Awesome
pytorch-inpainting-with-partial-conv
Official implementation is released by the authors.
Note that this is an ongoing re-implementation and I cannot fully reproduce the results. Suggestions and PRs are welcome!
This is an unofficial pytorch implementation of a paper, Image Inpainting for Irregular Holes Using Partial Convolutions [Liu+, arXiv2018].
Requirements
- Python 3.6+
- Pytorch 0.4.1+
pip install -r requirements.txt
Usage
Preprocess
-
download Places2 and place it somewhere. The dataset should contain
data_large
,val_large
, andtest_large
as the subdirectories. Don't forget to specify the root of the dataset by--root ROOT
when usingtrain.py
ortest.py
-
Generate masks by following [1] (saved under
./masks
by default). Note that the way of the mask generation is different from the original work
python generate_data.py
Train
CUDA_VISIBLE_DEVICES=<gpu_id> python train.py
Fine-tune
CUDA_VISIBLE_DEVICES=<gpu_id> python train.py --finetune --resume <checkpoint_name>
Test
CUDA_VISIBLE_DEVICES=<gpu_id> python test.py --snapshot <snapshot_path>
Results
Here are some results from the test set after the training of 500,000 iterations and fine-tuning (freezing BN in encoder) of 500,000 iterations. The model is available here, but I don't ensure the quality.
(Top to bottom: input, mask, image generated by the network, image which is combined with the original non-masked region of image, ground truth)