simple_pose 310 infer

modified: README.md modified: ascend310_infer/CMakeLists.txt modified: postprocess.py modified: preprocess.py
2021-06-18 10:18:17 +08:00 · 2021-06-18 10:18:17 +08:00 · 47950370c3
parent 48e384c5bd
commit 47950370c3
11 changed files with 692 additions and 0 deletions
--- a/model_zoo/official/cv/simple_pose/README.md
+++ b/model_zoo/official/cv/simple_pose/README.md
@ -17,6 +17,10 @@
        - [Training](#training)
        - [Distributed Training](#distributed-training)
    - [Evaluation Process](#evaluation-process)
+    - [Inference Process](#inference-process)
+        - [Export MindIR](#export-mindir)
+        - [Infer on Ascend310](#infer-on-ascend310)
+        - [result](#result)
 - [Model Description](#model-description)
    - [Performance](#performance)
 - [Description of Random Situation](#description-of-random-situation)
@ -365,6 +369,38 @@ Total boxes: 104125
 ...
 ```

+## Inference Process
+
+### [Export MindIR](#contents)
+
+```shell
+python export.py
+```
+
+The `TEST.MODEL_FILE` parameter is required
+`FILE_FORMAT` should be in ["AIR", "MINDIR"]
+
+### Infer on Ascend310
+
+Before performing inference, the mindir file must be exported by `export.py` script. We only provide an example of inference using MINDIR model.
+When the network processes datasets, if the last batch is insufficient, it will not be automatically supplemented, in a nutshell, batch_Size set to 1 will go better.
+
+```shell
+# Ascend310 inference
+bash run_infer_310.sh [MINDIR_PATH] [NEED_PREPROCESS] [DEVICE_ID]
+```
+
+- `NEED_PREPROCESS` means weather the dataset is processed in binary format, it's value is 'y' or 'n'.
+- `DEVICE_ID` is optional, default value is 0.
+
+### result
+
+Inference result is saved in current path, you can find result like this in acc.log file.
+
+```bash
+AP: 0.7036180026660003
+```
+
 # [Model Description](#contents)

 ## [Performance](#contents)
--- a/model_zoo/official/cv/simple_pose/ascend310_infer/CMakeLists.txt
+++ b/model_zoo/official/cv/simple_pose/ascend310_infer/CMakeLists.txt
@ -0,0 +1,14 @@
+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)
--- a/model_zoo/official/cv/simple_pose/ascend310_infer/build.sh
+++ b/model_zoo/official/cv/simple_pose/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="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make
--- a/model_zoo/official/cv/simple_pose/ascend310_infer/inc/utils.h
+++ b/model_zoo/official/cv/simple_pose/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
--- a/model_zoo/official/cv/simple_pose/ascend310_infer/src/main.cc
+++ b/model_zoo/official/cv/simple_pose/ascend310_infer/src/main.cc
@ -0,0 +1,130 @@
+/**
+ * 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, "", "mindir path");
+DEFINE_string(input0_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;
+}
--- a/model_zoo/official/cv/simple_pose/ascend310_infer/src/utils.cc
+++ b/model_zoo/official/cv/simple_pose/ascend310_infer/src/utils.cc
@ -0,0 +1,129 @@
+/**
+ * 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;
+}
--- a/model_zoo/official/cv/simple_pose/default_config.yaml
+++ b/model_zoo/official/cv/simple_pose/default_config.yaml
@ -69,6 +69,14 @@ TEST:
    BBOX_THRE: 1.0
    IMAGE_THRE: 0.0
    NMS_THRE: 1.0
+#export-related
+EXPORT:
+    FILE_NAME: 'simple_pose'
+    FILE_FORMAT: 'MINDIR'
+#310 infer-related
+INFER:
+    PRE_RESULT_PATH: './preprocess_Result'
+    POST_RESULT_PATH: './result_Files'
 ---

 # Help description for each configuration
--- a/model_zoo/official/cv/simple_pose/export.py
+++ b/model_zoo/official/cv/simple_pose/export.py
@ -0,0 +1,41 @@
+# 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.
