diff --git a/research/cv/ArbitraryStyleTransfer/README.md b/research/cv/ArbitraryStyleTransfer/README.md
index 301d81534816489bb908c081daebd11b3205e43f..8acadf07e56061bd0457ceabd53b123a27ecba55 100644
--- a/research/cv/ArbitraryStyleTransfer/README.md
+++ b/research/cv/ArbitraryStyleTransfer/README.md
@@ -9,6 +9,7 @@
     - [Script and Sample Code](#script-and-sample-code)
     - [Script Parameters](#script-parameters)
     - [Training Process](#training-process)
+    - [Ascend310 Inference Process](#ascend310-inference-process)
 - [Model Description](#model-description)
         - [Performance](#performance)
         - [Training Performance](#training-performance)  
@@ -104,7 +105,9 @@ style transfer
  鈹斺攢 inceptionv3py # inception-v3 network define  
 鈹溾攢 output #result
 鈹溾攢 test.py # generate style transfer images
-鈹斺攢 train.py # train script
+鈹溾攢 train.py # train
+鈹溾攢 export_for_310.py # export model for 310 infer
+鈹斺攢 ascend310_infer # 310 main
 ```
 
 ## [Script Parameters](#contents)
@@ -138,6 +141,27 @@ bash ./scripts/run_eval.sh [PLATFORM] [DEVICE_ID] [CONTENT_PATH] [STYLE_PATH] [I
 
 Evaluation result will be stored in the output. Under this, you can find style transfer pictures.
 
+## [Ascend310 Inference Process](#contents)
+
+### Export MINDIR file
+
+```bash
+python  export_for_310.py --ckpt_file [/path/to/ckpt_file] --inception_ckpt [/path/to/inception_ckpt]
+```
+
+### Ascend310 Inference
+
+- Run `run_infer_310.sh` for Ascend310 inference.
+
+```bash
+# infer
+bash run_infer_310.sh [MINDIR_PATH] [CONTENT_PATH] [STYLE_PATH] [DEVICE_ID]
+# example
+bash run_infer_310.sh ./style_transfer_model.mindir ./content_test/ ./style_test/ 0
+```
+
+Stylized pictures will be stored in the postprocess_Result path.
+
 # [Model Description](#contents)
 
 ## [Performance](#contents)
@@ -182,4 +206,4 @@ Evaluation result will be stored in the output. Under this, you can find style t
 
 # [ModelZoo Homepage](#contents)
 
-Please check the official [homepage](https://gitee.com/mindspore/models).
\ No newline at end of file
+Please check the official [homepage](https://gitee.com/mindspore/models).
diff --git a/research/cv/ArbitraryStyleTransfer/README_CN.md b/research/cv/ArbitraryStyleTransfer/README_CN.md
index 1d9eeb3cf3a8f8f7b65bf7a9452c7e0447e3c3c1..df1b565dd11e43a30773c2043d946bdf956484e3 100644
--- a/research/cv/ArbitraryStyleTransfer/README_CN.md
+++ b/research/cv/ArbitraryStyleTransfer/README_CN.md
@@ -9,6 +9,7 @@
     - [鑴氭湰鍜屾牱渚嬩唬鐮乚(#鑴氭湰鍜屾牱渚嬩唬涔�)
     - [鑴氭湰鍙傛暟](#鑴氭湰鍙傛暟)
     - [璁粌杩囩▼](#璁粌杩囩▼)
+    - [Ascend310鎺ㄧ悊杩囩▼](#ascend310鎺ㄧ悊杩囩▼)
 - [妯″瀷鎻忚堪](#妯″瀷鎻忚堪)
     - [鏁堟灉](#鏁堟灉)
         - [璁粌鏁堟灉](#璁粌鏁堟灉)  
@@ -71,7 +72,7 @@ Style Transfer Networks鐨勮缁冭繃绋嬮渶瑕侀鍏堣缁冪殑VGG16鍜孖nception-v3
 
 # [鑴氭湰鎻忚堪](#鍐呭)
 
-## [[鑴氭湰鍜屾牱渚嬩唬鐮乚(#鍐呭)
+## [鑴氭湰鍜屾牱渚嬩唬鐮乚(#鍐呭)
 
 ```shell
 style transfer
@@ -102,7 +103,9 @@ style transfer
  鈹斺攢 inceptionv3py # inception-v3 缃戠粶鐨勫畾涔�  
 鈹溾攢 output # 杈撳嚭缁撴灉鐨勬枃浠跺す
 鈹溾攢 test.py # 妯″瀷娴嬭瘯浠ｇ爜
-鈹斺攢 train.py # 妯″瀷璁粌浠ｇ爜
+鈹溾攢 train.py # 妯″瀷璁粌浠ｇ爜
+鈹溾攢 export_for_310.py # 涓�310鎺ㄧ悊瀵煎嚭妯″瀷
+鈹斺攢 ascend310_infer # 310鎺ㄧ悊绋嬪簭
 ```
 
 ## [鑴氭湰鍙傛暟](#鍐呭)
@@ -136,6 +139,27 @@ bash ./scripts/run_eval.sh [PLATFORM] [DEVICE_ID] [CONTENT_PATH] [STYLE_PATH] [I
 
 璇勪及缁撴灉灏嗗瓨鍌ㄥ湪杈撳嚭涓€傚湪閭ｉ噷锛屼綘鍙互鎵惧埌椋庢牸杞崲鍥剧墖銆�
 
+## [Ascend310鎺ㄧ悊杩囩▼](#鍐呭)
+
+### 瀵煎嚭 MINDIR 鏂囦欢
+
+```bash
+python  export_for_310.py --ckpt_file [/path/to/ckpt_file] --inception_ckpt [/path/to/inception_ckpt]
+```
+
+### Ascend310鎺ㄧ悊
+
+- 杩愯 `run_infer_310.sh` 浠ヨ繘琛�310鎺ㄧ悊.
