Pytorch error: type error

Hala_Hamdoun: <p>I am facing the following error <img src="https://discuss.pytorch.org/images/emoji/apple/slight_smile.png?v=9" title=":slight_smile:" class="emoji" alt=":slight_smile:"><br>
TypeError: only integer tensors of a single element can be converted to an index</p>
<p>Traceback (most recent call last):<br>
File “Main_mine.py”, line 377, in <br>
best_model, [losses_train, losses_valid, losses_epochs_train, losses_epochs_valid] = Train_Model(grud, train_dataloader, valid_dataloader , lr, epochs,patience)<br>
File “Main_mine.py”, line 206, in Train_Model<br>
outputs = model(inputs)<br>
File “/home/alaa/.local/lib/python3.5/site-packages/torch/nn/modules/module.py”, line 532, in <strong>call</strong><br>
result = self.forward(*input, **kwargs)<br>
File “/media/alaa/New Drive2/PHD/PHD3-Missing Values/Codes/archive(1)/run/GRU-D_my_version (copy)/LSTMD_mine.py”, line 168, in forward<br>
, torch.squeeze(Delta[:,i:i+1,:]))<br>
File “/media/alaa/New Drive2/PHD/PHD3-Missing Values/Codes/archive(1)/run/GRU-D_my_version (copy)/LSTMD_mine.py”, line 133, in step<br>
h = (delta_h) * (h)</p>
<p>TypeError: only integer tensors of a single element can be converted to an index</p>
<p>here is a piece of code</p>
<p>def step(self, x, x_last_obsv, x_mean, h, ct, mask, delta):</p>
<pre><code>    batch_size = x.shape[0]
    dim_size = x.shape[1]
    
    delta_x = torch.exp(-torch.max(self.zeros_x, self.gamma_x_l(delta)))
    delta_h = torch.exp(-torch.max(self.zeros_h, self.gamma_h_l(delta)))
    
    x = mask * x + (1 - mask) * (delta_x * x_last_obsv + (1 - delta_x) * x_mean)
    h = (delta_h) * (h)
    
    combined = torch.cat((x, h, mask), 1)
    i = F.sigmoid(self.il(combined))
    f = F.sigmoid(self.fl(combined))
    o = F.sigmoid(self.ol(combined))
    c = F.tanh(self.cl(combined))
    ct = f*ct + i*c
    h = o * (torch.tanh(ct))
    
    return h,ct</code></pre>

Kushagra_Bhatia: <p>Looking at the traceback, I believe the error is associated with <code>torch.squeeze(Delta[:,i:i+1,:]))</code>. However, you haven’t shared the code. It could also be due to your version of PyTorch. As some users have reported the same error previously. Have a look at <a href="https://github.com/chrisdxie/uois/issues/2" rel="nofollow noopener">https://github.com/chrisdxie/uois/issues/2</a></p>
<p>I would suggest you to share the piece of code associated with slicing the <code>Delta</code> tensor.</p>

Hala_Hamdoun: <p>def forward(self, input):<br>
batch_size = input.size(0)<br>
type_size = input.size(1)<br>
step_size = input.size(2)<br>
spatial_size = input.size(3)</p>
<pre><code>    Hidden_State = self.initHidden(batch_size)
    X = torch.squeeze(input[:,0,:,:])
    X_last_obsv = torch.squeeze(input[:,1,:,:])
    Mask = torch.squeeze(input[:,2,:,:])
    Delta = torch.squeeze(input[:,3,:,:])
    
    hidden = None
    for i in range(step_size):
        Hidden_State = self.step(torch.squeeze(X[:,i:i+1,:])\
                                 , torch.squeeze(X_last_obsv[:,i:i+1,:])\
                                 , torch.squeeze(self.X_mean[:,i:i+1,:])\
                                 , Hidden_State\
                                 , torch.squeeze(Mask[:,i:i+1,:])\
                                 , torch.squeeze(Delta[:,i:i+1,:]))
        
                                  
        if hidden is None:
            hidden = Hidden_State.unsqueeze(1)
        else:
            hidden = torch.cat((hidden, Hidden_State.unsqueeze(1)), 1)
       
            
    if self.output_last:
        prediction = self.output(hidden[:,-1,:])
        return prediction
    else:
        hidden = hidden.view(-1, self.hidden_size)
        prediction = self.output(hidden)
        return prediction</code></pre>

Hala_Hamdoun: <p>I have tried the solution in the link but nothing solved</p>