op.h"
+#include "minddata/dataset/kernels/image/dvpp/dvpp_decode_jpeg_op.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/CommonDataType.h"
+#include "minddata/dataset/kernels/image/image_utils.h"
+
+namespace mindspore {
+namespace dataset {
+// Compute() will be called when context=="Ascend310"
+Status DvppDecodeJpegOp::Compute(const std::shared_ptr<DeviceTensor> &input, std::shared_ptr<DeviceTensor> *output) {
+  IO_CHECK(input, output);
+  try {
+    CHECK_FAIL_RETURN_UNEXPECTED(input->GetDeviceBuffer() != nullptr, "The input image buffer on device is empty");
+    APP_ERROR ret = processor_->JPEG_D();
+    if (ret != APP_ERR_OK) {
+      processor_->Release();
+      std::string error = "Error in dvpp processing:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    std::shared_ptr<DvppDataInfo> DecodeOut(processor_->Get_Decode_DeviceData());
+    const TensorShape dvpp_shape({1, 1, 1});
+    const DataType dvpp_data_type(DataType::DE_UINT8);
+    mindspore::dataset::DeviceTensor::CreateEmpty(dvpp_shape, dvpp_data_type, output);
+    (*output)->SetAttributes(DecodeOut);
+    if (!((*output)->HasDeviceData())) {
+      std::string error = "[ERROR] Fail to get the Output result from memory!";
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+  } catch (const cv::Exception &e) {
+    std::string error = "[ERROR] Fail in DvppDecodeJpegOp:" + std::string(e.what());
+    RETURN_STATUS_UNEXPECTED(error);
+  }
+  return Status::OK();
+}
+// Compute() will be called when context=="CPU"
+Status DvppDecodeJpegOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
+  IO_CHECK(input, output);
+  if (!IsNonEmptyJPEG(input)) {
+    RETURN_STATUS_UNEXPECTED("DvppDecodeJpegOp only support process JPEG image.");
+  }
+  try {
+    CHECK_FAIL_RETURN_UNEXPECTED(input->GetBuffer() != nullptr, "The input image buffer is empty.");
+    unsigned char *buffer = const_cast<unsigned char *>(input->GetBuffer());
+    RawData imageInfo;
+    uint32_t filesize = input->SizeInBytes();
+    imageInfo.lenOfByte = filesize;
+    imageInfo.data = static_cast<void *>(buffer);
+    ResourceInfo resource;
+    resource.aclConfigPath = "";
+    resource.deviceIds.insert(mindspore::GlobalContext::GetGlobalDeviceID());
+    std::shared_ptr<ResourceManager> instance = ResourceManager::GetInstance();
+    APP_ERROR ret = instance->InitResource(resource);
+    if (ret != APP_ERR_OK) {
+      instance->Release();
+      std::string error = "Error in Init D-chip:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    int deviceId = *(resource.deviceIds.begin());
+    aclrtContext context = instance->GetContext(deviceId);
+    // Second part end where we initialize the resource of D-chip and set up all configures
+    MDAclProcess process(context, false);
+    ret = process.InitResource();
+    if (ret != APP_ERR_OK) {
+      instance->Release();
+      std::string error = "Error in Init resource:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    ret = process.JPEG_D(imageInfo);
+    if (ret != APP_ERR_OK) {
+      instance->Release();
+      std::string error = "Error in dvpp processing:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    // Third part end where we execute the core function of dvpp
+    auto data = std::static_pointer_cast<unsigned char>(process.Get_Memory_Data());
+    unsigned char *ret_ptr = data.get();
+    std::shared_ptr<DvppDataInfo> DecodeOut(process.Get_Decode_DeviceData());
+    dsize_t dvpp_length = DecodeOut->dataSize;
+    uint32_t decoded_height = DecodeOut->height;
+    uint32_t decoded_heightStride = DecodeOut->heightStride;
+    uint32_t decoded_width = DecodeOut->width;
+    uint32_t decoded_widthStride = DecodeOut->widthStride;
+    // std::cout << "Decoded size: " << decoded_width << ", " << decoded_height << std::endl;
+    const TensorShape dvpp_shape({dvpp_length, 1, 1});
+    const DataType dvpp_data_type(DataType::DE_UINT8);
+    mindspore::dataset::Tensor::CreateFromMemory(dvpp_shape, dvpp_data_type, ret_ptr, output);
+    (*output)->SetYuvShape(decoded_width, decoded_widthStride, decoded_height, decoded_heightStride);
+    if (!((*output)->HasData())) {
+      std::string error = "[ERROR] Fail to get the Output result from device memory!";
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    process.device_memory_release();
+    process.Release();
+    // Last part end where we transform the processed data into a tensor which can be applied in later units.
+  } catch (const cv::Exception &e) {
+    std::string error = "[ERROR] Fail in DvppDecodeJpegOp:" + std::string(e.what());
+    RETURN_STATUS_UNEXPECTED(error);
+  }
+  return Status::OK();
+}
+
+Status DvppDecodeJpegOp::SetAscendResource(const std::shared_ptr<MDAclProcess> &processor) {
+  processor_ = processor;
+  return Status::OK();
+}
+
+Status DvppDecodeJpegOp::OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) {
+  RETURN_IF_NOT_OK(TensorOp::OutputShape(inputs, outputs));
+  outputs.clear();
+  TensorShape out({-1, 1, 1});  // we don't know what is output image size, but we know it should be 3 channels
+  if (inputs[0].Rank() == 1) outputs.emplace_back(out);
+  if (!outputs.empty()) return Status::OK();
+  return Status(StatusCode::kMDUnexpectedError, "Input has a wrong shape");
+}
+
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/dvpp_decode_jpeg_op.h

@@ -0,0 +1,59 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_DECODE_JPEG_OP_H
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_DECODE_JPEG_OP_H
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "acl/acl.h"
+#include "mindspore/core/utils/log_adapter.h"
+#include "minddata/dataset/core/data_type.h"
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/ErrorCode.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/MDAclProcess.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/ResourceManager.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+class DvppDecodeJpegOp : public TensorOp {
+ public:
+  DvppDecodeJpegOp() { processor_ = nullptr; }
+
+  /// \brief Destructor
+  ~DvppDecodeJpegOp() = default;
+
+  Status Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) override;
+
+  Status Compute(const std::shared_ptr<DeviceTensor> &input, std::shared_ptr<DeviceTensor> *output) override;
+
+  Status OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) override;
+
+  std::string Name() const override { return kDvppDecodeJpegOp; }
+
+  Status SetAscendResource(const std::shared_ptr<MDAclProcess> &processor) override;
+
+ private:
+  std::shared_ptr<MDAclProcess> processor_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_DECODE_JPEG_OP_H

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/dvpp_decode_png_op.cc

@@ -0,0 +1,138 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <string>
+#include <vector>
+#include <iostream>
+#include "include/api/context.h"
+#include "minddata/dataset/core/cv_tensor.h"
+#include "minddata/dataset/core/data_type.h"
+#include "minddata/dataset/kernels/image/dvpp/dvpp_decode_resize_crop_jpeg_op.h"
+#include "minddata/dataset/kernels/image/dvpp/dvpp_decode_png_op.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/CommonDataType.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/MDAclProcess.h"
+#include "minddata/dataset/kernels/image/image_utils.h"
+
+namespace mindspore {
+namespace dataset {
+Status DvppDecodePngOp::Compute(const std::shared_ptr<DeviceTensor> &input, std::shared_ptr<DeviceTensor> *output) {
+  IO_CHECK(input, output);
+  try {
+    CHECK_FAIL_RETURN_UNEXPECTED(input->GetDeviceBuffer() != nullptr, "The input image buffer on device is empty");
+    APP_ERROR ret = processor_->PNG_D();
+    if (ret != APP_ERR_OK) {
+      processor_->Release();
+      std::string error = "Error in dvpp processing:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    std::shared_ptr<DvppDataInfo> DecodeOut(processor_->Get_Decode_DeviceData());
+    const TensorShape dvpp_shape({1, 1, 1});
+    const DataType dvpp_data_type(DataType::DE_UINT8);
+    mindspore::dataset::DeviceTensor::CreateEmpty(dvpp_shape, dvpp_data_type, output);
+    (*output)->SetAttributes(DecodeOut);
+    if (!((*output)->HasDeviceData())) {
+      std::string error = "[ERROR] Fail to get the Output result from memory!";
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+  } catch (const cv::Exception &e) {
+    std::string error = "[ERROR] Fail in DvppDecodeJpegOp:" + std::string(e.what());
+    RETURN_STATUS_UNEXPECTED(error);
+  }
+  return Status::OK();
+}
+
+Status DvppDecodePngOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
+  IO_CHECK(input, output);
+  if (!IsNonEmptyPNG(input)) {
+    RETURN_STATUS_UNEXPECTED("DvppDecodePngOp only support process PNG image.");
+  }
+  try {
+    CHECK_FAIL_RETURN_UNEXPECTED(input->GetBuffer() != nullptr, "The input image buffer is empty.");
+    unsigned char *buffer = const_cast<unsigned char *>(input->GetBuffer());
+    RawData imageInfo;
+    uint32_t filesize = input->SizeInBytes();
+    imageInfo.lenOfByte = filesize;
+    imageInfo.data = static_cast<void *>(buffer);
+    ResourceInfo resource;
+    resource.aclConfigPath = "";
+    resource.deviceIds.insert(mindspore::GlobalContext::GetGlobalDeviceID());
+    std::shared_ptr<ResourceManager> instance = ResourceManager::GetInstance();
+    APP_ERROR ret = instance->InitResource(resource);
+    if (ret != APP_ERR_OK) {
+      instance->Release();
+      std::string error = "Error in Init D-chip:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    int deviceId = *(resource.deviceIds.begin());
+    aclrtContext context = instance->GetContext(deviceId);
+    // Second part end where we initialize the resource of D-chip and set up all configures
+    MDAclProcess process(context, false);
+    ret = process.InitResource();
+    if (ret != APP_ERR_OK) {
+      instance->Release();
+      std::string error = "Error in Init resource:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+
+    ret = process.PNG_D(imageInfo);
+    if (ret != APP_ERR_OK) {
+      instance->Release();
+      std::string error = "Error in dvpp processing:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+
+    // Third part end where we execute the core function of dvpp
+    /* 测试Device内存
+     */
+    auto data = std::static_pointer_cast<unsigned char>(process.Get_Memory_Data());
+    unsigned char *ret_ptr = data.get();
+    std::shared_ptr<DvppDataInfo> DecodeOut(process.Get_Decode_DeviceData());
+    dsize_t dvpp_length = DecodeOut->dataSize;
+    // dsize_t decode_height = DecodeOut->height;
+    // dsize_t decode_width = DecodeOut->width;
+    const TensorShape dvpp_shape({dvpp_length, 1, 1});
+    const DataType dvpp_data_type(DataType::DE_UINT8);
+    mindspore::dataset::Tensor::CreateFromMemory(dvpp_shape, dvpp_data_type, ret_ptr, output);
+    if (!((*output)->HasData())) {
+      std::string error = "[ERROR] Fail to get the Output result from memory!";
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    process.device_memory_release();
+    process.Release();
+    // Last part end where we transform the processed data into a tensor which can be applied in later units.
+  } catch (const cv::Exception &e) {
+    std::string error = "[ERROR] Fail in DvppDecodePngOp:" + std::string(e.what());
+    RETURN_STATUS_UNEXPECTED(error);
+  }
+  return Status::OK();
+}
+
+Status DvppDecodePngOp::OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) {
+  RETURN_IF_NOT_OK(TensorOp::OutputShape(inputs, outputs));
+  outputs.clear();
+  TensorShape out({-1, 1, 1});  // we don't know what is output image size, but we know it should be 3 channels
+  if (inputs[0].Rank() == 1) outputs.emplace_back(out);
+  if (!outputs.empty()) return Status::OK();
+  return Status(StatusCode::kMDUnexpectedError, "Input has a wrong shape");
+}
+
+Status DvppDecodePngOp::SetAscendResource(const std::shared_ptr<MDAclProcess> &processor) {
+  processor_ = processor;
+  return Status::OK();
+}
+
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/dvpp_decode_png_op.h

@@ -0,0 +1,58 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_DECODE_PNG_OP_H
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_DECODE_PNG_OP_H
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "acl/acl.h"
+#include "mindspore/core/utils/log_adapter.h"
+#include "minddata/dataset/core/data_type.h"
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/ErrorCode.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/ResourceManager.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+class DvppDecodePngOp : public TensorOp {
+ public:
+  DvppDecodePngOp() {}
+
+  /// \brief Destructor
+  ~DvppDecodePngOp() = default;
+
+  Status Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) override;
+
+  Status Compute(const std::shared_ptr<DeviceTensor> &input, std::shared_ptr<DeviceTensor> *output) override;
+
+  Status OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) override;
+
+  std::string Name() const override { return kDvppDecodePngOp; }
+
+  Status SetAscendResource(const std::shared_ptr<MDAclProcess> &processor) override;
+
+ private:
+  std::shared_ptr<MDAclProcess> processor_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_DECODE_PNG_OP_H

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/dvpp_decode_resize_crop_jpeg_op.cc

@@ -17,20 +17,48 @@
 #include <string>
 #include <vector>
 #include <iostream>
-#include "minddata/dataset/kernels/image/dvpp/utils/AclProcess.h"
+#include "include/api/context.h"
 #include "minddata/dataset/core/cv_tensor.h"
-#include "minddata/dataset/kernels/image/image_utils.h"
-#include "minddata/dataset/kernels/image/dvpp/utils/CommonDataType.h"
 #include "minddata/dataset/core/data_type.h"
 #include "minddata/dataset/kernels/image/dvpp/dvpp_decode_resize_crop_jpeg_op.h"
-#include "include/api/context.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/CommonDataType.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/MDAclProcess.h"
+#include "minddata/dataset/kernels/image/image_utils.h"
 
 namespace mindspore {
 namespace dataset {
+Status DvppDecodeResizeCropJpegOp::Compute(const std::shared_ptr<DeviceTensor> &input,
+                                           std::shared_ptr<DeviceTensor> *output) {
+  IO_CHECK(input, output);
+  try {
+    CHECK_FAIL_RETURN_UNEXPECTED(input->GetDeviceBuffer() != nullptr, "The input image buffer on device is empty");
+    APP_ERROR ret = processor_->JPEG_DRC();
+    if (ret != APP_ERR_OK) {
+      processor_->Release();
+      std::string error = "Error in dvpp processing:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    std::shared_ptr<DvppDataInfo> CropOut(processor_->Get_Croped_DeviceData());
+    // std::cout << "Decoded size: " << decoded_width << ", " << decoded_height << std::endl;
+    const TensorShape dvpp_shape({1, 1, 1});
+    const DataType dvpp_data_type(DataType::DE_UINT8);
+    mindspore::dataset::DeviceTensor::CreateEmpty(dvpp_shape, dvpp_data_type, output);
+    (*output)->SetAttributes(CropOut);
+    if (!((*output)->HasDeviceData())) {
+      std::string error = "[ERROR] Fail to get the Output result from memory!";
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+  } catch (const cv::Exception &e) {
+    std::string error = "[ERROR] Fail in DvppDecodeResizeCropJpegOp:" + std::string(e.what());
+    RETURN_STATUS_UNEXPECTED(error);
+  }
+  return Status::OK();
+}
+
 Status DvppDecodeResizeCropJpegOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
   IO_CHECK(input, output);
   if (!IsNonEmptyJPEG(input)) {
-    RETURN_STATUS_UNEXPECTED("SoftDvppDecodeReiszeJpegOp only support process jpeg image.");
+    RETURN_STATUS_UNEXPECTED("DvppDecodeReiszeJpegOp only support process jpeg image.");
   }
   try {
     CHECK_FAIL_RETURN_UNEXPECTED(input->GetBuffer() != nullptr, "The input image buffer is empty.");
@@ -38,11 +66,7 @@ Status DvppDecodeResizeCropJpegOp::Compute(const std::shared_ptr<Tensor> &input,
     RawData imageInfo;
     uint32_t filesize = input->SizeInBytes();
     imageInfo.lenOfByte = filesize;
-    imageInfo.data = std::make_shared<uint8_t>();
-    imageInfo.data.reset(new uint8_t[filesize], std::default_delete<uint8_t[]>());
-    memcpy_s(imageInfo.data.get(), filesize, buffer, filesize);
-    // First part end, whose function is to transform data from a Tensor to imageinfo data structure which can be
-    // applied on device
+    imageInfo.data = static_cast<void *>(buffer);
     ResourceInfo resource;
     resource.aclConfigPath = "";
     resource.deviceIds.insert(mindspore::GlobalContext::GetGlobalDeviceID());
@@ -56,25 +80,26 @@ Status DvppDecodeResizeCropJpegOp::Compute(const std::shared_ptr<Tensor> &input,
     int deviceId = *(resource.deviceIds.begin());
     aclrtContext context = instance->GetContext(deviceId);
     // Second part end where we initialize the resource of D chip and set up all configures
-    AclProcess process(resized_width_, resized_height_, crop_width_, crop_height_, context);
-    process.set_mode(true);
-    ret = process.InitResource();
+    MDAclProcess processor(resized_width_, resized_height_, crop_width_, crop_height_, context, true);
+    ret = processor.InitResource();
     if (ret != APP_ERR_OK) {
       instance->Release();
       std::string error = "Error in Init resource:" + std::to_string(ret);
       RETURN_STATUS_UNEXPECTED(error);
     }
-    ret = process.Process(imageInfo);
+
+    ret = processor.JPEG_DRC(imageInfo);
     if (ret != APP_ERR_OK) {
       instance->Release();
       std::string error = "Error in dvpp processing:" + std::to_string(ret);
       RETURN_STATUS_UNEXPECTED(error);
     }
+
     // Third part end where we execute the core function of dvpp
-    auto data = std::static_pointer_cast<unsigned char>(process.Get_Memory_Data());
+    auto data = std::static_pointer_cast<unsigned char>(processor.Get_Memory_Data());
     unsigned char *ret_ptr = data.get();
-    std::shared_ptr<DvppDataInfo> CropOut = process.Get_Device_Memory_Data();
-    dsize_t dvpp_length = CropOut->dataSize;
+    std::shared_ptr<DvppDataInfo> CropOut(processor.Get_Croped_DeviceData());
+    uint32_t dvpp_length = CropOut->dataSize;
     const TensorShape dvpp_shape({dvpp_length, 1, 1});
     const DataType dvpp_data_type(DataType::DE_UINT8);
     mindspore::dataset::Tensor::CreateFromMemory(dvpp_shape, dvpp_data_type, ret_ptr, output);
@@ -82,8 +107,8 @@ Status DvppDecodeResizeCropJpegOp::Compute(const std::shared_ptr<Tensor> &input,
       std::string error = "[ERROR] Fail to get the Output result from memory!";
       RETURN_STATUS_UNEXPECTED(error);
     }
-    process.device_memory_release();
-    process.Release();
+    processor.device_memory_release();
+    processor.Release();
     // Last part end where we transform the processed data into a tensor which can be applied in later units.
   } catch (const cv::Exception &e) {
     std::string error = "[ERROR] Fail in DvppDecodeResizeCropJpegOp:" + std::string(e.what());
@@ -102,5 +127,12 @@ Status DvppDecodeResizeCropJpegOp::OutputShape(const std::vector<TensorShape> &i
   return Status(StatusCode::kMDUnexpectedError, "Input has a wrong shape");
 }
 
+Status DvppDecodeResizeCropJpegOp::SetAscendResource(const std::shared_ptr<MDAclProcess> &processor) {
+  processor_ = processor;
+  processor_->SetResizeParas(resized_width_, resized_height_);
+  processor_->SetCropParas(crop_width_, crop_height_);
+  return Status::OK();
+}
+
 }  // namespace dataset
 }  // namespace mindspore

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/dvpp_decode_resize_crop_jpeg_op.h

