PyTorch实现ResNet18-优快云博客

这篇博客介绍了如何使用PyTorch实现ResNet18网络，详细解析了ResNet-18的结构，并给出了CIFAR-10数据集上的预测代码及运行结果。

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论文链接：Deep residual learning for image recognition
译文地址：http://blog.youkuaiyun.com/wspba/article/details/57074389
解读博客：https://blog.youkuaiyun.com/wspba/article/details/57074389
Resnet 网络简介：
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ResNet-18结构为例：
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基本结点：

代码实现：

import torch
import torch.nn as nn
from torch.nn import functional as F


class RestNetBasicBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride):
        super(RestNetBasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)

    def forward(self, x):
        output = self.conv1(x)
        output = F.relu(self.bn1(output))
        output = self.conv2(output)
        output = self.bn2(output)
        return F.relu(x + output)


class RestNetDownBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride):
        super(RestNetDownBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride[<