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hainingfang avatar hainingfang commented on August 27, 2024

since it uses tensor.data = Binarize(tensor.data.org) in the code, so PyTorch won't record the grad op in the graph, in other words, no need to worry about the backward op, it won't happen at all.

from binarynet.pytorch.

whubaichuan avatar whubaichuan commented on August 27, 2024

I think the gradient will be set to 1 because of the code

    def backward(ctx,grad_output):
        #STE 
        grad_input=grad_output.sign()
        return grad_input,None,None,None

from binarynet.pytorch.

zouyingcao avatar zouyingcao commented on August 27, 2024

I think the gradient will be set to 1 because of the code

    def backward(ctx,grad_output):
        #STE 
        grad_input=grad_output.sign()
        return grad_input,None,None,None

There is a question that there is no grad_output.sign() in the current code version. Just grad_input=grad_output there. Is there any error about code update?

from binarynet.pytorch.

whubaichuan avatar whubaichuan commented on August 27, 2024

I think the gradient will be set to 1 because of the code

    def backward(ctx,grad_output):
        #STE 
        grad_input=grad_output.sign()
        return grad_input,None,None,None

There is a question that there is no grad_output.sign() in the current code version. Just grad_input=grad_output there. Is there any error about code update?

No, because 1.sign() = 1

from binarynet.pytorch.

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