AHDRNet-PyTorch

• This is the PyTorch implementation of Attention-guided Network for Ghost-free High Dynamic Range Imaging (CVPR 2019).
• The official code is at AHDRNet. However, there exists some problems in training and testing within the official implementation which are not solved.

1. Environment

• Python >= 3.7
• PyTorch >= 1.4.0
• opencv-python = 4.5.1
• imageio = 2.13.3
• matplotlib

2. Dataset

The training data and testing data is from Kalantari (ACM TOG 2017), the dataset can be downloade from Kalantari Dataset.

3. Quick Demo (Only for tiff format 48-depth images (same with the Kalantari Dataset) now, others in progress)

1. Clone this repository:

   git clone https://github.com/ytZhang99/AHDRNet-PyTorch.git
   

2. Place the test image folders in ./data/Test/:

   Test
   └── test_data
       ├── Name_A
       |   ├── 1.tif
       |   ├── 2.tif
       |   ├── 3.tif
       |   ├── exposure.txt
       |   └── HDRImg.hdr (optional)
       └── Name_B
   

3. Run the following command to test:

   python main.py --test_only
   

   The output images are placed in ./results/0_epoch/

4. Training

1. Place the training image folders in ./data/Train/:

   Train
   └── train_data
       ├── Name_A
       |   ├── 1.tif
       |   ├── 2.tif
       |   ├── 3.tif
       |   ├── exposure.txt
       |   └── HDRImg.hdr
       └── Name_B
   

2. Modify the main.sh file and run the following command to train:

   sh main.sh
   

   Notice that the default setting of this program is implementing validation on the test dataset after training, you can modify main.sh to close the validation progress.
3. The trained model is saved in ./ckp/, then you can test your own model:

   python main.py --test_only --model latest.pth
   python main.py --test_only --model best_checkpoint.pth (This model is accessible with validation)