Error computing embeddings

[tkram01]

When running the compute_embedding.py I get this error.

Using backend: pytorch
Downloading gin_supervised_contextpred_pre_trained.pth from https://data.dgl.ai/dgllife/pre_trained/gin_supervised_contextpred.pth...
Pretrained model loaded
Total data:  172988
  0%|                                                                                                                                                                                                            | 0/172988 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "/home/ec2-user/SynNet/scripts/compute_embedding.py", line 143, in <module>
    embeddings.append(model(smi))
  File "/home/ec2-user/miniconda3/envs/rdkit/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
TypeError: forward() missing 2 required positional arguments: 'categorical_node_feats' and 'categorical_edge_feats'

When trying to run the compute_embedding_mp.py I get the following error

Using backend: pytorch
Downloading gin_supervised_contextpred_pre_trained.pth from https://data.dgl.ai/dgllife/pre_trained/gin_supervised_contextpred.pth...
Pretrained model loaded
Total data:  172988
Traceback (most recent call last):
  File "/home/ec2-user/SynNet/scripts/compute_embedding_mp.py", line 29, in <module>
    embeddings = pool.map(gin_embedding, data)
NameError: name 'gin_embedding' is not defined

I think this can be resolved by changing gin_embedding to model but that then results in the above error.

[rociomer]

This has now been fixed. To summarize the problem, the 'model()' should have been replaced by the 'mol_embedding()' function (which creates the GIN embeddings from the pretrained model). I have replaced it now with the right function names, and moved this and related functions to syn_net/utils/predict_utils.py, as the mol_embedding() function is used by both scripts/compute_embedding.py and scripts/compute_embedding_mp.py