add ascend310_infer for retinanet_resnet152

2021-07-17 10:57:39 +08:00 · 2021-07-17 10:57:39 +08:00 · 1fbb229321
parent 0e025d2210
commit 1fbb229321
11 changed files with 5189 additions and 44 deletions
--- a/model_zoo/research/cv/retinanet_resnet152/README_CN.md
+++ b/model_zoo/research/cv/retinanet_resnet152/README_CN.md
@ -2,7 +2,7 @@

 <!-- TOC -->

- <span id="content">[Retinanet 描述](#-Retinanet-描述)</span>
+- [Retinanet 描述](#retinanet描述)
 - [模型架构](#模型架构)
 - [数据集](#数据集)
 - [环境要求](#环境要求)
@ -14,9 +14,16 @@
        - [运行](#运行)
        - [结果](#结果)
    - [评估过程](#评估过程)
-        - [用法](#usage)
-        - [运行](#running)
-        - [结果](#outcome)
+        - [用法](#用-法)
+        - [运行](#运-行)
+        - [结果](#结-果)
+    - [模型导出](#模型导出)
+        - [用途](#用途)
+        - [运行方式](#运行方式)
+    - [推理过程](#推理过程)
+        - [用途](#用-途)
+        - [运行命令](#运行命令)
+        - [运行结果](#运行结果)
    - [模型说明](#模型说明)
        - [性能](#性能)
            - [训练性能](#训练性能)
@ -26,7 +33,7 @@

 <!-- /TOC -->

-## [Retinanet 描述](#content)
+## [Retinanet描述](#content)

 RetinaNet算法源自2018年Facebook AI Research的论文 Focal Loss for Dense Object Detection。该论文最大的贡献在于提出了Focal Loss用于解决类别不均衡问题，从而创造了RetinaNet（One Stage目标检测算法）这个精度超越经典Two Stage的Faster-RCNN的目标检测网络。

@ -226,7 +233,7 @@ epoch time: 444237.851 ms, per step time: 484.976 ms

 ### [评估过程](#content)

-#### <span id="usage">用法</span>
+#### 用 法

 您可以使用python或shell脚本进行训练。shell脚本的用法如下:

@ -234,7 +241,7 @@ epoch time: 444237.851 ms, per step time: 484.976 ms
 sh scripts/run_eval.sh [DATASET] [DEVICE_ID]
 ```

-#### <span id="running">运行</span>
+#### 运 行

 ```eval运行
 # 验证示例
@ -248,7 +255,7 @@ sh scripts/run_eval.sh [DATASET] [DEVICE_ID]

 > checkpoint 可以在训练过程中产生.

-#### <span id="outcome">结果</span>
+#### 结 果

 计算结果将存储在示例路径中，您可以在 `eval.log` 查看.

