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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import logging
import os
from typing import Union
import torch
import torch.distributed as dist
from protenix.config import parse_configs, parse_sys_args
from protenix.config.extend_types import ListValue, RequiredValue
from protenix.utils.distributed import DIST_WRAPPER
from protenix.utils.logger import get_logger
from pxdbench.pxd_configs.eval import eval_configs
from pxdbench.tasks import get_task_class
from pxdbench.utils import convert_cif_to_pdb
logger = get_logger(__name__)
def run_task(input_data: dict, configs, device_id: int = 0, seed: int = None):
"""
Args:
input_data (dict): A dictionary containing the following keys:
task (str): The task to run. Example: "binder".
pdb_dir (str): The directory containing the PDB files.
pdb_names (list): The names of the PDB files.
cond_chains (list): The chains to condition on.
binder_chains (list): The chains to bind.
out_dir (str): The root directory. Outputs will be saved here.
configs (dict): A dictionary containing the configuration for the task.
device_id (int): Device ID to run the task on.
seed (int): Random seed to use.
Returns:
dict: A dictionary containing the results of the task.
"""
if device_id >= 0 and not torch.cuda.is_available():
raise ValueError("device_id must be -1 (CPU) or a valid GPU ID")
task = input_data["task"]
task_cls = get_task_class(task)
task_cfg = configs.get(task)
task = task_cls(input_data, task_cfg, device_id, seed)
return task.run()
def split_string(s):
return s.split(",")
def get_file_name_list(file_name_list: Union[str, list]):
"""
Get file names from a string or a file.
Args:
file_name_list (str or list): A string or a list containing file names.
If a string, it should be a comma-separated list of file names.
If a file, it should contain one file name per line.
Returns:
list: A list of file names.
"""
if isinstance(file_name_list, list):
return file_name_list
if os.path.exists(file_name_list):
file_names = []
with open(file_name_list, "r") as f:
for line in f.readlines():
line = line.strip()
if line:
file_names.append(line)
return file_names
else:
file_name_list = file_name_list.split(",")
return file_name_list
def find_files_with_ext(folder_path, ext="cif"):
pdb_files = []
for filename in os.listdir(folder_path):
if filename.endswith(f".{ext}"):
pdb_files.append(filename[: -(len(ext) + 1)])
return sorted(pdb_files)
def get_chains_from_pdb(pdb_path):
chains = set()
with open(pdb_path, "r") as f:
for line in f:
if line.startswith("ATOM") or line.startswith("HETATM"):
chain_id = line[21].strip()
if chain_id:
chains.add(chain_id)
return chains
def prepare_tasks_from_mmcif(
data_dir: str,
file_name_list: list,
binder_chains: list[str],
cond_chains: list[str],
dump_dir: str,
):
"""
Prepare tasks from mmCIF files.
Args:
data_dir (str): The directory containing the mmCIF files.
file_name_list (str or list): A string or a list containing file names.
If a string, it should be a comma-separated list of file names.
If a file, it should contain one file name per line.
binder_chains (list): The binder chain IDs.
cond_chains (list): The chains to condition on. Could be auto infered by binder_chains.
root_dir (str): The root directory. Outputs will be saved here.
Returns:
dict: A dictionary containing the results of the task.
"""
n_cond_chains, n_binder_chains = None, None
valid_file_name_list = []
for file_name in file_name_list:
mmcif_path = os.path.join(data_dir, f"{file_name}.cif")
if not os.path.exists(mmcif_path):
logger.warning(f"Could not find {mmcif_path}, skip!")
continue
else:
valid_file_name_list.append(file_name)
pdb_path = os.path.join(data_dir, "converted_pdbs", f"{file_name}.pdb")
os.makedirs(os.path.dirname(pdb_path), exist_ok=True)
new_cond_chains, new_binder_chains = convert_cif_to_pdb(
cif_path=mmcif_path,
out_pdb_path=pdb_path,
binder_chains=binder_chains,
)
if n_cond_chains is None:
n_cond_chains = list(new_cond_chains)
else:
assert set(n_cond_chains) == set(new_cond_chains)
if n_binder_chains is None:
n_binder_chains = list(new_binder_chains)
else:
assert set(n_binder_chains) == set(new_binder_chains)
logger.info(
f"Found {len(valid_file_name_list)} valid files, cond chains: {n_cond_chains}, binder chains: {n_binder_chains}"
)
input_data = {
"task": "binder",
"name": os.path.basename(data_dir),
"pdb_dir": os.path.join(data_dir, "converted_pdbs"),
"pdb_names": valid_file_name_list,
"cond_chains": n_cond_chains,
"binder_chains": n_binder_chains,
"out_dir": dump_dir,
}
return input_data
def prepare_tasks_from_pdb(
data_dir: str,
file_name_list: list,
binder_chains: list[str],
cond_chains: list[str],
dump_dir: str,
):
n_cond_chains = None
valid_file_name_list = []
for file_name in file_name_list:
pdb_path = os.path.join(data_dir, f"{file_name}.pdb")
if not os.path.exists(pdb_path):
logger.warning(f"Could not find {pdb_path}, skip!")
