编码器网络由一个卷积层(“Conv”)和四个卷积块(“ECB10”、“ECB20”、“ECB30”和“ECB40”)组成。每个块包含两个卷积层和一个最大池算子,可以确保编码器网络提取不同尺度的深度特征,代码
时间: 2023-08-24 14:05:04 浏览: 40
可能是这样的:
```
import torch.nn as nn
class Encoder(nn.Module):
def __init__(self):
super(Encoder, self).__init__()
self.conv = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)
self.ecb10 = EncodingConvBlock(32, 64)
self.ecb20 = EncodingConvBlock(64, 128)
self.ecb30 = EncodingConvBlock(128, 256)
self.ecb40 = EncodingConvBlock(256, 512)
def forward(self, x):
x = self.conv(x)
x = self.ecb10(x)
x = self.ecb20(x)
x = self.ecb30(x)
x = self.ecb40(x)
return x
class EncodingConvBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(EncodingConvBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(out_channels)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(out_channels)
self.relu2 = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu2(x)
x = self.maxpool(x)
return x
```
其中`EncodingConvBlock`是编码器网络中的一个卷积块,包含两个卷积层和一个最大池算子。`Encoder`是编码器网络的主体,由一个卷积层和四个卷积块组成。`forward`方法中按顺序调用各个层即可完成前向传播。