求完整yolov7-tiny减少检测头的代码，且在pytorch环境下

以下是在PyTorch环境下实现YOLOv7-tiny减少检测头的完整代码示例：

import torch
import torch.nn as nn

# 定义YOLOv7-tiny的主干网络
class TinyBody(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.relu1 = nn.LeakyReLU(0.1)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.relu2 = nn.LeakyReLU(0.1)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.relu3 = nn.LeakyReLU(0.1)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.bn4 = nn.BatchNorm2d(256)
        self.relu4 = nn.LeakyReLU(0.1)
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
        self.conv5 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.bn5 = nn.BatchNorm2d(512)
        self.relu5 = nn.LeakyReLU(0.1)
        self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
        self.conv6 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=1)
        self.bn6 = nn.BatchNorm2d(1024)
        self.relu6 = nn.LeakyReLU(0.1)
        self.conv7 = nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1)
        self.bn7 = nn.BatchNorm2d(1024)
        self.relu7 = nn.LeakyReLU(0.1)

    def forward(self, x):
        x = self.pool1(self.relu1(self.bn1(self.conv1(x))))
        x = self.pool2(self.relu2(self.bn2(self.conv2(x))))
        x = self.pool3(self.relu3(self.bn3(self.conv3(x))))
        x = self.pool4(self.relu4(self.bn4(self.conv4(x))))
        x = self.pool5(self.relu5(self.bn5(self.conv5(x))))
        x = self.relu6(self.bn6(self.conv6(x)))
        x = self.relu7(self.bn7(self.conv7(x)))
        return x

# 定义YOLOv7-tiny的检测头
class TinyHead(nn.Module):
    def __init__(self, in_channels, num_anchors, num_classes):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels, 256, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(256)
        self.relu1 = nn.LeakyReLU(0.1)
        self.conv2 = nn.Conv2d(256, num_anchors * (num_classes + 5), kernel_size=1)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.conv2(x)
        return x

# 将YOLOv7-tiny的检测头从3个缩小到2个
class YOLOv7Tiny(nn.Module):
    def __init__(self, num_classes, num_anchors):
        super().__init__()
        self.body = TinyBody()
        self.head1 = TinyHead(1024, num_anchors, num_classes)
        self.conv1 = nn.Conv2d(512, 256, kernel_size=1)
        self.upsample1 = nn.Upsample(scale_factor=2, mode='nearest')
        self.concat1 = nn.Concat(dim=1)
        self.head2 = TinyHead(384, num_anchors, num_classes)

    def forward(self, x):
        x_body = self.body(x)
        x1 = self.head1(x_body)
        x = self.conv1(x_body)
        x = self.upsample1(x)
        x = self.concat1([x, self.body.conv5])
        x2 = self.head2(x)
        return [x1, x2]

在上面的代码中，我们首先定义了YOLOv7-tiny的主干网络TinyBody，并定义了YOLOv7-tiny的检测头TinyHead。然后，我们定义了YOLOv7-tiny的整个网络结构YOLOv7Tiny，其中将YOLOv7-tiny的检测头从3个缩小到2个。最后，我们可以使用YOLOv7Tiny来进行目标检测任务。