From 64f7c7524252bf8448002e14e14cc30bdc343c6c Mon Sep 17 00:00:00 2001
From: mr_melos <1595862940@qq.com>
Date: Mon, 25 Apr 2022 20:27:44 +0800
Subject: [PATCH 1/9] =?UTF-8?q?GFocalV2=E7=A6=BB=E7=BA=BF=E6=8E=A8?=
 =?UTF-8?q?=E7=90=86710=E7=BB=93=E6=9E=9C?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md  |  8 ++++----
 .../cv/detection/GFocalV2/gfocal_postprocess.py      | 12 ++++++------
 .../cv/detection/GFocalV2/test/eval_acc_perf.sh      |  8 ++++----
 3 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md b/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
index 7b2c3f63d1..3a33d8e3f0 100644
--- a/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
+++ b/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
@@ -42,8 +42,8 @@ root
 │   │   ├── val2017
 ```
 
-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  
+5.[获取msame工具](https://gitee.com/ascend/tools/tree/master/msame)  
+参考链接教程安装msame推理工具
 
 ## 2 离线推理 
 
@@ -53,8 +53,8 @@ bash test/pth2om.sh
 bash test/eval_acc_perf.sh --datasets_path=/root/datasets  
 ```
  **评测结果：**   
-| 模型      | 在线推理精度  | 310离线推理精度  | 基准性能    | 310性能    |
+| 模型      | 在线推理精度  | 710离线推理精度  | 710性能    | 310性能    |
 | :------: | :------: | :------: | :------:  | :------:  | 
-| GFocalV2 bs1  | mAP：41.0% | mAP:40.6% |  7.9fps | 12.071fps | 
+| GFocalV2 bs1  | mAP：41.0% | mAP:40.6% |  9.59fps | 4.48fps | 
 
 备注：离线模型不支持多batch。
\ No newline at end of file
diff --git a/ACL_PyTorch/contrib/cv/detection/GFocalV2/gfocal_postprocess.py b/ACL_PyTorch/contrib/cv/detection/GFocalV2/gfocal_postprocess.py
index 49b2e77560..922a8a8d73 100644
--- a/ACL_PyTorch/contrib/cv/detection/GFocalV2/gfocal_postprocess.py
+++ b/ACL_PyTorch/contrib/cv/detection/GFocalV2/gfocal_postprocess.py
@@ -1,4 +1,4 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
+# Copyright 2022 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.
@@ -60,7 +60,7 @@ if __name__ == '__main__':
     cnt = 0
     for ids in coco_dataset.img_ids:
         cnt = cnt + 1
-        bin_file = glob.glob(bin_path + '/*0' + str(ids) + '_1.bin')[0]
+        bin_file = glob.glob(bin_path + '/*0' + str(ids) + '_output_0.txt')[0]
         bin_file = bin_file[bin_file.rfind('/') + 1:]
         bin_file = bin_file[:bin_file.rfind('_')]
         print(cnt - 1, bin_file)
@@ -69,11 +69,11 @@ if __name__ == '__main__':
         res_buff = []
         bbox_results = []
         cls_segms = []
-        if os.path.exists(path_base + "_" + "1" + ".bin") and os.path.exists(path_base + "_" + "2" + ".bin"):
-            bboxes = np.fromfile(path_base + "_" + str(flags.net_out_num - 1) + ".bin", dtype="float32")
+        if os.path.exists(path_base + "_" + "output_0" + ".txt") and os.path.exists(path_base + "_" + "output_1" + ".txt"):
+            bboxes = np.loadtxt(path_base + "_output_" + str(flags.net_out_num - 3) + ".txt", dtype="float32")
             bboxes = np.reshape(bboxes, [100, 5])
             bboxes = torch.from_numpy(bboxes)
-            labels = np.fromfile(path_base + "_" + str(flags.net_out_num - 2) + ".bin", dtype="int64")
+            labels = np.loadtxt(path_base + "_output_" + str(flags.net_out_num - 2) + ".txt", dtype="int64")
             labels = np.reshape(labels, [100, 1])
             labels = torch.from_numpy(labels)
 
