VGG with Shared Classified: RuntimeError: multi-target not supported at /pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu:15

username: CasellaJr
cooked: <p>I have an architecture with a shared classifier, that works on MNIST.<br>
I created the architecture considering a simple binary classification between even and odd numbers of MNIST. Now I am trying to generalize the classification to all the numbers, but I have this error:<br>
<code>RuntimeError: multi-target not supported at /pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu:15</code><br>
This is the link to my code: <a href="https://colab.research.google.com/drive/1ozkDlF_xH13NcsnPnE40d453EyycQzVl?usp=sharing" class="inline-onebox" rel="noopener nofollow ugc">Google Colaboratory</a><br>
However, here there are the most important parts of the code:<br>
The VGG:</p>
<pre><code class="lang-auto">class VGGBlock(nn.Module):
    def __init__(self, in_channels, out_channels,batch_norm=False):

        super().__init__()

        conv2_params = {'kernel_size': (3, 3),
                        'stride'     : (1, 1),
                        'padding'   : 1
                        }

        noop = lambda x : x

        self._batch_norm = batch_norm

        self.conv1 = nn.Conv2d(in_channels=in_channels,out_channels=out_channels , **conv2_params)
        #self.bn1 = nn.BatchNorm2d(out_channels) if batch_norm else noop
        self.bn1 = nn.GroupNorm(64, out_channels) if batch_norm else noop

        self.conv2 = nn.Conv2d(in_channels=out_channels,out_channels=out_channels, **conv2_params)
        #self.bn2 = nn.BatchNorm2d(out_channels) if batch_norm else noop
        self.bn2 = nn.GroupNorm(64, out_channels) if batch_norm else noop

        self.max_pooling = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))

    @property
    def batch_norm(self):
        return self._batch_norm

    def forward(self,x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = F.relu(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = F.relu(x)

        x = self.max_pooling(x)

        return x

class Classifier(nn.Module):

    def __init__(self,num_classes=10):
        super().__init__()

        self.classifier = nn.Sequential(
            nn.Linear(2048, 2048),
            nn.ReLU(True),
            nn.Dropout(p=0.5),
            nn.Linear(2048, 512),
            nn.ReLU(True),
            nn.Dropout(p=0.5),
            nn.Linear(512, num_classes)
        )

    def forward(self,x):

        return self.classifier(x)

class VGG16(nn.Module):

  def __init__(self, input_size, batch_norm=False):
    super(VGG16, self).__init__()

    self.in_channels,self.in_width,self.in_height = input_size

    self.block_1 = VGGBlock(self.in_channels,64,batch_norm=batch_norm)
    self.block_2 = VGGBlock(64, 128,batch_norm=batch_norm)
    self.block_3 = VGGBlock(128, 256,batch_norm=batch_norm)
    self.block_4 = VGGBlock(256,512,batch_norm=batch_norm)


  @property
  def input_size(self):
      return self.in_channels,self.in_width,self.in_height

  def forward(self, x):

    x = self.block_1(x)
    x = self.block_2(x)
    x = self.block_3(x)
    x = self.block_4(x)
    # x = self.avgpool(x)
    x = torch.flatten(x,1)

    return x
</code></pre>
<p>My custom loss:</p>
<pre><code class="lang-auto">class CombinedLoss(nn.Module):
    def __init__(self, loss_a, loss_b, loss_combo, _lambda=1.0):
        super().__init__()
        self.loss_a = loss_a
        self.loss_b = loss_b
        self.loss_combo = loss_combo

        self.register_buffer('_lambda',torch.tensor(float(_lambda),dtype=torch.float32))


    def forward(self,y_hat,y):

        return self.loss_a(y_hat[0],y[0]) + self.loss_b(y_hat[1],y[1]) + self._lambda * self.loss_combo(y_hat[2],torch.cat(y,0))
</code></pre>
<p>The training function:</p>
<pre><code class="lang-auto">def train(nets, loaders, optimizer, criterion, epochs=20, dev=None, save_param=False, model_name="valerio"):
    # try:
      nets = [n.to(dev) for n in nets]

      model_a = module_unwrap(nets[0], True)
      model_b = module_unwrap(nets[1], True)
      model_c = module_unwrap(nets[2], True)

      reg_loss = nn.MSELoss()

      criterion.to(dev)
      reg_loss.to(dev)

      # Initialize history
      history_loss = {"train": [], "val": [], "test": []}
      history_accuracy = {"train": [], "val": [], "test": []}
      # Store the best val accuracy
      best_val_accuracy = 0

