跑网络报错 输入通道数和卷积层期望通道数之间的不匹配
但是我已经修改了输入通道数 还是显示如下错误信息
Traceback (most recent call last): File "/mnt/workspace/code_1/DC/main.py", line 101, in <module> main(config)
File "/mnt/workspace/code_1/DC/main.py", line 64, in main solver.train() File "/mnt/workspace/code_1/DC/solver.py", line 179, in train SR = self.unet(images)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl return forward_call(*args, **kwargs) File "/mnt/workspace/code_1/DC/network.py", line 239, in forward x1 = self.RRCNN1(x)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl return forward_call(*args, **kwargs)
File "/mnt/workspace/code_1/DC/network.py", line 90, in forward x = self.Conv_1x1(x) File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/conv.py", line 460, in forward return self._conv_forward(input, self.weight, self.bias)
File "/usr/local/lib/python3.10/site-packages/torch/nn/modules/conv.py", line 456, in _conv_forward return F.conv2d(input, weight, bias, self.stride, RuntimeError: Given groups=1, weight of size [64, 3, 1, 1], expected input[1, 1, 256, 256] to have 3 channels, but got 1 channels instead
网络代码如下
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
def init_weights(net, init_type='normal', gain=0.02):
def init_func(m):
classname = m.__class__.__name__
if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
if init_type == 'normal':
init.normal_(m.weight.data, 0.0, gain)
elif init_type == 'xavier':
init.xavier_normal_(m.weight.data, gain=gain)
elif init_type == 'kaiming':
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
init.orthogonal_(m.weight.data, gain=gain)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
if hasattr(m, 'bias') and m.bias is not None:
init.constant_(m.bias.data, 0.0)
elif classname.find('BatchNorm2d') != -1:
init.normal_(m.weight.data, 1.0, gain)
init.constant_(m.bias.data, 0.0)
print('initialize network with %s' % init_type)
net.apply(init_func)
class conv_block(nn.Module):
def __init__(self,ch_in,ch_out):
super(conv_block,self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(ch_in, ch_out, kernel_size=3,stride=1,padding=1,bias=True),
nn.BatchNorm2d(ch_out),
nn.ReLU(inplace=True),
nn.Conv2d(ch_out, ch_out, kernel_size=3,stride=1,padding=1,bias=True),
nn.BatchNorm2d(ch_out),
nn.ReLU(inplace=True)
)
def forward(self,x):
x = self.conv(x)
return x
class up_conv(nn.Module):
def __init__(self,ch_in,ch_out):
super(up_conv,self).__init__()
self.up = nn.Sequential(
nn.Upsample(scale_factor=2),
nn.Conv2d(ch_in,ch_out,kernel_size=3,stride=1,padding=1,bias=True),
nn.BatchNorm2d(ch_out),
nn.ReLU(inplace=True)
)
def forward(self,x):
x = self.up(x)
return x
class Recurrent_block(nn.Module):
def __init__(self,ch_out,t=2):
