请写出peleeNet神经网络的pytorch代码

以下是PeleeNet神经网络的PyTorch代码：

import torch.nn as nn

class PeleeNet(nn.Module):
    def __init__(self, num_classes=100):
        super(PeleeNet, self).__init__()
        self.num_classes = num_classes

        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(32)
        self.relu = nn.ReLU(inplace=True)

        self.conv2 = nn.Sequential(
            nn.Conv2d(32, 16, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(16),
            nn.ReLU(inplace=True),
            nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True)
        )

        self.conv3_1 = nn.Sequential(
            nn.Conv2d(32, 32, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True)
        )

        self.conv3_2 = nn.Sequential(
            nn.Conv2d(64, 32, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True)
        )

        self.conv4_1 = nn.Sequential(
            nn.Conv2d(64, 64, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True)
        )

        self.conv4_2 = nn.Sequential(
            nn.Conv2d(128, 64, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True)
        )

        self.conv5_1 = nn.Sequential(
            nn.Conv2d(128, 128, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True)
        )

        self.conv5_2 = nn.Sequential(
            nn.Conv2d(256, 128, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True)
        )

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(256, num_classes)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        x = self.conv2(x)

        x = self.conv3_1(x)
        x = self.conv3_2(x)

        x = self.conv4_1(x)
        x = self.conv4_2(x)

        x = self.conv5_1(x)
        x = self.conv5_2(x)

        x = self.avgpool(x)
        x = x.view(x.size(), -1)
        x = self.fc(x)

        return x

希望对你有所帮助！