# Problem in U-NET

``````class DoubleConv(nn.Module):
def __init__(self, in_ch, out_ch):
super(DoubleConv, self).__init__()
self.conv = nn.Sequential(
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True))
def forward(self, x):
x = self.conv(x)
return x
class InConv(nn.Module):
def __init__(self, in_ch, out_ch):
super(InConv, self).__init__()
self.conv = DoubleConv(in_ch, out_ch)
def forward(self, x):
x = self.conv(x)
return x
class Down(nn.Module):
def __init__(self, in_ch, out_ch):
super(Down, self).__init__()
self.mpconv = nn.Sequential(
nn.MaxPool2d(2),
DoubleConv(in_ch, out_ch)
)
def forward(self, x):
x = self.mpconv(x)
return x
class Up(nn.Module):
def __init__(self, in_ch, out_ch, bilinear=True):
super(Up, self).__init__()
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
else:
self.up = nn.ConvTranspose2d(in_ch // 2, in_ch // 2, 2, stride=2)
self.conv = DoubleConv(in_ch, out_ch)
def forward(self, x1, x2):
x1 = self.up(x1)
diffY = x2.size()[2] - x1.size()[2]
diffX = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, (diffX // 2, diffX - diffX // 2,
diffY // 2, diffY - diffY // 2))
x = torch.cat([x2, x1], dim=1)
x = self.conv(x)
return x
class OutConv(nn.Module):
def __init__(self, in_ch, out_ch):
super(OutConv, self).__init__()
self.conv = nn.Conv2d(in_ch, out_ch, 1)
def forward(self, x):
x = self.conv(x)
return x
class Unet(nn.Module):
def __init__(self, in_channels, classes):
super(Unet, self).__init__()
self.n_channels = 3
self.n_classes = 2
self.inc = InConv(in_channels, 64)
self.down1 = Down(64, 128)
self.down2 = Down(128, 256)
self.down3 = Down(256, 512)
self.down4 = Down(512, 512)
self.up1 = Up(1024, 256)
self.up2 = Up(512, 128)
self.up3 = Up(256, 64)
self.up4 = Up(128, 64)
self.outc = OutConv(64, classes)
def forward(self, x):
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
x = self.up1(x5, x4)
x = self.up2(x, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
x = self.outc(x)
return x

``````

problem : RuntimeError: Given groups=1, weight of size [64, 4, 1, 1], expected input[6451, 1, 5, 5] to have 4 channels, but got 1 channels instead

help me plzz

It looks like there is an n_channels member of the Unet class that is defined but never used, and in_channels is defined when the Unet is created (this value seems like it is set to 4). However, the input has only a single channel? I would check how these parameters are used and whether the input data is correct because the shape seems very strange.

Hi @eqy yes a single channel. I prefer to get this architecture

help me plzz eqy

help me plzz

There is something wrong with your input. What is the input shape?

Hi @Hmrishav_Bandyopadhy I solved my problem, but I couldn’t do this architecture

can you help me plzz

This looks like the Unet architecture with some blocks skipped. You can see an example implementation in this tutorial video here

Hi @Hmrishav_Bandyopadhy I tested this code but

problem : RuntimeError: only batches of spatial targets supported (3D tensors) but got targets of dimension: 1

I prefer to detect two classes

Once again if you have targets of dimension 1 then there is an issue with the data that you are using. UNets are segmentation models which means that the input and output would be 2D data…Add a dimension for batch size and you have 3D tensors

just segmentation unet? do not make a classification

When I tested this code

``````class UNet(nn.Module):
def __init__(self):
super(UNet, self).__init__()
self.max_pool = nn.MaxPool2d(2,2)
self.down_conv1 = doubleconv(1,64)
self.down_conv2= doubleconv(64,128)
self.down_conv3= doubleconv(128,256)
self.down_conv4= doubleconv(256,512)
self.down_conv5= doubleconv(512,1024)

def forward (self,x) :

x1= self.down_conv1(x)
#x2= self.max_pool(x1)
x3= self.down_conv2(x1)
#x4= self.max_pool(x3)
x5= self.down_conv3(x3)
#x6= self.max_pool(x5)
x7= self.down_conv4(x5)
#x8= self.max_pool(x7)
x9= self.down_conv5(x7)
``````

RuntimeError: Given groups=1, weight of size [64, 1, 3, 3], expected input[1481, 128, 1, 1] to have 1 channels, but got 128 channels instead

help me plzz

I solved my problem