      # Process each epoch
      for epoch in range(epochs):
        # Initialize epoch variables
        sum_loss = {"train": 0, "val": 0, "test": 0}
        sum_accuracy = {"train": [0,0,0], "val": [0,0,0], "test": [0,0,0]}

        progbar = None
        # Process each split
        for split in ["train", "val", "test"]:
          if split == "train":
            for n in nets:
              n.train()
            widgets = [
              ' [', pb.Timer(), '] ',
              pb.Bar(),
              ' [', pb.ETA(), '] ', pb.Variable('ta','[Train Acc: {formatted_value}]')
            ]

            progbar = pb.ProgressBar(max_value=len(loaders[split][0]),widgets=widgets,redirect_stdout=True)

          else:
            for n in nets:
              n.eval()
          # Process each batch
          for j,((input_a, labels_a),(input_b, labels_b)) in enumerate(zip(loaders[split][0],loaders[split][1])):

            input_a = input_a.to(dev)
            input_b = input_b.to(dev)

            labels_a = labels_a.long().to(dev)
            labels_b = labels_b.long().to(dev)
            labels_a = labels_a.unsqueeze(1)
            labels_b = labels_b.unsqueeze(1)

            inputs = torch.cat([input_a,input_b],axis=0)
            labels = torch.cat([labels_a, labels_b])

            labels  = labels.unsqueeze(1)


            # Reset gradients
            optimizer.zero_grad()
            # Compute output
            features_a = nets[0](input_a)
            features_b = nets[1](input_b)
            features_c = nets[2](inputs)

            pred_a = torch.squeeze(nets[3](features_a))
            pred_b = torch.squeeze(nets[3](features_b))
            pred_c = torch.squeeze(nets[3](features_c))

            loss = criterion(pred_a, labels_a) + criterion(pred_b, labels_b) + criterion(pred_c, labels)

            for n in model_a:
              layer_a = model_a[n]
              layer_b = model_b[n]
              layer_c = model_c[n]
              if (isinstance(layer_a,nn.Conv2d)):
                loss += lambda_reg * reg_loss(combo_fn(layer_a.weight,layer_b.weight),layer_c.weight)
                if (layer_a.bias is not None):
                  loss += lambda_reg * reg_loss(combo_fn(layer_a.bias, layer_b.bias), layer_c.bias)

            # Update loss
            sum_loss[split] += loss.item()
            # Check parameter update
            if split == "train":
              # Compute gradients
              loss.backward()
              # Optimize
              optimizer.step()

            # Compute accuracy

            #https://discuss.pytorch.org/t/bcewithlogitsloss-and-model-accuracy-calculation/59293/ 2
            pred_labels_a = (pred_a &gt;= 0.0).long()  # Binarize predictions to 0 and 1
            pred_labels_b = (pred_b &gt;= 0.0).long()  # Binarize predictions to 0 and 1
            pred_labels_c = (pred_c &gt;= 0.0).long()  # Binarize predictions to 0 and 1


            batch_accuracy_a = (pred_labels_a == labels_a).sum().item() / len(labels_a)
            batch_accuracy_b = (pred_labels_b == labels_b).sum().item() / len(labels_b)
            batch_accuracy_c = (pred_labels_c == labels).sum().item() / len(labels)

            # Update accuracy
            sum_accuracy[split][0] += batch_accuracy_a
            sum_accuracy[split][1] += batch_accuracy_b
            sum_accuracy[split][2] += batch_accuracy_c


            if (split=='train'):
              progbar.update(j, ta=batch_accuracy_c)

        if (progbar is not None):
          progbar.finish()
        # Compute epoch loss/accuracy
        epoch_loss = {split: sum_loss[split] / len(loaders[split][0]) for split in ["train", "val", "test"]}
        epoch_accuracy = {split: [sum_accuracy[split][i] / len(loaders[split][0]) for i in range(len(sum_accuracy[split])) ] for split in ["train", "val", "test"]}

        # # Store params at the best validation accuracy
        # if save_param and epoch_accuracy["val"] &gt; best_val_accuracy:
        #   # torch.save(net.state_dict(), f"{net.__class__.__name__}_best_val.pth")
        #   torch.save(net.state_dict(), f"{model_name}_best_val.pth")
        #   best_val_accuracy = epoch_accuracy["val"]

        print(f"Epoch {epoch + 1}:")
        # Update history
        for split in ["train", "val", "test"]:
          history_loss[split].append(epoch_loss[split])
          history_accuracy[split].append(epoch_accuracy[split])
          # Print info
          print(f"\t{split}\tLoss: {epoch_loss[split]:0.5}\tVGG 1:{epoch_accuracy[split][0]:0.5}"
                f"\tVGG 2:{epoch_accuracy[split][1]:0.5}\tVGG *:{epoch_accuracy[split][2]:0.5}")

      if save_param:
        torch.save({'vgg_a':nets[0].state_dict(),'vgg_b':nets[1].state_dict(),'vgg_star':nets[2].state_dict(),'classifier':nets[3].state_dict()},f'{model_name}.pth')
</code></pre>
<p>The models:</p>
<pre><code class="lang-auto">model1 = VGG16((1,32,32),batch_norm=True)
model2 = VGG16((1,32,32),batch_norm=True)
model3 = VGG16((1,32,32),batch_norm=True)
classifier = Classifier(num_classes=10)

nets = [model1,model2,model3,classifier]
</code></pre>
<p>And the optimizer</p>
<pre><code class="lang-auto">optimizer = torch.optim.SGD(parameters, lr = 0.01)

