!17181 ascend 310 inference for xception

From: @yuzhenhua666
Reviewed-by: @c_34,@oacjiewen
Signed-off-by: @c_34

model_zoo/official/cv/xception/README.md

@@ -18,6 +18,9 @@
         - [Usage](#usage-1)
         - [Launch](#launch-1)
         - [Result](#result-1)
+    - [Export Process](#Export-process)
+    - [Inference Process](#Inference-process)
+        - [Inference](#Inference)
 - [Model description](#model-description)
     - [Performance](#performance)
         - [Training Performance](#training-performance)
@@ -73,12 +76,14 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 .
 └─Xception
   ├─README.md
+  ├─ascend310_infer                #application for 310 inference
   ├─scripts
     ├─run_standalone_train.sh      # launch standalone training with ascend platform(1p)
     ├─run_distribute_train.sh      # launch distributed training with ascend platform(8p)
     ├─run_train_gpu_fp32.sh        # launch standalone or distributed fp32 training with gpu platform(1p or 8p)
     ├─run_train_gpu_fp16.sh        # launch standalone or distributed fp16 training with gpu platform(1p or 8p)
     ├─run_eval.sh                  # launch evaluating with ascend platform
+    ├─run_infer_310.sh             # shell script for 310 inference
     └─run_eval_gpu.sh              # launch evaluating with gpu platform
   ├─src
     ├─config.py                    # parameter configuration
@@ -87,6 +92,7 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
     ├─loss.py                      # Customized CrossEntropy loss function
     └─lr_generator.py              # learning rate generator
   ├─train.py                       # train net
+  ├─postprogress.py                # post process for 310 inference
   ├─export.py                      # export net
   └─eval.py                        # eval net
 
@@ -176,6 +182,9 @@ sh scripts/run_train_gpu_fp16.sh 1 DATASET_PATH PRETRAINED_CKPT_PATH(optional)
 
 # infer example
 sh run_eval_gpu.sh DEVICE_ID DATASET_PATH CHECKPOINT_PATH
+
+#ascend310 infer example
+sh run_infer_310.sh MINDIR_PATH DATA_PATH LABEL_FILE DEVICE_ID
 ```
 
 > Notes: RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html), and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
@@ -274,6 +283,34 @@ result: {'Loss': 1.7797744848789312, 'Top_1_Acc': 0.7985777243589743, 'Top_5_Acc
 result: {'Loss': 1.7846775874590903, 'Top_1_Acc': 0.798735595390525, 'Top_5_Acc': 0.9498439500640204}
 ```
 
+## [Export process](#contents)
+
+```shell
+python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_format[EXPORT_FORMAT] --batch_size [BATCH_SIZE]
+```
+
+`EXPORT_FORMAT` should be in ["AIR",  "MINDIR"]
+
+## [Inference process](#contents)
+
+### Inference
+
+Before performing inference, we need to export model first. Air model can only be exported in Ascend 910 environment, mindir model can be exported in any environment.
+Current batch_ size can only be set to 1.
+
+```shell
+# Ascend310 inference
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [LABEL_FILE] [DEVICE_ID]
+```
+
+-Note: the Imagenet data set is used in densnet121 network. The label of the picture is the number from 0 after sorting the folder.
+
+Inference result will be stored in the script path, you can find result like the followings in acc.log.
+
+```shell
+Top_1_Acc: 0.79886%, Top_5_Acc: 0.94882%
+```
+
 # [Model description](#contents)
 
