code: # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
  """
  Modules to compute the matching cost and solve the corresponding LSAP.
  """
  import torch
  from scipy.optimize import linear_sum_assignment
  from torch import nn
  
  from util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou
  
  
  class HungarianMatcher(nn.Module):
      """This class computes an assignment between the targets and the predictions of the network
  
      For efficiency reasons, the targets don't include the no_object. Because of this, in general,
      there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,
      while the others are un-matched (and thus treated as non-objects).
      """
  
      def __init__(self, cost_class: float = 1, cost_bbox: float = 1, cost_giou: float = 1):
          """Creates the matcher
  
          Params:
              cost_class: This is the relative weight of the classification error in the matching cost
              cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates in the matching cost
              cost_giou: This is the relative weight of the giou loss of the bounding box in the matching cost
          """
          super().__init__()
          self.cost_class = cost_class
          self.cost_bbox = cost_bbox
          self.cost_giou = cost_giou
          assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, "all costs cant be 0"
  
      @torch.no_grad()
      def forward(self, outputs, targets):
          """ Performs the matching
  
          Params:
              outputs: This is a dict that contains at least these entries:
                   "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
                   "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates
  
              targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:
                   "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth
                             objects in the target) containing the class labels
                   "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates
  
          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 = outputs["pred_logits"].shape[:2]
  
          # We flatten to compute the cost matrices in a batch
          out_prob = outputs["pred_logits"].flatten(0, 1).softmax(-1)  # [batch_size * num_queries, num_classes]
          out_bbox = outputs["pred_boxes"].flatten(0, 1)  # [batch_size * num_queries, 4]
  
          # Also concat the target labels and boxes
          tgt_ids = torch.cat([v["labels"] for v in targets])
          tgt_bbox = torch.cat([v["boxes"] for v in targets])
  
          # Compute the classification cost. Contrary to the loss, we don't use the NLL,
          # but approximate it in 1 - proba[target class].
          # The 1 is a constant that doesn't change the matching, it can be ommitted.
          # 在out_prob中，dim1中每一维都代表了对每个该bbox的所属类别的概率，由于有92个类，所以有92个数字
          # 由于candidate box远比实际的box数量要多，因此并不知道到底哪个candidate能与gt box进行匹配
          # 所以先获取所有tgt_id，并在out_ptob中取出对应的概率，因为知道在众多candidate中必有一个bbox与某个gt bbox最为匹配
          # 之所以用减号就是想知道与理想概率1的差距，但这里加不加1其实无所谓
          cost_class = -out_prob[:, tgt_ids]
  
          # Compute the L1 cost between boxes
          cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)
  
          # Compute the giou cost betwen boxes
          cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox))
  
          # Final cost matrix
          C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou
          C = C.view(bs, num_queries, -1).cpu()
  
          sizes = [len(v["boxes"]) for v in targets]
          indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))]
          return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]
  
  
  def build_matcher(args):
      return HungarianMatcher(cost_class=args.set_cost_class, cost_bbox=args.set_cost_bbox, cost_giou=args.set_cost_giou)
  
apis: torch.as_tensor torch.no_grad torch.cdist scipy.optimize.linear_sum_assignment torch.cat