One of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [1, 2, 28, 2]], which is output 0 of SliceBackward, is at version 14; expected version 13 instead

111293: <p>Hello everyone, i am just a beginner in Pytorch, recently i try to adjust STN to make it works on fish eye image, but i am trapped in problem of “one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [1, 2, 28, 2]], which is output 0 of SliceBackward, is at version 14; expected version 13 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient.” when debugging, i try to fix it with some suggestion here but it still doesn’t work, could someone point out the problem in my code for me, thanks <img src="https://discuss.pytorch.org/images/emoji/apple/slight_smile.png?v=9" title=":slight_smile:" class="emoji" alt=":slight_smile:"></p>
<pre><code class="lang-auto">class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

        # Spatial transformer localization-network
        self.localization = nn.Sequential(
            nn.Conv2d(1, 8, kernel_size=7),
            nn.MaxPool2d(2, stride=2),
            nn.ReLU(True),
            nn.Conv2d(8, 10, kernel_size=5),
            nn.MaxPool2d(2, stride=2),
            nn.ReLU(True)
        )

        # Regressor for the 3 * 2 affine matrix
        self.fc_loc = nn.Sequential(
            nn.Linear(10 * 3 * 3, 2*14),
            # nn.ReLU(True),
            # nn.Linear(32, 3 * 2)
        )

        # Initialize the weights/bias with identity transformation
        self.fc_loc[0].weight.data.zero_()
        self.fc_loc[0].bias.data.copy_(torch.tensor([1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0], dtype=torch.float))

    # Spatial transformer network forward function
    def stn(self, x):
        xs = self.localization(x)
        xs = xs.view(-1, 10 * 3 * 3)
        theta = self.fc_loc(xs)
        theta = theta.view(-1, 14, 2)
        # print('theta size:',theta.size())
        output_2 = torch.zeros(x.size())
        print(type(output_2))

        #grid = torch.zeros(14, 1, 1, 2, 28,2)
        grid = torch.zeros(14, 1, 2, 28, 2)

        beta = torch.zeros(14,2,3)
        for i in range(14):
          beta[i,0,0] = theta[0,i,0]
          beta[i,0,2] = theta[0,i,1]

        for i in range(14):

          # print('grid_size:',grid[i,:,:,:,:].size())
          grid[i,:,:,:,:] = F.affine_grid(beta[i,:,:].unsqueeze(0), [1, 1, 2, 28]).clone()

          output_2[:,:,2*i:2*i+2,:] = F.grid_sample(x[:,:,2*i:2*i+2,:], grid[i,:,:,:,:]).clone()

        return output_2

    def forward(self, x_temp):
        # transform the input
        x = self.stn(x_temp)

        # Perform the usual forward pass
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)
</code></pre>

albanD: <p>Hi,</p>
<p>The error states that you are modifying inplace a Tensor that you are slicing. So an op like <code>your_tensor[ind] = xxx</code>.<br>
It states that because the original value of this Tensor is needed for backward computations, this is not valid.<br>
You should replace this by and out of place operations.</p>
<p>In particular, in your last for-loop, you don’t mneed to do <code>grid[i,:,:,:,:] = xxx</code> (an inplace op) you can simply store that in a temporary Tensor and give it to the next line <code>grid_elem = xxx</code>.<br>
If that does not fix the issue.<br>
For the grid sample that fills in output_2, I usually store all the results in a list and then <code>torch.cat()</code> the result to get the final result.</p>

111293: <p>nice! I try your first suggestion and change my code as “output_2[:,:,2<em>i:2</em>i+2,:] = F.grid_sample(x[:,:,2<em>i:2</em>i+2,:], F.affine_grid(beta[i,:,:].unsqueeze(0), [1, 1, 2, 28]))”, which avoids in-place operation here, and now I can continue my working. Thanks a lot!</p>