to(device) if (time.time() - t) > time_limit: LOGGER.warning(f'WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded') break # time limit exceeded最终的输出结果是:(Pdb) len(output) 29 (Pdb) output[0][0] tensor([5.56500e+02, 1.74750e+02, 6.34500e+02, 2.14250e+02, ...
assert log_[i].shape[0] == x[i].shape[0] if (time.time() - t) > time_limit: print(f'WARNING: NMS time limit {time_limit}s exceeded') break # time limit exceeded return output
print(time.time() - t) img3_ = torch.from_numpy(img3_).to('cuda:0') print(img3.shape, img3_.shape) YOLOv5后处理 Tensorrtx cpp版本 nms之前detect的个数限制为1000。nms是一张张处理的。 源码是infer之后进入nms过程。 void nms(std::vector<Yolo::Detection>& res, float *output, float ...
max_nms=30000# maximum number of boxes into torchvision.ops.nms() time_limit=10.0# seconds to quit after redundant=True# require redundant detections multi_label&=nc>1# multiple labels per box (adds 0.5ms/img) merge=False# use merge-NMS t=time.time() output=[torch.zeros((0,6),device=...
if i.shape[0] > max_det: # limit detections i = i[:max_det] if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) try: # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix ...
Regarding the NMS time limit warning, this may not necessarily be an issue unless you are seeing drastically poor performance or other issues during the training process. Since you have a relatively powerful server setup, I recommend ensuring that your dataset is properly formatted and optimized ...
Class Images Labels P R mAP@.5 mAP@.5:.95: 50%|█████ | 2/4 [00:30<00:25, 12.80s/it] WARNING: NMS time limit 1.260s exceeded Class Images Labels P R mAP@.5 mAP@.5:.95: 75%|███████▌ | 3/4 [00:32<00:08, 8.15s/it] WARNING: NMS time limit 1.260s ex...
break# time limit exceeded returnoutput deftest(cfg, data, Expand DownExpand Up@@ -78,7 +185,8 @@ def test(cfg, loss+=compute_loss(train_out,targets,model)[1][:3].cpu()# GIoU, obj, cls # Run NMS output=non_max_suppression(inf_out,conf_thres=conf_thres,nms_thres=nms_thres) ...
max_nms=30000# maximum numberofboxes into torchvision.ops.nms()time_limit=10.0# seconds to quit after redundant=True # require redundant detections multi_label&=nc>1# multiple labels perbox(adds0.5ms/img)merge=False # use merge-NMSt=time.time()output=[torch.zeros((0,6),device=prediction....
WARNING ⚠️ NMS time limit 0.550s exceeded image 2/2 /home/yolov5-7.0/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 14584.1ms Speed: 252.7ms pre-process, 14543.6ms inference, 5094.1ms NMS per image at shape (1, 3, 640, 640) Results saved to ../home/yolov5-7.0/runs/det...