DMFN (Dense Multi-scale Fusion Network)
This is an unoffical repository for reproducing model DMFN from the paper [Image Fine-grained Inpainting]. The original repository is here, but author have not commit the rest of implement code yet.
Prerequisites
- Python3.5 (or higher)
- pytorch 1.0(or higher) with GPU
- numpy
- OpenCV
- scipy
- tensorboardX
RESULT
Note that the following result maybe not as good as the paper because they are trained only in 1 epoch. You can get the final result in original author's github.
train
test
loss
Prepair the dataset
Download the dataset of celebA, unzip and split it to test/train dataset (or you can use my train/test file in CelebA/ ).
How to test
You can specify the folder address by the option --dataset_path, and set the pretrained model path by --load_model_dir when calling test.py as the following
python test.py ---dataset_path celeba_data --data_file img_align_celeba_png\test.txt --load_model_dir pretrained/1epoch
I train it only 1 epoch with single GPU, you can train it yourself for better performance or in custom dataset.
How to train
Use train.py as the following
python train.py ---dataset_path celeba_data --data_file img_align_celeba_png\test.txt --batch_size 8 --lr 2e-4
You can load the pretrained model by the option --load_model_dir too.
