DeepFillv2 Pytorch Lightning
Clean minimal implementation of Free-Form Image Inpainting with Gated Convolutions in pytorch lightning. Inspired from pytorch implementation by @avalonstrel.
The models lack support for guidance masks, but can be very easily added. Instead of contexual attention, uses self attention GAN. The dataset class lacks support for generating freeform masks.
Usage
usage: deepfillv2.py [-h] [--dataset DATASET] [--num_workers NUM_WORKERS]
[--image_size IMAGE_SIZE] [--bbox_shape BBOX_SHAPE]
[--bbox_randomness BBOX_RANDOMNESS]
[--bbox_margin BBOX_MARGIN] [--bbox_max_num BBOX_MAX_NUM]
[--vis_dataset VIS_DATASET] [--overfit]
[--max_epoch MAX_EPOCH] [--save_epoch SAVE_EPOCH]
[--lr LR] [--weight_decay WEIGHT_DECAY] [--l1_c_h L1_C_H]
[--l1_c_nh L1_C_NH] [--l1_r_h L1_R_H] [--l1_r_nh L1_R_NH]
[--gen_loss_alpha GEN_LOSS_ALPHA]
[--disc_loss_alpha DISC_LOSS_ALPHA]
[--batch_size BATCH_SIZE] [--input_nc INPUT_NC]
[--experiment EXPERIMENT]
[--visualization_set VISUALIZATION_SET]Arguments:
--dataset DATASET dataset name
--num_workers NUM_WORKERS num workers
--image_size IMAGE_SIZE input image size
--bbox_shape BBOX_SHAPE random box size
--bbox_randomness BBOX_RANDOMNESS variation in box size
--bbox_margin BBOX_MARGIN margin from boundaries for box
--bbox_max_num BBOX_MAX_NUM max num of boxes
--vis_dataset VIS_DATASET images to be visualized after each epoch
--overfit overfit
--max_epoch MAX_EPOCH number of epochs to train for
--save_epoch SAVE_EPOCH save every nth epoch
--lr LR learning rate, default=0.001
--weight_decay WEIGHT_DECAY weight decay
--l1_c_h L1_C_H reconstruction coarse weight for holes
--l1_c_nh L1_C_NH reconstruction coarse weight for non-holes
--l1_r_h L1_R_H reconstruction coarse weight for holes
--l1_r_nh L1_R_NH reconstruction coarse weight for non-holes
--gen_loss_alpha GEN_LOSS_ALPHA reconstruction coarse weight for non-holes
--disc_loss_alpha DISC_LOSS_ALPHA reconstruction coarse weight for non-holes
--batch_size BATCH_SIZE batch size
--input_nc INPUT_NC number of input channels + mask
--experiment EXPERIMENT experiment directory
--visualization_set VISUALIZATION_SET validation samples to be visualizedThe defaults should give reasonable performance in most cases.
Data
The datasets must be placed in the the following structure
├── dataset-name
├── images
# all the rgb images
├── split
├── train.txt
├── val.txt
├── test.txt
├── vis_0.txt
# names of images without extensionFor an example see the matterport folder in data directory.
Acknowledgements
Official repository for DeepFillv2 and pytorch implementation by @avalonstrel.