RuntimeError: DataLoader worker (pid(s) 296430) exited unexpectedly

[xuqinghan]

🐛 Bug

pytorch 1.7.1

train model with 10 worker.

To Reproduce (REQUIRED)

Input:

python train.py --batch 16 --epochs 200 --workers 10 --weights yolov5s.pt --data warship.yaml

Output:

Using CUDA device0 _CudaDeviceProperties(name='GeForce RTX 3080', total_memory=10015MB)

Namespace(adam=False, batch_size=16, bucket='', cache_images=False, cfg='', data='./data/warship.yaml', device='', epochs=200, evolve=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, logdir='runs/', multi_scale=False, name='', noautoanchor=False, nosave=False, notest=False, rect=False, resume=False, single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=10, world_size=1)
Start Tensorboard with "tensorboard --logdir runs/", view at http://localhost:6006/
Hyperparameters {'lr0': 0.01, 'lrf': 0.2, 'momentum': 0.937, 'weight_decay': 0.0005, 'warmup_epochs': 3.0, 'warmup_momentum': 0.8, 'warmup_bias_lr': 0.1, 'box': 0.05, 'cls': 0.5, 'cls_pw': 1.0, 'obj': 1.0, 'obj_pw': 1.0, 'iou_t': 0.2, 'anchor_t': 4.0, 'fl_gamma': 0.0, 'hsv_h': 0.015, 'hsv_s': 0.7, 'hsv_v': 0.4, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.5, 'mosaic': 1.0, 'mixup': 0.0}
Overriding model.yaml nc=80 with nc=8

                 from  n    params  module                                  arguments                     
  0                -1  1      3520  models.common.Focus                     [3, 32, 3]                    
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     19904  models.common.BottleneckCSP             [64, 64, 1]                   
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  1    161152  models.common.BottleneckCSP             [128, 128, 3]                 
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6                -1  1    641792  models.common.BottleneckCSP             [256, 256, 3]                 
  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  8                -1  1    656896  models.common.SPP                       [512, 512, [5, 9, 13]]        
  9                -1  1   1248768  models.common.BottleneckCSP             [512, 512, 1, False]          
 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1    378624  models.common.BottleneckCSP             [512, 256, 1, False]          
 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1     95104  models.common.BottleneckCSP             [256, 128, 1, False]          
 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1    313088  models.common.BottleneckCSP             [256, 256, 1, False]          
 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1   1248768  models.common.BottleneckCSP             [512, 512, 1, False]          
 24      [17, 20, 23]  1     35061  models.yolo.Detect                      [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model Summary: 191 layers, 7.27397e+06 parameters, 7.27397e+06 gradients

Transferred 364/370 items from yolov5s.pt
Optimizer groups: 62 .bias, 70 conv.weight, 59 other
Scanning labels ../warship/labels.cache (21200 found, 0 missing, 75 empty, 0 duplicate, for 21275 images): 21275it [00:01, 18689.53it/s]
Scanning labels ../warship/labels.cache (21200 found, 0 missing, 75 empty, 0 duplicate, for 21275 images): 21275it [00:01, 18503.85it/s]
Inconsistency detected by ld.so: dl-open.c: 560: dl_open_worker: Assertion `_dl_debug_initialize (0, args->nsid)->r_state == RT_CONSISTENT' failed!

Analyzing anchors... anchors/target = 4.07, Best Possible Recall (BPR) = 1.0000
Image sizes 640 train, 640 test
Using 10 dataloader workers
Logging results to runs/exp9
Starting training for 200 epochs...

     Epoch   gpu_mem       box       obj       cls     total   targets  img_size
     0/199     5.15G   0.05539   0.02971   0.02252    0.1076        49       640: 100%|███████████████████████████████████████████████████████████| 1330/1330 [03:20<00:00,  6.64it/s]
               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95:   0%|                                                  | 3/1330 [00:06<44:56,  2.03s/it]
Traceback (most recent call last):
  File "/home/xcsc/anaconda3/envs/yolo_v5/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 872, in _try_get_data
    data = self._data_queue.get(timeout=timeout)
  File "/home/xcsc/anaconda3/envs/yolo_v5/lib/python3.8/queue.py", line 178, in get
    raise Empty
_queue.Empty

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "train.py", line 460, in <module>
    train(hyp, opt, device, tb_writer)
  File "train.py", line 313, in train
    results, maps, times = test.test(opt.data,
  File "/home/xcsc/dev/yolo/yolov5-3.1/test.py", line 95, in test
    for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
  File "/home/xcsc/anaconda3/envs/yolo_v5/lib/python3.8/site-packages/tqdm/std.py", line 1167, in __iter__
    for obj in iterable:
  File "/home/xcsc/dev/yolo/yolov5-3.1/utils/datasets.py", line 91, in __iter__
    yield next(self.iterator)
  File "/home/xcsc/anaconda3/envs/yolo_v5/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
    data = self._next_data()
  File "/home/xcsc/anaconda3/envs/yolo_v5/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1068, in _next_data
    idx, data = self._get_data()
  File "/home/xcsc/anaconda3/envs/yolo_v5/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1024, in _get_data
    success, data = self._try_get_data()
  File "/home/xcsc/anaconda3/envs/yolo_v5/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 885, in _try_get_data
    raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str)) from e
RuntimeError: DataLoader worker (pid(s) 296430) exited unexpectedly

Expected behavior

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Environment

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• OS: Ubuntu 20.04
• GPU 3080
  CPU: intel i9-10900 (10c20t)

Additional context

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[github-actions]

Hello @xuqinghan, thank you for your interest in 🚀 YOLOv5! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Requirements

Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:

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[glenn-jocher]

@xuqinghan this error indicates you are attempting to use too many workers.

[github-actions]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

[Albertwindows]

Set --workers 0 to disable multiple process.