diff --git "a/onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNeXt50_Onnx\346\250\241\345\236\213\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274.md" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNeXt50_Onnx\346\250\241\345\236\213\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274.md"
similarity index 100%
rename from "onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNeXt50_Onnx\346\250\241\345\236\213\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274.md"
rename to "Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNeXt50_Onnx\346\250\241\345\236\213\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274.md"
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/LICENSE" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/LICENSE"
new file mode 100644
index 0000000000000000000000000000000000000000..eeac88fb9dc15a1427b41173cf5f136327230c49
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/LICENSE"
@@ -0,0 +1,201 @@
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diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/README.md" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/README.md"
new file mode 100644
index 0000000000000000000000000000000000000000..a37c7038bb37a6b948a1aa8ea7f508c8f5e1960d
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/README.md"
@@ -0,0 +1,345 @@
+# ResNeXt50 Onnx模型端到端推理指导
+-   [1 模型概述](#1-模型概述)
+	-   [1.1 论文地址](#11-论文地址)
+	-   [1.2 代码地址](#12-代码地址)
+-   [2 环境说明](#2-环境说明)
+	-   [2.1 深度学习框架](#21-深度学习框架)
+	-   [2.2 python第三方库](#22-python第三方库)
+-   [3 模型转换](#3-模型转换)
+	-   [3.1 pth转onnx模型](#31-pth转onnx模型)
+	-   [3.2 onnx转om模型](#32-onnx转om模型)
+-   [4 数据集预处理](#4-数据集预处理)
+	-   [4.1 数据集获取](#41-数据集获取)
+	-   [4.2 数据集预处理](#42-数据集预处理)
+	-   [4.3 生成数据集信息文件](#43-生成数据集信息文件)
+-   [5 离线推理](#5-离线推理)
+	-   [5.1 benchmark工具概述](#51-benchmark工具概述)
+	-   [5.2 离线推理](#52-离线推理)
+-   [6 精度对比](#6-精度对比)
+	-   [6.1 离线推理精度统计](#61-离线推理精度统计)
+	-   [6.2 开源精度](#62-开源精度)
+	-   [6.3 精度对比](#63-精度对比)
+-   [7 性能对比](#7-性能对比)
+	-   [7.1 npu性能数据](#71-npu性能数据)
+	-   [7.2 T4性能数据](#72-T4性能数据)
+	-   [7.3 性能对比](#73-性能对比)
+
+
+
+## 1 模型概述
+
+-   **[论文地址](#11-论文地址)**  
+
+-   **[代码地址](#12-代码地址)**  
+
+### 1.1 论文地址
+[ResNeXt50论文](https://arxiv.org/abs/1611.05431)  
+
+### 1.2 代码地址
+[ResNeXt50代码](https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py)  
+branch:master  
+commit id:b68adcf9a9280aef02fc08daed170d74d0892361  
+$\color{red}{说明：删除线用于说明READ.md必要的包含项，以下带有删除线的说明在README.md中需要删除}$  
+~~优先使用本任务提供的开源代码仓，填写分支与commit id，需要从github的commits中找到commit id，commit id是指基于该次提交时的模型代码做推理，通常选择稳定版本的最后一次提交，或代码仓最新的一次提交~~  
+
+
+## 2 环境说明
+
+-   **[深度学习框架](#21-深度学习框架)**  
+
+-   **[python第三方库](#22-python第三方库)**  
+
+### 2.1 深度学习框架
+```
+python3.7.5
+CANN 5.0.1
+
+pytorch >= 1.5.0
+torchvision >= 0.6.0
+onnx >= 1.7.0
+```
+~~目前推理310服务器安装的是蓝区商用版本CANN 5.0.1，库若无特殊版本要求以上三个库固定这么写，需要使用python3.7命令执行脚本，pip3.7命令安装库，torch使用1.5.0版本，如果开源模型代码导出onnx要求torch版本大于1.5.0，则使用1.8.0版本，并在此处说明~~
+
+### 2.2 python第三方库
+
+```
+numpy == 1.20.3
+Pillow == 8.2.0
+opencv-python == 4.5.2.54
+```
+~~requirements.txt中需要写明本模型离线推理所有必要依赖库的具体版本，版本号即是推理310服务器上推理时使用库的版本号~~  
+
+**说明：** 
+>   X86架构：pytorch，torchvision和onnx可以通过官方下载whl包安装，其它可以通过pip3.7 install 包名 安装
+>
+>   Arm架构：pytorch，torchvision和onnx可以通过源码编译安装，其它可以通过pip3.7 install 包名 安装
+
+## 3 模型转换
+
+-   **[pth转onnx模型](#31-pth转onnx模型)**  
+
+-   **[onnx转om模型](#32-onnx转om模型)**  
+
+### 3.1 pth转onnx模型
+
+1.下载pth权重文件  
+[ResNeXt50预训练pth权重文件](https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth)  
+文件md5sum: 1d6611049e6ef03f1d6afa11f6f9023e  
+```
+wget https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth
+```
+~~优先使用训练提供的权重文件，如果训练的权重文件网上能获则需给出网址，否则需要给出从哪获取权重文件。如果训练没有提供权重则使用开源代码仓的权重文件。需要给出权重文件名与通过md5sum命令计算的权重文件md5sum值~~  
+2.resnext50模型代码在torchvision里，安装torchvision，arm下需源码安装，参考torchvision官网，若安装过程报错请百度解决
+```
+git clone https://github.com/pytorch/vision
+cd vision
+git reset b68adcf9a9280aef02fc08daed170d74d0892361 --hard
+python3.7 setup.py install
+cd ..
+```
+~~如果需要对模型的开源代码做修改，以打patch的形式修改后再安装：patch -p1 < ../{patch_name}.diff~~  
+3.编写pth2onnx脚本resnext50_pth2onnx.py  
+~~如果模型开源代码仓没有安装脚本，可以通过sys.path.append(r"./vision")添加搜索路径，然后就可以引用模型开源代码仓的函数或类~~  
+ **说明：**  
+>注意目前ATC支持的onnx算子版本为11
+
+4.执行pth2onnx脚本，生成onnx模型文件
+```
+python3.7 resnext50_pth2onnx.py resnext50_32x4d-7cdf4587.pth resnext50.onnx
+```
+
+ **模型转换要点：**  
+~~对于CANN包算子有问题导致模型转换失败或需要规避才能转换成功，则需要在模型转换要点里写明定位主要过程，原因与措施~~  
+>此模型转换为onnx不需要修改开源代码仓代码，故不需要特殊说明  
+
+### 3.2 onnx转om模型
+
+1.设置环境变量
+```
+source env.sh
+```
+2.使用atc将onnx模型转换为om模型文件，工具使用方法可以参考CANN 5.0.1 开发辅助工具指南 (推理) 01
+```
+atc --framework=5 --model=./resnext50.onnx --input_format=NCHW --input_shape="image:16,3,224,224" --output=resnext50_bs16 --log=debug --soc_version=Ascend310
+```
+
+## 4 数据集预处理
+
+-   **[数据集获取](#41-数据集获取)**  
+
+-   **[数据集预处理](#42-数据集预处理)**  
+
+-   **[生成数据集信息文件](#43-生成数据集信息文件)**  
+
+### 4.1 数据集获取
+该模型使用[ImageNet官网](http://www.image-net.org)的5万张验证集进行测试，图片与标签分别存放在/root/datasets/imagenet/val与/root/datasets/imagenet/val_label.txt。
+
+### 4.2 数据集预处理
+1.预处理脚本imagenet_torch_preprocess.py
+
+2.执行预处理脚本，生成数据集预处理后的bin文件
+```
+python3.7 imagenet_torch_preprocess.py resnet /root/datasets/imagenet/val ./prep_dataset
+```
+### 4.3 生成数据集信息文件
+1.生成数据集信息文件脚本gen_dataset_info.py
+
+2.执行生成数据集信息脚本，生成数据集信息文件
+```
+python3.7 gen_dataset_info.py bin ./prep_dataset ./resnext50_prep_bin.info 224 224
+```
+第一个参数为模型输入的类型，第二个参数为生成的bin文件路径，第三个为输出的info文件，后面为宽高信息
+## 5 离线推理
+
+-   **[benchmark工具概述](#51-benchmark工具概述)**  
+
+-   **[离线推理](#52-离线推理)**  
+
+### 5.1 benchmark工具概述
+
+benchmark工具为华为自研的模型推理工具，支持多种模型的离线推理，能够迅速统计出模型在Ascend310上的性能，支持真实数据和纯推理两种模式，配合后处理脚本，可以实现诸多模型的端到端过程，获取工具及使用方法可以参考CANN 5.0.1 推理benchmark工具用户指南 01
+### 5.2 离线推理
+1.设置环境变量
+```
+source env.sh
+```
+2.执行离线推理
+```
+./benchmark.x86_64 -model_type=vision -device_id=0 -batch_size=16 -om_path=resnext50_bs16.om -input_text_path=./resnext50_prep_bin.info -input_width=224 -input_height=224 -output_binary=False -useDvpp=False
+```
+输出结果默认保存在当前目录result/dumpOutput_device{0}，模型只有一个名为class的输出，shape为bs * 1000，数据类型为FP32，对应1000个分类的预测结果，每个输入对应的输出对应一个_x.bin文件。
+
+## 6 精度对比
+
+-   **[离线推理精度](#61-离线推理精度)**  
+-   **[开源精度](#62-开源精度)**  
+-   **[精度对比](#63-精度对比)**  
+
+### 6.1 离线推理精度统计
+
+后处理统计TopN精度
+
+调用imagenet_acc_eval.py脚本推理结果与label比对，可以获得Accuracy Top5数据，结果保存在result.json中。
+```
+python3.7 imagenet_acc_eval.py result/dumpOutput_device0/ /root/datasets/imagenet/val_label.txt ./ result.json
+```
+第一个为benchmark输出目录，第二个为数据集配套标签，第三个是生成文件的保存目录，第四个是生成的文件名。  
+查看输出结果：
+```
+{"title": "Overall statistical evaluation", "value": [{"key": "Number of images", "value": "50000"}, {"key": "Number of classes", "value": "1000"}, {"key": "Top1 accuracy", "value": "77.62%"}, {"key": "Top2 accuracy", "value": "87.42%"}, {"key": "Top3 accuracy", "value": "90.79%"}, {"key": "Top4 accuracy", "value": "92.56%"}, {"key": "Top5 accuracy", "value": "93.69%"}]
+```
+经过对bs1与bs16的om测试，本模型batch1的精度与batch16的精度没有差别，精度数据均如上  
+~~因为batch可能影响精度，如果模型支持多batch的话，精度测试需要且仅测试bs1与bs16的精度~~  
+
+### 6.2 开源精度
+[torchvision官网精度](https://pytorch.org/vision/stable/models.html)
+```
+Model               Acc@1     Acc@5
+ResNeXt-50-32x4d    77.618    93.698
+```
+### 6.3 精度对比
+将得到的om离线模型推理TopN精度与该模型github代码仓上公布的精度对比，精度下降在1%范围之内，故精度达标。  
+ **精度调试：**  
+~~对于CANN包算子有问题导致精度不达标或需要规避才能达标，则需要在精度调试里写明定位主要过程，原因与措施~~  
+>没有遇到精度不达标的问题，故不需要进行精度调试  
+
+## 7 性能对比
+
+-   **[npu性能数据](#71-npu性能数据)**  
+-   **[T4性能数据](#72-T4性能数据)**  
+-   **[性能对比](#73-性能对比)**  
+
+~~性能数据需要测bs1，16，4，8，32的性能数据，且需要计算出单卡吞吐率。对于npu，bs1,16要在整个数据集上推理测性能，为了避免长期占用device，bs4,8,32也可以使用纯推理测性能~~  
+
+### 7.1 npu性能数据
