File size: 2,241 Bytes
13e95c8 | 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 | import os
import sys
from pathlib import Path
import logging
project_dir = Path(__file__).absolute().parent.parent
sys.path.append(str(project_dir))
import hydra
from hydra.core.hydra_config import HydraConfig
from omegaconf import DictConfig, OmegaConf
OmegaConf.register_new_resolver("eval", eval)
import torch
import torch.distributed as dist
from ml_collections.config_dict import ConfigDict
from src.utils.inference.dumper import DataDumper
from src.utils.inference.infer_runner import InferRunner
from src.data.dataloader import get_inference_dataloader
from src.model.odesign import ODesign
from src.utils.train.distributed import DIST_WRAPPER
from src.utils.misc import print_configs
logger = logging.getLogger(__name__)
@hydra.main(
version_base="1.3", config_path=str(project_dir/"configs"), config_name="inference"
)
def main(configs : DictConfig) -> None:
# Load configs
print_configs(configs)
os.environ["DATA_ROOT_DIR"] = configs.data_root_dir
os.environ["CKPT_ROOT_DIR"] = configs.ckpt_root_dir
configs = ConfigDict(
OmegaConf.to_container(configs.exp, resolve=True)
)
dump_dir = HydraConfig.get().runtime.output_dir
configs.dump_dir = dump_dir
error_dir = Path(dump_dir) / "errors"
if DIST_WRAPPER.rank == 0:
error_dir.mkdir(parents=True, exist_ok=True)
# Set up distributed env
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:
dist.init_process_group(backend="nccl")
# Init dataloader
infer_dl = get_inference_dataloader(configs=configs)
# Init model
model = ODesign(configs).to(device)
# Init dumper
dumper = DataDumper(base_dir=dump_dir)
# Init runner
infer_runner = InferRunner(
configs=configs,
dump_dir=dump_dir,
error_dir=error_dir,
device=device,
infer_dl=infer_dl,
model=model,
dumper=dumper,
)
infer_runner.run()
if __name__ == "__main__":
main()
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