resnet50代码

ResNet50是一种深度残差神经网络模型，由微软亚洲研究院于2015年提出，常用于计算机视觉任务，如图像分类、目标检测等。它特别设计了残差块（Residual Block），解决了深度学习训练过程中梯度消失的问题，使得模型能够更深，提升性能。

以下是ResNet50的一个基本框架概述，通常是在PyTorch或TensorFlow这样的深度学习库中实现的：

import torch.nn as nn

# ResNet的基本模块 - 残差块
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 // 4, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels // 4)
        self.conv2 = nn.Conv2d(out_channels // 4, out_channels // 4, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels // 4)
        self.conv3 = nn.Conv2d(out_channels // 4, out_channels, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.shortcut = (in_channels != out_channels) or (stride != 1)

    def forward(self, x):
        residual = x
        if self.shortcut:
            residual = nn.functional.avg_pool2d(x, stride=stride)
        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)
        
        return self.relu(out + residual) if self.shortcut else self.relu(out)

# 整个ResNet50模型
def resnet50(num_classes):
    return nn.Sequential(
        # ... 这里包含多个Bottleneck模块，通常分为几个阶段，每个阶段有不同的通道数和步长
        nn.AdaptiveAvgPool2d((1, 1)),
        nn.Flatten(),
        nn.Linear(2048, num_classes)  # 2048是ResNet50最后一层卷积后的特征维度
    )