+benchmark工具在整个数据集上推理时也会统计性能数据，但是推理整个数据集较慢，如果这么测性能那么整个推理期间需要确保独占device，使用npu-smi info可以查看device是否空闲。也可以使用benchmark纯推理功能测得性能数据，但是由于随机数不能模拟数据分布，纯推理功能测的有些模型性能数据可能不太准，benchmark纯推理功能测性能仅为快速获取大概的性能数据以便调试优化使用，可初步确认benchmark工具在整个数据集上推理时由于device也被其它推理任务使用了导致的性能不准的问题。模型的性能以使用benchmark工具在整个数据集上推理得到bs1与bs16的性能数据为准，对于使用benchmark工具测试的batch4，8，32的性能数据在README.md中如下作记录即可。  
+1.benchmark工具在整个数据集上推理获得性能数据  
+batch1的性能，benchmark工具在整个数据集上推理后生成result/perf_vision_batchsize_1_device_0.txt：  
+```
+[e2e] throughputRate: 243.034, latency: 205733
+[data read] throughputRate: 258.963, moduleLatency: 3.86155
+[preprocess] throughputRate: 258.404, moduleLatency: 3.86991
+[infer] throughputRate: 244.435, Interface throughputRate: 382.328, moduleLatency: 3.35758
+[post] throughputRate: 244.435, moduleLatency: 4.09107
+```
+Interface throughputRate: 382.328，382.328x4=1529.312既是batch1 310单卡吞吐率  
+batch16的性能，benchmark工具在整个数据集上推理后生成result/perf_vision_batchsize_16_device_1.txt：  
+```
+[e2e] throughputRate: 173.173, latency: 288729
+[data read] throughputRate: 174.62, moduleLatency: 5.72673
+[preprocess] throughputRate: 174.357, moduleLatency: 5.73535
+[infer] throughputRate: 173.844, Interface throughputRate: 519.634, moduleLatency: 3.36724
+[post] throughputRate: 10.865, moduleLatency: 92.0383
+```
+Interface throughputRate: 519.634，519.634x4=2078.536既是batch16 310单卡吞吐率  
+batch4性能：
+```
+[e2e] throughputRate: 232.98, latency: 214611
+[data read] throughputRate: 235.537, moduleLatency: 4.24562
+[preprocess] throughputRate: 235.147, moduleLatency: 4.25266
+[infer] throughputRate: 234.437, Interface throughputRate: 492.99, moduleLatency: 3.48397
+[post] throughputRate: 58.6087, moduleLatency: 17.0623
+```
+batch4 310单卡吞吐率：492.99x4=1971.96fps  
+batch8性能：
+```
+[e2e] throughputRate: 211.307, latency: 236622
+[data read] throughputRate: 212.246, moduleLatency: 4.71152
+[preprocess] throughputRate: 211.931, moduleLatency: 4.71851
+[infer] throughputRate: 211.927, Interface throughputRate: 496.378, moduleLatency: 3.45797
+[post] throughputRate: 26.4906, moduleLatency: 37.7493
+```
+batch8 310单卡吞吐率：496.378x4=1985.512fps  
+batch32性能：
+```
+[e2e] throughputRate: 122.942, latency: 406696
+[data read] throughputRate: 123.244, moduleLatency: 8.11402
+[preprocess] throughputRate: 123.143, moduleLatency: 8.12064
+[infer] throughputRate: 123.207, Interface throughputRate: 377.787, moduleLatency: 4.10655
+[post] throughputRate: 3.8514, moduleLatency: 259.646
+```
+batch32 310单卡吞吐率：377.787x4=1511.148fps  
+
+### 7.2 T4性能数据
+在装有T4卡的服务器上测试gpu性能，测试过程请确保卡没有运行其他任务，TensorRT版本：7.2.3.4，cuda版本：11.0，cudnn版本：8.2  
+~~目前T4服务器安装的cuda,cudnn,TensorRT版本如上~~  
+batch1性能：
+```
+trtexec --onnx=resnext50.onnx --fp16 --shapes=image:1x3x224x224 --threads
+```
+gpu T4是4个device并行执行的结果，mean是时延（tensorrt的时延是batch个数据的推理时间），即吞吐率的倒数乘以batch。其中--fp16是算子精度，目前算子精度只测--fp16的。注意--shapes是onnx的输入节点名与shape，当onnx输入节点的batch为-1时，可以用同一个onnx文件测不同batch的性能，否则用固定batch的onnx测不同batch的性能不准  
+```
+[03/24/2021-03:54:47] [I] GPU Compute
+[03/24/2021-03:54:47] [I] min: 1.26575 ms
+[03/24/2021-03:54:47] [I] max: 4.41528 ms
+[03/24/2021-03:54:47] [I] mean: 1.31054 ms
+[03/24/2021-03:54:47] [I] median: 1.30151 ms
+[03/24/2021-03:54:47] [I] percentile: 1.40723 ms at 99%
+[03/24/2021-03:54:47] [I] total compute time: 2.9972 s
+```
+batch1 t4单卡吞吐率：1000/(1.31054/1)=763.044fps  
+
+batch16性能：
+```
+trtexec --onnx=resnext50.onnx --fp16 --shapes=image:16x3x224x224 --threads
+```
+```
+[03/24/2021-03:57:22] [I] GPU Compute
+[03/24/2021-03:57:22] [I] min: 12.5645 ms
+[03/24/2021-03:57:22] [I] max: 14.8437 ms
+[03/24/2021-03:57:22] [I] mean: 12.9561 ms
+[03/24/2021-03:57:22] [I] median: 12.8541 ms
+[03/24/2021-03:57:22] [I] percentile: 14.8377 ms at 99%
+[03/24/2021-03:57:22] [I] total compute time: 3.03173 s
+```
+batch16 t4单卡吞吐率：1000/(12.9561/16)=1234.940fps  
+
+batch4性能：
+```
+[05/27/2021-03:16:26] [I] GPU Compute
+[05/27/2021-03:16:26] [I] min: 3.77515 ms
+[05/27/2021-03:16:26] [I] max: 4.07959 ms
+[05/27/2021-03:16:26] [I] mean: 3.92862 ms
+[05/27/2021-03:16:26] [I] median: 3.9552 ms
+[05/27/2021-03:16:26] [I] percentile: 4.07324 ms at 99%
+[05/27/2021-03:16:26] [I] total compute time: 3.0054 s
+```
+batch4 t4单卡吞吐率：1000/(3.92862/4)=1018.169fps  
+
+batch8性能：
+```
+[05/27/2021-03:14:52] [I] GPU Compute
+[05/27/2021-03:14:52] [I] min: 6.52148 ms
+[05/27/2021-03:14:52] [I] max: 7.22937 ms
+[05/27/2021-03:14:52] [I] mean: 6.80709 ms
+[05/27/2021-03:14:52] [I] median: 6.78735 ms
+[05/27/2021-03:14:52] [I] percentile: 7.08972 ms at 99%
+[05/27/2021-03:14:52] [I] total compute time: 3.01554 s
+```
+batch8 t4单卡吞吐率：1000/(6.80709/8)=1175.245fps  
+
+batch32性能：
+```
+[05/27/2021-03:13:11] [I] GPU Compute
+[05/27/2021-03:13:11] [I] min: 23.126 ms
+[05/27/2021-03:13:11] [I] max: 26.0043 ms
+[05/27/2021-03:13:11] [I] mean: 24.2826 ms
+[05/27/2021-03:13:11] [I] median: 24.2343 ms
+[05/27/2021-03:13:11] [I] percentile: 25.6355 ms at 99%
+[05/27/2021-03:13:11] [I] total compute time: 3.05961 s
+```
+batch32 t4单卡吞吐率：1000/(24.2826/32)=1317.816fps  
+
+### 7.3 性能对比
+batch1：382.328x4 > 1000x1/(1.31054/1)  
+batch16：519.634x4 > 1000x1/(12.9561/16)  
+310单个device的吞吐率乘4即单卡吞吐率比T4单卡的吞吐率大，故310性能高于T4性能，性能达标。  
+对于batch1与batch16，310性能均高于T4性能1.2倍，该模型放在Benchmark/cv/classification目录下。  
+~~对比bs1和16，小于1倍放于Research，1-1.2倍放于Official，大于1.2倍放于Benchmark，而实际提交代码时目前都放在Research目录下~~  
+ **性能优化：**  
+~~对于CANN包算子有问题导致性能不达标或需要规避才能达标，则需要在性能优化里写明定位主要过程，原因与措施~~  
+>没有遇到性能不达标的问题，故不需要进行性能优化  
+
+~~如果蓝区商用版本测精度或性能不达标，蓝区最新社区CANN版本测可以达标，这里需要写出原因与最新蓝区社区CANN包版本，用最新版本测。如果是无法规避的算子缺陷导致性能不达标，这里需要添加性能不达标的原因与解决方案。如果onnx因包含自定义算子不支持推理，需要说明性能是在t4上测的在线推理，如果模型不支持batch 16，也需要说明一下~~
+
+
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/env.sh" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/env.sh"
new file mode 100644
index 0000000000000000000000000000000000000000..49be8f16a045f6e4e61666ecc8a5da811c15fd80
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/env.sh"
@@ -0,0 +1,8 @@
+#! /bin/bash
+
+export install_path=/usr/local/Ascend/ascend-toolkit/latest
+export PATH=/usr/local/python3.7.5/bin:${install_path}/atc/ccec_compiler/bin:${install_path}/atc/bin:$PATH
+export PYTHONPATH=${install_path}/atc/python/site-packages:$PYTHONPATH
+export LD_LIBRARY_PATH=${install_path}/atc/lib64:${install_path}/acllib/lib64:$LD_LIBRARY_PATH
+export ASCEND_OPP_PATH=${install_path}/opp
+export ASCEND_AICPU_PATH=/usr/local/Ascend/ascend-toolkit/latest
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/gen_dataset_info.py" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/gen_dataset_info.py"
new file mode 100644
index 0000000000000000000000000000000000000000..80c2b0fc300d7037330a166b23c562015cd17148
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/gen_dataset_info.py"
@@ -0,0 +1,60 @@
+# 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 sys
+import cv2
+from glob import glob
+
+
+def get_bin_info(file_path, info_name, width, height):
+    bin_images = glob(os.path.join(file_path, '*.bin'))
+    with open(info_name, 'w') as file:
+        for index, img in enumerate(bin_images):
+            content = ' '.join([str(index), img, width, height])
+            file.write(content)
+            file.write('\n')
+
+
+def get_jpg_info(file_path, info_name):
+    extensions = ['jpg', 'jpeg', 'JPG', 'JPEG']
+    image_names = []
+    for extension in extensions:
+        image_names.append(glob(os.path.join(file_path, '*.' + extension)))  
+    with open(info_name, 'w') as file:
+        for image_name in image_names:
+            if len(image_name) == 0:
+                continue
+            else:
+                for index, img in enumerate(image_name):
+                    img_cv = cv2.imread(img)
+                    shape = img_cv.shape
+                    width, height = shape[1], shape[0]
+                    content = ' '.join([str(index), img, str(width), str(height)])
+                    file.write(content)
