We develop a new approach for image inpainting that does a better job of reproducing filled regions exhibiting fine details inspired by our understanding of how artists work: lines first, color next. We propose a two-stage adversarial model EdgeConnect that comprises of an edge generator followed by an image completion network. The edge generator hallucinates edges of the missing region (both regular and irregular) of the image, and the image completion network fills in the missing regions using hallucinated edges as a priori. Detailed description of the system can be found in our paper.
- Python 3
- PyTorch 1.0
- NVIDIA GPU + CUDA cuDNN
- Clone this repo:
git clone https://github.com/knazeri/edge-connect.git
cd edge-connect- Install PyTorch and dependencies from http://pytorch.org
- Install python requirements:
pip install -r requirements.txtWe use Places2, CelebA and Paris Street-View datasets. To train a model on the full dataset, download datasets from official websites. Our model is trained on the irregular mask dataset provided by Liu et al.. You can download publically available train/test mask dataset from their website.
After downloading, run scripts/flist.py to generate train, test and validation set file lists. For example, to generate the training set file list on Places2 dataset run:
mkdir datasets
python ./scripts/flist.py --path path_to_places2_train_set --output ./datasets/places_train.flistDownload the pre-trained models using the following links and copy them under ./checkpoints directory.
Places2 | CelebA | Paris-StreetView
Alternatively, you can run the following script to automatically download the pre-trained models:
bash ./scripts/download_model.shTo train the model, create a config.yaml file similar to the example config file and copy it under your checkpoints directory. Read the configuration guide for more information on model configuration.
EdgeConnect is trained in three stages: 1) training the edge model, 2) training the inpaint model and 3) training the joint model. To train the model:
python train.py --model [stage] --checkpoints [path to checkpoints]For example to train the edge model on Places2 dataset under ./checkpoints/places2 directory:
python train.py --model 1 --checkpoints ./checkpoints/places2Convergence of the model differs from dataset to dataset. For example Places2 dataset converges in one of two epochs, while smaller datasets like CelebA require almost 40 epochs to converge. You can set the number of training iterations by changing MAX_ITERS value in the configuration file.
To test the model, create a config.yaml file similar to the example config file and copy it under your checkpoints directory. Read the configuration guide for more information on model configuration.
You can test the model on all three stages: 1) edge model, 2) inpaint model and 3) joint model. In each case, you need to provide an input image (image with a mask) and a grayscale mask file. Please make sure that the mask file covers the entire mask region in the input image. To test the model:
python test.py \
--model [stage] \
--checkpoints [path to checkpoints] \
--input [path to input directory or file] \
--mask [path to masks directory or mask file] \
--output [path to the output directory]We provide some test examples under ./examples directory. Please download the pre-trained models and run:
python test.py \
--checkpoints ./checkpoints/places2
--input ./examples/places2/images
--mask ./examples/places2/masks
--output ./checkpoints/resultsThis script will inpaint all images in ./examples/places2/images using their corresponding masks in ./examples/places2/mask directory and saves the results in ./checkpoints/results directory. By default test.py script is run on stage 3 (--model=3).
To evaluate the model, you need to first run the model in test mode against your validartion set and save the results on disk. We provide a utility ./scripts/metrics.py to evaluate the model using PSNR, SSIM and Mean Absolute Error:
python ./scripts/metrics.py --data-path [path to validation set] --output-path [path to model output]To measure the Fréchet Inception Distance (FID score) run ./scripts/fid_score.py. We utilize the PyTorch implementation of FID from here which uses the pretrained weights from PyTorch's Inception model.
python ./scripts/fid_score.py --path [path to validation, path to model output] --gpu [GPU id to use]The model configuration is stored in a config.yaml file under your checkpoints directory. The following tables provide the documentation for all the options available in the configuration file:
| Option | Description |
|---|---|
| MODE | 1: train, 2: test, 3: eval |
| MODEL | 1: edge model, 2: inpaint model, 3: edge-inpaint model, 4: joint model |
| MASK | 1: random block, 2: half, 3: external, 4: external + random block, 5: external + random block + half |
| EDGE | 1: canny, 2: external |
| NMS | 0: no non-max-suppression, 1: non-max-suppression on the external edges |
| SEED | random number generator seed |
| GPU | list of gpu ids, comma separated list e.g. [0,1] |
| DEBUG | 0: no debug, 1: debugging mode |
| VERBOSE | 0: no verbose, 1: output detailed statistics in the output console |
| Option | Description |
|---|---|
| TRAIN_FLIST | text file containing training set files list |
| VAL_FLIST | text file containing validation set files list |
| TEST_FLIST | text file containing test set files list |
| TRAIN_EDGE_FLIST | text file containing training set external edges files list (only with EDGE=2) |
| VAL_EDGE_FLIST | text file containing validation set external edges files list (only with EDGE=2) |
| TEST_EDGE_FLIST | text file containing test set external edges files list (only with EDGE=2) |
| TRAIN_MASK_FLIST | text file containing training set masks files list (only with MASK=3, 4, 5) |
| VAL_MASK_FLIST | text file containing validation set masks files list (only with MASK=3, 4, 5) |
| TEST_MASK_FLIST | text file containing test set masks files list (only with MASK=3, 4, 5) |
| Option | Default | Description |
|---|---|---|
| LR | 0.0001 | learning rate |
| D2G_LR | 0.1 | discriminator/generator learning rate ratio |
| BETA1 | 0.0 | adam optimizer beta1 |
| BETA2 | 0.9 | adam optimizer beta2 |
| BATCH_SIZE | 8 | input batch size |
| INPUT_SIZE | 256 | input image size for training. (0 for original size) |
| SIGMA | 2 | standard deviation of the Gaussian filter used in Canny edge detector (0: random, -1: no edge) |
| MAX_ITERS | 2e7 | maximum number of iterations to train the model |
| EDGE_THRESHOLD | 0.5 | edge detection threshold (0-1) |
| L1_LOSS_WEIGHT | 1 | l1 loss weight |
| FM_LOSS_WEIGHT | 10 | feature-matching loss weight |
| STYLE_LOSS_WEIGHT | 1 | style loss weight |
| CONTENT_LOSS_WEIGHT | 1 | perceptual loss weight |
| INPAINT_ADV_LOSS_WEIGHT | 0.01 | adversarial loss weight |
| GAN_LOSS | nsgan | nsgan: non-saturating gan, lsgan: least squares GAN, hinge: hinge loss GAN |
| GAN_POOL_SIZE | 0 | fake images pool size |
| SAVE_INTERVAL | 1000 | how many iterations to wait before saving model (0: never) |
| EVAL_INTERVAL | 0 | how many iterations to wait before evaluating the model (0: never) |
| LOG_INTERVAL | 10 | how many iterations to wait before logging training loss (0: never) |
| SAMPLE_INTERVAL | 1000 | how many iterations to wait before saving sample (0: never) |
| SAMPLE_SIZE | 12 | number of images to sample on each samling interval |
Licensed under a Creative Commons Attribution-NonCommercial 4.0 International.
Except where otherwise noted, this content is published under a CC BY-NC license, which means that you can copy, remix, transform and build upon the content as long as you do not use the material for commercial purposes and give appropriate credit and provide a link to the license.
If you use this code for your research, please cite our paper EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning:
@inproceedings{nazeri2019edgeconnect,
title={EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning},
author={Nazeri, Kamyar and Ng, Eric and Joseph, Tony and Qureshi, Faisal and Ebrahimi, Mehran},
journal={arXiv preprint},
year={2019},
}