YOLOv7：第一篇之实践和推理

目录

• 1. 下载源码
• 2. 推理
• 3. onnx导出
• 4. 网络配置yaml 文件解读
• 5.推理参数

 

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1. 下载源码

git clone  https://github.com/WongKinYiu/yolov7.git

本文下载代码时间：2022.08.03

可能需要安装的库：

清华源加速安装：

-i https://pypi.tuna.tsinghua.edu.cn/simple

需要安装 yaml：

pip install pyyaml # 注意这里是 pyyaml，使用yaml 会安装失败找不到包。

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2. 推理

python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg

首次使用会自动下载 yolov7.pt 文件到主目录。

输出信息：

Namespace(weights=['yolov7.pt'], source='inference/images/horses.jpg', img_size=640, conf_thres=0.25, iou_thres=0.45, device='', view_img=False, save_txt=False, save_conf=False, nosave=False, classes=None, agnostic_nms=False, augment=False, update=False, project='runs/detect', name='exp', exist_ok=False, no_trace=False)
YOLOR 🚀 v0.1-47-g4f6e390 torch 1.10.1+cu102 CPU
 
Downloading https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt to yolov7.pt...
100%|██████████████████████████████████████| 72.1M/72.1M [02:16<00:00, 553kB/s]
 
Fusing layers... 
RepConv.fuse_repvgg_block
RepConv.fuse_repvgg_block
RepConv.fuse_repvgg_block
/home/xx/programs/miniconda3/envs/yolo/lib/python3.9/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
Model Summary: 306 layers, 36905341 parameters, 6652669 gradients, 104.7 GFLOPS
 Convert model to Traced-model... 
 traced_script_module saved! 
 model is traced! 
 
 The image with the result is saved in: runs/detect/exp/horses.jpg
Done. (0.359s)

需要注意的是上面的内容：

Model Summary: 306 layers, 36905341 parameters, 6652669 gradients, 104.7 GFLOPS
 Convert model to Traced-model... 
 traced_script_module saved! 
 model is traced! 

暂时不太清楚啥意思。

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3. onnx导出

python export.py --weights yolov7.pt --grid --end2end --simplify \
		--topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640

这里我们主要关注得到的 yolov7.onnx ，我们使用 netron 进行可视化，以结合源码查看网络架构。

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4. 网络配置yaml 文件解读

文件路径：cfg/training/yolov7.yaml

点击查看代码

# parameters
nc: 80  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple
 
# anchors
anchors:
  - [12,16, 19,36, 40,28]  # P3/8
  - [36,75, 76,55, 72,146]  # P4/16
  - [142,110, 192,243, 459,401]  # P5/32
 
# yolov7 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [32, 3, 1]],  # 0
  
   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2      
   [-1, 1, Conv, [64, 3, 1]],  # 2
   
   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4    # -2
   [-1, 1, Conv, [64, 1, 1]],   # 4      # -6
   [-2, 1, Conv, [64, 1, 1]],   # 5      # -5
   [-1, 1, Conv, [64, 3, 1]],   # 6
   [-1, 1, Conv, [64, 3, 1]],   # 7      # -3
   [-1, 1, Conv, [64, 3, 1]],   # 8
   [-1, 1, Conv, [64, 3, 1]],   # 9      # -1
   [[-1, -3, -5, -6], 1, Concat, [1]],   # 10  
   [-1, 1, Conv, [256, 1, 1]],  # 11
         
   [-1, 1, MP, []],   # 12
   [-1, 1, Conv, [128, 1, 1]],  # 13     # -3
   [-3, 1, Conv, [128, 1, 1]],  # 14
   [-1, 1, Conv, [128, 3, 2]],  # 15     # -1
   [[-1, -3], 1, Concat, [1]],  # 16-P3/8       # -2
   [-1, 1, Conv, [128, 1, 1]],  # 17     # -6
   [-2, 1, Conv, [128, 1, 1]],  # 18     # -5
   [-1, 1, Conv, [128, 3, 1]],  # 19
   [-1, 1, Conv, [128, 3, 1]],  # 20     # -3
   [-1, 1, Conv, [128, 3, 1]],  # 21
   [-1, 1, Conv, [128, 3, 1]],  # 22     # -1
   [[-1, -3, -5, -6], 1, Concat, [1]],  # 23
   [-1, 1, Conv, [512, 1, 1]],  # 24     # -3
         
   [-1, 1, MP, []],   # 25
   [-1, 1, Conv, [256, 1, 1]],  # 26     # -3
   [-3, 1, Conv, [256, 1, 1]],  # 27
   [-1, 1, Conv, [256, 3, 2]],  # 28     # -1
   [[-1, -3], 1, Concat, [1]],  # 29-P4/16     # -2
   [-1, 1, Conv, [256, 1, 1]],  # 30     # -6
   [-2, 1, Conv, [256, 1, 1]],  # 31     # -5
   [-1, 1, Conv, [256, 3, 1]],  # 32
   [-1, 1, Conv, [256, 3, 1]],  # 33     # -3
   [-1, 1, Conv, [256, 3, 1]],  # 34
   [-1, 1, Conv, [256, 3, 1]],  # 35     # -1
   [[-1, -3, -5, -6], 1, Concat, [1]],  # 36
   [-1, 1, Conv, [1024, 1, 1]],  # 37     # -3
         
