No save model checkpoints for site prediction

[wjs20]

Hi

Thanks for making your code public.

I want to run main_inference.py for site prediction. The only pretrained model you provide is this one for search prediction dMaSIF_search_3layer_12A_16dim

when I try and run it for site prediction, it fails with a can't load state dict error

Python -W ignore -u main_inference.py --experiment_name dMaSIF_search_3layer_12A_16dim --batch_size 64 --embedding_layer dMaSIF --site True --emb_dims 16 --device cuda:0 --radius 12.0 --n_layers 3
RuntimeError: Error(s) in loading state_dict for dMaSIF:
        Missing key(s) in state_dict: "net_out.0.weight", "net_out.0.bias", "net_out.2.weight", "net_out.2.bias", "net_out.4.weight", "net_out.4.bias". 
        Unexpected key(s) in state_dict: "orientation_scores2.0.weight", "orientation_scores2.0.bias", "orientation_scores2.2.weight", "orientation_scores2.2.bias", "conv2.layers.0.net_in.0.weight", "conv2.layers.0.net_in.0.bias", "conv2.layers.0.net_in.2.weight", "conv2.layers.0.net_in.2.bias", "conv2.layers.0.norm_in.weight", "conv2.layers.0.norm_in.bias", "conv2.layers.0.conv.0.weight", "conv2.layers.0.conv.0.bias", "conv2.layers.0.conv.2.weight", "conv2.layers.0.conv.2.bias", "conv2.layers.0.net_out.0.weight", "conv2.layers.0.net_out.0.bias", "conv2.layers.0.net_out.2.weight", "conv2.layers.0.net_out.2.bias", "conv2.layers.0.norm_out.weight", "conv2.layers.0.norm_out.bias", "conv2.layers.1.net_in.0.weight", "conv2.layers.1.net_in.0.bias", "conv2.layers.1.net_in.2.weight", "conv2.layers.1.net_in.2.bias", "conv2.layers.1.norm_in.weight", "conv2.layers.1.norm_in.bias", "conv2.layers.1.conv.0.weight", "conv2.layers.1.conv.0.bias", "conv2.layers.1.conv.2.weight", "conv2.layers.1.conv.2.bias", "conv2.layers.1.net_out.0.weight", "conv2.layers.1.net_out.0.bias", "conv2.layers.1.net_out.2.weight", "conv2.layers.1.net_out.2.bias", "conv2.layers.1.norm_out.weight", "conv2.layers.1.norm_out.bias", "conv2.layers.2.net_in.0.weight", "conv2.layers.2.net_in.0.bias", "conv2.layers.2.net_in.2.weight", "conv2.layers.2.net_in.2.bias", "conv2.layers.2.norm_in.weight", "conv2.layers.2.norm_in.bias", "conv2.layers.2.conv.0.weight", "conv2.layers.2.conv.0.bias", "conv2.layers.2.conv.2.weight", "conv2.layers.2.conv.2.bias", "conv2.layers.2.net_out.0.weight", "conv2.layers.2.net_out.0.bias", "conv2.layers.2.net_out.2.weight", "conv2.layers.2.net_out.2.bias", "conv2.layers.2.norm_out.weight", "conv2.layers.2.norm_out.bias", "conv2.linear_layers.0.0.weight", "conv2.linear_layers.0.0.bias", "conv2.linear_layers.0.2.weight", "conv2.linear_layers.0.2.bias", "conv2.linear_layers.1.0.weight", "conv2.linear_layers.1.0.bias", "conv2.linear_layers.1.2.weight", "conv2.linear_layers.1.2.bias", "conv2.linear_layers.2.0.weight", "conv2.linear_layers.2.0.bias", "conv2.linear_layers.2.2.weight", "conv2.linear_layers.2.2.bias", "conv2.linear_transform.0.weight", "conv2.linear_transform.0.bias", "conv2.linear_transform.1.weight", "conv2.linear_transform.1.bias", "conv2.linear_transform.2.weight", "conv2.linear_transform.2.bias"

Am I using the command wrong? or is the model just not available?

[2mal16]

Have a look at the colab repo, there are some site models available.

https://github.com/casperg92/MaSIF_colab/tree/main/models

[wjs20]

Weirdly they don't seem to have nearly the accuracy that is stated in the paper :/

The average ROC_AUC I got from running their script was ~0.62 not .80+.

Have you got good results out of those site_models?