super(Recurrent_block,self).__init__()
self.t = t
self.ch_out = ch_out
self.conv = nn.Sequential(
nn.Conv2d(ch_out,ch_out,kernel_size=3,stride=1,padding=1,bias=True),
nn.BatchNorm2d(ch_out),
nn.ReLU(inplace=True)
)
def forward(self,x):
for i in range(self.t):
if i==0:
x1 = self.conv(x)
x1 = self.conv(x+x1)
return x1
class RRCNN_block(nn.Module):
def __init__(self,ch_in,ch_out,t=2):
super(RRCNN_block,self).__init__()
self.RCNN = nn.Sequential(
Recurrent_block(ch_out,t=t),
Recurrent_block(ch_out,t=t)
)
self.Conv_1x1 = nn.Conv2d(ch_in,ch_out,kernel_size=1,stride=1,padding=0)
def forward(self,x):
x = self.Conv_1x1(x)
x1 = self.RCNN(x)
return x+x1
class single_conv(nn.Module):
def __init__(self,ch_in,ch_out):
super(single_conv,self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(ch_in, ch_out, kernel_size=3,stride=1,padding=1,bias=True),
nn.BatchNorm2d(ch_out),
nn.ReLU(inplace=True)
)
def forward(self,x):
x = self.conv(x)
return x
class Attention_block(nn.Module):
def __init__(self,F_g,F_l,F_int):
super(Attention_block,self).__init__()
self.W_g = nn.Sequential(
nn.Conv2d(F_g, F_int, kernel_size=1,stride=1,padding=0,bias=True),
nn.BatchNorm2d(F_int)
)
self.W_x = nn.Sequential(
nn.Conv2d(F_l, F_int, kernel_size=1,stride=1,padding=0,bias=True),
nn.BatchNorm2d(F_int)
)
self.psi = nn.Sequential(
nn.Conv2d(F_int, 1, kernel_size=1,stride=1,padding=0,bias=True),
nn.BatchNorm2d(1),
nn.Sigmoid()
)
self.relu = nn.ReLU(inplace=True)
def forward(self,g,x):
g1 = self.W_g(g)
x1 = self.W_x(x)
psi = self.relu(g1+x1)
psi = self.psi(psi)
return x*psi
class U_Net(nn.Module):
def __init__(self,img_ch=1,output_ch=1):
super(U_Net,self).__init__()
self.Maxpool = nn.MaxPool2d(kernel_size=2,stride=2)
self.Conv1 = conv_block(ch_in=img_ch,ch_out=64)
self.Conv2 = conv_block(ch_in=64,ch_out=128)
self.Conv3 = conv_block(ch_in=128,ch_out=256)
self.Conv4 = conv_block(ch_in=256,ch_out=512)
self.Conv5 = conv_block(ch_in=512,ch_out=1024)
self.Up5 = up_conv(ch_in=1024,ch_out=512)
self.Up_conv5 = conv_block(ch_in=1024, ch_out=512)
self.Up4 = up_conv(ch_in=512,ch_out=256)
self.Up_conv4 = conv_block(ch_in=512, ch_out=256)
self.Up3 = up_conv(ch_in=256,ch_out=128)
self.Up_conv3 = conv_block(ch_in=256, ch_out=128)
self.Up2 = up_conv(ch_in=128,ch_out=64)
self.Up_conv2 = conv_block(ch_in=128, ch_out=64)
self.Conv_1x1 = nn.Conv2d(64,output_ch,kernel_size=1,stride=1,padding=0)
def forward(self,x):
# encoding path
x1 = self.Conv1(x)
x2 = self.Maxpool(x1)
x2 = self.Conv2(x2)
x3 = self.Maxpool(x2)
x3 = self.Conv3(x3)
x4 = self.Maxpool(x3)
x4 = self.Conv4(x4)
x5 = self.Maxpool(x4)
x5 = self.Conv5(x5)
# decoding + concat path
d5 = self.Up5(x5)
d5 = torch.cat((x4,d5),dim=1)