+                    file.write('\n')
+
+
+if __name__ == '__main__':
+    file_type = sys.argv[1]
+    file_path = sys.argv[2]
+    info_name = sys.argv[3]
+    if file_type == 'bin':
+        width = sys.argv[4]
+        height = sys.argv[5]
+        assert len(sys.argv) == 6, 'The number of input parameters must be equal to 5'
+        get_bin_info(file_path, info_name, width, height)
+    elif file_type == 'jpg':
+        assert len(sys.argv) == 4, 'The number of input parameters must be equal to 3'
+        get_jpg_info(file_path, info_name)
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/imagenet_acc_eval.py" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/imagenet_acc_eval.py"
new file mode 100644
index 0000000000000000000000000000000000000000..0e1db27e816a0cf3ec4fb21ee23e691315f3f959
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/imagenet_acc_eval.py"
@@ -0,0 +1,183 @@
+# 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 sys
+import json
+import numpy as np
+import time
+
+np.set_printoptions(threshold=sys.maxsize)
+
+LABEL_FILE = "HiAI_label.json"
+
+
+def gen_file_name(img_name):
+    full_name = img_name.split('/')[-1]
+    index = full_name.rfind('.')
+    return full_name[:index]
+
+
+def cre_groundtruth_dict(gtfile_path):
+    """
+    :param filename: file contains the imagename and label number
+    :return: dictionary key imagename, value is label number
+    """
+    img_gt_dict = {}
+    for gtfile in os.listdir(gtfile_path):
+        if (gtfile != LABEL_FILE):
+            with open(os.path.join(gtfile_path, gtfile), 'r') as f:
+                gt = json.load(f)
+                ret = gt["image"]["annotations"][0]["category_id"]
+                img_gt_dict[gen_file_name(gtfile)] = ret
+    return img_gt_dict
+
+
+def cre_groundtruth_dict_fromtxt(gtfile_path):
+    """
+    :param filename: file contains the imagename and label number
+    :return: dictionary key imagename, value is label number
+    """
+    img_gt_dict = {}
+    with open(gtfile_path, 'r')as f:
+        for line in f.readlines():
+            temp = line.strip().split(" ")
+            imgName = temp[0].split(".")[0]
+            imgLab = temp[1]
+            img_gt_dict[imgName] = imgLab
+    return img_gt_dict
+
+
+def load_statistical_predict_result(filepath):
+    """
+    function:
+    the prediction esult file data extraction
+    input:
+    result file:filepath
+    output:
+    n_label:numble of label
+    data_vec: the probabilitie of prediction in the 1000
+    :return: probabilities, numble of label, in_type, color
+    """
+    with open(filepath, 'r')as f:
+        data = f.readline()
+        temp = data.strip().split(" ")
+        n_label = len(temp)
+        if data == '':
+            n_label = 0
+        data_vec = np.zeros((n_label), dtype=np.float32)
+        in_type = ''
+        color = ''
+        if n_label == 0:
+            in_type = f.readline()
+            color = f.readline()
+        else:
+            for ind, prob in enumerate(temp):
+                data_vec[ind] = np.float32(prob)
+    return data_vec, n_label, in_type, color
+
+
+def create_visualization_statistical_result(prediction_file_path,
+                                            result_store_path, json_file_name,
+                                            img_gt_dict, topn=5):
+    """
+    :param prediction_file_path:
+    :param result_store_path:
+    :param json_file_name:
+    :param img_gt_dict:
+    :param topn:
+    :return:
+    """
+    writer = open(os.path.join(result_store_path, json_file_name), 'w')
+    table_dict = {}
+    table_dict["title"] = "Overall statistical evaluation"
+    table_dict["value"] = []
+
+    count = 0
+    resCnt = 0
+    n_labels = 0
+    count_hit = np.zeros(topn)
+    for tfile_name in os.listdir(prediction_file_path):
+        count += 1
+        temp = tfile_name.split('.')[0]
+        index = temp.rfind('_')
+        img_name = temp[:index]
+        filepath = os.path.join(prediction_file_path, tfile_name)
+        ret = load_statistical_predict_result(filepath)
+        prediction = ret[0]
+        n_labels = ret[1]
+        sort_index = np.argsort(-prediction)
+        gt = img_gt_dict[img_name]
+        if (n_labels == 1000):
+            realLabel = int(gt)
+        elif (n_labels == 1001):
+            realLabel = int(gt) + 1
+        else:
+            realLabel = int(gt)
+
+        resCnt = min(len(sort_index), topn)
+        for i in range(resCnt):
+            if (str(realLabel) == str(sort_index[i])):
+                count_hit[i] += 1
+                break
+
+    if 'value' not in table_dict.keys():
+        print("the item value does not exist!")
+    else:
+        table_dict["value"].extend(
+            [{"key": "Number of images", "value": str(count)},
+             {"key": "Number of classes", "value": str(n_labels)}])
+        if count == 0:
+            accuracy = 0
+        else:
+            accuracy = np.cumsum(count_hit) / count
+        for i in range(resCnt):
+            table_dict["value"].append({"key": "Top" + str(i + 1) + " accuracy",
+                                        "value": str(
+                                            round(accuracy[i] * 100, 2)) + '%'})
+        json.dump(table_dict, writer)
+    writer.close()
+
+
+if __name__ == '__main__':
+    start = time.time()
+    try:
+        # txt file path
+        folder_davinci_target = sys.argv[1]
+        # annotation files path, "val_label.txt"
+        annotation_file_path = sys.argv[2]
+        # the path to store the results json path
+        result_json_path = sys.argv[3]
+        # result json file name
+        json_file_name = sys.argv[4]
+    except IndexError:
+        print("Stopped!")
+        exit(1)
+
+    if not (os.path.exists(folder_davinci_target)):
+        print("target file folder does not exist.")
+
+    if not (os.path.exists(annotation_file_path)):
+        print("Ground truth file does not exist.")
+
+    if not (os.path.exists(result_json_path)):
+        print("Result folder doesn't exist.")
+
+    img_label_dict = cre_groundtruth_dict_fromtxt(annotation_file_path)
+    create_visualization_statistical_result(folder_davinci_target,
+                                            result_json_path, json_file_name,
+                                            img_label_dict, topn=5)
+
+    elapsed = (time.time() - start)
+    print("Time used:", elapsed)
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/imagenet_torch_preprocess.py" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/imagenet_torch_preprocess.py"
new file mode 100644
index 0000000000000000000000000000000000000000..65b50a502a600bc3a8ee420cbdbe3c6f13adc02a
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/imagenet_torch_preprocess.py"
@@ -0,0 +1,116 @@
+# 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 sys
+from PIL import Image
+import numpy as np
+import multiprocessing
+
+
+model_config = {
+    'resnet': {
+        'resize': 256,
+        'centercrop': 224,
+        'mean': [0.485, 0.456, 0.406],
+        'std': [0.229, 0.224, 0.225],
+    },
+    'inceptionv3': {
+        'resize': 342,
+        'centercrop': 299,
+        'mean': [0.485, 0.456, 0.406],
+        'std': [0.229, 0.224, 0.225],
+    },
+    'inceptionv4': {
+        'resize': 342,
+        'centercrop': 299,
+        'mean': [0.5, 0.5, 0.5],
+        'std': [0.5, 0.5, 0.5],
+    },
+}
+
+
+def center_crop(img, output_size):
+    if isinstance(output_size, int):
+        output_size = (int(output_size), int(output_size))
+    image_width, image_height = img.size
+    crop_height, crop_width = output_size
+    crop_top = int(round((image_height - crop_height) / 2.))
