Shape mismatch error

username: ismail
cooked: <p>i am facing this error <strong>ValueError: Input and output must have the same number of spatial dimensions, but got input with spatial dimensions of [256, 256] and output size of torch.Size([256])</strong>. Please provide input tensor in (N, C, d1, d2, …,dK) format and output size in (o1, o2, …,oK) format.</p>
<p>this is a ink.detection model code. What seems to be the cause of this error .</p>
<p>here is the code snippet of subvlomedataset and as well as model</p>
<pre><code class="lang-auto">class SubvolumeDataset(Dataset):
    def __init__(self, data_paths, mask_paths, label_paths=None, device='cpu', resize_shape=(256, 256)):
        self.data_paths = data_paths
        self.mask_paths = mask_paths
        self.label_paths = label_paths
        self.device = device
        self.resize_shape = resize_shape
        self.subvolume_shape = (1,) + self.resize_shape  

    def __len__(self):
        return len(self.data_paths)

    def __getitem__(self, idx):
        data_path = self.data_paths[idx]
        mask_path = self.get_corresponding_mask_path(data_path)
       

        data = cv2.imread(data_path, cv2.IMREAD_GRAYSCALE)
        mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE) if mask_path is not None else None
        

        

        # Resize data and mask to the specified shape
        data = cv2.resize(data, self.resize_shape)
        mask = cv2.resize(mask, self.resize_shape, interpolation=cv2.INTER_NEAREST) if mask is not None else None

        if self.label_paths:
            label_path = self.get_corresponding_label_path(data_path)
            label = cv2.imread(label_path, cv2.IMREAD_GRAYSCALE)
        else:
            label = None

        data_tensor = torch.from_numpy(data).unsqueeze(0).to(self.device).float()
        mask_tensor = (
           torch.from_numpy(mask).unsqueeze(0).to(self.device).float()
           if mask is not None
           else torch.zeros((1,) + self.subvolume_shape).to(self.device)
        )
        label_tensor = torch.from_numpy(label).unsqueeze(0).to(self.device).float() if label is not None else torch.zeros_like(data_tensor)

        #Resize label_tensor to match the size of data_tensor
        if label_tensor is not None and label_tensor.ndim == 3:
            label_tensor = label_tensor.unsqueeze(0)  # Add an extra dimension for batch
            label_tensor = F.interpolate(label_tensor, size=data_tensor.shape[2:], mode='nearest')
            label_tensor = label_tensor.squeeze(0)  # Remove the extra dimension

        return data_tensor, mask_tensor, label_tensor

    def get_corresponding_mask_path(self, data_path):
        # Generate the corresponding mask path
        mask_path = data_path.replace('surface_volume', 'mask')
        return mask_path

    def get_corresponding_label_path(self, data_path):
        # Generate the corresponding label path
        label_path = data_path.replace('surface_volume', 'inklabels')
        return label_path
</code></pre>
<p>This is the con2D model</p>
<pre><code class="lang-auto">class SegmentationModel(nn.Module):
    def __init__(self, in_channels):
        super(SegmentationModel, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, 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, 1, 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>i really can’t understand what seems to be issue here…</p>

username: ptrblck
cooked: <p>The error seems to be raised in the loss function and based on its message it assume you are using <code>nn.CrossEntropyLoss</code>.<br>
If so, note that the model output is supposed to have the shape <code>[batch_size, nb_classes, *]</code> containing logits and the target the shape <code>[batch_size, *]</code> containing class indices, where <code>*</code> denotes additional dimensions. If you want to pass the target containing probabilities instead its shape would have to match the model output’s shape.</p>
<p>Could you thus check the shape of the model output as well as target and make sure they are as explained above?</p>

username: ismail
cooked: <p>As you can see from the mention code that it has used <strong>nn.BCEWithLogitsLoss()</strong> so what changes do i have to make now?</p>
<pre><code class="lang-auto"># Set up the device for GPU usage
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Define the model
model = SegmentationModel(in_channels).to(device)

# Define the loss function
criterion = nn.BCEWithLogitsLoss()

# Define the optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# Define the number of epochs
epochs = 10


# Start the training loop
for epoch in range(epochs):
    print(f'Starting epoch {epoch + 1}/{epochs}')
    print('-' * 10)

    train_loss = 0.0

    # Set the model to training mode
    model.train()

    # Iterate over the training data
    for batch in train_dataloader:

        images, masks, labels = batch

        # Extract the images and masks from the batch
        

        # Move the images and masks to the GPU
        images = images.to(device)
        masks = masks.to(device)

        # Clear the gradients
        optimizer.zero_grad()

        # Forward pass
        outputs = model(images)

        masks = F.interpolate(masks.float().unsqueeze(1), size=outputs.shape[2:], mode='nearest')
        masks = masks.squeeze(1).long()

        # Calculate the loss
        loss = criterion(outputs, masks)

        # Backward pass
        loss.backward()

        # Update the weights
        optimizer.step()

        # Update the training loss
        train_loss += loss.item() * images.size(0)

    # Print the loss for this epoch
    print(f'Loss: {train_loss / len(train_dataloader):.4f}')

print('Training complete.')

