min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height 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...
max_nms: 进入筛选预测框的上线 time_limit: 时间限制 multi_label: 多标签标识 merge:merge-nms标识max_wh = 7680 # (pixels) maximum box width and height max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() time_limit = 0.5 + 0.05 * bs # seconds to quit after redundant ...
1、timeout(没用到) 这个函数是自定义的timeout超时函数,如果某个程序执行超时,就会触发超时处理函数_timeout_handler 返回超时异常信息。但是这个函数没用到,代码中都是使用库函数自己定义的timeout,没用用这个自定义的timeout函数。所以这个函数可以了解下就行,不过这种超时提示的代码还是有必要学习的。
Model summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPs Class Images Labels P R mAP@.5 mAP@WARNING: NMS time limit 1.260s exceeded Class Images Labels P R mAP@.5 mAP@ all 128 782 0.67 0.65 0.7 0.461 person 128 208 0.803 0.724 0.804 0.525 bicycle 128 6 0.551 0.667 0.62...
torchvision.ops.nms(boxes, scores, iou_thres) # NMS if i.shape[0] > max_det: # limit detections i = i[:max_det] output[xi] = x[i] if mps: output[xi] = output[xi].to(device) if (time.time() - t) > time_limit: print(f'WARNING ⚠️ NMS time limit {time_limit:.3f...
# Batched NMS c = x[:, 5:6] * (0 if agnostic else max_wh) # classes boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores i = torchvision.ops.boxes.nms(boxes, scores, iou_thres) if i.shape[0] > max_det: # limit detections ...
Class Images Labels P R mAP@.5 mAP@WARNING: NMS time limit 0.450s exceeded Class Images Labels P R mAP@.5 mAP@ all 5 1750 0.0111 0.00571 0.00616 0.00106 Epoch gpu_mem box obj cls labels img_size 3/99 3.46G 0.1326 1.078 0 8882 640: 100%|███ ...
[INFO] nms_time_limit 20.0 2024-11-02 03:33:08,085 [INFO] recompute False 2024-11-02 03:33:08,085 [INFO] recompute_layers 0 2024-11-02 03:33:08,085 [INFO] seed 2 2024-11-02 03:33:08,085 [INFO] summary True 2024-11-02 03:33:08,085 [INFO] profiler False 2024-11-02 ...
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....
(time.time() - t) > time_limit: print(f'WARNING: NMS time limit {time_limit}s exceeded') break # time limit exceeded return output def strip_optimizer(f='weights/best.pt', s=''): # from utils.general import *; strip_optimizer() # Strip optimizer from 'f' to finalize training...