+
+```bash
+# 310鎺ㄧ悊
+bash run_infer_310.sh [MINDIR_PATH] [CONTENT_PATH] [STYLE_PATH] [DEVICE_ID]
+# 绀轰緥
+bash run_infer_310.sh ./style_transfer_model.mindir ./content_test/ ./style_test/ 0
+```
+
+椋庢牸鍖栧浘鐗囧皢瀛樺偍鍦� postprocess_Result 鏂囦欢澶逛腑.
+
 # [妯″瀷鎻忚堪](#鍐呭)
 
 ## [鏁堟灉](#鍐呭)
@@ -182,4 +206,4 @@ bash ./scripts/run_eval.sh [PLATFORM] [DEVICE_ID] [CONTENT_PATH] [STYLE_PATH] [I
 
 # [ModelZoo 涓婚〉](#鍐呭)
 
-璇风偣鍑昏繘鍏ュ畼鏂筟涓婚〉](https://gitee.com/mindspore/models).
\ No newline at end of file
+璇风偣鍑昏繘鍏ュ畼鏂筟涓婚〉](https://gitee.com/mindspore/models).
diff --git a/research/cv/ArbitraryStyleTransfer/ascend310_infer/CMakeLists.txt b/research/cv/ArbitraryStyleTransfer/ascend310_infer/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f936989f21405ae4ba068d1284d62f4e1b8613a5
--- /dev/null
+++ b/research/cv/ArbitraryStyleTransfer/ascend310_infer/CMakeLists.txt
@@ -0,0 +1,15 @@
+cmake_minimum_required(VERSION 3.14.1)
+project(Ascend310Infer)
+add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O2 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
+set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
+option(MINDSPORE_PATH "mindspore install path" "")
+include_directories(${MINDSPORE_PATH})
+include_directories(${MINDSPORE_PATH}/include)
+include_directories(${PROJECT_SRC_ROOT})
+find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
+file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
+
+add_executable(main src/main.cc src/utils.cc)
+target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)
+find_package(gflags REQUIRED)
\ No newline at end of file
diff --git a/research/cv/ArbitraryStyleTransfer/ascend310_infer/build.sh b/research/cv/ArbitraryStyleTransfer/ascend310_infer/build.sh
new file mode 100644
index 0000000000000000000000000000000000000000..713d7f657ddfa5f75b069351c55f8447f77c72d0
--- /dev/null
+++ b/research/cv/ArbitraryStyleTransfer/ascend310_infer/build.sh
@@ -0,0 +1,29 @@
+#!/bin/bash
+# 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.
