AttributeError: 'ConfigDict' object has no attribute 'device'

[ccsimp1e]

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1. I have searched related issues but cannot get the expected help.
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3. The bug has not been fixed in the latest version.

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2. Did you make any modifications on the code or config? Did you understand what you have modified?
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[hhaAndroid]

@ccsimp1e Please complete the information, thank you.

[Hiyao-yy]

AttributeError: 'ConfigDict' object has no attribute 'device'

[ModestYjx]

[Hiyao-yy]

谢谢你，以后要常联系哦

[ModestYjx]

error:

AttributeError: 'ConfigDict' object has no attribute 'device'
Traceback (most recent call last):
  File "./train.py", line 193, in <module>
    main()
  File "./train.py", line 182, in main
    train_detector(
  File "/home/yjxun/code/mmdetection/mmdet/apis/train.py", line 158, in train_detector
    cfg.device,
  File "/home/yjxun/anaconda3/envs/UniverseNet/lib/python3.8/site-packages/mmcv/utils/config.py", line 519, in __getattr__
    return getattr(self._cfg_dict, name)
  File "/home/yjxun/anaconda3/envs/UniverseNet/lib/python3.8/site-packages/mmcv/utils/config.py", line 50, in __getattr__
    raise ex
AttributeError: 'ConfigDict' object has no attribute 'device'
WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 17762 closing signal SIGTERM
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 17758) of binary: /home/yjxun/anaconda3/envs/Unive
rseNet/bin/python
Traceback (most recent call last):
  File "/home/yjxun/anaconda3/envs/UniverseNet/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/home/yjxun/anaconda3/envs/UniverseNet/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/yjxun/anaconda3/envs/UniverseNet/lib/python3.8/site-packages/torch/distributed/launch.py", line 193, in <module>
    main()
  File "/home/yjxun/anaconda3/envs/UniverseNet/lib/python3.8/site-packages/torch/distributed/launch.py", line 189, in main
    launch(args)
  File "/home/yjxun/anaconda3/envs/UniverseNet/lib/python3.8/site-packages/torch/distributed/launch.py", line 174, in launch
    run(args)
  File "/home/yjxun/anaconda3/envs/UniverseNet/lib/python3.8/site-packages/torch/distributed/run.py", line 752, in run
    elastic_launch(
  File "/home/yjxun/anaconda3/envs/UniverseNet/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/home/yjxun/anaconda3/envs/UniverseNet/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 245, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
./train.py FAILED
-----------------------------------------------------------

repo

https://github.com/hunto/LightViT         （based on mmdetection）

command

bash ./dist_train.sh /home/yjxun/code/LightViT/detection/configs/mask_rcnn_lightvit_b
ase_fpn_3x_ms_coco.py 4

mmdet version:

TorchVision: 0.13.0
OpenCV: 4.6.0
MMCV: 1.6.0
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.2
MMDetection: 2.25.1+e2452a1

other log

optimizer_config = dict(grad_clip=None)
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[27, 33])
runner = dict(type='EpochBasedRunner', max_epochs=36)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
find_unused_parameters = True
total_epochs = 36
work_dir = './work_dirs/mask_rcnn_lightvit_base_fpn_3x_ms_coco'
gpu_ids = range(0, 4)

and I changed codes here compared to LightViT:

        bbox_head=dict(
            type='Shared2FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=3,
            bbox_coder=dict(

        mask_head=dict(
            type='FCNMaskHead',
            num_convs=4,
            in_channels=256,
            conv_out_channels=256,
            num_classes=3,
            loss_mask=dict(
                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),

class CocoDataset(CustomDataset):

    # CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
    #            'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
    #            'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
    #            'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
    #            'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
    #            'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
    #            'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
    #            'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
    #            'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
    #            'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
    #            'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
    #            'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
    #            'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
    #            'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush')

    # try for pre-trained model
    CLASSES = ('person', 'car', 'motorcycle')

def coco_classes():
    # return [
    #     'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
    #     'truck', 'boat', 'traffic_light', 'fire_hydrant', 'stop_sign',
    #     'parking_meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
    #     'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
    #     'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
    #     'sports_ball', 'kite', 'baseball_bat', 'baseball_glove', 'skateboard',
    #     'surfboard', 'tennis_racket', 'bottle', 'wine_glass', 'cup', 'fork',
    #     'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
    #     'broccoli', 'carrot', 'hot_dog', 'pizza', 'donut', 'cake', 'chair',
    #     'couch', 'potted_plant', 'bed', 'dining_table', 'toilet', 'tv',
    #     'laptop', 'mouse', 'remote', 'keyboard', 'cell_phone', 'microwave',
    #     'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
    #     'scissors', 'teddy_bear', 'hair_drier', 'toothbrush'
    # ]

    # try for pre-trained model
    return [
        'person', 'car', 'motorcycle'
    ]

[ModestYjx]

In short: On the basis of LightViT, I use my 'human vehicle non' data set to reduce the number of categories and category names of the coco data set to its subset 'person', 'car', 'motor cycle'. This problem arises.

[BIGWangYuDong]

this error occurred here https://github.com/open-mmlab/mmdetection/blob/ca11860f4f3c3ca2ce8340e2686eeaec05b29111/tools/train.py#L201 please update your train.py file before running the code

[BIGWangYuDong]

If you still meet the same problem, please feel free to re-open this issue

[ding19980201]

@BIGWangYuDong dear sir ，what does update your train.py mean i still can not fix even i add cfg.device='cuda'