This project is built from IDN, and thanks for the contributions of all the other researchers those who made their codes accessible.
- PyTorch>=1.0.0
- Numpy 1.15.4
- Pillow 5.4.1
- h5py 2.8.0
- tqdm 4.30.0
##result
The DIV2K, Set5 dataset converted to HDF5 can be downloaded from the links below.Otherwise, you can use prepare.py to create custom dataset.
| Dataset | Scale | Type | Link |
|---|---|---|---|
| DIV2K | 2 | Train | Download |
| DIV2K | 3 | Train | Download |
| DIV2K | 4 | Train | Download |
| Set5 | 2 | Eval | Download |
| Set5 | 3 | Eval | Download |
| Set5 | 4 | Eval | Download |
The Flickr2K dataset can be downloaded from the links below,and then you can use prepare.py to create custom dataset.
https://link.csdn.net/?target=http%3A%2F%2Fcv.snu.ac.kr%2Fresearch%2FEDSR%2FFlickr2K.tar
##for preparedata: python3 prepare.py --images-dir ../../DIV2K/DIV2K_train_HR --output-path ./h5file_DIV2K_train_HR_x4_train --scale 4 --eval False
##for train: python3 train.py --choose_net DRLN_BlancedAttention --train_file ./h5file_mirflickr_train_HR_x3_train --eval_file ./h5file_Set5_x4_test
##for eval all SR size && all networks(you should download checkpoints first); python3 eval_allsize_allnet.py
##for eval dingle image: python3 eval_singleimg.py --lr_image_file ./savedimg/Set5/4/EDSR_blanced_attention_2.png --hr_image_file ../classical_SR_datasets/Set5/Set5/butterfly.png
##for infer all size && all networks SR images(the SR images will be saved in the direct ./savedimg/*): python3 infer_allsize_allnet.py
##checkpoints We provide all network && all size checkpoints to prove that our experiments are convincing. you can get them from:https://pan.baidu.com/s/1gy-3jcikT2h-QfRduwoibg password: 2ubm
If the password fails or any other questions, please contact me:[email protected]
##Our Attention mechanism is very tiny and efficient, and has also been proved to be efficient in semantic segmentation missions,especially for light-weight models.