(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.

        """
        labels = []
        for img in imgs:
            with chainer.using_config('train', False), \
                    chainer.function.no_backprop_mode():
                if self.scales is not None:
                    scores = _multiscale_predict(
                        self._tile_predict, img, self.scales)
                else:
                    scores = self._tile_predict(img)
            labels.append(chainer.backends.cuda.to_cpu(
                self.xp.argmax(scores, axis=0).astype(np.int32)))
        return labels
def predict ( self , imgs ) : labels = [ ] for img in imgs : with chainer . using_config ( 'train' , False ) , chainer . function . no_backprop_mode ( ) : if self . scales is not None : scores = _multiscale_predict ( self . _tile_predict , img , self . scales ) else : scores = self . _tile_predict ( img ) labels . append ( chainer . backends . cuda . to_cpu ( self . xp . argmax ( scores , axis = 0 ) . astype ( np . int32 ) ) ) return labels