Train.py - _pickle.UnpicklingError: STACK_GLOBAL requires str

[MorphSeur]

Dear Yolov7,

I got the following error when I run train.py on a custom dataset with the weight (pretrained COCO) https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt

Do you have any solution?

Thanks,

Namespace(adam=False, artifact_alias='latest', batch_size=2, bbox_interval=-1, bucket='', cache_images=False, cfg='cfg/training/yolov7.yaml', data='/path/yolov7/data/dataset22.yaml', device='0', entity=None, epochs=300, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.p5.yaml', image_weights=False, img_size=[640, 640], label_smoothing=0.0, linear_lr=False, local_rank=-1, multi_scale=False, name='exp', noautoanchor=False, nosave=False, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs/train/exp6', save_period=-1, single_cls=False, sync_bn=False, total_batch_size=2, upload_dataset=False, weights='/path/yolov7/yolov7x.pt', workers=8, world_size=1)
tensorboard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/
/path/.conda/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/path/.conda/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/path/.conda/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/path/.conda/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/path/.conda/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/path/.conda/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
hyperparameters: lr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, 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=0.2, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.0, paste_in=0.15
wandb: Install Weights & Biases for YOLOR logging with 'pip install wandb' (recommended)
Overriding model.yaml nc=80 with nc=103

                 from  n    params  module                                  arguments                     
  0                -1  1       928  models.common.Conv                      [3, 32, 3, 1]                 
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  1      8320  models.common.Conv                      [128, 64, 1, 1]               
  5                -2  1      8320  models.common.Conv                      [128, 64, 1, 1]               
  6                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                
  7                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                
  8                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                
  9                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                
 10  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           
 11                -1  1     66048  models.common.Conv                      [256, 256, 1, 1]              
 12                -1  1         0  models.common.MP                        []                            
 13                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 14                -3  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 16          [-1, -3]  1         0  models.common.Concat                    [1]                           
 17                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 18                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 19                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 20                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 21                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 22                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 23  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           
 24                -1  1    263168  models.common.Conv                      [512, 512, 1, 1]              
 25                -1  1         0  models.common.MP                        []                            
 26                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 27                -3  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 28                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 29          [-1, -3]  1         0  models.common.Concat                    [1]                           
 30                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 31                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 32                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 33                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 34                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 35                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 36  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           
 37                -1  1   1050624  models.common.Conv                      [1024, 1024, 1, 1]            
 38                -1  1         0  models.common.MP                        []                            
 39                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             
 40                -3  1    525312  models.common.Conv                      [1024, 512, 1, 1]             
 41                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              
 42          [-1, -3]  1         0  models.common.Concat                    [1]                           
 43                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             
 44                -2  1    262656  models.common.Conv                      [1024, 256, 1, 1]             
 45                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 46                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 47                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 48                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 49  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           
 50                -1  1   1050624  models.common.Conv                      [1024, 1024, 1, 1]            
 51                -1  1   7609344  models.common.SPPCSPC                   [1024, 512, 1]                
 52                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 53                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 54                37  1    262656  models.common.Conv                      [1024, 256, 1, 1]             
 55          [-1, -2]  1         0  models.common.Concat                    [1]                           
 56                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 57                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 58                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              
 59                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 60                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 61                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 62[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           
 63                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             
 64                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 65                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 66                24  1     65792  models.common.Conv                      [512, 128, 1, 1]              
 67          [-1, -2]  1         0  models.common.Concat                    [1]                           
 68                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 69                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 70                -1  1     73856  models.common.Conv                      [128, 64, 3, 1]               
 71                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                
 72                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                
 73                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                
 74[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           
 75                -1  1     65792  models.common.Conv                      [512, 128, 1, 1]              
 76                -1  1         0  models.common.MP                        []                            
 77                -1  1     16640  models.common.Conv                      [128, 128, 1, 1]              
 78                -3  1     16640  models.common.Conv                      [128, 128, 1, 1]              
 79                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 80      [-1, -3, 63]  1         0  models.common.Concat                    [1]                           
 81                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 82                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 83                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              
 84                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 85                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 86                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 87[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           
 88                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             
 89                -1  1         0  models.common.MP                        []                            
 90                -1  1     66048  models.common.Conv                      [256, 256, 1, 1]              
 91                -3  1     66048  models.common.Conv                      [256, 256, 1, 1]              
 92                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 93      [-1, -3, 51]  1         0  models.common.Concat                    [1]                           
 94                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             
 95                -2  1    525312  models.common.Conv                      [1024, 512, 1, 1]             
 96                -1  1   1180160  models.common.Conv                      [512, 256, 3, 1]              
 97                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 98                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 99                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
100[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           
101                -1  1   1049600  models.common.Conv                      [2048, 512, 1, 1]             
102                75  1    328704  models.common.RepConv                   [128, 256, 3, 1]              
103                88  1   1312768  models.common.RepConv                   [256, 512, 3, 1]              
104               101  1   5246976  models.common.RepConv                   [512, 1024, 3, 1]             
105   [102, 103, 104]  1    584344  models.yolo.IDetect                     [103, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]]
/path/.conda/lib/python3.7/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2228.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
Model Summary: 415 layers, 37746744 parameters, 37746744 gradients, 106.5 GFLOPS

