diff --git a/research/cv/SRGAN/README.md b/research/cv/SRGAN/README.md
index d1bce2c0c4562041a5885513d67d5286339ed680..25e9d87290e28d8753ec23ff279421f728769e46 100644
--- a/research/cv/SRGAN/README.md
+++ b/research/cv/SRGAN/README.md
@@ -9,6 +9,10 @@
     - [Script and Sample Code](#script-and-sample-code)
     - [Script Parameters](#script-parameters)
     - [Training Process](#training-process)
+- [Inference Process](#inference-process)
+    - [Export MindIR](#export-mindir)
+    - [Infer on Ascend310](#infer-on-ascend310)
+    - [Result](#result)
 - [Model Description](#model-description)
     - [Performance](#performance)
         - [Training Performance](#training-performance)  
@@ -123,6 +127,34 @@ eg: sh run_eval.sh ./ckpt/best.ckpt ./Set14/LR ./Set14/HR 0
 
 Evaluation result will be stored in the scripts/result. Under this, you can find generator pictures.
 
+# [Inference Process](#contents)
+
+## [Export MindIR](#contents)
+
+```shell
+python export.py --config_path [CONFIG_PATH] --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
+```
+
+The ckpt_file parameter is required,
+`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
+
+## [Infer on Ascend310](#contents)
+
+Before performing inference, the mindir file must be exported by `export.py` script. We only provide an example of inference using MINDIR model.
+
+```shell
+# Ascend310 inference
+bash run_infer_310.sh [MINDIR_PATH] [TEST_LR_PATH] [TEST_GT_PATH] [NEED_PREPROCESS] [DEVICE_ID]
+```
+
+### [Result](#contents)
+
+Inference result is saved in current path, you can find result like this in acc.log file.
+
+```bash
+'avg psnr': 27.4
+```
+
 # [Model Description](#contents)
 
 ## [Performance](#contents)
diff --git a/research/cv/SRGAN/ascend310_infer/CMakeLists.txt b/research/cv/SRGAN/ascend310_infer/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..49478f950647bd15851ae9b931c04ed190ef9cbf
--- /dev/null
+++ b/research/cv/SRGAN/ascend310_infer/CMakeLists.txt
@@ -0,0 +1,16 @@
+cmake_minimum_required(VERSION 3.14.1)
+project(Ascend310Infer)
+find_package(OpenCV 2 REQUIRED)
+find_package(gflags REQUIRED)
+add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
+set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
+option(MINDSPORE_PATH "mindspore install path" "")
+include_directories(${OpenCV_INCLUDE_DIRS})
+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} ${OpenCV_LIBS} gflags)
diff --git a/research/cv/SRGAN/ascend310_infer/build.sh b/research/cv/SRGAN/ascend310_infer/build.sh
new file mode 100644
index 0000000000000000000000000000000000000000..922df6cb4d87095802306ba30c7741fe64ca79f1
--- /dev/null
+++ b/research/cv/SRGAN/ascend310_infer/build.sh
@@ -0,0 +1,28 @@
+#!/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 
+  mkdir out
+fi
+
+cd out || exit
+
+if [ -f "Makefile" ]; then
+  make clean
+fi
+
+cmake ..  -DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make
diff --git a/research/cv/SRGAN/ascend310_infer/inc/utils.h b/research/cv/SRGAN/ascend310_infer/inc/utils.h
new file mode 100644
index 0000000000000000000000000000000000000000..b052ca372f90e10fbaecde73e2459650e17c8474
--- /dev/null
+++ b/research/cv/SRGAN/ascend310_infer/inc/utils.h
@@ -0,0 +1,32 @@
+/*
+ * 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_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& imageFile, const std::vector<mindspore::MSTensor> &outputs);
+#endif
diff --git a/research/cv/SRGAN/ascend310_infer/src/main.cc b/research/cv/SRGAN/ascend310_infer/src/main.cc
new file mode 100644
index 0000000000000000000000000000000000000000..9e8c1169ca76a972ec3ac1e844cfb6e4a3751cc1
--- /dev/null
+++ b/research/cv/SRGAN/ascend310_infer/src/main.cc
@@ -0,0 +1,131 @@
+/*
+ * 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 <sys/time.h>
+#include <gflags/gflags.h>
+#include <dirent.h>
+#include <iostream>
+#include <string>
+#include <algorithm>
+#include <iosfwd>
+#include <vector>
+#include <fstream>
+#include <sstream>
+