@@ -81,7 +81,7 @@ if __name__ == '__main__':
             bboxes = postprocess_bboxes(bboxes, img_size_dict[bin_file], flags.net_input_width, flags.net_input_height)
             bbox_results = [bbox2result(bboxes, labels[:, 0], 80)]
         else:
-            print("[ERROR] file not exist", path_base + "_" + str(1) + ".bin",path_base + "_" + str(2) + ".bin")
+            print("[ERROR] file not exist", path_base + "_output_" + str(1) + ".txt",path_base + "_output_" + str(2) + ".txt")
 
         result = bbox_results
         results.extend(result)
diff --git a/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/eval_acc_perf.sh b/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/eval_acc_perf.sh
index a25afb777e..c9d1782de2 100644
--- a/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/eval_acc_perf.sh
+++ b/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/eval_acc_perf.sh
@@ -13,7 +13,7 @@ if [ $? != 0 ]; then
     echo "fail!"
     exit -1
 fi
-python3.7 get_info.py bin val2017_bin gfocal.info 1216 800
+python3.7 get_info.py bin ${datasets_path}/coco/val2017_bin gfocal.info 1216 800
 if [ $? != 0 ]; then
     echo "fail!"
     exit -1
@@ -24,13 +24,13 @@ if [ $? != 0 ]; then
     exit -1
 fi
 source env.sh
-rm -rf result/dumpOutput_device0
-./benchmark.${arch} -model_type=vision -om_path=gfocal_bs1.om -device_id=0 -batch_size=1 -input_text_path=gfocal.info -input_width=1216 -input_height=800 -useDvpp=false -output_binary=true
+rm -rf ${datasets_path}/coco/val2017_bin
+./msame --model "./gfocal_bs1.om" --input "${datasets_path}/coco/val2017_bin" --output "./out/" --outfmt TXT
 if [ $? != 0 ]; then
     echo "fail!"
     exit -1
 fi
-python3.7 gfocal_postprocess.py --bin_data_path=./result/dumpOutput_device0/ --test_annotation=gfocal_jpeg.info --net_out_num=3 --net_input_height=800 --net_input_width=1216
+python3.7 gfocal_postprocess.py --bin_data_path=./out/ --test_annotation=gfocal_jpeg.info --net_out_num=3 --net_input_height=800 --net_input_width=1216
 if [ $? != 0 ]; then
     echo "fail!"
     exit -1
-- 
Gitee


