ykiiiiii / grade_if Goto Github PK

Hi, i want to know if this repository will be update? now it is nothing, thank you!

Hi, i run the inverse_folding.ipynb , and the fourth cell shows "AttributeError:'EGNN_Sparse' object has no attribute check_input". The torch_geometric version is the same as yours, .2.2.0. and the new 2.4.0 also has the bug. i don't know how to solve this bug. could you please help me?

Hi, I want to ask for help. when i run download_pdb.py, i got the error that segmentation fault(core dumped). some days before, when i trained the model, in the training process, i also met this error. almostly, i can not successfully run, but i don't know the reason, because the error time is also different, and the error is strange...I have tried some meatures,replace the version, re-install conda, and re-install the system, but the error still exists...

Hi, I want to ask why the perplexity is multiplied by 0.01 at this line perplexity.
What do you mean by saying "for the same scale" ? Thanks! @ykiiiiii

Dear developer,

try to run the example, but it gave an error

in egnn_pytorch_geometric.py
File "diffusion\model\egnn_pytorch\egnn_pytorch_geometric.py", line 250, in forward
hidden_out, coors_out = self.propagate(edge_index, x=feats, edge_attr=edge_attr_feats,
File "diffusion\model\egnn_pytorch\egnn_pytorch_geometric.py", line 273, in propagate
size = self.check_input(edge_index, size)
AttributeError: 'EGNN_Sparse' object has no attribute 'check_input'

after comment out #size = self.check_input(edge_index, size)
AttributeError: 'EGNN_Sparse' object has no attribute 'collect'

Hi, could you release the ckpt and config about the TS50 and T500 dataset? because I follow the setting in paper that hidden_size boost to 256, the recoveries of the two datasets are reduced by about 5 percentage points. Maybe the mean_attr.pt is not suitable for these dataset? I am confused.. so hope to receive reply.

thanks!

I noticed on line 565 of cath_imem_2nd.py that you delete the last residue of the PDB file:
graph = self.remove_node(graph, graph.x.shape[0]-1)

Why is this done? Is it safe to comment out this line (code seems to run fine without it)? Is the last residue deleted when computing metrics with the baseline methods as well?

Thanks!