pytorch yolov3 代码详解_YOLOv3作者JR官方Pytorch检测代码

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<h2>一：前言</h2>
<p>代码链接：</p>

ayooshkathuria/YOLO_v3_tutorial_from_scratchgithub.com
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<p>官方注释翻译：</p>

深度智能：从零开始实现YOLO v3（Part1）zhuanlan.zhihu.com
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<p>推荐注释(来自王若霄师兄 @王若霄的工作)：</p>

王若霄：超详细的Pytorch版yolov3代码中文注释详解（一）zhuanlan.zhihu.com
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<p><b>本文进行的工作：</b></p>
<p>上面已经对代码各模块进行了详细解读，此次工作是<b>完整进行一次detect(</b>detect.py文件<b>).</b></p>
<p><b>请详细研究cfg网络层信息：</b>https://zhuanlan.zhihu.com/p/36920744（找到<b>配置文件</b>）</p>
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<h2>>..> 我是真的蠢！！！tensorflow、pytorch图片的输入格式都是（H，W）</h2>
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<p><b>重要网络参考：</b></p>
<p>他是完整的一次检测网络，有输入输出、route、residual，注意<b>索引index(layer)</b>和代码的索引前后关系相同(如：route -4 ，往后索引四层，表中：83 route 79，就是往后4层到达79 ).</p>
<p>下面会多次使用此表！</p>
<div class="blockcode">
  <pre class="blockcode"><code>Demo
layer    filters size             input             output
0 conv    32  3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x  32  0.299 BFLOPs
1 conv    64  3 x 3 / 2 416 x 416 x  32 -> 208 x 208 x  64  1.595 BFLOPs
2 conv    32  1 x 1 / 1 208 x 208 x  64 -> 208 x 208 x  32  0.177 BFLOPs
3 conv    64  3 x 3 / 1 208 x 208 x  32 -> 208 x 208 x  64  1.595 BFLOPs
4 res 1                208 x 208 x  64 -> 208 x 208 x  64
5 conv 128  3 x 3 / 2 208 x 208 x  64 -> 104 x 104 x 128  1.595 BFLOPs
6 conv    64  1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x  64  0.177 BFLOPs
7 conv 128  3 x 3 / 1 104 x 104 x  64 -> 104 x 104 x 128  1.595 BFLOPs
8 res 5                104 x 104 x 128 -> 104 x 104 x 128
9 conv    64  1 x 1 / 1 104 x 104 x 128 -> 104 x 104 x  64  0.177 BFLOPs
10 conv 128  3 x 3 / 1 104 x 104 x  64 -> 104 x 104 x 128  1.595 BFLOPs
11 res 8                104 x 104 x 128 -> 104 x 104 x 128
12 conv 256  3 x 3 / 2 104 x 104 x 128 -> 52 x  52 x 256  1.595 BFLOPs
13 conv 128  1 x 1 / 1 52 x  52 x 256 -> 52 x  52 x 128  0.177 BFLOPs
14 conv 256  3 x 3 / 1 52 x  52 x 128 -> 52 x  52 x 256  1.595 BFLOPs
15 res 12                52 x  52 x 256 -> 52 x  52 x 256
16 conv 128  1 x 1 / 1 52 x  52 x 256 -> 52 x  52 x 128  0.177 BFLOPs
17 conv 256  3 x 3 / 1 52 x  52 x 128 -> 52 x  52 x 256  1.595 BFLOPs
18 res 15                52 x  52 x 256 -> 52 x  52 x 256
19 conv 128  1 x 1 / 1 52 x  52 x 256 -> 52 x  52 x 128  0.177 BFLOPs
20 conv 256  3 x 3 / 1 52 x  52 x 128 -> 52 x  52 x 256  1.595 BFLOPs
21 res 18                52 x  52 x 256 -> 52 x  52 x 256
22 conv 128  1 x 1 / 1 52 x  52 x 256 -> 52 x  52 x 128  0.177 BFLOPs
23 conv 256  3 x 3 / 1 52 x  52 x 128 -> 52 x  52 x 256  1.595 BFLOPs
24 res 21                52 x  52 x 256 -> 52 x  52 x 256
25 conv 128  1 x 1 / 1 52 x  52 x 256 -> 52 x  52 x 128  0.177 BFLOPs
26 conv 256  3 x 3 / 1 52 x  52 x 128 -> 52 x  52 x 256  1.595 BFLOPs
27 res 24                52 x  52 x 256 -> 52 x  52 x 256
28 conv 128  1 x 1 / 1 52 x  52 x 256 -> 52 x  52 x 128  0.177 BFLOPs
29 conv 256  3 x 3 / 1 52 x  52 x 128 -> 52 x  52 x 256  1.595 BFLOPs
30 res 27                52 x  52 x 256 -> 52 x  52 x 256
31 conv 128  1 x 1 / 1 52 x  52 x 256 -> 52 x  52 x 128  0.177 BFLOPs
32 conv 256  3 x 3 / 1 52 x  52 x 128 -> 52 x  52 x 256  1.595 BFLOPs
33 res 30                52 x  52 x 256 -> 52 x  52 x 256
34 conv 128  1 x 1 / 1 52 x  52 x 256 -> 52 x  52 x 128  0.177 BFLOPs
35 conv 256  3 x 3 / 1 52 x  52 x 128 -> 52 x  52 x 256  1.595 BFLOPs
36 res 33                52 x  52 x 256 -> 52 x  52 x 256
37 conv 512  3 x 3 / 2 52 x  52 x 256 -> 26 x  26 x 512  1.595 BFLOPs
38 conv 256  1 x 1 / 1 26 x  26 x 512 -> 26 x  26 x 256  0.177 BFLOPs
39 conv 512  3 x 3 / 1 26 x  26 x 256 -> 26 x  26 x 512  1.595 BFLOPs