@@ -14,21 +14,21 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_DVPP_DECODE_RESIZE_CROP_JPEG_OP_H
-#define MINDSPORE_DVPP_DECODE_RESIZE_CROP_JPEG_OP_H
+#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_DECODE_RESIZE_CROP_JPEG_OP_H
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_DECODE_RESIZE_CROP_JPEG_OP_H
 
 #include <memory>
 #include <string>
 #include <vector>
 
-#include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/kernels/tensor_op.h"
-#include "minddata/dataset/util/status.h"
-#include "minddata/dataset/core/data_type.h"
+#include "acl/acl.h"
 #include "mindspore/core/utils/log_adapter.h"
+#include "minddata/dataset/core/data_type.h"
+#include "minddata/dataset/core/tensor.h"
 #include "minddata/dataset/kernels/image/dvpp/utils/ResourceManager.h"
 #include "minddata/dataset/kernels/image/dvpp/utils/ErrorCode.h"
-#include "acl/acl.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/status.h"
 
 namespace mindspore {
 namespace dataset {
@@ -44,17 +44,23 @@ class DvppDecodeResizeCropJpegOp : public TensorOp {
   ~DvppDecodeResizeCropJpegOp() = default;
 
   Status Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) override;
+
+  Status Compute(const std::shared_ptr<DeviceTensor> &input, std::shared_ptr<DeviceTensor> *output) override;
+
   Status OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) override;
 
   std::string Name() const override { return kDvppDecodeResizeCropJpegOp; }
 
+  Status SetAscendResource(const std::shared_ptr<MDAclProcess> &processor) override;
+
  private:
   int32_t crop_height_;
   int32_t crop_width_;
   int32_t resized_height_;
   int32_t resized_width_;
+  std::shared_ptr<MDAclProcess> processor_;
 };
 }  // namespace dataset
 }  // namespace mindspore
 
-#endif  // MINDSPORE_DVPP_DECODE_RESIZE_CROP_JPEG_OP_H
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_DECODE_RESIZE_CROP_JPEG_OP_H

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/dvpp_decode_resize_jpeg_op.cc

@@ -0,0 +1,134 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <string>
+#include <vector>
+#include <iostream>
+#include "include/api/context.h"
+#include "minddata/dataset/core/cv_tensor.h"
+#include "minddata/dataset/core/data_type.h"
+#include "minddata/dataset/kernels/image/dvpp/dvpp_decode_resize_jpeg_op.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/CommonDataType.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/MDAclProcess.h"
+#include "minddata/dataset/kernels/image/image_utils.h"
+
+namespace mindspore {
+namespace dataset {
+Status DvppDecodeResizeJpegOp::Compute(const std::shared_ptr<DeviceTensor> &input,
+                                       std::shared_ptr<DeviceTensor> *output) {
+  IO_CHECK(input, output);
+  try {
+    CHECK_FAIL_RETURN_UNEXPECTED(input->GetDeviceBuffer() != nullptr, "The input image buffer on device is empty");
+    APP_ERROR ret = processor_->JPEG_DR();
+    if (ret != APP_ERR_OK) {
+      processor_->Release();
+      std::string error = "Error in dvpp processing:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    std::shared_ptr<DvppDataInfo> ResizeOut(processor_->Get_Resized_DeviceData());
+    const TensorShape dvpp_shape({1, 1, 1});
+    const DataType dvpp_data_type(DataType::DE_UINT8);
+    mindspore::dataset::DeviceTensor::CreateEmpty(dvpp_shape, dvpp_data_type, output);
+    (*output)->SetAttributes(ResizeOut);
+    if (!((*output)->HasDeviceData())) {
+      std::string error = "[ERROR] Fail to get the Output result from memory!";
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+  } catch (const cv::Exception &e) {
+    std::string error = "[ERROR] Fail in DvppDecodeResizeJpegOp:" + std::string(e.what());
+    RETURN_STATUS_UNEXPECTED(error);
+  }
+  return Status::OK();
+}
+
+Status DvppDecodeResizeJpegOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
+  IO_CHECK(input, output);
+  if (!IsNonEmptyJPEG(input)) {
+    RETURN_STATUS_UNEXPECTED("DvppDecodeReiszeJpegOp only support process jpeg image.");
+  }
+  try {
+    CHECK_FAIL_RETURN_UNEXPECTED(input->GetBuffer() != nullptr, "The input image buffer is empty.");
+    unsigned char *buffer = const_cast<unsigned char *>(input->GetBuffer());
+    RawData imageInfo;
+    uint32_t filesize = input->SizeInBytes();
+    imageInfo.lenOfByte = filesize;
+    imageInfo.data = static_cast<void *>(buffer);
+    ResourceInfo resource;
+    resource.aclConfigPath = "";
+    resource.deviceIds.insert(mindspore::GlobalContext::GetGlobalDeviceID());
+    std::shared_ptr<ResourceManager> instance = ResourceManager::GetInstance();
+    APP_ERROR ret = instance->InitResource(resource);
+    if (ret != APP_ERR_OK) {
+      instance->Release();
+      std::string error = "Error in Init D-chip:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    int deviceId = *(resource.deviceIds.begin());
+    aclrtContext context = instance->GetContext(deviceId);
+    // Second part end where we initialize the resource of D-chip and set up all configures
+    MDAclProcess process(resized_width_, resized_height_, context, false);
+    ret = process.InitResource();
+    if (ret != APP_ERR_OK) {
+      instance->Release();
+      std::string error = "Error in Init resource:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+
+    ret = process.JPEG_DR(imageInfo);
+    if (ret != APP_ERR_OK) {
+      instance->Release();
+      std::string error = "Error in dvpp processing:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+
+    // Third part end where we execute the core function of dvpp
+    auto data = std::static_pointer_cast<unsigned char>(process.Get_Memory_Data());
+    unsigned char *ret_ptr = data.get();
+    std::shared_ptr<DvppDataInfo> ResizeOut(process.Get_Resized_DeviceData());
+    dsize_t dvpp_length = ResizeOut->dataSize;
+    const TensorShape dvpp_shape({dvpp_length, 1, 1});
+    const DataType dvpp_data_type(DataType::DE_UINT8);
+    mindspore::dataset::Tensor::CreateFromMemory(dvpp_shape, dvpp_data_type, ret_ptr, output);
+    if (!((*output)->HasData())) {
+      std::string error = "[ERROR] Fail to get the Output result from memory!";
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    process.device_memory_release();
+    process.Release();
+    // Last part end where we transform the processed data into a tensor which can be applied in later units.
+  } catch (const cv::Exception &e) {
+    std::string error = "[ERROR] Fail in DvppDecodeResizeJpegOp:" + std::string(e.what());
+    RETURN_STATUS_UNEXPECTED(error);
+  }
+  return Status::OK();
+}
+
+Status DvppDecodeResizeJpegOp::OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) {
+  RETURN_IF_NOT_OK(TensorOp::OutputShape(inputs, outputs));
+  outputs.clear();
+  TensorShape out({-1, 1, 1});  // we don't know what is output image size, but we know it should be 1 channels
+  if (inputs[0].Rank() == 1) outputs.emplace_back(out);
+  if (!outputs.empty()) return Status::OK();
+  return Status(StatusCode::kMDUnexpectedError, "Input has a wrong shape");
+}
+
+Status DvppDecodeResizeJpegOp::SetAscendResource(const std::shared_ptr<MDAclProcess> &processor) {
+  processor_ = processor;
+  processor_->SetResizeParas(resized_width_, resized_height_);
+  return Status::OK();
+}
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/dvpp_decode_resize_jpeg_op.h

@@ -0,0 +1,62 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_DECODE_RESIZE_JPEG_OP_H
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_DECODE_RESIZE_JPEG_OP_H
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "acl/acl.h"
+#include "mindspore/core/utils/log_adapter.h"
+#include "minddata/dataset/core/data_type.h"
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/ErrorCode.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/ResourceManager.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+class DvppDecodeResizeJpegOp : public TensorOp {
+ public:
+  DvppDecodeResizeJpegOp(int32_t resized_height, int32_t resized_width)
+      : resized_height_(resized_height), resized_width_(resized_width) {}
+
+  /// \brief Destructor
+  ~DvppDecodeResizeJpegOp() = default;
+
+  Status Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) override;
+
+  Status Compute(const std::shared_ptr<DeviceTensor> &input, std::shared_ptr<DeviceTensor> *output) override;
+
+  Status OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) override;
+
+  std::string Name() const override { return kDvppDecodeResizeJpegOp; }
+
+  Status SetAscendResource(const std::shared_ptr<MDAclProcess> &processor) override;
+
+ private:
+  int32_t resized_height_;
+  int32_t resized_width_;
+
+  std::shared_ptr<MDAclProcess> processor_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_DECODE_RESIZE_JPEG_OP_H

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/dvpp_resize_jpeg_op.cc

@@ -0,0 +1,147 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <string>
+#include <vector>
+#include <iostream>
+#include "include/api/context.h"
+#include "minddata/dataset/core/cv_tensor.h"
+#include "minddata/dataset/core/data_type.h"
+#include "minddata/dataset/core/device_tensor.h"
+#include "minddata/dataset/kernels/image/dvpp/dvpp_resize_jpeg_op.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/CommonDataType.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/MDAclProcess.h"
+#include "minddata/dataset/kernels/image/image_utils.h"
+
+namespace mindspore {
+namespace dataset {
+Status DvppResizeJpegOp::Compute(const std::shared_ptr<DeviceTensor> &input, std::shared_ptr<DeviceTensor> *output) {
+  IO_CHECK(input, output);
+  try {
+    CHECK_FAIL_RETURN_UNEXPECTED(input->GetDeviceBuffer() != nullptr, "The input image buffer is empty.");
+    std::string last_step = "Decode";
+    std::shared_ptr<DvppDataInfo> imageinfo(processor_->Get_Decode_DeviceData());
+    if (!imageinfo->data) {
+      last_step = "Crop";
+    }
+    APP_ERROR ret = processor_->JPEG_R(last_step);
+    if (ret != APP_ERR_OK) {
+      processor_->Release();
+      std::string error = "Error in dvpp processing:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    std::shared_ptr<DvppDataInfo> ResizeOut(processor_->Get_Resized_DeviceData());
+    const TensorShape dvpp_shape({1, 1, 1});
+    const DataType dvpp_data_type(DataType::DE_UINT8);
+    mindspore::dataset::DeviceTensor::CreateEmpty(dvpp_shape, dvpp_data_type, output);
+    (*output)->SetAttributes(ResizeOut);  // Set attributes for output DeviceTensor
+    if (!((*output)->HasDeviceData())) {
+      std::string error = "[ERROR] Fail to get the Output result from device memory!";
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+  } catch (const cv::Exception &e) {
+    std::string error = "[ERROR] Fail in DvppResizeJpegOp:" + std::string(e.what());
+    RETURN_STATUS_UNEXPECTED(error);
+  }
+  return Status::OK();
+}
+
+Status DvppResizeJpegOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
+  IO_CHECK(input, output);
+  try {
+    CHECK_FAIL_RETURN_UNEXPECTED(input->GetBuffer() != nullptr, "The input image buffer is empty.");
+    unsigned char *buffer = const_cast<unsigned char *>(input->GetBuffer());
+    DvppDataInfo imageinfo;
+    imageinfo.dataSize = input->SizeInBytes();
+    imageinfo.data = static_cast<uint8_t *>(buffer);
+    std::vector<uint32_t> yuv_shape_ = input->GetYuvShape();
+    imageinfo.width = yuv_shape_[0];
+    imageinfo.widthStride = yuv_shape_[1];
+    imageinfo.height = yuv_shape_[2];
+    imageinfo.heightStride = yuv_shape_[3];
+    imageinfo.format = PIXEL_FORMAT_YUV_SEMIPLANAR_420;
+    ResourceInfo resource;
+    resource.aclConfigPath = "";
+    resource.deviceIds.insert(mindspore::GlobalContext::GetGlobalDeviceID());
+    std::shared_ptr<ResourceManager> instance = ResourceManager::GetInstance();
+    APP_ERROR ret = instance->InitResource(resource);
+    if (ret != APP_ERR_OK) {
+      instance->Release();
+      std::string error = "Error in Init D-chip:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    int deviceId = *(resource.deviceIds.begin());
+    aclrtContext context = instance->GetContext(deviceId);
+    // Second part end where we initialize the resource of D-chip and set up all configures
+    MDAclProcess process(resized_width_, resized_height_, context, false);
+    ret = process.InitResource();
+    if (ret != APP_ERR_OK) {
+      instance->Release();
+      std::string error = "Error in Init resource:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+
+    ret = process.JPEG_R(imageinfo);
+    if (ret != APP_ERR_OK) {
+      instance->Release();
+      std::string error = "Error in dvpp processing:" + std::to_string(ret);
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+
+    // Third part end where we execute the core function of dvpp
+    auto data = std::static_pointer_cast<unsigned char>(process.Get_Memory_Data());
+    unsigned char *ret_ptr = data.get();
+    std::shared_ptr<DvppDataInfo> ResizeOut(process.Get_Resized_DeviceData());
+    dsize_t dvpp_length = ResizeOut->dataSize;
+    const TensorShape dvpp_shape({dvpp_length, 1, 1});
+    uint32_t resized_height = ResizeOut->height;
+    uint32_t resized_heightStride = ResizeOut->heightStride;
+    uint32_t resized_width = ResizeOut->width;
+    uint32_t resized_widthStride = ResizeOut->widthStride;
+    const DataType dvpp_data_type(DataType::DE_UINT8);
+    mindspore::dataset::Tensor::CreateFromMemory(dvpp_shape, dvpp_data_type, ret_ptr, output);
+    (*output)->SetYuvShape(resized_width, resized_widthStride, resized_height, resized_heightStride);
+    if (!((*output)->HasData())) {
+      std::string error = "[ERROR] Fail to get the Output result from memory!";
+      RETURN_STATUS_UNEXPECTED(error);
+    }
+    process.device_memory_release();
+    process.Release();
+    // Last part end where we transform the processed data into a tensor which can be applied in later units.
+  } catch (const cv::Exception &e) {
+    std::string error = "[ERROR] Fail in DvppResizeJpegOp:" + std::string(e.what());
+    RETURN_STATUS_UNEXPECTED(error);
+  }
+  return Status::OK();
+}
+
+Status DvppResizeJpegOp::SetAscendResource(const std::shared_ptr<MDAclProcess> &processor) {
+  processor_ = processor;
+  processor_->SetResizeParas(resized_width_, resized_height_);
+  return Status::OK();
+}
+
+Status DvppResizeJpegOp::OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) {
+  RETURN_IF_NOT_OK(TensorOp::OutputShape(inputs, outputs));
+  outputs.clear();
+  TensorShape out({-1, 1, 1});  // we don't know what is output image size, but we know it should be 1 channels
+  if (inputs[0].Rank() == 1) outputs.emplace_back(out);
+  if (!outputs.empty()) return Status::OK();
+  return Status(StatusCode::kMDUnexpectedError, "Input has a wrong shape");
+}
+
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/dvpp_resize_jpeg_op.h

@@ -0,0 +1,63 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_RESIZE_JPEG_OP_H
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_RESIZE_JPEG_OP_H
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "acl/acl.h"
+#include "minddata/dataset/core/data_type.h"
+#include "minddata/dataset/core/device_tensor.h"
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/ErrorCode.h"
+#include "minddata/dataset/kernels/image/dvpp/utils/ResourceManager.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/status.h"
+#include "mindspore/core/utils/log_adapter.h"
+
+namespace mindspore {
+namespace dataset {
+class DvppResizeJpegOp : public TensorOp {
+ public:
+  DvppResizeJpegOp(int32_t resized_height, int32_t resized_width)
+      : resized_height_(resized_height), resized_width_(resized_width) {}
+
+  /// \brief Destructor
+  ~DvppResizeJpegOp() = default;
+
+  Status Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) override;
+
+  Status Compute(const std::shared_ptr<DeviceTensor> &input, std::shared_ptr<DeviceTensor> *output) override;
+
+  Status OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) override;
+
+  std::string Name() const override { return kDvppDecodeResizeJpegOp; }
+
+  Status SetAscendResource(const std::shared_ptr<MDAclProcess> &processor) override;
+
+ private:
+  int32_t resized_height_;
+  int32_t resized_width_;
+
+  std::shared_ptr<MDAclProcess> processor_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_DVPP_DVPP_RESIZE_JPEG_OP_H