From e059f9fb37f9ae22ba53cfbaca4af33694619bb0 Mon Sep 17 00:00:00 2001
From: mr_melos <1595862940@qq.com>
Date: Tue, 26 Apr 2022 15:57:59 +0800
Subject: [PATCH 2/9] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E4=BA=86GPU=E6=8E=A8?=
 =?UTF-8?q?=E7=90=86=E4=BF=A1=E6=81=AF=EF=BC=8C=E4=BF=AE=E6=AD=A3=E4=BA=86?=
 =?UTF-8?q?=E8=84=9A=E6=9C=AC?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md      | 8 ++++----
 .../contrib/cv/detection/GFocalV2/test/eval_acc_perf.sh  | 9 ++++++---
 ACL_PyTorch/contrib/cv/detection/GFocalV2/test/parse.py  | 2 +-
 3 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md b/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
index 3a33d8e3f0..0ccf7dcadb 100644
--- a/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
+++ b/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
@@ -50,11 +50,11 @@ root
 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  
+bash test/eval_acc_perf.sh --datasets_path=/root/dataset  
 ```
  **评测结果：**   
-| 模型      | 在线推理精度  | 710离线推理精度  | 710性能    | 310性能    |
-| :------: | :------: | :------: | :------:  | :------:  | 
-| GFocalV2 bs1  | mAP：41.0% | mAP:40.6% |  9.59fps | 4.48fps | 
+| 模型      | 在线推理精度  | 710离线推理精度  | 710性能    | 310性能    |T4性能    |
+| :------: | :------: | :------: | :------:  | :------:  | :------:  | 
+| GFocalV2 bs1  | mAP：41.0% | mAP:40.6% |  9.59fps | 4.48fps | 9.4fps | 
 
 备注：离线模型不支持多batch。
\ No newline at end of file
diff --git a/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/eval_acc_perf.sh b/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/eval_acc_perf.sh
index c9d1782de2..1cd7e79c58 100644
--- a/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/eval_acc_perf.sh
+++ b/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/eval_acc_perf.sh
@@ -24,13 +24,16 @@ if [ $? != 0 ]; then
     exit -1
 fi
 source env.sh
-rm -rf ${datasets_path}/coco/val2017_bin
-./msame --model "./gfocal_bs1.om" --input "${datasets_path}/coco/val2017_bin" --output "./out/" --outfmt TXT
+rm -rf ./val2017_bin
+./msame/out/msame --model "./gfocal_bs1.om" --input "./val2017_bin" --output "./out/" --outfmt TXT
 if [ $? != 0 ]; then
     echo "fail!"
     exit -1
 fi
-python3.7 gfocal_postprocess.py --bin_data_path=./out/ --test_annotation=gfocal_jpeg.info --net_out_num=3 --net_input_height=800 --net_input_width=1216
+cd ./out/2*/
+dir=${PWD##*/}
+cd ../../
+python3.7 gfocal_postprocess.py --bin_data_path=./out/${dir} --test_annotation=gfocal_jpeg.info --net_out_num=3 --net_input_height=800 --net_input_width=1216
 if [ $? != 0 ]; then
     echo "fail!"
     exit -1
diff --git a/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/parse.py b/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/parse.py
index b9c74f41d7..6da8978fc3 100644
--- a/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/parse.py
+++ b/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/parse.py
@@ -29,4 +29,4 @@ if __name__ == '__main__':
             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
+        print('710 bs{} fps:{}'.format(result_txt.split('_')[3], fps))
\ No newline at end of file
-- 
Gitee


From 411d38bd443e26d95961b18dd4a4d2a6d34f6df5 Mon Sep 17 00:00:00 2001
From: mr_melos <1595862940@qq.com>
Date: Thu, 5 May 2022 17:12:16 +0800
Subject: [PATCH 3/9] =?UTF-8?q?=E4=BF=AE=E6=AD=A3=E4=BA=86readme=E7=9A=843?=
 =?UTF-8?q?10=E6=80=A7=E8=83=BD?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md b/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
index 0ccf7dcadb..d23e7afa60 100644
--- a/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
+++ b/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
@@ -55,6 +55,6 @@ bash test/eval_acc_perf.sh --datasets_path=/root/dataset
  **评测结果：**   
 | 模型      | 在线推理精度  | 710离线推理精度  | 710性能    | 310性能    |T4性能    |
 | :------: | :------: | :------: | :------:  | :------:  | :------:  | 
-| GFocalV2 bs1  | mAP：41.0% | mAP:40.6% |  9.59fps | 4.48fps | 9.4fps | 
+| GFocalV2 bs1  | mAP：41.0% | mAP:40.6% |  9.59fps | 17.92fps | 9.4fps | 
 
 备注：离线模型不支持多batch。
\ No newline at end of file
-- 
Gitee


From aaba1f829b79c226b28b35d55590178ff82d1994 Mon Sep 17 00:00:00 2001
From: mr-melos <1595862940@qq.com>
Date: Tue, 9 Aug 2022 23:35:01 +0800
Subject: [PATCH 4/9] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E8=AF=84=E4=BC=B0?=
 =?UTF-8?q?=E8=84=9A=E6=9C=AC?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../cv/detection/GFocalV2/gfocal_postprocess.py      |  2 +-
 .../cv/detection/GFocalV2/test/eval_acc_perf.sh      | 12 +++++-------
 2 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/ACL_PyTorch/contrib/cv/detection/GFocalV2/gfocal_postprocess.py b/ACL_PyTorch/contrib/cv/detection/GFocalV2/gfocal_postprocess.py