# Define a loss

criterion = nn.CrossEntropyLoss()

n_params = 0
</code></pre>
<p>The error raise when I run the train:<br>
train(nets, loaders, optimizer, criterion, epochs=50, dev=dev,save_param=True)</p>

username: ptrblck
cooked: <p>The error is raised in <code>nn.CrossEntropyLoss</code> or <code>nn.NLLLoss</code>, if an invalid <code>target</code> shape is provided.<br>
For a multi-class classification these loss functions expect a model output in the shape <code>[batch_size, nb_classes]</code> and a target in the shape <code>[batch_size]</code> containing values in the range <code>[0, nb_classes-1]</code>.<br>
Based on the error I guess your <code>target</code> has more dimensions, so you would need to remove them (e.g. via <code>squeeze</code>).</p>

username: CasellaJr
cooked: <p>If I change this part in the training function,</p>
<pre><code class="lang-auto">for j,((input_a, labels_a),(input_b, labels_b)) in enumerate(zip(loaders[split][0],loaders[split][1])):

            input_a = input_a.to(dev)
            input_b = input_b.to(dev)

            labels_a = labels_a.long().to(dev)
            labels_b = labels_b.long().to(dev)
            labels_a = labels_a.squeeze()
            labels_b = labels_b.squeeze()
            #labels_a = labels_a.unsqueeze(1)
            #labels_b = labels_b.unsqueeze(1)

            #labels_a = labels_a.argmax(-1)
            #labels_b = labels_b.argmax(-1)

            inputs = torch.cat([input_a,input_b],axis=0)
            labels = torch.cat([labels_a, labels_b])

            labels  = labels.squeeze()
            #labels = labels.argmax(-1)
</code></pre>
<p>I get this error:</p>
<pre><code class="lang-auto">RuntimeError: The size of tensor a (10) must match the size of tensor b (512) at non-singleton dimension 1


</code></pre>

username: ptrblck
cooked: <p>What was the shape of <code>label</code> before squeezing it and which shape does the model output have?</p>

username: CasellaJr
cooked: <p>Before and after squeezing there is the same shape, before:<br>
<code>torch.Size([512])</code><br>
and after:<br>
<code>torch.Size([512])</code><br>
This is clear because there are not dimensions of size 1.<br>
And the output size of my model3 is <code>torch.Size([1, 2048])</code></p>
<p>Edit: labels_a and labels_b have torch.Size([512])<br>
Then, there is a torch.cat of labels_a and labels_b and the new labels torch.Size([1024])</p>

username: ptrblck
cooked: <p>I don’t quite understand the issues, as they seem to point to different errors.</p>
<ol>
<li>The first one (<code>multi-target not supported</code>) is raised e.g. if your target has additional dimensions as seen here:</li>
</ol>
<pre><code class="lang-python">criterion = nn.CrossEntropyLoss()

output = torch.randn(1, 2048)
target = torch.randint(0, 2048, (1,))
loss = criterion(output, target) # works

target = target.unsqueeze(1)
loss = criterion(output, target)
&gt; RuntimeError: 1D target tensor expected, multi-target not supported
</code></pre>
<ol start="2">
<li>
<p>The second issue (size mismatch) could be created by a wrong reshape operation, which changes the batch size.</p>
</li>
<li>
<p>The last post would indicate that the model output is <code>[batch_size=1, nb_classes=2048]</code>, while the target would use <code>[batch_size=1024]</code>, which is also wrong.</p>
</li>
</ol>
<p>Could you thus explain your use case a bit more, i.e. what is the expected batch size and how many classes are you working with?</p>

username: CasellaJr
cooked: <p>Finally I solved the issues and now it seems it works; in particular I changed these lines:</p>
<pre><code class="lang-auto">#pred_labels_a = (pred_a &gt;= 0.0).long()  # Binarize predictions to 0 and 1
            #pred_labels_b = (pred_b &gt;= 0.0).long()  # Binarize predictions to 0 and 1
            #pred_labels_c = (pred_c &gt;= 0.0).long()  # Binarize predictions to 0 and 1
</code></pre>
<p>into:</p>
<pre><code class="lang-auto">_,pred_label_a = torch.max(pred_a, dim = 1)
            pred_labels_a = (pred_label_a == labels_a).float()

            _,pred_label_b = torch.max(pred_b, dim = 1)
            pred_labels_b = (pred_label_b == labels_b).float()

            _,pred_label_c = torch.max(pred_c, dim = 1)
            pred_labels_c = (pred_label_c == labels).float()
</code></pre>
<p>I have 10 classes. This is the notebook if you want to see: <a href="https://colab.research.google.com/drive/1ozkDlF_xH13NcsnPnE40d453EyycQzVl?usp=sharing" class="inline-onebox" rel="noopener nofollow ugc">Google Colaboratory</a></p>