continue
else:
valid_file_name_list.append(file_name)
if cond_chains == [""]:
chains = get_chains_from_pdb(pdb_path)
new_cond_chains = chains - set(binder_chains)
else:
new_cond_chains = cond_chains
if n_cond_chains is None:
n_cond_chains = list(new_cond_chains)
else:
assert set(n_cond_chains) == set(new_cond_chains)
return {
"task": "binder",
"pdb_dir": data_dir,
"name": os.path.basename(data_dir),
"pdb_names": valid_file_name_list,
"cond_chains": n_cond_chains,
"binder_chains": binder_chains,
"out_dir": dump_dir,
}
def prepare_tasks_from_json(json_path):
"""
Prepare tasks from a JSON file.
Args:
json_path (str): Path to the JSON file.
Returns:
list: List of tasks.
"""
with open(json_path, "r") as f:
tasks = json.load(f)
if isinstance(tasks, dict):
tasks = [tasks]
return tasks
class EvalRunner(object):
def __init__(self, configs):
self.configs = configs
self.init_env()
def init_env(self) -> None:
self.print(
f"Distributed environment: world size: {DIST_WRAPPER.world_size}, "
+ f"global rank: {DIST_WRAPPER.rank}, local rank: {DIST_WRAPPER.local_rank}"
)
self.use_cuda = torch.cuda.device_count() > 0
if self.use_cuda:
self.device = torch.device("cuda:{}".format(DIST_WRAPPER.local_rank))
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
all_gpu_ids = ",".join(str(x) for x in range(torch.cuda.device_count()))
devices = os.getenv("CUDA_VISIBLE_DEVICES", all_gpu_ids)
logging.info(
f"LOCAL_RANK: {DIST_WRAPPER.local_rank} - CUDA_VISIBLE_DEVICES: [{devices}]"
)
torch.cuda.set_device(self.device)
else:
self.device = torch.device("cpu")
if DIST_WRAPPER.world_size > 1:
dist.init_process_group(backend="nccl")
logging.info("Finished init ENV.")
def print(self, msg: str):
if DIST_WRAPPER.rank == 0:
logger.info(msg)
def run(self, input_data_list):
for input_data in input_data_list:
run_task(
input_data,
self.configs,
device_id=DIST_WRAPPER.local_rank,
seed=self.configs.seed,
)
logging.info("Eval done!")
def main():
# Configs
config_dict = {
"file_name_list": "",
"json_path": "",
"data_dir": "",
"dump_dir": RequiredValue(str),
"cond_chains": ListValue([""]),
"binder_chains": ListValue([""]), # required
"is_mmcif": False,
"orig_seqs_json": "",
"seed": 2025,
**eval_configs,
}
configs = parse_configs(config_dict, arg_str=parse_sys_args())
# Prepare tasks
if configs.json_path:
logger.info("Prepare tasks from json file.")
input_data_list = prepare_tasks_from_json(configs.json_path)
if DIST_WRAPPER.world_size > 1:
input_data_list = input_data_list[
DIST_WRAPPER.rank :: DIST_WRAPPER.world_size
]
for input_data in input_data_list:
input_data["out_dir"] += f"_rank{DIST_WRAPPER.rank}"
else:
if not configs.file_name_list:
if configs.is_mmcif:
fn_list = find_files_with_ext(configs.data_dir, "cif")
else:
fn_list = find_files_with_ext(configs.data_dir, "pdb")
else:
fn_list = get_file_name_list(configs.file_name_list)
if DIST_WRAPPER.world_size > 1:
fn_list = fn_list[DIST_WRAPPER.rank :: DIST_WRAPPER.world_size]
if configs.is_mmcif:
logger.info("Prepare tasks from mmCIF files.")
input_data = prepare_tasks_from_mmcif(
data_dir=configs.data_dir,
file_name_list=fn_list,
binder_chains=configs.binder_chains,
cond_chains=configs.cond_chains,
dump_dir=configs.dump_dir,
)
else:
logger.info("Prepare tasks from PDB files.")
input_data = prepare_tasks_from_pdb(
data_dir=configs.data_dir,
file_name_list=fn_list,
binder_chains=configs.binder_chains,
cond_chains=configs.cond_chains,
dump_dir=configs.dump_dir,
)
if len(configs.orig_seqs_json) > 0:
input_data["orig_seqs_json"] = configs.orig_seqs_json
if DIST_WRAPPER.world_size > 1:
input_data["out_dir"] += f"_rank{DIST_WRAPPER.rank}"
input_data_list = [input_data]
# Run task
runner = EvalRunner(configs)
runner.run(input_data_list)
if __name__ == "__main__":
main()
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