index 922a8a8d73..454b43fb29 100644
--- a/ACL_PyTorch/contrib/cv/detection/GFocalV2/gfocal_postprocess.py
+++ b/ACL_PyTorch/contrib/cv/detection/GFocalV2/gfocal_postprocess.py
@@ -62,7 +62,7 @@ if __name__ == '__main__':
         cnt = cnt + 1
         bin_file = glob.glob(bin_path + '/*0' + str(ids) + '_output_0.txt')[0]
         bin_file = bin_file[bin_file.rfind('/') + 1:]
-        bin_file = bin_file[:bin_file.rfind('_')]
+        bin_file = bin_file[:bin_file.find('_')]
         print(cnt - 1, bin_file)
         path_base = os.path.join(bin_path, bin_file)
 
diff --git a/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/eval_acc_perf.sh b/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/eval_acc_perf.sh
index 1cd7e79c58..ba2fa14e5b 100644
--- a/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/eval_acc_perf.sh
+++ b/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/eval_acc_perf.sh
@@ -23,17 +23,15 @@ if [ $? != 0 ]; then
     echo "fail!"
     exit -1
 fi
-source env.sh
-rm -rf ./val2017_bin
-./msame/out/msame --model "./gfocal_bs1.om" --input "./val2017_bin" --output "./out/" --outfmt TXT
+source /usr/local/Ascend/ascend-toolkit/set_env.sh
+rm -rf ./out/
+./tools/msame/out/msame --model "./gfocal_bs1.om" --input "./val2017_bin" --output "./out/" --outfmt TXT
 if [ $? != 0 ]; then
     echo "fail!"
     exit -1
 fi
-cd ./out/2*/
-dir=${PWD##*/}
-cd ../../
-python3.7 gfocal_postprocess.py --bin_data_path=./out/${dir} --test_annotation=gfocal_jpeg.info --net_out_num=3 --net_input_height=800 --net_input_width=1216
+
+python3.7 gfocal_postprocess.py --bin_data_path=./out/2* --test_annotation=gfocal_jpeg.info --net_out_num=3 --net_input_height=800 --net_input_width=1216
 if [ $? != 0 ]; then
     echo "fail!"
     exit -1
-- 
Gitee


From 0e0d905a0da718aac787a7638de38b873c0fa046 Mon Sep 17 00:00:00 2001
From: mr-melos <1595862940@qq.com>
Date: Wed, 10 Aug 2022 13:07:40 +0800
Subject: [PATCH 5/9] =?UTF-8?q?=E4=BF=AE=E6=94=B9readme=E6=96=87=E6=A1=A3?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../contrib/cv/detection/GFocalV2/README.md   | 195 ++++++++++++++++--
 .../cv/detection/GFocalV2/requirements.txt    |   4 +-
 .../cv/detection/GFocalV2/test/pth2om.sh      |   2 +-
 3 files changed, 176 insertions(+), 25 deletions(-)

diff --git a/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md b/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
index d23e7afa60..8325382890 100644
--- a/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
+++ b/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
@@ -1,23 +1,93 @@
 # GFocalV2模型PyTorch离线推理指导
 
-## 1 环境准备 
+- [GFocalV2模型PyTorch离线推理指导](#GFocalV2模型PyTorch离线推理指导)
+	- [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 生成预处理数据集信息文件](#43-生成预处理数据集信息文件)
+	- [5 离线推理](#5-离线推理)
+		- [5.1 msame工具概述](#51-msame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)
+	- [7 性能对比](#7-性能对比)
+        - [7.1 310P性能数据](#71-310p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.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  
+## 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
+[GfocalV2论文](https://arxiv.org/abs/1807.01544)
+论文主要引入边界框不确定性的统计量来高效地指导定位质量估计，从而提升one-stage的检测器性能
+
+### 1.2 代码地址
+
+[GfocalV2 Pytorch实现代码](https://github.com/implus/GFocalV2)
 ```
-pip3.7 install -r requirements.txt  
+branch=master 
+commit_id=bfcc2b9fbbcad714cff59dacc8fb1111ce381cda
 ```
-说明：PyTorch选用开源1.8.0版本
 
+## 2 环境说明 
 
+-   **[深度学习框架](#21-深度学习框架)**  
 
-2.获取，修改与安装开源模型代码  
-安装mmcv
-```shell
-git clone https://github.com/open-mmlab/mmcv -b v1.2.7
-cd mmcv
-MMCV_WITH_OPS=1 pip3.7 install -e .
-cd ..
+-   **[python第三方库](#22-python第三方库)**  
+
+### 2.1 深度学习框架
+```
+CANN == 5.1.RC1
+pytorch == 1.8.0
+torchvision == 0.9.0
+onnx == 1.9.0
+```
+
+### 2.2 python第三方库
+
+```
+mmcv-full == 1.2.4 
+mmdet == 2.6.0
+opencv-python == 4.5.1.48
+numpy == 1.21.6
+pillow == 7.2.0
+```
+
+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
+
+```
+pip install -r requirements.txt  
 ```
+
+
+
+## 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.获取pth权重文件  
+
+[gfocalv2预训练的pth权重文件](https://drive.google.com/file/d/1wSE9-c7tcQwIDPC6Vm_yfOokdPfmYmy7/view?usp=sharing)
+
+2.获取GFocalV2源码
+
 获取GFocalV2代码
 ```
 git clone https://github.com/implus/GFocalV2.git -b master
@@ -27,12 +97,50 @@ patch -p1 < ../GFocalV2.diff
 python3.7 setup.py develop
 cd ..
 ```
+3.使用./GFocalV2/tools/pytorch2onnx.py进行onnx的转换，在目录下生成gfocal.onnx
+
+```
+python3.7 ./GFocalV2/tools/pytorch2onnx.py ./GFocalV2/configs/gfocal/gfocal_r50_fpn_1x.py ./gfocal_r50_fpn_1x.pth --output-file gfocal.onnx --input-img ./GFocalV2/demo/demo.jpg --shape 800 1216 --show
+```
+
+
+
+### 3.2 onnx转om模型
+
+1.设置环境变量
+
+```
+source /usr/local/Ascend/ascend-toolkit/set_env.sh
+```
 
