class_docstr: Head of the Grounding DINO: Marrying DINO with Grounded Pre-Training for
  Open-Set Object Detection.
  
  Args:
      contrastive_cfg (dict, optional): Contrastive config that contains
        keys like ``max_text_len``. Defaults to dict(max_text_len=256).
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docstr: Get targets in denoising part for one image.
  
  Args:
      gt_instances (:obj:`InstanceData`): Ground truth of instance
          annotations. It should includes ``bboxes`` and ``labels``
          attributes.
      img_meta (dict): Meta information for one image.
      dn_meta (Dict[str, int]): The dictionary saves information about
        group collation, including 'num_denoising_queries' and
        'num_denoising_groups'. It will be used for split outputs of
        denoising and matching parts and loss calculation.
  
  Returns:
      tuple[Tensor]: a tuple containing the following for one image.
  
      - labels (Tensor): Labels of each image.
      - label_weights (Tensor]): Label weights of each image.
      - bbox_targets (Tensor): BBox targets of each image.
      - bbox_weights (Tensor): BBox weights of each image.
      - pos_inds (Tensor): Sampled positive indices for each image.
      - neg_inds (Tensor): Sampled negative indices for each image.
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code: def _get_dn_targets_single(
      self,
      gt_instances: InstanceData,
      img_meta: dict,
      dn_meta: Dict[str, int],
  ) -> tuple:
      """Get targets in denoising part for one image.
  
      Args:
          gt_instances (:obj:`InstanceData`): Ground truth of instance
              annotations. It should includes ``bboxes`` and ``labels``
              attributes.
          img_meta (dict): Meta information for one image.
          dn_meta (Dict[str, int]): The dictionary saves information about
            group collation, including 'num_denoising_queries' and
            'num_denoising_groups'. It will be used for split outputs of
            denoising and matching parts and loss calculation.
  
      Returns:
          tuple[Tensor]: a tuple containing the following for one image.
  
          - labels (Tensor): Labels of each image.
          - label_weights (Tensor]): Label weights of each image.
          - bbox_targets (Tensor): BBox targets of each image.
          - bbox_weights (Tensor): BBox weights of each image.
          - pos_inds (Tensor): Sampled positive indices for each image.
          - neg_inds (Tensor): Sampled negative indices for each image.
      """
      gt_bboxes = gt_instances.bboxes
      gt_labels = gt_instances.labels
      num_groups = dn_meta["num_denoising_groups"]
      num_denoising_queries = dn_meta["num_denoising_queries"]
      num_queries_each_group = int(num_denoising_queries / num_groups)
      device = gt_bboxes.device
  
      if len(gt_labels) > 0:
          t = torch.arange(
              len(gt_labels), dtype=torch.long, device=device
          )
          t = t.unsqueeze(0).repeat(num_groups, 1)
          pos_assigned_gt_inds = t.flatten()
          pos_inds = torch.arange(
              num_groups, dtype=torch.long, device=device
          )
          pos_inds = pos_inds.unsqueeze(1) * num_queries_each_group + t
          pos_inds = pos_inds.flatten()
      else:
          pos_inds = pos_assigned_gt_inds = gt_bboxes.new_tensor(
              [], dtype=torch.long
          )
  
      neg_inds = pos_inds + num_queries_each_group // 2
      # label targets
      # this change
      labels = gt_bboxes.new_full(
          (num_denoising_queries, self.max_text_len),
          0,
          dtype=torch.float32,
      )
      labels[pos_inds] = gt_instances.positive_maps[
          pos_assigned_gt_inds
      ]
      label_weights = gt_bboxes.new_ones(num_denoising_queries)
  
      # bbox targets
      bbox_targets = torch.zeros(
          num_denoising_queries, 4, device=device
      )
      bbox_weights = torch.zeros(
          num_denoising_queries, 4, device=device
      )
      bbox_weights[pos_inds] = 1.0
      img_h, img_w = img_meta["img_shape"]
  
      # DETR regress the relative position of boxes (cxcywh) in the image.
      # Thus the learning target should be normalized by the image size, also
      # the box format should be converted from defaultly x1y1x2y2 to cxcywh.
      factor = gt_bboxes.new_tensor(
          [img_w, img_h, img_w, img_h]
      ).unsqueeze(0)
      gt_bboxes_normalized = gt_bboxes / factor
      gt_bboxes_targets = bbox_xyxy_to_cxcywh(gt_bboxes_normalized)
      bbox_targets[pos_inds] = gt_bboxes_targets.repeat([num_groups, 1])
  
      return (
          labels,
          label_weights,
          bbox_targets,
          bbox_weights,
          pos_inds,
          neg_inds,
      )
  
imports: import copy
  import math
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from mmcv.cnn import Linear
  from mmengine.model import constant_init
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.models.losses import QualityFocalLoss
  from mmdet.registry import MODELS
  from mmdet.structures import SampleList
  from mmdet.structures.bbox import bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh
  from mmdet.utils import InstanceList, reduce_mean
  from ..layers import inverse_sigmoid
  from .atss_vlfusion_head import convert_grounding_to_cls_scores
  from .dino_head import DINOHead
label_desc: Use image node_id 12 for calling the GroundingDINOHead obj's underlying member method code with example usage: obj._get_dn_targets_single(gt_instances, img_meta, dn_meta) and returns: labels,  label_weights,  bbox_targets,  bbox_weights,  pos_inds,  neg_inds