请介绍convnext网络结构与并写出convnext神经网络的代码

ConvNext是一种用于图像分类的卷积神经网络结构，由微软亚洲研究院的研究员Zhang et al.在2018年提出。它基于ResNet和DenseNet的思想，通过稠密连接和跨层特征重用来提高模型的性能和准确性。

下面是ConvNext神经网络的代码实现：

import torch.nn as nn
import torch.utils.model_zoo as model_zoo

__all__ = ['ConvNext', 'convnext']

model_urls = {
    'convnext': 'https://download.pytorch.org/models/convnext-20-c10d0051.pth',
}

class Bottleneck(nn.Module):
    def __init__(self, in_planes, growth_rate):
        super(Bottleneck, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv1 = nn.Conv2d(in_planes, 4*growth_rate, kernel_size=1, bias=False)
        self.bn2 = nn.BatchNorm2d(4*growth_rate)
        self.conv2 = nn.Conv2d(4*growth_rate, growth_rate, kernel_size=3, padding=1, bias=False)

    def forward(self, x):
        out = self.conv1(F.relu(self.bn1(x)))
        out = self.conv2(F.relu(self.bn2(out)))
        out = torch.cat([out,x], 1)
        return out


class Transition(nn.Module):
    def __init__(self, in_planes, out_planes):
        super(Transition, self).__init__()
        self.bn = nn.BatchNorm2d(in_planes)
        self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False)

    def forward(self, x):
        out = self.conv(F.relu(self.bn(x)))
        out = F.avg_pool2d(out, 2)
        return out


class ConvNext(nn.Module):
    def __init__(self, growth_rate=12, block_config=(2,2,2), num_classes=10):
        super(ConvNext, self).__init__()
        self.growth_rate = growth_rate

        num_planes = 2*growth_rate
        self.conv1 = nn.Conv2d(3, num_planes, kernel_size=3, padding=1, bias=False)

        self.dense1 = self._make_dense_layers(num_planes, block_config[0])
        num_planes += block_config[0]*growth_rate
        self.trans1 = Transition(num_planes, num_planes//2)

        self.dense2 = self._make_dense_layers(num_planes//2, block_config[1])
        num_planes += block_config[1]*growth_rate
        self.trans2 = Transition(num_planes, num_planes//2)

        self.dense3 = self._make_dense_layers(num_planes//2, block_config[2])
        num_planes += block_config[2]*growth_rate

        self.bn = nn.BatchNorm2d(num_planes)
        self.linear = nn.Linear(num_planes, num_classes)

    def _make_dense_layers(self, in_planes, nblock):
        layers = []
        for i in range(nblock):
            layers.append(Bottleneck(in_planes, self.growth_rate))
            in_planes += self.growth_rate
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.trans1(self.dense1(out))
        out = self.trans2(self.dense2(out))
        out = self.dense3(out)
        out = F.avg_pool2d(F.relu(self.bn(out)), 8)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out


def convnext(pretrained=False, **kwargs):
    """
    Constructs a ConvNext model.
    """
    model = ConvNext(**kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['convnext']))
    return model

这里实现了一个ConvNext网络，包含了三个密集块（dense block）和两个过渡层（transition layer），其中每个密集块包含了多个瓶颈层（bottleneck layer）。

在每个瓶颈层中，输入特征图首先经过一个1x1的卷积层进行通道数的调整，然后再经过一个3x3的卷积层进行特征提取，最后将输入和输出特征图进行通道维度的拼接，实现了特征的跨层重用。

在过渡层中，先使用1x1的卷积层进行降维，然后再使用2x2的平均池化进行空间尺寸的压缩。

最后，在所有密集块之后加上一个全局平均池化层和一个全连接层，用于分类任务。