代码和图片如下:
import torch
from torch import nn
from torch.nn import functional as F
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(ConvBlock, self).__init__()
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size=7, padding=3, bias=False)
self.relu = nn.ReLU()
def forward(self, x):
return self.relu(self.conv(x))
class DownSample(nn.Module):
def __init__(self, in_channels, out_channels):
super(DownSample, self).__init__()
self.conv = nn.Conv1d(in_channels, out_channels, kernel_size=7, stride=4, padding=3, bias=False)
def forward(self, x):
return self.conv(x)
class UpSample(nn.Module):
def __init__(self, in_channels, out_channels, skip_channels):
super(UpSample, self).__init__()
self.up_conv = nn.ConvTranspose1d(in_channels, out_channels, kernel_size=4, stride=2, padding=1,
output_padding=1, bias=False)
# 上卷积操作后,特征图的通道数变为 out_channels
# 拼接跳跃连接后,总通道数为 out_channels + skip_channels
self.conv_block = ConvBlock((out_channels + skip_channels), out_channels)
def forward(self, x, skip_connection):
x = self.up_conv(x)
skip_connection = F.interpolate(skip_connection, size=x.shape[2:], mode='nearest')
x = torch.cat((x, skip_connection), dim=1) # 在这里拼接上卷积结果和跳跃连接
return self.conv_block(x)
class UNet(nn.Module):
def __init__(self):
super(UNet, self).__init__()
self.c1 = ConvBlock(3, 8)
self.enc1 = ConvBlock(8, 8)
self.d1 = DownSample(8, 8)
self.enc2 = ConvBlock(8, 11)
self.d2 = DownSample(11, 11)
self.enc3 = ConvBlock(11, 16)
self.d3 = DownSample(16, 16)
self.enc4 = ConvBlock(16, 22)
self.d4 = DownSample(22, 22)
self.enc5 = ConvBlock(22, 32)
self.dec1 = UpSample(32, 22,22)
self.enc6 = ConvBlock(44, 22)
self.dec2 = UpSample(22, 16,16)
self.enc7 = ConvBlock(32, 16)
self.dec3 = UpSample(16, 11,11)
self.enc8 = ConvBlock(22, 11)
self.dec4 = UpSample(11, 8,8)
self.enc9 = ConvBlock(16, 8)
self.final_conv = nn.Conv1d(8, 1, kernel_size=1)
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
c1 = self.c1(x)
enc1 = self.enc1(c1)
d1 = self.d1(enc1)
enc2 = self.enc2(d1)
d2 = self.d2(enc2)
enc3 = self.enc3(d2)
d3 = self.d3(enc3)
enc4 = self.enc4(d3)
d4 = self.d4(enc4)
enc5 =self.enc5(d4)
dec1 = self.dec1(enc5, enc4)
enc6 = self.enc6(dec1)
dec2 = self.dec2(enc6, enc3)
enc7 = self.enc6(dec2)
dec3 = self.dec3(enc7, enc2)
enc8 = self.enc6(dec3)
dec4 = self.dec4(enc8, enc1)
enc9 = self.enc6(dec4)
final = self.final_conv(dec4)
return self.softmax(final)
if __name__ == '__main__':
x = torch.randn(1, 3, 3001)
net = UNet()
output = net(x).squeeze(1)
print(output.shape)
![img](https://img-mid.csdnimg.cn/release/static/image/mid/ask/a48b02806d3c47a6bdce6498ede2c2b7.jpeg "#left")
unet神经网络处理一维信号数据,上采样部分和跳跃连接部分一直报错,该怎么修改?
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2条回答 默认 最新
- 一直免费一直爽 2024-08-17 15:07关注
以下回复参考:皆我百晓生、券券喵儿等免费微信小程序作答:
根据您提供的代码,我发现在
UpSample
类的forward
方法中,您使用了torch.cat((x, skip_connection), dim=1)
来拼接上卷积结果和跳跃连接。这里的问题是,拼接后的通道数可能超过了out_channels + skip_channels
的限制。为了解决这个问题,您可以在拼接之前将跳跃连接的通道数调整为out_channels
。修改后的
UpSample
类如下:class UpSample(nn.Module): def __init__(self, in_channels, out_channels, skip_channels): super(UpSample, self).__init__() self.up_conv = nn.ConvTranspose1d(in_channels, out_channels, kernel_size=4, stride=2, padding=1, output_padding=1, bias=False) # 上卷积操作后,特征图的通道数变为 out_channels # 拼接跳跃连接后,总通道数为 out_channels + skip_channels self.conv_block = ConvBlock((out_channels + skip_channels), out_channels) def forward(self, x, skip_connection): x = self.up_conv(x) skip_connection = F.interpolate(skip_connection, size=x.shape[2:], mode='nearest') # 调整跳跃连接的通道数为 out_channels skip_connection = nn.Conv1d(skip_connection.shape[1], x.shape[1], kernel_size=1)(skip_connection) x = torch.cat((x, skip_connection), dim=1) # 在这里拼接上卷积结果和跳跃连接 return self.conv_block(x)
这样修改后,您的
UNet
网络应该可以正常运行了。解决 无用评论 打赏 举报
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