Anchor Boxes

https://d2l.ai/chapter_computer-vision/anchor.html


Then I found
It didn’t show when I switched to tab "pytorch"at first.
@anirudh

So does match_anchor_to_bbox.
So does offset_inverse and multibox_detection.
@anirudh
Is there some way to fix these?

This is a known issue. The tab rendering is managed by d2lbook.

match_anchor_to_bbox doesn’t faithfully implement the algorithm presented in the text.

# Find the largest iou for each bbox
anc_i = torch.argmax(jaccard, dim=0)
box_j = torch.arange(num_gt_boxes, device=device)
anchors_bbox_map[anc_i] = box_j

In particular, there might be cases where anc_i is the max of multiple columns (j). The code above will assign the last j to anc_i. It should be noted that the code is a simplification of the algorithm.

Hi @gcy, can you please elaborate the same? Also, feel free to open a PR to fix something that might be wrong! :slight_smile:

@anirudh Using the algorithm, we will have the result in the left image, whereas the code gives the right. The table has the same layout as in Fig. 13.4.2.

Thanks @gcy for raising this issue. I think it should be fixed with the following change. Let me know what do you think about the same. I’ll then make a PR.

#@save
def match_anchor_to_bbox(ground_truth, anchors, device, iou_threshold=0.5):
    """Assign ground-truth bounding boxes to anchor boxes similar to them."""
    num_anchors, num_gt_boxes = anchors.shape[0], ground_truth.shape[0]
    # Element `x_ij` in the `i^th` row and `j^th` column is the IoU
    # of the anchor box `anc_i` to the ground-truth bounding box `box_j`
    jaccard = box_iou(anchors, ground_truth)
    # Initialize the tensor to hold assigned ground truth bbox for each anchor
    anchors_bbox_map = torch.full((num_anchors,), -1, dtype=torch.long,
                                  device=device)
    # Assign ground truth bounding box according to the threshold
    max_ious, indices = torch.max(jaccard, dim=1)
    anc_i = torch.nonzero(max_ious >= 0.5).reshape(-1)
    box_j = indices[max_ious >= 0.5]
    anchors_bbox_map[anc_i] = box_j
    # Find the largest iou for each bbox
    col_discard = torch.full((num_anchors,), -1)
    row_discard = torch.full((num_gt_boxes,), -1)
    for _ in range(num_gt_boxes):
      max_idx = torch.argmax(jaccard)
      gt_idx = (max_idx % num_gt_boxes).long()
      anc_idx = (max_idx / num_gt_boxes).long()
      anchors_bbox_map[anc_idx] = gt_idx
      jaccard[:, gt_idx] = col_discard
      jaccard[anc_idx, :] = row_discard
    return anchors_bbox_map

Yes, exactly! I would suggest changing gt_idx to something like box_idx to make the naming more consistent.