Awesome
[ECCV 2018] A PyTorch implementation of Patch2Image
- This is a pytorch implementation of the paper, Unsupervised Holistic Image Generation from Key Local Patches. (ECCV 2018).
- Paper link: https://arxiv.org/abs/1703.10730 (warning: this is an old version. Latest version will be uploaded!)
Concept
Examples (Faces)
Examples (Cars)
Requirements
- Python2 or 3
- Cuda device (NVIDIA GTX1080Ti was used to test)
- Pytorch
- Visdom (optional)
- Tensorflow & Tensorboard (optional)
Preparing dataset
Download dataset via visiting celebA or CompCar.
For celebA dataset,
You can download using download.py
> python download.py celebA
For compcar dataset, Download the entire compcar dataset and some pre-processing is required.
You should crop the car patches using the ground truth bounding boxes, resize them 128*128 resolution, and save them in a single directory.
Key-patches
We already extracted key patches from celebA and compcar dataset and save the bounding box coordinates to celebA_allbbs.mat and compcar_allbbs.mat.
You can extract key patches and use your own key patches.
Training celebA dataset
Run
python main.py --db_name=celebA --dataset_root=YOUR_DATA_ROOT --is_crop=True --image_size=108 --output_size=64 --model_structure=unet
The resolution of output image can be enlarged by --output_size=128 or --output_size=256 options.
Training compcar dataset
Run
python main.py --db_name=compcar --dataset_root=YOUR_DATA_ROOT --is_crop=False --image_size=128 --output_size=128 --conv_dim=64 --batch_size=32 --model_structure=unet
Misc.
Modify the options output_size, conv_dim, or batch_size to prevent out-of-memory error.