class ResDown(nn.Module): def __init__(self, channel_in, channel_out, scale=2): super(ResDown, self).__init__() self.conv1 = nn.Conv2d(channel_in, channel_out//2, 3, 1, 1) # 输入，输出，核大小，步长，填充， self.batch_norm1 = nn.BatchNorm2d(channel_out//2, 0.8) # 批数据标准化处理 self.conv2 = nn.Conv2d(channel_out//2, channel_out, 3, scale, 1) self.batch_norm2 = nn.BatchNorm2d(channel_out, 0.8) self.conv3 = nn.Conv2d(channel_in, channel_out, 3, scale, 1) self.activation = nn.LeakyReLU(0.2) def forward(self, x): skip = self.conv3(x) x = self.conv1(x) x = self.batch_norm1(x) x = self.activation(x) x = self.conv2(x) x = self.batch_norm2(x) x = self.activation(x + skip) return x

这是一个用于图像降采样的卷积神经网络模块，被称为 ResDown。它接受一个维度为 (batch_size, channel_in, height, width) 的输入张量 x，并对其执行以下操作：

1. 首先，使用一个 3x3 的卷积核和填充大小为 1 的卷积层将输入张量 x 降采样，输出张量的通道数被切割为原来的一半。然后，对输出张量进行批数据标准化处理并使用 LeakyReLU 激活函数进行非线性变换。

2. 接着，使用一个 3x3 的卷积核和步长为 scale 的卷积层对输出张量进行降采样，输出张量的通道数被增加为 channel_out。然后，对输出张量进行批数据标准化处理并使用 LeakyReLU 激活函数进行非线性变换。

3. 最后，使用一个 3x3 的卷积核和步长为 scale 的卷积层对输入张量 x 进行降采样，输出张量的通道数被增加为 channel_out。

4. 将步骤 2 和步骤 3 的输出张量相加，并使用 LeakyReLU 激活函数进行非线性变换。最终输出张量的维度为 (batch_size, channel_out, height/scale, width/scale)。

相关问题

class ResDown(nn.Module):

这是一个PyTorch的神经网络模块，用于下采样图像或特征图。其实现方式为ResNet中的下采样模块，通常用于图像分类或目标检测等任务中。以下是该模块的代码实现：

import torch.nn as nn

class ResDown(nn.Module):
    def __init__(self, in_channels, out_channels, stride):
        super(ResDown, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.conv3 = nn.Conv2d(out_channels, out_channels * 4, kernel_size=1, stride=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channels * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = nn.Sequential(
            nn.Conv2d(in_channels, out_channels * 4, kernel_size=1, stride=stride, bias=False),
            nn.BatchNorm2d(out_channels * 4)
        )

    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)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        identity = self.downsample(identity)

        out += identity
        out = self.relu(out)

        return out

该模块输入的张量shape为$[batch\_size, in\_channels, height, width]$，输出的张量shape为$[batch\_size, out\_channels * 4, height / stride, width / stride]$。其中，$stride$表示下采样的步长。