+#include "include/api/model.h"
+#include "include/api/context.h"
+#include "include/api/types.h"
+#include "include/api/serialization.h"
+#include "include/dataset/execute.h"
+#include "include/dataset/vision.h"
+#include "inc/utils.h"
+
+using mindspore::Context;
+using mindspore::Serialization;
+using mindspore::Model;
+using mindspore::Status;
+using mindspore::MSTensor;
+using mindspore::dataset::Execute;
+using mindspore::ModelType;
+using mindspore::GraphCell;
+using mindspore::kSuccess;
+
+DEFINE_string(mindir_path, "./SRGAN_model.mindir", "mindir path");
+DEFINE_string(input0_path, "./scripts/preprocess_path", "input0 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;
+  }
+
+  auto input0_files = GetAllFiles(FLAGS_input0_path);
+
+  if (input0_files.empty()) {
+    std::cout << "ERROR: input data empty." << std::endl;
+    return 1;
+  }
+
+  std::map<double, double> costTime_map;
+  size_t size = input0_files.size();
+
+  for (size_t i = 0; i < size; ++i) {
+    struct timeval start = {0};
+    struct timeval end = {0};
+    double startTimeMs;
+    double endTimeMs;
+    std::vector<MSTensor> inputs;
+    std::vector<MSTensor> outputs;
+    std::cout << "Start predict input files:" << input0_files[i] << std::endl;
+
+    auto input0 = ReadFileToTensor(input0_files[i]);
+
+    inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
+                        input0.Data().get(), input0.DataSize());
+    gettimeofday(&start, nullptr);
+    ret = model.Predict(inputs, &outputs);
+    gettimeofday(&end, nullptr);
+    if (ret != kSuccess) {
+      std::cout << "Predict " << input0_files[i] << " 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));
+    WriteResult(input0_files[i], outputs);
+  }
+  double average = 0.0;
+  int inferCount = 0;
+
+  for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
+    double diff = 0.0;
+    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/SRGAN/ascend310_infer/src/utils.cc b/research/cv/SRGAN/ascend310_infer/src/utils.cc
new file mode 100644
index 0000000000000000000000000000000000000000..4af8a81639144d8a57421256a66d8011dbe5e09f
--- /dev/null
+++ b/research/cv/SRGAN/ascend310_infer/src/utils.cc
@@ -0,0 +1,127 @@
+/*
+ * 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 <fstream>
+#include <algorithm>
+#include <iostream>
+#include "inc/utils.h"
+
+using mindspore::MSTensor;
+using mindspore::DataType;
+
+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& imageFile, 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 = imageFile.rfind('/');
+    std::string fileName(imageFile, pos + 1);
+    fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin");
+    std::string outFileName = homePath + "/" + fileName;
+    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/SRGAN/eval.py b/research/cv/SRGAN/eval.py
index 8ad35559472b734326138944c8f8b04204fa5d5f..8dd9bec7363ae5e12d9e9b07fc0694f08d0a7fc1 100644
--- a/research/cv/SRGAN/eval.py
+++ b/research/cv/SRGAN/eval.py
@@ -24,15 +24,15 @@ from mindspore import context
 import mindspore.ops as ops
 from src.model.generator import Generator
 from src.dataset.testdataset import create_testdataset
-
+from PIL import Image
 
 set_seed(1)
 parser = argparse.ArgumentParser(description="SRGAN eval")
-parser.add_argument("--test_LR_path", type=str, default='/data/Set14/LR')
-parser.add_argument("--test_GT_path", type=str, default='/data/Set14/HR')
+parser.add_argument("--test_LR_path", type=str, default='../Set14/LR')
+parser.add_argument("--test_GT_path", type=str, default='../Set14/HR')
 parser.add_argument("--res_num", type=int, default=16)
 parser.add_argument("--scale", type=int, default=4)
-parser.add_argument("--generator_path", type=str, default='./ckpt/best.ckpt')
+parser.add_argument("--generator_path", type=str, default='./ckpt/pre_trained_model_400.ckpt')
 parser.add_argument("--mode", type=str, default='train')
 parser.add_argument("--device_id", type=int, default=0, help="device id, default: 0.")
