Repository: tensorflow/models
File: research/object_detection/core/target_assigner.py (Python)

Code before:
    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.7,
                                           unmatched_threshold=0.3,
                                           force_match_for_each_row=True,
                                           use_matmul_gather=use_matmul_gather)
    box_coder_instance = faster_rcnn_box_coder.FasterRcnnBoxCoder(
        scale_factors=[10.0, 10.0, 5.0, 5.0])

  elif reference == 'FasterRCNN' and stage == 'detection':
    similarity_calc = sim_calc.IouSimilarity()
    # Uses all proposals with IOU < 0.5 as candidate negatives.
    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
                                           negatives_lower_than_unmatched=True,
                                           use_matmul_gather=use_matmul_gather)
    box_coder_instance = faster_rcnn_box_coder.FasterRcnnBoxCoder(
        scale_factors=[10.0, 10.0, 5.0, 5.0])

  elif reference == 'FastRCNN':
    similarity_calc = sim_calc.IouSimilarity()
    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
                                           unmatched_threshold=0.1,
                                           force_match_for_each_row=False,
                                           negatives_lower_than_unmatched=False,
                                           use_matmul_gather=use_matmul_gather)
    box_coder_instance = faster_rcnn_box_coder.FasterRcnnBoxCoder()

  elif reference == 'DETR':
    return DETRTargetAssigner()

  else:
    raise ValueError('No valid combination of reference and stage.')

  return TargetAssigner(similarity_calc, matcher, box_coder_instance,
                        negative_class_weight=negative_class_weight)


def batch_assign(target_assigner,
                 anchors_batch,
                 gt_box_batch,
                 gt_class_targets_batch,
                 unmatched_class_label=None,
                 gt_weights_batch=None,
                 class_predictions=None):
  """Batched assignment of classification and regression targets.

  Args:
    target_assigner: a target assigner.
    anchors_batch: BoxList representing N box anchors or list of BoxList objects
      with length batch_size representing anchor sets.
    gt_box_batch: a list of BoxList objects with length batch_size
      representing groundtruth boxes for each image in the batch
    gt_class_targets_batch: a list of tensors with length batch_size, where

Review comment (suggestion):
we don't need to add it here. In DETR meta architecture you can directly create `DETRTargetAssigner()`

Code after:
                                           unmatched_threshold=0.3,
                                           force_match_for_each_row=True,
                                           use_matmul_gather=use_matmul_gather)
    box_coder_instance = faster_rcnn_box_coder.FasterRcnnBoxCoder(
        scale_factors=[10.0, 10.0, 5.0, 5.0])

  elif reference == 'FasterRCNN' and stage == 'detection':
    similarity_calc = sim_calc.IouSimilarity()
    # Uses all proposals with IOU < 0.5 as candidate negatives.
    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
                                           negatives_lower_than_unmatched=True,
                                           use_matmul_gather=use_matmul_gather)
    box_coder_instance = faster_rcnn_box_coder.FasterRcnnBoxCoder(
        scale_factors=[10.0, 10.0, 5.0, 5.0])

  elif reference == 'FastRCNN':
    similarity_calc = sim_calc.IouSimilarity()
    matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
                                           unmatched_threshold=0.1,
                                           force_match_for_each_row=False,
                                           negatives_lower_than_unmatched=False,
                                           use_matmul_gather=use_matmul_gather)
    box_coder_instance = faster_rcnn_box_coder.FasterRcnnBoxCoder()

  else:
    raise ValueError('No valid combination of reference and stage.')

  return TargetAssigner(similarity_calc, matcher, box_coder_instance,
                        negative_class_weight=negative_class_weight)


def batch_assign(target_assigner,
                 anchors_batch,
                 gt_box_batch,
                 gt_class_targets_batch,
                 unmatched_class_label=None,
                 gt_weights_batch=None):
  """Batched assignment of classification and regression targets.

  Args:
    target_assigner: a target assigner.
    anchors_batch: BoxList representing N box anchors or list of BoxList objects
      with length batch_size representing anchor sets.
    gt_box_batch: a list of BoxList objects with length batch_size
      representing groundtruth boxes for each image in the batch
    gt_class_targets_batch: a list of tensors with length batch_size, where
      each tensor has shape [num_gt_boxes_i, classification_target_size] and
      num_gt_boxes_i is the number of boxes in the ith boxlist of
      gt_box_batch.
    unmatched_class_label: a float32 tensor with shape [d_1, d_2, ..., d_k]
      which is consistent with the classification target for each