Awesome
LMLT(Low-to-high Multi-Level Vision Transformer)
Jeongsoo Kim, Jongho Nang, Junsuk Choe<sup>*</sup>
<sup>*</sup> : Corresponding author
Requirements
# Install Packages
pip install -r requirements.txt
pip install matplotlib
# Install BasicSR
python3 setup.py develop
Dataset
We used DIV2K, Flickr2K as Training dataset. You can download two datasets at https://github.com/dslisleedh/Download_df2k/blob/main/download_df2k.sh and prepare other test datasets at https://github.com/XPixelGroup/BasicSR/blob/master/docs/DatasetPreparation.md#Common-Image-SR-Datasets
And also, you'd better extract subimages using
python3 scripts/data_preparation/extract_subimages.py
By running the code above, you may get subimages of training datasets.
Training
You can train LMLT following commands below
python3 basicsr/train.py -opt options/train/LMLT/train_tiny(base, large)_DF2K_X2(3, 4).yml
And also, we set torch.backends.cudnn.benchmark
to True
to accelerate training process so that results can be fluctuated a little. If you want to get fixed output, you should set it to False
and set torch.backends.cudnn.deterministic
to True
.
Test
You can test LMLT following commands below
python3 basicsr/test.py -opt options/test/LMLT/test_tiny(base, large)_benchmark_X2(3, 4).yml
Result
Result table with #Param and #FLOPs
Result table with GPU Consumption and AVG Time
Results
We will provide visual results of LMLT_Base x4 scale soon.
If you want to see only architecture, please refer to LMLT.py
.