resnet50有多少个参数？

ResNet50是一个非常深的卷积神经网络，它具有 50 层深度，包含多个卷积层、池化层、全连接层和批归一化层。它的参数数量是非常庞大的，但是可以通过计算每一层的参数数量来得到近似值。

具体地，ResNet50的网络结构可以分为五个阶段，每个阶段包含多个卷积层和池化层。其中第一层是一个 7x7 的卷积层，输入通道数为 3，输出通道数为 64。第二层到第五层分别包含 3、4、6、3 个残差模块。每个残差模块包含两个 3x3 的卷积层和一个跳跃连接，其中第一个卷积层的输入通道数和输出通道数相同，第二个卷积层的输入通道数和输出通道数翻倍。

根据这些信息，我们可以使用以下代码来计算ResNet50的参数数量：

import torch
import torch.nn as nn

class ResNet50(nn.Module):
    def __init__(self):
        super(ResNet50, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(64, 64, 3)
        self.layer2 = self._make_layer(256, 128, 4, stride=2)
        self.layer3 = self._make_layer(512, 256, 6, stride=2)
        self.layer4 = self._make_layer(1024, 512, 3, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(2048, 1000)

    def _make_layer(self, in_channels, out_channels, blocks, stride=1):
        layers = []
        layers.append(Bottleneck(in_channels, out_channels, stride))
        for i in range(1, blocks):
            layers.append(Bottleneck(out_channels * 4, out_channels))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

class Bottleneck(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.conv3 = nn.Conv2d(out_channels, out_channels * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channels * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = nn.Sequential(
            nn.Conv2d(in_channels, out_channels * 4, kernel_size=1, stride=stride, bias=False),
            nn.BatchNorm2d(out_channels * 4),
        )

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out

model = ResNet50()
params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print("ResNet50 has {} parameters.".format(params))

上述代码中，我们定义了一个ResNet50模型，并使用`sum(p.numel() for p in model.parameters() if p.requires_grad)`来计算模型中所有需要学习的参数数量。在这个示例中，ResNet50模型的参数数量为23,508,032。