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/utils/AclProcess.cc

@@ -1,255 +0,0 @@
-/*
- * Copyright (c) 2020.Huawei Technologies Co., Ltd. All rights reserved.
- * 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 "AclProcess.h"
-#include <sys/stat.h>
-#include <sys/time.h>
-#include <thread>
-
-namespace {
-const int BUFFER_SIZE = 2048;
-const mode_t DEFAULT_FILE_PERMISSION = 0077;
-}  // namespace
-
-mode_t SetFileDefaultUmask() { return umask(DEFAULT_FILE_PERMISSION); }
-
-/*
- * @description: Constructor
- * @param: resizeWidth specifies the resized width
- * @param: resizeHeight specifies the resized hegiht
- * @param: stream is used to maintain the execution order of operations
- * @param: context is used to manage the life cycle of objects
- * @param: dvppCommon is a class for decoding and resizing
- */
-AclProcess::AclProcess(uint32_t resizeWidth, uint32_t resizeHeight, uint32_t cropWidth, uint32_t cropHeight,
-                       aclrtContext context, aclrtStream stream, std::shared_ptr<DvppCommon> dvppCommon)
-    : resizeWidth_(resizeWidth),
-      resizeHeight_(resizeHeight),
-      cropWidth_(cropWidth),
-      cropHeight_(cropHeight),
-      context_(context),
-      stream_(stream),
-      dvppCommon_(dvppCommon) {
-  repeat_ = true;
-}
-
-/*
- * @description: Release AclProcess resources
- * @return: aclError which is error code of ACL API
- */
-APP_ERROR AclProcess::Release() {
-  // Release objects resource
-  APP_ERROR ret = dvppCommon_->DeInit();
-  dvppCommon_->ReleaseDvppBuffer();
-
-  if (ret != APP_ERR_OK) {
-    MS_LOG(ERROR) << "Failed to deinitialize dvppCommon_, ret = " << ret;
-    return ret;
-  }
-  MS_LOG(INFO) << "dvppCommon_ object deinitialized successfully";
-  dvppCommon_.reset();
-
-  // Release stream
-  if (stream_ != nullptr) {
-    ret = aclrtDestroyStream(stream_);
-    if (ret != APP_ERR_OK) {
-      MS_LOG(ERROR) << "Failed to destroy stream, ret = " << ret;
-      stream_ = nullptr;
-      return ret;
-    }
-    stream_ = nullptr;
-  }
-  MS_LOG(INFO) << "The stream is destroyed successfully";
-  return APP_ERR_OK;
-}
-
-/*
- * @description: Initialize DvppCommon object
- * @return: aclError which is error code of ACL API
- */
-APP_ERROR AclProcess::InitModule() {
-  // Create Dvpp JpegD object
-  dvppCommon_ = std::make_shared<DvppCommon>(stream_);
-  if (dvppCommon_ == nullptr) {
-    MS_LOG(ERROR) << "Failed to create dvppCommon_ object";
-    return APP_ERR_COMM_INIT_FAIL;
-  }
-  MS_LOG(INFO) << "DvppCommon object created successfully";
-  APP_ERROR ret = dvppCommon_->Init();
-  if (ret != APP_ERR_OK) {
-    MS_LOG(ERROR) << "Failed to initialize dvppCommon_ object, ret = " << ret;
-    return ret;
-  }
-  MS_LOG(INFO) << "DvppCommon object initialized successfully";
-  return APP_ERR_OK;
-}
-
-/*
- * @description: Initialize AclProcess resources
- * @return: aclError which is error code of ACL API
- */
-APP_ERROR AclProcess::InitResource() {
-  APP_ERROR ret = aclrtSetCurrentContext(context_);
-  if (ret != APP_ERR_OK) {
-    MS_LOG(ERROR) << "Failed to get ACL context, ret = " << ret;
-    return ret;
-  }
-  MS_LOG(INFO) << "The context is created successfully";
-  ret = aclrtCreateStream(&stream_);  // Create stream for application
-  if (ret != APP_ERR_OK) {
-    MS_LOG(ERROR) << "Failed to create ACL stream, ret = " << ret;
-    return ret;
-  }
-  MS_LOG(INFO) << "The stream is created successfully";
-  // Initialize dvpp module
-  if (InitModule() != APP_ERR_OK) {
-    return APP_ERR_COMM_INIT_FAIL;
-  }
-  return APP_ERR_OK;
-}
-
-/*
- * @description: Read image files, and perform decoding and scaling
- * @param: imageFile specifies the image path to be processed
- * @return: aclError which is error code of ACL API
- */
-APP_ERROR AclProcess::Preprocess(const RawData &ImageInfo) {
-  // Decode process
-  APP_ERROR ret = dvppCommon_->CombineJpegdProcess(ImageInfo, PIXEL_FORMAT_YUV_SEMIPLANAR_420, true);
-  if (ret != APP_ERR_OK) {
-    MS_LOG(ERROR) << "Failed to process decode, ret = " << ret << ".";
-    return ret;
-  }
-  // Get output of decoded jpeg image
-  std::shared_ptr<DvppDataInfo> decodeOutData = dvppCommon_->GetDecodedImage();
-  if (decodeOutData == nullptr) {
-    MS_LOG(ERROR) << "Decode output buffer is null.";
-    return APP_ERR_COMM_INVALID_POINTER;
-  }
-  // Define output of resize jpeg image
-  DvppDataInfo resizeOut;
-  resizeOut.width = resizeWidth_;
-  resizeOut.height = resizeHeight_;
-  resizeOut.format = PIXEL_FORMAT_YUV_SEMIPLANAR_420;
-  // Run resize application function
-  ret = dvppCommon_->CombineResizeProcess(*(decodeOutData.get()), resizeOut, true);
-  if (ret != APP_ERR_OK) {
-    MS_LOG(ERROR) << "Failed to process resize, ret = " << ret << ".";
-    return ret;
-  }
-  // Get output of resize jpeg image
-  std::shared_ptr<DvppDataInfo> resizeOutData = dvppCommon_->GetResizedImage();
-  if (resizeOutData == nullptr) {
-    MS_LOG(ERROR) << "resize output buffer is null.";
-    return APP_ERR_COMM_INVALID_POINTER;
-  }
-  // Define output of crop jpeg image
-  DvppDataInfo cropOut;
-  cropOut.width = cropWidth_;
-  cropOut.height = cropHeight_;
-  cropOut.format = PIXEL_FORMAT_YUV_SEMIPLANAR_420;
-  // Define input of crop jpeg image
-  DvppCropInputInfo cropInfo;
-  cropInfo.dataInfo = *(resizeOutData.get());
-  // Define crop parameters
-  CropRoiConfig cropCfg;
-  CropConfigFilter(cropCfg, cropInfo);
-  ret = dvppCommon_->CombineCropProcess(cropInfo, cropOut, true);
-  if (ret != APP_ERR_OK) {
-    MS_LOG(ERROR) << "Failed to process center crop, ret = " << ret << ".";
-    return ret;
-  }
-  return APP_ERR_OK;
-}
-
-/*
- * @description: Decode and scale the picture, and write the result to a file
- * @param: imageFile specifies the image path to be processed
- * @return: aclError which is error code of ACL API
- */
-APP_ERROR AclProcess::Process(const RawData &ImageInfo) {
-  struct timeval begin = {0};
-  struct timeval end = {0};
-  gettimeofday(&begin, nullptr);
-  // deal with image
-  APP_ERROR ret = Preprocess(ImageInfo);
-  if (ret != APP_ERR_OK) {
-    MS_LOG(ERROR) << "Failed to preprocess, ret = " << ret;
-    return ret;
-  }
-  gettimeofday(&end, nullptr);
-  // Calculate the time cost of preprocess
-  const double costMs = SEC2MS * (end.tv_sec - begin.tv_sec) + (end.tv_usec - begin.tv_usec) / SEC2MS;
-  const double fps = 1 * SEC2MS / costMs;
-  MS_LOG(INFO) << "[dvpp Delay] cost: " << costMs << "ms\tfps: " << fps;
-  // Get output of resize module
-  std::shared_ptr<DvppDataInfo> CropOutData = dvppCommon_->GetCropedImage();
-  if (CropOutData->dataSize == 0) {
-    MS_LOG(ERROR) << "CropOutData return NULL";
-    return APP_ERR_COMM_INVALID_POINTER;
-  }
-  // Alloc host memory for the inference output according to the size of output
-  void *resHostBuf = nullptr;
-  ret = aclrtMallocHost(&resHostBuf, CropOutData->dataSize);
-  if (ret != APP_ERR_OK) {
-    MS_LOG(ERROR) << "Failed to allocate memory from host ret = " << ret;
-    return ret;
-  }
-  std::shared_ptr<void> outBuf(resHostBuf, aclrtFreeHost);
-  processedInfo_ = outBuf;
-  // Memcpy the output data from device to host
-  ret = aclrtMemcpy(outBuf.get(), CropOutData->dataSize, CropOutData->data, CropOutData->dataSize,
-                    ACL_MEMCPY_DEVICE_TO_HOST);
-  if (ret != APP_ERR_OK) {
-    MS_LOG(ERROR) << "Failed to copy memory from device to host, ret = " << ret;
-    return ret;
-  }
-  return APP_ERR_OK;
-}
-
-void AclProcess::CropConfigFilter(CropRoiConfig &cfg, DvppCropInputInfo &cropinfo) {
-  cfg.up = (resizeHeight_ - cropHeight_) / 2;
-  if (cfg.up % 2 != 0) {
-    cfg.up++;
-  }
-  cfg.down = resizeHeight_ - (resizeHeight_ - cropHeight_) / 2;
-  if (cfg.down % 2 == 0) {
-    cfg.down--;
-  }
-  cfg.left = (resizeWidth_ - cropWidth_) / 2;
-  if (cfg.left % 2 != 0) {
-    cfg.left++;
-  }
-  cfg.right = resizeWidth_ - (resizeWidth_ - cropWidth_) / 2;
-  if (cfg.right % 2 == 0) {
-    cfg.right--;
-  }
-  cropinfo.roi = cfg;
-}
-
-/*
- * @description: Obtain result data of memory
- * @param: processed_data is result data info pointer
- * @return: Address of data in the memory
- */
-std::shared_ptr<void> AclProcess::Get_Memory_Data() { return processedInfo_; }
-
-std::shared_ptr<DvppDataInfo> AclProcess::Get_Device_Memory_Data() { return dvppCommon_->GetCropedImage(); }
-
-void AclProcess::set_mode(bool flag) { repeat_ = flag; }
-
-bool AclProcess::get_mode() { return repeat_; }
-
-void AclProcess::device_memory_release() { dvppCommon_->ReleaseDvppBuffer(); }

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/utils/AclProcess.h

@@ -1,80 +0,0 @@
-/*
- * Copyright (c) 2020.Huawei Technologies Co., Ltd. All rights reserved.
- * 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 ACLMANAGER_H
-#define ACLMANAGER_H
-
-#include <stdio.h>
-#include <unistd.h>
-#include <sys/stat.h>
-#include <string.h>
-#include <sys/types.h>
-#include <string>
-#include <map>
-#include <climits>
-#include <iostream>
-#include <memory>
-#include "acl/acl.h"
-#include "CommonDataType.h"
-#include "mindspore/core/utils/log_adapter.h"
-#include "ErrorCode.h"
-#include "DvppCommon.h"
-
-mode_t SetFileDefaultUmask();
-
-class AclProcess {
- public:
-  AclProcess(uint32_t resizeWidth, uint32_t resizeHeight, uint32_t cropWidth, uint32_t cropHeight, aclrtContext context,
-             aclrtStream stream = nullptr, std::shared_ptr<DvppCommon> dvppCommon = nullptr);
-
-  ~AclProcess() {}
-
-  // Release all the resource
-  APP_ERROR Release();
-  // Create resource for this sample
-  APP_ERROR InitResource();
-  // Process the result
-  APP_ERROR Process(const RawData &ImageInfo);
-  // API for access memory
-  std::shared_ptr<void> Get_Memory_Data();
-  // API for access device memory
-  std::shared_ptr<DvppDataInfo> Get_Device_Memory_Data();
-  // change output method
-  void set_mode(bool flag);
-  // Get the mode of Acl process
-  bool get_mode();
-  // Crop definition
-  void CropConfigFilter(CropRoiConfig &cfg, DvppCropInputInfo &cropinfo);
-  // D-chip memory release
-  void device_memory_release();
-
- private:
-  // Initialize the modules used by this sample
-  APP_ERROR InitModule();
-  // Preprocess the input image
-  APP_ERROR Preprocess(const RawData &ImageInfo);
-
-  aclrtContext context_;
-  aclrtStream stream_;
-  std::shared_ptr<DvppCommon> dvppCommon_;  // dvpp object
-  std::shared_ptr<void> processedInfo_;     // processed data
-  uint32_t resizeWidth_;                    // dvpp resize width
-  uint32_t resizeHeight_;                   // dvpp resize height
-  uint32_t cropWidth_;                      // dvpp crop width
-  uint32_t cropHeight_;                     // dvpp crop height
-  bool repeat_;                             // Repeatly process image or not
-};
-
-#endif

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

@@ -4,7 +4,7 @@ set_property(SOURCE ${_CURRENT_SRC_FILES} PROPERTY COMPILE_DEFINITIONS SUBMODULE
 add_definitions(-DENABLE_DVPP_INTERFACE)
 
 add_library(dvpp-utils OBJECT
-        AclProcess.cc
+        MDAclProcess.cc
         DvppCommon.cc
         ErrorCode.cpp
         ResourceManager.cc

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/utils/CommonDataType.h

@@ -21,7 +21,6 @@
 #include <stdio.h>
 #include <iostream>
 #include <memory>
-#include <string>
 #include <vector>
 #include "acl/acl.h"
 #include "acl/ops/acl_dvpp.h"
@@ -42,15 +41,6 @@ const int JPEG_WIDTH_ALIGN = 128;
 const int JPEG_HEIGHT_ALIGN = 16;
 const int VPC_OFFSET_ALIGN = 2;
 
-// Tensor Descriptor
-struct Tensor {
-  aclDataType dataType;       // Tensor data type
-  int numDim;                 // Number of dimensions of Tensor
-  std::vector<int64_t> dims;  // Dimension vector
-  aclFormat format;           // Format of tensor, e.g. ND, NCHW, NC1HWC0
-  std::string name;           // Name of tensor
-};
-
 // Data type of tensor
 enum OpAttrType {
   BOOL = 0,
@@ -92,8 +82,8 @@ struct ImageInfo {
 
 // Description of data in device
 struct RawData {
-  size_t lenOfByte;            // Size of memory, bytes
-  std::shared_ptr<void> data;  // Smart pointer of data
+  size_t lenOfByte;  // Size of memory, bytes
+  void *data;        // Pointer of data
 };
 
 // Description of data in device

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/utils/DvppCommon.cc

@@ -15,6 +15,7 @@
 
 #include <iostream>
 #include <memory>
+
 #include "mindspore/core/utils/log_adapter.h"
 #include "DvppCommon.h"
 #include "CommonDataType.h"
@@ -185,11 +186,16 @@ void DvppCommon::ReleaseDvppBuffer() {
  */
 APP_ERROR DvppCommon::GetVpcDataSize(uint32_t width, uint32_t height, acldvppPixelFormat format, uint32_t &vpcSize) {
   // Check the invalid format of VPC function and calculate the output buffer size
-  if (format != PIXEL_FORMAT_YUV_SEMIPLANAR_420 && format != PIXEL_FORMAT_YVU_SEMIPLANAR_420) {
-    MS_LOG(ERROR) << "Format[" << format << "] for VPC is not supported, just support NV12 or NV21.";
+  if (format != PIXEL_FORMAT_YUV_SEMIPLANAR_420 && format != PIXEL_FORMAT_YVU_SEMIPLANAR_420 &&
+      format != PIXEL_FORMAT_RGB_888) {
+    MS_LOG(ERROR) << "Format[" << format << "] for VPC is not supported, just support NV12 or NV21 or RGB888.";
     return APP_ERR_COMM_INVALID_PARAM;
   }
   uint32_t widthStride = DVPP_ALIGN_UP(width, VPC_WIDTH_ALIGN);
+  if (format == PIXEL_FORMAT_RGB_888) {
+    widthStride *= 3;
+  }
+
   uint32_t heightStride = DVPP_ALIGN_UP(height, VPC_HEIGHT_ALIGN);
   vpcSize = widthStride * heightStride * YUV_BGR_SIZE_CONVERT_3 / YUV_BGR_SIZE_CONVERT_2;
   return APP_ERR_OK;
@@ -254,12 +260,17 @@ APP_ERROR DvppCommon::GetVpcInputStrideSize(uint32_t width, uint32_t height, acl
 APP_ERROR DvppCommon::GetVpcOutputStrideSize(uint32_t width, uint32_t height, acldvppPixelFormat format,
                                              uint32_t &widthStride, uint32_t &heightStride) {
   // Check the invalidty of output format and calculate the output width and height
-  if (format != PIXEL_FORMAT_YUV_SEMIPLANAR_420 && format != PIXEL_FORMAT_YVU_SEMIPLANAR_420) {
-    MS_LOG(ERROR) << "Output format[" << format << "] for VPC is not supported, just support NV12 or NV21.";
+  if (format != PIXEL_FORMAT_YUV_SEMIPLANAR_420 && format != PIXEL_FORMAT_YVU_SEMIPLANAR_420 &&
+      format != PIXEL_FORMAT_RGB_888) {
+    MS_LOG(ERROR) << "Output format[" << format << "] for VPC is not supported, just support NV12 or NV21 or RGB888.";
     return APP_ERR_COMM_INVALID_PARAM;
   }
 
   widthStride = DVPP_ALIGN_UP(width, VPC_STRIDE_WIDTH);
+  if (format == PIXEL_FORMAT_RGB_888) {
+    widthStride *= 3;
+  }
+
   heightStride = DVPP_ALIGN_UP(height, VPC_STRIDE_HEIGHT);
   return APP_ERR_OK;
 }
@@ -373,8 +384,9 @@ APP_ERROR DvppCommon::SetDvppPicDescData(const DvppDataInfo &dataInfo, acldvppPi
  * @return: APP_ERR_OK if success, other values if failure
  */
 APP_ERROR DvppCommon::CheckResizeParams(const DvppDataInfo &input, const DvppDataInfo &output) {
-  if (output.format != PIXEL_FORMAT_YUV_SEMIPLANAR_420 && output.format != PIXEL_FORMAT_YVU_SEMIPLANAR_420) {
-    MS_LOG(ERROR) << "Output format[" << output.format << "] for VPC is not supported, just support NV12 or NV21.";
+  if (output.format != PIXEL_FORMAT_YUV_SEMIPLANAR_420 && output.format != PIXEL_FORMAT_YVU_SEMIPLANAR_420 &&
+      output.format != PIXEL_FORMAT_RGB_888) {
+    MS_LOG(ERROR) << "Output format[" << output.format << "] for VPC is not supported, only NV12 or NV21 or RGB888.";
     return APP_ERR_COMM_INVALID_PARAM;
   }
   if (((float)output.height / input.height) < MIN_RESIZE_SCALE ||
@@ -419,6 +431,7 @@ APP_ERROR DvppCommon::ResizeProcess(acldvppPicDesc &inputDesc, acldvppPicDesc &o
       return ret;
     }
   }
+
   return APP_ERR_OK;
 }
 
@@ -770,6 +783,7 @@ APP_ERROR DvppCommon::CombineCropProcess(DvppCropInputInfo &input, DvppDataInfo
   if (ret != APP_ERR_OK) {
     return ret;
   }
+  // cropImage_所持有的成员变量 uint8_t *data通过acldvppMalloc()接口申请，位于Device上
   cropImage_ = std::make_shared<DvppDataInfo>();
   cropImage_->width = output.width;
   cropImage_->height = output.height;
@@ -839,6 +853,44 @@ APP_ERROR DvppCommon::JpegDecode(DvppDataInfo &input, DvppDataInfo &output, bool
   return APP_ERR_OK;
 }
 
+/*
+ * @description: Set the description of the output image and decode
+ * @param: input specifies the input image information
+ * @param: output specifies the output image information
+ * @param: withSynchronize specifies whether to execute synchronously
+ * @return: APP_ERR_OK if success, other values if failure
+ * @attention: This function can be called only when the DvppCommon object is initialized with Init
+ */
+APP_ERROR DvppCommon::PngDecode(DvppDataInfo &input, DvppDataInfo &output, bool withSynchronize) {
+  // Return special error code when the DvppCommon object is initialized with InitVdec
+  if (isVdec_) {
+    MS_LOG(ERROR) << "PngDecode cannot be called by the DvppCommon object which is initialized with InitVdec.";
+    return APP_ERR_DVPP_OBJ_FUNC_MISMATCH;
+  }
+
+  acldvppPicDesc *outputDesc = acldvppCreatePicDesc();
+  decodeOutputDesc_.reset(outputDesc, g_picDescDeleter);
+
+  APP_ERROR ret = SetDvppPicDescData(output, *decodeOutputDesc_);
+  if (ret != APP_ERR_OK) {
+    return ret;
+  }
+
+  ret = acldvppPngDecodeAsync(dvppChannelDesc_, input.data, input.dataSize, decodeOutputDesc_.get(), dvppStream_);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to decode png, ret = " << ret << ".";
+    return ret;
+  }
+  if (withSynchronize) {
+    ret = aclrtSynchronizeStream(dvppStream_);
+    if (ret != APP_ERR_OK) {
+      MS_LOG(ERROR) << "Failed to synchronize stream, ret = " << ret << ".";
+      return APP_ERR_DVPP_JPEG_DECODE_FAIL;
+    }
+  }
+  return APP_ERR_OK;
+}
+
 /*
  * @description: Get the aligned width and height of the image after decoding
  * @param: width specifies the width before alignment
@@ -853,6 +905,17 @@ void DvppCommon::GetJpegDecodeStrideSize(uint32_t width, uint32_t height, uint32
   heightStride = DVPP_ALIGN_UP(height, JPEGD_STRIDE_HEIGHT);
 }
 
+void DvppCommon::GetPngDecodeStrideSize(uint32_t width, uint32_t height, uint32_t &widthStride, uint32_t &heightStride,
+                                        acldvppPixelFormat format) {
+  if (format == PIXEL_FORMAT_RGB_888) {
+    widthStride = DVPP_ALIGN_UP(width * 3, JPEGD_STRIDE_WIDTH);
+    heightStride = DVPP_ALIGN_UP(height, JPEGD_STRIDE_HEIGHT);
+  } else {
+    widthStride = DVPP_ALIGN_UP(width * 4, JPEGD_STRIDE_WIDTH);
+    heightStride = DVPP_ALIGN_UP(height, JPEGD_STRIDE_HEIGHT);
+  }
+}
+
 /*
  * @description: Get picture width and height and number of channels from image data
  * @param: data specifies the memory to store the image data
@@ -886,6 +949,39 @@ APP_ERROR DvppCommon::GetJpegImageInfo(const void *data, uint32_t dataSize, uint
   return APP_ERR_OK;
 }
 
