(self, imgs)
return labels
Conduct semantic segmentation from images.

        Args:
            imgs (iterable of numpy.ndarray): Arrays holding images.
                All images are in CHW and RGB format
                and the range of their values are :math:`[0, 255]`.

        Returns:
            list of numpy.ndarray:

            List of integer labels predicted from each image in the input list.
Conduct semantic segmentation from images.
Conduct semantic segmentation from images .
def predict(self, imgs):
        """Conduct semantic segmentation from images.

        Args:
            imgs (iterable of numpy.ndarray): Arrays holding images.
                All images are in CHW and RGB format
                and the range of their values are :math:`[0, 255]`.

        Returns:
            list of numpy.ndarray:

            List of integer labels predicted from each image in the input list.

        """

        with chainer.using_config('train', False), \
                chainer.function.no_backprop_mode():
            labels = []
            n_aug = len(self.scales) if self.flip else len(self.scales) * 2

            for img in imgs:
                score = 0
                for scale in self.scales:
                    score += self._get_proba(img, scale, False) / n_aug
                    if self.flip:
                        score += self._get_proba(img, scale, True) / n_aug

                label = np.argmax(score, axis=0).astype(np.int32)
                labels.append(label)
        return labels
def predict ( self , imgs ) : with chainer . using_config ( 'train' , False ) , chainer . function . no_backprop_mode ( ) : labels = [ ] n_aug = len ( self . scales ) if self . flip else len ( self . scales ) * 2 for img in imgs : score = 0 for scale in self . scales : score += self . _get_proba ( img , scale , False ) / n_aug if self . flip : score += self . _get_proba ( img , scale , True ) / n_aug label = np . argmax ( score , axis = 0 ) . astype ( np . int32 ) labels . append ( label ) return labels