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Limit detections without explicit if condition (#10502)

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* limit detections without explicit if condition

Signed-off-by: Yonghye Kwon <developer.0hye@gmail.com>

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* cleanup indexing code for limit detections

Signed-off-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Yonghye Kwonpre-commit-ci[bot]Glenn Jocher
 authored and GitHub committed 2022-12-18 13:03:01 +01:00
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commit 2c35c1b318
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utils/general.py
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@@ -978,8 +978,7 @@ def non_max_suppression(
c = x[:, 5:6] * (0 if agnostic else max_wh) # classes c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
if i.shape[0] > max_det: # limit detections i = i[:max_det] # limit detections
i = i[:max_det]
if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
# update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix