If you get interested in this repository, I recommend you to see Nvidia's official Pytorch implementation.
https://nv-adlr.github.io/publication/partialconv-inpainting
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)
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.
#To try default setting
python generate_windows.py 256 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)
Firstly, check implementation FAQ
other differences:
This repository utilizes the codes of following impressive repositories