yolov5网络结构图怎么画

要画Yolov5网络结构图，可以使用一些工具，比如Graphviz和PyTorch的nn.Module。

以下是使用PyTorch的nn.Module画Yolov5网络结构图的示例代码：

import torch
import torch.nn as nn

class Conv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=None, groups=1, bias=False):
        super().__init__()
        if padding is None:
            padding = (kernel_size - 1) // 2
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, 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 CSPBlock(nn.Module):
    def __init__(self, in_channels, out_channels, num_blocks, use_residual=True):
        super().__init__()

        self.use_residual = use_residual

        mid_channels = out_channels // 2

        self.conv1 = Conv(in_channels, mid_channels, 1)
        self.conv2 = Conv(in_channels, mid_channels, 1)
        self.conv3 = Conv(2 * mid_channels, out_channels, 1)

        self.blocks = nn.Sequential(
            *[ResidualBlock(mid_channels, mid_channels, use_residual) for _ in range(num_blocks)]
        )

    def forward(self, x):
        x1 = self.conv1(x)
        x2 = self.conv2(x)
        x2 = self.blocks(x2)
        x = torch.cat((x1, x2), dim=1)
        x = self.conv3(x)

        if self.use_residual:
            x = x + x1

        return x

class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, use_residual=True):
        super().__init__()

        self.conv1 = Conv(in_channels, out_channels, 1)
        self.conv2 = Conv(out_channels, in_channels, 3, padding=1)

        self.use_residual = use_residual

    def forward(self, x):
        y = self.conv1(x)
        y = self.conv2(y)

        if self.use_residual:
            y = y + x

        return y

class Yolov5(nn.Module):
    def __init__(self, num_classes=80):
        super().__init__()

        self.backbone = nn.Sequential(
            Conv(3, 32, 3, stride=2),
            CSPBlock(32, 64, num_blocks=1),
            CSPBlock(64, 128, num_blocks=3),
            CSPBlock(128, 256, num_blocks=15),
            CSPBlock(256, 512, num_blocks=15),
            CSPBlock(512, 1024, num_blocks=7),
        )

        self.neck = nn.Sequential(
            Conv(1024, 512, 1),
            nn.Upsample(scale_factor=2),
            CSPBlock(1024, 512, num_blocks=3, use_residual=False),
            Conv(512, 256, 1),
            nn.Upsample(scale_factor=2),
            CSPBlock(512, 256, num_blocks=3, use_residual=False),
            Conv(256, 128, 1),
            nn.Upsample(scale_factor=2),
            CSPBlock(256, 128, num_blocks=3, use_residual=False),
        )

        self.head = nn.Sequential(
            Conv(128, 256, 3, padding=1),
            nn.Conv2d(256, 3 * (num_classes + 5), 1)
        )

    def forward(self, x):
        x = self.backbone(x)
        x = self.neck(x)
        x = self.head(x)
        return x

这里定义了Conv、CSPBlock、ResidualBlock和Yolov5四个类，分别表示卷积层、CSP块、残差块和整个Yolov5网络。

可以使用如下代码将网络结构图保存到文件中：

from torchviz import make_dot

model = Yolov5()
x = torch.randn(1, 3, 416, 416)
y = model(x)

make_dot(y, params=dict(model.named_parameters())).render("yolov5", format="png")

这里使用了torchviz库的make_dot函数生成网络结构图，并将其保存为yolov5.png文件。