# Save the trained model
torch.save(model.state_dict(), 'segmentation_model.pt')
</code></pre>

username: ismail
cooked: <p>And I have also tried running code again with <code>nn.CrossEntropyLoss</code> . but it didn’t fix the error either.</p>

username: ismail
cooked: <p><a class="mention" href="/u/ptrblck">@ptrblck</a> hi could you please tell me whats the possible error here.</p>

username: ptrblck
cooked: <p>It seems the error message might be raised from <code>F.interpolate</code> and you should thus check the provided <code>size</code> as well as the shape of the input tensor.<br>
<a href="https://discuss.pytorch.org/t/functional-spatial-dimensions/175791">This topic</a> discusses the same issue.</p>

username: ismail
cooked: <p>could you show me how ?</p>

username: ptrblck
cooked: <p>My linked post shows an example how to run <code>F.interpolate</code>. Does it not work for you or is something else failing?</p>

username: ismail
cooked: <p>Yes, i have tried to reshape output images and mask using F.interpolate but it didn’t work too, could you please check</p>
<pre><code class="lang-auto">
# Set up the device for GPU usage
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Define the model
model = SegmentationModel(in_channels).to(device)

# Define the loss function
criterion = nn.CrossEntropyLoss()

# Define the optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# Define the number of epochs
epochs = 10

# Start the training loop
for epoch in range(epochs):
    print(f'Starting epoch {epoch + 1}/{epochs}')
    print('-' * 10)

    train_loss = 0.0

    # Set the model to training mode
    model.train()

    # Iterate over the training data
    for batch  in train_dataloader:
        
        # Move the images and masks to the GPU
        images = images.to(device)
        masks = masks.to(device)
        labels = labels.to(device)

        # Resize the input tensor to match the spatial dimensions of the target tensor
        **resized_images = F.interpolate(images, size=(4096, 4096), mode='bilinear', align_corners=False)**

        # Resize the mask tensor to match the spatial dimensions of the input tensor
        **resized_masks = F.interpolate(masks.unsqueeze(1).float(), size=(4096, 4096), mode='nearest').squeeze(1).long()**

        # Clear the gradients
        optimizer.zero_grad()

        # Forward pass
        **outputs = model(resized_images)**

        # Resize the output tensor to match the spatial dimensions of the target tensor
        **resized_outputs = F.interpolate(outputs, size=(256, 256), mode='bilinear', align_corners=False)**

        # Calculate the loss
        loss = criterion(resized_outputs, masks)

        # Backward pass
        loss.backward()

        # Update the weights
        optimizer.step()

        # Update the training loss
        train_loss += loss.item() * images.size(0)

    # Print the loss for this epoch
    print(f'Loss: {train_loss / len(train_dataloader):.4f}')

print('Training complete.')

# Save the trained model
torch.save(model.state_dict(), 'segmentation_model.pt')

</code></pre>

username: ismail
cooked: <p><a class="mention" href="/u/ptrblck">@ptrblck</a> please have a look at this.</p>

username: ptrblck
cooked: <p>I don’t know which shapes are initially used but the code works for me:</p>
<pre><code class="lang-python">images = torch.randn(1, 3, 224, 224)
# Resize the input tensor to match the spatial dimensions of the target tensor
resized_images = F.interpolate(images, size=(4096, 4096), mode='bilinear', align_corners=False)

masks = torch.randint(0, 2, (1, 224, 224))
# Resize the mask tensor to match the spatial dimensions of the input tensor
resized_masks = F.interpolate(masks.unsqueeze(1).float(), size=(4096, 4096), mode='nearest').squeeze(1).long()

outputs = torch.randn(1, 10, 4096, 4096)
# Resize the output tensor to match the spatial dimensions of the target tensor
resized_outputs = F.interpolate(outputs, size=(256, 256), mode='bilinear', align_corners=False)
</code></pre>
<p>I would also like to ask to for some patience.</p>