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import argparse |
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import os |
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import pathlib |
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import yaml |
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from pytorch_lightning import Trainer |
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from pytorch_lightning.callbacks import ModelCheckpoint |
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from pytorch_lightning.loggers.csv_logs import CSVLogger |
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from pytorch_lightning.loggers import TensorBoardLogger |
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from dataset import DataModule |
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from lightning_module import ( |
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PretrainLightningModule, |
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SSLStepLightningModule, |
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SSLDualLightningModule, |
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) |
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def get_arg(): |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--config_path", required=True, type=pathlib.Path) |
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parser.add_argument("--ckpt_path", required=True, type=pathlib.Path) |
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parser.add_argument( |
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"--stage", required=True, type=str, choices=["pretrain", "ssl-step", "ssl-dual"] |
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) |
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parser.add_argument("--run_name", required=True, type=str) |
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return parser.parse_args() |
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def eval(args, config, output_path): |
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csvlogger = CSVLogger(save_dir=output_path, name="test_log") |
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trainer = Trainer( |
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gpus=-1, |
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deterministic=False, |
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auto_select_gpus=True, |
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benchmark=True, |
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logger=[csvlogger], |
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default_root_dir=os.getcwd(), |
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) |
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if config["general"]["stage"] == "pretrain": |
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model = PretrainLightningModule(config).load_from_checkpoint( |
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checkpoint_path=args.ckpt_path, config=config |
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) |
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elif config["general"]["stage"] == "ssl-step": |
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model = SSLStepLightningModule(config).load_from_checkpoint( |
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checkpoint_path=args.ckpt_path, config=config |
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) |
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elif config["general"]["stage"] == "ssl-dual": |
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model = SSLDualLightningModule(config).load_from_checkpoint( |
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checkpoint_path=args.ckpt_path, config=config |
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) |
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else: |
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raise NotImplementedError() |
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datamodule = DataModule(config) |
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trainer.test(model=model, verbose=True, datamodule=datamodule) |
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if __name__ == "__main__": |
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args = get_arg() |
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config = yaml.load(open(args.config_path, "r"), Loader=yaml.FullLoader) |
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output_path = str(pathlib.Path(config["general"]["output_path"]) / args.run_name) |
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config["general"]["stage"] = str(getattr(args, "stage")) |
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eval(args, config, output_path) |
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