IResNet pytorch代码

IResNet通常是指Inverted Residual Networks（倒置残差块）在PyTorch框架下的实现。这是一种在深度学习特别是卷积神经网络（CNNs）中常用的结构，它在ResNet（残差网络）的基础上进行了改进，通过更小的计算代价实现了更深的网络。

在PyTorch中，要创建一个简单的IResNet模型，你需要首先导入必要的库，如`torch`, `torch.nn`等，然后定义基础的模块，比如`BasicBlock`和`Bottleneck`（对于更深的网络），接着构造包含这些模块的残差块，并构建整个网络。以下是一个简化的例子：

import torch
import torch.nn as nn

# 定义基本的ResNet块
class BasicBlock(nn.Module):
    # ...

class Bottleneck(nn.Module):
    # ...

# 倒置残差块的实例
def iresnet_block(block, in_channels, out_channels, stride=1):
    return [
        block(in_channels, out_channels, stride=stride),
        block(out_channels, out_channels)
    ]

# 构建整个IResNet模型
class IResNet(nn.Module):
    def __init__(self, block, layers, num_classes):
        super(IResNet, self).__init__()
        self.in_channels = 64
        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)

        # 主干部分
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)

        # 全局平均池化和分类层
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, block, out_channels, blocks, stride=1):
        strides = [stride] + [1] * (blocks - 1)
        layers = []
        for stride in strides:
            layers.append(iresnet_block(block, self.in_channels, out_channels, stride))
            self.in_channels = out_channels * block.expansion
        return nn.Sequential(*layers)

# 使用示例:
ires_net = IResNet(BasicBlock, [2, 2, 2, 2], num_classes=1000)