test.py

import pytorch_lightning as pl
import argparse
import pprint
from loguru import logger as loguru_logger
from config.defaultmf import get_cfg_defaults
from model.data import MultiSceneDataModule
from model.lightning_loftr import PL_LoFTR


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(
        '--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='inference', 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()
    # init default-cfg and merge it with the main- and data-cfg
    config = get_cfg_defaults()
    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.LOFTR.MATCH_COARSE.THR = args.thr

    # lightning module
    model = PL_LoFTR(config, pretrained_ckpt=args.ckpt_path, dump_dir=args.dump_dir)

    # lightning data
    data_module = MultiSceneDataModule(args, config)

    # 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)