+    crop_left = int(round((image_width - crop_width) / 2.))
+    return img.crop((crop_left, crop_top, crop_left + crop_width, crop_top + crop_height))
+
+
+def resize(img, size, interpolation=Image.BILINEAR):
+    if isinstance(size, int):
+        w, h = img.size
+        if (w <= h and w == size) or (h <= w and h == size):
+            return img
+        if w < h:
+            ow = size
+            oh = int(size * h / w)
+            return img.resize((ow, oh), interpolation)
+        else:
+            oh = size
+            ow = int(size * w / h)
+            return img.resize((ow, oh), interpolation)
+    else:
+        return img.resize(size[::-1], interpolation)
+
+
+def gen_input_bin(mode_type, file_batches, batch):
+    i = 0
+    for file in file_batches[batch]:
+        i = i + 1
+        print("batch", batch, file, "===", i)
+
+        # RGBA to RGB
+        image = Image.open(os.path.join(src_path, file)).convert('RGB')
+        image = resize(image, model_config[mode_type]['resize']) # Resize
+        image = center_crop(image, model_config[mode_type]['centercrop']) # CenterCrop
+        img = np.array(image, dtype=np.float32)
+        img = img.transpose(2, 0, 1) # ToTensor: HWC -> CHW
+        img = img / 255. # ToTensor: div 255
+        img -= np.array(model_config[mode_type]['mean'], dtype=np.float32)[:, None, None] # Normalize: mean
+        img /= np.array(model_config[mode_type]['std'], dtype=np.float32)[:, None, None] # Normalize: std
+        img.tofile(os.path.join(save_path, file.split('.')[0] + ".bin"))
+
+
+def preprocess(mode_type, src_path, save_path):
+    files = os.listdir(src_path)
+    file_batches = [files[i:i + 500] for i in range(0, 50000, 500) if files[i:i + 500] != []]
+    thread_pool = multiprocessing.Pool(len(file_batches))
+    for batch in range(len(file_batches)):
+        thread_pool.apply_async(gen_input_bin, args=(mode_type, file_batches, batch))
+    thread_pool.close()
+    thread_pool.join()
+    print("in thread, except will not report! please ensure bin files generated.")
+
+
+if __name__ == '__main__':
+    if len(sys.argv) < 4:
+        raise Exception("usage: python3 xxx.py [model_type] [src_path] [save_path]")
+    mode_type = sys.argv[1]
+    src_path = sys.argv[2]
+    save_path = sys.argv[3]
+    src_path = os.path.realpath(src_path)
+    save_path = os.path.realpath(save_path)
+    if mode_type not in model_config:
+        model_type_help = "model type: "
+        for key in model_config.keys():
+            model_type_help += key
+            model_type_help += ' '
+        raise Exception(model_type_help)
+    if not os.path.isdir(save_path):
+        os.makedirs(os.path.realpath(save_path))
+    preprocess(mode_type, src_path, save_path)
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/requirements.txt" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/requirements.txt"
new file mode 100644
index 0000000000000000000000000000000000000000..8f0de9561f6cdbfe3facbf31905aee0e9bf7fa9e
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/requirements.txt"
@@ -0,0 +1,6 @@
+torch == 1.5.0
+torchvision == 0.6.0
+onnx == 1.7.0
+numpy == 1.20.3
+Pillow == 8.2.0
+opencv-python == 4.5.2.54
\ No newline at end of file
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/resnext50_pth2onnx.py" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/resnext50_pth2onnx.py"
new file mode 100644
index 0000000000000000000000000000000000000000..8e180e9245b3f8964b98196e080e3e6e8835077b
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/resnext50_pth2onnx.py"
@@ -0,0 +1,35 @@
+# 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 sys
+import torch
+import torch.onnx
+import torchvision.models as models
+
+def pth2onnx(input_file, output_file):
+    model = models.resnext50_32x4d(pretrained=False)
+    checkpoint = torch.load(input_file, map_location=None)
+    model.load_state_dict(checkpoint)
+
+    model.eval()
+    input_names = ["image"]
+    output_names = ["class"]
+    dynamic_axes = {'image': {0: '-1'}, 'class': {0: '-1'}}
+    dummy_input = torch.randn(1, 3, 224, 224)
+    torch.onnx.export(model, dummy_input, output_file, input_names = input_names, dynamic_axes = dynamic_axes, output_names = output_names, verbose=True, opset_version=11)
+
+if __name__ == "__main__":
+    input_file = sys.argv[1]
+    output_file = sys.argv[2]
+    pth2onnx(input_file, output_file)
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/test/README.md" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/test/README.md"
new file mode 100644
index 0000000000000000000000000000000000000000..c25279493efdab395677a1e991caa2467ca60a11
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/test/README.md"
@@ -0,0 +1,33 @@
+环境准备：  
+
+1.数据集路径  
+通用的数据集统一放在/root/datasets/或/opt/npu/  
+本模型数据集放在/root/datasets/  
+
+2.进入工作目录  
+cd ResNext50  
+
+3.安装必要的依赖，测试环境可能已经安装其中的一些不同版本的库了，故手动测试时不推荐使用该命令安装  
+pip3.7 install -r requirements.txt 
+
+4.获取，修改与安装开源模型代码  
+git clone https://github.com/pytorch/vision   
+cd vision  
+如果修改了模型代码，交付了{model_name}.diff  
+patch -p1 < ../{model_name}.diff  
+如果模型代码需要安装，则安装模型代码(如果没有安装脚本，pth2onnx等脚本需要引用模型代码的类或函数，可通过sys.path.append(r"./vision")添加搜索路径的方式)  
+python3.7 setup.py install  
+cd ..  
+
+5.获取权重文件  
+wget https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth  
+
+6.获取benchmark工具  
+将benchmark.x86_64 benchmark.aarch64放在当前目录  
+
+7.310上执行，执行时确保device空闲  
+bash test/pth2om.sh  
+bash test/eval_acc_perf.sh --datasets_path=/root/datasets  
+
+8.在t4环境上将onnx文件与perf_t4.sh放在同一目录  
+然后执行bash perf_t4.sh，执行时确保gpu空闲  
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/test/eval_acc_perf.sh" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/test/eval_acc_perf.sh"
new file mode 100644
index 0000000000000000000000000000000000000000..46fcfc68792b801f73d1f214c9fb97a0a0020731
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/test/eval_acc_perf.sh"
@@ -0,0 +1,69 @@
+#!/bin/bash
+
+datasets_path="/root/datasets/"
+
+for para in $*
+do
+    if [[ $para == --datasets_path* ]]; then
+        datasets_path=`echo ${para#*=}`
+    fi
+done
+
+arch=`uname -m`
+rm -rf ./prep_dataset
+python3.7 imagenet_torch_preprocess.py resnet ${datasets_path}/imagenet/val ./prep_dataset
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+python3.7 gen_dataset_info.py bin ./prep_dataset ./resnext50_prep_bin.info 224 224
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+source env.sh
+rm -rf result/dumpOutput_device0
+./benchmark.${arch} -model_type=vision -device_id=0 -batch_size=1 -om_path=resnext50_bs1.om -input_text_path=./resnext50_prep_bin.info -input_width=224 -input_height=224 -output_binary=False -useDvpp=False
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+rm -rf result/dumpOutput_device1
+./benchmark.${arch} -model_type=vision -device_id=1 -batch_size=16 -om_path=resnext50_bs16.om -input_text_path=./resnext50_prep_bin.info -input_width=224 -input_height=224 -output_binary=False -useDvpp=False
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+python3.7 imagenet_acc_eval.py result/dumpOutput_device0/ ${datasets_path}/imagenet/val_label.txt ./ result_bs1.json
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+python3.7 imagenet_acc_eval.py result/dumpOutput_device1/ ${datasets_path}/imagenet/val_label.txt ./ result_bs16.json
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+echo "====accuracy data===="
+python3.7 test/parse.py result_bs1.json
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+python3.7 test/parse.py result_bs16.json
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+echo "====performance data===="
+python3.7 test/parse.py result/perf_vision_batchsize_1_device_0.txt
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+python3.7 test/parse.py result/perf_vision_batchsize_16_device_1.txt
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+echo "success"
\ No newline at end of file
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/test/parse.py" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/test/parse.py"
new file mode 100644
index 0000000000000000000000000000000000000000..b9c74f41d7848e1250356f14472b237a18bb3489
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/test/parse.py"
@@ -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.
