def hard_example_mining(dist_mat, is_pos, is_neg):
    """For each anchor, find the hardest positive and negative sample.
    Args:
      dist_mat: pair wise distance between samples, shape [N, M]
      is_pos: positive index with shape [N, M]
      is_neg: negative index with shape [N, M]
    Returns:
      dist_ap: pytorch Variable, distance(anchor, positive); shape [N]
      dist_an: pytorch Variable, distance(anchor, negative); shape [N]
      p_inds: pytorch LongTensor, with shape [N];
        indices of selected hard positive samples; 0 <= p_inds[i] <= N - 1
      n_inds: pytorch LongTensor, with shape [N];
        indices of selected hard negative samples; 0 <= n_inds[i] <= N - 1
    NOTE: Only consider the case in which all labels have same num of samples,
      thus we can cope with all anchors in parallel.
    """

    assert len(dist_mat.size()) == 2

    # `dist_ap` means distance(anchor, positive)
    # both `dist_ap` and `relative_p_inds` with shape [N]
    dist_ap, _ = torch.max(dist_mat * is_pos, dim=1)
    # `dist_an` means distance(anchor, negative)
    # both `dist_an` and `relative_n_inds` with shape [N]
    dist_an, _ = torch.min(dist_mat * is_neg + is_pos * 1e9, dim=1)

    return dist_ap, dist_an
For each anchor, find the hardest positive and negative sample.
    Args:
      dist_mat: pair wise distance between samples, shape [N, M]
      is_pos: positive index with shape [N, M]
      is_neg: negative index with shape [N, M]
    Returns:
      dist_ap: pytorch Variable, distance(anchor, positive); shape [N]
      dist_an: pytorch Variable, distance(anchor, negative); shape [N]
      p_inds: pytorch LongTensor, with shape [N];
        indices of selected hard positive samples; 0 <= p_inds[i] <= N - 1
      n_inds: pytorch LongTensor, with shape [N];
        indices of selected hard negative samples; 0 <= n_inds[i] <= N - 1
    NOTE: Only consider the case in which all labels have same num of samples,
      thus we can cope with all anchors in parallel.
    
fastreid/modeling/losses/triplet_loss.py