import torch

from torch import nn

def conv3x3(in_planes, out_planes, stride=1):

    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False)  # 定义卷积层

class BasicBlock(nn.Module):

    def __init__(self, inplanes, outplanes, stride, downsample=None):

        super(BasicBlock, self).__init__()

        self.conv1 = conv3x3(inplanes, outplanes, stride=stride)  # 第一个卷积

        self.bn = nn.BatchNorm2d(outplanes) # 定义batch_norm层

        self.relu = nn.ReLU(inplace=True) # 定义激活层

        self.conv2 = conv3x3(outplanes, outplanes, stride=stride) # 第二个卷积

        self.bn2 = nn.BatchNorm1d(outplanes) # 进行标准化操作

        self.downsample = downsample # 进行维度的降低, 通常使用卷积操作来进行维度的降低

    def forward(self, x):

        residual = x   # 原始的残差模块 

        x = self.conv1(x)  # 第一次卷积

        x = self.bn(x) # 归一化操作

        x = self.relu(x) # 激活操作

        x = self.conv2(x) # 第二次卷积

        out = self.bn2(x) # 归一化操作

        if self.downsample is not None:

            residual = self.downsample(x) # 是否需要对原始的样本做降采样操作

        out += residual # 进行加和操作

        out = self.relu(out) # 进行激活操作

        return out