   [-1, 1, MP, []],   # 38
   [-1, 1, Conv, [512, 1, 1]],  # 39     # -3
   [-3, 1, Conv, [512, 1, 1]],  # 40
   [-1, 1, Conv, [512, 3, 2]],  # 41     # -1
   [[-1, -3], 1, Concat, [1]],  # 42-P5/32     # -2
   [-1, 1, Conv, [256, 1, 1]],  # 43     # -6
   [-2, 1, Conv, [256, 1, 1]],  # 44     # -5
   [-1, 1, Conv, [256, 3, 1]],  # 45
   [-1, 1, Conv, [256, 3, 1]],  # 46     # -3
   [-1, 1, Conv, [256, 3, 1]],  # 47
   [-1, 1, Conv, [256, 3, 1]],  # 48     # -1
   [[-1, -3, -5, -6], 1, Concat, [1]],  # 49
   [-1, 1, Conv, [1024, 1, 1]],  # 50
  ]
 
# yolov7 head
head:
  [[-1, 1, SPPCSPC, [512]], # 51
  
   [-1, 1, Conv, [256, 1, 1]],  # 52
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],  # 53     # -2
   [37, 1, Conv, [256, 1, 1]], # route backbone P4  # 54     # -1
   [[-1, -2], 1, Concat, [1]],  # 55     # -2
   
   [-1, 1, Conv, [256, 1, 1]],  # 56     # -6
   [-2, 1, Conv, [256, 1, 1]],  # 57     # -5
   [-1, 1, Conv, [128, 3, 1]],  # 58     # -4
   [-1, 1, Conv, [128, 3, 1]],  # 59     # -3
   [-1, 1, Conv, [128, 3, 1]],  # 60     # -2
   [-1, 1, Conv, [128, 3, 1]],  # 61     # -1
   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],  # 62
   [-1, 1, Conv, [256, 1, 1]], # 63
   
   [-1, 1, Conv, [128, 1, 1]],  # 64
   [-1, 1, nn.Upsample, [None, 2, 'nearest']], # 65     # -2
   [24, 1, Conv, [128, 1, 1]], # route backbone P3  # 66
   [[-1, -2], 1, Concat, [1]],  # 67     # -2
   
   [-1, 1, Conv, [128, 1, 1]],  # 68     # -6
   [-2, 1, Conv, [128, 1, 1]],  # 69     # -5
   [-1, 1, Conv, [64, 3, 1]],    # 70     # -4
   [-1, 1, Conv, [64, 3, 1]],   # 71     # -3
   [-1, 1, Conv, [64, 3, 1]],   # 72     # -2
   [-1, 1, Conv, [64, 3, 1]],  # 73     # -1
   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],  # 74
   [-1, 1, Conv, [128, 1, 1]], # 75
      
   [-1, 1, MP, []],  # 76
   [-1, 1, Conv, [128, 1, 1]],  # 77     # -3
   [-3, 1, Conv, [128, 1, 1]],  # 78
   [-1, 1, Conv, [128, 3, 2]],  # 79     # -1
   [[-1, -3, 63], 1, Concat, [1]],  # 80     # -2
   
   [-1, 1, Conv, [256, 1, 1]],  # 81
   [-2, 1, Conv, [256, 1, 1]],  # 82
   [-1, 1, Conv, [128, 3, 1]],  # 83
   [-1, 1, Conv, [128, 3, 1]],  # 84
   [-1, 1, Conv, [128, 3, 1]],  # 85
   [-1, 1, Conv, [128, 3, 1]],  # 86
   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],   # 87
   [-1, 1, Conv, [256, 1, 1]], # 88     # -3
      
   [-1, 1, MP, []],  # 89
   [-1, 1, Conv, [256, 1, 1]],  # 90     # -3
   [-3, 1, Conv, [256, 1, 1]],  # 91
   [-1, 1, Conv, [256, 3, 2]],  # 92     # -1
   [[-1, -3, 51], 1, Concat, [1]],  # 93     # -2
   
   [-1, 1, Conv, [512, 1, 1]],  # 94
   [-2, 1, Conv, [512, 1, 1]],  # 95
   [-1, 1, Conv, [256, 3, 1]],  # 96
   [-1, 1, Conv, [256, 3, 1]],  # 97
   [-1, 1, Conv, [256, 3, 1]],  # 98
   [-1, 1, Conv, [256, 3, 1]],  # 99
   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],  # 100
   [-1, 1, Conv, [512, 1, 1]], # 101
   
   [75, 1, RepConv, [256, 3, 1]],  # 102
   [88, 1, RepConv, [512, 3, 1]],  # 103
   [101, 1, RepConv, [1024, 3, 1]],  # 104
 
   [[102,103,104], 1, IDetect, [nc, anchors]],   # Detect(P3, P4, P5) # 105  # IDetect 训练和部署不一样
  ]
 

已完成：
- 翻译下面的论文：
https://arxiv.org/pdf/2101.03697.pdf

【论文阅读】RepVGG: Making VGG-style ConvNets Great Again

待完成：

• 理解YOLOv7 中的 RepConv啥意思。

• 画出 YOLOv7的网络架构图

• 有时间的话，将YOLOv7 部分代码模块化，显然现在的YOLOv7的模型搭建还不够模块化。

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5.推理参数

使用 detect.py 来检测：

python .\detect.py --source 0
# source：0 调用笔记本摄像头
#        'xx.jpg' 直接检测图片
#        '传入文件夹'  检测文件夹的目录
#        '传入视频文件' 直接检测视频文件