Size mismatch when loading pre-trained models #37
Comments
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Thanks for reaching out. Can you provide more details? What is your train/test data? What are other hyperparameters? |
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My train/test data is crystal structures downloaded from Materials Project database (using MPRester API). As for parameters, I think I didn't change any default ones other than epochs: |
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And for the size mismatch problem, I'm wondering if my environment is different from the environment of pre-trained models. The packages in my vritual environment are listed as follows: |
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Dear author, |
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yeah，i also found this problem |
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I know, it’s because there are differences in prediction codes between models trained using GPU and models trained using CPU. You can go to some tutorials online and it’s easier to solve. |
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Hi,
When I try to load pre-trained models to test predict.py, I was noticed as follows:
python predict.py pre-trained/final-energy-per-atom.pth.tar mp/
=> loading model params 'pre-trained/final-energy-per-atom.pth.tar'
=> loaded model params 'pre-trained/final-energy-per-atom.pth.tar'
=> loading model 'pre-trained/final-energy-per-atom.pth.tar'
Traceback (most recent call last):
File "E:\cgcnn-master\predict.py", line 298, in
main()
File "E:\cgcnn-master\predict.py", line 94, in main
model.load_state_dict(checkpoint['state_dict'])
File "C:\ProgramData\Anaconda3\envs\cgcnn1\lib\site-packages\torch\nn\modules\module.py", line 1497, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for CrystalGraphConvNet:
size mismatch for convs.0.fc_full.weight: copying a param with shape torch.Size([128, 169]) from checkpoint, the shape in current model is torch.Size([128, 179]).
size mismatch for convs.1.fc_full.weight: copying a param with shape torch.Size([128, 169]) from checkpoint, the shape in current model is torch.Size([128, 179]).
size mismatch for convs.2.fc_full.weight: copying a param with shape torch.Size([128, 169]) from checkpoint, the shape in current model is torch.Size([128, 179]).
size mismatch for convs.3.fc_full.weight: copying a param with shape torch.Size([128, 169]) from checkpoint, the shape in current model is torch.Size([128, 179]).
btw, then I tried to train my own model and use it to predict. The errors above didn't show up, but I got a TOO large MAE.
(cgcnn) E:\cgcnn-master>python predict.py E:\cgcnn-master\trained_files\from_cmd\mp-2\mp_model_best.pth.tar mp/
=> loading model params 'E:\cgcnn-master\trained_files\from_cmd\mp-2\mp_model_best.pth.tar'
=> loaded model params 'E:\cgcnn-master\trained_files\from_cmd\mp-2\mp_model_best.pth.tar'
=> loading model 'E:\cgcnn-master\trained_files\from_cmd\mp-2\mp_model_best.pth.tar'
=> loaded model 'E:\cgcnn-master\trained_files\from_cmd\mp-2\mp_model_best.pth.tar' (epoch 484, validation 0.05862389877438545)
C:\ProgramData\Anaconda3\envs\cgcnn\lib\site-packages\pymatgen\io\cif.py:1155: UserWarning: Issues encountered while parsing CIF: Some fractional coordinates rounded to ideal values to avoid issues with finite precision.
warnings.warn("Issues encountered while parsing CIF: " + "\n".join(self.warnings))
Test: [0/74] Time 26.633 (26.633) Loss inf (inf) MAE 5.977 (5.977)
Test: [10/74] Time 24.787 (27.052) Loss inf (inf) MAE 6.005 (6.013)
Test: [20/74] Time 28.383 (28.096) Loss inf (inf) MAE 5.941 (6.010)
Test: [30/74] Time 31.305 (28.518) Loss inf (inf) MAE 6.081 (6.008)
Test: [40/74] Time 30.491 (29.037) Loss inf (inf) MAE 5.860 (6.010)
Test: [50/74] Time 35.822 (29.651) Loss inf (inf) MAE 6.035 (6.008)
Test: [60/74] Time 33.488 (30.191) Loss inf (inf) MAE 6.033 (6.012)
Test: [70/74] Time 34.823 (30.565) Loss inf (inf) MAE 5.955 (6.008)
** MAE 6.009
Thanks for your attention!
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