YOLOv5s网络结构详解

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模型配置文件

YOLO v5的模型配置文件都一样，区别在层深depth_multiple和宽度width_multiple控制不一样。YOLO v5s是最简洁的一个模型，深度为1就是说没有重复模块，因此方便用来分析其结构。模型的具体深度需要跑一下才能看到，或者将depth_multiple与各层 number相乘，按下式计算：

n = max(round(n * gd), 1) if n > 1 else n  # depth gain

下面给出了具体的 YOLO v5s 参数配置信息：

                 from  n    params  module                                  arguments                       layer            cin    cout
---------------------------------------------------------------------------------------------------------------------------------------------
  0                -1  1      3520  models.common.Focus                     [3, 32, 3]                    	Focus          	   3	  32
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                	Conv           	  32	  64
  2                -1  1     19904  models.common.BottleneckCSP             [64, 64, 1]                   	BottleneckCSP  	  64	  64
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               	Conv           	  64	 128
  4                -1  1    161152  models.common.BottleneckCSP             [128, 128, 3]                 	BottleneckCSP  	 128	 128
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              	Conv           	 128	 256
  6                -1  1    641792  models.common.BottleneckCSP             [256, 256, 3]                 	BottleneckCSP  	 256	 256
  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              	Conv           	 256	 512
  8                -1  1    656896  models.common.SPP                       [512, 512, [5, 9, 13]]        	SPP            	 512	 512
  9                -1  1   1248768  models.common.BottleneckCSP             [512, 512, 1, False]          	BottleneckCSP  	 512	 512
 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              	Conv           	 512	 256
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          	Upsample       	 512	 256
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           	Concat         	 512	 512
 13                -1  1    378624  models.common.BottleneckCSP             [512, 256, 1, False]          	BottleneckCSP  	 512	 256
 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              	Conv           	 256	 128
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          	Upsample       	 256	 128
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           	Concat         	 256	 256
 17                -1  1     95104  models.common.BottleneckCSP             [256, 128, 1, False]          	BottleneckCSP  	 256	 128
 18                -1  1      2322  torch.nn.modules.conv.Conv2d            [128, 18, 1, 1]               	Conv2d         	 128	 255
 19                -2  1    147712  models.common.Conv                      [128, 128, 3, 2]              	Conv           	 128	 128
 20          [-1, 14]  1         0  models.common.Concat                    [1]                           	Concat         	 128	 256
 21                -1  1    313088  models.common.BottleneckCSP             [256, 256, 1, False]          	BottleneckCSP  	 256	 256
 22                -1  1      4626  torch.nn.modules.conv.Conv2d            [256, 18, 1, 1]               	Conv2d         	 256	 255
 23                -2  1    590336  models.common.Conv                      [256, 256, 3, 2]              	Conv           	 256	 256
 24          [-1, 10]  1         0  models.common.Concat                    [1]                           	Concat         	 256	 512
 25                -1  1   1248768  models.common.BottleneckCSP             [512, 512, 1, False]          	BottleneckCSP  	 512	 512
 26                -1  1      9234  torch.nn.modules.conv.Conv2d            [512, 18, 1, 1]               	Conv2d         	 512	 255
 27      [-1, 22, 18]  1         0  Detect                                  [1, anchors						Detect         	 512	 255

网络可视化

根据配置文件定义，将网络进行图1划分：

归纳整理得到图2：

搭建网络

根据网络划分和梳理的连接就可以自行搭建网络了。

class YoloModel(nn.Module):
    anchors = [[116, 90, 156, 198, 373, 326],
               [30, 61, 62, 45, 59, 119],
               [10, 13, 16, 30, 33, 23]]

    def __init__(self, class_num=1, input_ch=3):
        super(YoloModel, self).__init__()
        self.build_model(class_num)

        # Build strides, anchors
        s = 128  # 2x min stride
        self.Detect.stride = torch.tensor(
            [s / x.shape[-2] for x in self.forward(torch.zeros(1, input_ch, s, s))])  # forward
        self.Detect.anchors /= self.Detect.stride.view(-1, 1, 1)
        check_anchor_order(self.Detect)
        self.stride = self.Detect.stride
        # print('Strides: %s' % self.Detect.stride.tolist())  # [8.0, 16.0, 32.0]
        print("Input size must be multiple of", self.stride.max().item())

        torch_utils.initialize_weights(self)
        self._initialize_biases()  # only run once
        # model_info(self)

    def build_model(self, class_num):
        # output channels
        self