+# ============================================================================
+if [ -d out ]; then
+    rm -rf out
+fi
+
+mkdir out
+cd out || exit
+
+if [ -f "Makefile" ]; then
+  make clean
+fi
+
+cmake .. \
+    -DMINDSPORE_PATH="`pip show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make
diff --git a/research/cv/ArbitraryStyleTransfer/ascend310_infer/inc/utils.h b/research/cv/ArbitraryStyleTransfer/ascend310_infer/inc/utils.h
new file mode 100644
index 0000000000000000000000000000000000000000..13eb133d68b8dd4229b5d3f4f93aef5d9ba43c3e
--- /dev/null
+++ b/research/cv/ArbitraryStyleTransfer/ascend310_infer/inc/utils.h
@@ -0,0 +1,33 @@
+/**
+ * Copyright 2022 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_INFERENCE_UTILS_H_
+#define MINDSPORE_INFERENCE_UTILS_H_
+
+#include <sys/stat.h>
+#include <dirent.h>
+#include <vector>
+#include <string>
+#include <memory>
+#include "include/api/types.h"
+
+std::vector<std::string> GetAllFiles(std::string_view dirName);
+DIR *OpenDir(std::string_view dirName);
+std::string RealPath(std::string_view path);
+mindspore::MSTensor ReadFileToTensor(const std::string &file);
+int WriteResult(const std::string &contentFile, const std::string &styleFile,
+                const std::vector<mindspore::MSTensor> &outputs);
+#endif
diff --git a/research/cv/ArbitraryStyleTransfer/ascend310_infer/src/main.cc b/research/cv/ArbitraryStyleTransfer/ascend310_infer/src/main.cc
new file mode 100644
index 0000000000000000000000000000000000000000..ca0f2e7f9360f34d930c46f13537dae019416bed
--- /dev/null
+++ b/research/cv/ArbitraryStyleTransfer/ascend310_infer/src/main.cc
@@ -0,0 +1,148 @@
+/**
+ * Copyright 2022 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 <dirent.h>
+#include <gflags/gflags.h>
+#include <sys/time.h>
+#include <algorithm>
+#include <fstream>
+#include <iosfwd>
+#include <iostream>
+#include <sstream>
+#include <string>
+#include <vector>
+
+#include "inc/utils.h"
+#include "include/api/context.h"
+#include "include/api/model.h"
+#include "include/api/serialization.h"
+#include "include/api/types.h"
+#include "include/dataset/execute.h"
+#include "include/dataset/vision.h"
+
+using mindspore::Context;
+using mindspore::GraphCell;
+using mindspore::kSuccess;
+using mindspore::Model;
+using mindspore::ModelType;
+using mindspore::MSTensor;
+using mindspore::Serialization;
+using mindspore::Status;
+using mindspore::dataset::Execute;
+
+DEFINE_string(mindir_path, "", "mindir path");
+DEFINE_string(input_path, ".", "input path");
+DEFINE_int32(device_id, 0, "device id");
+
+int main(int argc, char **argv) {
+  gflags::ParseCommandLineFlags(&argc, &argv, true);
+  if (RealPath(FLAGS_mindir_path).empty()) {
+    std::cout << "Invalid mindir" << std::endl;
+    return 1;
+  }
+
+  auto context = std::make_shared<Context>();
+  auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
+  ascend310->SetDeviceID(FLAGS_device_id);
+  context->MutableDeviceInfo().push_back(ascend310);
+  mindspore::Graph graph;
+  Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
+
+  Model model;
+  Status ret = model.Build(GraphCell(graph), context);
+  if (ret != kSuccess) {
+    std::cout << "ERROR: Build failed." << std::endl;
+    return 1;
+  }
+  std::vector<MSTensor> model_inputs = model.GetInputs();
+  if (model_inputs.empty()) {
+    std::cout << "Invalid model, inputs is empty." << std::endl;
+    return 1;
+  }
+  std::map<double, double> costTime_map;
+  // get content file path
+  auto content_files = GetAllFiles(FLAGS_input_path + "/content/");
+  if (content_files.empty()) {
+    std::cout << "ERROR: content data empty." << std::endl;
+    return 1;
+  }
+  size_t content_size = content_files.size();
+  // get style file path
+  auto style_files = GetAllFiles(FLAGS_input_path + "/style/");
+
+  if (style_files.empty()) {
+    std::cout << "ERROR: style data empty." << std::endl;
+    return 1;
+  }
+  size_t style_size = style_files.size();
+
+  for (size_t i = 0; i < content_size; ++i) {
+    for (size_t j = 0; j < style_size; ++j) {
+      struct timeval start = {0};
+      struct timeval end = {0};
+      double startTimeMs;
+      double endTimeMs;
+      std::vector<MSTensor> inputs;
+      std::vector<MSTensor> outputs;
+      std::cout << "Start predict content files:" << content_files[i]
+                << " with style file:" << style_files[j] << std::endl;
+
+      auto input0 = ReadFileToTensor(content_files[i]);
+      auto input1 = ReadFileToTensor(style_files[j]);
+
+      inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(),
+                          model_inputs[0].Shape(), input0.Data().get(),
+                          input0.DataSize());
+
+      inputs.emplace_back(model_inputs[1].Name(), model_inputs[1].DataType(),
+                          model_inputs[1].Shape(), input1.Data().get(),
+                          input1.DataSize());
+
+      gettimeofday(&start, nullptr);
+      ret = model.Predict(inputs, &outputs);
+      gettimeofday(&end, nullptr);
+      if (ret != kSuccess) {
+        std::cout << "Predict " << style_files[j] << " failed." << std::endl;
+        return 1;
+      }
+      startTimeMs = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
+      endTimeMs = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
+      costTime_map.insert(std::pair<double, double>(startTimeMs, endTimeMs));
