Transformer for Image Harmonization and Beyond [Paper]

Zonghui Guo, Zhaorui Gu, Bing Zheng, Junyu Dong, Haiyong Zheng
IEEE Transactions on Pattern Analysis and Machine Intelligence

Here we provide the PyTorch implementation and pre-trained model of our latest version, if you require the code of our previous ICCV version ("Image Harmonization With Transformer"), please click the released version.

Prerequisites

• Linux
• Python 3
• CPU or NVIDIA GPU + CUDA CuDNN

Train/Test

• Download iHarmony4 dataset.

• Train our HT+ model (FC-TRE-DeCNN):

CUDA_VISIBLE_DEVICES=0 python train.py --model ht --tr_r_enc_head x --tr_r_enc_layers x --name experiment_name --dataset_root <dataset_dir> --dataset_name IHD --batch_size xx --init_port xxxx

• Test our HT+ model (FC-TRE-DeCNN):

CUDA_VISIBLE_DEVICES=0 python test.py --model ht --tr_r_enc_head x --tr_r_enc_layers x --name experiment_name --dataset_root <dataset_dir> --dataset_name IHD --batch_size xx --init_port xxxx

• Train our DHT+ model:

CUDA_VISIBLE_DEVICES=0 python train.py --model dht --light_use_mask --tr_r_enc_head x --tr_r_enc_layers x  --tr_i_dec_head x --tr_i_dec_layers x --tr_l_dec_head x --tr_l_dec_layers x --name DHT_experiment_name --dataset_root <dataset_dir> --dataset_name IHD --batch_size xx --init_port xxxx

• Test our DHT+ model:

CUDA_VISIBLE_DEVICES=0 python test.py --model dht --light_use_mask --tr_r_enc_head x --tr_r_enc_layers x  --tr_i_dec_head x --tr_i_dec_layers x --tr_l_dec_head x --tr_l_dec_layers x --name DHT_experiment_name --dataset_root <dataset_dir> --dataset_name IHD --batch_size xx --init_port xxxx

Apply a pre-trained model

• Download pre-trained models from Google Drive or BaiduCloud (access code: vmrg), and put latest_net_G.pth in the directory checkpoints/HT_2H9L_allihd or checkpoints/DHT_2H9L_allihd. Run:

# Our HT model
CUDA_VISIBLE_DEVICES=0 python test.py --model ht --tr_r_enc_head 2 --tr_r_enc_layers 9 --name HT_2H9L_allihd --dataset_root <dataset_dir> --dataset_name IHD --batch_size xx --init_port xxxx
# Our CNN-DHT model
CUDA_VISIBLE_DEVICES=0 python test.py --model dht --light_use_mask --tr_r_enc_head 2 --tr_r_enc_layers 9  --tr_i_dec_head 2 --tr_i_dec_layers 9 --tr_l_dec_head 2 --tr_l_dec_layers 9 --name DHT_2H9L_allihd --dataset_root <dataset_dir> --dataset_name IHD --batch_size xx --init_port xxxx

Evaluation

We provide the code in ih_evaluation.py. Run:

CUDA_VISIBLE_DEVICES=0 python evaluation/ih_evaluation.py --dataroot <dataset_dir> --result_root  results/experiment_name/test_latest/images/ --evaluation_type our --dataset_name ALL

Real composite image harmonnization

More compared results can be found at Google Drive or BaduCloud (access code: n37b).

Bibtex

If you use this code for your research, please cite our papers.

@article{guo2022transformer,
  title={Transformer for Image Harmonization and Beyond},
  author={Guo, Zonghui and Gu, Zhaorui and Zheng, Bing and Dong, Junyu and Zheng, Haiyong},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2022},
  publisher={IEEE}
}

@InProceedings{Guo_2021_ICCV,
    author    = {Guo, Zonghui and Guo, Dongsheng and Zheng, Haiyong and Gu, Zhaorui and Zheng, Bing and Dong, Junyu},
    title     = {Image Harmonization With Transformer},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {14870-14879}
}

Acknowledgement

For some of the data modules and model functions used in this source code, we need to acknowledge the repositories of DoveNet, CycleGAN, SpiralNet and IntrinsicHarmony.