 ## [Performance](#contents)

model_zoo/official/cv/xception/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} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
+set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
+option(MINDSPORE_PATH "mindspore install path" "")
+include_directories(${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)

model_zoo/official/cv/xception/ascend310_infer/build.sh

@@ -0,0 +1,23 @@
+#!/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
+cmake .. \
+    -DMINDSPORE_PATH="`pip show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
+make

model_zoo/official/cv/xception/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

model_zoo/official/cv/xception/ascend310_infer/src/main.cc

@@ -0,0 +1,154 @@
+/**
+ * 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 "../inc/utils.h"
+#include "include/dataset/execute.h"
+#include "include/dataset/transforms.h"
+#include "include/dataset/vision.h"
+#include "include/dataset/vision_ascend.h"
+#include "include/api/types.h"
+#include "include/api/model.h"
+#include "include/api/serialization.h"
+#include "include/api/context.h"
+
+using mindspore::Serialization;
+using mindspore::Model;
+using mindspore::Context;
+using mindspore::Status;
+using mindspore::ModelType;
+using mindspore::Graph;
+using mindspore::GraphCell;
+using mindspore::kSuccess;
+using mindspore::MSTensor;
+using mindspore::DataType;
+using mindspore::dataset::Execute;
+using mindspore::dataset::TensorTransform;
+using mindspore::dataset::vision::Decode;
+using mindspore::dataset::vision::Resize;
+using mindspore::dataset::vision::CenterCrop;
+using mindspore::dataset::vision::Normalize;
+using mindspore::dataset::vision::HWC2CHW;
+
+using mindspore::dataset::transforms::TypeCast;
+
+
+DEFINE_string(model_path, "", "model path");
+DEFINE_string(dataset_path, ".", "dataset path");
+DEFINE_int32(device_id, 0, "device id");
+
+int main(int argc, char **argv) {
+    gflags::ParseCommandLineFlags(&argc, &argv, true);
+    if (RealPath(FLAGS_model_path).empty()) {
+      std::cout << "Invalid model" << std::endl;
+      return 1;
+    }
+
+    auto context = std::make_shared<Context>();
+    auto ascend310_info = std::make_shared<mindspore::Ascend310DeviceInfo>();
+    ascend310_info->SetDeviceID(FLAGS_device_id);
+    context->MutableDeviceInfo().push_back(ascend310_info);
+
+    Graph graph;
+    Status ret = Serialization::Load(FLAGS_model_path, ModelType::kMindIR, &graph);
+    if (ret != kSuccess) {
+        std::cout << "Load model failed." << std::endl;
+        return 1;
+    }
+
+    Model model;
+    ret = model.Build(GraphCell(graph), context);
+    if (ret != kSuccess) {
+        std::cout << "ERROR: Build failed." << std::endl;
+        return 1;
+    }
+
+    std::vector<MSTensor> modelInputs = model.GetInputs();
+
+    auto all_files = GetAllFiles(FLAGS_dataset_path);
+    if (all_files.empty()) {
+        std::cout << "ERROR: no input data." << std::endl;
+        return 1;
+    }
+
+    std::shared_ptr<TensorTransform> decode(new Decode());
+    std::shared_ptr<TensorTransform> resize(new Resize({320}));
+    std::shared_ptr<TensorTransform> centerCrop(new CenterCrop({299}));
+    std::shared_ptr<TensorTransform> normalize(new Normalize({127.5, 127.5, 127.5}, {127.5, 127.5, 127.5}));
+    std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW());
+
+    mindspore::dataset::Execute transform({decode, resize, centerCrop, normalize, hwc2chw});
+
+    std::map<double, double> costTime_map;
+
+    size_t size = all_files.size();
+    for (size_t i = 0; i < size; ++i) {
+        struct timeval start;
+        struct timeval end;
+        double startTime_ms;
+        double endTime_ms;
+        std::vector<MSTensor> inputs;
+        std::vector<MSTensor> outputs;
+
+        std::cout << "Start predict input files:" << all_files[i] << std::endl;
+        mindspore::MSTensor image =  ReadFileToTensor(all_files[i]);
+
+        transform(image, &image);
+
+        inputs.emplace_back(modelInputs[0].Name(), modelInputs[0].DataType(), modelInputs[0].Shape(),
+                            image.Data().get(), image.DataSize());
+
+        gettimeofday(&start, NULL);
+        model.Predict(inputs, &outputs);
+        gettimeofday(&end, NULL);
+
+        startTime_ms = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
+        endTime_ms = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
+        costTime_map.insert(std::pair<double, double>(startTime_ms, endTime_ms));
+        WriteResult(all_files[i], outputs);
+    }
+    double average = 0.0;
+    int infer_cnt = 0;
+    for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
+        double diff = 0.0;
+        diff = iter->second - iter->first;
+        average += diff;
+        infer_cnt++;
+    }
+
+    average = average / infer_cnt;
+
+    std::stringstream timeCost;
+    timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << infer_cnt << std::endl;
+    std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << infer_cnt << std::endl;
+
+    std::string file_name = "./time_Result" + std::string("/test_perform_static.txt");
+    std::ofstream file_stream(file_name.c_str(), std::ios::trunc);
+    file_stream << timeCost.str();
+    file_stream.close();
+    costTime_map.clear();
+  return 0;
+}

