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https://github.com/AAnoosheh/ComboGAN


https://github.com/AAnoosheh/ComboGAN

cyclegan domain-adaptation gan image-manipulation neural-network

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README 

        

# ComboGAN



This is our ongoing PyTorch implementation for ComboGAN.

Code was written by [Asha Anoosheh](https://github.com/aanoosheh) (built upon [CycleGAN](https://github.com/junyanz/CycleGAN))



#### [[ComboGAN Paper]](https://arxiv.org/pdf/1712.06909.pdf)




If you use this code for your research, please cite:



ComboGAN: Unrestrained Scalability for Image Domain Translation

[Asha Anoosheh](http://ashaanoosheh.com),  [Eirikur Augustsson](https://relational.github.io/), [Radu Timofte](http://www.vision.ee.ethz.ch/~timofter/), [Luc van Gool](https://www.vision.ee.ethz.ch/en/members/get_member.cgi?id=1)

In Arxiv, 2017.














## Prerequisites

- Linux or macOS

- Python 3

- CPU or NVIDIA GPU + CUDA CuDNN



## Getting Started

### Installation

- Install PyTorch and dependencies from http://pytorch.org

- Install Torch vision from the source.

```bash

git clone https://github.com/pytorch/vision

cd vision

python setup.py install

```

- Install python libraries [visdom](https://github.com/facebookresearch/visdom) and [dominate](https://github.com/Knio/dominate).

```bash

pip install visdom

pip install dominate

```

- Clone this repo:

```bash

git clone https://github.com/AAnoosheh/ComboGAN.git

cd ComboGAN

```



### ComboGAN training

Our ready datasets can be downloaded using `./datasets/download_dataset.sh `.



A pretrained model for the 14-painters dataset can be found [HERE](https://www.dropbox.com/s/t8s6x0bu52d73s0/paint14_pretrained.zip?dl=0). Place under `./checkpoints/` and test using the instructions below, with args `--name paint14_pretrained --dataroot ./datasets/painters_14 --n_domains 14 --which_epoch 1150`.



Example running scripts can be found in the `scripts` directory.



- Train a model:

```

python train.py --name  --dataroot ./datasets/ --n_domains  --niter  --niter_decay 

```

Checkpoints will be saved by default to `./checkpoints//`

- Fine-tuning/Resume training:

```

python train.py --continue_train --which_epoch  --name  --dataroot ./datasets/ --n_domains  --niter  --niter_decay 

```

- Test the model:

```

python test.py --phase test --name  --dataroot ./datasets/ --n_domains  --which_epoch  --serial_test

```

The test results will be saved to a html file here: `./results///index.html`.



## Training/Testing Details

- Flags: see `options/train_options.py` for training-specific flags; see `options/test_options.py` for test-specific flags; and see `options/base_options.py` for all common flags.

- Dataset format: The desired data directory (provided by `--dataroot`) should contain subfolders of the form `train*/` and `test*/`, and they are loaded in alphabetical order. (Note that a folder named train10 would be loaded before train2, and thus all checkpoints and results would be ordered accordingly.)

- CPU/GPU (default `--gpu_ids 0`): set`--gpu_ids -1` to use CPU mode; set `--gpu_ids 0,1,2` for multi-GPU mode. You need a large batch size (e.g. `--batchSize 32`) to benefit from multiple GPUs.

- Visualization: during training, the current results and loss plots can be viewed using two methods. First, if you set `--display_id` > 0, the results and loss plot will appear on a local graphics web server launched by [visdom](https://github.com/facebookresearch/visdom). To do this, you should have `visdom` installed and a server running by the command `python -m visdom.server`. The default server URL is `http://localhost:8097`. `display_id` corresponds to the window ID that is displayed on the `visdom` server. The `visdom` display functionality is turned on by default. To avoid the extra overhead of communicating with `visdom` set `--display_id 0`. Secondly, the intermediate results are also saved to `./checkpoints//web/index.html`. To avoid this, set the `--no_html` flag.

- Preprocessing: images can be resized and cropped in different ways using `--resize_or_crop` option. The default option `'resize_and_crop'` resizes the image to be of size `(opt.loadSize, opt.loadSize)` and does a random crop of size `(opt.fineSize, opt.fineSize)`. `'crop'` skips the resizing step and only performs random cropping. `'scale_width'` resizes the image to have width `opt.fineSize` while keeping the aspect ratio. `'scale_width_and_crop'` first resizes the image to have width `opt.loadSize` and then does random cropping of size `(opt.fineSize, opt.fineSize)`.



NOTE: one should **not** expect ComboGAN to work on just any combination of input and output datasets (e.g. `dogs<->houses`). We find it works better if two datasets share similar visual content. For example, `landscape painting<->landscape photographs` works much better than `portrait painting <-> landscape photographs`.