Torch implementation of various types of GANs (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN, EBGAN). Note that EBGAN and BEGAN implementation is still not stable yet. I am working on this.
- Torch7
- python2.7
- cuda
- other torch packages (display, hdf5, image ...)
- download training data:
python download.py --datasets <dataset>
(e.g) python run.py --datasets celebA
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The training data folder should look like :
<train_data_root>
|--classA
|--image1A
|--image2B ...
|--classB
|--image1B
|--image2B ...
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- run GANs training:
Note that you need to change parameter options in "script/opts.lua" for each GANs.
python run.py --type <gan_type>
(e.g) python run.py --type dcgan
step by step instruction:
1. set server-related options(ip, port, etc.) in "script.opts.lua"
2. run server (python server.py --type <gan_type>)
3. open web browser, and connect. (https://<server_ip>:<server_port>)
you will see like this:
| training | Final |
|---|---|
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- brought dataloader code from (DCGAN)
- referenced the code from (Context-encoder)
MinchulShin, @nashory
Will keep updating other types of GANs.
Any insane bug reports or questions are welcome. (min.stellastra[at]gmail.com) :-)