 i = 0
@@ -46,12 +46,11 @@ if __name__ == '__main__':
     load_param_into_net(generator, params)
     op = ops.ReduceSum(keep_dims=False)
     psnr_list = []
-
+    i = 0
     print("=======starting test=====")
     for data in test_data_loader:
         lr = data['LR']
         gt = data['HR']
-
         bs, c, h, w = lr.shape[:4]
         gt = gt[:, :, : h * args.scale, : w *args.scale]
 
@@ -67,7 +66,7 @@ if __name__ == '__main__':
         output = output.transpose(1, 2, 0)
         gt = gt.asnumpy()
         gt = gt.transpose(1, 2, 0)
-
+        result = Image.fromarray((output * 255.0).astype(np.uint8))
         y_output = rgb2ycbcr(output)[args.scale:-args.scale, args.scale:-args.scale, :1]
         y_gt = rgb2ycbcr(gt)[args.scale:-args.scale, args.scale:-args.scale, :1]
 
diff --git a/research/cv/SRGAN/export.py b/research/cv/SRGAN/export.py
index 1f325b4ef4d4b7010f91a7299e4bd6eff5e900fa..7001c1e3ef1a4ae60d92549a3283d5fb3d6ab642 100644
--- a/research/cv/SRGAN/export.py
+++ b/research/cv/SRGAN/export.py
@@ -24,9 +24,9 @@ from src.model.generator import Generator
 
 parser = argparse.ArgumentParser(description="SRGAN export")
 parser.add_argument('--file_name', type=str, default='SRGAN', help='output file name prefix.')
-parser.add_argument('--file_format', type=str, choices=['AIR', 'ONNX', 'MINDIR'], default='AIR', \
+parser.add_argument('--file_format', type=str, choices=['AIR', 'ONNX', 'MINDIR'], default='MINDIR', \
                     help='file format')
-parser.add_argument("--generator_path", type=str, default='./scripts/srgan0/src/ckpt/G_model_1000.ckpt')
+parser.add_argument("--generator_path", type=str, default='')
 parser.add_argument("--device_id", type=int, default=0, help="device id, default: 0.")
 
 if __name__ == '__main__':
@@ -35,8 +35,9 @@ if __name__ == '__main__':
     generator = Generator(4)
     params = load_checkpoint(args.generator_path)
     load_param_into_net(generator, params)
-    generator.set_train(True)
-    input_shp = [16, 3, 96, 96]
+    generator.set_train(False)
+    input_shp = [1, 3, 126, 126]
     input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
     G_file = f"{args.file_name}_model"
+    generator(input_array)
     export(generator, input_array, file_name=G_file, file_format=args.file_format)
diff --git a/research/cv/SRGAN/postprocess.py b/research/cv/SRGAN/postprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..af2e76a664db72897a7a2636792a56f2f23305bf
--- /dev/null
+++ b/research/cv/SRGAN/postprocess.py
@@ -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.
+# ============================================================================
+
+"""postprocess"""
+import os
+import argparse
+from PIL import Image
+import numpy as np
+from src.dataset.testdataset import create_testdataset
+from mindspore import context
+from skimage.color import rgb2ycbcr
+from skimage.metrics import peak_signal_noise_ratio
+parser = argparse.ArgumentParser(description="SRGAN eval")
+parser.add_argument("--test_LR_path", type=str, default='/home/SRGANprofile/Set14/LR')
+parser.add_argument("--test_GT_path", type=str, default='/home/SRGANprofile/Set14/HR')
+parser.add_argument("--result_path", type=str, default='./result_Files')
+parser.add_argument("--device_id", type=int, default=0, help="device id, default: 0.")
