yolov5网络结构代码详解

Yolov5 是一种用于目标检测的神经网络模型，它是由 Alexey Bochkovskiy 在 YOLO (You Only Look Once) 系列模型的基础上进行改进和优化而来的。下面是 Yolov5 的网络结构代码的详解：

import torch
import torch.nn as nn
import torch.nn.functional as F


class Conv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, groups=1, bias=False):
        super(Conv, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding=(kernel_size - 1) // 2, groups=groups, bias=bias)
        self.bn = nn.BatchNorm2d(out_channels)
        self.act = nn.ReLU()
    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.act(x)
        return x


class Bottleneck(nn.Module):
    def __init__(self, in_channels, out_channels, shortcut=True, groups=1, expansion=0.5):
        super(Bottleneck, self).__init__()
        hidden_channels = int(out_channels * expansion)

        self.conv1 = Conv(in_channels, hidden_channels, 1)
        self.conv2 = Conv(hidden_channels, out_channels, 3, groups=groups)
        self.shortcut = shortcut and in_channels == out_channels
        if self.shortcut:
            self.shortcut_conv = Conv(in_channels, out_channels, 1)

    def forward(self, x):
        shortcut = x
        x = self.conv1(x)
        x = self.conv2(x)
        if self.shortcut:
            shortcut = self.shortcut_conv(shortcut)
        x += shortcut
        return x


class CSPDarknet(nn.Module):
    def __init__(self, layers, channels=(32, 64, 128, 256, 512, 1024), expansion=0.5):
        super(CSPDarknet, self).__init__()

        self.in_channels = channels[0]
        self.conv1 = Conv(3, self.in_channels, kernel_size=3, stride=1)

        self.stages = nn.ModuleList()
        for i, (in_channels, out_channels) in enumerate(zip(channels[:-1], channels[1:])):
            if i == 0:
                self.stages.append(nn.Sequential(
                    Conv(in_channels, out_channels, kernel_size=3, stride=2),
                    Bottleneck(out_channels, out_channels, groups=1, expansion=1)))
            else:
                self.stages.append(nn.Sequential(
                    Conv(in_channels, out_channels // 2, kernel_size=1),
                    Bottleneck(out_channels // 2, out_channels // 2, groups=1, expansion=1),
                    Conv(out_channels // 2, out_channels // 2, kernel_size=1),
                    Bottleneck(out_channels // 2, out_channels // 2, groups=1, expansion=1),
                    Conv(out_channels // 2, out_channels, kernel_size=1),
                    Bottleneck(out_channels, out_channels, groups=1, expansion=1)))

                self.stages.append(Conv(out_channels * 2, out_channels, kernel_size=1))

        self.init_weights()

    def forward(self, x):
        x = self.conv1(x)
        x = self.stages[0](x)
        route = x
        x = self.stages[1](x)
        x = self.stages[2](x)
        x = self.stages[3](x)
        x = self.stages[4](x)
        x = self.stages[5](x)
        return route, x

    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)


class YOLOv5(nn.Module):
    def __init__(self, num_classes=80, expansion=0.5, width_mult=1.0):
        super(YOLOv5, self).__init__()

        channels = (64, 128, 256, 512, 1024)
        channels = [int(c * width_mult) for c in channels]

        self.backbone = CSPDarknet(layers=[1, 2, 8, 8, 4], channels=channels, expansion=expansion)

        self.num_classes = num_classes
        self.num_anchors = 3
        self.num_attrib = num_classes + 5

        self.heads = self._make_head(channels)

    def forward(self, x):
        x = self.backbone(x)

        outputs = []
        for i, head in enumerate(self.heads):
            outputs.append(head(x))

        return outputs

    def _make_head(self, channels):
        heads = nn.ModuleList()
        for i in range(3):
            heads.append(nn.Sequential(
                Conv(channels[-1], channels[-1] // 2, kernel_size=1),
                Conv(channels[-1] // 2, channels[-1], kernel_size=3),
                Conv(channels[-1], channels[-1] // 2, kernel_size=1),
                Conv(channels[-1] // 2, channels[-1], kernel_size=3),
                Conv(channels[-1], self.num_attrib * self.num_anchors, kernel_size=1, bias=True)))

        return heads

以上是 Yolov5 的网络结构代码的详解。代码中包含了一些常用的卷积和规范化层，还有 CSPDarknet 和 YOLOv5 的主要结构。YOLOv5 的网络结构包括了主干网络 CSPDarknet 和三个输出头，每个输出头负责预测不同尺度的目标框。在 forward 方法中，通过对主干网络和输出头进行调用，得到最终的输出结果。

这部分代码只是 Yolov5 网络结构的实现，模型的训练和推理过程还需要其他的代码进行支持。希望这些信息能够帮助到你！如有任何疑问，请随时提出。