Awesome
ESRT
Efficient Transformer for Single Image Super-Resolution
Update
#######22.03.17########
The result images of our method are collected in fold "/result".
Environment
- pytorch >=1.0
- python 3.6
- numpy
Model
<p align="center"> <img src="figs/esrt.png" width="960"> <br /> <em> The overall architecture of the proposed Efficient SR Transformer (ESRT). </em> </p> <p align="center"> <img src="figs/EMHA.png" width="960"> <br /> <em> Efficient Transformer and Efficient Multi-Head Attention. </em> </p>Train
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dataset: DIV2K
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prepare
Like IMDN, convert png files in DIV2K to npy files:
python scripts/png2npy.py --pathFrom /path/to/DIV2K/ --pathTo /path/to/DIV2K_decoded/ -
Training
python train.py --scale 2 --patch_size 96
python train.py --scale 3 --patch_size 144
python train.py --scale 4 --patch_size 192
If you want a better result, use 128/192/256 patch_size for each scale.
Test
Example:
- test B100 X4
python test.py --is_y --test_hr_folder dataset/benchmark/B100/HR/ --test_lr_folder dataset/benchmark/B100/LR_bicubic/X4/ --output_folder results/B100/x4 --checkpoint experiment/checkpoint/x4/epoch_990.pth --upscale_factor 4