Reproduction of Nvidia image inpainting paper "Image Inpainting for Irregular Holes Using Partial Convolutions" https://arxiv.org/abs/1804.07723
1,000 iteration results (completion, output, mask) "completion" represents the input images whose masked pixels are replaced with the corresonded pixels of the output images
10,000 iteration results (completion, output, mask)
100,000 iteration results (completion, output, mask)
- Python 3.7.11
- chainer 6.7.0
- opencv (only for cv.imread, you can replace it with PIL)
- PIL
or see the requirements.txt
Edit common/paths.py
train_place2 = "/yourpath/place2/data_256/"
val_place2 = "/yourpath/place2/val_256/"
test_place2 = "/yourpath/test_256/"In this implementation, masks are automatically generated in advance.
python generate_windows.py image_size generate_num"image_size" indicates image size of masks.
"generate_num" indicates the number of masks to generate.
Default implementation uses image_size=256 and generate_num=1000.
Note that original paper uses 512x512 image and generate mask with different way.
python train.py -g 0 -g represents gpu option.(utilize gpu of No.0)
- Place2
- CelebA
- COMPLEXITY COMPARISON
All training and testing base on same 2080 Ti.
- Place2
- CelebA
Firstly, check implementation FAQ
- C(0)=0 in first implementation (already fix in latest version)
- Masks are generated using random walk by generate_window.py
- To use chainer VGG pre-traied model, I re-scaled input of the model. See updater.vgg_extract. It includes cropping, so styleloss in outside of crop box is ignored.)
- Padding is to make scale of height and width input:output=2:1 in encoder stage.
other differences:
- image_size=256x256 (original: 512x512)
This repository utilizes the codes of following impressive repositories
If you have any question, feel free to contact wiwi61666166@gmail.com