u

YOLOv5Prune/README.md

@@ -16,7 +16,7 @@ yolov5s是非常优秀的轻量级检测网络，但是有时候模型依然比
 
 ### 1.2 支持的版本
 
-本样例配套的CANN版本为[5.0.5](https://www.hiascend.com/software/cann/commercial)。支持的SDK版本为[2.0.4](https://www.hiascend.com/software/Mindx-sdk)。
+本样例配套的CANN版本为[5.0.4](https://www.hiascend.com/software/cann/commercial)。支持的SDK版本为[2.0.4](https://www.hiascend.com/software/Mindx-sdk)。
 
 MindX SDK安装前准备可参考《用户指南》，[安装教程](https://gitee.com/ascend/mindxsdk-referenceapps/blob/master/docs/quickStart/1-1安装SDK开发套件.md)
 
@@ -176,7 +176,7 @@ $$
 
 ## 4 模型转换
 
-ONNX模型可通过[地址]()获取，下载后使用模型转换工具 ATC 将 pb 模型转换为 om 模型，模型转换工具相关介绍参考链接：[https://support.huaweicloud.com/tg-cannApplicationDev330/atlasatc_16_0005.html](https://gitee.com/link?target=https%3A%2F%2Fsupport.huaweicloud.com%2Ftg-cannApplicationDev330%2Fatlasatc_16_0005.html) 。
+ONNX模型可通过[地址](https://mindx.sdk.obs.cn-north-4.myhuaweicloud.com/ascend_community_projects/yolov5prune/prune_onnx.rar)获取，下载后使用模型转换工具 ATC 将 pb 模型转换为 om 模型，模型转换工具相关介绍参考链接：[https://support.huaweicloud.com/tg-cannApplicationDev330/atlasatc_16_0005.html](https://gitee.com/link?target=https%3A%2F%2Fsupport.huaweicloud.com%2Ftg-cannApplicationDev330%2Fatlasatc_16_0005.html) 。
 
 模型转换，步骤如下：
 

YOLOv5Prune/compute_mAP/reval_voc.py

@@ -66,23 +66,22 @@ def do_eval(devkit_path, image_set, classes, output_dir):
         rec, prec, ap = evaluate(
             filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.55)
         aps += [ap]
-        print('AP for {} = {:.4f}'.format(cls, ap))
+        
         os.system('touch '+os.path.join(output_dir, cls + '_pr.pkl'))
         with open(os.path.join(output_dir, cls + '_pr.pkl'), 'rb+') as f:
             pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
+    print('Results:')
+    print('~~~~~~~~')
     print('Mean AP = {:.4f}'.format(np.mean(aps)))
     print('~~~~~~~~')
-    print('Results:')
-    for ap in aps:
-        print('{:.3f}'.format(ap))
-    print('{:.3f}'.format(np.mean(aps)))
+    for ap, cls in zip(aps, classes):
+        print('AP for {} = {:.4f}'.format(cls, ap))
 
 
 if __name__ == '__main__':
     args = parse_args()
 
     res_dir = os.path.abspath(args.output_dir[0])
-    print(output_dir)
     with open(args.class_file, 'r') as file:
         lines = file.readlines()
     class_name = [t.strip('\n') for t in lines]

YOLOv5Prune/compute_mAP/voc_eval.py

@@ -69,7 +69,6 @@ def evaluate(detpath,
     imagenames = [x.strip() for x in lines]
 
     if os.path.isfile(cachefile):
-        print('cachefile = ', cachefile)
         with open(cachefile, 'rb') as f:
             recs = pickel.load(f)
     else:

YOLOv5Prune/main.cpp

@@ -23,7 +23,7 @@
 #include "opencv4/opencv2/opencv.hpp"
 const int color_list[20][3] = { {216, 82, 24}, {236, 176, 31}, {125, 46, 141}, {118, 171, 47}, {76, 189, 237}, {238, 19, 46},
                             {76, 76, 76}, {153, 153, 153}, {255, 0, 0}, {255, 127, 0}, {190, 190, 0}, {0, 255, 0}, {0, 0, 255},
-                            {170, 0, 255}, {84, 84, 0}, {84, 170, 0}, {84, 255, 0}, {170, 84, 0}, {170, 170, 0}, {170, 255, 0}}
+                            {170, 0, 255}, {84, 84, 0}, {84, 170, 0}, {84, 255, 0}, {170, 84, 0}, {170, 170, 0}, {170, 255, 0}};
 
 float pad_w = 0.0, pad_h = 0.0;
 float ratio = 1.0;
@@ -207,7 +207,7 @@ void SaveImage(const std::string& result, const cv::Mat src, const std::string&
         cv::Rect rect(it.x0, it.y0, it.x1 - it.x0, it.y1 - it.y0);
         cv::rectangle(src, rect, color);
         char text[256];
-        sprintf(text, "%s %.1f%", it.className.c_str(), it.conf);
+        sprintf(text, "%s %.2f", it.className.c_str(), it.conf);
         int baseLine = 0;
         double fontScale = 0.4;
         cv::Scalar fontColor = cv::Scalar(255, 255, 255);
@@ -219,7 +219,6 @@ void SaveImage(const std::string& result, const cv::Mat src, const std::string&
 }
 void SaveTxt(const std::string& result, const std::string& line)
 {
-    // web::json::value jsonText = web::json::value::parse(result);
     auto res = ParseResult(result);
     for (auto it : res) {
         std::ofstream outfile("./txt_result/det_test_" + it.className + ".txt", std::ios::app);
@@ -247,6 +246,7 @@ int work(const std::string& id, MxStream::MxStreamManager& mxStreamManager)
     std::string img_path = imageSetPath+'/'+id+".jpg";
     MxStream::MxstDataInput dataBuffer;
     cv::Mat src;
+    int ret;
     if (task == "eval") {
         src = cv::imread(img_path);
         cv::Mat img = letterBox(src);
@@ -278,10 +278,6 @@ int work(const std::string& id, MxStream::MxStreamManager& mxStreamManager)
         dataBuffer.dataPtr = nullptr;
         return ret;
     }
-    double time =  (double)(clock() - start) / CLOCKS_PER_SEC;
-    time_min = (std::min)(time_min, time);
-    time_max = (std::max)(time_max, time);
-    time_avg += time;
     std::string result = std::string((char *)output->dataPtr, output->dataSize);
     if (saveImage == true) { SaveImage(result, src, id); }
     if (saveTxt == true) { SaveTxt(result, id); }
@@ -317,7 +313,12 @@ int run()
     if (in) {
         while (getline(in, line)) {
             loop_num++;
+            auto start = clock();
             work(line, mxStreamManager);
+            double time =  (double)(clock() - start) / CLOCKS_PER_SEC;
+            time_min = (std::min)(time_min, time);
+            time_max = (std::max)(time_max, time);
+            time_avg += time;
         }
         time_avg /= loop_num;
     }

YOLOv5Prune/models/yolov5/atc.sh

@@ -14,12 +14,13 @@
 
 cd ./models/yolov5/
 atc \
---model=prune55_t.onnx \
+--model=prune60_t.onnx \
 --framework=5 \
---output=./prune55_t \
+--output=./prune60_t \
 --input_format=NCHW \
 --input_shape="images:1,3,512,512" \
 --enable_small_channel=1 \
+--insert_op_conf=./aipp_yolov5.cfg \
 --soc_version=Ascend310 \
---out_nodes="Transpose_260:0;Transpose_308:0;Transpose_356:0" \
+--out_nodes="Transpose_260:0;Transpose_520:0;Transpose_780:0" \
 cd -