main.py

# Copyright 2017 The BEGAN-tensorflow Authors(Taehoon Kim). All Rights Reserved.
#
# 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.
# ==============================================================================
# MIT License
#
# Modifications copyright (c) 2018 Image & Vision Computing Lab, Institute of Information Science, Academia Sinica
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# ==============================================================================
import numpy as np
import tensorflow as tf
from trainer import Trainer
from config import get_config
from utils import prepare_dirs_and_logger, save_config

def main(config):
    prepare_dirs_and_logger(config)

    rng = np.random.RandomState(config.random_seed)
    tf.set_random_seed(config.random_seed)

    if config.is_train:
        data_path = config.data_path
        batch_size = config.batch_size

    else:
        setattr(config, 'batch_size', 1)


    trainer = Trainer(config)

    if config.is_train:
        save_config(config)
        trainer.train()
    else:
        if not config.load_path:
            raise Exception("[!] You should specify `load_path` to load a pretrained model")
        trainer.test()

if __name__ == "__main__":
    config, unparsed = get_config()
    main(config)