text: # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
  from typing import List
  import torch
  
  from detectron2.layers import nonzero_tuple
  
  
  class Matcher(object):
      """
      This class assigns to each predicted "element" (e.g., a box) a ground-truth
      element. Each predicted element will have exactly zero or one matches; each
      ground-truth element may be matched to zero or more predicted elements.
  
      The matching is determined by the MxN match_quality_matrix, that characterizes
      how well each (ground-truth, prediction)-pair match each other. For example,
      if the elements are boxes, this matrix may contain box intersection-over-union
      overlap values.
  
      The matcher returns (a) a vector of length N containing the index of the
      ground-truth element m in [0, M) that matches to prediction n in [0, N).
      (b) a vector of length N containing the labels for each prediction.
      """
  
      def __init__(
          self, thresholds: List[float], labels: List[int], allow_low_quality_matches: bool = False
      ):
          """
          Args:
              thresholds (list): a list of thresholds used to stratify predictions
                  into levels.
              labels (list): a list of values to label predictions belonging at
                  each level. A label can be one of {-1, 0, 1} signifying
                  {ignore, negative class, positive class}, respectively.
              allow_low_quality_matches (bool): if True, produce additional matches
                  for predictions with maximum match quality lower than high_threshold.
                  See set_low_quality_matches_ for more details.
  
              For example,
                  thresholds = [0.3, 0.5]
                  labels = [0, -1, 1]
                  All predictions with iou < 0.3 will be marked with 0 and
                  thus will be considered as false positives while training.
                  All predictions with 0.3 <= iou < 0.5 will be marked with -1 and
                  thus will be ignored.
                  All predictions with 0.5 <= iou will be marked with 1 and
                  thus will be considered as true positives.
          """
          # Add -inf and +inf to first and last position in thresholds
          thresholds = thresholds[:]
          assert thresholds[0] > 0
          thresholds.insert(0, -float("inf"))
          thresholds.append(float("inf"))
          # Currently torchscript does not support all + generator
          assert all([low <= high for (low, high) in zip(thresholds[:-1], thresholds[1:])])
          assert all([l in [-1, 0, 1] for l in labels])
          assert len(labels) == len(thresholds) - 1
          self.thresholds = thresholds
          self.labels = labels
          self.allow_low_quality_matches = allow_low_quality_matches
  
      def __call__(self, match_quality_matrix):
          """
          Args:
              match_quality_matrix (Tensor[float]): an MxN tensor, containing the
                  pairwise quality between M ground-truth elements and N predicted
                  elements. All elements must be >= 0 (due to the us of `torch.nonzero`
                  for selecting indices in :meth:`set_low_quality_matches_`).
  
          Returns:
              matches (Tensor[int64]): a vector of length N, where matches[i] is a matched
                  ground-truth index in [0, M)
              match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates
                  whether a prediction is a true or false positive or ignored
          """
          assert match_quality_matrix.dim() == 2
          if match_quality_matrix.numel() == 0:
              default_matches = match_quality_matrix.new_full(
                  (match_quality_matrix.size(1),), 0, dtype=torch.int64
              )
              # When no gt boxes exist, we define IOU = 0 and therefore set labels
              # to `self.labels[0]`, which usually defaults to background class 0
              # To choose to ignore instead, can make labels=[-1,0,-1,1] + set appropriate thresholds
              default_match_labels = match_quality_matrix.new_full(
                  (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8
              )
              return default_matches, default_match_labels
  
          assert torch.all(match_quality_matrix >= 0)
  
          # match_quality_matrix is M (gt) x N (predicted)
          # Max over gt elements (dim 0) to find best gt candidate for each prediction
          matched_vals, matches = match_quality_matrix.max(dim=0)
  
          match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8)
  
          for (l, low, high) in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]):
              low_high = (matched_vals >= low) & (matched_vals < high)
              match_labels[low_high] = l
  
          if self.allow_low_quality_matches:
              self.set_low_quality_matches_(match_labels, match_quality_matrix)
  
          return matches, match_labels
  
      def set_low_quality_matches_(self, match_labels, match_quality_matrix):
          """
          Produce additional matches for predictions that have only low-quality matches.
          Specifically, for each ground-truth G find the set of predictions that have
          maximum overlap with it (including ties); for each prediction in that set, if
          it is unmatched, then match it to the ground-truth G.
  
          This function implements the RPN assignment case (i) in Sec. 3.1.2 of
          :paper:`Faster R-CNN`.
          """
          # For each gt, find the prediction with which it has highest quality
          highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1)
          # Find the highest quality match available, even if it is low, including ties.
          # Note that the matches qualities must be positive due to the use of
          # `torch.nonzero`.
          _, pred_inds_with_highest_quality = nonzero_tuple(
              match_quality_matrix == highest_quality_foreach_gt[:, None]
          )
          # If an anchor was labeled positive only due to a low-quality match
          # with gt_A, but it has larger overlap with gt_B, it's matched index will still be gt_B.
          # This follows the implementation in Detectron, and is found to have no significant impact.
          match_labels[pred_inds_with_highest_quality] = 1
  
  
  class SampleMatcher(object):
      """
      This class assigns to each predicted "element" (e.g., a box) a ground-truth
      element. Each predicted element will have exactly zero or one matches; each
      ground-truth element may be matched to zero or more predicted elements.
  
