class_docstr: Assign a corresponding gt bbox or background to each bbox.
  
  Each proposals will be assigned with `-1`, `0`, or a positive integer
  indicating the ground truth index.
  
  - -1: don't care
  - 0: negative sample, no assigned gt
  - positive integer: positive sample, index (1-based) of assigned gt
  
  Args:
      pos_iou_thr (float): IoU threshold for positive bboxes.
      neg_iou_thr (float or tuple): IoU threshold for negative bboxes.
      min_pos_iou (float): Minimum iou for a bbox to be considered as a
          positive bbox. Positive samples can have smaller IoU than
          pos_iou_thr due to the 4th step (assign max IoU sample to each gt).
      gt_max_assign_all (bool): Whether to assign all bboxes with the same
          highest overlap with some gt to that gt.
docstr: Assign gt to bboxes. The process is very much like the max iou
  assigner, except that positive samples are constrained within the cell
  that the gt boxes fell in.
  
  This method assign a gt bbox to every bbox (proposal/anchor), each bbox
  will be assigned with -1, 0, or a positive number. -1 means don't care,
  0 means negative sample, positive number is the index (1-based) of
  assigned gt.
  The assignment is done in following steps, the order matters.
  
  1. assign every bbox to -1
  2. assign proposals whose iou with all gts <= neg_iou_thr to 0
  3. for each bbox within a cell, if the iou with its nearest gt >
      pos_iou_thr and the center of that gt falls inside the cell,
      assign it to that bbox
  4. for each gt bbox, assign its nearest proposals within the cell the
      gt bbox falls in to itself.
  
  Args:
      bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4).
      box_responsible_flags (Tensor): flag to indicate whether box is
          responsible for prediction, shape(n, )
      gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
      gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
  
  Returns:
      :obj:`AssignResult`: The assign result.
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code: def assign(
      self, bboxes, box_responsible_flags, gt_bboxes, gt_labels=None
  ):
      """Assign gt to bboxes. The process is very much like the max iou
      assigner, except that positive samples are constrained within the cell
      that the gt boxes fell in.
  
      This method assign a gt bbox to every bbox (proposal/anchor), each bbox
      will be assigned with -1, 0, or a positive number. -1 means don't care,
      0 means negative sample, positive number is the index (1-based) of
      assigned gt.
      The assignment is done in following steps, the order matters.
  
      1. assign every bbox to -1
      2. assign proposals whose iou with all gts <= neg_iou_thr to 0
      3. for each bbox within a cell, if the iou with its nearest gt >
          pos_iou_thr and the center of that gt falls inside the cell,
          assign it to that bbox
      4. for each gt bbox, assign its nearest proposals within the cell the
          gt bbox falls in to itself.
  
      Args:
          bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4).
          box_responsible_flags (Tensor): flag to indicate whether box is
              responsible for prediction, shape(n, )
          gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
          gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
  
      Returns:
          :obj:`AssignResult`: The assign result.
      """
      num_gts, num_bboxes = gt_bboxes.size(0), bboxes.size(0)
  
      # compute iou between all gt and bboxes
      overlaps = self.iou_calculator(gt_bboxes, bboxes)
  
      # 1. assign -1 by default
      assigned_gt_inds = overlaps.new_full(
          (num_bboxes,), -1, dtype=torch.long
      )
  
      if num_gts == 0 or num_bboxes == 0:
          # No ground truth or boxes, return empty assignment
          max_overlaps = overlaps.new_zeros((num_bboxes,))
          if num_gts == 0:
              # No truth, assign everything to background
              assigned_gt_inds[:] = 0
          if gt_labels is None:
              assigned_labels = None
          else:
              assigned_labels = overlaps.new_full(
                  (num_bboxes,), -1, dtype=torch.long
              )
          return AssignResult(
              num_gts,
              assigned_gt_inds,
              max_overlaps,
              labels=assigned_labels,
          )
  
      # 2. assign negative: below
      # for each anchor, which gt best overlaps with it
      # for each anchor, the max iou of all gts
      # shape of max_overlaps == argmax_overlaps == num_bboxes
      max_overlaps, argmax_overlaps = overlaps.max(dim=0)
  
      if isinstance(self.neg_iou_thr, float):
          assigned_gt_inds[
              (max_overlaps >= 0) & (max_overlaps <= self.neg_iou_thr)
          ] = 0
      elif isinstance(self.neg_iou_thr, (tuple, list)):
          assert len(self.neg_iou_thr) == 2
          assigned_gt_inds[
              (max_overlaps > self.neg_iou_thr[0])
              & (max_overlaps <= self.neg_iou_thr[1])
          ] = 0
  
      # 3. assign positive: falls into responsible cell and above
      # positive IOU threshold, the order matters.
      # the prior condition of comparision is to filter out all
      # unrelated anchors, i.e. not box_responsible_flags
      overlaps[:, ~box_responsible_flags.type(torch.bool)] = -1.0
  
      # calculate max_overlaps again, but this time we only consider IOUs
      # for anchors responsible for prediction
      max_overlaps, argmax_overlaps = overlaps.max(dim=0)
  
      # for each gt, which anchor best overlaps with it
      # for each gt, the max iou of all proposals
      # shape of gt_max_overlaps == gt_argmax_overlaps == num_gts
      gt_max_overlaps, gt_argmax_overlaps = overlaps.max(dim=1)
  
      pos_inds = (
          max_overlaps > self.pos_iou_thr
      ) & box_responsible_flags.type(torch.bool)
      assigned_gt_inds[pos_inds] = argmax_overlaps[pos_inds] + 1
  
      # 4. assign positive to max overlapped anchors within responsible cell
      for i in range(num_gts):
          if gt_max_overlaps[i] > self.min_pos_iou:
              if self.gt_max_assign_all:
                  max_iou_inds = (
                      overlaps[i, :] == gt_max_overlaps[i]
                  ) & box_responsible_flags.type(torch.bool)
                  assigned_gt_inds[max_iou_inds] = i + 1
              elif box_responsible_flags[gt_argmax_overlaps[i]]:
                  assigned_gt_inds[gt_argmax_overlaps[i]] = i + 1
  
      # assign labels of positive anchors
      if gt_labels is not None:
          assigned_labels = assigned_gt_inds.new_full((num_bboxes,), -1)
          pos_inds = torch.nonzero(
              assigned_gt_inds > 0, as_tuple=False
          ).squeeze()
          if pos_inds.numel() > 0:
              assigned_labels[pos_inds] = gt_labels[
                  assigned_gt_inds[pos_inds] - 1
              ]
  
      else:
          assigned_labels = None
  
      return AssignResult(
          num_gts,
          assigned_gt_inds,
          max_overlaps,
          labels=assigned_labels,
      )
  
file_path: insightface/detection/scrfd/mmdet/core/bbox/assigners/grid_assigner.py
imports: import torch
  from ..builder import BBOX_ASSIGNERS
  from ..iou_calculators import build_iou_calculator
  from .assign_result import AssignResult
  from .base_assigner import BaseAssigner
label_desc: Use image node_id 2 for calling the GridAssigner obj's underlying member method code with example usage: obj.assign(bboxes, box_responsible_flags, gt_bboxes, gt_labels) and returns: AssignResult, AssignResult