+/*
+ * @description: Get picture width and height and number of channels from PNG image data
+ * @param: data specifies the memory to store the image data
+ * @param: dataSize specifies the size of the image data
+ * @param: width is used to save the image width
+ * @param: height is used to save the image height
+ * @param: components is used to save the number of channels
+ * @return: APP_ERR_OK if success, other values if failure
+ */
+APP_ERROR DvppCommon::GetPngImageInfo(const void *data, uint32_t dataSize, uint32_t &width, uint32_t &height,
+                                      int32_t &components) {
+  uint32_t widthTmp;
+  uint32_t heightTmp;
+  int32_t componentsTmp;
+  APP_ERROR ret = acldvppPngGetImageInfo(data, dataSize, &widthTmp, &heightTmp, &componentsTmp);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to get image info of PNG, ret = " << ret << ".";
+    return ret;
+  }
+  if (widthTmp > MAX_PNGD_WIDTH || widthTmp < MIN_PNGD_WIDTH) {
+    MS_LOG(ERROR) << "Input width is invalid, not in [" << MIN_PNGD_WIDTH << ", " << MAX_PNGD_WIDTH << "].";
+    return APP_ERR_COMM_INVALID_PARAM;
+  }
+  if (heightTmp > MAX_PNGD_HEIGHT || heightTmp < MIN_PNGD_HEIGHT) {
+    MS_LOG(ERROR) << "Input height is invalid, not in [" << MIN_PNGD_HEIGHT << ", " << MAX_PNGD_HEIGHT << "].";
+    return APP_ERR_COMM_INVALID_PARAM;
+  }
+  width = widthTmp;
+  height = heightTmp;
+  components = componentsTmp;
+  return APP_ERR_OK;
+}
+
 /*
  * @description: Get the size of the buffer for storing decoded images based on the image data, size, and format
  * @param: data specifies the memory to store the image data
@@ -906,6 +1002,26 @@ APP_ERROR DvppCommon::GetJpegDecodeDataSize(const void *data, uint32_t dataSize,
   return APP_ERR_OK;
 }
 
+/*
+ * @description: Get the size of the buffer for storing decoded images based on the PNG image data, size, and format
+ * @param: data specifies the memory to store the image data
+ * @param: dataSize specifies the size of the image data
+ * @param: format specifies the image format
+ * @param: decSize is used to store the result size
+ * @return: APP_ERR_OK if success, other values if failure
+ */
+APP_ERROR DvppCommon::GetPngDecodeDataSize(const void *data, uint32_t dataSize, acldvppPixelFormat format,
+                                           uint32_t &decSize) {
+  uint32_t outputSize;
+  APP_ERROR ret = acldvppPngPredictDecSize(data, dataSize, format, &outputSize);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to predict decode size of png image, ret = " << ret << ".";
+    return ret;
+  }
+  decSize = outputSize;
+  return APP_ERR_OK;
+}
+
 /*
  * @description: Decode the image specified by imageInfo and save the result to member variable decodedImage_
  * @param: imageInfo specifies image information
@@ -923,24 +1039,27 @@ APP_ERROR DvppCommon::CombineJpegdProcess(const RawData &imageInfo, acldvppPixel
   }
 
   int32_t components;
+  // Member variable of inputImage_, uint8_t *data will be on device
   inputImage_ = std::make_shared<DvppDataInfo>();
   inputImage_->format = format;
   APP_ERROR ret =
-    GetJpegImageInfo(imageInfo.data.get(), imageInfo.lenOfByte, inputImage_->width, inputImage_->height, components);
+    // GetJpegImageInfo(imageInfo.data.get(), imageInfo.lenOfByte, inputImage_->width, inputImage_->height, components);
+    GetJpegImageInfo(imageInfo.data, imageInfo.lenOfByte, inputImage_->width, inputImage_->height, components);
   if (ret != APP_ERR_OK) {
     MS_LOG(ERROR) << "Failed to get input image info, ret = " << ret << ".";
     return ret;
   }
 
-  // Get the buffer size of decode output according to the input data and output format
+  // Get the buffer size(On device) of decode output according to the input data and output format
   uint32_t outBuffSize;
-  ret = GetJpegDecodeDataSize(imageInfo.data.get(), imageInfo.lenOfByte, format, outBuffSize);
+  // ret = GetJpegDecodeDataSize(imageInfo.data.get(), imageInfo.lenOfByte, format, outBuffSize);
+  ret = GetJpegDecodeDataSize(imageInfo.data, imageInfo.lenOfByte, format, outBuffSize);
   if (ret != APP_ERR_OK) {
     MS_LOG(ERROR) << "Failed to get size of decode output buffer, ret = " << ret << ".";
     return ret;
   }
 
-  // In TransferImageH2D function, device buffer will be alloced to store the input image
+  // In TransferImageH2D function, device buffer will be alloced to store the input image before decode
   // Need to pay attention to release of the buffer
   ret = TransferImageH2D(imageInfo, inputImage_);
   if (ret != APP_ERR_OK) {
@@ -976,6 +1095,150 @@ APP_ERROR DvppCommon::CombineJpegdProcess(const RawData &imageInfo, acldvppPixel
   return APP_ERR_OK;
 }
 
+APP_ERROR DvppCommon::SinkCombineJpegdProcess(std::shared_ptr<DvppDataInfo> &input,
+                                              std::shared_ptr<DvppDataInfo> &output, bool withSynchronize) {
+  // Both input and output are locate on device, so we must release them if fail in decode
+  APP_ERROR ret = JpegDecode(*input, *output, withSynchronize);
+  if (ret != APP_ERR_OK) {
+    // Release the output buffer when decode failed, otherwise release it after use
+    RELEASE_DVPP_DATA(inputImage_->data);
+    inputImage_->data = nullptr;
+    RELEASE_DVPP_DATA(decodedImage_->data);
+    decodedImage_->data = nullptr;
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR DvppCommon::SinkCombinePngdProcess(std::shared_ptr<DvppDataInfo> &input,
+                                             std::shared_ptr<DvppDataInfo> &output, bool withSynchronize) {
+  // Both input and output are locate on device, so we must release them if fail in decode
+  APP_ERROR ret = PngDecode(*input, *output, withSynchronize);
+  if (ret != APP_ERR_OK) {
+    // Release the output buffer when decode failed, otherwise release it after use
+    RELEASE_DVPP_DATA(inputImage_->data);
+    inputImage_->data = nullptr;
+    RELEASE_DVPP_DATA(decodedImage_->data);
+    decodedImage_->data = nullptr;
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+/*
+ * @description: Decode the image specified by imageInfo and save the result to member variable decodedImage_
+ * This function is for PNG format image
+ * @param: imageInfo specifies image information
+ * @param: format specifies the image format
+ * @param: withSynchronize specifies whether to execute synchronously
+ * @return: APP_ERR_OK if success, other values if failure
+ * @attention: This function can be called only when the DvppCommon object is initialized with Init
+ */
+APP_ERROR DvppCommon::CombinePngdProcess(const RawData &imageInfo, acldvppPixelFormat format, bool withSynchronize) {
+  // Return special error code when the DvppCommon object is initialized with InitVdec
+  if (isVdec_) {
+    MS_LOG(ERROR) << "CombinePngdProcess cannot be called by the DvppCommon object which is initialized with InitVdec.";
+    return APP_ERR_DVPP_OBJ_FUNC_MISMATCH;
+  }
+
+  int32_t components;
+  inputImage_ = std::make_shared<DvppDataInfo>();
+  inputImage_->format = format;
+  APP_ERROR ret =
+    // GetJpegImageInfo(imageInfo.data.get(), imageInfo.lenOfByte, inputImage_->width, inputImage_->height, components);
+    GetPngImageInfo(imageInfo.data, imageInfo.lenOfByte, inputImage_->width, inputImage_->height, components);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to get input image info, ret = " << ret << ".";
+    return ret;
+  }
+
+  // Get the buffer size of decode output according to the input data and output format
+  uint32_t outBuffSize;
+  // ret = GetJpegDecodeDataSize(imageInfo.data.get(), imageInfo.lenOfByte, format, outBuffSize);
+  ret = GetPngDecodeDataSize(imageInfo.data, imageInfo.lenOfByte, format, outBuffSize);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to get size of decode output buffer, ret = " << ret << ".";
+    return ret;
+  }
+
+  // In TransferImageH2D function, device buffer will be alloced to store the input image
+  // Need to pay attention to release of the buffer
+  ret = TransferImageH2D(imageInfo, inputImage_);
+  if (ret != APP_ERR_OK) {
+    return ret;
+  }
+
+  decodedImage_ = std::make_shared<DvppDataInfo>();
+  decodedImage_->format = format;
+  decodedImage_->width = inputImage_->width;
+  decodedImage_->height = inputImage_->height;
+  GetPngDecodeStrideSize(inputImage_->width, inputImage_->height, decodedImage_->widthStride,
+                         decodedImage_->heightStride, format);
+  decodedImage_->dataSize = outBuffSize;
+  // Malloc dvpp buffer to store the output data after decoding
+  // Need to pay attention to release of the buffer
+  ret = acldvppMalloc((void **)&decodedImage_->data, decodedImage_->dataSize);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to malloc memory on dvpp, ret = " << ret << ".";
+    RELEASE_DVPP_DATA(inputImage_->data);
+    return ret;
+  }
+  ret = PngDecode(*inputImage_, *decodedImage_, withSynchronize);
+  if (ret != APP_ERR_OK) {
+    // Release the output buffer when decode failed, otherwise release it after use
+    RELEASE_DVPP_DATA(inputImage_->data);
+    inputImage_->data = nullptr;
+    RELEASE_DVPP_DATA(decodedImage_->data);
+    decodedImage_->data = nullptr;
+    return ret;
+  }
+
+  return APP_ERR_OK;
+}
+
+/*
+ * @description: Transfer data from host to device
+ * @param: imageInfo specifies the image data on the host
+ * @return: APP_ERR_OK if success, other values if failure
+ */
+APP_ERROR DvppCommon::TransferYuvDataH2D(const DvppDataInfo &imageinfo) {
+  if (imageinfo.dataSize <= 0) {
+    MS_LOG(ERROR) << "The input buffer size on host should not be empty.";
+    return APP_ERR_COMM_INVALID_PARAM;
+  }
+  uint8_t *device_ptr = nullptr;
+  APP_ERROR ret = acldvppMalloc((void **)&device_ptr, imageinfo.dataSize);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to malloc " << imageinfo.dataSize << " bytes on dvpp, ret = " << ret << ".";
+    return ret;
+  }
+  ret = aclrtMemcpyAsync(device_ptr, imageinfo.dataSize, imageinfo.data, imageinfo.dataSize, ACL_MEMCPY_HOST_TO_DEVICE,
+                         dvppStream_);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to copy " << imageinfo.dataSize << " bytes from host to device, ret = " << ret << ".";
+    RELEASE_DVPP_DATA(device_ptr);
+    return ret;
+  }
+  ret = aclrtSynchronizeStream(dvppStream_);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to synchronize stream, ret = " << ret << ".";
+    RELEASE_DVPP_DATA(device_ptr);
+    return ret;
+  }
+  /* Important!!! decodedImage_ speifies the image in deocded format(RGB OR YUV)
+   * Not essentailly to be the image after decode.(Specifies the data not in RAW encode format)
+   * It can also be the image after resize(Very important)
+   */
+  decodedImage_ = std::make_shared<DvppDataInfo>();
+  decodedImage_->data = device_ptr;
+  decodedImage_->dataSize = imageinfo.dataSize;
+  decodedImage_->height = imageinfo.height;
+  decodedImage_->heightStride = imageinfo.heightStride;
+  decodedImage_->width = imageinfo.width;
+  decodedImage_->widthStride = imageinfo.widthStride;
+  return APP_ERR_OK;
+}
+
 /*
  * @description: Transfer data from host to device
  * @param: imageInfo specifies the image data on the host
@@ -983,7 +1246,13 @@ APP_ERROR DvppCommon::CombineJpegdProcess(const RawData &imageInfo, acldvppPixel
  * @return: APP_ERR_OK if success, other values if failure
  */
 APP_ERROR DvppCommon::TransferImageH2D(const RawData &imageInfo, const std::shared_ptr<DvppDataInfo> &jpegInput) {
-  uint8_t *inDevBuff = nullptr;
+  // Check image buffer size validity
+  if (imageInfo.lenOfByte <= 0) {
+    MS_LOG(ERROR) << "The input buffer size on host should not be empty.";
+    return APP_ERR_COMM_INVALID_PARAM;
+  }
+
+  uint8_t *inDevBuff = nullptr;  // This pointer will be on device
   APP_ERROR ret = acldvppMalloc((void **)&inDevBuff, imageInfo.lenOfByte);
   if (ret != APP_ERR_OK) {
     MS_LOG(ERROR) << "Failed to malloc " << imageInfo.lenOfByte << " bytes on dvpp, ret = " << ret << ".";
@@ -991,8 +1260,10 @@ APP_ERROR DvppCommon::TransferImageH2D(const RawData &imageInfo, const std::shar
   }
 
   // Copy the image data from host to device
-  ret = aclrtMemcpyAsync(inDevBuff, imageInfo.lenOfByte, imageInfo.data.get(), imageInfo.lenOfByte,
-                         ACL_MEMCPY_HOST_TO_DEVICE, dvppStream_);
+  // ret = aclrtMemcpyAsync(inDevBuff, imageInfo.lenOfByte, imageInfo.data.get(), imageInfo.lenOfByte,
+  //                       ACL_MEMCPY_HOST_TO_DEVICE, dvppStream_);
+  ret = aclrtMemcpyAsync(inDevBuff, imageInfo.lenOfByte, imageInfo.data, imageInfo.lenOfByte, ACL_MEMCPY_HOST_TO_DEVICE,
+                         dvppStream_);
   if (ret != APP_ERR_OK) {
     MS_LOG(ERROR) << "Failed to copy " << imageInfo.lenOfByte << " bytes from host to device, ret = " << ret << ".";
     RELEASE_DVPP_DATA(inDevBuff);
@@ -1011,6 +1282,114 @@ APP_ERROR DvppCommon::TransferImageH2D(const RawData &imageInfo, const std::shar
   return APP_ERR_OK;
 }
 
+/*
+ * Sink RawData(On host) into DvppDataInfo(On device)
+ */
+APP_ERROR DvppCommon::SinkImageH2D(const RawData &imageInfo, acldvppPixelFormat format) {
+  if (isVdec_) {
+    MS_LOG(ERROR)
+      << "CombineJpegdProcess cannot be called by the DvppCommon object which is initialized with InitVdec.";
+    return APP_ERR_DVPP_OBJ_FUNC_MISMATCH;
+  }
+
+  int32_t components;
+  // Member variable of inputImage_, uint8_t *data will be on device
+  inputImage_ = std::make_shared<DvppDataInfo>();
+  inputImage_->format = format;
+  APP_ERROR ret =
+    // GetJpegImageInfo(imageInfo.data.get(), imageInfo.lenOfByte, inputImage_->width, inputImage_->height, components);
+    GetJpegImageInfo(imageInfo.data, imageInfo.lenOfByte, inputImage_->width, inputImage_->height, components);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to get input image info, ret = " << ret << ".";
+    return ret;
+  }
+
+  // Get the buffer size(On device) of decode output according to the input data and output format
+  uint32_t outBufferSize;
+  // ret = GetJpegDecodeDataSize(imageInfo.data.get(), imageInfo.lenOfByte, format, outBuffSize);
+  ret = GetJpegDecodeDataSize(imageInfo.data, imageInfo.lenOfByte, format, outBufferSize);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to get size of decode output buffer, ret = " << ret << ".";
+    return ret;
+  }
+  // In TransferImageH2D function, device buffer will be alloced to store the input image before decode
+  // Need to pay attention to release of the buffer
+  ret = TransferImageH2D(imageInfo, inputImage_);
+  if (ret != APP_ERR_OK) {
+    return ret;
+  }
+  // This part is to define the data after decode (MALLOC ON DEVICE!!)
+  decodedImage_ = std::make_shared<DvppDataInfo>();
+  decodedImage_->format = format;
+  decodedImage_->width = inputImage_->width;
+  decodedImage_->height = inputImage_->height;
+  GetJpegDecodeStrideSize(inputImage_->width, inputImage_->height, decodedImage_->widthStride,
+                          decodedImage_->heightStride);
+  // Obtain all the attributes of inputImage_
+  GetJpegDecodeStrideSize(inputImage_->width, inputImage_->height, inputImage_->widthStride, inputImage_->heightStride);
+  decodedImage_->dataSize = outBufferSize;
+  // Malloc dvpp buffer to store the output data after decoding
+  // Need to pay attention to release of the buffer
+  ret = acldvppMalloc((void **)&decodedImage_->data, decodedImage_->dataSize);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to malloc memory on dvpp, ret = " << ret << ".";
+    RELEASE_DVPP_DATA(inputImage_->data);
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR DvppCommon::SinkImageH2D(const RawData &imageInfo) {
+  if (isVdec_) {
+    MS_LOG(ERROR) << "CombinePngdProcess cannot be called by the DvppCommon object which is initialized with InitVdec.";
+    return APP_ERR_DVPP_OBJ_FUNC_MISMATCH;
+  }
+
+  int32_t components;
+  inputImage_ = std::make_shared<DvppDataInfo>();
+  acldvppPixelFormat format = PIXEL_FORMAT_RGB_888;
+  inputImage_->format = format;
+  APP_ERROR ret =
+    GetPngImageInfo(imageInfo.data, imageInfo.lenOfByte, inputImage_->width, inputImage_->height, components);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to get input image info, ret = " << ret << ".";
+    return ret;
+  }
+
+  // Get the buffer size of decode output according to the input data and output format
+  uint32_t outBuffSize;
+  // ret = GetJpegDecodeDataSize(imageInfo.data.get(), imageInfo.lenOfByte, format, outBuffSize);
+  ret = GetPngDecodeDataSize(imageInfo.data, imageInfo.lenOfByte, format, outBuffSize);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to get size of decode output buffer, ret = " << ret << ".";
+    return ret;
+  }
+
+  // In TransferImageH2D function, device buffer will be alloced to store the input image
+  // Need to pay attention to release of the buffer
+  ret = TransferImageH2D(imageInfo, inputImage_);
+  if (ret != APP_ERR_OK) {
+    return ret;
+  }
+
+  decodedImage_ = std::make_shared<DvppDataInfo>();
+  decodedImage_->format = format;
+  decodedImage_->width = inputImage_->width;
+  decodedImage_->height = inputImage_->height;
+  GetPngDecodeStrideSize(inputImage_->width, inputImage_->height, decodedImage_->widthStride,
+                         decodedImage_->heightStride, format);
+  decodedImage_->dataSize = outBuffSize;
+  // Malloc dvpp buffer to store the output data after decoding
+  // Need to pay attention to release of the buffer
+  ret = acldvppMalloc((void **)&decodedImage_->data, decodedImage_->dataSize);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to malloc memory on dvpp, ret = " << ret << ".";
+    RELEASE_DVPP_DATA(inputImage_->data);
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
 /*
  * @description: Create and set the description of a video stream
  * @param: data specifies the information about the video stream