+      std::cout << content_files[i] + "_" + style_files[j] << std::endl;
+      WriteResult(content_files[i], style_files[j], outputs);
+    }
+  }
+  double average = 0.0;
+  int inferCount = 0;
+
+  for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
+    double diff = iter->second - iter->first;
+    average += diff;
+    inferCount++;
+  }
+  average = average / inferCount;
+  std::stringstream timeCost;
+  timeCost << "NN inference cost average time: " << average
+           << " ms of infer_count " << inferCount << std::endl;
+  std::cout << "NN inference cost average time: " << average
+            << "ms of infer_count " << inferCount << std::endl;
+  std::string fileName =
+      "./time_Result" + std::string("/test_perform_static.txt");
+  std::ofstream fileStream(fileName.c_str(), std::ios::trunc);
+  fileStream << timeCost.str();
+  fileStream.close();
+  costTime_map.clear();
+  return 0;
+}
diff --git a/research/cv/ArbitraryStyleTransfer/ascend310_infer/src/utils.cc b/research/cv/ArbitraryStyleTransfer/ascend310_infer/src/utils.cc
new file mode 100644
index 0000000000000000000000000000000000000000..14b996fff2a6e28d29daac026412bda1f59e12fb
--- /dev/null
+++ b/research/cv/ArbitraryStyleTransfer/ascend310_infer/src/utils.cc
@@ -0,0 +1,137 @@
+/**
+ * Copyright 2022 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 "inc/utils.h"
+#include <algorithm>
+#include <fstream>
+#include <iostream>
+
+using mindspore::DataType;
+using mindspore::MSTensor;
+
+std::vector<std::string> GetAllFiles(std::string_view dirName) {
+  struct dirent *filename;
+  DIR *dir = OpenDir(dirName);
+  if (dir == nullptr) {
+    return {};
+  }
+  std::vector<std::string> res;
+  while ((filename = readdir(dir)) != nullptr) {
+    std::string dName = std::string(filename->d_name);
+    if (dName == "." || dName == ".." || filename->d_type != DT_REG) {
+      continue;
+    }
+    res.emplace_back(std::string(dirName) + "/" + filename->d_name);
+  }
+  std::sort(res.begin(), res.end());
+  for (auto &f : res) {
+    std::cout << "image file: " << f << std::endl;
+  }
+  return res;
+}
+
+int WriteResult(const std::string &contentFile, const std::string &styleFile,
+                const std::vector<MSTensor> &outputs) {
+  std::string homePath = "./result_Files";
+  for (size_t i = 0; i < outputs.size(); ++i) {
+    size_t outputSize;
+    std::shared_ptr<const void> netOutput;
+    netOutput = outputs[i].Data();
+    outputSize = outputs[i].DataSize();
+    int pos = contentFile.rfind('/');
+    std::string contentName(contentFile, pos + 1);
+    contentName.replace(contentName.find('.'),
+                        contentName.size() - contentName.find('.'), "");
+
+    pos = styleFile.rfind('/');
+    std::string styleName(styleFile, pos + 1);
+    styleName.replace(styleName.find('.'),
+                      styleName.size() - styleName.find('.'), "");
+
+    std::string outFileName = homePath + "/" + contentName + styleName + ".bin";
+    FILE *outputFile = fopen(outFileName.c_str(), "wb");
+    fwrite(netOutput.get(), outputSize, sizeof(char), outputFile);
+    fclose(outputFile);
+    outputFile = nullptr;
+  }
+  return 0;
+}
+
+mindspore::MSTensor ReadFileToTensor(const std::string &file) {
+  if (file.empty()) {
+    std::cout << "Pointer file is nullptr" << std::endl;
+    return mindspore::MSTensor();
+  }
+
+  std::ifstream ifs(file);
+  if (!ifs.good()) {
+    std::cout << "File: " << file << " is not exist" << std::endl;
+    return mindspore::MSTensor();
+  }
+
+  if (!ifs.is_open()) {
+    std::cout << "File: " << file << "open failed" << std::endl;
+    return mindspore::MSTensor();
+  }
+
+  ifs.seekg(0, std::ios::end);
+  size_t size = ifs.tellg();
+  mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8,
+                             {static_cast<int64_t>(size)}, nullptr, size);
+
+  ifs.seekg(0, std::ios::beg);
+  ifs.read(reinterpret_cast<char *>(buffer.MutableData()), size);
+  ifs.close();
+
+  return buffer;
+}
+
+DIR *OpenDir(std::string_view dirName) {
+  if (dirName.empty()) {
+    std::cout << " dirName is null ! " << std::endl;
+    return nullptr;
+  }
+  std::string realPath = RealPath(dirName);
+  struct stat s;
+  lstat(realPath.c_str(), &s);
+  if (!S_ISDIR(s.st_mode)) {
+    std::cout << "dirName is not a valid directory !" << std::endl;
+    return nullptr;
+  }
+  DIR *dir;
+  dir = opendir(realPath.c_str());
+  if (dir == nullptr) {
+    std::cout << "Can not open dir " << dirName << std::endl;
+    return nullptr;
+  }
+  std::cout << "Successfully opened the dir " << dirName << std::endl;
+  return dir;
+}
+
+std::string RealPath(std::string_view path) {
+  char realPathMem[PATH_MAX] = {0};
+  char *realPathRet = nullptr;
+  realPathRet = realpath(path.data(), realPathMem);
+
+  if (realPathRet == nullptr) {
+    std::cout << "File: " << path << " is not exist.";
+    return "";
+  }
+
+  std::string realPath(realPathMem);
+  std::cout << path << " realpath is: " << realPath << std::endl;
+  return realPath;
+}
diff --git a/research/cv/ArbitraryStyleTransfer/export_for_310.py b/research/cv/ArbitraryStyleTransfer/export_for_310.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ab994a643717a0f1c239bb55c2081417943353a
--- /dev/null
+++ b/research/cv/ArbitraryStyleTransfer/export_for_310.py
@@ -0,0 +1,78 @@
+# Copyright 2022 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.
