055c61d73514d471158ee36b83762802c8d4e3d4,lib/loss/clipped_weighted_huber_loss.py,ClippedWeightedHuberLoss,forward,#ClippedWeightedHuberLoss#Any#,22

Before Change

        mask = y > (self.delta ** 2)
        y -= mask * xp.square(abs(self.diff) - self.delta)
        y *= 0.5
        return xp.array(y.sum() / y.dtype.type(y.size), dtype=y.dtype),

    def backward(self, inputs, gy):
        xp = cuda.get_array_module(*inputs)

After Change

        diff = xp.abs(self.diff)
        y = xp.square(diff)
        diff -= diff.dtype.type(self.delta)
        xp.maximum(diff, 0, dtype=diff.dtype, out=diff)
        xp.square(diff, out=diff)
        y = (y - diff) * 0.5

Instances