+# ============================================================================
+import numpy as np
+
+from mindspore import Tensor, float32, context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
+
+from src.model import get_pose_net
+from src.model_utils.config import config
+from src.model_utils.device_adapter import get_device_id
+
+if __name__ == '__main__':
+    # set context
+    device_id = get_device_id()
+    context.set_context(mode=context.GRAPH_MODE,
+                        device_target="Ascend", save_graphs=False, device_id=device_id)
+
+    # init model
+    model = get_pose_net(config, is_train=False)
+    model.set_train(False)
+
+    # load parameters
+    ckpt_file = config.TEST.MODEL_FILE
+    print('loading model ckpt from {}'.format(ckpt_file))
+    load_param_into_net(model, load_checkpoint(ckpt_file))
+
+    input_shape = [config.TEST.BATCH_SIZE, 3, config.MODEL.IMAGE_SIZE[1], config.MODEL.IMAGE_SIZE[0]]
+    input_ids = Tensor(np.zeros(input_shape), float32)
+    export(model, input_ids, file_name=config.EXPORT.FILE_NAME, file_format=config.EXPORT.FILE_FORMAT)
--- a/model_zoo/official/cv/simple_pose/postprocess.py
+++ b/model_zoo/official/cv/simple_pose/postprocess.py
@ -0,0 +1,89 @@
+# 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.
+# ============================================================================
+import os
+import numpy as np
+
+from src.evaluate.coco_eval import evaluate
+from src.utils.transform import flip_back
+from src.predict import get_final_preds
+from src.dataset import flip_pairs
+from src.model_utils.config import config
+
+
+def get_acc():
+    '''calculate accuracy'''
+    ckpt_file = config.TEST.MODEL_FILE
+    output_dir = ckpt_file.split('.')[0]
+    if config.enable_modelarts:
+        output_dir = config.output_path
+    cfg = config
+
+    # init record
+    file_num = len(os.listdir(config.INFER.POST_RESULT_PATH)) // 2
+    num_samples = file_num * cfg.TEST.BATCH_SIZE
+    all_preds = np.zeros((num_samples, cfg.MODEL.NUM_JOINTS, 3),
+                         dtype=np.float32)
+    all_boxes = np.zeros((num_samples, 2))
+    image_id = []
+    idx = 0
+    bs = config.TEST.BATCH_SIZE
+    h, w = config.POSE_RESNET.HEATMAP_SIZE[1], config.POSE_RESNET.HEATMAP_SIZE[0]
+    shape = [bs, config.MODEL.NUM_JOINTS, h, w]
+
+    for i in range(file_num):
+        f = os.path.join(config.INFER.POST_RESULT_PATH, "sp_bs" + str(bs) + "_" + str(i) + "_0.bin")
+        output = np.fromfile(f, np.float32).reshape(shape)
+        if cfg.TEST.FLIP_TEST:
+            f = os.path.join(config.INFER.POST_RESULT_PATH, "sp_flip_bs" + str(bs) + "_" + str(i) + "_0.bin")
+            output_flipped = np.fromfile(f, np.float32).reshape(shape)
+            output_flipped = flip_back(output_flipped, flip_pairs)
+
+            # feature is not aligned, shift flipped heatmap for higher accuracy
+            if cfg.TEST.SHIFT_HEATMAP:
+                output_flipped[:, :, :, 1:] = \
+                    output_flipped.copy()[:, :, :, 0:-1]
+
+            output = (output + output_flipped) * 0.5
+
+        # meta data
+        center_path = os.path.join(config.INFER.PRE_RESULT_PATH, "center")
+        scale_path = os.path.join(config.INFER.PRE_RESULT_PATH, "scale")
+        score_path = os.path.join(config.INFER.PRE_RESULT_PATH, "score")
+        id_path = os.path.join(config.INFER.PRE_RESULT_PATH, "id")
+        file_name = "sp_bs" + str(bs) + "_" + str(i) + ".npy"
+        c = np.load(os.path.join(center_path, file_name))
+        s = np.load(os.path.join(scale_path, file_name))
+        score = np.load(os.path.join(score_path, file_name))
+        file_id = np.load(os.path.join(id_path, file_name))
+
+        # pred by heatmaps
+        preds, maxvals = get_final_preds(cfg, output.copy(), c, s)
+        num_images, _ = preds.shape[:2]
+        all_preds[idx:idx + num_images, :, 0:2] = preds[:, :, 0:2]
+        all_preds[idx:idx + num_images, :, 2:3] = maxvals
+        # double check this all_boxes parts
+        all_boxes[idx:idx + num_images, 0] = np.prod(s * 200, 1)
+        all_boxes[idx:idx + num_images, 1] = score
+        image_id.extend(file_id)
+        idx += num_images
+
+
+    print(all_preds[:idx].shape, all_boxes[:idx].shape, len(image_id))
+    _, perf_indicator = evaluate(
+        cfg, all_preds[:idx], output_dir, all_boxes[:idx], image_id)
+    print("AP:", perf_indicator)
+
+if __name__ == '__main__':
+    get_acc()
--- a/model_zoo/official/cv/simple_pose/preprocess.py
+++ b/model_zoo/official/cv/simple_pose/preprocess.py
@ -0,0 +1,64 @@
+# 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.
