用python写Unet

我可以提供一个简单的Python示例，用于实现一个简单的U-Net架构：import torch
import torch.nn as nnclass UNet(nn.Module):
def __init__(self, in_channels, out_channels, init_features=64):
super(UNet, self).__init__()
features = init_features
self.encoder1 = self.encoder_block(in_channels, features)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.encoder2 = self.encoder_block(features, features * 2)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.encoder3 = self.encoder_block(features * 2, features * 4)
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.encoder4 = self.encoder_block(features * 4, features * 8)
self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2) self.bottleneck = self.encoder_block(features * 8, features * 16) self.upconv4 = self.decoder_block(features * 16, features * 8)
self.decoder4 = self.encoder_block(features * 16, features * 8)
self.upconv3 = self.decoder_block(features * 8, features * 4)
self.decoder3 = self.encoder_block(features * 8, features * 4)
self.upconv2 = self.decoder_block(features * 4, features * 2)
self.decoder2 = self.encoder_block(features * 4, features * 2)
self.upconv1 = self.decoder_block(features * 2, features)
self.decoder1 = self.encoder_block(features * 2, features) self.conv = nn.Conv2d(in_channels=features, out_channels=out_channels, kernel_size=1) def forward(self, x):
enc1 = self.encoder1(x)
enc2 = self.encoder2(self.pool1(enc1))
enc3 = self.encoder3(self.pool2(enc2))
enc4 = self.encoder4(self.pool3(enc3)) bottleneck = self.bottleneck(self.pool4(enc4)) dec4 = self.upconv4(bottleneck)
dec4 = torch.cat((dec4, enc4), dim=1)
dec4 = self.decoder4(dec4)
dec3 = self.upconv3(dec4)
dec3 = torch.cat((dec3, enc3), dim=1)
dec3 = self.decoder3(dec3)
dec2 = self.upconv2(dec3)
dec2 = torch.cat((dec2, enc2), dim=1)
dec2 = self.decoder2(dec2)
dec1 = self.upconv1(dec2)
dec1 = torch.cat((dec1, enc1), dim=1)
dec1 = self.decoder1(dec1) return self.conv(dec1) @staticmethod
def encoder_block(in_channels, out_channels):
block = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True)
) return block @staticmethod
def decoder_block(in_channels, out_channels):
block = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
) return block