def get_seg_single(self, cate_preds, seg_preds, kernel_preds, featmap_size,
                       im_shape, scale_factor):
        """
        The code of this function is based on:
            https://github.com/WXinlong/SOLO/blob/master/mmdet/models/anchor_heads/solov2_head.py#L385
        """
        h = paddle.cast(im_shape[0], 'int32')
        w = paddle.cast(im_shape[1], 'int32')
        upsampled_size_out = [featmap_size[0] * 4, featmap_size[1] * 4]

        y = paddle.zeros(shape=cate_preds.shape, dtype='float32')
        inds = paddle.where(cate_preds > self.score_threshold, cate_preds, y)
        inds = paddle.nonzero(inds)
        cate_preds = paddle.reshape(cate_preds, shape=[-1])
        # Prevent empty and increase fake data
        ind_a = paddle.cast(paddle.shape(kernel_preds)[0:1], 'int64')
        ind_b = paddle.zeros(shape=[1], dtype='int64')
        inds_end = paddle.unsqueeze(paddle.concat([ind_a, ind_b]), 0)
        inds = paddle.concat([inds, inds_end])
        kernel_preds_end = paddle.ones(
            shape=[1, self.kernel_out_channels], dtype='float32')
        kernel_preds = paddle.concat([kernel_preds, kernel_preds_end])
        cate_preds = paddle.concat(
            [cate_preds, paddle.zeros(
                shape=[1], dtype='float32')])

        # cate_labels & kernel_preds
        cate_labels = inds[:, 1]
        kernel_preds = paddle.gather(kernel_preds, index=inds[:, 0])
        cate_score_idx = paddle.add(inds[:, 0] * self.cate_out_channels,
                                    cate_labels)
        cate_scores = paddle.gather(cate_preds, index=cate_score_idx)

        size_trans = np.power(self.seg_num_grids, 2)
        strides = []
        for _ind in range(len(self.segm_strides)):
            strides.append(
                paddle.full(
                    shape=[int(size_trans[_ind])],
                    fill_value=self.segm_strides[_ind],
                    dtype="int32"))
        strides = paddle.concat(strides)
        strides = paddle.concat(
            [strides, paddle.zeros(
                shape=[1], dtype='int32')])
        strides = paddle.gather(strides, index=inds[:, 0])

        # mask encoding.
        kernel_preds = paddle.unsqueeze(kernel_preds, [2, 3])
        seg_preds = F.conv2d(seg_preds, kernel_preds)
        seg_preds = F.sigmoid(paddle.squeeze(seg_preds, [0]))
        seg_masks = seg_preds > self.mask_threshold
        seg_masks = paddle.cast(seg_masks, 'float32')
        sum_masks = paddle.sum(seg_masks, axis=[1, 2])

        y = paddle.zeros(shape=paddle.shape(sum_masks), dtype='float32')
        keep = paddle.where(sum_masks > strides.cast(sum_masks.dtype), sum_masks, y)
        keep = paddle.nonzero(keep)
        keep = paddle.squeeze(keep, axis=[1])
        # Prevent empty and increase fake data
        keep_other = paddle.concat(
            [keep, paddle.cast(paddle.shape(sum_masks)[0:1] - 1, 'int64')])
        keep_scores = paddle.concat(
            [keep, paddle.cast(paddle.shape(sum_masks)[0:1], 'int64')])
        cate_scores_end = paddle.zeros(shape=[1], dtype='float32')
        cate_scores = paddle.concat([cate_scores, cate_scores_end])

        seg_masks = paddle.gather(seg_masks, index=keep_other)
        seg_preds = paddle.gather(seg_preds, index=keep_other)
        sum_masks = paddle.gather(sum_masks, index=keep_other)
        cate_labels = paddle.gather(cate_labels, index=keep_other)
        cate_scores = paddle.gather(cate_scores, index=keep_scores)

        # mask scoring.
        seg_mul = paddle.cast(seg_preds * seg_masks, 'float32')
        seg_scores = paddle.sum(seg_mul, axis=[1, 2]) / sum_masks
        cate_scores *= seg_scores
        # Matrix NMS
        seg_preds, cate_scores, cate_labels = self.mask_nms(
            seg_preds, seg_masks, cate_labels, cate_scores, sum_masks=sum_masks)
        ori_shape = im_shape[:2] / scale_factor + 0.5
        ori_shape = paddle.cast(ori_shape, 'int32')
        seg_preds = F.interpolate(
            paddle.unsqueeze(seg_preds, 0),
            size=upsampled_size_out,
            mode='bilinear',
            align_corners=False,
            align_mode=0)
        seg_preds = paddle.slice(
            seg_preds, axes=[2, 3], starts=[0, 0], ends=[h, w])
        seg_masks = paddle.squeeze(
            F.interpolate(
                seg_preds,
                size=ori_shape[:2],
                mode='bilinear',
                align_corners=False,
                align_mode=0),
            axis=[0])
        seg_masks = paddle.cast(seg_masks > self.mask_threshold, 'uint8')
        return seg_masks, cate_labels, cate_scores

        The code of this function is based on:
            https://github.com/WXinlong/SOLO/blob/master/mmdet/models/anchor_heads/solov2_head.py#L385
        
ppdet/modeling/heads/solov2_head.py