-3.获取权重文件  
+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 V100R020C10 开发辅助工具指南 (推理) 01](https://support.huawei.com/enterprise/zh/doc/EDOC1100164868?idPath=23310P424%7C251366513%7C22892968%7C251168373)，需要指定输出节点以去除无用输出，可以使用netron开源可视化工具查看具体的输出节点名：
 
-[gfocalv2预训练的pth权重文件](https://drive.google.com/file/d/1wSE9-c7tcQwIDPC6Vm_yfOokdPfmYmy7/view?usp=sharing)  
+使用atc将onnx模型 ${chip_name}可通过npu-smi info指令查看
+
+执行ATC命令
+
+```shell
+atc --framework=5 --model=./gfocal.onnx --output=gfocal_bs1 --input_format=NCHW --input_shape="input.1:1,3,800,1216" --log=debug --soc_version=Ascend310P3
+```
+参数说明：\
+--model：为ONNX模型文件。 \
+--framework：5代表ONNX模型。\
+--output：输出的OM模型。\
+--input_format：输入数据的格式。\
+--input_shape：输入数据的shape。\
+--log：日志级别。\
+--soc_version：处理器型号。\
 
-4.数据集     
+## 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COCO数据集
 [coco2017](https://cocodataset.org/#download)，下载其中val2017图片及其标注文件，放入服务器/root/dataset/coco/文件夹，val2017目录存放coco数据集的验证集图片，annotations目录存放coco数据集的instances_val2017.json，文件目录结构如下：
 ```
 root
@@ -41,20 +149,63 @@ root
 │   │   ├── annotations
 │   │   ├── val2017
 ```
+### 4.2 数据集预处理
+1.预处理脚本gfocal_preprocess.py
+
+2.执行预处理脚本，生成数据集预处理后的bin文件
+
+```shell
+python3.7 gfocal_preprocess.py --image_src_path=${datasets_path}/coco/val2017 --bin_file_path=val2017_bin --model_input_height=800 --model_input_width=1216
+```
 
-5.[获取msame工具](https://gitee.com/ascend/tools/tree/master/msame)  
-参考链接教程安装msame推理工具
+### 4.3 生成预处理数据集信息文件
+1.生成数据集信息文件脚本get_info.py
 
-## 2 离线推理 
+2.执行生成数据集信息脚本，生成数据集信息文件
 
-310上执行，执行时使npu-smi info查看设备状态，确保device空闲  
+```shell
+python3.7 get_info.py jpg ${datasets_path}/coco/val2017 gfocal_jpeg.info
+```
+第一个参数为模型输入的类型，第二个参数为数据集路径，第三个为输出的info文件
+
+
+## 5 离线推理 
+-   **[msame工具概述](#51-msame工具概述)**  
+
+-   **[离线推理](#52-离线推理)**  
+
+### 5.1 msame工具概述
+
+msame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[参考链接](https://gitee.com/ascend/tools/tree/master/msame#https://gitee.com/link?target=https%3A%2F%2Fobs-book.obs.cn-east-2.myhuaweicloud.com%2Fcjl%2Fmsame.zip)
+
+### 5.2 离线推理
+1.设置环境变量
+
+```
+source /usr/local/Ascend/ascend-toolkit/set_env.sh
+```
+2.将编译完成的可执行文件放到当前目录下，执行离线推理，执行时使npu-smi info查看设备状态，确保device空闲
+
+```shell
+./tools/msame/out/msame --model "./gfocal_bs1.om" --input "./val2017_bin" --output "./out/" --outfmt TXT
+```
+
+## 6 精度对比 
+调用gfocal_postprocess.py：
+```shell
+python3.7 gfocal_postprocess.py --bin_data_path=./out/2* --test_annotation=gfocal_jpeg.info --net_out_num=3 --net_input_height=800 --net_input_width=1216
 ```
-bash test/pth2om.sh  
-bash test/eval_acc_perf.sh --datasets_path=/root/dataset  
+参数"--bin_data_path"为推理生成结果文件的路径。
+执行完后会打印出精度。
+## 7 性能对比
 ```
  **评测结果：**   
-| 模型      | 在线推理精度  | 710离线推理精度  | 710性能    | 310性能    |T4性能    |
+| 模型      | 在线推理精度  | 310P离线推理精度  | 310P性能    | 310性能    |T4性能    |
 | :------: | :------: | :------: | :------:  | :------:  | :------:  | 
-| GFocalV2 bs1  | mAP：41.0% | mAP:40.6% |  9.59fps | 17.92fps | 9.4fps | 
+| GFocalV2 bs1  | mAP：41.0% | mAP:40.6% |  34.49fps | 17.92fps | 9.4fps | 
 
