I am working with an autoencoder model which takes an input image and reconstructs it for the output. I am using MSELoss here. I want to perform certain transformations on the model output (matrix multiplication with some other matrix, addition, then matrix multiplication again) before computing the loss on it. I tried implementing this but got the error:

one of the variables needed for gradient computation has been modified by an inplace operation.

Is there a workaround to perform these operations on the output?

Based on the error message it seems you are manipulating a tensor inplace.
Could you check, if you are using inplace operations (e.g. via a += b) and remove them?

Thank you! I tried removing them and made the operations store their result in a different variable. Now, when I pass the new variable to the loss and backpropagate, there is no gradient being passed through the network. The code looks something like this:

output = model(input)
results = torch.zeros((batch_size, 16384))
# Applying the custom transformation (each sample in the batch
# undergoes the same operation but with different parameters)
for i in range (0, batch_size):
results[i] = measurement(output[i], i)
results = Variable(results, requires_grad = True)
loss = criterion(results, target)
# ===================backward====================
optimizer.zero_grad()
loss.backward()
optimizer.step()

Is there a reason why the flow of gradients might be getting interrupted?

will detach it from the computation graph, so remove this line of code.
Also, Variables are deprecated since PyTorch 0.4.0 so you shouldnâ€™t use them anymore in any case.