model_zoo/official/cv/xception/ascend310_infer/src/utils.cc

@@ -0,0 +1,147 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ * 
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * 
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * 
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "inc/utils.h"
+
+#include <fstream>
+#include <algorithm>
+#include <iostream>
+
+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> dirs;
+    std::vector<std::string> files;
+    while ((filename = readdir(dir)) != nullptr) {
+        std::string dName = std::string(filename->d_name);
+        if (dName == "." || dName == "..") {
+            continue;
+        } else if (filename->d_type == DT_DIR) {
+            dirs.emplace_back(std::string(dirName) + "/" + filename->d_name);
+        } else if (filename->d_type == DT_REG) {
+            files.emplace_back(std::string(dirName) + "/" + filename->d_name);
+        } else {
+            continue;
+        }
+    }
+
+    for (auto d : dirs) {
+        dir = OpenDir(d);
+        while ((filename = readdir(dir)) != nullptr) {
+            std::string dName = std::string(filename->d_name);
+            if (dName == "." || dName == ".." || filename->d_type != DT_REG) {
+                continue;
+            }
+            files.emplace_back(std::string(d) + "/" + filename->d_name);
+        }
+    }
+    std::sort(files.begin(), files.end());
+    for (auto &f : files) {
+        std::cout << "image file: " << f << std::endl;
+    }
+    return files;
+}
+
+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;
+}

model_zoo/official/cv/xception/export.py

@@ -23,13 +23,13 @@ from src.config import config_ascend, config_gpu
 
 parser = argparse.ArgumentParser(description="Image classification")
 parser.add_argument("--device_id", type=int, default=0, help="Device id")
+parser.add_argument("--batch_size", type=int, default=1, help="batch size")
 parser.add_argument("--ckpt_file", type=str, required=True, help="xception ckpt file.")
 parser.add_argument("--width", type=int, default=299, help="input width")
 parser.add_argument("--height", type=int, default=299, help="input height")
 parser.add_argument("--file_name", type=str, default="xception", help="xception output file name.")
-parser.add_argument("--file_format", type=str, choices=["AIR", "ONNX", "MINDIR"],
-                    default="MINDIR", help="file format")
-parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="GPU",
+parser.add_argument("--file_format", type=str, choices=["AIR", "MINDIR"], default="MINDIR", help="file format")
+parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
                     help="device target")
 
 args = parser.parse_args()
@@ -52,5 +52,5 @@ if __name__ == "__main__":
     load_param_into_net(net, param_dict)
     net.set_train(False)
 