+parser.add_argument("--scale", type=int, default=4)
+args = parser.parse_args()
+context.set_context(mode=context.GRAPH_MODE, device_id=args.device_id)
+
+def unpadding(img, target_shape):
+    a, b = target_shape[0], target_shape[1]
+    img_h, img_w, _ = img.shape
+    if img_h > a:
+        img = img[:a, :, :]
+    if img_w > b:
+        img = img[:, :b, :]
+    return img
+
+
+if __name__ == '__main__':
+    i = 0
+    args = parser.parse_args()
+    test_ds = create_testdataset(1, args.test_LR_path, args.test_GT_path)
+    test_data_loader = test_ds.create_dict_iterator(output_numpy=True)
+    sr_list = []
+    psnr_list = []
+    for j in range(0, 12):
+        f_name = os.path.join(args.result_path, "SRGAN_data_" + str(j) + "_0.bin")
+        sr = np.fromfile(f_name, np.float32).reshape(3, 800, 800)
+        sr_list.append(sr)
+    for data in test_data_loader:
+        lr = data['LR']
+        sr = sr_list[i]
+        i = i+1
+        gt = data['HR']
+        bs, c, h, w = lr.shape[:4]
+        gt = gt[:, :, : h * 4, : w *4]
+        gt = gt[0]
+        gt = (gt + 1.0) / 2.0
+        gt = gt.transpose(1, 2, 0)
+        output = sr.transpose(1, 2, 0)
+        output = unpadding(output, gt.shape)
+        output = (output + 1.0) / 2.0
+        result = Image.fromarray((output * 255.0).astype(np.uint8))
+        y_output = rgb2ycbcr(output)[args.scale:-args.scale, args.scale:-args.scale, :1]
+        y_gt = rgb2ycbcr(gt)[args.scale:-args.scale, args.scale:-args.scale, :1]
+        psnr = peak_signal_noise_ratio(y_output / 255.0, y_gt / 255.0, data_range=1.0)
+        psnr = float('%.2f' % psnr)
+        psnr_list.append(psnr)
+    x = np.mean(psnr_list)
+    x = float('%.2f' % x)
+    print("avg PSNR:", x)
+ 
\ No newline at end of file
diff --git a/research/cv/SRGAN/preprocess.py b/research/cv/SRGAN/preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..19a5557b63c35974cdea0c529a2c4b4e7ccc56e9
--- /dev/null
+++ b/research/cv/SRGAN/preprocess.py
@@ -0,0 +1,57 @@
+# 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.
+# ============================================================================
+
+"""preprocess"""
+import os
+import argparse
+import numpy as np
+from mindspore import context
+from src.dataset.testdataset import create_testdataset
+
+parser = argparse.ArgumentParser(description="SRGAN eval")
+parser.add_argument("--test_LR_path", type=str, default='./Set14/LR')
+parser.add_argument("--test_GT_path", type=str, default='./Set14/HR')
+parser.add_argument("--result_path", type=str, default='./preprocess_path')
+parser.add_argument("--device_id", type=int, default=1, help="device id, default: 0.")
