No save model checkpoints for site prediction #35
Description
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?