loss.py 文件源码

def backward(self, grad_output):
        input, target = self.saved_tensors
        grad_input = input.new().resize_as_(input).copy_(target)
        grad_input[torch.mul(torch.eq(target, -1), torch.gt(input, self.margin))] = 0

        if self.size_average:
            grad_input.mul_(1. / input.nelement())

        if grad_output[0] != 1:
            grad_input.mul_(grad_output[0])

        return grad_input, None