+# 710性能是否超过基准： 是
+310P vs 310: bs1:710=(34.49/17.92) 1.92倍基准
+310P vs T4: bs1:710=(34.49/9.4) 3.67倍基准
+性能在310P上的性能超过310的1.2倍,超过T4性能的1.6倍,性能达标
 备注：离线模型不支持多batch。
\ No newline at end of file
diff --git a/ACL_PyTorch/contrib/cv/detection/GFocalV2/requirements.txt b/ACL_PyTorch/contrib/cv/detection/GFocalV2/requirements.txt
index 7609cac2ce..475b8abc43 100644
--- a/ACL_PyTorch/contrib/cv/detection/GFocalV2/requirements.txt
+++ b/ACL_PyTorch/contrib/cv/detection/GFocalV2/requirements.txt
@@ -1,5 +1,5 @@
 torch == 1.8.0
 torchvision == 0.9.0
 onnx == 1.9.0
-numpy == 1.19.4
-opencv-python == 4.4.0.46
\ No newline at end of file
+numpy == 1.21.6
+opencv-python == 4.5.1.48
\ No newline at end of file
diff --git a/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/pth2om.sh b/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/pth2om.sh
index e484971191..81d2226855 100644
--- a/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/pth2om.sh
+++ b/ACL_PyTorch/contrib/cv/detection/GFocalV2/test/pth2om.sh
@@ -1,7 +1,7 @@
 rm -rf gfocal.onnx
 python3.7 ./GFocalV2/tools/pytorch2onnx.py ./GFocalV2/configs/gfocal/gfocal_r50_fpn_1x.py ./gfocal_r50_fpn_1x.pth --output-file gfocal.onnx --input-img ./GFocalV2/demo/demo.jpg --shape 800 1216 --show
 source env.sh
-atc --framework=5 --model=./gfocal.onnx --output=gfocal_bs1 --input_format=NCHW --input_shape="input.1:1,3,800,1216" --log=debug --soc_version=Ascend310
+atc --framework=5 --model=./gfocal.onnx --output=gfocal_bs1 --input_format=NCHW --input_shape="input.1:1,3,800,1216" --log=debug --soc_version=Ascend310P3
 if [ -f "gfocal_bs1.om" ]; then
     echo "success"
 else
-- 
Gitee


From eb918ed3722488ecbffdb5bc9e2ba85208ea2e08 Mon Sep 17 00:00:00 2001
From: mr-melos <1595862940@qq.com>
Date: Wed, 10 Aug 2022 13:10:31 +0800
Subject: [PATCH 6/9] =?UTF-8?q?=E4=BF=AE=E6=94=B9readme=E6=96=87=E6=A1=A3?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md b/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
index 8325382890..c5f9fd6ecd 100644
--- a/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
+++ b/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
@@ -115,7 +115,7 @@ source /usr/local/Ascend/ascend-toolkit/set_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 V100R020C10 开发辅助工具指南 (推理) 01](https://support.huawei.com/enterprise/zh/doc/EDOC1100164868?idPath=23310P424%7C251366513%7C22892968%7C251168373)，需要指定输出节点以去除无用输出，可以使用netron开源可视化工具查看具体的输出节点名：
 
