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feat: support backbone-only CIF output and parallel-seed dump_dir #8

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feat: support backbone-only CIF output and parallel-seed dump_dir #8

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10 changes: 7 additions & 3 deletions scripts/inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,10 +32,14 @@ def main(configs : DictConfig) -> None:
print_configs(configs)
os.environ["DATA_ROOT_DIR"] = configs.data_root_dir
os.environ["CKPT_ROOT_DIR"] = configs.ckpt_root_dir
hydra_output_dir = HydraConfig.get().runtime.output_dir
configs = ConfigDict(
OmegaConf.to_container(configs.exp, resolve=True)
)
dump_dir = HydraConfig.get().runtime.output_dir
# exp.dump_dir allows the caller to separate data output from hydra's
# working directory, which is needed when parallel seed workers each
# require a distinct hydra.run.dir but share one output directory.
dump_dir = configs.get("dump_dir", None) or hydra_output_dir
configs.dump_dir = dump_dir
error_dir = Path(dump_dir) / "errors"
if DIST_WRAPPER.rank == 0:
Expand All @@ -45,7 +49,7 @@ def main(configs : DictConfig) -> None:
logger.info(
f"Distributed environment: world size: {DIST_WRAPPER.world_size}, "
+ f"global rank: {DIST_WRAPPER.rank}, local rank: {DIST_WRAPPER.local_rank}"
)
)
device = torch.device("cuda:{}".format(DIST_WRAPPER.local_rank))
torch.cuda.set_device(device)
if DIST_WRAPPER.world_size > 1:
Expand All @@ -72,7 +76,7 @@ def main(configs : DictConfig) -> None:
)

infer_runner.run()


if __name__ == "__main__":
main()
34 changes: 17 additions & 17 deletions src/utils/inference/dumper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -127,25 +127,25 @@ def _save_structure_sequence(
new_atom_array,
per_chain_edits=per_chain_edits,
design_modality=design_modality,
)

output_fpath = os.path.join(
prediction_save_dir,
f"{sample_name}_seed_{seed}_bb_{rank}_seq_{seq_var_idx}.cif",
)

if b_factor is not None:
# b_factor.shape == [N_sample, N_atom]
new_atom_array.set_annotation("b_factor", np.round(b_factor[idx], 2))

save_structure_cif(
atom_array=new_atom_array,
pred_coordinate=pred_coordinates[idx],
output_fpath=output_fpath,
entity_poly_type=entity_poly_type,
pdb_id=sample_name,
)

output_fpath = os.path.join(
prediction_save_dir,
f"{sample_name}_seed_{seed}_bb_{rank}_seq_{seq_var_idx}.cif",
)

if b_factor is not None:
# b_factor.shape == [N_sample, N_atom]
new_atom_array.set_annotation("b_factor", np.round(b_factor[idx], 2))

save_structure_cif(
atom_array=new_atom_array,
pred_coordinate=pred_coordinates[idx],
output_fpath=output_fpath,
entity_poly_type=entity_poly_type,
pdb_id=sample_name,
)

def _apply_sequence_variant_to_atom_array(
self,
atom_array: AtomArray,
Expand Down