+# ============================================================================
+import os
+import numpy as np
+
+from src.dataset import keypoint_dataset
+from src.model_utils.config import config
+
+def get_bin():
+    ''' get bin files'''
+    valid_dataset, _ = keypoint_dataset(
+        config,
+        bbox_file=config.TEST.COCO_BBOX_FILE,
+        train_mode=False,
+        num_parallel_workers=config.TEST.DATALOADER_WORKERS,
+    )
+    inputs_path = os.path.join(config.INFER.PRE_RESULT_PATH, "00_data")
+    os.makedirs(inputs_path)
+
+    center_path = os.path.join(config.INFER.PRE_RESULT_PATH, "center")
+    os.makedirs(center_path)
+
+    scale_path = os.path.join(config.INFER.PRE_RESULT_PATH, "scale")
+    os.makedirs(scale_path)
+
+    score_path = os.path.join(config.INFER.PRE_RESULT_PATH, "score")
+    os.makedirs(score_path)
+
+    id_path = os.path.join(config.INFER.PRE_RESULT_PATH, "id")
+    os.makedirs(id_path)
+
+    for i, item in enumerate(valid_dataset.create_dict_iterator(output_numpy=True)):
+        file_name = "sp_bs" + str(config.TEST.BATCH_SIZE) + "_" + str(i) + ".bin"
+        # input data
+        inputs = item['image']
+        inputs_file_path = os.path.join(inputs_path, file_name)
+        inputs.tofile(inputs_file_path)
+        if config.TEST.FLIP_TEST:
+            inputs_flipped = inputs[:, :, :, ::-1]
+            file_name = "sp_flip_bs" + str(config.TEST.BATCH_SIZE) + "_" + str(i) + ".bin"
+            inputs_file_path = os.path.join(inputs_path, file_name)
+            inputs_flipped.tofile(inputs_file_path)
+        file_name = "sp_bs" + str(config.TEST.BATCH_SIZE) + "_" + str(i) + ".npy"
+        np.save(os.path.join(center_path, file_name), item['center'])
+        np.save(os.path.join(scale_path, file_name), item['scale'])
+        np.save(os.path.join(score_path, file_name), item['score'])
+        np.save(os.path.join(id_path, file_name), item['id'])
+    print("=" * 20, "export bin files finished", "=" * 20)
+
+
+if __name__ == '__main__':
+    get_bin()
--- a/model_zoo/official/cv/simple_pose/scripts/run_infer_310.sh
+++ b/model_zoo/official/cv/simple_pose/scripts/run_infer_310.sh
@ -0,0 +1,120 @@
+#!/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 2 || $# -gt 3 ]]; then
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [NEED_PREPROCESS] [DEVICE_ID]
+    NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+
+get_real_path(){
+    if [ "${1:0:1}" == "/" ]; then
+        echo "$1"
+    else
+        echo "$(realpath -m $PWD/$1)"
+    fi
+}
+model=$(get_real_path $1)
+
+if [ "$2" == "y" ] || [ "$2" == "n" ];then
+    need_preprocess=$2
+else
+  echo "weather need preprocess or not, it's value must be in [y, n]"
+  exit 1
+fi
+
+device_id=0
+if [ $# == 3 ]; then
+    device_id=$3
+fi
+
+echo "mindir name: "$model
+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=$ASCEND_HOME/opp
+fi
+
+function preprocess_data()
+{
+    if [ -d preprocess_Result ]; then
+        rm -rf ./preprocess_Result
+    fi
+    mkdir preprocess_Result
+    python3.7 ../preprocess.py
+}
+
+function compile_app()
+{
+    cd ../ascend310_infer || exit
+    bash build.sh &> build.log
+}
+
+function infer()
+{
+    cd - || exit
+    if [ -d result_Files ]; then
+        rm -rf ./result_Files
+    fi
+    if [ -d time_Result ]; then
+        rm -rf ./time_Result
+    fi
+    mkdir result_Files
+    mkdir time_Result
+
+    ../ascend310_infer/out/main --mindir_path=$model --input0_path=./preprocess_Result/00_data --device_id=$device_id &> infer.log
+
+}
+
+function cal_acc()
+{
+    python3.7 ../postprocess.py &> acc.log
+}
+
+if [ $need_preprocess == "y" ]; then
+    preprocess_data
+    if [ $? -ne 0 ]; then
+        echo "preprocess dataset failed"
+        exit 1
+    fi
+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