Transferred 169/566 items from /path/yolov7/yolov7x.pt
Scaled weight_decay = 0.0005
Optimizer groups: 95 .bias, 95 conv.weight, 98 other
Traceback (most recent call last):
  File "train.py", line 609, in <module>
    train(hyp, opt, device, tb_writer)
  File "train.py", line 248, in train
    image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: '))
  File "/path/yolov7/utils/datasets.py", line 78, in create_dataloader
    prefix=prefix)
  File "/path/yolov7/utils/datasets.py", line 392, in __init__
    cache, exists = torch.load(cache_path), True  # load
  File "/path/.conda/lib/python3.7/site-packages/torch/serialization.py", line 713, in load
    return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
  File "/path/.conda/lib/python3.7/site-packages/torch/serialization.py", line 920, in _legacy_load
    magic_number = pickle_module.load(f, **pickle_load_args)
_pickle.UnpicklingError: STACK_GLOBAL requires str```

[cnavarrete]

Delete the labels.cache in each train, val and test folders. I had this exact same problem a few days ago, I had first trained a yolov5 model and that created labels.cache files in the folders, when you try to train with yolov7, the program will look for label.cache files, the problem is that these files are not compatible between each yolo version. So you should just delete them.

[MorphSeur]

@cnavarrete,

Great! I just did it. It works!!

How about the results you got? Are they better than the one of Yolov5?

Many thanks,

[LateLinux]

Delete the labels.cache in each train, val and test folders. I had this exact same problem a few days ago, I had first trained a yolov5 model and that created labels.cache files in the folders, when you try to train with yolov7, the program will look for label.cache files, the problem is that these files are not compatible between each yolo version. So you should just delete them.

Many thanks! It helps me a lot!

[jjjonathan14]

thanks

[trantuankhoi]

Delete the labels.cache in each train, val and test folders. I had this exact same problem a few days ago, I had first trained a yolov5 model and that created labels.cache files in the folders, when you try to train with yolov7, the program will look for label.cache files, the problem is that these files are not compatible between each yolo version. So you should just delete them.

many thanks

[pms5343]

Delete the labels.cache in each train, val and test folders. I had this exact same problem a few days ago, I had first trained a yolov5 model and that created labels.cache files in the folders, when you try to train with yolov7, the program will look for label.cache files, the problem is that these files are not compatible between each yolo version. So you should just delete them.

enormous gratitude

[zyl-hub]

Delete the labels.cache in each train, val and test folders. I had this exact same problem a few days ago, I had first trained a yolov5 model and that created labels.cache files in the folders, when you try to train with yolov7, the program will look for label.cache files, the problem is that these files are not compatible between each yolo version. So you should just delete them.

thanks, this helps me a lot