def loss_single(self, cls_scores, mask_preds, gt_labels_list,
                    gt_masks_list, img_metas):
        """Loss function for outputs from a single decoder layer.

        Args:
            cls_scores (Tensor): Mask score logits from a single decoder layer
                for all images. Shape (batch_size, num_queries,
                cls_out_channels). Note `cls_out_channels` should includes
                background.
            mask_preds (Tensor): Mask logits for a pixel decoder for all
                images. Shape (batch_size, num_queries, h, w).
            gt_labels_list (list[Tensor]): Ground truth class indices for each
                image, each with shape (num_gts, ).
            gt_masks_list (list[Tensor]): Ground truth mask for each image,
                each with shape (num_gts, h, w).
            img_metas (list[dict]): List of image meta information.

        Returns:
            tuple[Tensor]: Loss components for outputs from a single \
                decoder layer.
        """
        num_imgs = cls_scores.size(0)
        cls_scores_list = [cls_scores[i] for i in range(num_imgs)]
        mask_preds_list = [mask_preds[i] for i in range(num_imgs)]
        (labels_list, label_weights_list, mask_targets_list, mask_weights_list,
         num_total_pos,
         num_total_neg) = self.get_targets(cls_scores_list, mask_preds_list,
                                           gt_labels_list, gt_masks_list,
                                           img_metas)
        # shape (batch_size, num_queries)
        labels = torch.stack(labels_list, dim=0)
        # shape (batch_size, num_queries)
        label_weights = torch.stack(label_weights_list, dim=0)
        # shape (num_total_gts, h, w)
        mask_targets = torch.cat(mask_targets_list, dim=0)
        # shape (batch_size, num_queries)
        mask_weights = torch.stack(mask_weights_list, dim=0)

        # classfication loss
        # shape (batch_size * num_queries, )
        cls_scores = cls_scores.flatten(0, 1)
        labels = labels.flatten(0, 1)
        label_weights = label_weights.flatten(0, 1)

        class_weight = cls_scores.new_tensor(self.class_weight)
        loss_cls = self.loss_cls(
            cls_scores,
            labels,
            label_weights,
            avg_factor=class_weight[labels].sum())

        num_total_masks = reduce_mean(cls_scores.new_tensor([num_total_pos]))
        num_total_masks = max(num_total_masks, 1)

        # extract positive ones
        # shape (batch_size, num_queries, h, w) -> (num_total_gts, h, w)
        mask_preds = mask_preds[mask_weights > 0]

        if mask_targets.shape[0] == 0:
            # zero match
            loss_dice = mask_preds.sum()
            loss_mask = mask_preds.sum()
            return loss_cls, loss_mask, loss_dice

        with torch.no_grad():
            points_coords = get_uncertain_point_coords_with_randomness(
                mask_preds.unsqueeze(1), None, self.num_points,
                self.oversample_ratio, self.importance_sample_ratio)
            # shape (num_total_gts, h, w) -> (num_total_gts, num_points)
            mask_point_targets = point_sample(
                mask_targets.unsqueeze(1).float(), points_coords).squeeze(1)
        # shape (num_queries, h, w) -> (num_queries, num_points)
        mask_point_preds = point_sample(
            mask_preds.unsqueeze(1), points_coords).squeeze(1)

        # dice loss
        loss_dice = self.loss_dice(
            mask_point_preds, mask_point_targets, avg_factor=num_total_masks)

        # mask loss
        # shape (num_queries, num_points) -> (num_queries * num_points, )
        mask_point_preds = mask_point_preds.reshape(-1,1)
        # shape (num_total_gts, num_points) -> (num_total_gts * num_points, )
        mask_point_targets = mask_point_targets.reshape(-1)
        loss_mask = self.loss_mask(
            mask_point_preds,
            mask_point_targets,
            avg_factor=num_total_masks * self.num_points)

        return loss_cls, loss_mask, loss_dice
Loss function for outputs from a single decoder layer.

        Args:
            cls_scores (Tensor): Mask score logits from a single decoder layer
                for all images. Shape (batch_size, num_queries,
                cls_out_channels). Note `cls_out_channels` should includes
                background.
            mask_preds (Tensor): Mask logits for a pixel decoder for all
                images. Shape (batch_size, num_queries, h, w).
            gt_labels_list (list[Tensor]): Ground truth class indices for each
                image, each with shape (num_gts, ).
            gt_masks_list (list[Tensor]): Ground truth mask for each image,
                each with shape (num_gts, h, w).
            img_metas (list[dict]): List of image meta information.

        Returns:
            tuple[Tensor]: Loss components for outputs from a single                 decoder layer.
        
EVA-01/seg/mmseg_custom/models/decode_heads/mask2former_head.py