+
+import sys
+import json
+import re
+
+if __name__ == '__main__':
+    if sys.argv[1].endswith('.json'):
+        result_json = sys.argv[1]
+        with open(result_json, 'r') as f:
+            content = f.read()
+        tops = [i.get('value') for i in json.loads(content).get('value') if 'Top' in i.get('key')]
+        print('om {} top1:{} top5:{}'.format(result_json.split('_')[1].split('.')[0], tops[0], tops[4]))
+    elif sys.argv[1].endswith('.txt'):
+        result_txt = sys.argv[1]
+        with open(result_txt, 'r') as f:
+            content = f.read()
+        txt_data_list = [i.strip() for i in re.findall(r':(.*?),', content.replace('\n', ',') + ',')]
+        fps = float(txt_data_list[7].replace('samples/s', '')) * 4
+        print('310 bs{} fps:{}'.format(result_txt.split('_')[3], fps))
\ No newline at end of file
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/test/perf_t4.sh" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/test/perf_t4.sh"
new file mode 100644
index 0000000000000000000000000000000000000000..aee41c4db7a3e56e608c84c89f5cac1d532458d8
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/test/perf_t4.sh"
@@ -0,0 +1,22 @@
+#!/bin/bash
+
+# T4上执行：
+trtexec --onnx=resnext50.onnx --fp16 --shapes=image:1x3x224x224 --threads > resnext50_bs1.log
+perf_str=`grep "GPU.* mean.*ms$" resnext50_bs1.log`
+if [ -n "$perf_str" ]; then
+    perf_num=`echo $perf_str | awk -F' ' '{print $16}'`
+else
+    perf_str=`grep "mean.*ms$" resnext50_bs1.log`
+    perf_num=`echo $perf_str | awk -F' ' '{print $4}'`
+fi
+awk 'BEGIN{printf "t4 bs1 fps:%.3f\n", 1000*1/('$perf_num'/1)}'
+
+trtexec --onnx=resnext50.onnx --fp16 --shapes=image:16x3x224x224 --threads > resnext50_bs16.log
+perf_str=`grep "GPU.* mean.*ms$" resnext50_bs16.log`
+if [ -n "$perf_str" ]; then
+    perf_num=`echo $perf_str | awk -F' ' '{print $16}'`
+else
+    perf_str=`grep "mean.*ms$" resnext50_bs16.log`
+    perf_num=`echo $perf_str | awk -F' ' '{print $4}'`
+fi
+awk 'BEGIN{printf "t4 bs16 fps:%.3f\n", 1000*1/('$perf_num'/16)}'
\ No newline at end of file
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/test/pth2om.sh" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/test/pth2om.sh"
new file mode 100644
index 0000000000000000000000000000000000000000..eaf285c653a1a928765ca6c650d557b89e364607
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/ResNext50/test/pth2om.sh"
@@ -0,0 +1,13 @@
+#!/bin/bash
+
+rm -rf resnext50.onnx
+python3.7 resnext50_pth2onnx.py resnext50_32x4d-7cdf4587.pth resnext50.onnx
+source env.sh
+rm -rf resnext50_bs1.om resnext50_bs16.om
+atc --framework=5 --model=./resnext50.onnx --input_format=NCHW --input_shape="image:1,3,224,224" --output=resnext50_bs1 --log=debug --soc_version=Ascend310
+atc --framework=5 --model=./resnext50.onnx --input_format=NCHW --input_shape="image:16,3,224,224" --output=resnext50_bs16 --log=debug --soc_version=Ascend310
+if [ -f "resnext50_bs1.om" ] && [ -f "resnext50_bs16.om" ]; then
+    echo "success"
+else
+    echo "fail!"
+fi
\ No newline at end of file
diff --git "a/onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/segmentation/ssd_detection.diff" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/segmentation/ssd_detection.diff"
similarity index 100%
rename from "onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/segmentation/ssd_detection.diff"
rename to "Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/segmentation/ssd_detection.diff"
diff --git "a/onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/segmentation/\345\237\272\344\272\216detectron2\350\256\255\347\273\203\347\232\204npu\346\235\203\351\207\215\347\232\204maskrcnn_Onnx\346\250\241\345\236\213\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274.md" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/segmentation/\345\237\272\344\272\216detectron2\350\256\255\347\273\203\347\232\204npu\346\235\203\351\207\215\347\232\204maskrcnn_Onnx\346\250\241\345\236\213\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274.md"
similarity index 100%
rename from "onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/segmentation/\345\237\272\344\272\216detectron2\350\256\255\347\273\203\347\232\204npu\346\235\203\351\207\215\347\232\204maskrcnn_Onnx\346\250\241\345\236\213\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274.md"
rename to "Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/segmentation/\345\237\272\344\272\216detectron2\350\256\255\347\273\203\347\232\204npu\346\235\203\351\207\215\347\232\204maskrcnn_Onnx\346\250\241\345\236\213\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274.md"
diff --git "a/onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/segmentation/\345\237\272\344\272\216\345\274\200\346\272\220mmdetection\351\242\204\350\256\255\347\273\203\347\232\204maskrcnn_Onnx\346\250\241\345\236\213\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274.md" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/segmentation/\345\237\272\344\272\216\345\274\200\346\272\220mmdetection\351\242\204\350\256\255\347\273\203\347\232\204maskrcnn_Onnx\346\250\241\345\236\213\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274.md"
similarity index 100%
rename from "onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/segmentation/\345\237\272\344\272\216\345\274\200\346\272\220mmdetection\351\242\204\350\256\255\347\273\203\347\232\204maskrcnn_Onnx\346\250\241\345\236\213\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274.md"
rename to "Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/segmentation/\345\237\272\344\272\216\345\274\200\346\272\220mmdetection\351\242\204\350\256\255\347\273\203\347\232\204maskrcnn_Onnx\346\250\241\345\236\213\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274.md"
diff --git a/docs/.keep "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/nlp/.keep"
similarity index 100%
rename from docs/.keep
rename to "Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/nlp/.keep"
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/LICENSE" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/LICENSE"
new file mode 100644
index 0000000000000000000000000000000000000000..56ee3c8c4cc2b4b32e0975d17258f9ba515fdbcc
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/LICENSE"
@@ -0,0 +1,201 @@
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\ No newline at end of file
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/README.md" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/README.md"
new file mode 100644
index 0000000000000000000000000000000000000000..c5f37d0c9ef5f29c0ff618e9df896b417da223e9
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/README.md"
@@ -0,0 +1,53 @@
+$\color{red}{说明：删除线用于READ.md的说明，以下带有删除线的说明在README.md中需要删除}$  
+
+# ReID-strong-baseline模型PyTorch离线推理指导
+
+## 1 环境准备 
+
+1.安装必要的依赖，测试环境可能已经安装其中的一些不同版本的库了，故手动测试时不推荐使用该命令安装  
+```
+pip3.7 install -r requirements.txt  
+```
+~~需要使用python3.7命令执行脚本，pip3.7命令安装库，torch使用1.5.0版本，如果开源模型代码导出onnx要求torch版本大于1.5.0，则使用1.8.0版本，并在此处说明。onnx可以选择1.9.0。requirements.txt中需要写明本模型离线推理所有必要依赖库的具体版本，版本号即是推理310服务器上推理时使用库的版本号，常用库包括numpy，Pillow，opencv-python等。目前atc工具支持的onnx算子版本opset_version为11。~~
+
+
+2.获取，修改与安装开源模型代码  
+```
+git clone https://github.com/michuanhaohao/reid-strong-baseline -b master   
+cd reid-strong-baseline  
+git reset 3da7e6f03164a92e696cb6da059b1cd771b0346d --hard
+cd ..  
+```
+~~优先使用本任务提供的开源代码仓，并基于分支与commit id的代码做离线推理，分支一般选择master或稳定的版本，需要从github的commits中找到commit id，通常选择稳定版本的最后一次提交，或代码仓最新的一次提交~~  
+~~如果需要对模型的开源代码做修改，以打patch的形式修改后再安装：patch -p1 < ../{patch_name}.diff~~  
+~~如果开源模型代码仓没有安装脚本，可以通过sys.path.append(r"./reid-strong-baseline")添加搜索路径，然后在pth2onnx脚本中就可以引用模型代码的函数或类~~  
+
+3.获取权重文件  
+
+[market_resnet50_model_120_rank1_945.pth](https://drive.google.com/open?id=1hn0sXLZ5yJcxtmuY-ItQfYD7hBtHwt7A)  
+~~优先使用训练提供的权重文件，如果训练的权重文件网上能获则需给出网址，否则需要给出从哪获取权重文件。如果训练没有提供权重则使用开源代码仓的权重文件。需要给出权重文件名~~  
+
+4.数据集     
+[获取Market1501](http://www.liangzheng.org/Project/project_reid.html)，并重命名为market1501  
+
+5.[获取benchmark工具](https://support.huawei.com/enterprise/zh/ascend-computing/cann-pid-251168373/software/)  
+将benchmark.x86_64或benchmark.aarch64放到当前目录  
+
+## 2 离线推理 
+
+310上执行，执行时使npu-smi info查看设备状态，确保device空闲  
+```
+bash test/pth2om.sh  
+bash test/eval_acc_perf.sh --datasets_path=/root/datasets  
+```
+ **评测结果：**   
+| 模型      | 官网pth精度  | 310离线推理精度  | gpu性能    | 310性能    |
+| :------: | :------: | :------: | :------:  | :------:  | 
+| ReID-strong-baseline bs1  | [rank1:94.5% mAP:85.9%](https://github.com/michuanhaohao/reid-strong-baseline) | rank1:94.5% mAP:85.9% |  992.9994fps | 1446.188fps | 
+| ReID-strong-baseline bs16 | [rank1:94.5% mAP:85.9%](https://github.com/michuanhaohao/reid-strong-baseline) | rank1:94.5% mAP:85.9% | 2211.7074fps | 2588.56fps | 
+
+备注：  
+加上TEST.NECK_FEAT "('before')" TEST.FEAT_NORM "('no')"导出的onnx可以进行离线推理  
+不加TEST.NECK_FEAT "('before')" TEST.FEAT_NORM "('no')"导出的onnx转换的om精度与官网精度一致  
+
+
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/ReID_postprocess.py" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/ReID_postprocess.py"
new file mode 100644
index 0000000000000000000000000000000000000000..0af4cb769d1e1e998e62e37aee621d542d94115a
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/ReID_postprocess.py"
@@ -0,0 +1,73 @@
+# 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 sys
+sys.path.append('./reid-strong-baseline')
+import os
+import argparse
+import glob
+import re
+import numpy as np
+import torch
+from utils.reid_metric import R1_mAP
+
+def get_pred_label(label_dir, pre_dir):
+    img_paths = glob.glob(os.path.join(label_dir, '*.jpg'))
+    pattern = re.compile(r'([-\d]+)_c(\d)')
+
+    outputs = []
+    for img_path in img_paths:
+        pid, camid = map(int, pattern.search(img_path).groups())
+        if pid == -1: continue  # junk images are just ignored
+        camid -= 1  # index starts from 0
+
+        filename = img_path.split("/")[-1]
+        if filename[-8:] == ".jpg.jpg":
+            bin_file = filename[:-8] + "_1.bin"
+        else:
+            bin_file = filename[:-4] + "_1.bin"
+        output = np.fromfile(os.path.join(pre_dir, bin_file), dtype="float32")
+        output = torch.from_numpy(output)
+        output = output.unsqueeze(0)
+
+        pid = torch.from_numpy(np.array([pid,]))
+        camid = torch.from_numpy(np.array([camid,]))
+        outputs.append((output, pid, camid))
+
+    return outputs
+
+def eval(query_dir, gallery_dir, pred_dir):
+
+    query = get_pred_label(query_dir, pred_dir)
+    gallery = get_pred_label(gallery_dir, pred_dir)
+    outputs = query + gallery
+
+    num_query = 3368
+    eval = R1_mAP(num_query, max_rank=50, feat_norm="yes")
+    eval.reset()
+    for output in outputs:
+        eval.update(output)
+    cmc, mAP = eval.compute()
+    print('Validation Results')
+    print("mAP: {:.1%}".format(mAP))
+    for r in [1, 5, 10]:
+        print("CMC curve, Rank-{:<3}:{:.1%}".format(r, cmc[r - 1]))
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--query_dir", default="./data/market1501/query")
+    parser.add_argument("--gallery_dir", default="./data/market1501/bounding_box_test")
+    parser.add_argument("--pred_dir", default="./result/dumpOutput_device0/")
+    args = parser.parse_args()
+    eval(args.query_dir, args.gallery_dir, args.pred_dir)
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/ReID_preprocess.py" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/ReID_preprocess.py"
new file mode 100644
index 0000000000000000000000000000000000000000..c296fb121ebb4f91f918d885d51b9288c451d70e
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/ReID_preprocess.py"
@@ -0,0 +1,56 @@
+# 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 sys
+import numpy as np
+from PIL import Image
+from torchvision import transforms
+import multiprocessing
+
+preprocess = transforms.Compose([
+        transforms.Resize([256, 128]),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+def gen_input_bin(file_batches, batch):
+    i = 0
+    for file in file_batches[batch]:
+        if ".db" in file:
+            continue
+        i = i + 1
+        print("batch", batch, file, "===", i)
+
+        input_image = Image.open(os.path.join(src_path, file)).convert('RGB')
+        input_tensor = preprocess(input_image)
+        img = np.array(input_tensor).astype(np.float32)
+        img.tofile(os.path.join(save_path, file.split('.')[0] + ".bin"))
+
+def ReID_preprocess(src_path, save_path):
+    files = os.listdir(src_path)
+    file_batches = [files[i:i + 500] for i in range(0, 50000, 500) if files[i:i + 500] != []]
+    thread_pool = multiprocessing.Pool(len(file_batches))
+    for batch in range(len(file_batches)):
+        thread_pool.apply_async(gen_input_bin, args=(file_batches, batch))
+    thread_pool.close()
+    thread_pool.join()
+    print("in thread, except will not report! please ensure bin files generated.")