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/utils/DvppCommon.h

@@ -16,8 +16,6 @@
 #ifndef DVPP_COMMON_H
 #define DVPP_COMMON_H
 
-#include <vector>
-#include <memory>
 #include "CommonDataType.h"
 #include "ErrorCode.h"
 #include "acl/ops/acl_dvpp.h"
@@ -61,6 +59,8 @@ struct DeviceStreamData {
 
 const uint32_t JPEGD_STRIDE_WIDTH = 128;  // Jpegd module output width need to align up to 128
 const uint32_t JPEGD_STRIDE_HEIGHT = 16;  // Jpegd module output height need to align up to 16
+const uint32_t PNGD_STRIDE_WIDTH = 128;   // Pngd module output width need to align up to 128
+const uint32_t PNGD_STRIDE_HEIGHT = 16;   // Pngd module output height need to align up to 16
 const uint32_t JPEGE_STRIDE_WIDTH = 16;   // Jpege module input width need to align up to 16
 const uint32_t JPEGE_STRIDE_HEIGHT = 1;   // Jpege module input height remains unchanged
 const uint32_t VPC_STRIDE_WIDTH = 16;     // Vpc module output width need to align up to 16
@@ -77,6 +77,10 @@ const uint32_t MAX_JPEGD_WIDTH = 8192;    // Max width of jpegd module
 const uint32_t MAX_JPEGD_HEIGHT = 8192;   // Max height of jpegd module
 const uint32_t MIN_JPEGD_WIDTH = 32;      // Min width of jpegd module
 const uint32_t MIN_JPEGD_HEIGHT = 32;     // Min height of jpegd module
+const uint32_t MAX_PNGD_WIDTH = 4096;     // Max width of pngd module
+const uint32_t MAX_PNGD_HEIGHT = 4096;    // Max height of pngd module
+const uint32_t MIN_PNGD_WIDTH = 32;       // Min width of pngd module
+const uint32_t MIN_PNGD_HEIGHT = 32;      // Min height of pngd module
 const uint32_t MAX_JPEGE_WIDTH = 8192;    // Max width of jpege module
 const uint32_t MAX_JPEGE_HEIGHT = 8192;   // Max height of jpege module
 const uint32_t MIN_JPEGE_WIDTH = 32;      // Min width of jpege module
@@ -126,10 +130,16 @@ class DvppCommon {
   static APP_ERROR GetVpcOutputStrideSize(uint32_t width, uint32_t height, acldvppPixelFormat format,
                                           uint32_t &widthStride, uint32_t &heightStride);
   static void GetJpegDecodeStrideSize(uint32_t width, uint32_t height, uint32_t &widthStride, uint32_t &heightStride);
+  static void GetPngDecodeStrideSize(uint32_t width, uint32_t height, uint32_t &widthStride, uint32_t &heightStride,
+                                     acldvppPixelFormat format);
   static APP_ERROR GetJpegImageInfo(const void *data, uint32_t dataSize, uint32_t &width, uint32_t &height,
                                     int32_t &components);
+  static APP_ERROR GetPngImageInfo(const void *data, uint32_t dataSize, uint32_t &width, uint32_t &height,
+                                   int32_t &components);
   static APP_ERROR GetJpegDecodeDataSize(const void *data, uint32_t dataSize, acldvppPixelFormat format,
                                          uint32_t &decSize);
+  static APP_ERROR GetPngDecodeDataSize(const void *data, uint32_t dataSize, acldvppPixelFormat format,
+                                        uint32_t &decSize);
   static APP_ERROR GetJpegEncodeStrideSize(std::shared_ptr<DvppDataInfo> &input);
   static APP_ERROR SetEncodeLevel(uint32_t level, acldvppJpegeConfig &jpegeConfig);
   static APP_ERROR GetVideoDecodeStrideSize(uint32_t width, uint32_t height, acldvppPixelFormat format,
@@ -143,6 +153,8 @@ class DvppCommon {
   APP_ERROR VpcCrop(const DvppCropInputInfo &input, const DvppDataInfo &output, bool withSynchronize);
   APP_ERROR JpegDecode(DvppDataInfo &input, DvppDataInfo &output, bool withSynchronize);
 
+  APP_ERROR PngDecode(DvppDataInfo &input, DvppDataInfo &output, bool withSynchronize);
+
   APP_ERROR JpegEncode(DvppDataInfo &input, DvppDataInfo &output, acldvppJpegeConfig *jpegeConfig,
                        bool withSynchronize);
 
@@ -156,8 +168,17 @@ class DvppCommon {
                                  VpcProcessType processType = VPC_PT_DEFAULT);
   APP_ERROR CombineCropProcess(DvppCropInputInfo &input, DvppDataInfo &output, bool withSynchronize);
   APP_ERROR CombineJpegdProcess(const RawData &imageInfo, acldvppPixelFormat format, bool withSynchronize);
+
+  APP_ERROR SinkCombineJpegdProcess(std::shared_ptr<DvppDataInfo> &input, std::shared_ptr<DvppDataInfo> &output,
+                                    bool withSynchronize);
+  APP_ERROR SinkCombinePngdProcess(std::shared_ptr<DvppDataInfo> &input, std::shared_ptr<DvppDataInfo> &output,
+                                   bool withSynchronize);
+
   APP_ERROR CombineJpegeProcess(const RawData &imageInfo, uint32_t width, uint32_t height, acldvppPixelFormat format,
                                 bool withSynchronize);
+  // New feature for PNG format image decode
+  APP_ERROR CombinePngdProcess(const RawData &imageInfo, acldvppPixelFormat format, bool withSynchronize);
+
   // The following interface can be called only when the DvppCommon object is initialized with InitVdec
   APP_ERROR CombineVdecProcess(std::shared_ptr<DvppDataInfo> data, void *userData);
 
@@ -168,6 +189,15 @@ class DvppCommon {
   std::shared_ptr<DvppDataInfo> GetEncodedImage();
   std::shared_ptr<DvppDataInfo> GetCropedImage();
 
+  // Transfer DvppDataInfo from host to device, aim at resize and crop
+  APP_ERROR TransferYuvDataH2D(const DvppDataInfo &imageinfo);
+  // Transfer RawData(image) from host to device, aim at decode
+  APP_ERROR TransferImageH2D(const RawData &imageInfo, const std::shared_ptr<DvppDataInfo> &jpegInput);
+  // Transfer RawData(image on host) to inputImage_(data on device), this is for data sink mode
+  APP_ERROR SinkImageH2D(const RawData &imageInfo, acldvppPixelFormat format);
+  // This overload function is for PNG image sink, hence we ignore the pixel format
+  APP_ERROR SinkImageH2D(const RawData &imageInfo);
+
   // Release the memory that is allocated in the interfaces which are started with "Combine"
   void ReleaseDvppBuffer();
   APP_ERROR VdecSendEosFrame() const;
@@ -185,7 +215,6 @@ class DvppCommon {
                         bool withSynchronize);
   APP_ERROR CheckResizeParams(const DvppDataInfo &input, const DvppDataInfo &output);
   APP_ERROR CheckCropParams(const DvppCropInputInfo &input);
-  APP_ERROR TransferImageH2D(const RawData &imageInfo, const std::shared_ptr<DvppDataInfo> &jpegInput);
   APP_ERROR CreateStreamDesc(std::shared_ptr<DvppDataInfo> data);
   APP_ERROR DestroyResource();
 

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/utils/ErrorCode.cpp

@@ -37,13 +37,14 @@ std::string GetAppErrCodeInfo(const APP_ERROR err) {
   }
 }
 
-void AssertErrorCode(const int code, const std::string &file, const std::string &function, const int line) {
+void AssertErrorCode(const int code, const std::string file, const std::string function, const int line) {
   if (code != APP_ERR_OK) {
     MS_LOG(ERROR) << "Failed at " << file << "->" << function << "->" << line << ": error code=" << code;
+    exit(code);
   }
 }
 
-void CheckErrorCode(const int code, const std::string &file, const std::string &function, const int line) {
+void CheckErrorCode(const int code, const std::string file, const std::string function, const int line) {
   if (code != APP_ERR_OK) {
     MS_LOG(ERROR) << "Failed at " << file << "->" << function << "->" << line << ": error code=" << code;
   }

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/utils/ErrorCode.h

@@ -139,7 +139,7 @@ enum {
   APP_ERROR_FACE_WEB_USE_PART_SUCCESS = APP_ERROR_FACE_WEB_USE_BASE + 4,  // Web: partial search succeeded
   APP_ERROR_FACE_WEB_USE_NO_FACE = APP_ERROR_FACE_WEB_USE_BASE + 5,       // Web: no face detected
   APP_ERR_QUEUE_END,  // Not an error code, define the range of blocking queue
-                      // error code
+  // error code
 };
 const std::string APP_ERR_ACL_LOG_STRING[] = {
   [APP_ERR_OK] = "Success",
@@ -249,10 +249,10 @@ const std::string APP_ERR_FACE_LOG_STRING[] = {
 };
 
 std::string GetAppErrCodeInfo(APP_ERROR err);
-void AssertErrorCode(const int code, const std::string &file, const std::string &function, const int line);
-void CheckErrorCode(const int code, const std::string &file, const std::string &function, const int line);
+void AssertErrorCode(const int code, const std::string file, const std::string function, const int line);
+void CheckErrorCode(const int code, const std::string file, const std::string function, const int line);
 
 #define RtAssert(code) AssertErrorCode(code, __FILE__, __FUNCTION__, __LINE__);
 #define RtCheckError(code) CheckErrorCode(code, __FILE__, __FUNCTION__, __LINE__);
 
