Bicubic++: Slim, Slimmer, Slimmest - Designing an Industry-Grade Super-Resolution Network (🚀 Winner of NTIRE RTSR Challange Track 2 (x3 SR) @ CVPR 2023)
##note:This is an unofficial code repo,some code is inherited from bicubic-plusplus,this method adds ~1dB on Bicubic upscaling PSNR for all tested SR datasets and runs with ~0.5ms(2000FPS) per image on RTX4090,only inference time is included, not pre-processing and post-processing time.
git clone https://github.com/Ysnower/bicubic-plusplus.git
2.Install the dependencies. Python 3.7 PyTorch 1.13.1 opencv-python 4.5.1.48 onnxruntime 1.10.0 onnxruntime-gpu 1.10.0
my gpu is RTX4090 with CUDA 12.1
only train on DIV2K dataset,you can use your own data, the number of hr images must be equal to lr images.
Download DIV2K data password:xb5r and unzip the DIV2K.zip to the datasets. I used 860 images for training and 40 images for validation.
1.Set validation & training dataset paths in configs/conf.yaml (data.val.lr_path, data.val.hr_path, data.train.lr_path, data.train.hr_path). Set loader.train.batch_size and loader.val.batch_size according to your dataset.
note: if use degradation(blur is True or img_noise is True), training will slow down,you can use degradation offline to generate lr data to the lr_path.
The scale/sr_rate must be divisible by patch_cropsize.
2.Run train code.
python3 train.py
python3 inference.py
You can change the img_path in the inference.py file to your image location.
1.pytorch to onnx model
python3 torch2onnx.py
You can change the model_path to your pytorch model location and change the export_onnx_file to your onnx model name.
2.onnxruntime inference
python3 onnx_inference.py