CDTNet: High-Resolution Image Harmonization via Collaborative Dual Transformations (CVPR 2022)

Unofficial implementation of "High-Resolution Image Harmonization via Collaborative Dual Transformations (CVPR 2022)" in PyTorch.

Example Results

Prerequisites

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

Datasets

• HAdobe5k

Train

1. Download HRNet-W18-C model(hrnetv2_w18_imagenet_pretrained.pth) in HRNets
2. Put it in the pretrained folder.
3. Run:

   CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python train.py --model iih_base --name iih_base_allidh_test --dataset_root ~/IHD/ --dataset_name HAdobe5k --batch_size 80 --init_port 50000

Test

Train a new model

If you train a new model with the command above, latest_net_G.pth and latest_net_P2P.pth will be generated in the directory checkpoints/iih_base_lt_allihd.

Run:

CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python test.py --model iih_base --name iih_base_allidh_test --dataset_root ~/IHD/ --dataset_name HAdobe5k --batch_size 80 --init_port 50000

Apply pre-trained model

1. Download pre-trained models (model_G and model_P2P)
2. Change their names to latest_net_G.pth and latest_net_P2P.pth respectively.
3. Put them in the directory checkpoints/iih_base_lt_allihd.
4. Run (use the command above)

Quantitative Result

Acknowledgement

We borrowed some of the data modules and model functions from repo of IntrinsicHarmony, iSSAM, and 3DLUT.