+# ============================================================================
+
+"""file for evaling"""
+import argparse
+import numpy as np
+
+import mindspore
+from mindspore import Tensor, Parameter
+import mindspore.nn as nn
+import mindspore.ops as ops
+from mindspore.common import set_seed
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train.serialization import export
+from src.inceptionv3 import inceptionv3
+from src.model import get_model
+
+set_seed(1)
+parser = argparse.ArgumentParser(description="style transfer train")
+# data loader
+parser.add_argument("--inception_ckpt", type=str, default='./pretrained_model/inceptionv3.ckpt')
+parser.add_argument('--file_format', type=str, choices=['AIR', 'ONNX', 'MINDIR'], default='MINDIR', \
+                    help='file format')
+parser.add_argument("--ckpt_path", type=str, default='./ckpt/style_transfer_model_0100.ckpt')
+parser.add_argument('--platform', type=str, choices=['Ascend', 'GPU'], default='Ascend', help='Ascend or GPU')
+parser.add_argument("--device_id", type=int, default=0, help="device id, default: 0.")
+parser.add_argument("--image_size", type=int, default=256, help='image size, default: image_size.')
+parser.add_argument('--file_name', type=str, default='style_transfer', help='output file name prefix.')
+parser.add_argument("--style_dim", type=int, default=100,
+                    help="Style vector dimension. default: 100")
+parser.add_argument('--init_type', type=str, default='normal', choices=("normal", "xavier"), \
+                    help='network initialization, default is normal.')
+parser.add_argument('--init_gain', type=float, default=0.02, \
+                    help='scaling factor for normal, xavier and orthogonal, default is 0.02.')
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    image_size = args.image_size
+    transfer_net = get_model(args)
+    params = load_checkpoint(args.ckpt_path)
+    load_param_into_net(transfer_net, params)
+
+
+    class stylization(nn.Cell):
+        def __init__(self):
+            super(stylization, self).__init__()
+            self.inception = inceptionv3(args.inception_ckpt)
+            self.transfer_net = transfer_net
+            self.scale = Parameter(Tensor(np.array([2, 2, 2]).reshape([1, 3, 1, 1]), mindspore.float32))
+            self.offset = Parameter(Tensor(np.array([1, 1, 1]).reshape([1, 3, 1, 1]), mindspore.float32))
+            self.op_concat = ops.Concat(axis=0)
+
+        def construct(self, content, style_shifted):
+            s_in_feat = self.inception(style_shifted)
+            c_in_feat = self.inception(content[1])
+            interporated_stylied_img = self.transfer_net.construct_interpolation_310(content[0], s_in_feat, c_in_feat)
+            return self.op_concat([style_shifted, interporated_stylied_img, content[0]])
+
+
+    model = stylization()
+    input1_shape = [2, 1, 3, args.image_size, args.image_size]
+    input2_shape = [1, 3, args.image_size, args.image_size]
+    input_array_1 = Tensor(np.random.uniform(-1.0, 1.0, size=input1_shape).astype(np.float32))
+    input_array_2 = Tensor(np.random.uniform(-1.0, 1.0, size=input2_shape).astype(np.float32))
+    G_file = f"{args.file_name}_model"
+    export(model, input_array_1, input_array_2, file_name=G_file, file_format=args.file_format)
diff --git a/research/cv/ArbitraryStyleTransfer/postprocess.py b/research/cv/ArbitraryStyleTransfer/postprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..bffae964b9b71bc2c0a9a8830adc056180c82fb6
--- /dev/null
+++ b/research/cv/ArbitraryStyleTransfer/postprocess.py
@@ -0,0 +1,77 @@
+# Copyright 2022 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.
