(Image from https://github.com/JingyunLiang/SwinIR/tree/main/testsets)
Automatically downloads the onnx and prototxt files on the first run. It is necessary to be connected to the Internet while downloading.
For the sample image,
$ python3 swinir.py --onnx
Please be careful that onnxruntime is used bacause ailia model is not implemented.
If you want to specify the input image, put the image path after the --input
option.
You can use --savepath
option to change the name of the output file to save.
$ python3 swinir.py --input IMAGE_PATH --savepath SAVE_IMAGE_PATH --onnx
(ex) $ python3 swinir.py --input input_classical.png --savepath example.png --onnx
By adding the --model_name
option, you can choose the model.
$ python3 swinir.py --model_name MODEL_NAME --onnx
(ex) $ python3 swinir.py --model_name classical --onnx
(ex) $ python3 swinir.py --model_name lightweight --onnx
(ex) $ python3 swinir.py --model_name real --onnx
(ex) $ python3 swinir.py --model_name gray --onnx
(ex) $ python3 swinir.py --model_name color --onnx
(ex) $ python3 swinir.py --model_name jpeg --onnx
By adding the --video option, you can input the video. If you pass 0 as an argument to VIDEO_PATH, you can use the webcam input instead of the video file.
$ python3 swinir.py --video VIDEO_PATH --onnx
(ex) $ python3 swinir.py --video demo.mp4 --onnx
(ex) $ python3 swinir.py --video demo.mp4 -s output2.mp4 --onnx
(ex) $ python3 swinir.py --video demo.mp4 --model_name classical --onnx
(ex) $ python3 swinir.py --video demo.mp4 -s output.mp4 --model_name lightweight --onnx
- (Github) SwinIR: Image Restoration Using Swin Transformer
- (Paper) SwinIR: Image Restoration Using Swin Transformer
Pytorch 1.7.1
ONNX opset=11
001_classicalSR_DIV2K_s48w8_SwinIR-M_x2.onnx.prototxt 002_lightweightSR_DIV2K_s64w8_SwinIR-S_x2.onnx.prototxt 003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.onnx.prototxt 004_grayDN_DFWB_s128w8_SwinIR-M_noise25.onnx.prototxt 005_colorDN_DFWB_s128w8_SwinIR-M_noise25.onnx.prototxt 006_CAR_DFWB_s126w7_SwinIR-M_jpeg10.onnx.prototxt