+args = parser.parse_args()
+context.set_context(mode=context.GRAPH_MODE, device_id=args.device_id)
+
+def padding(_img, target_shape):
+    h, w = target_shape[0], target_shape[1]
+    img_h, img_w, _ = _img.shape
+    dh, dw = h - img_h, w - img_w
+    if dh < 0 or dw < 0:
+        raise RuntimeError(f"target_shape is bigger than img.shape, {target_shape} > {_img.shape}")
+    if dh != 0 or dw != 0:
+        _img = np.pad(_img, ((0, dh), (0, dw), (0, 0)), "constant")
+    return _img
+if __name__ == '__main__':
+    test_ds = create_testdataset(1, args.test_LR_path, args.test_GT_path)
+    test_data_loader = test_ds.create_dict_iterator(output_numpy=True)
+    i = 0
+    img_path = args.result_path
+    if not os.path.exists(img_path):
+        os.makedirs(img_path)
+    for data in test_data_loader:
+        file_name = "SRGAN_data"  + "_" + str(i) + ".bin"
+        file_path = img_path + "/" + file_name
+        lr = data['LR']
+        lr = lr[0]
+        lr = lr.transpose(1, 2, 0)
+        org_img = padding(lr, [200, 200])
+        org_img = org_img.transpose(2, 0, 1)
+        img = org_img.copy()
+        img.tofile(file_path)
+        i = i + 1
diff --git a/research/cv/SRGAN/scripts/run_infer_310.sh b/research/cv/SRGAN/scripts/run_infer_310.sh
new file mode 100644
index 0000000000000000000000000000000000000000..2f0d65d63c88d48220e3c86bdb128cdac00a986f
--- /dev/null
+++ b/research/cv/SRGAN/scripts/run_infer_310.sh
@@ -0,0 +1,122 @@
+#!/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 [[ $# -lt 4 || $# -gt 5 ]]; then
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [TEST_LR_PATH] [TEST_GT_PATH] [NEED_PREPROCESS] [DEVICE_ID]
+    NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+
+get_real_path(){
+    if [ "${1:0:1}" == "/" ]; then
+        echo "$1"
+    else
+        echo "$(realpath -m $PWD/$1)"
+    fi
+}
+model=$(get_real_path $1)
+test_LR_path=$(get_real_path $2)
+test_GT_path=$(get_real_path $3)
+
+if [ "$4" == "y" ] || [ "$4" == "n" ];then
+    need_preprocess=$4
+else
+  echo "weather need preprocess or not, it's value must be in [y, n]"
+  exit 1
+fi
+
+device_id=0
+if [ $# == 5 ]; then
+    device_id=$5
+fi
+
+echo "mindir name: "$model
+echo "test_LR_path: "$test_LR_path
+echo "test_GT_path: "$test_GT_path
+echo "need preprocess: "$need_preprocess
+echo "device id: "$device_id
+
+export ASCEND_HOME=/usr/local/Ascend/
+if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
+    export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
+    export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$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=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
+    export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
+    export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
+else
+    export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
+    export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/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/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
+    export ASCEND_OPP_PATH=$ASCEND_HOME/opp
+fi
+
+function preprocess_data()
+{
+    if [ -d preprocess_path ]; then
+        rm -rf ./preprocess_path
+    fi
+    mkdir preprocess_path
+    python3.7 ../preprocess.py --test_LR_path=$test_LR_path --test_GT_path=$test_GT_path  --result_path=./preprocess_path/
+}
+
+function compile_app()
+{
+    cd ../ascend310_infer || exit
+    bash build.sh &> build.log
+}
+
+function infer()
+{
+    cd - || exit
+    if [ -d result_Files ]; then
+        rm -rf ./result_Files
+    fi
+    if [ -d time_Result ]; then
+        rm -rf ./time_Result
+    fi
+    mkdir result_Files
+    mkdir time_Result
+
+    ../ascend310_infer/out/main --mindir_path=$model --input0_path=./preprocess_path --device_id=$device_id &> infer.log
+
+}
+
+function cal_acc()
+{
+    if [ -d infer_output ]; then
+        rm -rf ./infer_output
+    fi
