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merge_batch NMS method

This commit is contained in:
Glenn Jocher 2020-03-25 23:24:57 -07:00
parent 3265d50f69
commit aa0c64b5ac
1 changed files with 9 additions and 6 deletions
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15
utils/utils.py
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@ -558,9 +558,15 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_label=T
boxes += c.view(-1, 1) * max_wh boxes += c.view(-1, 1) * max_wh
if method == 'vision_batch': if method == 'vision_batch':
i = torchvision.ops.boxes.nms(boxes, scores, iou_thres) i = torchvision.ops.boxes.nms(boxes, scores, iou_thres)
elif method == 'merge_batch': # Merge NMS
i = torchvision.ops.boxes.nms(boxes, scores, iou_thres)
iou = box_iou(boxes, boxes[i]).tril_() # upper triangular iou matrix
weights = (iou > conf_thres) * scores.view(-1, 1)
weights /= weights.sum(0)
pred[i, :4] = torch.matmul(weights.T, pred[:, :4]) # merged_boxes(n,4) = weights(n,n) * boxes(n,4)
elif method == 'fast_batch': # FastNMS from https://github.com/dbolya/yolact elif method == 'fast_batch': # FastNMS from https://github.com/dbolya/yolact
iou = box_iou(boxes, boxes).triu_(diagonal=1) # upper triangular iou matrix iou = box_iou(boxes, boxes).triu_(diagonal=1) # upper triangular iou matrix
i = iou.max(dim=0)[0] < iou_thres i = iou.max(0)[0] < iou_thres
output[image_i] = pred[i] output[image_i] = pred[i]
continue continue
@ -577,10 +583,7 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_label=T
elif n > 500: elif n > 500:
dc = dc[:500] # limit to first 500 boxes: https://github.com/ultralytics/yolov3/issues/117 dc = dc[:500] # limit to first 500 boxes: https://github.com/ultralytics/yolov3/issues/117
if method == 'vision': if method == 'or': # default
det_max.append(dc[torchvision.ops.boxes.nms(dc[:, :4], dc[:, 4], iou_thres)])
elif method == 'or': # default
# METHOD1 # METHOD1
# ind = list(range(len(dc))) # ind = list(range(len(dc)))
# while len(ind): # while len(ind):
@ -629,7 +632,7 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, multi_label=T
if len(det_max): if len(det_max):
det_max = torch.cat(det_max) # concatenate det_max = torch.cat(det_max) # concatenate
output[image_i] = det_max[(-det_max[:, 4]).argsort()] # sort output[image_i] = det_max[det_max[:, 4].argsort(descending=True)] # sort
return output return output