d5 = self.Up_conv5(d5)
d4 = self.Up4(d5)
d4 = torch.cat((x3,d4),dim=1)
d4 = self.Up_conv4(d4)
d3 = self.Up3(d4)
d3 = torch.cat((x2,d3),dim=1)
d3 = self.Up_conv3(d3)
d2 = self.Up2(d3)
d2 = torch.cat((x1,d2),dim=1)
d2 = self.Up_conv2(d2)
d1 = self.Conv_1x1(d2)
return d1
class R2U_Net(nn.Module):
def __init__(self,img_ch=1,output_ch=1,t=2):
super(R2U_Net,self).__init__()
self.Maxpool = nn.MaxPool2d(kernel_size=2,stride=2)
self.Upsample = nn.Upsample(scale_factor=2)
self.RRCNN1 = RRCNN_block(ch_in=img_ch,ch_out=64,t=t)
self.RRCNN2 = RRCNN_block(ch_in=64,ch_out=128,t=t)
self.RRCNN3 = RRCNN_block(ch_in=128,ch_out=256,t=t)
self.RRCNN4 = RRCNN_block(ch_in=256,ch_out=512,t=t)
self.RRCNN5 = RRCNN_block(ch_in=512,ch_out=1024,t=t)
self.Up5 = up_conv(ch_in=1024,ch_out=512)
self.Up_RRCNN5 = RRCNN_block(ch_in=1024, ch_out=512,t=t)
self.Up4 = up_conv(ch_in=512,ch_out=256)
self.Up_RRCNN4 = RRCNN_block(ch_in=512, ch_out=256,t=t)
self.Up3 = up_conv(ch_in=256,ch_out=128)
self.Up_RRCNN3 = RRCNN_block(ch_in=256, ch_out=128,t=t)
self.Up2 = up_conv(ch_in=128,ch_out=64)
self.Up_RRCNN2 = RRCNN_block(ch_in=128, ch_out=64,t=t)
self.Conv_1x1 = nn.Conv2d(64,output_ch,kernel_size=1,stride=1,padding=0)
def forward(self,x):
# encoding path
x1 = self.RRCNN1(x)
x2 = self.Maxpool(x1)
x2 = self.RRCNN2(x2)
x3 = self.Maxpool(x2)
x3 = self.RRCNN3(x3)
x4 = self.Maxpool(x3)
x4 = self.RRCNN4(x4)
x5 = self.Maxpool(x4)
x5 = self.RRCNN5(x5)
# decoding + concat path
d5 = self.Up5(x5)
d5 = torch.cat((x4,d5),dim=1)
d5 = self.Up_RRCNN5(d5)
d4 = self.Up4(d5)
d4 = torch.cat((x3,d4),dim=1)
d4 = self.Up_RRCNN4(d4)
d3 = self.Up3(d4)
d3 = torch.cat((x2,d3),dim=1)
d3 = self.Up_RRCNN3(d3)
d2 = self.Up2(d3)
d2 = torch.cat((x1,d2),dim=1)
d2 = self.Up_RRCNN2(d2)
d1 = self.Conv_1x1(d2)
return d1
class AttU_Net(nn.Module):
def __init__(self,img_ch=1,output_ch=1):
super(AttU_Net,self).__init__()
self.Maxpool = nn.MaxPool2d(kernel_size=2,stride=2)
self.Conv1 = conv_block(ch_in=img_ch,ch_out=64)
self.Conv2 = conv_block(ch_in=64,ch_out=128)
self.Conv3 = conv_block(ch_in=128,ch_out=256)
self.Conv4 = conv_block(ch_in=256,ch_out=512)
self.Conv5 = conv_block(ch_in=512,ch_out=1024)
self.Up5 = up_conv(ch_in=1024,ch_out=512)
self.Att5 = Attention_block(F_g=512,F_l=512,F_int=256)
self.Up_conv5 = conv_block(ch_in=1024, ch_out=512)
self.Up4 = up_conv(ch_in=512,ch_out=256)
self.Att4 = Attention_block(F_g=256,F_l=256,F_int=128)
self.Up_conv4 = conv_block(ch_in=512, ch_out=256)
self.Up3 = up_conv(ch_in=256,ch_out=128)
self.Att3 = Attention_block(F_g=128,F_l=128,F_int=64)
self.Up_conv3 = conv_block(ch_in=256, ch_out=128)
self.Up2 = up_conv(ch_in=128,ch_out=64)
self.Att2 = Attention_block(F_g=64,F_l=64,F_int=32)