+# ============================================================================
+"""postprocess"""
+import argparse
+import os
+
+import imageio
+import numpy as np
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--bin_path", type=str, help='bin_path, path to binary files generated by 310 model, default: None')
+parser.add_argument("--image_size", type=int, default=256, help='image size, default: image_size.')
+parser.add_argument("--output_dir", type=str, default='output_dir',
+                    help='output_path, path to store output, default: None')
+parser.add_argument("--output_dir_interpolation", type=str, default='output_dir',
+                    help='output_path, path to store output, default: None')
+args = parser.parse_args()
+
+
+def convert(img):
+    img = np.swapaxes(img, 0, 1)
+    img = np.swapaxes(img, 1, 2)
+    return img
+
+
+def post_process(img):
+    img = convert(img)
+    img = (img + 1.0) / 2.0
+    img = np.clip(img, 0, 1.0)
+    return img
+
+
+if __name__ == "__main__":
+    bin_path = args.bin_path
+    image_size = args.image_size
+    content_list = os.listdir(args.bin_path)
+
+    for i in range(0, len(content_list)):
+        pic_path = os.path.join(args.bin_path, content_list[i])
+        b = np.fromfile(pic_path, dtype=np.float32, count=8 * 3 * image_size * image_size)
+        b = np.reshape(b, (8, 3, image_size, image_size))
+        # get output images
+        output = ()
+        style_shifted = convert(b[0])
+        std = np.array([0.229, 0.224, 0.225]).reshape([1, 1, 3])
+        mean = np.array([0.485, 0.456, 0.406]).reshape([1, 1, 3])
+        style = style_shifted * std + mean
+        style = np.clip(style, 0, 1.0)
+        output = (output + (style,))
+        for j in range(1, 7):
+            output = ((post_process(b[j]),) + output)
+        print(b[7].max())
+        print(b[7].min())
+        original = post_process(b[7])
+        output = ((original,) + output)
+        # save images
+        content_name = content_list[i].replace(".bin", "")
+        image_path_interpolation = os.path.join(args.output_dir_interpolation, content_name) + ".png"
+        output_interpolation = np.concatenate(output, axis=1)
+        imageio.imsave(image_path_interpolation, output_interpolation)
+
+        image_path = os.path.join(args.output_dir, content_name) + ".png"
+        output_single = np.concatenate((output[0], output[7], output[6]), axis=1)
+        imageio.imsave(image_path, output_single)
+        print("%d / %d , %s \n" % (i + 1, len(content_list), content_name))
diff --git a/research/cv/ArbitraryStyleTransfer/preprocess.py b/research/cv/ArbitraryStyleTransfer/preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..8781832993bdde27942dcd9bcb7a251be9612307
--- /dev/null
+++ b/research/cv/ArbitraryStyleTransfer/preprocess.py
@@ -0,0 +1,77 @@
+# Copyright 2022 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.
+# ============================================================================
+"""preprocess"""
+import argparse
+import os
+
+import numpy as np
+from PIL import Image
+
+parser = argparse.ArgumentParser('preprocess')
+parser.add_argument("--content_path", type=str, help='content_path, default: None')
+parser.add_argument("--style_path", type=str, help='style_path, default: None')
+parser.add_argument("--image_size", type=int, default=256, help='image size, default: image_size.')
+parser.add_argument('--output_path', type=str, default="./preprocess_Result", help='eval data dir')
+args = parser.parse_args()
+
+if __name__ == "__main__":
+
+    if not os.path.exists(args.output_path):
+        os.makedirs(args.output_path)
+    image_dir_style = os.path.join(args.output_path, "style")
+    if not os.path.exists(image_dir_style):
+        os.makedirs(image_dir_style)
+    image_dir_content = os.path.join(args.output_path, "content")
+    if not os.path.exists(image_dir_content):
+        os.makedirs(image_dir_content)
+    content_list = os.listdir(args.content_path)
+    for j in range(0, len(content_list)):
+        pic_path = os.path.join(args.content_path, content_list[j])
+        # content_pic = crop_and_resize(pic_path, size=256)
+        img_c = Image.open(pic_path).convert("RGB")
+        img_c = np.array(img_c.resize((256, 256)))
+        img_c = (img_c / 127.5) - 1.0
+        img_c = img_c.transpose(2, 0, 1).astype(np.float32)
+        img_c = np.reshape(img_c, (1, 3, 256, 256))
+        file_name = content_list[j].replace(".jpg", "") + ".bin"
+        image_path_content = os.path.join(args.output_path, "content", file_name)
+        content_pic_shifted = img_c.copy()
+        content_pic_shifted += 1
+        content_pic_shifted /= 2
+        content_pic_shifted -= np.array([0.485, 0.456, 0.406]).reshape([1, 3, 1, 1])
+        content_pic_shifted /= np.array([0.229, 0.224, 0.225]).reshape([1, 3, 1, 1])
+        img_c = np.reshape(img_c, (1, 1, 3, 256, 256))
+        content_pic_shifted = np.reshape(content_pic_shifted, (1, 1, 3, 256, 256))
+        output = np.concatenate((img_c, content_pic_shifted), axis=0)
+        output.tofile(image_path_content)
+
+    style_list = os.listdir(args.style_path)
+
+    for j in range(0, len(style_list)):
+        pic_path = os.path.join(args.style_path, style_list[j])
+        img_c = Image.open(pic_path).convert("RGB")
+        img_c = np.array(img_c.resize((256, 256)))
+        img_c = (img_c / 127.5) - 1.0
+        img_c = img_c.transpose(2, 0, 1).astype(np.float32)
+        img_c = np.reshape(img_c, (1, 3, 256, 256))
+        style_pic = img_c
+        style_pic += 1
+        style_pic /= 2
+        style_pic -= np.array([0.485, 0.456, 0.406]).reshape([1, 3, 1, 1])
+        style_pic /= np.array([0.229, 0.224, 0.225]).reshape([1, 3, 1, 1])
+        file_name = style_list[j].replace(".jpg", "") + ".bin"
+        image_path_style = os.path.join(args.output_path, "style", file_name)
+        style_pic.tofile(image_path_style)
+    print("Export bin files finished!")