+    mkdir infer_output
+    python3.7 ../postprocess.py --test_LR_path=$test_LR_path --test_GT_path=$test_GT_path --device_id=$device_id &> acc.log
+}
+
+if [ $need_preprocess == "y" ]; then
+    preprocess_data
+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
+cal_acc
+if [ $? -ne 0 ]; then
+    echo "calculate accuracy failed"
+    exit 1
+fi
\ No newline at end of file
diff --git a/research/cv/SRGAN/src/dataset/testdataset.py b/research/cv/SRGAN/src/dataset/testdataset.py
index 1fda8fe3c8296f31e37b4369624e4bf3fb819760..256d329cc93625c20a4d63565c5551199d2b01fa 100644
--- a/research/cv/SRGAN/src/dataset/testdataset.py
+++ b/research/cv/SRGAN/src/dataset/testdataset.py
@@ -57,8 +57,10 @@ class mydata:
         return  img_item['LR'], img_item['GT']
 
 def create_testdataset(batchsize, LR_path, GT_path):
-    """create testdataset"""
+    """create testdataset
+    # noqa: DAR201
+    """
     dataset = mydata(LR_path, GT_path, in_memory=False)
-    DS = ds.GeneratorDataset(dataset, column_names=["LR", "HR"])
-    DS = DS.batch(batchsize)
-    return DS
+    dataloader = ds.GeneratorDataset(dataset, column_names=["LR", "HR"], shuffle=False)
+    dataloader = dataloader.batch(batchsize)
+    return dataloader
diff --git a/research/cv/SRGAN/src/dataset/traindataset.py b/research/cv/SRGAN/src/dataset/traindataset.py
index 64d02e9c1d69debf7e354d504b7965ec327d4737..4dea59e269205bb5bc7aaaa7727cb16505a7569d 100644
--- a/research/cv/SRGAN/src/dataset/traindataset.py
+++ b/research/cv/SRGAN/src/dataset/traindataset.py
@@ -18,6 +18,7 @@
 import os
 import random
 import math
+import multiprocessing
 import numpy as np
 from PIL import Image
 import mindspore.dataset as ds
@@ -110,18 +111,26 @@ class MySampler():
 
 def create_traindataset(batchsize, LR_path, GT_path):
     """"create SRGAN dataset"""
+
+    device_num = int(os.getenv("RANK_SIZE", "1"))
+    rank_id = int(os.getenv("RANK_ID", "0"))
+    cores = multiprocessing.cpu_count()
+    num_parallel_workers = int(cores / device_num)
     parallel_mode = context.get_auto_parallel_context("parallel_mode")
     if parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]:
         dataset = mydata(LR_path, GT_path, in_memory=True)
-        sampler = MySampler(dataset, local_rank=0, world_size=4)
-        device_num = int(os.getenv("RANK_SIZE"))
-        rank_id = int(os.getenv("DEVICE_ID"))
-        sampler = MySampler(dataset, local_rank=rank_id, world_size=4)
-        DS = ds.GeneratorDataset(dataset, column_names=['LR', 'HR'], shuffle=True,
-                                 num_shards=device_num, shard_id=rank_id, sampler=sampler)
-        DS = DS.batch(batchsize, drop_remainder=True)
+        sampler = MySampler(dataset, local_rank=rank_id, world_size=device_num)
+        dataloader = ds.GeneratorDataset(dataset, column_names=['LR', 'HR'], shuffle=True,
+                                         num_shards=device_num, shard_id=rank_id, sampler=sampler,
+                                         python_multiprocessing=True,
+                                         num_parallel_workers=min(12, num_parallel_workers)
+                                         )
+        dataloader = dataloaderS.batch(batchsize, drop_remainder=True,
+                                       num_parallel_workers=min(8, num_parallel_workers))
     else:
         dataset = mydata(LR_path, GT_path, in_memory=True)
-        DS = ds.GeneratorDataset(dataset, column_names=['LR', 'HR'], shuffle=True)
-        DS = DS.batch(batchsize)
-    return DS
+        dataloader = ds.GeneratorDataset(dataset, column_names=['LR', 'HR'], shuffle=True,
+                                         python_multiprocessing=True,
+                                         num_parallel_workers=min(12, num_parallel_workers))
+        dataloader = dataloader.batch(batchsize, drop_remainder=True, num_parallel_workers=min(8, num_parallel_workers))
+    return dataloader