self.Up_conv2 = conv_block(ch_in=128, ch_out=64)
self.Conv_1x1 = nn.Conv2d(64,output_ch,kernel_size=1,stride=1,padding=0)
def forward(self,x):
# encoding path
x1 = self.Conv1(x)
x2 = self.Maxpool(x1)
x2 = self.Conv2(x2)
x3 = self.Maxpool(x2)
x3 = self.Conv3(x3)
x4 = self.Maxpool(x3)
x4 = self.Conv4(x4)
x5 = self.Maxpool(x4)
x5 = self.Conv5(x5)
# decoding + concat path
d5 = self.Up5(x5)
x4 = self.Att5(g=d5,x=x4)
d5 = torch.cat((x4,d5),dim=1)
d5 = self.Up_conv5(d5)
d4 = self.Up4(d5)
x3 = self.Att4(g=d4,x=x3)
d4 = torch.cat((x3,d4),dim=1)
d4 = self.Up_conv4(d4)
d3 = self.Up3(d4)
x2 = self.Att3(g=d3,x=x2)
d3 = torch.cat((x2,d3),dim=1)
d3 = self.Up_conv3(d3)
d2 = self.Up2(d3)
x1 = self.Att2(g=d2,x=x1)
d2 = torch.cat((x1,d2),dim=1)
d2 = self.Up_conv2(d2)
d1 = self.Conv_1x1(d2)
return d1
class R2AttU_Net(nn.Module):
def __init__(self,img_ch=1,output_ch=1,t=2):
super(R2AttU_Net,self).__init__()
self.Maxpool = nn.MaxPool2d(kernel_size=2,stride=2)
self.Upsample = nn.Upsample(scale_factor=2)
self.RRCNN1 = RRCNN_block(ch_in=img_ch,ch_out=64,t=t)
self.RRCNN2 = RRCNN_block(ch_in=64,ch_out=128,t=t)
self.RRCNN3 = RRCNN_block(ch_in=128,ch_out=256,t=t)
self.RRCNN4 = RRCNN_block(ch_in=256,ch_out=512,t=t)
self.RRCNN5 = RRCNN_block(ch_in=512,ch_out=1024,t=t)
self.Up5 = up_conv(ch_in=1024,ch_out=512)
self.Att5 = Attention_block(F_g=512,F_l=512,F_int=256)
self.Up_RRCNN5 = RRCNN_block(ch_in=1024, ch_out=512,t=t)
self.Up4 = up_conv(ch_in=512,ch_out=256)
self.Att4 = Attention_block(F_g=256,F_l=256,F_int=128)
self.Up_RRCNN4 = RRCNN_block(ch_in=512, ch_out=256,t=t)
self.Up3 = up_conv(ch_in=256,ch_out=128)
self.Att3 = Attention_block(F_g=128,F_l=128,F_int=64)
self.Up_RRCNN3 = RRCNN_block(ch_in=256, ch_out=128,t=t)
self.Up2 = up_conv(ch_in=128,ch_out=64)
self.Att2 = Attention_block(F_g=64,F_l=64,F_int=32)
self.Up_RRCNN2 = RRCNN_block(ch_in=128, ch_out=64,t=t)
self.Conv_1x1 = nn.Conv2d(64,output_ch,kernel_size=1,stride=1,padding=0)
def forward(self,x):
# encoding path
x1 = self.RRCNN1(x)
x2 = self.Maxpool(x1)
x2 = self.RRCNN2(x2)
x3 = self.Maxpool(x2)
x3 = self.RRCNN3(x3)
x4 = self.Maxpool(x3)
x4 = self.RRCNN4(x4)
x5 = self.Maxpool(x4)
x5 = self.RRCNN5(x5)
# decoding + concat path
d5 = self.Up5(x5)
x4 = self.Att5(g=d5,x=x4)
d5 = torch.cat((x4,d5),dim=1)
d5 = self.Up_RRCNN5(d5)
d4 = self.Up4(d5)
x3 = self.Att4(g=d4,x=x3)
d4 = torch.cat((x3,d4),dim=1)
d4 = self.Up_RRCNN4(d4)
d3 = self.Up3(d4)
x2 = self.Att3(g=d3,x=x2)
d3 = torch.cat((x2,d3),dim=1)
d3 = self.Up_RRCNN3(d3)
d2 = self.Up2(d3)
x1 = self.Att2(g=d2,x=x1)
d2 = torch.cat((x1,d2),dim=1)
d2 = self.Up_RRCNN2(d2)
d1 = self.Conv_1x1(d2)
return d1
或是是我的数据集出现问题了吗 用的是DC1000牙齿分割的一个数据集 原链接找不到了
