File size: 5,167 Bytes
d766458 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | # Copyright 2025 ByteDance and/or its affiliates.
#
# Licensed under Creative Commons Attribution-NonCommercial 4.0
# International License (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# http://creativecommons.org/licenses/by-nc/4.0/
#
# 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 logging
import os
from pathlib import Path
from typing import Any
import torch
import torch.distributed as dist
from natsort import natsorted
from protenix.config import parse_configs, parse_sys_args
from protenix.config.extend_types import RequiredValue
from protenix.utils.distributed import DIST_WRAPPER
from pxdbench.pxd_configs.eval import eval_configs
from pxdbench.run import find_files_with_ext, run_task
from pxdbench.utils import convert_cifs_to_pdbs
logger = logging.getLogger(__name__)
def scan_tasks(data_dir, is_mmcif=False):
data_dir = Path(data_dir).resolve()
paths = []
pattern = "*.cif" if is_mmcif else "*.pdb"
for pdb_file in data_dir.rglob(pattern):
paths.append(os.path.dirname(pdb_file))
return sorted(list(set(paths)))
class EvalRunner(object):
def __init__(self, configs: Any) -> None:
self.configs = configs
self.root_dir = self.configs.data_dir
self.dump_dir = self.configs.dump_dir
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_dirs = scan_tasks(self.root_dir, self.configs.is_mmcif)
valid_input_dirs = []
for data_dir in input_dirs:
exp_name = os.path.relpath(data_dir, self.root_dir)
if os.path.exists(
os.path.join(self.dump_dir, exp_name, "summary_output.json")
):
self.print(f"Found summary file for {exp_name} - Skip!")
continue
valid_input_dirs.append(data_dir)
valid_input_dirs = natsorted(valid_input_dirs)
logging.info(f"There are {len(valid_input_dirs)} tasks to evaluate")
print(valid_input_dirs)
sub_input_dirs = valid_input_dirs[DIST_WRAPPER.rank :: DIST_WRAPPER.world_size]
for i, data_dir in enumerate(sub_input_dirs):
logging.info(
f"Begin to evaluate [{i + 1}/{len(sub_input_dirs)}]: {data_dir}"
)
exp_name = os.path.relpath(data_dir, self.root_dir)
dump_dir = os.path.join(self.dump_dir, exp_name)
os.makedirs(dump_dir, exist_ok=True)
if self.configs.is_mmcif:
pdb_dir, pdb_names, _, _ = convert_cifs_to_pdbs(
data_dir,
out_pdb_dir=os.path.join(data_dir, "converted_pdbs"),
)
else:
pdb_dir = data_dir
pdb_names = find_files_with_ext(data_dir, "pdb")
logging.info(f"There are {len(pdb_names)} pdbs in this task.")
input_data = {
"task": "monomer",
"name": exp_name,
"pdb_dir": pdb_dir,
"pdb_names": pdb_names,
"out_dir": dump_dir,
}
run_task(
input_data,
self.configs,
device_id=DIST_WRAPPER.local_rank,
seed=self.configs.seed,
)
logging.info("Eval done!")
def main():
config_dict = {
"data_dir": RequiredValue(str),
"dump_dir": RequiredValue(str),
"is_mmcif": False,
"seed": 2025,
**eval_configs,
}
configs = parse_configs(config_dict, arg_str=parse_sys_args())
runner = EvalRunner(configs)
runner.run()
if __name__ == "__main__":
main()
|