7d96731b9a50e5455a1e31f676508af42801b7ae,chainercv/links/model/deeplab/deeplab_v3_plus.py,DeepLabV3plus,predict,#DeepLabV3plus#Any#,129

Before Change

                x = chainer.Variable(self.xp.asarray(img[np.newaxis]))
                x = self.__call__(x)
                score = F.resize_images(x, crop)[0, :, :H, :W].array
            score = chainer.backends.cuda.to_cpu(score)
            label = np.argmax(score, axis=0).astype(np.int32)
            labels.append(label)
        return labels

After Change

                chainer.function.no_backprop_mode():
            labels = []
            score = 0
            n_aug = len(self.scales) if self.flip else len(self.scales) * 2

            for img in imgs:
                for scale in self.scales:
                    score += self._get_proba(img, scale, False) / n_aug

Instances