深度学习——残差网络ResNet基础结构理解+注释

灰灰学姐

已于 2024-01-30 19:50:18 修改

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分类专栏：深度学习神经网络文章标签：深度学习网络人工智能

于 2024-01-06 23:15:30 首次发布

本文链接：https://blog.youkuaiyun.com/m0_51965393/article/details/135433807

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本文详细介绍了ResNetV1架构中的基本残差块（BasicBlockV1b和BottleneckV1b）以及ResNetV1类的构造，包括卷积层、批归一化层的使用，以及不同类型的块和输出步长的选择。

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import torch
import torch.nn as nn

class BasicBlockV1b(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
                 previous_dilation=1, norm_layer=nn.BatchNorm2d):
        super(BasicBlockV1b, self).__init__()
        # 定义了一个二维卷积用于提取特征
        self.conv1 = nn.Conv2d(inplanes, planes, 3, stride,
                               dilation, dilation, bias=False)
        # 定义批归一化层，用于加速训练过程并提高模型稳定性
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(True)
        self.conv2 = nn.Conv2d(planes, planes, 3, 1, previous_dilation,
                               dilation=previous_dilation, bias=False)
        self.bn2 = norm_layer(planes)
        # 设置成下采样，用于调整残差块的输入与输出维度
        self.downsample = downsample
        self.stride = stride

    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)
        # 检查是否有下采样操作，通常是卷积层和批归一化层的组合，用于调整identity的维度与out匹配
        if self.downsample is not None:
            identity = self.downsample(x)
        # 将处理过的输出与跳跃连接输出相加，能减少梯度消失问题
        out += identity
        out = self.relu(out)

        return out

# 定义更复杂的残差块
class BottleneckV1b(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
                 previous_dilation=1, norm_layer=nn.BatchNorm2d):
        super(BottleneckV1b, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
        self.bn1 = norm_layer(planes)
        self.conv2 = nn.Conv2d(planes, planes,

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