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main.py
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main.py
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"""Training and evaluation"""
import run_lib
from absl import app, flags
from ml_collections.config_flags import config_flags
import logging
import os
from rdkit import RDLogger
FLAGS = flags.FLAGS
config_flags.DEFINE_config_file(
'config', None, 'Training configuration.', lock_config=True
)
flags.DEFINE_string('workdir', None, 'Work directory.')
flags.DEFINE_enum('mode', None, ['train', 'eval'],
'Running mode: train or eval')
flags.DEFINE_string('eval_folder', 'eval', 'The folder name for storing evaluation results')
flags.DEFINE_boolean('deterministic', True, 'Set random seed for reproducibility.')
flags.mark_flags_as_required(['workdir', 'config', 'mode'])
def main(argv):
# Set random seed
if FLAGS.deterministic:
run_lib.set_random_seed(FLAGS.config)
# Ignore info output by RDKit
RDLogger.DisableLog('rdApp.error')
RDLogger.DisableLog('rdApp.warning')
if FLAGS.mode == 'train':
# Create the working directory
if not os.path.exists(FLAGS.workdir):
os.makedirs(FLAGS.workdir)
# Set logger so that it outputs to both console and file
gfile_stream = open(os.path.join(FLAGS.workdir, 'stdout.txt'), 'a')
handler = logging.StreamHandler(gfile_stream)
formatter = logging.Formatter('%(levelname)s - %(filename)s - %(asctime)s - %(message)s')
handler.setFormatter(formatter)
logger = logging.getLogger()
logger.addHandler(handler)
logger.setLevel('INFO')
# Run the training pipeline
run_lib.train(FLAGS.config, FLAGS.workdir)
elif FLAGS.mode == 'eval':
# Run the evaluation pipeline
gfile_stream = open(os.path.join(FLAGS.workdir, 'eval_stdout.txt'), 'a')
handler = logging.StreamHandler(gfile_stream)
formatter = logging.Formatter('%(levelname)s - %(filename)s - %(asctime)s - %(message)s')
handler.setFormatter(formatter)
logger = logging.getLogger()
logger.addHandler(handler)
logger.setLevel('INFO')
run_lib.evaluate(FLAGS.config, FLAGS.workdir, FLAGS.eval_folder)
else:
raise ValueError(f"Mode {FLAGS.mode} not recognized.")
if __name__ == '__main__':
app.run(main)