      The matching is determined by the MxN match_quality_matrix, that characterizes
      how well each (ground-truth, prediction)-pair match each other. For example,
      if the elements are boxes, this matrix may contain box intersection-over-union
      overlap values.
  
      The matcher returns (a) a vector of length N containing the index of the
      ground-truth element m in [0, M) that matches to prediction n in [0, N).
      (b) a vector of length N containing the labels for each prediction.
      """
  
      def __init__(
          self, thresholds: List[float], labels: List[int], allow_low_quality_matches: bool = False, match_num=1
      ):
          """
          Args:
              thresholds (list): a list of thresholds used to stratify predictions
                  into levels.
              labels (list): a list of values to label predictions belonging at
                  each level. A label can be one of {-1, 0, 1} signifying
                  {ignore, negative class, positive class}, respectively.
              allow_low_quality_matches (bool): if True, produce additional matches
                  for predictions with maximum match quality lower than high_threshold.
                  See set_low_quality_matches_ for more details.
  
              For example,
                  thresholds = [0.3, 0.5]
                  labels = [0, -1, 1]
                  All predictions with iou < 0.3 will be marked with 0 and
                  thus will be considered as false positives while training.
                  All predictions with 0.3 <= iou < 0.5 will be marked with -1 and
                  thus will be ignored.
                  All predictions with 0.5 <= iou will be marked with 1 and
                  thus will be considered as true positives.
          """
          # Add -inf and +inf to first and last position in thresholds
          thresholds = thresholds[:]
          assert thresholds[0] > 0
          thresholds.insert(0, -float("inf"))
          thresholds.append(float("inf"))
          # Currently torchscript does not support all + generator
          assert all([low <= high for (low, high) in zip(thresholds[:-1], thresholds[1:])])
          assert all([l in [-1, 0, 1] for l in labels])
          assert len(labels) == len(thresholds) - 1
          self.thresholds = thresholds
          self.labels = labels
          self.match_num = match_num
          self.allow_low_quality_matches = allow_low_quality_matches
  
      def __call__(self, match_quality_matrix):
          """
          Args:
              match_quality_matrix (Tensor[float]): an MxN tensor, containing the
                  pairwise quality between M ground-truth elements and N predicted
                  elements. All elements must be >= 0 (due to the us of `torch.nonzero`
                  for selecting indices in :meth:`set_low_quality_matches_`).
  
          Returns:
              matches (Tensor[int64]): a vector of length N, where matches[i] is a matched
                  ground-truth index in [0, M)
              match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates
                  whether a prediction is a true or false positive or ignored
          """
          assert match_quality_matrix.dim() == 2
          if match_quality_matrix.numel() == 0:
              default_matches = match_quality_matrix.new_full(
                  (match_quality_matrix.size(1),), 0, dtype=torch.int64
              )
              # When no gt boxes exist, we define IOU = 0 and therefore set labels
              # to `self.labels[0]`, which usually defaults to background class 0
              # To choose to ignore instead, can make labels=[-1,0,-1,1] + set appropriate thresholds
              default_match_labels = match_quality_matrix.new_full(
                  (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8
              )
              return default_matches, default_match_labels
  
          assert torch.all(match_quality_matrix >= 0)
  
          # match_quality_matrix is M (gt) x N (predicted)
          # Max over gt elements (dim 0) to find best gt candidate for each prediction
          matched_vals, matches = match_quality_matrix.topk(k=self.match_num, dim=0)
          # matched_vals_, matches_ = match_quality_matrix.max(dim=0)
          mask = torch.randint(0, self.match_num, (matches.size(1),)).cuda()
          # print(mask, matches)
          matches = matches.T.gather(1, mask.view(-1,1)).T[0]
          matched_vals = matched_vals.T.gather(1, mask.view(-1,1)).T[0]
          
          match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8)
  
          for (l, low, high) in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]):
              low_high = (matched_vals >= low) & (matched_vals < high)
              match_labels[low_high] = l
  
          if self.allow_low_quality_matches:
              self.set_low_quality_matches_(match_labels, match_quality_matrix)
  
          return matches, match_labels
  
      def set_low_quality_matches_(self, match_labels, match_quality_matrix):
          """
          Produce additional matches for predictions that have only low-quality matches.
          Specifically, for each ground-truth G find the set of predictions that have
          maximum overlap with it (including ties); for each prediction in that set, if
          it is unmatched, then match it to the ground-truth G.
  
          This function implements the RPN assignment case (i) in Sec. 3.1.2 of
          :paper:`Faster R-CNN`.
          """
          # For each gt, find the prediction with which it has highest quality
          highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1)
          # Find the highest quality match available, even if it is low, including ties.
          # Note that the matches qualities must be positive due to the use of
          # `torch.nonzero`.
          _, pred_inds_with_highest_quality = nonzero_tuple(
              match_quality_matrix == highest_quality_foreach_gt[:, None]
          )
          # If an anchor was labeled positive only due to a low-quality match
          # with gt_A, but it has larger overlap with gt_B, it's matched index will still be gt_B.
          # This follows the implementation in Detectron, and is found to have no significant impact.
          match_labels[pred_inds_with_highest_quality] = 1