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import pytorch_lightning as pl
import argparse
import pprint
from loguru import logger as loguru_logger
from src.config.default import get_cfg_defaults
from src.utils.profiler import build_profiler
from src.lightning_trainer.data import MultiSceneDataModule
from src.lightning_trainer.trainer import PL_Trainer
def parse_args():
# init a costum parser which will be added into pl.Trainer parser
# check documentation: https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html#trainer-flags
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'data_cfg_path', type=str, help='data config path')
parser.add_argument(
'main_cfg_path', type=str, help='main config path')
parser.add_argument(
'--ckpt_path', type=str, default="weights/indoor_ds.ckpt", help='path to the checkpoint')
parser.add_argument(
'--dump_dir', type=str, default=None, help="if set, the matching results will be dump to dump_dir")
parser.add_argument(
'--profiler_name', type=str, default=None, help='options: [inference, pytorch], or leave it unset')
parser.add_argument(
'--batch_size', type=int, default=1, help='batch_size per gpu')
parser.add_argument(
'--num_workers', type=int, default=2)
parser.add_argument(
'--thr', type=float, default=None, help='modify the coarse-level matching threshold.')
parser = pl.Trainer.add_argparse_args(parser)
return parser.parse_args()
if __name__ == '__main__':
# parse arguments
args = parse_args()
pprint.pprint(vars(args))
# init default-cfg and merge it with the main- and data-cfg
config = get_cfg_defaults()
config.merge_from_file(args.main_cfg_path)
config.merge_from_file(args.data_cfg_path)
pl.seed_everything(config.TRAINER.SEED) # reproducibility
# tune when testing
if args.thr is not None:
config.MODEL.MATCH_COARSE.THR = args.thr
loguru_logger.info(f"Args and config initialized!")
# lightning module
profiler = build_profiler(args.profiler_name)
model = PL_Trainer(config, pretrained_ckpt=args.ckpt_path, profiler=profiler, dump_dir=args.dump_dir)
loguru_logger.info(f"Model-lightning initialized!")
# lightning data
data_module = MultiSceneDataModule(args, config)
loguru_logger.info(f"DataModule initialized!")
# lightning trainer
trainer = pl.Trainer.from_argparse_args(args, replace_sampler_ddp=False, logger=False)
loguru_logger.info(f"Start testing!")
trainer.test(model, datamodule=data_module, verbose=False)
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