I am getting this channel mismacth error RuntimeError: Given groups=1, weight of size [64, 2, 3, 3], expected input[1, 1, 256, 256] to have 2 channels, but got 1 channels instead

username: ismail
cooked: <p>I am getting this channel mismacth error <strong>RuntimeError: Given groups=1, weight of size [64, 2, 3, 3], expected input[1, 1, 256, 256] to have 2 channels, but got 1 channels instead</strong></p>
<p>in_channels = 2 but still i cant uderstand the cause of error.</p>
<p>Here is the segmentation model</p>
<pre><code class="lang-auto">



class SegmentationModel(nn.Module):
    def __init__(self, in_channels):
        super(SegmentationModel, self).__init__()
        self.conv1 = nn.Conv2d(2, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.conv5 = nn.Conv2d(512, 1024, kernel_size=3, padding=1)
        self.upconv1 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2)
        self.upconv2 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
        self.upconv3 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
        self.upconv4 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
        self.final_conv = nn.Conv2d(64, 2, kernel_size=1)

    def forward(self, x):
        # Assuming x is of shape [batch_size, channels, height, width]
        x1 = F.relu(self.conv1(x))
        x2 = F.relu(self.conv2(x1))
        x3 = F.relu(self.conv3(x2))
        x4 = F.relu(self.conv4(x3))
        x5 = F.relu(self.conv5(x4))
        
        x6 = F.relu(self.upconv1(x5))
        x7 = F.relu(self.upconv2(x6))
        x8 = F.relu(self.upconv3(x7))
        x9 = F.relu(self.upconv4(x8))
        
        output = self.final_conv(x9)

        

        
        
        return output
</code></pre>
<p>Here are the input output shapes:</p>
<p>Image shape: torch.Size([2, 256, 256])</p>
<p>Mask shape: torch.Size([2, 256, 256])</p>
<p>Output shape: torch.Size([1, 2, 4096, 4096])</p>
<p>Resized output shape: torch.Size([1, 2, 256, 256])</p>
<p>Resized_Image shape: torch.Size([1, 2, 256, 256])</p>
<p>Resized_Mask shape: torch.Size([1, 2, 256, 256])</p>

username: ptrblck
cooked: <p>Your model works fine with the mentioned shapes:</p>
<pre><code class="lang-python">class SegmentationModel(nn.Module):
    def __init__(self, in_channels):
        super(SegmentationModel, self).__init__()
        self.conv1 = nn.Conv2d(2, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.conv5 = nn.Conv2d(512, 1024, kernel_size=3, padding=1)
        self.upconv1 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2)
        self.upconv2 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
        self.upconv3 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
        self.upconv4 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
        self.final_conv = nn.Conv2d(64, 2, kernel_size=1)

    def forward(self, x):
        # Assuming x is of shape [batch_size, channels, height, width]
        x1 = F.relu(self.conv1(x))
        x2 = F.relu(self.conv2(x1))
        x3 = F.relu(self.conv3(x2))
        x4 = F.relu(self.conv4(x3))
        x5 = F.relu(self.conv5(x4))

        x6 = F.relu(self.upconv1(x5))
        x7 = F.relu(self.upconv2(x6))
        x8 = F.relu(self.upconv3(x7))
        x9 = F.relu(self.upconv4(x8))

        output = self.final_conv(x9)
        return output

model = SegmentationModel(2)
x = torch.randn(2, 256, 256)
out = model(x)
print(out.shape)
# torch.Size([2, 4096, 4096])

x = torch.randn(1, 2, 256, 256)
out = model(x)
print(out.shape)
# torch.Size([1, 2, 4096, 4096])
</code></pre>
<p>so I guess some of your images have indeed a single channel and you would need to make sure they are using 2 channels.</p>

username: ismail
cooked: <p>how would i know which images ?</p>

username: ptrblck
cooked: <p>You could return the image name additionally to the actual data in the <code>Dataset.__getitem__</code> method and print the image name (or path) once the code breaks.</p>

username: ismail
cooked: <p>here is the complete error traceback may be it can help you identify issue more prominently</p>
<pre><code class="lang-auto">---------
RuntimeError                              Traceback (most recent call last)
Cell In[18], line 51
     47 optimizer.zero_grad()
     50  # Forward pass
---&gt; 51 outputs = model(resized_images)
     53 # Resize the output tensor to match the spatial dimensions of the target tensor
     54 resized_outputs = F.interpolate(outputs, size=(256, 256), mode='bilinear', align_corners=False)

File /usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
   1496 # If we don't have any hooks, we want to skip the rest of the logic in
   1497 # this function, and just call forward.
   1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1499         or _global_backward_pre_hooks or _global_backward_hooks
   1500         or _global_forward_hooks or _global_forward_pre_hooks):
-&gt; 1501     return forward_call(*args, **kwargs)
   1502 # Do not call functions when jit is used
   1503 full_backward_hooks, non_full_backward_hooks = [], []

Cell In[16], line 17, in SegmentationModel.forward(self, x)
     15 def forward(self, x):
     16     # Assuming x is of shape [batch_size, channels, height, width]
---&gt; 17     x1 = F.relu(self.conv1(x))
     18     x2 = F.relu(self.conv2(x1))
     19     x3 = F.relu(self.conv3(x2))

File /usr/local/lib/python3.8/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
   1496 # If we don't have any hooks, we want to skip the rest of the logic in
   1497 # this function, and just call forward.
   1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1499         or _global_backward_pre_hooks or _global_backward_hooks
   1500         or _global_forward_hooks or _global_forward_pre_hooks):
-&gt; 1501     return forward_call(*args, **kwargs)
   1502 # Do not call functions when jit is used
   1503 full_backward_hooks, non_full_backward_hooks = [], []

File /usr/local/lib/python3.8/site-packages/torch/nn/modules/conv.py:463, in Conv2d.forward(self, input)
    462 def forward(self, input: Tensor) -&gt; Tensor:
--&gt; 463     return self._conv_forward(input, self.weight, self.bias)

File /usr/local/lib/python3.8/site-packages/torch/nn/modules/conv.py:459, in Conv2d._conv_forward(self, input, weight, bias)
    455 if self.padding_mode != 'zeros':
    456     return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
    457                     weight, bias, self.stride,
    458                     _pair(0), self.dilation, self.groups)
--&gt; 459 return F.conv2d(input, weight, bias, self.stride,
    460                 self.padding, self.dilation, self.groups)

RuntimeError: Given groups=1, weight of size [64, 2, 3, 3], expected input[1, 1, 256, 256] to have 2 channels, but got 1 channels instead
```</code></pre>

username: ptrblck
cooked: <p>The error points to the first conv layer and explains the input doesn’t have the expected two channels. You would thus still need to narrow down which image has a single channel only and either remove it or increase the channels to two by e.g. repeating it. Iterate the dataset and print the shape for all data tensors. At least one of them should have a single channel.</p>