-#endif  // ERROR_CODE_H_
+#endif  // ERROR_CODE_H_

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/utils/MDAclProcess.cc

@@ -0,0 +1,966 @@
+/*
+ * Copyright (c) 2020.Huawei Technologies Co., Ltd. All rights reserved.
+ * 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/core/constants.h"
+#include "minddata/dataset/core/tensor_shape.h"
+#include "minddata/dataset/kernels/image/image_utils.h"
+#include "MDAclProcess.h"
+#include <sys/time.h>
+#include <thread>
+#include <sys/stat.h>
+
+namespace {
+const int BUFFER_SIZE = 2048;
+const mode_t DEFAULT_FILE_PERMISSION = 0077;
+}  // namespace
+
+mode_t SetFileDefaultUmask() { return umask(DEFAULT_FILE_PERMISSION); }
+
+/*
+ * @description: Constructor
+ * @param: resizeWidth specifies the resized width
+ * @param: resizeHeight specifies the resized hegiht
+ * @param: stream is used to maintain the execution order of operations
+ * @param: context is used to manage the life cycle of objects
+ * @param: dvppCommon is a class for decoding and resizing
+ */
+MDAclProcess::MDAclProcess(uint32_t resizeWidth, uint32_t resizeHeight, uint32_t cropWidth, uint32_t cropHeight,
+                           aclrtContext context, bool is_crop, aclrtStream stream,
+                           std::shared_ptr<DvppCommon> dvppCommon)
+    : resizeWidth_(resizeWidth),
+      resizeHeight_(resizeHeight),
+      cropWidth_(cropWidth),
+      cropHeight_(cropHeight),
+      context_(context),
+      stream_(stream),
+      contain_crop_(is_crop),
+      dvppCommon_(dvppCommon),
+      processedInfo_(nullptr) {}
+
+MDAclProcess::MDAclProcess(uint32_t ParaWidth, uint32_t ParaHeight, aclrtContext context, bool is_crop,
+                           aclrtStream stream, std::shared_ptr<DvppCommon> dvppCommon)
+    : contain_crop_(is_crop), context_(context), stream_(stream), dvppCommon_(dvppCommon), processedInfo_(nullptr) {
+  if (is_crop) {
+    resizeWidth_ = 0;
+    resizeHeight_ = 0;
+    cropWidth_ = ParaWidth;
+    cropHeight_ = ParaHeight;
+  } else {
+    resizeWidth_ = ParaWidth;
+    resizeHeight_ = ParaHeight;
+    cropWidth_ = 0;
+    cropHeight_ = 0;
+  }
+}
+
+MDAclProcess::MDAclProcess(aclrtContext context, bool is_crop, aclrtStream stream,
+                           std::shared_ptr<DvppCommon> dvppCommon)
+    : resizeWidth_(0),
+      resizeHeight_(0),
+      cropWidth_(0),
+      cropHeight_(0),
+      contain_crop_(is_crop),
+      context_(context),
+      stream_(stream),
+      dvppCommon_(dvppCommon),
+      processedInfo_(nullptr) {}
+/*
+ * @description: Release MDAclProcess resources
+ * @return: aclError which is error code of ACL API
+ */
+APP_ERROR MDAclProcess::Release() {
+  // Release objects resource
+  APP_ERROR ret = dvppCommon_->DeInit();
+  dvppCommon_->ReleaseDvppBuffer();
+
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to deinitialize dvppCommon_, ret = " << ret;
+    return ret;
+  }
+  MS_LOG(INFO) << "dvppCommon_ object deinitialized successfully";
+  dvppCommon_.reset();
+
+  // Release stream
+  if (stream_ != nullptr) {
+    ret = aclrtDestroyStream(stream_);
+    if (ret != APP_ERR_OK) {
+      MS_LOG(ERROR) << "Failed to destroy stream, ret = " << ret;
+      stream_ = nullptr;
+      return ret;
+    }
+    stream_ = nullptr;
+  }
+  MS_LOG(INFO) << "The stream is destroyed successfully";
+  return APP_ERR_OK;
+}
+
+/*
+ * @description: Initialize DvppCommon object
+ * @return: aclError which is error code of ACL API
+ */
+APP_ERROR MDAclProcess::InitModule() {
+  // Create Dvpp JpegD object
+  dvppCommon_ = std::make_shared<DvppCommon>(stream_);
+  if (dvppCommon_ == nullptr) {
+    MS_LOG(ERROR) << "Failed to create dvppCommon_ object";
+    return APP_ERR_COMM_INIT_FAIL;
+  }
+  MS_LOG(INFO) << "DvppCommon object created successfully";
+  APP_ERROR ret = dvppCommon_->Init();
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to initialize dvppCommon_ object, ret = " << ret;
+    return ret;
+  }
+  MS_LOG(INFO) << "DvppCommon object initialized successfully";
+  return APP_ERR_OK;
+}
+
+/*
+ * @description: Initialize MDAclProcess resources
+ * @return: aclError which is error code of ACL API
+ */
+APP_ERROR MDAclProcess::InitResource() {
+  APP_ERROR ret = aclrtSetCurrentContext(context_);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to get ACL context, ret = " << ret;
+    return ret;
+  }
+  MS_LOG(INFO) << "The context is created successfully";
+  ret = aclrtCreateStream(&stream_);  // Create stream for application
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to create ACL stream, ret = " << ret;
+    return ret;
+  }
+  MS_LOG(INFO) << "The stream is created successfully";
+  // Initialize dvpp module
+  if (InitModule() != APP_ERR_OK) {
+    return APP_ERR_COMM_INIT_FAIL;
+  }
+  return APP_ERR_OK;
+}
+
+std::shared_ptr<DvppCommon> MDAclProcess::GetDeviceModule() { return dvppCommon_; }
+
+/*
+ * Sink data from Tensor(On host) to DeviceTensor(On device)
+ * Two cases are different, jpeg and png
+ */
+APP_ERROR MDAclProcess::H2D_Sink(std::shared_ptr<mindspore::dataset::Tensor> &input,
+                                 std::shared_ptr<mindspore::dataset::DeviceTensor> &device_input) {
+  RawData imageinfo;
+  uint32_t filesize = input->SizeInBytes();
+  // MS_LOG(INFO) << "Filesize on host is: " << filesize;
+  imageinfo.lenOfByte = filesize;
+  unsigned char *buffer = const_cast<unsigned char *>(input->GetBuffer());
+  imageinfo.data = static_cast<void *>(buffer);
+  // Transfer RawData(Raw image) from host to device, which we call sink
+  APP_ERROR ret;
+  if (IsNonEmptyJPEG(input)) {  // case JPEG
+    ret = dvppCommon_->SinkImageH2D(imageinfo, PIXEL_FORMAT_YUV_SEMIPLANAR_420);
+  } else {  // case PNG
+    ret = dvppCommon_->SinkImageH2D(imageinfo);
+  }
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to transport Tensor to device, ret = " << ret;
+    return ret;
+  }
+  auto deviceInputData = dvppCommon_->GetInputImage();
+  // std::cout << "[DEBUG]Sink data sunccessfully, Filesize on device is: " << deviceInputData->dataSize << std::endl;
+  const mindspore::dataset::DataType dvpp_data_type(mindspore::dataset::DataType::DE_UINT8);
+  const mindspore::dataset::TensorShape dvpp_shape({1, 1, 1});
+  mindspore::dataset::DeviceTensor::CreateEmpty(dvpp_shape, dvpp_data_type, &device_input);
+  device_input->SetAttributes(deviceInputData);
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::D2H_Pop(std::shared_ptr<mindspore::dataset::DeviceTensor> &device_output,
+                                std::shared_ptr<mindspore::dataset::Tensor> &output) {
+  void *resHostBuf = nullptr;
+  APP_ERROR ret = aclrtMallocHost(&resHostBuf, device_output->DeviceDataSize());
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to allocate memory from host ret = " << ret;
+    return ret;
+  }
+  std::shared_ptr<void> outBuf(resHostBuf, aclrtFreeHost);
+  processedInfo_ = outBuf;
+  // Memcpy the output data from device to host
+  ret = aclrtMemcpy(outBuf.get(), device_output->DeviceDataSize(), device_output->GetDeviceBuffer(),
+                    device_output->DeviceDataSize(), ACL_MEMCPY_DEVICE_TO_HOST);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to copy memory from device to host, ret = " << ret;
+    return ret;
+  }
+  auto data = std::static_pointer_cast<unsigned char>(processedInfo_);
+  unsigned char *ret_ptr = data.get();
+
+  mindspore::dataset::dsize_t dvppDataSize = device_output->DeviceDataSize();
+  const mindspore::dataset::TensorShape dvpp_shape({dvppDataSize, 1, 1});
+  uint32_t _output_width_ = device_output->GetYuvStrideShape()[0];
+  uint32_t _output_widthStride_ = device_output->GetYuvStrideShape()[1];
+  uint32_t _output_height_ = device_output->GetYuvStrideShape()[2];
+  uint32_t _output_heightStride_ = device_output->GetYuvStrideShape()[3];
+  const mindspore::dataset::DataType dvpp_data_type(mindspore::dataset::DataType::DE_UINT8);
+  mindspore::dataset::Tensor::CreateFromMemory(dvpp_shape, dvpp_data_type, ret_ptr, &output);
+  output->SetYuvShape(_output_width_, _output_widthStride_, _output_height_, _output_heightStride_);
+  if (!output->HasData()) {
+    return APP_ERR_COMM_ALLOC_MEM;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::JPEG_D(const RawData &ImageInfo) {
+  struct timeval begin = {0};
+  struct timeval end = {0};
+  gettimeofday(&begin, nullptr);
+  // deal with image
+  APP_ERROR ret = JPEG_D_(ImageInfo);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to decode, ret = " << ret;
+    return ret;
+  }
+  gettimeofday(&end, nullptr);
+  // Calculate the time cost of preprocess
+  const double costMs = SEC2MS * (end.tv_sec - begin.tv_sec) + (end.tv_usec - begin.tv_usec) / SEC2MS;
+  const double fps = 1 * SEC2MS / costMs;
+  MS_LOG(INFO) << "[dvpp decode Delay] cost: " << costMs << "ms\tfps: " << fps;
+  // Get output of resize module
+  std::shared_ptr<DvppDataInfo> DecodeOutData = dvppCommon_->GetDecodedImage();
+  if (!DecodeOutData) {
+    MS_LOG(ERROR) << "Decode Data returns NULL";
+    return APP_ERR_COMM_INVALID_POINTER;
+  }
+
+  // Alloc host memory for the inference output according to the size of output
+  void *resHostBuf = nullptr;
+  ret = aclrtMallocHost(&resHostBuf, DecodeOutData->dataSize);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to allocate memory from host ret = " << ret;
+    return ret;
+  }
+  std::shared_ptr<void> outBuf(resHostBuf, aclrtFreeHost);
+  processedInfo_ = outBuf;
+  // Memcpy the output data from device to host
+  ret = aclrtMemcpy(outBuf.get(), DecodeOutData->dataSize, DecodeOutData->data, DecodeOutData->dataSize,
+                    ACL_MEMCPY_DEVICE_TO_HOST);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to copy memory from device to host, ret = " << ret;
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::JPEG_D() {
+  struct timeval begin = {0};
+  struct timeval end = {0};
+  gettimeofday(&begin, nullptr);
+  // deal with image
+  APP_ERROR ret = JPEG_D_();
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to decode, ret = " << ret;
+    return ret;
+  }
+  gettimeofday(&end, nullptr);
+  // Calculate the time cost of preprocess
+  const double costMs = SEC2MS * (end.tv_sec - begin.tv_sec) + (end.tv_usec - begin.tv_usec) / SEC2MS;
+  const double fps = 1 * SEC2MS / costMs;
+  MS_LOG(INFO) << "[dvpp decode Delay] cost: " << costMs << "ms\tfps: " << fps;
+  // Get output of resize module
+  std::shared_ptr<DvppDataInfo> DecodeOutData = dvppCommon_->GetDecodedImage();
+  if (!DecodeOutData) {
+    MS_LOG(ERROR) << "Decode Data returns NULL";
+    return APP_ERR_COMM_INVALID_POINTER;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::JPEG_D_(const RawData &ImageInfo) {
+  APP_ERROR ret = dvppCommon_->CombineJpegdProcess(ImageInfo, PIXEL_FORMAT_YUV_SEMIPLANAR_420, true);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to process decode, ret = " << ret << ".";
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::JPEG_D_() {
+  auto input_ = dvppCommon_->GetInputImage();
+  auto decode_output_ = dvppCommon_->GetDecodedImage();
+  APP_ERROR ret = dvppCommon_->SinkCombineJpegdProcess(input_, decode_output_, true);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to process decode, ret = " << ret << ".";
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::JPEG_R(const DvppDataInfo &ImageInfo) {
+  struct timeval begin = {0};
+  struct timeval end = {0};
+  gettimeofday(&begin, nullptr);
+  // deal with image
+  APP_ERROR ret = JPEG_R_(ImageInfo);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to resize, ret = " << ret;
+    return ret;
+  }
+  gettimeofday(&end, nullptr);
+  // Calculate the time cost of preprocess
+  const double costMs = SEC2MS * (end.tv_sec - begin.tv_sec) + (end.tv_usec - begin.tv_usec) / SEC2MS;
+  const double fps = 1 * SEC2MS / costMs;
+  MS_LOG(INFO) << "[dvpp resize Delay] cost: " << costMs << "ms\tfps: " << fps;
+  // Get output of resize module
+  std::shared_ptr<DvppDataInfo> ResizeOutData = dvppCommon_->GetResizedImage();
+  if (!ResizeOutData) {
+    MS_LOG(ERROR) << "Resize Data returns NULL";
+    return APP_ERR_COMM_INVALID_POINTER;
+  }
+  // Alloc host memory for the inference output according to the size of output
+  void *resHostBuf = nullptr;
+  ret = aclrtMallocHost(&resHostBuf, ResizeOutData->dataSize);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to allocate memory from host ret = " << ret;
+    return ret;
+  }
+  std::shared_ptr<void> outBuf(resHostBuf, aclrtFreeHost);
+  processedInfo_ = outBuf;
+  // Memcpy the output data from device to host
+  ret = aclrtMemcpy(outBuf.get(), ResizeOutData->dataSize, ResizeOutData->data, ResizeOutData->dataSize,
+                    ACL_MEMCPY_DEVICE_TO_HOST);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to copy memory from device to host, ret = " << ret;
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::JPEG_R(std::string &last_step) {
+  struct timeval begin = {0};
+  struct timeval end = {0};
+  gettimeofday(&begin, nullptr);
+  // deal with image
+  APP_ERROR ret = JPEG_R_(last_step);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to resize, ret = " << ret;
+    return ret;
+  }
+  gettimeofday(&end, nullptr);
+  // Calculate the time cost of preprocess
+  const double costMs = SEC2MS * (end.tv_sec - begin.tv_sec) + (end.tv_usec - begin.tv_usec) / SEC2MS;
+  const double fps = 1 * SEC2MS / costMs;
+  MS_LOG(INFO) << "[dvpp resize Delay] cost: " << costMs << "ms\tfps: " << fps;
+  // Get output of resize module
+  std::shared_ptr<DvppDataInfo> ResizeOutData = dvppCommon_->GetResizedImage();
+  if (!ResizeOutData) {
+    MS_LOG(ERROR) << "Resize Data returns NULL";
+    return APP_ERR_COMM_INVALID_POINTER;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::JPEG_R_(const DvppDataInfo &ImageInfo) {
+  APP_ERROR ret = dvppCommon_->TransferYuvDataH2D(ImageInfo);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to copy data from host to device, ret = " << ret << ".";
+    return ret;
+  }
+  std::shared_ptr<DvppDataInfo> decoded_image = dvppCommon_->GetDecodedImage();
+  uint32_t pri_h = decoded_image->heightStride;
+  uint32_t pri_w = decoded_image->widthStride;
+  // Define the resize shape
+  DvppDataInfo resizeOut;
+  ResizeConfigFilter(resizeOut, pri_w, pri_h);
+  ret = dvppCommon_->CombineResizeProcess(*decoded_image, resizeOut, true);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to process resize, ret = " << ret << ".";
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::JPEG_R_(std::string &last_step) {
+  std::shared_ptr<DvppDataInfo> input_image = std::make_shared<DvppDataInfo>();
+  if (last_step == "Decode") {
+    input_image = dvppCommon_->GetDecodedImage();
+  } else {
+    input_image = dvppCommon_->GetCropedImage();
+  }
+  if (!input_image->data) {
+    MS_LOG(ERROR) << "Failed to get data for resize, please verify last step operation";
+    return APP_ERR_DVPP_RESIZE_FAIL;
+  }
+  uint32_t pri_h = input_image->heightStride;
+  uint32_t pri_w = input_image->widthStride;
+  DvppDataInfo resizeOut;
+  ResizeConfigFilter(resizeOut, pri_w, pri_h);
+  APP_ERROR ret = dvppCommon_->CombineResizeProcess(*input_image, resizeOut, true);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to process resize, ret = " << ret << ".";
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::JPEG_C(const DvppDataInfo &ImageInfo) {
+  struct timeval begin = {0};
+  struct timeval end = {0};
+  gettimeofday(&begin, nullptr);
+  // deal with image
+  APP_ERROR ret = JPEG_C_(ImageInfo);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to crop image, ret = " << ret;
+    return ret;
+  }
+  gettimeofday(&end, nullptr);
+  // Calculate the time cost of preprocess
+  const double costMs = SEC2MS * (end.tv_sec - begin.tv_sec) + (end.tv_usec - begin.tv_usec) / SEC2MS;
+  const double fps = 1 * SEC2MS / costMs;
+  MS_LOG(INFO) << "[dvpp crop Delay] cost: " << costMs << "ms\tfps: " << fps;
+  // Get output of resize module
+  std::shared_ptr<DvppDataInfo> CropOutData = dvppCommon_->GetCropedImage();
+  if (!CropOutData) {
+    MS_LOG(ERROR) << "Crop Data returns NULL";
+    return APP_ERR_COMM_INVALID_POINTER;
+  }
+  // Alloc host memory for the inference output according to the size of output
+  void *resHostBuf = nullptr;
+  ret = aclrtMallocHost(&resHostBuf, CropOutData->dataSize);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to allocate memory from host ret = " << ret;
+    return ret;
+  }
+  std::shared_ptr<void> outBuf(resHostBuf, aclrtFreeHost);
+  processedInfo_ = outBuf;
+  // Memcpy the output data from device to host
+  ret = aclrtMemcpy(outBuf.get(), CropOutData->dataSize, CropOutData->data, CropOutData->dataSize,
+                    ACL_MEMCPY_DEVICE_TO_HOST);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to copy memory from device to host, ret = " << ret;
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::JPEG_C(std::string &last_step) {
+  struct timeval begin = {0};
+  struct timeval end = {0};
+  gettimeofday(&begin, nullptr);
+  // deal with image
+  APP_ERROR ret = JPEG_C_(last_step);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to crop image, ret = " << ret;
+    return ret;
+  }
+  gettimeofday(&end, nullptr);
+  // Calculate the time cost of preprocess
+  const double costMs = SEC2MS * (end.tv_sec - begin.tv_sec) + (end.tv_usec - begin.tv_usec) / SEC2MS;
+  const double fps = 1 * SEC2MS / costMs;
+  MS_LOG(INFO) << "[dvpp crop Delay] cost: " << costMs << "ms\tfps: " << fps;
+  // Get output of resize module
+  std::shared_ptr<DvppDataInfo> CropOutData = dvppCommon_->GetCropedImage();
+  if (!CropOutData) {
+    MS_LOG(ERROR) << "Crop Data returns NULL";
+    return APP_ERR_COMM_INVALID_POINTER;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::JPEG_C_(const DvppDataInfo &ImageInfo) {
+  APP_ERROR ret = dvppCommon_->TransferYuvDataH2D(ImageInfo);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to copy data from host to device, ret = " << ret << ".";
+    return ret;
+  }
+  // Unneccessary to be image after resize, maybe after decode, we store both of them in DecodedImage()
+  std::shared_ptr<DvppDataInfo> resized_image = dvppCommon_->GetDecodedImage();
+  uint32_t pri_h = resized_image->heightStride;
+  uint32_t pri_w = resized_image->widthStride;
+  // Validate the crop shape
+  DvppDataInfo cropOut;
+  cropOut.width = cropWidth_;
+  cropOut.height = cropHeight_;
+  if (cropOut.width > pri_w || cropOut.height > pri_h) {
+    MS_LOG(ERROR) << "Crop size can not excceed resize, please verify your input [CROP SIZE] parameters";
+    return APP_ERR_COMM_INVALID_PARAM;
+  }
+  cropOut.format = PIXEL_FORMAT_YUV_SEMIPLANAR_420;
+  DvppCropInputInfo cropInfo;
+  cropInfo.dataInfo = *resized_image;
+  // Define crop area
+  CropRoiConfig cropCfg;
+  CropConfigFilter(cropCfg, cropInfo, *resized_image);
+  ret = dvppCommon_->CombineCropProcess(cropInfo, cropOut, true);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to process center crop, ret = " << ret << ".";
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::JPEG_C_(std::string &last_step) {
+  std::shared_ptr<DvppDataInfo> input_image = std::make_shared<DvppDataInfo>();
+  if (last_step == "Resize") {
+    input_image = dvppCommon_->GetResizedImage();
+  } else {
+    input_image = dvppCommon_->GetDecodedImage();
+  }
+  if (!input_image->data) {
+    MS_LOG(ERROR) << "Failed to get input data for crop, please verify last step operation";
+    return APP_ERR_DVPP_CROP_FAIL;
+  }
+  uint32_t pri_h = input_image->heightStride;
+  uint32_t pri_w = input_image->widthStride;
+  DvppDataInfo cropOut;
+  cropOut.width = cropWidth_;
+  cropOut.height = cropHeight_;
+  if (cropOut.width > pri_w || cropOut.height > pri_h) {
+    MS_LOG(ERROR) << "Crop size can not excceed resize, please verify your input [CROP SIZE] parameters";
+    return APP_ERR_COMM_INVALID_PARAM;
+  }
+  cropOut.format = PIXEL_FORMAT_YUV_SEMIPLANAR_420;
+  DvppCropInputInfo cropInfo;
+  cropInfo.dataInfo = *input_image;
+  // Define crop area
+  CropRoiConfig cropCfg;
+  CropConfigFilter(cropCfg, cropInfo, *input_image);
+  APP_ERROR ret = dvppCommon_->CombineCropProcess(cropInfo, cropOut, true);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to process center crop, ret = " << ret << ".";
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::PNG_D(const RawData &ImageInfo) {
+  struct timeval begin = {0};
+  struct timeval end = {0};
+  gettimeofday(&begin, nullptr);
+  // deal with image
+  APP_ERROR ret = PNG_D_(ImageInfo);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to decode, ret = " << ret;
+    return ret;
+  }
+  gettimeofday(&end, nullptr);
+  // Calculate the time cost of preprocess
+  const double costMs = SEC2MS * (end.tv_sec - begin.tv_sec) + (end.tv_usec - begin.tv_usec) / SEC2MS;
+  const double fps = 1 * SEC2MS / costMs;
+  MS_LOG(INFO) << "[dvpp Delay] cost: " << costMs << "ms\tfps: " << fps;
+  // Get output of resize module
+  std::shared_ptr<DvppDataInfo> DecodeOutData = dvppCommon_->GetDecodedImage();
+  if (!DecodeOutData) {
+    MS_LOG(ERROR) << "ResizedOutData returns NULL";
+    return APP_ERR_COMM_INVALID_POINTER;
+  }
+  // Alloc host memory for the inference output according to the size of output
+  void *resHostBuf = nullptr;
+  ret = aclrtMallocHost(&resHostBuf, DecodeOutData->dataSize);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to allocate memory from host ret = " << ret;
+    return ret;
+  }
+  std::shared_ptr<void> outBuf(resHostBuf, aclrtFreeHost);
+  processedInfo_ = outBuf;
+  // Memcpy the output data from device to host
+  ret = aclrtMemcpy(outBuf.get(), DecodeOutData->dataSize, DecodeOutData->data, DecodeOutData->dataSize,
+                    ACL_MEMCPY_DEVICE_TO_HOST);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to copy memory from device to host, ret = " << ret;
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::PNG_D() {
+  struct timeval begin = {0};
+  struct timeval end = {0};
+  gettimeofday(&begin, nullptr);
+  // deal with image
+  APP_ERROR ret = PNG_D_();
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to decode, ret = " << ret;
+    return ret;
+  }
+  gettimeofday(&end, nullptr);
+  // Calculate the time cost of preprocess
+  const double costMs = SEC2MS * (end.tv_sec - begin.tv_sec) + (end.tv_usec - begin.tv_usec) / SEC2MS;
+  const double fps = 1 * SEC2MS / costMs;
+  MS_LOG(INFO) << "[dvpp decode Delay] cost: " << costMs << "ms\tfps: " << fps;
+  // Get output of resize module
+  std::shared_ptr<DvppDataInfo> DecodeOutData = dvppCommon_->GetDecodedImage();
+  if (!DecodeOutData) {
+    MS_LOG(ERROR) << "Decode Data returns NULL";
+    return APP_ERR_COMM_INVALID_POINTER;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::PNG_D_(const RawData &ImageInfo) {
+  APP_ERROR ret = dvppCommon_->CombinePngdProcess(ImageInfo, PIXEL_FORMAT_RGB_888, true);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to process decode, ret = " << ret << ".";
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::PNG_D_() {
+  auto input_ = dvppCommon_->GetInputImage();
+  auto decode_output_ = dvppCommon_->GetDecodedImage();
+  APP_ERROR ret = dvppCommon_->SinkCombinePngdProcess(input_, decode_output_, true);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to process decode, ret = " << ret << ".";
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+/*
+ * @description: Decode and scale the picture, and write the result to a file
+ * @param: imageFile specifies the image path to be processed
+ * @return: aclError which is error code of ACL API
+ */
+APP_ERROR MDAclProcess::JPEG_DRC(const RawData &ImageInfo) {
+  struct timeval begin = {0};
+  struct timeval end = {0};
+  gettimeofday(&begin, nullptr);
+  // deal with image
+  APP_ERROR ret = JPEG_DRC_(ImageInfo);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to decode or resize or crop, ret = " << ret;
+    return ret;
+  }
+  gettimeofday(&end, nullptr);
+  // Calculate the time cost of preprocess
+  const double costMs = SEC2MS * (end.tv_sec - begin.tv_sec) + (end.tv_usec - begin.tv_usec) / SEC2MS;
+  const double fps = 1 * SEC2MS / costMs;
+  MS_LOG(INFO) << "[dvpp Delay] cost: " << costMs << "ms\tfps: " << fps;
+  // Get output of resize module
+  /*  测试Device内存
+   */
+  std::shared_ptr<DvppDataInfo> CropOutData = dvppCommon_->GetCropedImage();
+  if (CropOutData->dataSize == 0) {
+    MS_LOG(ERROR) << "CropOutData return NULL";
+    return APP_ERR_COMM_INVALID_POINTER;
+  }
+  // Alloc host memory for the inference output according to the size of output
+  void *resHostBuf = nullptr;
+  ret = aclrtMallocHost(&resHostBuf, CropOutData->dataSize);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to allocate memory from host ret = " << ret;
+    return ret;
+  }
+  std::shared_ptr<void> outBuf(resHostBuf, aclrtFreeHost);
+  processedInfo_ = outBuf;
+  // Memcpy the output data from device to host
+  ret = aclrtMemcpy(outBuf.get(), CropOutData->dataSize, CropOutData->data, CropOutData->dataSize,
+                    ACL_MEMCPY_DEVICE_TO_HOST);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to copy memory from device to host, ret = " << ret;
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::JPEG_DRC() {
+  struct timeval begin = {0};
+  struct timeval end = {0};
+  gettimeofday(&begin, nullptr);
+  // deal with image
+  APP_ERROR ret = JPEG_D_();
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to decode, ret = " << ret;
+    return ret;
+  }
+  std::string last_step = "Decode";
+  ret = JPEG_R_(last_step);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to resize, ret = " << ret;
+    return ret;
+  }
+  last_step = "Resize";
+  ret = JPEG_C_(last_step);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to crop, ret = " << ret;
+    return ret;
+  }
+  // Get output of crop module
+  std::shared_ptr<DvppDataInfo> CropOutData = dvppCommon_->GetCropedImage();
+  if (!CropOutData) {
+    MS_LOG(ERROR) << "Decode Data returns NULL";
+    return APP_ERR_COMM_INVALID_POINTER;
+  }
+  gettimeofday(&end, nullptr);
+  // Calculate the time cost of preprocess
+  const double costMs = SEC2MS * (end.tv_sec - begin.tv_sec) + (end.tv_usec - begin.tv_usec) / SEC2MS;
+  const double fps = 1 * SEC2MS / costMs;
+  MS_LOG(INFO) << "[dvpp (Decode + Resize + Crop) Delay] cost: " << costMs << "ms\tfps: " << fps;
+  return APP_ERR_OK;
+}
+
+/*
+ * @description: Read image files, and perform decoding and scaling
+ * @param: imageFile specifies the image path to be processed
+ * @return: aclError which is error code of ACL API
+ */
+APP_ERROR MDAclProcess::JPEG_DRC_(const RawData &ImageInfo) {
+  // Decode process
+  APP_ERROR ret = dvppCommon_->CombineJpegdProcess(ImageInfo, PIXEL_FORMAT_YUV_SEMIPLANAR_420, true);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to process decode, ret = " << ret << ".";
+    return ret;
+  }
+  // Get output of decoded jpeg image, decodeOutData locates on device
+  std::shared_ptr<DvppDataInfo> decodeOutData = dvppCommon_->GetDecodedImage();
+
+  if (decodeOutData == nullptr) {
+    MS_LOG(ERROR) << "Decode output buffer is null.";
+    return APP_ERR_COMM_INVALID_POINTER;
+  }
+  uint32_t pri_h = decodeOutData->heightStride;
+  uint32_t pri_w = decodeOutData->widthStride;
+  // Define output of resize jpeg image
+  DvppDataInfo resizeOut;
+  ResizeConfigFilter(resizeOut, pri_w, pri_h);
+  // Run resize application function
+  ret = dvppCommon_->CombineResizeProcess(*decodeOutData, resizeOut, true);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to process resize, ret = " << ret << ".";
+    return ret;
+  }
+  // Get output of resize jpeg image, resizeOutData locates on device
+  std::shared_ptr<DvppDataInfo> resizeOutData = dvppCommon_->GetResizedImage();
+  if (resizeOutData == nullptr) {
+    MS_LOG(ERROR) << "resize output buffer is null.";
+    return APP_ERR_COMM_INVALID_POINTER;
+  }
+  // Define output of crop jpeg image
+  DvppDataInfo cropOut;
+  cropOut.width = cropWidth_;
+  cropOut.height = cropHeight_;
+  cropOut.format = PIXEL_FORMAT_YUV_SEMIPLANAR_420;
+  // Define input of crop jpeg image
+  DvppCropInputInfo cropInfo;
+  cropInfo.dataInfo = *resizeOutData;
+  // Define crop area
+  CropRoiConfig cropCfg;
+  CropConfigFilter(cropCfg, cropInfo, resizeOut);
+  ret = dvppCommon_->CombineCropProcess(cropInfo, cropOut, true);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to process center crop, ret = " << ret << ".";
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::JPEG_DR(const RawData &ImageInfo) {
+  struct timeval begin = {0};
+  struct timeval end = {0};
+  gettimeofday(&begin, nullptr);
+  // deal with image
+  APP_ERROR ret = JPEG_DR_(ImageInfo);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to decode or resize, ret = " << ret;
+    return ret;
+  }
+  gettimeofday(&end, nullptr);
+  // Calculate the time cost of preprocess
+  const double costMs = SEC2MS * (end.tv_sec - begin.tv_sec) + (end.tv_usec - begin.tv_usec) / SEC2MS;
+  const double fps = 1 * SEC2MS / costMs;
+  MS_LOG(INFO) << "[dvpp Delay] cost: " << costMs << "ms\tfps: " << fps;
+  // Get output of resize module
+  std::shared_ptr<DvppDataInfo> ResizeOutData = dvppCommon_->GetResizedImage();
+  if (!ResizeOutData) {
+    MS_LOG(ERROR) << "ResizedOutData returns NULL";
+    return APP_ERR_COMM_INVALID_POINTER;
+  }
+  // Alloc host memory for the inference output according to the size of output
+  void *resHostBuf = nullptr;
+  ret = aclrtMallocHost(&resHostBuf, ResizeOutData->dataSize);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to allocate memory from host ret = " << ret;
+    return ret;
+  }
+  std::shared_ptr<void> outBuf(resHostBuf, aclrtFreeHost);
+  processedInfo_ = outBuf;
+  // Memcpy the output data from device to host
+  ret = aclrtMemcpy(outBuf.get(), ResizeOutData->dataSize, ResizeOutData->data, ResizeOutData->dataSize,
+                    ACL_MEMCPY_DEVICE_TO_HOST);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to copy memory from device to host, ret = " << ret;
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::JPEG_DR() {
+  struct timeval begin = {0};
+  struct timeval end = {0};
+  gettimeofday(&begin, nullptr);
+  // deal with image
+  APP_ERROR ret = JPEG_D_();
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to decode, ret = " << ret;
+    return ret;
+  }
+  std::string last_step = "Decode";
+  ret = JPEG_R_(last_step);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to resize, ret = " << ret;
+    return ret;
+  }
+  // Get output of resize module
+  std::shared_ptr<DvppDataInfo> ResizeOutData = dvppCommon_->GetResizedImage();
+  if (!ResizeOutData) {
+    MS_LOG(ERROR) << "Decode Data returns NULL";
+    return APP_ERR_COMM_INVALID_POINTER;
+  }
+  gettimeofday(&end, nullptr);
+  // Calculate the time cost of preprocess
+  const double costMs = SEC2MS * (end.tv_sec - begin.tv_sec) + (end.tv_usec - begin.tv_usec) / SEC2MS;
+  const double fps = 1 * SEC2MS / costMs;
+  MS_LOG(INFO) << "[dvpp (Decode + Resize) Delay] cost: " << costMs << "ms\tfps: " << fps;
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::JPEG_DR_(const RawData &ImageInfo) {
+  // Decode process
+  APP_ERROR ret = dvppCommon_->CombineJpegdProcess(ImageInfo, PIXEL_FORMAT_YUV_SEMIPLANAR_420, true);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to process decode, ret = " << ret << ".";
+    return ret;
+  }
+  // Get output of decoded jpeg image
+  std::shared_ptr<DvppDataInfo> decodeOutData = dvppCommon_->GetDecodedImage();
+  if (decodeOutData == nullptr) {
+    MS_LOG(ERROR) << "Decode output buffer is null.";
+    return APP_ERR_COMM_INVALID_POINTER;
+  }
+  // Define output of resize jpeg image
+  uint32_t pri_h = decodeOutData->heightStride;
+  uint32_t pri_w = decodeOutData->widthStride;
+  DvppDataInfo resizeOut;
+  ResizeConfigFilter(resizeOut, pri_w, pri_h);
+  // Run resize application function
+  ret = dvppCommon_->CombineResizeProcess(*decodeOutData, resizeOut, true);
+  if (ret != APP_ERR_OK) {
+    MS_LOG(ERROR) << "Failed to process resize, ret = " << ret << ".";
+    return ret;
+  }
+  return APP_ERR_OK;
+}
+
+void MDAclProcess::CropConfigFilter(CropRoiConfig &cfg, DvppCropInputInfo &cropinfo, DvppDataInfo &resizeinfo) {
+  if (resizeHeight_ != 0) {
+    cfg.up = (resizeHeight_ - cropHeight_) / 2;
+    if (cfg.up % 2 != 0) {
+      cfg.up++;
+    }
+    cfg.down = resizeHeight_ - (resizeHeight_ - cropHeight_) / 2;
+    if (cfg.down % 2 == 0) {
+      cfg.down--;
+    }
+  } else {
+    cfg.up = (resizeinfo.height - cropHeight_) / 2;
+    if (cfg.up % 2 != 0) {
+      cfg.up++;
+    }
+    cfg.down = resizeinfo.height - (resizeinfo.height - cropHeight_) / 2;
+    if (cfg.down % 2 == 0) {
+      cfg.down--;
+    }
+  }
+  if (resizeWidth_ != 0) {
+    cfg.left = (resizeWidth_ - cropWidth_) / 2;
+    if (cfg.left % 2 != 0) {
+      cfg.left++;
+    }
+    cfg.right = resizeWidth_ - (resizeWidth_ - cropWidth_) / 2;
+    if (cfg.right % 2 == 0) {
+      cfg.right--;
+    }
+  } else {
+    cfg.left = (resizeinfo.width - cropWidth_) / 2;
+    if (cfg.left % 2 != 0) {
+      cfg.left++;
+    }
+    cfg.right = resizeinfo.width - (resizeinfo.width - cropWidth_) / 2;
+    if (cfg.right % 2 == 0) {
+      cfg.right--;
+    }
+  }
+  cropinfo.roi = cfg;
+}
+
+APP_ERROR MDAclProcess::ResizeConfigFilter(DvppDataInfo &resizeinfo, const uint32_t pri_w_, const uint32_t pri_h_) {
+  if (resizeWidth_ != 0) {  // 如果输入参数个数为2，按指定参数缩放
+    resizeinfo.width = resizeWidth_;
+    resizeinfo.widthStride = DVPP_ALIGN_UP(resizeWidth_, VPC_STRIDE_WIDTH);
+    resizeinfo.height = resizeHeight_;
+    resizeinfo.heightStride = DVPP_ALIGN_UP(resizeHeight_, VPC_STRIDE_HEIGHT);
+  } else {  // 如果输入参数个数为1，保持原图片比例缩放
+    if (pri_h_ >= pri_w_) {
+      resizeinfo.width = resizeHeight_;  // 若输入参数个数为1，则只有resizeHeight_有值
+      resizeinfo.widthStride = DVPP_ALIGN_UP(resizeinfo.width, VPC_STRIDE_WIDTH);
+      resizeinfo.height = uint32_t(resizeHeight_ * pri_h_ / pri_w_);
+      resizeinfo.heightStride = DVPP_ALIGN_UP(resizeinfo.height, VPC_STRIDE_HEIGHT);
+    } else {
+      resizeinfo.width = uint32_t(resizeHeight_ * pri_w_ / pri_h_);
+      resizeinfo.widthStride = DVPP_ALIGN_UP(resizeinfo.width, VPC_STRIDE_WIDTH);
+      resizeinfo.height = resizeHeight_;
+      resizeinfo.heightStride = DVPP_ALIGN_UP(resizeinfo.height, VPC_STRIDE_HEIGHT);
+    }
+  }
+  resizeinfo.format = PIXEL_FORMAT_YUV_SEMIPLANAR_420;
+  return APP_ERR_OK;
+}
+/*
+ * @description: Obtain result data of memory
+ * @param: processed_data is result data info pointer
+ * @return: Address of data in the memory
+ */
+std::shared_ptr<void> MDAclProcess::Get_Memory_Data() { return processedInfo_; }
+
+std::shared_ptr<DvppDataInfo> MDAclProcess::Get_Croped_DeviceData() { return dvppCommon_->GetCropedImage(); }
+
+std::shared_ptr<DvppDataInfo> MDAclProcess::Get_Resized_DeviceData() { return dvppCommon_->GetResizedImage(); }
+
+std::shared_ptr<DvppDataInfo> MDAclProcess::Get_Decode_DeviceData() { return dvppCommon_->GetDecodedImage(); }
+
+APP_ERROR MDAclProcess::SetResizeParas(uint32_t width, uint32_t height) {
+  resizeWidth_ = width;
+  resizeHeight_ = height;
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::SetCropParas(uint32_t width, uint32_t height) {
+  cropWidth_ = width;
+  cropHeight_ = height;
+  return APP_ERR_OK;
+}
+
+APP_ERROR MDAclProcess::device_memory_release() {
+  dvppCommon_->ReleaseDvppBuffer();
+  MS_LOG(INFO) << "Device memory release successfully";
+  return APP_ERR_OK;
+}
+
+std::vector<uint32_t> MDAclProcess::Get_Primary_Shape() {
+  std::vector<uint32_t> pri_shape;
+  if (!dvppCommon_) {
+    pri_shape.emplace_back(dvppCommon_->GetDecodedImage()->heightStride);
+    pri_shape.emplace_back(dvppCommon_->GetDecodedImage()->widthStride);
+  }
+  return pri_shape;
+}

