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[CVPR 2020] GAN Compression: Efficient Architectures for Interactive Conditional GANs
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# GAN Compression ### [project](https://hanlab.mit.edu/projects/gancompression/) | [paper](https://arxiv.org/abs/2003.08936) | [videos](https://www.youtube.com/playlist?list=PL80kAHvQbh-r5R8UmXhQK1ndqRvPNw_ex) | [slides](https://hanlab.mit.edu/projects/gancompression/resources/546-slides.pdf) **[NEW!]** GAN Compression is accepted by T-PAMI! We released our T-PAMI version in the [arXiv v4](https://arxiv.org/abs/2003.08936)! **[NEW!]** We release the codes of our [interactive demo](interactive_demo) and include the [TVM](https://github.com/apache/tvm) tuned model. It achieves **8FPS** on Jetson Nano GPU now! **[NEW!]** Add support to the [MUNIT](https://github.com/NVlabs/MUNIT), a multimodal unsupervised image-to-image translation approach! Please follow the [test commands](#munit) to test the pre-trained models and the [tutorial](docs/tutorials/fast_gan_compression.md) to train your own models!  *We introduce GAN Compression, a general-purpose method for compressing conditional GANs. Our method reduces the computation of widely-used conditional GAN models, including pix2pix, CycleGAN, MUNIT, and GauGAN, by 9-29x while preserving the visual fidelity. Our method is effective for a wide range of generator architectures, learning objectives, and both paired and unpaired settings.* GAN Compression: Efficient Architectures for Interactive Conditional GANs<br> [Muyang Li](https://lmxyy.me/), [Ji Lin](http://linji.me/), [Yaoyao Ding](https://yaoyaoding.com/), [Zhijian Liu](http://zhijianliu.com/), [Jun-Yan Zhu](https://www.cs.cmu.edu/~junyanz/), and [Song Han](https://songhan.mit.edu/)<br> MIT, Adobe Research, SJTU<br> In CVPR 2020. ## Demos <p align="center"> <img src="imgs/demo_xavier.gif" width=600> </p> ## Overview *GAN Compression framework: ① Given a pre-trained teacher generator G', we distill a smaller “once-for-all” student generator G that contains all possible channel numbers through weight sharing. We choose different channel numbers for the student generator G at each training step. ② We then extract many sub-generators from the “once-for-all” generator and evaluate their performance. No retraining is needed, which is the advantage of the “once-for-all” generator. ③ Finally, we choose the best sub-generator given the compression ratio target and performance target (FID or mIoU) using either brute-force or evolutionary search method. Optionally, we perform additional fine-tuning, and obtain the final compressed model.* ## Performance  *GAN Compression reduces the computation of pix2pix, cycleGAN and GauGAN by 9-21x, and model size by 4.6-33x.* ## Colab Notebook PyTorch Colab notebook: [CycleGAN](https://colab.research.google.com/github/mit-han-lab/gan-compression/blob/master/cycle_gan.ipynb) and [pix2pix](https://colab.research.google.com/github/mit-han-lab/gan-compression/blob/master/pix2pix.ipynb). ## Prerequisites * Linux * Python 3 * CPU or NVIDIA GPU + CUDA CuDNN ## Getting Started ### Installation - Clone this repo: ```shell git clone [email protected]:mit-han-lab/gan-compression.git cd gan-compression ``` - Install [PyTorch](https://pytorch.org) 1.4 and other dependencies (e.g., torchvision). - For pip users, please type the command `pip install -r requirements.txt`. - For Conda users, we provide an installation script `scripts/conda_deps.sh`. Alternatively, you can create a new Conda environment using `conda env create -f environment.yml`. ### CycleGAN #### Setup * Download the CycleGAN dataset (e.g., horse2zebra). ```shell bash datasets/download_cyclegan_dataset.sh horse2zebra ``` * Get the statistical information for the ground-truth images for your dataset to compute FID. We provide pre-prepared real statistic information for several datasets. For example, ```shell bash datasets/download_real_stat.sh horse2zebra A bash datasets/download_real_stat.sh horse2zebra B ```
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