add train py

train.py

@@ -0,0 +1,44 @@
+import os
+import hydra
+import lightning.pytorch as pl
+from lightning.pytorch.callbacks import LearningRateMonitor
+
+from src.data.data_module import DataModule
+
+def init_callbacks(cfg):
+    lr_monitor = LearningRateMonitor(logging_interval="epoch")
+    checkpoint_monitor = hydra.utils.instantiate(cfg.checkpoint_monitor)
+    return [checkpoint_monitor, lr_monitor]
+
+@hydra.main(version_base=None, config_path="config", config_name="global_config")
+def main(cfg):
+    # fix the seed
+    pl.seed_everything(cfg.train_seed, workers=True)
+
+    # create directories for training outputs
+    os.makedirs(os.path.join(cfg.experiment_output_path, "training"), exist_ok=True)
+
+    # initialize data
+    data_module = DataModule(cfg.data, cfg.model.network)
+
+    # initialize model
+    model = hydra.utils.instantiate(cfg.model.model_name, cfg)
+
+    # initialize logger
+    logger = hydra.utils.instantiate(cfg.logger)
+
+    # initialize callbacks
+    callbacks = init_callbacks(cfg)
+
+    # initialize trainer
+    trainer = hydra.utils.instantiate(cfg.trainer, callbacks=callbacks, logger=logger)
+
+    # check the checkpoint
+    if cfg.ckpt_path is not None:
+        assert os.path.exists(cfg.ckpt_path), "Error: Checkpoint path does not exist."
+
+    # start training
+    trainer.fit(model=model, datamodule=data_module, ckpt_path=cfg.ckpt_path)
+
+if __name__ == '__main__':
+    main()