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).
class_docstr_tok: ["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",")","."]
docstr: Compute regression and classification targets for one image.
  
  Outputs from a single decoder layer of a single feature level are used.
  
  Args:
      cls_score (Tensor): Box score logits from a single decoder layer
          for one image. Shape [num_queries, cls_out_channels].
      bbox_pred (Tensor): Sigmoid outputs from a single decoder layer
          for one image, with normalized coordinate (cx, cy, w, h) and
          shape [num_queries, 4].
      gt_instances (:obj:`InstanceData`): Ground truth of instance
          annotations. It should includes ``bboxes`` and ``labels``
          attributes.
      img_meta (dict): Meta information for one image.
  
  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.
docstr_tok: ["Compute","regression","and","classification","targets","for","one","image",".","Outputs","from","a","single","decoder","layer","of","a","single","feature","level","are","used",".","Args",":","cls_score","(","Tensor",")",":","Box","score","logits","from","a","single","decoder","layer","for","one","image",".","Shape","[","num_queries",",","cls_out_channels","]",".","bbox_pred","(","Tensor",")",":","Sigmoid","outputs","from","a","single","decoder","layer","for","one","image",",","with","normalized","coordinate","(","cx",",","cy",",","w",",","h",")","and","shape","[","num_queries",",","4","]",".","gt_instances","(",":","obj",":","`","InstanceData","`",")",":","Ground","truth","of","instance","annotations",".","It","should","includes","``","bboxes","``","and","``","labels","``","attributes",".","img_meta","(","dict",")",":","Meta","information","for","one","image",".","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","."]
code: def _get_targets_single(
      self,
      cls_score: Tensor,
      bbox_pred: Tensor,
      gt_instances: InstanceData,
      img_meta: dict,
  ) -> tuple:
      """Compute regression and classification targets for one image.
  
      Outputs from a single decoder layer of a single feature level are used.
  
      Args:
          cls_score (Tensor): Box score logits from a single decoder layer
              for one image. Shape [num_queries, cls_out_channels].
          bbox_pred (Tensor): Sigmoid outputs from a single decoder layer
              for one image, with normalized coordinate (cx, cy, w, h) and
              shape [num_queries, 4].
          gt_instances (:obj:`InstanceData`): Ground truth of instance
              annotations. It should includes ``bboxes`` and ``labels``
              attributes.
          img_meta (dict): Meta information for one image.
  
      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.
      """
      img_h, img_w = img_meta["img_shape"]
      factor = bbox_pred.new_tensor(
          [img_w, img_h, img_w, img_h]
      ).unsqueeze(0)
      num_bboxes = bbox_pred.size(0)
      # convert bbox_pred from xywh, normalized to xyxy, unnormalized
      bbox_pred = bbox_cxcywh_to_xyxy(bbox_pred)
      bbox_pred = bbox_pred * factor
  
      pred_instances = InstanceData(scores=cls_score, bboxes=bbox_pred)
      # assigner and sampler
      assign_result = self.assigner.assign(
          pred_instances=pred_instances,
          gt_instances=gt_instances,
          img_meta=img_meta,
      )
      gt_bboxes = gt_instances.bboxes
  
      pos_inds = (
          torch.nonzero(assign_result.gt_inds > 0, as_tuple=False)
          .squeeze(-1)
          .unique()
      )
      neg_inds = (
          torch.nonzero(assign_result.gt_inds == 0, as_tuple=False)
          .squeeze(-1)
          .unique()
      )
      pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1
      pos_gt_bboxes = gt_bboxes[pos_assigned_gt_inds.long(), :]
  
      # Major changes. The labels are 0-1 binary labels for each bbox
      # and text tokens.
      labels = gt_bboxes.new_full(
          (num_bboxes, 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_bboxes)
  
      # bbox targets
      bbox_targets = torch.zeros_like(bbox_pred, dtype=gt_bboxes.dtype)
      bbox_weights = torch.zeros_like(bbox_pred, dtype=gt_bboxes.dtype)
      bbox_weights[pos_inds] = 1.0
  
      # 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.
      pos_gt_bboxes_normalized = pos_gt_bboxes / factor
      pos_gt_bboxes_targets = bbox_xyxy_to_cxcywh(
          pos_gt_bboxes_normalized
      )
      bbox_targets[pos_inds] = pos_gt_bboxes_targets
      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 4 for calling the GroundingDINOHead obj's underlying member method code with example usage: obj._get_targets_single(cls_score, bbox_pred, gt_instances, img_meta) and returns: labels,  label_weights,  bbox_targets,  bbox_weights,  pos_inds,  neg_inds