From 02802e67f24e72374a1a4bcef4e5241b9e3b2add Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Tue, 31 Mar 2020 18:18:08 -0700 Subject: [PATCH] merge NMS full matrix --- utils/utils.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/utils/utils.py b/utils/utils.py index a8286780..109c78bd 100755 --- a/utils/utils.py +++ b/utils/utils.py @@ -555,10 +555,11 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_label=T boxes, scores = x[:, :4].clone() + c.view(-1, 1) * max_wh, x[:, 4] # boxes (offset by class), scores if method == 'merge': # Merge NMS (boxes merged using weighted mean) i = torchvision.ops.boxes.nms(boxes, scores, iou_thres) - if n < 5000: # update boxes - weights = (box_iou(boxes, boxes).tril_() > iou_thres) * scores.view(-1, 1) # box weights - weights /= weights.sum(0) # normalize - x[:, :4] = torch.mm(weights.T, x[:, :4]) # merged_boxes(n,4) = weights(n,n) * boxes(n,4) + # weights = (box_iou(boxes, boxes).tril_() > iou_thres) * scores.view(-1, 1) # box weights + # weights /= weights.sum(0) # normalize + # x[:, :4] = torch.mm(weights.T, x[:, :4]) + weights = (box_iou(boxes[i], boxes) > iou_thres) * scores[None] # box weights + x[i, :4] = torch.mm(weights / weights.sum(1, keepdim=True), x[:, :4]) # boxes(i,4) = w(i,n) * boxes(n,4) elif method == 'vision': i = torchvision.ops.boxes.nms(boxes, scores, iou_thres) elif method == 'fast': # FastNMS from https://github.com/dbolya/yolact