Yolov5深度学习报错RuntimeError: DataLoader worker (pid(s) 2516, 1768) exited unexpectedly

报错RuntimeError: DataLoader worker (pid(s) 2516, 1768) exited unexpectedly"/>

Yolov5深度学习报错RuntimeError: DataLoader worker (pid(s) 2516, 1768) exited unexpectedly

项目场景：

在利用Yolov5进行深度学习的时候碰到个问题，我用的是GPU进行学习的。

问题描述

在开始学习的时候报错，RuntimeError: DataLoader worker (pid(s) 2516, 1768) exited unexpectedly。

原因分析：

因为我是使用GPU进行学习的，Anaconda的虚拟内存也是分配足够的，那么问题就应该是CPU线程数的设置，在此之前我试过调整batch size，但是没效果。

解决方案：

在train.py的文件里面有个--workers的参数，将其设置为0即可

下面这个是我的设置，大家可以参考一下~~~

def parse_opt(known=False):parser = argparse.ArgumentParser()parser.add_argument('--weights', type=str, default=ROOT / 'yolov5x.pt', help='initial weights path') #初始权重值parser.add_argument('--cfg', type=str, default='yolov5_Scan_FDDI/PLC_model.yaml', help='model.yaml path') #训练模型文件parser.add_argument('--data', type=str, default=ROOT / 'yolov5_Scan_FDDI/PLC_parameter.yaml', help='dataset.yaml path') #数据集参数文件parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path') #超参数设置parser.add_argument('--epochs', type=int, default=100) #训练轮数parser.add_argument('--batch-size', type=int, default=4, help='total batch size for all GPUs, -1 for autobatch') #batch sizeparser.add_argument('--imgsz', '--img', '--img-size', type=int, default=320, help='train, val image size (pixels)') #图片大小parser.add_argument('--rect', action='store_true', help='rectangular training')parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') #断续训练parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')parser.add_argument('--noval', action='store_true', help='only validate final epoch')parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')parser.add_argument('--noplots', action='store_true', help='save no plot files')parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') #GPUparser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer')parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')parser.add_argument('--workers', type=int, default=0, help='max dataloader workers (per RANK in DDP mode)') #CPU线程数设置parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name')parser.add_argument('--name', default='exp', help='save to project/name')parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')parser.add_argument('--quad', action='store_true', help='quad dataloader')parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler')parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2')parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')parser.add_argument('--seed', type=int, default=0, help='Global training seed')parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')# Weights & Biases argumentsparser.add_argument('--entity', default=None, help='W&B: Entity')parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='W&B: Upload data, "val" option')parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval')parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')opt = parser.parse_known_args()[0] if known else parser.parse_args()return opt