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/utils/MDAclProcess.h

@@ -0,0 +1,139 @@
+/*
+ * Copyright (c) 2020.Huawei Technologies Co., Ltd. All rights reserved.
+ * 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 MDACLMANAGER_H
+#define MDACLMANAGER_H
+
+#include <climits>
+#include <string>
+#include <string.h>
+#include <map>
+#include <iostream>
+#include <memory>
+#include "acl/acl.h"
+#include "CommonDataType.h"
+#include "minddata/dataset/core/tensor_shape.h"
+#include "minddata/dataset/core/data_type.h"
+#include "mindspore/ccsrc/minddata/dataset/core/device_tensor.h"
+#include "mindspore/ccsrc/minddata/dataset/core/tensor.h"
+#include "mindspore/core/utils/log_adapter.h"
+#include "mindspore/ccsrc/minddata/dataset/util/status.h"
+#include "ErrorCode.h"
+#include "DvppCommon.h"
+#include <stdio.h>
+#include <unistd.h>
+#include <sys/stat.h>
+#include <sys/types.h>
+
+mode_t SetFileDefaultUmask();
+
+class MDAclProcess {
+ public:
+  MDAclProcess(uint32_t resizeWidth, uint32_t resizeHeight, uint32_t cropWidth, uint32_t cropHeight,
+               aclrtContext context, bool is_crop = true, aclrtStream stream = nullptr,
+               std::shared_ptr<DvppCommon> dvppCommon = nullptr);
+
+  MDAclProcess(uint32_t ParaWidth, uint32_t ParaHeight, aclrtContext context, bool is_crop = false,
+               aclrtStream stream = nullptr, std::shared_ptr<DvppCommon> dvppCommon = nullptr);
+
+  MDAclProcess(aclrtContext context, bool is_crop = false, aclrtStream stream = nullptr,
+               std::shared_ptr<DvppCommon> dvppCommon = nullptr);
+
+  ~MDAclProcess(){};
+
+  // Release all the resource
+  APP_ERROR Release();
+  // Create resource for this sample
+  APP_ERROR InitResource();
+  // Process the result
+  APP_ERROR JPEG_DRC(const RawData &ImageInfo);
+  APP_ERROR JPEG_DRC();
+  // Procss the image without crop
+  APP_ERROR JPEG_DR(const RawData &ImageInfo);
+  APP_ERROR JPEG_DR();
+  // Process the JPEG image only with decode
+  APP_ERROR JPEG_D(const RawData &ImageInfo);
+  APP_ERROR JPEG_D();
+  // Process the JPEG image only with resize
+  APP_ERROR JPEG_R(const DvppDataInfo &ImageInfo);
+  APP_ERROR JPEG_R(std::string &last_step);
+  // Process the JPEG image only with crop
+  APP_ERROR JPEG_C(const DvppDataInfo &ImageInfo);
+  APP_ERROR JPEG_C(std::string &last_step);
+  // Process the PNG image only with decode
+  APP_ERROR PNG_D(const RawData &ImageInfo);
+  APP_ERROR PNG_D();
+  // API for access memory
+  std::shared_ptr<void> Get_Memory_Data();
+  // API for access device memory of croped data
+  std::shared_ptr<DvppDataInfo> Get_Croped_DeviceData();
+  // API for access device memory of resized data
+  std::shared_ptr<DvppDataInfo> Get_Resized_DeviceData();
+  // API for access device memory of decode data
+  std::shared_ptr<DvppDataInfo> Get_Decode_DeviceData();
+
+  APP_ERROR H2D_Sink(std::shared_ptr<mindspore::dataset::Tensor> &input,
+                     std::shared_ptr<mindspore::dataset::DeviceTensor> &device_input);
+
+  APP_ERROR D2H_Pop(std::shared_ptr<mindspore::dataset::DeviceTensor> &device_output,
+                    std::shared_ptr<mindspore::dataset::Tensor> &output);
+
+  // D-chip memory release
+  APP_ERROR device_memory_release();
+
+  std::shared_ptr<DvppCommon> GetDeviceModule();
+
+  std::vector<uint32_t> Get_Primary_Shape();
+
+  // Set Dvpp parameters
+  APP_ERROR SetResizeParas(uint32_t width, uint32_t height);
+  APP_ERROR SetCropParas(uint32_t width, uint32_t height);
+
+ private:
+  // Crop definition
+  void CropConfigFilter(CropRoiConfig &cfg, DvppCropInputInfo &cropinfo, DvppDataInfo &resizeinfo);
+  // Resize definition
+  APP_ERROR ResizeConfigFilter(DvppDataInfo &resizeinfo, const uint32_t pri_w_, const uint32_t pri_h_);
+  // Initialize DVPP modules used by this sample
+  APP_ERROR InitModule();
+  // Dvpp process with crop
+  APP_ERROR JPEG_DRC_(const RawData &ImageInfo);
+  // Dvpp process without crop
+  APP_ERROR JPEG_DR_(const RawData &ImageInfo);
+  // Impl of JPEG_D
+  APP_ERROR JPEG_D_(const RawData &ImageInfo);
+  APP_ERROR JPEG_D_();
+  // Impl of JPEG_R
+  APP_ERROR JPEG_R_(const DvppDataInfo &ImageInfo);
+  APP_ERROR JPEG_R_(std::string &last_step);
+  // Impl of JPEG_C
+  APP_ERROR JPEG_C_(const DvppDataInfo &ImageInfo);
+  APP_ERROR JPEG_C_(std::string &last_step);
+  // Impl of PNG_D
+  APP_ERROR PNG_D_(const RawData &ImageInfo);
+  APP_ERROR PNG_D_();
+
+  aclrtContext context_;
+  aclrtStream stream_;
+  std::shared_ptr<DvppCommon> dvppCommon_;  // dvpp object
+  std::shared_ptr<void> processedInfo_;     // processed data (On host)
+  uint32_t resizeWidth_;                    // dvpp resize width
+  uint32_t resizeHeight_;                   // dvpp resize height
+  uint32_t cropWidth_;                      // dvpp crop width
+  uint32_t cropHeight_;                     // dvpp crop height
+  bool contain_crop_;                       // Initialize with crop or not
+};
+
+#endif

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/utils/ResourceManager.cc

@@ -29,15 +29,15 @@ std::shared_ptr<ResourceManager> ResourceManager::ptr_ = nullptr;
  */
 APP_ERROR ExistFile(const std::string &filePath) {
   struct stat fileSat = {0};
-  char c[PATH_MAX_ASCEND + 1] = {0x00};
-  size_t count = filePath.copy(c, PATH_MAX_ASCEND + 1);
+  char c[PATH_MAX + 1] = {0x00};
+  size_t count = filePath.copy(c, PATH_MAX + 1);
   if (count != filePath.length()) {
     MS_LOG(ERROR) << "Failed to strcpy" << c;
     return APP_ERR_COMM_FAILURE;
   }
   // Get the absolute path of input directory
-  char path[PATH_MAX_ASCEND + 1] = {0x00};
-  if ((strlen(c) > PATH_MAX_ASCEND) || (realpath(c, path) == nullptr)) {
+  char path[PATH_MAX + 1] = {0x00};
+  if ((strlen(c) > PATH_MAX) || (realpath(c, path) == nullptr)) {
     MS_LOG(ERROR) << "Failed to get canonicalize path";
     return APP_ERR_COMM_EXIST;
   }
@@ -85,7 +85,6 @@ APP_ERROR ResourceManager::InitResource(ResourceInfo &resourceInfo) {
     MS_LOG(INFO) << "Acl has been initialized, skip.";
     return APP_ERR_OK;
   }
-
   std::string &aclConfigPath = resourceInfo.aclConfigPath;
   APP_ERROR ret;
   if (aclConfigPath.length() == 0) {

mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/utils/ResourceManager.h

@@ -16,20 +16,18 @@
 #ifndef RESOURCEMANAGER_H
 #define RESOURCEMANAGER_H
 
-#include <sys/stat.h>
 #include <vector>
 #include <set>
 #include <cstring>
-#include <string>
 #include <unordered_map>
-#include <memory>
 #include <mutex>
 #include "CommonDataType.h"
 #include "ErrorCode.h"
+#include <sys/stat.h>
 #include "mindspore/core/utils/log_adapter.h"
 #include "mindspore/ccsrc/cxx_api/graph/acl/acl_env_guard.h"
 
-#define PATH_MAX_ASCEND 4096
+#define PATH_MAX 4096
 
 enum ModelLoadMethod {
   LOAD_FROM_FILE = 0,       // Loading from file, memory of model and weights are managed by ACL
@@ -63,10 +61,12 @@ struct ResourceInfo {
 APP_ERROR ExistFile(const std::string &filePath);
 
 class ResourceManager {
+  friend APP_ERROR ExistFile(const std::string &filePath);
+
  public:
-  ResourceManager() {}
+  ResourceManager(){};
 
-  ~ResourceManager() {}
+  ~ResourceManager(){};
 
   // Get the Instance of resource manager
   static std::shared_ptr<ResourceManager> GetInstance();
@@ -89,4 +89,4 @@ class ResourceManager {
   std::shared_ptr<mindspore::AclEnvGuard> acl_env_;
 };
 
-#endif
+#endif

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

@@ -142,6 +142,12 @@ bool IsNonEmptyJPEG(const std::shared_ptr<Tensor> &input) {
   return input->SizeInBytes() > kJpegMagicLen && memcmp(input->GetBuffer(), kJpegMagic, kJpegMagicLen) == 0;
 }
 
+bool IsNonEmptyPNG(const std::shared_ptr<Tensor> &input) {
+  const unsigned char *kPngMagic = (unsigned char *)"\x89\x50\x4E\x47";
+  constexpr size_t kPngMagicLen = 4;
+  return input->SizeInBytes() > kPngMagicLen && memcmp(input->GetBuffer(), kPngMagic, kPngMagicLen) == 0;
+}
+
 Status Decode(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
   if (IsNonEmptyJPEG(input)) {
     return JpegCropAndDecode(input, output);
@@ -953,7 +959,7 @@ Status Erase(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *outp
       RETURN_STATUS_UNEXPECTED("CutOut: load image failed.");
     }
     if (input_cv->Rank() != 3 || num_channels != 3) {
-      RETURN_STATUS_UNEXPECTED("CutOut: image shape is not <H,W,C>.");
+      RETURN_STATUS_UNEXPECTED("CutOut: image shape is not <H,W,C> or <H,W>.");
     }
     cv::Mat input_img = input_cv->mat();
     int32_t image_h = input_cv->shape()[0];