@ -271,6 +278,82 @@ sh scripts/run_eval.sh [DATASET] [DEVICE_ID]
 mAP: 0.3571988469737286
 ```

+### [模型导出](#content)
+
+#### 用途
+
+导出模型前要修改config.py文件中的checkpoint_path配置项，值为checkpoint的路径。
+
+```shell
+python export.py --file_name [RUN_PLATFORM] --file_format[EXPORT_FORMAT] --checkpoint_path [CHECKPOINT PATH]
+```
+
+`EXPORT_FORMAT` 可选 ["AIR", "MINDIR"]
+
+#### 运行方式
+
+```运行
+python export.py
+```
+
+- 在modelarts上导出MindIR
+
+```Modelarts
+在ModelArts上导出MindIR示例
+# (1) 选择a(修改yaml文件参数)或者b(ModelArts创建训练作业修改参数)其中一种方式。
+#       a. 设置 "enable_modelarts=True"
+#          设置 "file_name=retinanet"
+#          设置 "file_format=MINDIR"
+#          设置 "checkpoint_path=/cache/data/checkpoint/checkpoint file name"
+
+#       b. 增加 "enable_modelarts=True" 参数在modearts的界面上。
+#          在modelarts的界面上设置方法a所需要的参数
+#          注意：路径参数不需要加引号
+# (2)设置网络配置文件的路径 "_config_path=/The path of config in default_config.yaml/"
+# (3) 在modelarts的界面上设置代码的路径 "/path/retinanet"。
+# (4) 在modelarts的界面上设置模型的启动文件 "export.py" 。
+# (5) 在modelarts的界面上设置模型的数据路径 ".../MindRecord_COCO"(选择MindRecord_COCO文件夹路径) ,
+# MindIR的输出路径"Output file path" 和模型的日志路径 "Job log path" 。
+```
+
+### [推理过程](#content)
+
+#### 用 途
+
+在推理之前需要在昇腾910环境上完成模型的导出。推理时要将iscrowd为true的图片排除掉。在ascend310_infer目录下保存了去排除后的图片id。
+还需要修改config.py文件中的coco_root、val_data_type、instances_set配置项，值分别取coco数据集的目录，推理所用数据集的目录名称，推理完成后计算精度用的annotation文件，instances_set是用val_data_type拼接起来的，要保证文件正确并且存在。
+
+```shell
+# Ascend310 inference
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DEVICE_ID]
+```
+
+#### 运行命令
+
+```运行
+bash run_infer_310.sh  [MINDIR_PATH] [DATASET_PATH] [DEVICE_ID]
+```
+
+#### 运行结果
+
+推理的结果保存在当前目录下，在acc.log日志文件中可以找到类似以下的结果。
+
+```mAP
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.356
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.499
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.396
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.145
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.380
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.506
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.308
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.446
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.457
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.179
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.483
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.647
+mAP: 0.35625723922139957
+```
+
 ## [模型说明](#content)

 ### [性能](#content)
--- a/model_zoo/research/cv/retinanet_resnet152/ascend310_infer/CMakeLists.txt
+++ b/model_zoo/research/cv/retinanet_resnet152/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)
--- a/model_zoo/research/cv/retinanet_resnet152/ascend310_infer/build.sh
+++ b/model_zoo/research/cv/retinanet_resnet152/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
--- a/model_zoo/research/cv/retinanet_resnet152/ascend310_infer/image_id.txt
+++ b/model_zoo/research/cv/retinanet_resnet152/ascend310_infer/image_id.txt
--- a/model_zoo/research/cv/retinanet_resnet152/ascend310_infer/inc/utils.h
+++ b/model_zoo/research/cv/retinanet_resnet152/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/research/cv/retinanet_resnet152/ascend310_infer/src/main.cc
+++ b/model_zoo/research/cv/retinanet_resnet152/ascend310_infer/src/main.cc
@ -0,0 +1,153 @@
+/**
+ * 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 "../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::Normalize;
+using mindspore::dataset::vision::HWC2CHW;
+
+DEFINE_string(model_path, "", "model path");
+DEFINE_string(dataset_path, ".", "dataset path");
+DEFINE_int32(device_id, 0, "device id");
+DEFINE_string(precision_mode, "allow_fp32_to_fp16", "precision mode");
+DEFINE_string(op_select_impl_mode, "high_precision", "op impl mode");
+DEFINE_string(buffer_optimize_mode, "off_optimize", "buffer optimize mode");
+
+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);
+    ascend310_info->SetPrecisionMode(FLAGS_precision_mode);
+    ascend310_info->SetOpSelectImplMode(FLAGS_op_select_impl_mode);
+    ascend310_info->SetBufferOptimizeMode(FLAGS_buffer_optimize_mode);
+    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;
+    }
+
+    auto decode = Decode();
+    auto resize = Resize({640, 640});
+    auto normalize = Normalize({123.675, 116.28, 103.53}, {58.395, 57.12, 57.375});
+    auto hwc2chw = HWC2CHW();
+
+    mindspore::dataset::Execute transform({decode, resize, 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;
+    char tmpCh[256] = {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;
+
+    snprintf(tmpCh, sizeof(tmpCh), "NN inference cost average time: %4.3f ms of infer_count %d\n", average, infer_cnt);
+    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 << tmpCh;
+    file_stream.close();
+    costTime_map.clear();
+  return 0;
+}
--- a/model_zoo/research/cv/retinanet_resnet152/ascend310_infer/src/utils.cc
+++ b/model_zoo/research/cv/retinanet_resnet152/ascend310_infer/src/utils.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 "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> 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/research/cv/retinanet_resnet152/export.py
+++ b/model_zoo/research/cv/retinanet_resnet152/export.py
@ -12,43 +12,36 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-
-"""Export file"""
-
+"""export for retinanet"""
 import argparse
 import numpy as np
-
-from mindspore import dtype as mstype
+import mindspore.common.dtype as mstype
 from mindspore import context, Tensor
-from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
-
-from src.retinahead import retinahead
-from src.backbone import resnet152
+from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
