def forward(self,
                boxes,
                logits,
                gt_bbox,
                gt_class,
                masks=None,
                gt_mask=None):
        r"""
        Args:
            boxes (Tensor): [b, query, 4]
            logits (Tensor): [b, query, num_classes]
            gt_bbox (List(Tensor)): list[[n, 4]]
            gt_class (List(Tensor)): list[[n, 1]]
            masks (Tensor|None): [b, query, h, w]
            gt_mask (List(Tensor)): list[[n, H, W]]

        Returns:
            A list of size batch_size, containing tuples of (index_i, index_j) where:
                - index_i is the indices of the selected predictions (in order)
                - index_j is the indices of the corresponding selected targets (in order)
            For each batch element, it holds:
                len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
        """
        bs, num_queries = boxes.shape[:2]

        num_gts = [len(a) for a in gt_class]
        if sum(num_gts) == 0:
            return [(paddle.to_tensor(
                [], dtype=paddle.int64), paddle.to_tensor(
                    [], dtype=paddle.int64)) for _ in range(bs)]

        # We flatten to compute the cost matrices in a batch
        # [batch_size * num_queries, num_classes]
        logits = logits.detach()
        out_prob = F.sigmoid(logits.flatten(
            0, 1)) if self.use_focal_loss else F.softmax(logits.flatten(0, 1))
        # [batch_size * num_queries, 4]
        out_bbox = boxes.detach().flatten(0, 1)

        # Also concat the target labels and boxes
        if 'npu' in paddle.device.get_device():
            gt_class = [tensor.to(paddle.int32) for tensor in gt_class]
            
        tgt_ids = paddle.concat(gt_class).flatten()
        tgt_bbox = paddle.concat(gt_bbox)

        # Compute the classification cost
        out_prob = paddle.gather(out_prob, tgt_ids, axis=1)
        if self.use_focal_loss:
            neg_cost_class = (1 - self.alpha) * (out_prob**self.gamma) * (-(
                1 - out_prob + 1e-8).log())
            pos_cost_class = self.alpha * (
                (1 - out_prob)**self.gamma) * (-(out_prob + 1e-8).log())
            cost_class = pos_cost_class - neg_cost_class
        else:
            cost_class = -out_prob

        # Compute the L1 cost between boxes
        cost_bbox = (
            out_bbox.unsqueeze(1) - tgt_bbox.unsqueeze(0)).abs().sum(-1)

        # Compute the giou cost betwen boxes
        giou_loss = self.giou_loss(
            bbox_cxcywh_to_xyxy(out_bbox.unsqueeze(1)),
            bbox_cxcywh_to_xyxy(tgt_bbox.unsqueeze(0))).squeeze(-1)
        cost_giou = giou_loss - 1

        # Final cost matrix
        C = self.matcher_coeff['class'] * cost_class + \
            self.matcher_coeff['bbox'] * cost_bbox + \
            self.matcher_coeff['giou'] * cost_giou
        # Compute the mask cost and dice cost
        if self.with_mask:
            assert (masks is not None and gt_mask is not None,
                    'Make sure the input has `mask` and `gt_mask`')
            # all masks share the same set of points for efficient matching
            sample_points = paddle.rand([bs, 1, self.num_sample_points, 2])
            sample_points = 2.0 * sample_points - 1.0

            out_mask = F.grid_sample(
                masks.detach(), sample_points, align_corners=False).squeeze(-2)
            out_mask = out_mask.flatten(0, 1)

            tgt_mask = paddle.concat(gt_mask).unsqueeze(1)
            sample_points = paddle.concat([
                a.tile([b, 1, 1, 1]) for a, b in zip(sample_points, num_gts)
                if b > 0
            ])
            tgt_mask = F.grid_sample(
                tgt_mask, sample_points, align_corners=False).squeeze([1, 2])

            with paddle.amp.auto_cast(enable=False):
                # binary cross entropy cost
                pos_cost_mask = F.binary_cross_entropy_with_logits(
                    out_mask, paddle.ones_like(out_mask), reduction='none')
                neg_cost_mask = F.binary_cross_entropy_with_logits(
                    out_mask, paddle.zeros_like(out_mask), reduction='none')
                cost_mask = paddle.matmul(
                    pos_cost_mask, tgt_mask, transpose_y=True) + paddle.matmul(
                        neg_cost_mask, 1 - tgt_mask, transpose_y=True)
                cost_mask /= self.num_sample_points

                # dice cost
                out_mask = F.sigmoid(out_mask)
                numerator = 2 * paddle.matmul(
                    out_mask, tgt_mask, transpose_y=True)
                denominator = out_mask.sum(
                    -1, keepdim=True) + tgt_mask.sum(-1).unsqueeze(0)
                cost_dice = 1 - (numerator + 1) / (denominator + 1)

                C = C + self.matcher_coeff['mask'] * cost_mask + \
                    self.matcher_coeff['dice'] * cost_dice

        C = C.reshape([bs, num_queries, -1])
        C = [a.squeeze(0) for a in C.chunk(bs)]
        sizes = [a.shape[0] for a in gt_bbox]
        if hasattr(paddle.Tensor, "contiguous"):
            indices = [
                linear_sum_assignment(c.split(sizes, -1)[i].contiguous().numpy())
                for i, c in enumerate(C)
            ]
        else:
            indices = [
                linear_sum_assignment(c.split(sizes, -1)[i].numpy())
                for i, c in enumerate(C)
            ]
        return [(paddle.to_tensor(
            i, dtype=paddle.int64), paddle.to_tensor(
                j, dtype=paddle.int64)) for i, j in indices]

        Args:
            boxes (Tensor): [b, query, 4]
            logits (Tensor): [b, query, num_classes]
            gt_bbox (List(Tensor)): list[[n, 4]]
            gt_class (List(Tensor)): list[[n, 1]]
            masks (Tensor|None): [b, query, h, w]
            gt_mask (List(Tensor)): list[[n, H, W]]

        Returns:
            A list of size batch_size, containing tuples of (index_i, index_j) where:
                - index_i is the indices of the selected predictions (in order)
                - index_j is the indices of the corresponding selected targets (in order)
            For each batch element, it holds:
                len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
        
ppdet/modeling/transformers/matchers.py