CosineEmbeddingCriterion.py 文件源码

def updateGradInput(self, input, y):
        v1  = input[0]
        v2  = input[1]

        gw1 = self.gradInput[0]
        gw2 = self.gradInput[1]
        gw1.resize_as_(v1).copy_(v2)
        gw2.resize_as_(v1).copy_(v1)

        torch.mul(self.buffer, self.w1, self.w22)
        gw1.addcmul_(-1, self.buffer.expand_as(v1), v1)
        gw1.mul_(self.w.expand_as(v1))

        torch.mul(self.buffer, self.w1, self.w32)
        gw2.addcmul_(-1, self.buffer.expand_as(v1), v2)
        gw2.mul_(self.w.expand_as(v1))

        # self._idx = self._outputs <= 0
        torch.le(self._idx, self._outputs, 0)
        self._idx = self._idx.view(-1, 1).expand(gw1.size())
        gw1[self._idx] = 0
        gw2[self._idx] = 0

        torch.eq(self._idx, y, 1)
        self._idx = self._idx.view(-1, 1).expand(gw2.size())
        gw1[self._idx] = gw1[self._idx].mul_(-1)
        gw2[self._idx] = gw2[self._idx].mul_(-1)

        if self.sizeAverage:
           gw1.div_(y.size(0))
           gw2.div_(y.size(0))

        return self.gradInput