+from src.retinahead import  retinahead, retinanetInferWithDecoder
 from src.config import config
+from src.box_utils import default_boxes
+from src.backbone import resnet152

-parser = argparse.ArgumentParser(description="retinanet_resnet152 export")
-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="Checkpoint file path.")
-parser.add_argument("--file_name", type=str, default="retinanet_resnet152", help="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="Ascend",
-                    help="device target")
-args = parser.parse_args()

-context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
-if args.device_target == "Ascend":
-    context.set_context(device_id=args.device_id)
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='retinanet evaluation')
+    parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
+    parser.add_argument("--run_platform", type=str, default="Ascend", choices=("Ascend"),
+                        help="run platform, only support Ascend.")
+    parser.add_argument("--file_format", type=str, choices=["AIR", "MINDIR"], default="MINDIR", help="file format")
+    parser.add_argument("--batch_size", type=int, default=1, help="batch size")
+    parser.add_argument("--file_name", type=str, default="retinanet", help="output file name.")
+    args_opt = parser.parse_args()
+    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.run_platform, device_id=args_opt.device_id)

-if __name__ == "__main__":
-    network = retinahead(backbone=resnet152(80), config=config, is_training=False)
-
-    param_dict = load_checkpoint(args.ckpt_file)
-    load_param_into_net(network, param_dict)
-
-    network.set_train(False)
-
-    shape = [args.batch_size, 3] + [640, 640]
+    backbone = resnet152(config.num_classes)
+    net = retinahead(backbone, config)
+    net = retinanetInferWithDecoder(net, Tensor(default_boxes), config)
+    param_dict = load_checkpoint(config.checkpoint_path)
+    net.init_parameters_data()
+    load_param_into_net(net, param_dict)
+    net.set_train(False)
+    shape = [args_opt.batch_size, 3] + config.img_shape
    input_data = Tensor(np.zeros(shape), mstype.float32)
-
-    export(network, input_data, file_name=args.file_name, file_format=args.file_format)
+    export(net, input_data, file_name=args_opt.file_name, file_format=args_opt.file_format)
--- a/model_zoo/research/cv/retinanet_resnet152/postprocess.py
+++ b/model_zoo/research/cv/retinanet_resnet152/postprocess.py
@ -0,0 +1,76 @@
+# 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.
+# ============================================================================
+
+"""Evaluation for retinanet"""
+
+import os
+import argparse
+import numpy as np
+from PIL import Image
+from src.coco_eval import metrics
+from src.config import config
+
+parser = argparse.ArgumentParser(description='retinanet evaluation')
+parser.add_argument("--result_path", type=str, required=True, help="result file path.")
+parser.add_argument("--img_path", type=str, required=True, help="image file path.")
+parser.add_argument("--img_id_file", type=str, required=True, help="image id file.")
+args = parser.parse_args()
+
+def get_pred(result_path, img_id):
+    boxes_file = os.path.join(result_path, img_id + '_0.bin')
+    scores_file = os.path.join(result_path, img_id + '_1.bin')
+
+    boxes = np.fromfile(boxes_file, dtype=np.float32).reshape(76725, 4)
+    scores = np.fromfile(scores_file, dtype=np.float32).reshape(76725, config.num_classes)
+    return boxes, scores
+
+def get_img_size(file_name):
+    img = Image.open(file_name)
+    return img.size
+
+def get_img_id(img_id_file):
+    f = open(img_id_file)
+    lines = f.readlines()
+
+    ids = []
+    for line in lines:
+        ids.append(int(line))
+
+    return ids
+
+def cal_acc(result_path, img_path, img_id_file):
+    """Calculate acc"""
+    ids = get_img_id(img_id_file)
+    imgs = os.listdir(img_path)
+    pred_data = []
+
+    for img in imgs:
+        img_id = img.split('.')[0]
+        if int(img_id) not in ids:
+            continue
+        boxes, box_scores = get_pred(result_path, img_id)
+
+        w, h = get_img_size(os.path.join(img_path, img))
+        img_shape = np.array((h, w), dtype=np.float32)
+        pred_data.append({"boxes": boxes,
+                          "box_scores": box_scores,
+                          "img_id": int(img_id),
+                          "image_shape": img_shape})
+
+    mAP = metrics(pred_data)
+    print(f"mAP: {mAP}")
+
+if __name__ == '__main__':
+    cal_acc(args.result_path, args.img_path, args.img_id_file)
--- a/model_zoo/research/cv/retinanet_resnet152/requirements.txt
+++ b/model_zoo/research/cv/retinanet_resnet152/requirements.txt
@ -1,4 +0,0 @@
-numpy
-pycocotools
-easydict
-opencv-python
--- a/model_zoo/research/cv/retinanet_resnet152/scripts/run_infer_310.sh
+++ b/model_zoo/research/cv/retinanet_resnet152/scripts/run_infer_310.sh
@ -0,0 +1,104 @@
+#!/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 || $# -gt 3 ]]; then 
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
+    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+model=$(get_real_path $1)
+data_path=$(get_real_path $2)
+
+device_id=0
+
+if [ $# == 3 ]; then
+    device_id=$3
+fi
+
+echo $model
+echo $data_path
+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 [ $? -ne 0 ]; then
+        echo "execute inference failed"
+        exit 1
+    fi
+}
+
+function cal_acc()
+{
+    python ../postprocess.py --result_path=result_Files --img_path=$data_path --img_id_file=../ascend310_infer/image_id.txt &> acc.log
+    if [ $? -ne 0 ]; then
+        echo "calculate accuracy failed"
+        exit 1
+    fi
+}
+
+
+
+compile_app
+infer
+cal_acc