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save the features.pkl file of the monomer #184

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save the features.pkl file of the monomer #184

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4fc8977
save prediction output pickle file of monomer
Feb 23, 2023
e897d65
Merge branch 'hpcaitech:main' into main
double-vin Dec 2, 2023
5f08e3f
save features.pkl of the monomer
double-vin Dec 2, 2023
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37 changes: 23 additions & 14 deletions inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -341,24 +341,13 @@ def inference_monomer_model(args):
print("running in monomer mode...")
config = model_config(args.model_name)

template_featurizer = templates.TemplateHitFeaturizer(
mmcif_dir=args.template_mmcif_dir,
max_template_date=args.max_template_date,
max_hits=config.data.predict.max_templates,
kalign_binary_path=args.kalign_binary_path,
release_dates_path=args.release_dates_path,
obsolete_pdbs_path=args.obsolete_pdbs_path
)

use_small_bfd = args.preset == 'reduced_dbs' # (args.bfd_database_path is None)
if use_small_bfd:
assert args.bfd_database_path is not None
else:
assert args.bfd_database_path is not None
assert args.uniref30_database_path is not None

data_processor = data_pipeline.DataPipeline(template_featurizer=template_featurizer,)

output_dir_base = args.output_dir

random_seed = args.data_random_seed
Expand Down Expand Up @@ -423,11 +412,31 @@ def inference_monomer_model(args):
use_small_bfd=use_small_bfd,
no_cpus=args.cpus,
)
t = time.perf_counter()
alignment_runner.run(fasta_path, local_alignment_dir)
print(f"Alignment data time: {time.perf_counter() - t}")

feature_dict = data_processor.process_fasta(fasta_path=fasta_path,
alignment_dir=local_alignment_dir)

features_output_path = os.path.join(local_alignment_dir, 'features.pkl')
if os.path.exists(features_output_path):
feature_dict = pickle.load(open(features_output_path, 'rb'))

else:
template_featurizer = templates.TemplateHitFeaturizer(
mmcif_dir=args.template_mmcif_dir,
max_template_date=args.max_template_date,
max_hits=config.data.predict.max_templates,
kalign_binary_path=args.kalign_binary_path,
release_dates_path=args.release_dates_path,
obsolete_pdbs_path=args.obsolete_pdbs_path
)

data_processor = data_pipeline.DataPipeline(template_featurizer=template_featurizer,)

feature_dict = data_processor.process_fasta(fasta_path=fasta_path,
alignment_dir=local_alignment_dir)
with open(features_output_path, 'wb') as f:
pickle.dump(feature_dict, f, protocol=4)

# Remove temporary FASTA file
os.remove(fasta_path)

Expand Down