bbox_outside_weights normalized incorrectly (anchor_target_layer_fpn.py:136)

[rschwieterman]

the positive and negative weights are normalized by num_examples in anchor_target_layer_fpn.py. num_examples is calculated based on index i outside of i's batch loop, thus the only number of examples that matter is the last batch, all weights for every example in the batch will be normalized by the last example in the batch, rather than on a example by example basis OR by a num_example that considers the entire batch.

    for i in range(batch_size):
      ... ...
    offset = torch.arange(0, batch_size)*gt_boxes.size(1)

    argmax_overlaps = argmax_overlaps + offset.view(batch_size, 1).type_as(argmax_overlaps)
    bbox_targets = _compute_targets_batch(anchors, gt_boxes.view(-1,5 [argmax_overlaps.view(-1), :].view(batch_size, -1, 5))

    # use a single value instead of 4 values for easy index.
    bbox_inside_weights[labels==1] = cfg.TRAIN.RPN_BBOX_INSIDE_WEIGHTS[0]

    if cfg.TRAIN.RPN_POSITIVE_WEIGHT < 0:
        #note that i = batch_size-1
        num_examples = torch.sum(labels[i] >= 0)
        positive_weights = 1.0 / num_examples
        negative_weights = 1.0 / num_examples
    else:
        assert ((cfg.TRAIN.RPN_POSITIVE_WEIGHT > 0) &
                (cfg.TRAIN.RPN_POSITIVE_WEIGHT < 1))

    bbox_outside_weights[labels == 1] = positive_weights
    bbox_outside_weights[labels == 0] = negative_weights