experiment_baseline.py

from utils import USE_GPU, init_experiment
from pytorch_lightning import Trainer
from test_tube import Experiment, HyperOptArgumentParser
from models.baseline import CNN as Network
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
import os

config = {
    'epochs': 1,
    'patience': 50,
    'earlystopping_metric': 'val_loss', # 'val_prauc'
    'earlystopping_mode': 'min' # 'max'
}

def epochs_500():
    global config
    config['epochs'] = 500


def epochs_100():
    global config
    config['epochs'] = 100
    config['patience'] = 20


def epochs_20():
    global config
    config['epochs'] = 20


def run(hparams):
    init_experiment(comment=hparams.experiment_name)
    from utils import CURR_RUN_PATH, logger # import these after init_experiment
    logger.info(CURR_RUN_PATH)
    logger.info(f"tensorboard --logdir={CURR_RUN_PATH}")
    exp = Experiment(save_dir=CURR_RUN_PATH)

    def setup_config():
        def print_config():
            global config
            st = '---------CONFIG--------\n'
            for k in config.keys():
                st += k+':'+str(config.get(k))+'\n'
            return st

        conf = hparams.config
        if conf is not None:
            conf_func = globals()[conf]
            try:
                conf_func()
            except:
                logger.error(f"Config {conf} not defined")

        logger.info(print_config())

    setup_config()
    global config
    # parameters used in the baseline (read from main.py and solver.py)
    # n_epochs = 500
    # lr = 1e-4
    # num_class = 56
    # batch_size = 32

    early_stop = EarlyStopping(
        monitor=config['earlystopping_metric'],
        patience=config['patience'],
        verbose=True,
        mode=config['earlystopping_mode']
    )

    checkpoint_callback = ModelCheckpoint(
        filepath=os.path.join(CURR_RUN_PATH, 'best.ckpt'),
        save_best_only=True,
        verbose=True,
        monitor='val_loss',
        mode='min'
    )

    if USE_GPU:
        trainer = Trainer(gpus=[0], distributed_backend='ddp',
                          experiment=exp, max_nb_epochs=config['epochs'], train_percent_check=hparams.train_percent,
                          fast_dev_run=False,
                          early_stop_callback=early_stop,
                          checkpoint_callback=checkpoint_callback
    )
    else:
        trainer = Trainer(experiment=exp, max_nb_epochs=1, train_percent_check=0.01,
                          fast_dev_run=True)

    model = Network(num_class=56)  # TODO num_class

    print(model)

    trainer.fit(model)
    trainer.test()


if __name__=='__main__':
    parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)
    parent_parser.add_argument('--experiment_name', type=str,
                               default='pt_lightning_exp_a', help='test tube exp name')
    parent_parser.add_argument('--config', type=str, help='config function to run')
    #TODO : multiple arguments for --config using nargs='+' is not working with the test_tube
    # implementation of argument parser
    parent_parser.add_argument('--train_percent', type=float,
                               default=1.0, help='how much train data to use')
    parser = Network.add_model_specific_args(parent_parser)
    hyperparams = parser.parse_args()
    run(hyperparams)