CycleGAN 开源镜像.rar

# The Simplest CycleGAN Full Implementation Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks ## Requirement Check the `requirements.txt` ## TODO - replay buffer ## Run It will automatically download the data in `data.py`. ```python python3 train.py ``` ## Distributed Training GAN-like networks are particularly challenging given that they often use multiple optimizers. In addition, GANs also consume a large amont of GPU memory and are usually batch-size sensitive. To speed up training, we thus use a novel [KungFu](https://github.com/lsds/KungFu) distributed training library. KungFu is easy to install and run (compared to today's Horovod library which depends on OpenMPI). You can install it using a few lines by following the [instruction](https://github.com/lsds/KungFu#install). KungFu is also very fast and scalable, compared to Horovod and parameter servers, making it an attractive option for GAN networks. In the following, we assume that you have added `kungfu-run` into the `$PATH`. (i) To run on a machine with 4 GPUs: ```bash kungfu-run -np 4 python3 train.py --parallel --kf-optimizer=sma ``` The default KungFu optimizer is `sma` which implements synchronous model averaging. The `sma` decouple batch size and the number of GPUs, making it hyper-parameter-robust during scaling. You can also use other KungFu optimizers: `sync-sgd` (which is the same as the DistributedOptimizer in Horovod) and `async-sgd` if you train your model in a cluster that has limited bandwidth and straggelers. (ii) To run on 2 machines (which have the nic `eth0` with IPs as `192.168.0.1` and `192.168.0.2`): ```bash kungfu-run -np 8 -H 192.168.0.1:4,192.168.0.1:4 -nic eth0 python3 train.py --parallel --kf-optimizer=sma ``` ## Results <a href="https://join.slack.com/t/tensorlayer/shared_invite/enQtMjUyMjczMzU2Njg4LWI0MWU0MDFkOWY2YjQ4YjVhMzI5M2VlZmE4YTNhNGY1NjZhMzUwMmQ2MTc0YWRjMjQzMjdjMTg2MWQ2ZWJhYzc"> <div align="center"> <img src="results/_sample_A.png" width="80%" height="50%"/> </div> </a> <a href="https://join.slack.com/t/tensorlayer/shared_invite/enQtMjUyMjczMzU2Njg4LWI0MWU0MDFkOWY2YjQ4YjVhMzI5M2VlZmE4YTNhNGY1NjZhMzUwMmQ2MTc0YWRjMjQzMjdjMTg2MWQ2ZWJhYzc"> <div align="center"> <img src="results/199_a2b.png" width="80%" height="50%"/> </div> </a> ## Author - @zsdonghao - @luomai ### Discussion - [TensorLayer Slack](https://join.slack.com/t/tensorlayer/shared_invite/enQtMjUyMjczMzU2Njg4LWI0MWU0MDFkOWY2YjQ4YjVhMzI5M2VlZmE4YTNhNGY1NjZhMzUwMmQ2MTc0YWRjMjQzMjdjMTg2MWQ2ZWJhYzc) - [TensorLayer WeChat](https://github.com/tensorlayer/tensorlayer-chinese/blob/master/docs/wechat_group.md) ### License - For academic and non-commercial use only. - For commercial use, please contact tensorlayer@gmail.com.