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main_fastmri.py
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main_fastmri.py
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# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Training and evaluation"""
import run_lib_fastmri
from absl import app
from absl import flags
from ml_collections.config_flags import config_flags
import logging
import os
import tensorflow as tf
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", "train_regression", "eval"], "Running mode: train, train_regression, or eval")
flags.DEFINE_string("eval_folder", "eval",
"The folder name for storing evaluation results")
flags.mark_flags_as_required(["workdir", "config", "mode"])
def main(argv):
print(FLAGS.config)
if FLAGS.mode == "train" or FLAGS.mode == "train_regression":
# Create the working directory
tf.io.gfile.makedirs(FLAGS.workdir)
# Set logger so that it outputs to both console and file
# Make logging work for both disk and Google Cloud Storage
gfile_stream = open(os.path.join(FLAGS.workdir, 'stdout.txt'), 'w')
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
if FLAGS.mode == "train":
run_lib_fastmri.train(FLAGS.config, FLAGS.workdir)
elif FLAGS.mode == "train_regression":
run_lib_fastmri.train_regression(FLAGS.config, FLAGS.workdir)
elif FLAGS.mode == "eval":
# Run the evaluation pipeline
run_lib_fastmri.evaluate(FLAGS.config, FLAGS.workdir, FLAGS.eval_folder)
else:
raise ValueError(f"Mode {FLAGS.mode} not recognized.")
if __name__ == "__main__":
app.run(main)