Mmgen

Highlights

- Add more experiments for conditional GANs: SNGAN, SAGAN, and BigGAN
- Refact Translation Model (88, 126, 127, 145)

New Features

- Use PyTorch Sphinx theme 123
- Support torchserve for unconditional models 131

Fix bugs and Improvements

- Add CI for python3.9 110
- Add support for PyTorch1.9 115
- Add pre-commit hook for spell checking 135

Highlights

- Support conditional GANs: Projection GAN, SNGAN, SAGAN, and BigGAN

New Features

- Add support for persistent_workers in PyTorch >= 1.7.0 71
- Support warm-up for EMA 55

Fix bugs and Improvements

- Fix failing to build docs 64
- Revise the logic of `num_classes` in basic conditional gan 69
- Support dynamic eval internal in eval hook 73

Highlights

- Support new methods: LSGAN, GGAN.
- Support mixed-precision training (FP16): official PyTorch Implementation and APEX (11, 20)

New Features

- Add the experiment of MNIST in DCGAN (24)
- Add support for uploading checkpoints to `Ceph` system (cloud server) (27)
- Add the functionality of saving the best checkpoint in GenerativeEvalHook (21)

Fix bugs and Improvements

- Fix loss of sample-cfg argument (13)
- Add `pbar` to offline eval and fix bug in grayscale image evaluation/saving (23)
- Fix error when data_root option in val_cfg or test_cfg are set as None (28)
- Change latex in quick_run.md to svg url and fix number of checkpoints in modelzoo_statistics.md (34)

**Highlights**

- MMGeneration is released.

**Main Features**

- High-quality Training Performance: We currently support training on Unconditional GANs (`DCGAN`, `WGAN-GP`, `PGGAN`, `StyleGANV1`, `StyleGANV2`, `Positional Encoding in GANs`), Internal GANs (`SinGAN`), and Image Translation Models (`Pix2Pix`, `CycleGAN`). Support for conditional models will come soon.
- Powerful Application Toolkit: A plentiful toolkit containing multiple applications in GANs is provided to users. GAN interpolation, GAN projection, and GAN manipulations are integrated into our framework. It's time to play with your GANs!
- Efficient Distributed Training for Generative Models: For the highly dynamic training in generative models, we adopt a new way to train dynamic models with `MMDDP`.
- New Modular Design for Flexible Combination: A new design for complex loss modules is proposed for customizing the links between modules, which can achieve flexible combination among different modules.