对tph-yolov5增加超分网络的代码

以下是对tph-yolov5增加超分网络的代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.cuda.amp import autocast

from models.common import Conv
from models.yolo import Detect
from models.super_resolution import SuperResolutionNet

class TPH(nn.Module):
    def __init__(self, num_classes, input_channels=3, super_res_scale=4):
        super(TPH, self).__init__()
        self.num_classes = num_classes
        self.input_channels = input_channels
        self.super_res_scale = super_res_scale

        # Super Resolution Network
        self.super_res = SuperResolutionNet(scale=self.super_res_scale)

        # Backbone
        self.backbone = nn.Sequential(
            Conv(self.input_channels, 32, 3, 1),
            nn.MaxPool2d(2, 2),
            Conv(32, 64, 3, 1),
            nn.MaxPool2d(2, 2),
            Conv(64, 128, 3, 1),
            Conv(128, 64, 1, 1),
            Conv(64, 128, 3, 1),
            nn.MaxPool2d(2, 2),
            Conv(128, 256, 3, 1),
            Conv(256, 128, 1, 1),
            Conv(128, 256, 3, 1),
            nn.MaxPool2d(2, 2),
            Conv(256, 512, 3, 1),
            Conv(512, 256, 1, 1),
            Conv(256, 512, 3, 1),
            Conv(512, 256, 1, 1),
            Conv(256, 512, 3, 1),
        )

        # Neck
        self.neck = nn.Sequential(
            Conv(512, 256, 1, 1),
            Conv(256, 512, 3, 1),
            Conv(512, 256, 1, 1),
            Conv(256, 512, 3, 1),
            Conv(512, 256, 1, 1),
        )

        # Head
        self.head = nn.Sequential(
            Conv(256, 512, 3, 1),
            nn.Conv2d(512, (self.num_classes + 5) * 3, 1, 1, bias=True),
            Detect(num_classes=self.num_classes)
        )

    @autocast()
    def forward(self, x):
        # Super Resolution Network
        x = self.super_res(x)

        # Backbone
        x = self.backbone(x)

        # Neck
        x = self.neck(x)

        # Head
        x = self.head(x)

        return x

在这个示例中，我们在原始的TPH模型中添加了一个超分辨率网络。该网络将输入图像放大一定倍数，并将其用作TPH模型的输入。这可以提高模型对细节的感知能力，从而提高检测精度。

注意，这只是一个示例代码，并不是在所有情况下都适用的通用代码。根据您的具体需求，您可能需要修改或完全重写代码。