-使用atc将onnx模型 ${chip_name}可通过npu-smi info指令查看
+使用atc将onnx模型转换为om模型
 
 执行ATC命令
 
-- 
Gitee


From 994cee4fe6cddfc6fb2d4c502c331a4e328b9e60 Mon Sep 17 00:00:00 2001
From: mr-melos <1595862940@qq.com>
Date: Wed, 10 Aug 2022 14:31:47 +0800
Subject: [PATCH 7/9] =?UTF-8?q?=E4=BF=AE=E6=94=B9readme=E6=96=87=E6=A1=A3?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md         | 6 +++---
 .../contrib/cv/detection/GFocalV2/gfocal_postprocess.py     | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md b/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
index c5f9fd6ecd..2339afaf7d 100644
--- a/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
+++ b/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
@@ -49,7 +49,7 @@ commit_id=bfcc2b9fbbcad714cff59dacc8fb1111ce381cda
 
 ### 2.1 深度学习框架
 ```
-CANN == 5.1.RC1
+CANN == 5.1.RC2
 pytorch == 1.8.0
 torchvision == 0.9.0
 onnx == 1.9.0
@@ -115,12 +115,12 @@ source /usr/local/Ascend/ascend-toolkit/set_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 V100R020C10 开发辅助工具指南 (推理) 01](https://support.huawei.com/enterprise/zh/doc/EDOC1100164868?idPath=23310P424%7C251366513%7C22892968%7C251168373)，需要指定输出节点以去除无用输出，可以使用netron开源可视化工具查看具体的输出节点名：
 
-使用atc将onnx模型转换为om模型
+使用atc将onnx模型转换为om模型 ${chip_name}可通过npu-smi info指令查看
 
 执行ATC命令
 
 ```shell
-atc --framework=5 --model=./gfocal.onnx --output=gfocal_bs1 --input_format=NCHW --input_shape="input.1:1,3,800,1216" --log=debug --soc_version=Ascend310P3
+atc --framework=5 --model=./gfocal.onnx --output=gfocal_bs1 --input_format=NCHW --input_shape="input.1:1,3,800,1216" --log=debug --soc_version=Ascend${chip_name}
 ```
 参数说明：\
 --model：为ONNX模型文件。 \
diff --git a/ACL_PyTorch/contrib/cv/detection/GFocalV2/gfocal_postprocess.py b/ACL_PyTorch/contrib/cv/detection/GFocalV2/gfocal_postprocess.py
index 454b43fb29..70cec06f3f 100644
--- a/ACL_PyTorch/contrib/cv/detection/GFocalV2/gfocal_postprocess.py
+++ b/ACL_PyTorch/contrib/cv/detection/GFocalV2/gfocal_postprocess.py
@@ -81,7 +81,7 @@ if __name__ == '__main__':
             bboxes = postprocess_bboxes(bboxes, img_size_dict[bin_file], flags.net_input_width, flags.net_input_height)
             bbox_results = [bbox2result(bboxes, labels[:, 0], 80)]
         else:
-            print("[ERROR] file not exist", path_base + "_output_" + str(1) + ".txt",path_base + "_output_" + str(2) + ".txt")
+            print("[ERROR] file not exist", path_base + "_output_" + str(0) + ".txt",path_base + "_output_" + str(1) + ".txt")
 
         result = bbox_results
         results.extend(result)
-- 
Gitee


From ba2ec8fcc11a040b6cdffad81a334460427ea649 Mon Sep 17 00:00:00 2001
From: mr-melos <1595862940@qq.com>
Date: Wed, 10 Aug 2022 16:46:11 +0800
Subject: [PATCH 8/9] =?UTF-8?q?=E4=BF=AE=E6=94=B9readme=E6=96=87=E6=A1=A3?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../contrib/cv/detection/GFocalV2/README.md   | 39 +++++++++++++++++++
 1 file changed, 39 insertions(+)

diff --git a/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md b/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
index 2339afaf7d..6334dbecc4 100644
--- a/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
+++ b/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
@@ -117,6 +117,8 @@ source /usr/local/Ascend/ascend-toolkit/set_env.sh
 
 使用atc将onnx模型转换为om模型 ${chip_name}可通过npu-smi info指令查看
 
+![输入图片说明](https://images.gitee.com/uploads/images/2022/0704/095450_881600a3_7629432.png "屏幕截图.png")
+
 执行ATC命令
 
