81 lines
3.0 KiB
Python
81 lines
3.0 KiB
Python
# Imports
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# =============================================================================
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import warnings
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import torch
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
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from ml_lib.modules.util import LightningBaseModule
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from ml_lib.utils.config import Config
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from ml_lib.utils.loggers import Logger
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warnings.filterwarnings('ignore', category=FutureWarning)
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warnings.filterwarnings('ignore', category=UserWarning)
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def run_lightning_loop(config_obj):
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# Logging
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# ================================================================================
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# Logger
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with Logger(config_obj) as logger:
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# Callbacks
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# =============================================================================
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# Checkpoint Saving
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checkpoint_callback = ModelCheckpoint(
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filepath=str(logger.log_dir / 'ckpt_weights'),
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verbose=True, save_top_k=0,
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)
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# =============================================================================
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# Early Stopping
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# TODO: For This to work, one must set a validation step and End Eval and Score
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early_stopping_callback = EarlyStopping(
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monitor='val_loss',
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min_delta=0.0,
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patience=0,
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)
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# Model
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# =============================================================================
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# Init
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model: LightningBaseModule = config_obj.model_class(config_obj.model_paramters)
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model.init_weights(torch.nn.init.xavier_normal_)
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# Trainer
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# =============================================================================
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trainer = Trainer(max_epochs=config_obj.train.epochs,
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show_progress_bar=True,
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weights_save_path=logger.log_dir,
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gpus=[0] if torch.cuda.is_available() else None,
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check_val_every_n_epoch=10,
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# num_sanity_val_steps=config_obj.train.num_sanity_val_steps,
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# row_log_interval=(model.n_train_batches * 0.1), # TODO: Better Value / Setting
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# log_save_interval=(model.n_train_batches * 0.2), # TODO: Better Value / Setting
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checkpoint_callback=checkpoint_callback,
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logger=logger,
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fast_dev_run=config_obj.main.debug,
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early_stop_callback=None
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)
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# Train It
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trainer.fit(model)
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# Save the last state & all parameters
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trainer.save_checkpoint(config_obj.exp_path.log_dir / 'weights.ckpt')
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model.save_to_disk(config_obj.exp_path)
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# Evaluate It
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if config_obj.main.eval:
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trainer.test()
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return model
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if __name__ == "__main__":
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from _templates.new_project._parameters import args
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config = Config.read_namespace(args)
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trained_model = run_lightning_loop(config)
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