-    image = Tensor(np.zeros([config.batch_size, 3, args.height, args.width], np.float32))
+    image = Tensor(np.zeros([args.batch_size, 3, args.height, args.width], np.float32))
     export(net, image, file_name=args.file_name, file_format=args.file_format)

model_zoo/official/cv/xception/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.
+# ============================================================================
+'''post process for 310 inference'''
+import os
+import argparse
+import numpy as np
+
+parser = argparse.ArgumentParser(description='post process for 310 inference')
+parser.add_argument("--result_path", type=str, required=True, help="result file path")
+parser.add_argument("--label_file", type=str, required=True, help="label file")
+args = parser.parse_args()
+
+def get_top5_acc(top5_arg, gt_class):
+    sub_count = 0
+    for top5, gt in zip(top5_arg, gt_class):
+        if gt in top5:
+            sub_count += 1
+    return sub_count
+
+def read_label(label_file):
+    f = open(label_file, "r")
+    lines = f.readlines()
+
+    img_label = {}
+    for line in lines:
+        img_id = line.split(":")[0]
+        label = line.split(":")[1]
+        img_label[img_id] = label
+
+    return img_label
+
+def cal_acc(result_path, label_file):
+    img_label = read_label(label_file)
+
+    img_tot = 0
+    top1_correct = 0
+    top5_correct = 0
+
+    files = os.listdir(result_path)
+    for file in files:
+        full_file_path = os.path.join(result_path, file)
+        if os.path.isfile(full_file_path):
+            result = np.fromfile(full_file_path, dtype=np.float32).reshape(1, 1000)
+            gt_classes = int(img_label[file[:-6]])
+
+            top1_output = np.argmax(result, (-1))
+            top5_output = np.argsort(result)[:, -5:]
+
+            t1_correct = np.equal(top1_output, gt_classes).sum()
+            top1_correct += t1_correct
+            top5_correct += get_top5_acc(top5_output, [gt_classes])
+            img_tot += 1
+
+    results = [[top1_correct], [top5_correct], [img_tot]]
+
+    results = np.array(results)
+    top1_correct = results[0, 0]
+    top5_correct = results[1, 0]
+    img_tot = results[2, 0]
+    acc1 = 100.0 * top1_correct / img_tot
+    acc5 = 100.0 * top5_correct / img_tot
+    print('Top_1_Acc={}%, Top_5_Acc={}%'.format(acc1, acc5))
+
+if __name__ == "__main__":
+    cal_acc(args.result_path, args.label_file)

model_zoo/official/cv/xception/scripts/run_infer_310.sh

@@ -0,0 +1,107 @@
+#!/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 3 || $# -gt 4 ]]; then 
+    echo "Usage: sh run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [LABEL_FILE] [DEVICE_ID]
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+model=$(get_real_path $1)
+data_path=$(get_real_path $2)
+label_file=$(get_real_path $3)
+if [ $# == 4 ]; then
+    device_id=$4
+elif [ $# == 3 ]; then
+    if [ -z $device_id ]; then
+        device_id=0
+    else
+        device_id=$device_id
+    fi
+fi
+
+echo $model
+echo $data_path
+echo $label_file
+echo $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 PYTHONPATH=${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
+    export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
+else
+    export PATH=$ASCEND_HOME/atc/ccec_compiler/bin:$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 compile_app()
+{
+    cd ../ascend310_infer || exit
+    if [ -f "Makefile" ]; then
+        make clean
+    fi
+    sh build.sh &> build.log
+
+    if [ $? -ne 0 ]; then
+        echo "compile app code failed"
+        exit 1
+    fi
+    cd - || exit
+}
+
+function infer()
+{
+    if [ -d result_Files ]; then
+        rm -rf ./result_Files
+    fi
+     if [ -d time_Result ]; then
+        rm -rf ./time_Result
+    fi
+    mkdir result_Files
+    mkdir time_Result
+    ../ascend310_infer/out/main --model_path=$model --dataset_path=$data_path --device_id=$device_id &> infer.log
+
+    if [ $? -ne 0 ]; then
+        echo "execute inference failed"
+        exit 1
+    fi
+}
+
+function cal_acc()
+{
+    python ../postprocess.py --label_file=$label_file --result_path=result_Files &> acc.log
+    if [ $? -ne 0 ]; then
+        echo "calculate accuracy failed"
+        exit 1
+    fi
+}
+
+compile_app
+infer
+cal_acc