+
+if __name__ == '__main__':
+    src_path = sys.argv[1]
+    save_path = sys.argv[2]
+    if not os.path.isdir(save_path):
+        os.makedirs(os.path.realpath(save_path))
+    ReID_preprocess(src_path, save_path)
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/ReID_pth2onnx.py" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/ReID_pth2onnx.py"
new file mode 100644
index 0000000000000000000000000000000000000000..659451d4ade18d31b45aecf33889d3b548e4c5ba
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/ReID_pth2onnx.py"
@@ -0,0 +1,63 @@
+# 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 sys
+import argparse
+import torch
+import torch.onnx
+sys.path.append('./reid-strong-baseline')
+from config import cfg
+from modeling import build_model
+
+from collections import OrderedDict
+def proc_nodes_module(checkpoint):
+    new_state_dict = OrderedDict()
+    for k, v in checkpoint.items():
+        if "classifier" in k:
+            continue
+        new_state_dict[k] = v
+    return new_state_dict
+
+def main():
+    parser = argparse.ArgumentParser(description="ReID Baseline Inference")
+    parser.add_argument(
+        "--config_file", default="", help="path to config file", type=str
+    )
+    parser.add_argument("opts", help="Modify config options using the command-line", default=None,
+                        nargs=argparse.REMAINDER)
+    args = parser.parse_args()
+
+    cfg.merge_from_file(args.config_file)
+    cfg.merge_from_list(args.opts)
+    cfg.freeze()
+
+    num_classes = 751
+    model = build_model(cfg, num_classes)
+    checkpoint = torch.load(cfg.TEST.WEIGHT, map_location='cpu')
+    #checkpoint = proc_nodes_module(checkpoint)
+    model.load_state_dict(checkpoint)
+    model.eval()
+
+    input_names = ["image"]
+    output_names = ["class"]
+    dynamic_axes = {'image': {0: '-1'}, 'class': {0: '-1'}}
+    dummy_input = torch.randn(1, 3, 256, 128)
+    export_onnx_file = "ReID.onnx"
+
+    torch.onnx.export(model, dummy_input, export_onnx_file, input_names=input_names, dynamic_axes=dynamic_axes,
+                      output_names=output_names, opset_version=11, verbose=True)
+
+if __name__ == '__main__':
+    main()
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/env.sh" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/env.sh"
new file mode 100644
index 0000000000000000000000000000000000000000..49be8f16a045f6e4e61666ecc8a5da811c15fd80
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/env.sh"
@@ -0,0 +1,8 @@
+#! /bin/bash
+
+export install_path=/usr/local/Ascend/ascend-toolkit/latest
+export PATH=/usr/local/python3.7.5/bin:${install_path}/atc/ccec_compiler/bin:${install_path}/atc/bin:$PATH
+export PYTHONPATH=${install_path}/atc/python/site-packages:$PYTHONPATH
+export LD_LIBRARY_PATH=${install_path}/atc/lib64:${install_path}/acllib/lib64:$LD_LIBRARY_PATH
+export ASCEND_OPP_PATH=${install_path}/opp
+export ASCEND_AICPU_PATH=/usr/local/Ascend/ascend-toolkit/latest
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/gen_dataset_info.py" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/gen_dataset_info.py"
new file mode 100644
index 0000000000000000000000000000000000000000..f80f45a34c450d57f0ea49d93167892d93a30e88
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/gen_dataset_info.py"
@@ -0,0 +1,60 @@
+# 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 sys
+import cv2
+from glob import glob
+
+
+def get_bin_info(file_path, info_name, width, height):
+    bin_images = glob(os.path.join(file_path, '*.bin'))
+    with open(info_name, 'w') as file:
+        for index, img in enumerate(bin_images):
+            content = ' '.join([str(index), img, width, height])
+            file.write(content)
+            file.write('\n')
+
+
+def get_jpg_info(file_path, info_name):
+    extensions = ['jpg', 'jpeg', 'JPG', 'JPEG']
+    image_names = []
+    for extension in extensions:
+        image_names.append(glob(os.path.join(file_path, '*.' + extension)))  
+    with open(info_name, 'w') as file:
+        for image_name in image_names:
+            if len(image_name) == 0:
+                continue
+            else:
+                for index, img in enumerate(image_name):
+                    img_cv = cv2.imread(img)
+                    shape = img_cv.shape
+                    width, height = shape[1], shape[0]
+                    content = ' '.join([str(index), img, str(width), str(height)])
+                    file.write(content)
+                    file.write('\n')
+
+
+if __name__ == '__main__':
+    file_type = sys.argv[1]
+    file_path = sys.argv[2]
+    info_name = sys.argv[3]
+    if file_type == 'bin':
+        width = sys.argv[4]
+        height = sys.argv[5]
+        assert len(sys.argv) == 6, 'The number of input parameters must be equal to 5'
+        get_bin_info(file_path, info_name, width, height)
+    elif file_type == 'jpg':
+        assert len(sys.argv) == 4, 'The number of input parameters must be equal to 3'
+        get_jpg_info(file_path, info_name)
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/modelzoo_level.txt" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/modelzoo_level.txt"
new file mode 100644
index 0000000000000000000000000000000000000000..9e95396651cc4382fe60ee1ee053674f527a448c
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/modelzoo_level.txt"
@@ -0,0 +1,4 @@
+FuncStatus:OK
+PrecisionStatus:OK
+AutoTune:OK
+PerfStatus:POK
\ No newline at end of file
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/requirements.txt" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/requirements.txt"
new file mode 100644
index 0000000000000000000000000000000000000000..4cda32191fe9ee542596389f75f4377bd38521d8
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/requirements.txt"
@@ -0,0 +1,8 @@
+torch == 1.5.0
+torchvision == 0.6.0
+onnx == 1.7.0
+numpy == 1.20.3
+Pillow == 8.2.0
+opencv-python == 4.5.2.54
+yacs == 0.1.8
+pytorch-ignite == 0.4.5
\ No newline at end of file
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/test/eval_acc_perf.sh" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/test/eval_acc_perf.sh"
new file mode 100644
index 0000000000000000000000000000000000000000..1bfa7cf5aa72efe9118b025030f52d9c17b89248
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/test/eval_acc_perf.sh"
@@ -0,0 +1,72 @@
+#!/bin/bash
+
+datasets_path="/root/datasets/"
+
+for para in $*
+do
+    if [[ $para == --datasets_path* ]]; then
+        datasets_path=`echo ${para#*=}`
+    fi
+done
+
+arch=`uname -m`
+rm -rf ./prep_dataset_query
+rm -rf ./prep_dataset_gallery
+python3.7 ReID_preprocess.py ${datasets_path}/market1501/query ./prep_dataset_query
+python3.7 ReID_preprocess.py ${datasets_path}/market1501/bounding_box_test ./prep_dataset_gallery
+mv prep_dataset_gallery/* prep_dataset_query/
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+python3.7 gen_dataset_info.py bin ./prep_dataset_query ./prep_bin.info 128 256
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+source env.sh
+rm -rf result/dumpOutput_device0
+./benchmark.${arch} -model_type=vision -device_id=0 -batch_size=1 -om_path=./ReID_bs1.om -input_text_path=./prep_bin.info -input_width=128 -input_height=256 -output_binary=True -useDvpp=False
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+rm -rf result/dumpOutput_device1
+./benchmark.${arch} -model_type=vision -device_id=1 -batch_size=16 -om_path=./ReID_bs16.om -input_text_path=./prep_bin.info -input_width=128 -input_height=256 -output_binary=True -useDvpp=False
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+python3.7 ReID_postprocess.py --query_dir=${datasets_path}/market1501/query --gallery_dir=${datasets_path}/market1501/bounding_box_test --pred_dir=./result/dumpOutput_device0 > result_bs1.json
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+python3.7 ReID_postprocess.py --query_dir=${datasets_path}/market1501/query --gallery_dir=${datasets_path}/market1501/bounding_box_test --pred_dir=./result/dumpOutput_device1 > result_bs16.json
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+echo "====accuracy data===="
+python3.7 test/parse.py result_bs1.json
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+python3.7 test/parse.py result_bs16.json
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+echo "====performance data===="
+python3.7 test/parse.py result/perf_vision_batchsize_1_device_0.txt
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+python3.7 test/parse.py result/perf_vision_batchsize_16_device_1.txt
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+echo "success"
