rubenalv
commented
1 year ago @yzhang-github-pub, I could create a working environment with the conda installation I submitted in https://github.com/LBM-EPFL/PeSTo/issues/2 My numpy version is 1.25.
With that environment I can run the example in PeSTo/ /apply_model.ipynb, which I have edited to convert into a python script below. I tested it running it interactively from PeSTo/ install folder, using the example in PeSTo/examples/issue_19_04_2023/2CUA_A.pdb, and compared the output with the website https://pesto.epfl.ch/. Note that for the results to match, you have to select "chainA" only on the website (if you select chainA and chainB, the PPI probabilities change).
import os
import sys
import h5py
import json
import numpy as np
import torch as pt
import pandas as pd
import matplotlib.pyplot as plt
from tqdm import tqdm
from glob import glob
from src.dataset import StructuresDataset, collate_batch_features, select_by_sid, select_by_interface_types
from src.data_encoding import encode_structure, encode_features, extract_topology, categ_to_resnames, resname_to_categ
from src.structure import data_to_structure, encode_bfactor, concatenate_chains, split_by_chain
from src.structure_io import save_pdb, read_pdb
from src.scoring import bc_scoring, bc_score_names
# data parameters
data_path = "examples/issue_19_04_2023"
# model parameters
save_path = "model/save/i_v4_1_2021-09-07_11-21" # 91
# select saved model
model_filepath = os.path.join(save_path, 'model_ckpt.pt')
# add module to path
if save_path not in sys.path:
sys.path.insert(0, save_path)
# load functions
from config import config_model, config_data
from data_handler import Dataset
from model import Model
# define device
device = pt.device("cpu")
# create model
model = Model(config_model)
# reload model
model.load_state_dict(pt.load(model_filepath, map_location=pt.device("cpu")))
# set model to inference
model = model.eval().to(device)
# find pdb files and ignore already predicted oins
pdb_filepaths = glob(os.path.join(data_path, "*.pdb"), recursive=True)
pdb_filepaths = [fp for fp in pdb_filepaths if "_i" not in fp]
# create dataset loader with preprocessing
dataset = StructuresDataset(pdb_filepaths, with_preprocessing=True)
# debug print
print(len(dataset))
# run model on all subunits
with pt.no_grad():
for subunits, filepath in tqdm(dataset):
# concatenate all chains together
structure = concatenate_chains(subunits)
# encode structure and features
X, M = encode_structure(structure)
#q = pt.cat(encode_features(structure), dim=1)
q = encode_features(structure)[0]
# extract topology
ids_topk, _, _, _, _ = extract_topology(X, 64)
# pack data and setup sink (IMPORTANT)
X, ids_topk, q, M = collate_batch_features([[X, ids_topk, q, M]])
# run model
z = model(X.to(device), ids_topk.to(device), q.to(device), M.float().to(device))
# for all predictions
for i in range(z.shape[1]):
# prediction
p = pt.sigmoid(z[:,i])
# encode result
structure = encode_bfactor(structure, p.cpu().numpy())
# save results
output_filepath = filepath[:-4]+'_i{}.pdb'.format(i)
save_pdb(split_by_chain(structure), output_filepath)
yzhang-github-pub
lfkrapp
Dear authors,
Thanks for developing this very useful tool. I am testing PeSTo, and got an error with numpy:
_model/save/i_v4_1_2021-09-07_11-21/src/structure.py", line 99, in tag_hetatm_chains structure = tag_hetatmchains(structure) AttributeError: module 'numpy' has no attribute 'object'
This is perhaps a numpy version issue. When I created the environment, conda automatically installed numpy 1.25. Can you please tell the numpy version that works? Or even better specify versions in pesto.yml file? Thanks.