(self, imgs)
return labels
Conduct semantic segmentations 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 segmentations from images.
Conduct semantic segmentations from images .
def predict(self, imgs):
        """Conduct semantic segmentations 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:
            C, H, W = img.shape
            with chainer.using_config('train', False), \
                    chainer.function.no_backprop_mode():
                x = chainer.Variable(self.xp.asarray(img[np.newaxis]))
                score = self.forward(x)[0].array
            score = chainer.backends.cuda.to_cpu(score)
            if score.shape != (C, H, W):
                dtype = score.dtype
                score = resize(score, (H, W)).astype(dtype)

            label = np.argmax(score, axis=0).astype(np.int32)
            labels.append(label)
        return labels
def predict ( self , imgs ) : labels = [ ] for img in imgs : C , H , W = img . shape with chainer . using_config ( 'train' , False ) , chainer . function . no_backprop_mode ( ) : x = chainer . Variable ( self . xp . asarray ( img [ np . newaxis ] ) ) score = self . forward ( x ) [ 0 ] . array score = chainer . backends . cuda . to_cpu ( score ) if score . shape != ( C , H , W ) : dtype = score . dtype score = resize ( score , ( H , W ) ) . astype ( dtype ) label = np . argmax ( score , axis = 0 ) . astype ( np . int32 ) labels . append ( label ) return labels