def forward(self, inputs):
        """
        Get SOLOv2MaskHead output.

        Args:
            inputs(list[Tensor]): feature map from each necks with shape of [N, C, H, W]
        Returns:
            ins_pred(Tensor): Output of SOLOv2MaskHead head
        """
        feat_all_level = F.relu(self.convs_all_levels[0](inputs[0]))
        for i in range(1, self.range_level):
            input_p = inputs[i]
            if i == (self.range_level - 1):
                input_feat = input_p
                x_range = paddle.linspace(
                    -1, 1, input_feat.shape[-1], dtype='float32')
                y_range = paddle.linspace(
                    -1, 1, input_feat.shape[-2], dtype='float32')
                y, x = paddle.meshgrid([y_range, x_range])
                x = paddle.unsqueeze(x, [0, 1])
                y = paddle.unsqueeze(y, [0, 1])
                y = paddle.expand(
                    y, shape=[input_feat.shape[0], 1, -1, -1])
                x = paddle.expand(
                    x, shape=[input_feat.shape[0], 1, -1, -1])
                coord_feat = paddle.concat([x, y], axis=1)
                input_p = paddle.concat([input_p, coord_feat], axis=1)
            feat_all_level = paddle.add(feat_all_level,
                                        self.convs_all_levels[i](input_p))
        ins_pred = F.relu(self.conv_pred(feat_all_level))

        return ins_pred

        Get SOLOv2MaskHead output.

        Args:
            inputs(list[Tensor]): feature map from each necks with shape of [N, C, H, W]
        Returns:
            ins_pred(Tensor): Output of SOLOv2MaskHead head
        
ppdet/modeling/heads/solov2_head.py