diff --git a/research/cv/ArbitraryStyleTransfer/scripts/run_infer_310.sh b/research/cv/ArbitraryStyleTransfer/scripts/run_infer_310.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f1d25395c97706e4a950e4500416f96a4bdc551f
--- /dev/null
+++ b/research/cv/ArbitraryStyleTransfer/scripts/run_infer_310.sh
@@ -0,0 +1,118 @@
+#!/bin/bash
+# Copyright 2022 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.
+# ============================================================================
+if [[ $# -lt 3 || $# -gt 4 ]]; then
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [CONTENT_PATH] [STYLE_PATH] [DEVICE_ID]
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+get_real_path(){
+
+  if [ "${1:0:1}" == "/" ]; then
+        echo "$1"
+    else
+        echo "$(realpath -m $PWD/$1)"
+    fi
+}
+model=$(get_real_path $1)
+content_path=$(get_real_path $2)
+style_path=$(get_real_path $3)
+device_id=0
+if [ $# == 4 ]; then
+    device_id=$4
+fi
+echo "mindir name: "$model
+echo "content path: "$content_path
+echo "style path: "$style_path
+echo "device id: "$device_id
+
+export ASCEND_HOME=/usr/local/Ascend/
+if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
+    export PATH=$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
+    export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
+    export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
+    export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
+else
+    export PATH=$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
+    export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
+    export PYTHONPATH=$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
+    export ASCEND_OPP_PATH=$ASCEND_HOME/opp
+fi
+
+function preprocess_data()
+{
+   if [ -d preprocess_Result ]; then
+       rm -rf ./preprocess_Result
+    fi
+    mkdir preprocess_Result
+    python3.7 ../preprocess.py --content_path $content_path --style_path $style_path --output_path='./preprocess_Result/'
+}
+function compile_app()
+{
+    cd ../ascend310_infer/ || exit
+    bash build.sh &> build.log
+}
+
+function infer()
+{
+    cd - || exit
+    if [ -d result_Files ]; then
+        rm -rf ./result_Files
+    fi
+    if [ -d time_Result ]; then
+        rm -rf ./time_Result
+    fi
+    mkdir result_Files
+    mkdir time_Result
+
+    ../ascend310_infer/out/main --mindir_path=$model --input_path=./preprocess_Result --device_id=$device_id &> infer.log
+}
+
+function post_process()
+{
+    if [ -d postprocess_Result ]; then
+       rm -rf ./postprocess_Result
+    fi
+    mkdir postprocess_Result
+    if [ -d postprocess_Result_interpolation ]; then
+       rm -rf ./postprocess_Result_interpolation
+    fi
+    mkdir postprocess_Result_interpolation
+    python3.7 ../postprocess.py --bin_path='./result_Files' --output_dir='./postprocess_Result/' --output_dir_interpolation='./postprocess_Result_interpolation/' &> postprocess.log
+}
+
+preprocess_data
+if [ $? -ne 0 ]; then
+    echo "preprocess dataset failed"
+    exit 1
+fi
+
+compile_app
+if [ $? -ne 0 ]; then
+    echo "compile app code failed"
+    exit 1
+fi
+
+infer
+if [ $? -ne 0 ]; then
+    echo " execute inference failed"
+    exit 1
+fi
+
+post_process
+if [ $? -ne 0 ]; then
+    echo " execute post_process failed"
+    exit 1
+fi
\ No newline at end of file
diff --git a/research/cv/ArbitraryStyleTransfer/src/model.py b/research/cv/ArbitraryStyleTransfer/src/model.py
index 1d184cb2011eacd4a6a695bbcc666c0ada0a8a5e..3876e6fba159465240e38bf4780c89b3971d77b5 100644
--- a/research/cv/ArbitraryStyleTransfer/src/model.py
+++ b/research/cv/ArbitraryStyleTransfer/src/model.py
@@ -47,6 +47,7 @@ class style_transfer_model(nn.Cell):
         super(style_transfer_model, self).__init__()
         self.style_prediction_network = networks.style_prediction_network(args, style_dim=style_dim)