\ No newline at end of file
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/test/parse.py" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/test/parse.py"
new file mode 100644
index 0000000000000000000000000000000000000000..6d5a1293288dce8bfc70f79ecf6551acafed6b81
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/test/parse.py"
@@ -0,0 +1,33 @@
+# 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 sys
+import json
+import re
+
+if __name__ == '__main__':
+    if sys.argv[1].endswith('.json'):
+        result_json = sys.argv[1]
+        with open(result_json, 'r') as f:
+            content = f.read()
+        #tops = [i.get('value') for i in json.loads(content).get('value') if 'Top' in i.get('key')]
+        #print('om {} top1:{} top5:{}'.format(result_json.split('_')[1].split('.')[0], tops[0], tops[4]))
+        print(content)
+    elif sys.argv[1].endswith('.txt'):
+        result_txt = sys.argv[1]
+        with open(result_txt, 'r') as f:
+            content = f.read()
+        txt_data_list = [i.strip() for i in re.findall(r':(.*?),', content.replace('\n', ',') + ',')]
+        fps = float(txt_data_list[7].replace('samples/s', '')) * 4
+        print('310 bs{} fps:{}'.format(result_txt.split('_')[3], fps))
\ No newline at end of file
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/test/perf_g.sh" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/test/perf_g.sh"
new file mode 100644
index 0000000000000000000000000000000000000000..a1c5a64463d3165909d67bffe256b4f69732e50e
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/test/perf_g.sh"
@@ -0,0 +1,19 @@
+trtexec --onnx=ReID.onnx  --fp16 --shapes=image:1x3x256x128 > ReID_bs1.log
+perf_str=`grep "GPU.* mean.*ms$" ReID_bs1.log`
+if [ -n "$perf_str" ]; then
+    perf_num=`echo $perf_str | awk -F' ' '{print $16}'`
+else
+    perf_str=`grep "mean.*ms$" ReID_bs1.log`
+    perf_num=`echo $perf_str | awk -F' ' '{print $4}'`
+fi
+awk 'BEGIN{printf "t4 bs1 fps:%.3f\n", 1000*1/('$perf_num'/1)}'
+
+trtexec --onnx=ReID.onnx  --fp16 --shapes=image:16x3x256x128 > ReID_bs16.log
+perf_str=`grep "GPU.* mean.*ms$" ReID_bs16.log`
+if [ -n "$perf_str" ]; then
+    perf_num=`echo $perf_str | awk -F' ' '{print $16}'`
+else
+    perf_str=`grep "mean.*ms$" ReID_bs16.log`
+    perf_num=`echo $perf_str | awk -F' ' '{print $4}'`
+fi
+awk 'BEGIN{printf "t4 bs16 fps:%.3f\n", 1000*1/('$perf_num'/16)}'
\ No newline at end of file
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/test/pth2om.sh" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/test/pth2om.sh"
new file mode 100644
index 0000000000000000000000000000000000000000..393275a3c0ecde085fcc643e29e828d28d81dd67
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/official/cv/ReID/ReID-strong-baseline/test/pth2om.sh"
@@ -0,0 +1,19 @@
+#!/bin/bash
+
+rm -rf ReID.onnx
+python3.7 ReID_pth2onnx.py --config_file='reid-strong-baseline/configs/softmax_triplet_with_center.yml' MODEL.PRETRAIN_CHOICE "('self')" TEST.WEIGHT "('market_resnet50_model_120_rank1_945.pth')" TEST.NECK_FEAT "('before')" TEST.FEAT_NORM "('no')"
+if [ $? != 0 ]; then
+    echo "fail!"
+    exit -1
+fi
+
+rm -rf ReID_bs1.om ReID_bs16.om
+source env.sh
+atc --framework=5 --model=ReID.onnx --output=ReID_bs1 --input_format=NCHW --input_shape="image:1,3,256,128" --log=debug --soc_version=Ascend310
+atc --framework=5 --model=ReID.onnx --output=ReID_bs16 --input_format=NCHW --input_shape="image:16,3,256,128" --log=debug --soc_version=Ascend310 --auto_tune_mode="RL,GA"
+
+if [ -f "ReID_bs1.om" ] && [ -f "ReID_bs16.om" ]; then
+    echo "success"
+else
+    echo "fail!"
+fi
\ No newline at end of file
diff --git "a/onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/.keep" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/research/.keep"
similarity index 100%
rename from "onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/.keep"
rename to "Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/ONNX\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274/research/.keep"
diff --git "a/AscendPytorch\346\250\241\345\236\213\346\216\250\347\220\206\344\274\227\346\231\272FAQ.md" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-FAQ.md"
similarity index 100%
rename from "AscendPytorch\346\250\241\345\236\213\346\216\250\347\220\206\344\274\227\346\231\272FAQ.md"
rename to "Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-FAQ.md"
diff --git "a/docs/XxxxNet\347\275\221\347\273\234\346\250\241\345\236\213[\344\272\244\344\273\230\345\206\205\345\256\271]\346\265\213\350\257\225\346\212\245\345\221\212.docx" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-XxxxNet\347\275\221\347\273\234\346\250\241\345\236\213[\344\272\244\344\273\230\345\206\205\345\256\271]\346\265\213\350\257\225\346\212\245\345\221\212.docx"
similarity index 100%
rename from "docs/XxxxNet\347\275\221\347\273\234\346\250\241\345\236\213[\344\272\244\344\273\230\345\206\205\345\256\271]\346\265\213\350\257\225\346\212\245\345\221\212.docx"
rename to "Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-XxxxNet\347\275\221\347\273\234\346\250\241\345\236\213[\344\272\244\344\273\230\345\206\205\345\256\271]\346\265\213\350\257\225\346\212\245\345\221\212.docx"
diff --git "a/onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/.keep" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-Xxx\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274\344\271\246.docx"
similarity index 100%
rename from "onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/.keep"
rename to "Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-Xxx\346\250\241\345\236\213\346\216\250\347\220\206\346\214\207\345\257\274\344\271\246.docx"
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-Xxx\346\250\241\345\236\213\346\265\213\350\257\225\346\212\245\345\221\212.docx" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-Xxx\346\250\241\345\236\213\346\265\213\350\257\225\346\212\245\345\221\212.docx"
new file mode 100644
index 0000000000000000000000000000000000000000..fd5fb68b511e1abc0ed06f9b2da249026fa813cb
Binary files /dev/null and "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-Xxx\346\250\241\345\236\213\346\265\213\350\257\225\346\212\245\345\221\212.docx" differ
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-issue\346\250\241\346\235\277.md" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-issue\346\250\241\346\235\277.md"
new file mode 100644
index 0000000000000000000000000000000000000000..d47164e36c4d1d3c7ab1cf5234ea6fb06bacc3f0
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-issue\346\250\241\346\235\277.md"
@@ -0,0 +1,21 @@
+标题：
+[众智-PyTorch离线推理] [问题求助] - xx算子耗时长
+
+
+一、问题现象（附截图）：
+xx模型迁移到Ascend310上，pytorch->onnx->om，模型性能不达标，原因为算子性能差，profiling数据截图如下：
+
+
+
+
+二、软件版本:
+-- Pytorch 版本 (源码或二进制):
+-- Python 版本 (e.g., Python 3.7.5):
+-- 操作系统版本 (e.g., Ubuntu 18.04):
+-- CANN 版本 (e.g., CANNN 5.0.1):
+
+
+提供附件：
+1. profiling原始数据
+2. onnx模型与bs16的om模型
+3.PyToch离线推理xxx模型性能不达标测试报告.docx
\ No newline at end of file
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-models_result.xlsx" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-models_result.xlsx"
new file mode 100644
index 0000000000000000000000000000000000000000..1f95acd26098ea6f384121566b7ab6b8b8ef27b3
Binary files /dev/null and "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-models_result.xlsx" differ
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-\347\216\257\345\242\203\351\203\250\347\275\262\344\270\216\344\275\277\347\224\250\350\257\264\346\230\216.md" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-\347\216\257\345\242\203\351\203\250\347\275\262\344\270\216\344\275\277\347\224\250\350\257\264\346\230\216.md"
new file mode 100644
index 0000000000000000000000000000000000000000..5b38f1e95710f16a487ebb1da2bd36bcae99ec68
--- /dev/null
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-\347\216\257\345\242\203\351\203\250\347\275\262\344\270\216\344\275\277\347\224\250\350\257\264\346\230\216.md"
@@ -0,0 +1,115 @@
+# 310服务器部署与说明
+- CANN包安装  
+  - 1.获取CANN包  
+    https://www.hiascend.com/software/cann/community  
+  - 2.卸载老CANN包  
+    bash A300-3000-3010-npu-firmware_1.77.22.6.220.run --uninstall  
+    rm -rf /usr/local/Ascend  
+    reboot  
+  - 3.安装CANN包  
+    bash A300-3010-npu-driver_21.0.1_ubuntu18.04-x86_64.run --full  
+    bash A300-3000-3010-npu-firmware_1.77.22.6.220.run --full  
+    reboot  
+    bash Ascend-cann-toolkit_5.0.1_linux-x86_64.run --install  
+  - 4.获取benchmark工具  
+    unzip Ascend-cann-benchmark_5.0.1-Linux-x86_64.zip  
+