 ```shell
@@ -198,6 +200,43 @@ python3.7 gfocal_postprocess.py --bin_data_path=./out/2* --test_annotation=gfoca
 参数"--bin_data_path"为推理生成结果文件的路径。
 执行完后会打印出精度。
 ## 7 性能对比
+
+-   **[310性能数据](#71-310性能数据)**  
+-   **[310P性能数据](#72-310P性能数据)**  
+-   **[T4性能数据](#73-T4性能数据)** 
+-   **[性能对比](#74-性能对比)**  
+
+### 7.1 310性能数据
+
+batch1的性能：
+ 测试npu性能要确保device空闲，使用npu-smi info命令可查看device是否在运行其它推理任务
+
+```
+./tools/msame/out/msame --model "./gfocal_bs1.om" --input "./val2017_bin" --output "./out/" --outfmt TXT
+```
+执行数据集推理，推理完成时显示推理时间。
+```
+Inference average time : 223.21ms
+Inference average time without first time : 223.22ms
+```
+由于310为四芯片，计算fps时使用4*1000/223.21=17.92fps
+
+### 7.2 310P性能数据
+
+batch1的性能：
+ 测试npu性能要确保device空闲，使用npu-smi info命令可查看device是否在运行其它推理任务
+
+```
+./tools/msame/out/msame --model "./gfocal_bs1.om" --input "./val2017_bin" --output "./out/" --outfmt TXT
+```
+执行数据集推理，推理完成时显示推理时间。
+```
+Inference average time : 28.99ms
+Inference average time without first time : 28.99ms
+```
+计算fps时使用1000/28.99=34.49fps
+
+### 7.3 性能对比
 ```
  **评测结果：**   
 | 模型      | 在线推理精度  | 310P离线推理精度  | 310P性能    | 310性能    |T4性能    |
-- 
Gitee


From bc5ba0f69ad2b18eb3c4081c2ed9e4118e131392 Mon Sep 17 00:00:00 2001
From: mr-melos <1595862940@qq.com>
Date: Wed, 10 Aug 2022 17:06:00 +0800
Subject: [PATCH 9/9] =?UTF-8?q?=E4=BF=AE=E6=94=B9readme=E6=96=87=E6=A1=A3?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../contrib/cv/detection/GFocalV2/README.md   | 23 ++++++++++++++-----
 1 file changed, 17 insertions(+), 6 deletions(-)

diff --git a/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md b/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
index 6334dbecc4..65cf695a90 100644
--- a/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
+++ b/ACL_PyTorch/contrib/cv/detection/GFocalV2/README.md
@@ -199,6 +199,14 @@ python3.7 gfocal_postprocess.py --bin_data_path=./out/2* --test_annotation=gfoca
 ```
 参数"--bin_data_path"为推理生成结果文件的路径。
 执行完后会打印出精度。
+```
+Average Precision(AP)@[ IoU=0.50:0.95 | area=all | maxDets=100 ] =0.406
+```
+[官网精度](https://github.com/implus/GFocalV2)
+```
+Average Precision(AP)@[ IoU=0.50:0.95 | area=all | maxDets=100 ] =0.410
+```
+
 ## 7 性能对比
 
 -   **[310性能数据](#71-310性能数据)**  
@@ -237,14 +245,17 @@ Inference average time without first time : 28.99ms
 计算fps时使用1000/28.99=34.49fps
 
 ### 7.3 性能对比
-```
- **评测结果：**   
-| 模型      | 在线推理精度  | 310P离线推理精度  | 310P性能    | 310性能    |T4性能    |
-| :------: | :------: | :------: | :------:  | :------:  | :------:  | 
-| GFocalV2 bs1  | mAP：41.0% | mAP:40.6% |  34.49fps | 17.92fps | 9.4fps | 
 
+**评测结果：** 
+
+| 模型      |  310P性能    | 310性能    |T4性能  |310P/310 |310P/T4| 
+| :------: |  :------:  | :------:  | :------:  |:------:  |  :------:  | 
+| GFocalV2 bs1  |  34.49fps |17.92fps | 9.4fps |1.92| 3.67|
+
+```
 # 710性能是否超过基准： 是
 310P vs 310: bs1:710=(34.49/17.92) 1.92倍基准
 310P vs T4: bs1:710=(34.49/9.4) 3.67倍基准
 性能在310P上的性能超过310的1.2倍,超过T4性能的1.6倍,性能达标
-备注：离线模型不支持多batch。
\ No newline at end of file
+备注：离线模型不支持多batch。
+```
\ No newline at end of file
-- 
Gitee