         self.style_transfer_network = networks.style_transfer_network(args, style_dim=style_dim)
+        self.concat_ops = ops.Concat(axis=0)
 
     def construct(self, content_img, style_feat):
         """construct"""
@@ -66,6 +67,10 @@ class style_transfer_model(nn.Cell):
         stylied_img_6 = self.style_transfer_network(content_img, style_vector_1 * 0.0 + style_vector_2 * 1.0)
         out = [stylied_img_1, stylied_img_2, stylied_img_3, stylied_img_4, stylied_img_5, stylied_img_6]
         return out
+    def construct_interpolation_310(self, content_img, style_feat_1, style_feat_2):
+        """construct_interpolation for 310"""
+        out = self.construct_interpolation(content_img, style_feat_1, style_feat_2)
+        return self.concat_ops(out)
 
 
 class TrainOnestepStyleTransfer(nn.Cell):
diff --git a/research/cv/ArbitraryStyleTransfer/src/networks.py b/research/cv/ArbitraryStyleTransfer/src/networks.py
index a849a9e4a1ce7d8007cfdd23a961ee8bb490deee..f893d1e2513fabe70c3e76a97b4ac1871a484dfd 100644
--- a/research/cv/ArbitraryStyleTransfer/src/networks.py
+++ b/research/cv/ArbitraryStyleTransfer/src/networks.py
@@ -15,10 +15,12 @@
 
 
 """arbitrary style transfer network."""
+import numpy as np
+import mindspore
 import mindspore.nn as nn
 from mindspore import dtype as mstype
 from mindspore import Parameter
-from mindspore import ops
+from mindspore import ops, Tensor
 from mindspore.common import initializer as init
 
 
@@ -37,18 +39,24 @@ class InstanceNorm2d(nn.Cell):
                  beta_init='zeros'):
         super().__init__()
         self.num_features = num_features
-        self.eps = eps
         self.moving_mean = Parameter(init.initializer('zeros', num_features), name="mean", requires_grad=False)
         self.moving_variance = Parameter(init.initializer('ones', num_features), name="variance", requires_grad=False)
         self.gamma = Parameter(init.initializer(gamma_init, num_features), name="gamma", requires_grad=affine)
         self.beta = Parameter(init.initializer(beta_init, num_features), name="beta", requires_grad=affine)
-
+        self.sqrt = ops.Sqrt()
+        self.eps = Tensor(np.array([eps]), mindspore.float32)
+        self.cast = ops.Cast()
     def construct(self, x):
         """calculate InstanceNorm output"""
         mean = ops.ReduceMean(keep_dims=True)(x, (2, 3))
-        var = ops.ReduceMean(keep_dims=True)(((x - mean) ** 2), (2, 3))
-        std = (var + self.eps) ** 0.5
-        x = (x - mean) / std * self.gamma.reshape(1, -1, 1, 1) + self.beta.reshape(1, -1, 1, 1)
+        mean = self.cast(mean, mindspore.float32)
+        tmp = x - mean
+        tmp = tmp * tmp
+        var = ops.ReduceMean(keep_dims=True)(tmp, (2, 3))
+        std = self.sqrt(var+ self.eps)
+        gamma_t = self.cast(self.gamma, mindspore.float32)
+        beta_t = self.cast(self.beta, mindspore.float32)
+        x = (x - mean) / std * gamma_t.reshape(1, -1, 1, 1) + beta_t.reshape(1, -1, 1, 1)
         return x
 
 
@@ -273,6 +281,7 @@ class UpsampleConvInReluWithStyle(nn.Cell):
             self.instancenorm = InstanceNorm2d(channels_out)
         self.fc_beta = nn.Dense(style_dim, channels_out)
         self.fc_gamma = nn.Dense(style_dim, channels_out)
+        self.cast = ops.Cast()
         if activation:
             self.activation = activation()
         else:
@@ -290,7 +299,8 @@ class UpsampleConvInReluWithStyle(nn.Cell):
         gamma = self.fc_gamma(style)
         gamma = expand_dims(gamma, 2)
         gamma = expand_dims(gamma, 3)
-
+        gamma = self.cast(gamma, mindspore.float32)
+        beta = self.cast(beta, mindspore.float16)
         if self.upsample:
             x = self.upsample_layer(x, scale_factor=self.upsample)
         x = self.pad(x)