How to use the mmdet.core.multi_apply function in mmdet

def forward(self, feats):
        return multi_apply(self.forward_single, feats)

def forward(self, feats):
        return multi_apply(self.forward_single, feats, self.scales_bbox, self.scales_mask)

def forward(self, feats):
        return multi_apply(self.forward_single, feats)

gt_bboxes,
            img_metas,
            self.target_means,
            self.target_stds,
            cfg,
            gt_bboxes_ignore_list=gt_bboxes_ignore,
            gt_labels_list=gt_labels,
            label_channels=label_channels,
            sampling=self.sampling)
        if cls_reg_targets is None:
            return None
        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
         num_total_pos, num_total_neg) = cls_reg_targets
        num_total_samples = (
            num_total_pos + num_total_neg if self.sampling else num_total_pos)
        losses_cls, losses_bbox = multi_apply(
            self.loss_single,
            cls_scores,
            bbox_preds,
            labels_list,
            label_weights_list,
            bbox_targets_list,
            bbox_weights_list,
            num_total_samples=num_total_samples,
            cfg=cfg)
        return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)

all_bbox_preds = torch.cat([
            b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
            for b in bbox_preds
        ], -2)
        all_bbox_targets = torch.cat(bbox_targets_list,
                                     -2).view(num_images, -1, 4)
        all_bbox_weights = torch.cat(bbox_weights_list,
                                     -2).view(num_images, -1, 4)

        # check NaN and Inf
        assert torch.isfinite(all_cls_scores).all().item(), \
            'classification scores become infinite or NaN!'
        assert torch.isfinite(all_bbox_preds).all().item(), \
            'bbox predications become infinite or NaN!'

        losses_cls, losses_bbox = multi_apply(
            self.loss_single,
            all_cls_scores,
            all_bbox_preds,
            all_labels,
            all_label_weights,
            all_bbox_targets,
            all_bbox_weights,
            num_total_samples=num_total_pos,
            cfg=cfg)
        return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)

def forward(self, feats):
        return multi_apply(self.forward_single, feats)

gt_bboxes,
            img_metas,
            self.target_means,
            self.target_stds,
            cfg,
            gt_bboxes_ignore_list=gt_bboxes_ignore,
            gt_labels_list=gt_labels,
            label_channels=label_channels,
            sampling=self.sampling)
        if cls_reg_targets is None:
            return None
        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
         num_total_pos, num_total_neg) = cls_reg_targets
        num_total_samples = (
            num_total_pos + num_total_neg if self.sampling else num_total_pos)
        losses_cls, losses_bbox = multi_apply(
            self.loss_single,
            cls_scores,
            bbox_preds,
            labels_list,
            label_weights_list,
            bbox_targets_list,
            bbox_weights_list,
            num_total_samples=num_total_samples,
            cfg=cfg)
        return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)

gt_bboxes,
            img_metas,
            cfg.refine,
            gt_bboxes_ignore_list=gt_bboxes_ignore,
            gt_labels_list=gt_labels,
            label_channels=label_channels,
            sampling=self.sampling)
        (labels_list, label_weights_list, bbox_gt_list_refine,
         candidate_list_refine, bbox_weights_list_refine, num_total_pos_refine,
         num_total_neg_refine) = cls_reg_targets_refine
        num_total_samples_refine = (
            num_total_pos_refine +
            num_total_neg_refine if self.sampling else num_total_pos_refine)

        # compute loss
        losses_cls, losses_pts_init, losses_pts_refine = multi_apply(
            self.loss_single,
            cls_scores,
            pts_coordinate_preds_init,
            pts_coordinate_preds_refine,
            labels_list,
            label_weights_list,
            bbox_gt_list_init,
            bbox_weights_list_init,
            bbox_gt_list_refine,
            bbox_weights_list_refine,
            self.point_strides,
            num_total_samples_init=num_total_samples_init,
            num_total_samples_refine=num_total_samples_refine)
        loss_dict_all = {
            'loss_cls': losses_cls,
            'loss_pts_init': losses_pts_init,

s.permute(0, 2, 3, 1).reshape(
                num_images, -1, self.cls_out_channels) for s in cls_scores
        ], 1)
        all_labels = torch.cat(labels_list, -1).view(num_images, -1)
        all_label_weights = torch.cat(label_weights_list,
                                      -1).view(num_images, -1)
        all_bbox_preds = torch.cat([
            b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
            for b in bbox_preds
        ], -2)
        all_bbox_targets = torch.cat(bbox_targets_list,
                                     -2).view(num_images, -1, 4)
        all_bbox_weights = torch.cat(bbox_weights_list,
                                     -2).view(num_images, -1, 4)

        losses_cls, losses_bbox = multi_apply(
            self.loss_single,
            all_cls_scores,
            all_bbox_preds,
            all_labels,
            all_label_weights,
            all_bbox_targets,
            all_bbox_weights,
            num_total_samples=num_total_pos,
            cfg=cfg)
        return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)

cfg,
             gt_bboxes_ignore=None):
        
        cls_reg_targets = self.fsaf_target(
            cls_scores,
            bbox_preds,
            gt_bboxes,
            gt_labels,
            img_metas,
            cfg,
            gt_bboxes_ignore_list=gt_bboxes_ignore)
        
        (cls_targets_list, reg_targets_list) = cls_reg_targets
        
        level_list = [i for i in range(len(self.feat_strides))]
        loss_cls, loss_reg, norm_cls, norm_reg = multi_apply(
             self.loss_single,
             cls_scores,
             bbox_preds,
             cls_targets_list,
             reg_targets_list,
             level_list,
             img_metas=img_metas,
             cfg=cfg,
             gt_bboxes_ignore=None)
        
        loss_cls = sum(loss_cls)/sum(norm_cls)
        loss_reg = sum(loss_reg)/sum(norm_reg)
        
        return dict(loss_cls=loss_cls, loss_bbox=loss_reg)