Size mismatch when loading pre-trained models

[Rich2333]

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!