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ValueError: Object should have `state_dict` method.

See original GitHub issue

hi，when I train your reid_baseline model ,I met a error that I can’t solve. the error is as follows:

Traceback (most recent call last): File “/home/lc/Desktop/reid_baseline/tools/train.py”, line 87, in <module> main() File “/home/lc/Desktop/reid_baseline/tools/train.py”, line 83, in main train(cfg) File “/home/lc/Desktop/reid_baseline/tools/train.py”, line 46, in train num_query File “/home/lc/Desktop/reid_baseline/engine/trainer.py”, line 150, in do_train trainer.run(train_loader, max_epochs=epochs) File “/usr/local/lib/python3.5/dist-packages/pytorch_ignite-0.2.0-py3.5.egg/ignite/engine/engine.py”, line 388, in run File “/usr/local/lib/python3.5/dist-packages/pytorch_ignite-0.2.0-py3.5.egg/ignite/engine/engine.py”, line 352, in _handle_exception File “/usr/local/lib/python3.5/dist-packages/pytorch_ignite-0.2.0-py3.5.egg/ignite/engine/engine.py”, line 379, in run File “/usr/local/lib/python3.5/dist-packages/pytorch_ignite-0.2.0-py3.5.egg/ignite/engine/engine.py”, line 287, in _fire_event File “/usr/local/lib/python3.5/dist-packages/pytorch_ignite-0.2.0-py3.5.egg/ignite/handlers/checkpoint.py”, line 172, in call File “/usr/local/lib/python3.5/dist-packages/pytorch_ignite-0.2.0-py3.5.egg/ignite/handlers/checkpoint.py”, line 130, in _save File “/usr/local/lib/python3.5/dist-packages/pytorch_ignite-0.2.0-py3.5.egg/ignite/handlers/checkpoint.py”, line 144, in _internal_save ValueError: Object should have state_dict method.

How should I fix it?

Issue Analytics

State:
Created 4 years ago
Comments:19

Top GitHub Comments

3reactions

vfdev-5commented, May 21, 2019

@yuliuyu your problem is here: https://github.com/L1aoXingyu/reid_baseline/blob/852bb8ae8bd6635527e5a39609f8beed81be26c0/engine/trainer.py#L108-L109

Change it to this and it should be fine:

trainer.add_event_handler(Events.EPOCH_COMPLETED, checkpointer, {'model': model, 'optimizer': optimizer})

0reactions

jhxingcommented, Jul 11, 2019

@jhxing you need to remove .state_dict()

trainer.add_event_handler(Events.EPOCH_COMPLETED, checkpointer, {'model': model,
                                                                     'optimizer': optimizer,
                                                                     'center_param': center_criterion,
'optimizer_center': optimizer_center})

Thank you for your patient help, I will try it. Thank you angin !

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