pixel-styler

Refactored CycleGAN / pix2pix

<img src=’docs/horse2zebra.gif’ width=384>

This is a concise refactoring version of official PyTorch implementation for image-to-image translation.

• More readable and reusable.
• Use abstract externel OneGAN estimator with tailored functional closures.
• Use TensorBoard to visualize the scalar and image in training procedure.

Prerequisites

• Python 3.6+
• PyTorch >= 0.3 and torchvision >= 0.2
• OneGAN >= 0.3.1

Getting Started

CycleGAN train/test

• Download a CycleGAN dataset (e.g. maps):

  bash ./datasets/download_cyclegan_dataset.sh maps
  

• Train a model:

  python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan --no_dropout
  

• To view training results and loss, run tensorboard --logdir logs and open the URL http://localhost:6006
• Test the model:

  python test.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan --no_dropout
  

pix2pix train/test

• Download a pix2pix dataset (e.g.facades):

  bash ./datasets/download_pix2pix_dataset.sh facades
  

• Train a model:

  python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --dataset_mode aligned \
  --which_model_netG unet_256 --which_direction BtoA --lambda_A 100 --no_lsgan --norm batch --pool_size 0
  

• To view training results and loss, run tensorboard --logdir logs and open the URL http://localhost:6006
• Test the model:

  python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --dataset_mode aligned \
  --which_model_netG unet_256 --which_direction BtoA --norm batch
  

Apply a pre-trained model (CycleGAN)

If you would like to apply a pre-trained model to a collection of input photos (without image pairs), please use --dataset_mode single and --model test options. Here’s command to apply a model to Facade label maps (stored in the directory facades/testB).

  python test.py --dataroot ./datasets/facades/testA/ --name {pre_trained_model_name} --model test --dataset_mode single

You might want to specify --which_model_netG to match the generator architecture of the trained model.

Apply a pre-trained model (pix2pix)

Download the pre-trained models using ./demo/pretrained_models/download_pix2pix_model.sh. For example, if you would like to download label2photo model on the Facades dataset,

bash demo/pretrained_models/download_pix2pix_model.sh facades_label2photo

Then generate the results using

  python test.py --dataroot ./datasets/facades/ --name facades_label2photo_pretrained --model pix2pix --dataset_mode aligned \
  --which_model_netG unet_256 --which_direction BtoA --norm batch

Note that we specified --which_direction BtoA to accomodate the fact that the Facades dataset’s A to B direction is photos to labels.

Also, the models that are currently available to download can be found by reading the output of bash demo/pretrained_models/download_pix2pix_model.sh

Training/test Details

• CPU/GPU (default --gpu_ids 0): set--gpu_ids -1 to use CPU mode; set --gpu_ids 0,1,2 for multi-GPU mode
• Preprocessing: images can be resized and cropped in different ways using --resize_or_crop
  • The default option 'resize_and_crop' resizes the image to (opt.loadSize, opt.loadSize) and random crop to (opt.fineSize, opt.fineSize)
  • 'crop' performs random cropping
  • 'scale_width' resizes the image’s width to opt.fineSize while keeping the aspect ratio
  • 'scale_width_and_crop' resizes the image to opt.loadSize and random crop to (opt.fineSize, opt.fineSize)
• Fine-tuning/Resume training: use --continue_train and load the model based on which_epoch. By default, epoch count is 1, set --epoch_count <int> to specify a starting epoch count

CycleGAN Datasets

Download the CycleGAN datasets using the following script. Some of the datasets are collected by other researchers. Please cite their papers if you use the data.

  bash ./datasets/download_cyclegan_dataset.sh dataset_name

• facades: 400 images from the CMP Facades dataset. [Citation]
• cityscapes: 2975 images from the Cityscapes training set. [Citation]
• maps: 1096 training images scraped from Google Maps.
• horse2zebra: 939 horse images and 1177 zebra images downloaded from ImageNet using keywords wild horse and zebra
• apple2orange: 996 apple images and 1020 orange images downloaded from ImageNet using keywords apple and navel orange.
• summer2winter_yosemite: 1273 summer Yosemite images and 854 winter Yosemite images were downloaded using Flickr API. See more details in our paper.
• monet2photo, vangogh2photo, ukiyoe2photo, cezanne2photo: The art images were downloaded from Wikiart. The real photos are downloaded from Flickr using the combination of the tags landscape and landscapephotography. The training set size of each class is Monet:1074, Cezanne:584, Van Gogh:401, Ukiyo-e:1433, Photographs:6853.
• iphone2dslr_flower: both classes of images were downlaoded from Flickr. The training set size of each class is iPhone:1813, DSLR:3316. See more details in our paper.

To train on your own datasets, you need to create a data folder with two subdirectories trainA and trainB that contain images from domain A and B. You can test the model on training set by setting --phase train with test.py, and can also create subdirectories testA and testB if you have test data.

pix2pix datasets

Download the pix2pix datasets using the following script. Some of the datasets are collected by other researchers. Please cite their papers if you use the data.

  bash ./datasets/download_pix2pix_dataset.sh dataset_name

• facades: 400 images from CMP Facades dataset. [Citation]
• cityscapes: 2975 images from the Cityscapes training set. [Citation]
• maps: 1096 training images scraped from Google Maps
• edges2shoes: 50k training images from UT Zappos50K dataset. Edges are computed by HED edge detector + post-processing. [Citation]
• edges2handbags: 137K Amazon Handbag images from iGAN project. Edges are computed by HED edge detector + post-processing. [Citation]

We provide a python script to generate pix2pix training data in the form of pairs of images {A,B}:

Create folder /path/to/data with subfolders A and B. A and B should each have their own subfolders train, val, test, etc. Put training images in style A at /path/to/data/A/train, and put corresponding images in style B at /path/to/data/B/train. Repeat for other data splits (val, test, etc).

Corresponding images in a pair {A,B} must be the same size and have the same filename, e.g., /path/to/data/A/train/1.jpg is considered to correspond to /path/to/data/B/train/1.jpg.

Once the data is formatted this way, use:

  python datasets/combine_A_and_B.py --fold_A /path/to/data/A --fold_B /path/to/data/B --fold_AB /path/to/data

This will combine each pair of images (A,B) into a single image file, ready for training.

CycleGAN

[Project] [Paper] [Torch]

Pix2pix

[Project] [Paper] [Torch]

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