111 lines
3.3 KiB
Python
111 lines
3.3 KiB
Python
import os
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from types import MethodType
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import hydra
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from hydra.utils import instantiate
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from omegaconf import DictConfig, OmegaConf
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from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
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from pytorch_lightning.loggers import MLFlowLogger
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from torch.optim.lr_scheduler import ReduceLROnPlateau
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from torch_audiomentations import BandPassFilter, Compose, Shift
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os.environ["HYDRA_FULL_ERROR"] = "1"
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JOB_ID = os.environ.get("SLURM_JOBID", "0")
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@hydra.main(config_path="train_config", config_name="config")
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def main(config: DictConfig):
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OmegaConf.save(config, "config_log.yaml")
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callbacks = []
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logger = MLFlowLogger(
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experiment_name=config.mlflow.experiment_name,
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run_name=config.mlflow.run_name,
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tags={"JOB_ID": JOB_ID},
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)
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parameters = config.hyperparameters
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apply_augmentations = Compose(
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[
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Shift(min_shift=0.0, max_shift=1.0, shift_unit="seconds", p=0.5),
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BandPassFilter(p=0.5),
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]
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)
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dataset = instantiate(config.dataset, augmentations=apply_augmentations)
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model = instantiate(
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config.model,
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dataset=dataset,
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lr=parameters.get("lr"),
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loss=parameters.get("loss"),
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metric=parameters.get("metric"),
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)
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direction = model.valid_monitor
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checkpoint = ModelCheckpoint(
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dirpath="./model",
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filename=f"model_{JOB_ID}",
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monitor="valid_loss",
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verbose=False,
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mode=direction,
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every_n_epochs=1,
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)
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callbacks.append(checkpoint)
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if parameters.get("Early_stop", False):
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early_stopping = EarlyStopping(
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monitor="val_loss",
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mode=direction,
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min_delta=0.0,
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patience=parameters.get("EarlyStopping_patience", 10),
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strict=True,
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verbose=False,
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)
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callbacks.append(early_stopping)
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def configure_optimizer(self):
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optimizer = instantiate(
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config.optimizer,
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lr=parameters.get("lr"),
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parameters=self.parameters(),
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)
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scheduler = ReduceLROnPlateau(
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optimizer=optimizer,
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mode=direction,
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factor=parameters.get("ReduceLr_factor", 0.1),
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verbose=True,
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min_lr=parameters.get("min_lr", 1e-6),
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patience=parameters.get("ReduceLr_patience", 3),
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)
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return {"optimizer": optimizer, "lr_scheduler": scheduler}
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model.configure_parameters = MethodType(configure_optimizer, model)
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trainer = instantiate(config.trainer, logger=logger, callbacks=callbacks)
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trainer.fit(model)
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trainer.test(model)
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logger.experiment.log_artifact(
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logger.run_id, f"{trainer.default_root_dir}/config_log.yaml"
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)
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saved_location = os.path.join(
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trainer.default_root_dir, "model", f"model_{JOB_ID}.ckpt"
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)
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if os.path.isfile(saved_location):
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logger.experiment.log_artifact(logger.run_id, saved_location)
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logger.experiment.log_param(
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logger.run_id,
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"num_train_steps_per_epoch",
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dataset.train__len__() / dataset.batch_size,
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)
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logger.experiment.log_param(
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logger.run_id,
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"num_valid_steps_per_epoch",
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dataset.val__len__() / dataset.batch_size,
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)
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if __name__ == "__main__":
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main()
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