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What is the tf.stop_gradient() equivalent (provides a way to not compute gradient with respect to some variables during back-propagation) in pytorch?
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Tensor. requires_grad. Is True if gradients need to be computed for this Tensor, False otherwise. The fact that gradients need to be computed for a Tensor do not mean that the grad attribute will be populated, see is_leaf for more details.
tf. stop_gradient provides a way to not compute gradient with respect to some variables during back-propagation.
detach() is used to detach a tensor from the current computational graph. It returns a new tensor that doesn't require a gradient. When we don't need a tensor to be traced for the gradient computation, we detach the tensor from the current computational graph.
If you know during the forward which part you want to block the gradients from, you can use . detach() on the output of this block to exclude it from the backward.
Could you check with x.detach().
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Tensors in pytorch have requires_grad attribute. Set it to False to prevent gradient computation for that tensors.
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