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

@@ -103,6 +103,8 @@ Status DecodeCv(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *o
 
 bool IsNonEmptyJPEG(const std::shared_ptr<Tensor> &input);
 
+bool IsNonEmptyPNG(const std::shared_ptr<Tensor> &input);
+
 void JpegSetSource(j_decompress_ptr c_info, const void *data, int64_t data_size);
 
 Status JpegCropAndDecode(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, int x = 0, int y = 0,

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

@@ -32,9 +32,6 @@
 #include "minddata/dataset/kernels/image/cut_out_op.h"
 #endif
 #include "minddata/dataset/kernels/image/decode_op.h"
-#ifdef ENABLE_ACL
-#include "minddata/dataset/kernels/image/dvpp/dvpp_decode_resize_crop_jpeg_op.h"
-#endif
 #ifndef ENABLE_ANDROID
 #include "minddata/dataset/kernels/image/equalize_op.h"
 #include "minddata/dataset/kernels/image/hwc_to_chw_op.h"
@@ -305,102 +302,6 @@ Status MixUpBatchOperation::to_json(nlohmann::json *out_json) {
 
 #endif
 
-#ifdef ENABLE_ACL
-// DvppDecodeResizeCropOperation
-DvppDecodeResizeCropOperation::DvppDecodeResizeCropOperation(const std::vector<uint32_t> &crop,
-                                                             const std::vector<uint32_t> &resize)
-    : crop_(crop), resize_(resize) {}
-
-Status DvppDecodeResizeCropOperation::ValidateParams() {
-  // size
-  if (crop_.empty() || crop_.size() > 2) {
-    std::string err_msg = "DvppDecodeResizeCropJpeg: crop resolution must be a vector of one or two elements, got: " +
-                          std::to_string(crop_.size());
-    MS_LOG(ERROR) << err_msg;
-    RETURN_STATUS_SYNTAX_ERROR(err_msg);
-  }
-  if (resize_.empty() || resize_.size() > 2) {
-    std::string err_msg = "DvppDecodeResizeCropJpeg: resize resolution must be a vector of one or two elements, got: " +
-                          std::to_string(resize_.size());
-    MS_LOG(ERROR) << err_msg;
-    RETURN_STATUS_SYNTAX_ERROR(err_msg);
-  }
-  if (*min_element(crop_.begin(), crop_.end()) < 32 || *max_element(crop_.begin(), crop_.end()) > 2048) {
-    std::string err_msg = "Dvpp module supports crop image with resolution in range [32, 2048], got Crop Parameters: ";
-    if (crop_.size() == 2) {
-      MS_LOG(ERROR) << err_msg << "[" << crop_[0] << ", " << crop_[1] << "]";
-    } else {
-      MS_LOG(ERROR) << err_msg << "[" << crop_[0] << ", " << crop_[0] << "]";
-    }
-    RETURN_STATUS_SYNTAX_ERROR(err_msg);
-  }
-  if (*min_element(resize_.begin(), resize_.end()) < 32 || *max_element(resize_.begin(), resize_.end()) > 2048) {
-    std::string err_msg =
-      "Dvpp module supports resize image with resolution in range [32, 2048], got Crop Parameters: ";
-    if (resize_.size() == 2) {
-      MS_LOG(ERROR) << err_msg << "[" << resize_[0] << ", " << resize_[1] << "]";
-    } else {
-      MS_LOG(ERROR) << err_msg << "[" << resize_[0] << ", " << resize_[0] << "]";
-    }
-    RETURN_STATUS_SYNTAX_ERROR(err_msg);
-  }
-  if (crop_.size() < resize_.size()) {
-    if (crop_[0] > MIN(resize_[0], resize_[1])) {
-      std::string err_msg =
-        "Each value of crop parameter must be smaller than corresponding resize parameter, for example: x[0] <= "
-        "y[0],  and x[1] <= y[1], please verify your input parameters.";
-      MS_LOG(ERROR) << err_msg;
-      RETURN_STATUS_SYNTAX_ERROR(err_msg);
-    }
-  }
-  if (crop_.size() > resize_.size()) {
-    if (MAX(crop_[0], crop_[1]) > resize_[0]) {
-      std::string err_msg =
-        "Each value of crop parameter must be smaller than corresponding resize parameter, for example: x[0] <= "
-        "y[0],  and x[1] <= y[1], please verify your input parameters.";
-      MS_LOG(ERROR) << err_msg;
-      RETURN_STATUS_SYNTAX_ERROR(err_msg);
-    }
-  }
-  if (crop_.size() == resize_.size()) {
-    for (int32_t i = 0; i < crop_.size(); ++i) {
-      if (crop_[i] > resize_[i]) {
-        std::string err_msg =
-          "Each value of crop parameter must be smaller than corresponding resize parameter, for example: x[0] <= "
-          "y[0],  and x[1] <= y[1], please verify your input parameters.";
-        MS_LOG(ERROR) << err_msg;
-        RETURN_STATUS_SYNTAX_ERROR(err_msg);
-      }
-    }
-  }
-  return Status::OK();
-}
-
-std::shared_ptr<TensorOp> DvppDecodeResizeCropOperation::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.
-  uint32_t cropHeight, cropWidth, resizeHeight, resizeWidth;
-  if (crop_.size() == 1) {
-    cropHeight = crop_[0];
-    cropWidth = crop_[0];
-  } else {
-    cropHeight = crop_[0];
-    cropWidth = crop_[1];
-  }
-  // User specified the width value.
-  if (resize_.size() == 1) {
-    resizeHeight = resize_[0];
-    resizeWidth = resize_[0];
-  } else {
-    resizeHeight = resize_[0];
-    resizeWidth = resize_[1];
-  }
-  std::shared_ptr<DvppDecodeResizeCropJpegOp> tensor_op =
-    std::make_shared<DvppDecodeResizeCropJpegOp>(cropHeight, cropWidth, resizeHeight, resizeWidth);
-  return tensor_op;
-}
-#endif
-
 // NormalizeOperation
 NormalizeOperation::NormalizeOperation(std::vector<float> mean, std::vector<float> std) : mean_(mean), std_(std) {}
 

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

@@ -42,7 +42,6 @@ constexpr char kCropOperation[] = "Crop";
 constexpr char kCutMixBatchOperation[] = "CutMixBatch";
 constexpr char kCutOutOperation[] = "CutOut";
 constexpr char kDecodeOperation[] = "Decode";
-constexpr char kDvppDecodeResizeCropOperation[] = "DvppDecodeResizeCrop";
 constexpr char kEqualizeOperation[] = "Equalize";
 constexpr char kHwcToChwOperation[] = "HwcToChw";
 constexpr char kInvertOperation[] = "Invert";
@@ -88,7 +87,6 @@ class CropOperation;
 class CutMixBatchOperation;
 class CutOutOperation;
 class DecodeOperation;
-class DvppDecodeResizeCropOperation;
 class EqualizeOperation;
 class HwcToChwOperation;
 class InvertOperation;
@@ -258,23 +256,6 @@ class DecodeOperation : public TensorOperation {
   bool rgb_;
 };
 
-class DvppDecodeResizeCropOperation : public TensorOperation {
- public:
-  explicit DvppDecodeResizeCropOperation(const std::vector<uint32_t> &crop, const std::vector<uint32_t> &resize);
-
-  ~DvppDecodeResizeCropOperation() = default;
-
-  std::shared_ptr<TensorOp> Build() override;
-
-  Status ValidateParams() override;
-
-  std::string Name() const override { return kDvppDecodeResizeCropOperation; }
-
- private:
-  std::vector<uint32_t> crop_;
-  std::vector<uint32_t> resize_;
-};
-
 class EqualizeOperation : public TensorOperation {
  public:
   ~EqualizeOperation() = default;

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

@@ -48,6 +48,13 @@ Status TensorOp::Compute(const TensorRow &input, TensorRow *output) {
                 "Is this TensorOp oneToOne? If no, please implement this Compute() in the derived class.");
 }
 
+Status TensorOp::Compute(const std::shared_ptr<DeviceTensor> &input, std::shared_ptr<DeviceTensor> *output) {
+  IO_CHECK(input, output);
+  return Status(StatusCode::kMDUnexpectedError,
+                "Wrong Compute() function is called. This is a function for operators which can be executed by"
+                "different device. If so, please implement it in the derived class.");
+}
+
 Status TensorOp::OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) {
   if (inputs.size() != NumInput())
     return Status(StatusCode::kMDUnexpectedError,
@@ -63,5 +70,11 @@ Status TensorOp::OutputType(const std::vector<DataType> &inputs, std::vector<Dat
   outputs = inputs;
   return Status::OK();
 }
+#ifdef ENABLE_ACL
+Status TensorOp::SetAscendResource(const std::shared_ptr<MDAclProcess> &processor) {
+  return Status(StatusCode::kMDUnexpectedError,
+                "This is a CPU operator which doesn't have Ascend Resource. Please verify your context");
+}
+#endif
 }  // namespace dataset
 }  // namespace mindspore

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

@@ -24,6 +24,10 @@
 #include "minddata/dataset/core/tensor.h"
 #include "minddata/dataset/core/tensor_row.h"
 #include "minddata/dataset/util/status.h"
+#include "minddata/dataset/core/device_tensor.h"
+#ifdef ENABLE_ACL
+#include "minddata/dataset/kernels/image/dvpp/utils/MDAclProcess.h"
+#endif
 
 #define IO_CHECK(input, output)                             \
   do {                                                      \
@@ -58,7 +62,12 @@ constexpr char kCenterCropOp[] = "CenterCropOp";
 constexpr char kCutMixBatchOp[] = "CutMixBatchOp";
 constexpr char kCutOutOp[] = "CutOutOp";
 constexpr char kCropOp[] = "CropOp";
+constexpr char kDvppCropJpegOp[] = "DvppCropJpegOp";
 constexpr char kDvppDecodeResizeCropJpegOp[] = "DvppDecodeResizeCropJpegOp";
+constexpr char kDvppDecodeResizeJpegOp[] = "DvppDecodeResizeJpegOp";
+constexpr char kDvppDecodeJpegOp[] = "DvppDecodeJpegOp";
+constexpr char kDvppDecodePngOp[] = "DvppDecodePngOp";
+constexpr char kDvppResizeJpegOp[] = "DvppResizeJpegOp";
 constexpr char kEqualizeOp[] = "EqualizeOp";
 constexpr char kHwcToChwOp[] = "HWC2CHWOp";
 constexpr char kInvertOp[] = "InvertOp";
@@ -168,6 +177,12 @@ class TensorOp {
   // @return Status
   virtual Status Compute(const TensorRow &input, TensorRow *output);
 
+  // Perform an operation on one DeviceTensor and produce one DeviceTensor. This is for 1-to-1 column MapOp
+  // @param input shares the ownership of the Tensor (increase the ref count).
+  // @param output the address to a shared_ptr where the result will be placed.
+  // @return Status
+  virtual Status Compute(const std::shared_ptr<DeviceTensor> &input, std::shared_ptr<DeviceTensor> *output);
+
   // Returns true oif the TensorOp takes one input and returns one output.
   // @return true/false
   bool OneToOne() { return NumInput() == 1 && NumOutput() == 1; }
@@ -201,6 +216,9 @@ class TensorOp {
   virtual std::string Name() const = 0;
 
   virtual Status to_json(nlohmann::json *out_json) { return Status::OK(); }
+#ifdef ENABLE_ACL
+  virtual Status SetAscendResource(const std::shared_ptr<MDAclProcess> &processor);
+#endif
 
  protected:
   bool is_deterministic_{true};

tests/st/cpp/CMakeLists.txt

@@ -7,6 +7,9 @@ include_directories(${CMAKE_SOURCE_DIR}/mindspore/core)
 include_directories(${CMAKE_BINARY_DIR})
 include_directories(${CUDA_INCLUDE_DIRS})
 
+if(ENABLE_ACL)
+    add_definitions(-D ENABLE_ACL)
+endif()
 file(GLOB_RECURSE CXX_ST_SRC RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} *.cc)
 add_executable(st_tests ${CXX_ST_SRC})
 target_link_libraries(st_tests PRIVATE mindspore_shared_lib _c_dataengine mindspore::gtest)

tests/st/cpp/dataset/test_de.cc

@@ -19,6 +19,9 @@
 #include "include/api/types.h"
 #include "minddata/dataset/include/execute.h"
 #include "minddata/dataset/include/vision.h"
+#ifdef ENABLE_ACL
+#include "minddata/dataset/include/vision_ascend.h"
+#endif
 #include "minddata/dataset/kernels/tensor_op.h"
 #include "include/api/model.h"
 #include "include/api/serialization.h"
@@ -39,10 +42,9 @@ TEST_F(TestDE, TestResNetPreprocess) {
   auto image = mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
 
   // Define transform operations
-  mindspore::dataset::Execute Transform({
-                                          Decode(), Resize({224, 224}),
-                                          Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}),
-                                          HWC2CHW()});
+  mindspore::dataset::Execute Transform(
+    {Decode(), Resize({224, 224}),
+     Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}), HWC2CHW()});
 
   // Apply transform on images
   Status rc = Transform(image, &image);
@@ -63,9 +65,9 @@ TEST_F(TestDE, TestDvpp) {
   auto image = MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
 
   // Define dvpp transform
-  std::vector<uint32_t> crop_size = {224, 224};
-  std::vector<uint32_t> resize_size = {256, 256};
-  mindspore::dataset::Execute Transform(DvppDecodeResizeCropJpeg(crop_size, resize_size));
+  std::vector<uint32_t> crop_paras = {224, 224};
+  std::vector<uint32_t> resize_paras = {256, 256};
+  mindspore::dataset::Execute Transform(DvppDecodeResizeCropJpeg(crop_paras, resize_paras));
 
   // Apply transform on images
   Status rc = Transform(image, &image);
@@ -75,16 +77,52 @@ TEST_F(TestDE, TestDvpp) {
   ASSERT_EQ(image.Shape().size(), 3);
   int32_t real_h = 0;
   int32_t real_w = 0;
-  int32_t remainder = crop_size[crop_size.size() - 1] % 16;
-  if (crop_size.size() == 1) {
-    real_h = (crop_size[0] % 2 == 0) ? crop_size[0] : crop_size[0] + 1;
-    real_w = (remainder == 0) ? crop_size[0] : crop_size[0] + 16 - remainder;
+  int32_t remainder = crop_paras[crop_paras.size() - 1] % 16;
+  if (crop_paras.size() == 1) {
+    real_h = (crop_paras[0] % 2 == 0) ? crop_paras[0] : crop_paras[0] + 1;
+    real_w = (remainder == 0) ? crop_paras[0] : crop_paras[0] + 16 - remainder;
   } else {
-    real_h = (crop_size[0] % 2 == 0) ? crop_size[0] : crop_size[0] + 1;
-    real_w = (remainder == 0) ? crop_size[1] : crop_size[1] + 16 - remainder;
+    real_h = (crop_paras[0] % 2 == 0) ? crop_paras[0] : crop_paras[0] + 1;
+    real_w = (remainder == 0) ? crop_paras[1] : crop_paras[1] + 16 - remainder;
   }
   ASSERT_EQ(image.Shape()[0], real_h * real_w * 1.5);  // For image in YUV format, each pixel takes 1.5 byte
   ASSERT_EQ(image.Shape()[1], 1);
   ASSERT_EQ(image.Shape()[2], 1);
 #endif
 }
+
+TEST_F(TestDE, TestDvppSinkMode) {
+#ifdef ENABLE_ACL
+  // Read images from target directory
+  std::shared_ptr<mindspore::dataset::Tensor> de_tensor;
+  mindspore::dataset::Tensor::CreateFromFile("./data/dataset/apple.jpg", &de_tensor);
+  auto image = MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_tensor));
+
+  // Define dvpp transform
+  std::vector<uint32_t> crop_paras = {224, 224};
+  std::vector<uint32_t> resize_paras = {256};
+  mindspore::dataset::Execute Transform({DvppDecodeJpeg(), DvppResizeJpeg(resize_paras), DvppCropJpeg(crop_paras)},
+                                        "Ascend310");
+
+  // Apply transform on images
+  Status rc = Transform(image, &image);
+
+  // Check image info
+  ASSERT_TRUE(rc.IsOk());
+  ASSERT_EQ(image.Shape().size(), 2);
+  int32_t real_h = 0;
+  int32_t real_w = 0;
+  int32_t remainder = crop_paras[crop_paras.size() - 1] % 16;
+  if (crop_paras.size() == 1) {
+    real_h = (crop_paras[0] % 2 == 0) ? crop_paras[0] : crop_paras[0] + 1;
+    real_w = (remainder == 0) ? crop_paras[0] : crop_paras[0] + 16 - remainder;
+  } else {
+    real_h = (crop_paras[0] % 2 == 0) ? crop_paras[0] : crop_paras[0] + 1;
+    real_w = (remainder == 0) ? crop_paras[1] : crop_paras[1] + 16 - remainder;
+  }
+  ASSERT_EQ(image.Shape()[0], real_h);  // For image in YUV format, each pixel takes 1.5 byte
+  ASSERT_EQ(image.Shape()[1], real_w);
+  ASSERT_EQ(image.DataSize(), 1.5 * real_w * real_h);
+  Transform.DeviceMemoryRelease();
+#endif
+}

tests/ut/cpp/CMakeLists.txt

@@ -70,6 +70,13 @@ if(ENABLE_MINDDATA)
                 )
         list(REMOVE_ITEM UT_SRCS ${PYTHON_RELATED_SRCS})
     endif()
+
+    if(NOT ENABLE_ACL)
+        set(ASCEND310_RELATED_SRCS
+                dataset/dvpp_decode_jpeg_test.cc
+                )
+        list(REMOVE_ITEM UT_SRCS ${ASCEND310_RELATED_SRCS})
+    endif()
 else()
     file(GLOB_RECURSE TEMP_UT_SRCS ./*.cc)
     foreach(OBJ ${TEMP_UT_SRCS})

tests/ut/cpp/dataset/CMakeLists.txt

@@ -149,6 +149,12 @@ if(ENABLE_PYTHON)
             )
 endif()
 
+if(ENABLE_ACL)
+    set(DE_UT_SRCS
+            ${DE_UT_SRCS}
+            dvpp_decode_jpeg_test.cc)
+endif()
+
 add_executable(de_ut_tests ${DE_UT_SRCS})
 
 set_target_properties(de_ut_tests PROPERTIES INSTALL_RPATH "$ORIGIN/../lib:$ORIGIN/../lib64")

tests/ut/cpp/dataset/dvpp_decode_jpeg_test.cc

@@ -0,0 +1,40 @@
+/**
+ * Copyright 2021Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "common/common.h"
+#include "common/cvop_common.h"
+#include "minddata/dataset/include/vision_ascend.h"
+#include "minddata/dataset/kernels/image/dvpp/dvpp_decode_jpeg_op.h"
+#include "utils/log_adapter.h"
+
+using namespace mindspore::dataset;
+using mindspore::LogStream;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::MsLogLevel::INFO;
+
+class MindDataTestDvppDecodeJpeg : public UT::CVOP::CVOpCommon {
+ protected:
+  MindDataTestDvppDecodeJpeg() : CVOpCommon() {}
+
+  std::shared_ptr<Tensor> output_tensor_;
+};
+
+TEST_F(MindDataTestDvppDecodeJpeg, TestOp1) {
+  MS_LOG(INFO) << "Doing testDvppDecodeJpeg.";
+  std::unique_ptr<DvppDecodeJpegOp> op(new DvppDecodeJpegOp());
+  EXPECT_TRUE(op->OneToOne());
+  Status s = op->Compute(input_tensor_, &output_tensor_);
+  EXPECT_EQ(s, Status::OK());
+}