+  >**说明：** 
+  >
+  >若报无HwHiAiUser用户则执行useradd HwHiAiUser  
+  >安装固件若报Not a physical-machine, firmware upgrade does not support.则不必安装固件  
+  >若报错ls: cannot access '.../5.0.1/x86_64-linux/toolkit/python/site-packages/bin': No such file or directory，则```export PATH=/usr/local/python3.7.5/bin:$PATH;export LD_LIBRARY_PATH=/usr/local/python3.7.5/lib:$LD_LIBRARY_PATH```  
+  >可以指定安装路径bash Ascend-cann-toolkit_5.0.1_linux-x86_64.run --install-path=/home/test --install，然后环境变量的CANN包路径也相应设置 为/home/test/Ascend/ascend-toolkit/latest  
+
+- 添加普通用户  
+  useradd -m your_name  
+  passwd your_name  
+  usermod -s /bin/bash your_name  
+
+  修改/etc/sudoers添加your_name ALL=(ALL:ALL) ALL  
+  your_name用户便可使用sudo命令  
+
+  普通用户通过sudo执行atc报错找不到动态库，需要修改：  
+  修改/etc/sudoers将Defaults env_reset改成Defaults !env_reset  
+  修改/etc/bash.bashrc添加```alias sudo='sudo env PATH=$PATH LD_LIBRARY_PATH=$LD_LIBRARY_PATH'```  
+
+- 请使用conda安装python的库，将python的库安装在自己的conda环境里，修改这些库的代码不会影响其他用户  
+  查看已有环境：  
+  conda env list  
+  创建自己的conda环境：  
+  conda create -n your_env_name python=3.7.5  
+  进入环境：  
+  conda activate your_env_name  
+  查看环境安装的python的库：  
+  conda list  
+  只在该环境中安装py软件包：  
+  https://anaconda.org/ 网址搜索包的安装命令  
+  conda install -c pytorch pytorch  
+  conda install -c pytorch torchvision  
+  conda install -c conda-forge onnx=1.9.0  
+  查看安装路径：  
+  python3.7  
+  import torchvision  
+  print(torchvision.__file__)  
+  退出环境：  
+  conda deactivate  
+  删除环境：  
+  conda remove -n your_env_name --all  
+
+- 使用说明：  
+  - 请联系华为方申请登录服务器的普通用户名与密码  
+  - /home/下每个用户创建一个自己的目录，原则上只允许用户在自己的这个目录下开发，不要修改其它目录的东西  
+  - 不要随意更新CANN包与驱动包，修改系统文件，系统密码等  
+  - /opt/npu是共享的数据集盘目录，该目录仅用来存放共享的数据集，不可向该目录盘写其它数据  
+  - 每个模型使用到的通用数据集都放在/root/datasets/目录，除/root/datasets与/opt/npu外，不应在其它目录存放通用的数据集  
+  - 环境中默认安装的商用版CANN包放在/root/commerce_packages/目录下  
+  - 如果需要安装最新社区版CANN包可以安装在/root/cann_community/目录下  
+
+# t4服务器部署与说明
+- 安装cuda,cudnn,tensorrt  
+  - 安装cuda  
+    https://developer.nvidia.cn/cuda-toolkit-archive:  
+    wget http://developer.download.nvidia.com/compute/cuda/11.0.2/local_installers/cuda_11.0.2_450.51.05_linux.run  
+    sh cuda_11.0.2_450.51.05_linux.run  
+    安装过程不要选driver  
+    修改/etc/bash.bashrc添加：  
+    ```
+    export CUDA_HOME=/usr/local/cuda  
+    export PATH=$PATH:$CUDA_HOME/bin  
+    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_HOME/lib64
+    ```  
+  - 安装cudnn  
+    https://developer.nvidia.cn/rdp/cudnn-download:  
+    wget https://developer.nvidia.cn/compute/machine-learning/cudnn/secure/8.2.0.53/11.3_04222021/cudnn-11.3-linux-x64-v8.2.0.53.tgz  
+    cp cuda/include/cudnn.h /usr/local/cuda/include/  
+    cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/  
+  - 安装tensorRT  
+    https://developer.nvidia.cn/nvidia-tensorrt-download:  
+    wget https://developer.nvidia.cn/compute/machine-learning/tensorrt/secure/7.2.3/tars/TensorRT-7.2.3.4.Ubuntu-18.04.x86_64-gnu.cuda-11.0.cudnn8.1.tar.gz  
+    tar zxvf TensorRT-7.2.3.4.Ubuntu-18.04.x86_64-gnu.cuda-11.0.cudnn8.1.tar.gz -C /usr/local/  
+    修改/etc/bash.bashrc添加：  
+    ```
+    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/TensorRT-7.2.3.4/lib  
+    export PATH=$PATH:/usr/local/TensorRT-7.2.3.4/targets/x86_64-linux-gnu/bin
+    ```  
+
+- 添加普通用户  
+  useradd -m your_name  
+  passwd your_name  
+  usermod -s /bin/bash your_name  
+
+  t4上在线推理需要使用sudo安装库：  
+  修改/etc/sudoers添加your_name ALL=(ALL:ALL) ALL  
+  your_name用户便可使用sudo命令  
+  配置t4代理以访问外网：  
+  export http_proxy=http://192.168.88.254:8080  
+  export https_proxy=http://192.168.88.254:8080  
+
+- 使用说明：  
+  - /home/下每个普通用户创建一个自己的目录，原则上只允许用户在自己的这个目录下开发，不要修改其它目录的东西  
+  - 测试时请确保t4卡没有运行其它测试任务，使用nvidia-smi查看卡是否处于空闲态  
+  - t4上使用trtexec一条命令即可测试onnx模型性能，一般模型性能测试在半小时到半天之间可完成，测试完成后请及时退出登录  
+  - 不要更新CUDA驱动包与tensorRT，修改系统文件，系统密码等  
+  - 如果onnx不支持离线推理，需要在t4上在线推理测试性能，建议共用一台t4给普通用户sudo权限测试在线推理，而其它t4用来测试onnx离线推理性能不需要给普通用户sudo权限  
+
+
diff --git "a/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-\350\277\233\345\261\225.xlsx" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-\350\277\233\345\261\225.xlsx"
new file mode 100644
index 0000000000000000000000000000000000000000..fdda2d26fc8f6411dc83e8d23ad7a7c55ae4fbe7
Binary files /dev/null and "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-\350\277\233\345\261\225.xlsx" differ
diff --git "a/AscendPyTorch\346\250\241\345\236\213\346\216\250\347\220\206\344\274\227\346\231\272\351\252\214\346\224\266\346\214\207\345\215\227.md" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-\351\252\214\346\224\266\346\214\207\345\215\227.md"
similarity index 95%
rename from "AscendPyTorch\346\250\241\345\236\213\346\216\250\347\220\206\344\274\227\346\231\272\351\252\214\346\224\266\346\214\207\345\215\227.md"
rename to "Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-\351\252\214\346\224\266\346\214\207\345\215\227.md"
index e0449aaa17214ddce93b90e67c663e7f3fabfe80..70c0cfcd29822866bf24f70ba4ef12660033079f 100644
--- "a/AscendPyTorch\346\250\241\345\236\213\346\216\250\347\220\206\344\274\227\346\231\272\351\252\214\346\224\266\346\214\207\345\215\227.md"
+++ "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/PyTorch\347\246\273\347\272\277\346\216\250\347\220\206-\351\252\214\346\224\266\346\214\207\345\215\227.md"
@@ -90,6 +90,7 @@
     ```
     - 在pr提交的内容栏里编辑issue的链接即可关联对应的issue，问题解决后issue将自动关闭
     - 示例链接 https://gitee.com/ascend/modelzoo/issues/I3FI5L?from=project-issue
+- [性能不达标issue模板](https://gitee.com/pengyeqing/ascend-pytorch-crowdintelligence-doc/blob/master/%E4%BC%97%E6%99%BA%E6%8E%A8%E7%90%86issue) 
     
 ### 附： pr检视
 
@@ -125,6 +126,14 @@
 该文件是验收测试说明，主要是准备环境，pip3.7 install -r requirements.txt可能会重新安装某版本pytorch，验收时根据需要决定是否执行  
 参见模板[test/README.md](https://gitee.com/ascend/modelzoo/tree/master/built-in/ACL_PyTorch/Benchmark/cv/classification/ResNext50/test/README.md)  
 
+- 如果使用补丁文件修改了模型代码则将补丁打入模型代码,如果需要引用模型代码仓的类或函数通过sys.path.append(r"./pytorch-nested-unet")添加搜索路径  
+  预处理脚本不需要improt该脚本没有使用的库  
+参见https://gitee.com/ascend/modelzoo/pulls/2309  
+参见https://gitee.com/ascend/modelzoo/pulls/2585  
+- 模型不支持动态onnx，性能不达标等特殊情况需要在pr备注与性能优化里说明  
+参见https://gitee.com/ascend/modelzoo/pulls/2122  
+参见https://gitee.com/ascend/modelzoo/pulls/2328  
+
 ### 附： 模型推理指导中的交付标准与规范
 - 交付标准
     - 精度：  
diff --git "a/onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/.keep" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/\346\241\210\344\276\213/\345\212\237\350\203\275\346\211\223\351\200\232/.keep"
similarity index 100%
rename from "onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/.keep"
rename to "Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/\346\241\210\344\276\213/\345\212\237\350\203\275\346\211\223\351\200\232/.keep"
diff --git "a/onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/.keep" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/\346\241\210\344\276\213/\346\200\247\350\203\275\344\274\230\345\214\226/.keep"
similarity index 100%
rename from "onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/classification/.keep"
rename to "Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/\346\241\210\344\276\213/\346\200\247\350\203\275\344\274\230\345\214\226/.keep"
diff --git "a/onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/segmentation/.keep" "b/Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/\346\241\210\344\276\213/\347\262\276\345\272\246\350\260\203\350\257\225/.keep"
similarity index 100%
rename from "onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/cv/segmentation/.keep"
rename to "Ascend-PyTorch\347\246\273\347\272\277\346\216\250\347\220\206\346\214\207\345\257\274/\346\241\210\344\276\213/\347\262\276\345\272\246\350\260\203\350\257\225/.keep"
diff --git a/docs/models_result.xlsx b/docs/models_result.xlsx
deleted file mode 100644
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diff --git "a/onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/nlp/.keep" "b/onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/benchmark/nlp/.keep"
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git "a/onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/official/.keep" "b/onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/official/.keep"
deleted file mode 100644
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diff --git "a/onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/research/.keep" "b/onnx\347\253\257\345\210\260\347\253\257\346\216\250\347\220\206\346\214\207\345\257\274/research/.keep"
deleted file mode 100644
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