The above shows an example outcome of the srgan model. The training dataset is oxford_iiit_pet. To do the training, I pretrained the generator and the discriminator.
The repo was designed to be run in Google Cloud and makes use of GCS for logging. It contains three training tasks, which are srresnet_task, discriminator_task and srgan_task. srresnet_task trains the generator of the srgan solely, discriminator_task trains the discriminator of the srgan solely and srgan_task trains both the generator and the discriminator jointly.
To train the srresnet, the generator solely, using
python3 -m trainer.srresnet_task --job-dir 'gs://<project>/<path to store tensorboard jobs>'
To train the discriminator solely, using
python3 -m trainer.discriminator_task --job-dir 'gs://<project>/<path to store tensorboard jobs>'
To train srgan, the generator and discriminator jointly, using
python3 -m trainer.srgan_task --job-dir 'gs://<project>/<path to store tensorboard jobs>'
To use pretrained weight, specify the weight paths by setting the parameters: g_weight and d_weight. E.g.
python3 -m trainer.srgan_task --g_weight '<pretrained generator weight path>' --d_weight 'pretrained discriminator weight path' --job-dir 'gs://<project>/<path to store tensorboard jobs>'
More custom training parameters can be set by reading the configuration file: trainer/config.py
Copyright 2019 Zisheng Liang
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.
@article{DBLP:journals/corr/LedigTHCATTWS16,
author = {Christian Ledig and
Lucas Theis and
Ferenc Huszar and
Jose Caballero and
Andrew P. Aitken and
Alykhan Tejani and
Johannes Totz and
Zehan Wang and
Wenzhe Shi},
title = {Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial
Network},
journal = {CoRR},
volume = {abs/1609.04802},
year = {2016},
url = {http://arxiv.org/abs/1609.04802},
archivePrefix = {arXiv},
eprint = {1609.04802},
timestamp = {Mon, 13 Aug 2018 16:48:38 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/LedigTHCATTWS16},
bibsource = {dblp computer science bibliography, https://dblp.org}
}