import argparse import os import requests import re """ How to use: download all the models: python main_download_pretrained_models.py --models "all" --model_dir "model_zoo" download DnCNN models: python main_download_pretrained_models.py --models "DnCNN" --model_dir "model_zoo" download SRMD models: python main_download_pretrained_models.py --models "SRMD" --model_dir "model_zoo" download BSRGAN models: python main_download_pretrained_models.py --models "BSRGAN" --model_dir "model_zoo" download FFDNet models: python main_download_pretrained_models.py --models "FFDNet" --model_dir "model_zoo" download DPSR models: python main_download_pretrained_models.py --models "DPSR" --model_dir "model_zoo" download SwinIR models: python main_download_pretrained_models.py --models "SwinIR" --model_dir "model_zoo" download VRT models: python main_download_pretrained_models.py --models "VRT" --model_dir "model_zoo" download other models: python main_download_pretrained_models.py --models "others" --model_dir "model_zoo" ------------------------------------------------------------------ download 'dncnn_15.pth' and 'dncnn_50.pth' python main_download_pretrained_models.py --models "dncnn_15.pth dncnn_50.pth" --model_dir "model_zoo" ------------------------------------------------------------------ download DnCNN models and 'BSRGAN.pth' python main_download_pretrained_models.py --models "DnCNN BSRGAN.pth" --model_dir "model_zoo" """ def download_pretrained_model(model_dir='model_zoo', model_name='dncnn3.pth'): if os.path.exists(os.path.join(model_dir, model_name)): print(f'already exists, skip downloading [{model_name}]') else: os.makedirs(model_dir, exist_ok=True) if 'SwinIR' in model_name: url = 'https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/{}'.format(model_name) elif 'VRT' in model_name: url = 'https://github.com/JingyunLiang/VRT/releases/download/v0.0/{}'.format(model_name) else: url = 'https://github.com/cszn/KAIR/releases/download/v1.0/{}'.format(model_name) r = requests.get(url, allow_redirects=True) print(f'downloading [{model_dir}/{model_name}] ...') open(os.path.join(model_dir, model_name), 'wb').write(r.content) print('done!') if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--models', type=lambda s: re.split(' |, ', s), default = "dncnn3.pth", help='comma or space delimited list of characters, e.g., "DnCNN", "DnCNN BSRGAN.pth", "dncnn_15.pth dncnn_50.pth"') parser.add_argument('--model_dir', type=str, default='model_zoo', help='path of model_zoo') args = parser.parse_args() print(f'trying to download {args.models}') method_model_zoo = {'DnCNN': ['dncnn_15.pth', 'dncnn_25.pth', 'dncnn_50.pth', 'dncnn3.pth', 'dncnn_color_blind.pth', 'dncnn_gray_blind.pth'], 'SRMD': ['srmdnf_x2.pth', 'srmdnf_x3.pth', 'srmdnf_x4.pth', 'srmd_x2.pth', 'srmd_x3.pth', 'srmd_x4.pth'], 'DPSR': ['dpsr_x2.pth', 'dpsr_x3.pth', 'dpsr_x4.pth', 'dpsr_x4_gan.pth'], 'FFDNet': ['ffdnet_color.pth', 'ffdnet_gray.pth', 'ffdnet_color_clip.pth', 'ffdnet_gray_clip.pth'], 'USRNet': ['usrgan.pth', 'usrgan_tiny.pth', 'usrnet.pth', 'usrnet_tiny.pth'], 'DPIR': ['drunet_gray.pth', 'drunet_color.pth', 'drunet_deblocking_color.pth', 'drunet_deblocking_grayscale.pth'], 'BSRGAN': ['BSRGAN.pth', 'BSRNet.pth', 'BSRGANx2.pth'], 'IRCNN': ['ircnn_color.pth', 'ircnn_gray.pth'], 'SwinIR': ['001_classicalSR_DF2K_s64w8_SwinIR-M_x2.pth', '001_classicalSR_DF2K_s64w8_SwinIR-M_x3.pth', '001_classicalSR_DF2K_s64w8_SwinIR-M_x4.pth', '001_classicalSR_DF2K_s64w8_SwinIR-M_x8.pth', '001_classicalSR_DIV2K_s48w8_SwinIR-M_x2.pth', '001_classicalSR_DIV2K_s48w8_SwinIR-M_x3.pth', '001_classicalSR_DIV2K_s48w8_SwinIR-M_x4.pth', '001_classicalSR_DIV2K_s48w8_SwinIR-M_x8.pth', '002_lightweightSR_DIV2K_s64w8_SwinIR-S_x2.pth', '002_lightweightSR_DIV2K_s64w8_SwinIR-S_x3.pth', '002_lightweightSR_DIV2K_s64w8_SwinIR-S_x4.pth', '003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth', '003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_PSNR.pth', '004_grayDN_DFWB_s128w8_SwinIR-M_noise15.pth', '004_grayDN_DFWB_s128w8_SwinIR-M_noise25.pth', '004_grayDN_DFWB_s128w8_SwinIR-M_noise50.pth', '005_colorDN_DFWB_s128w8_SwinIR-M_noise15.pth', '005_colorDN_DFWB_s128w8_SwinIR-M_noise25.pth', '005_colorDN_DFWB_s128w8_SwinIR-M_noise50.pth', '006_CAR_DFWB_s126w7_SwinIR-M_jpeg10.pth', '006_CAR_DFWB_s126w7_SwinIR-M_jpeg20.pth', '006_CAR_DFWB_s126w7_SwinIR-M_jpeg30.pth', '006_CAR_DFWB_s126w7_SwinIR-M_jpeg40.pth'], 'VRT': ['001_VRT_videosr_bi_REDS_6frames.pth', '002_VRT_videosr_bi_REDS_16frames.pth', '003_VRT_videosr_bi_Vimeo_7frames.pth', '004_VRT_videosr_bd_Vimeo_7frames.pth', '005_VRT_videodeblurring_DVD.pth', '006_VRT_videodeblurring_GoPro.pth', '007_VRT_videodeblurring_REDS.pth', '008_VRT_videodenoising_DAVIS.pth'], 'others': ['msrresnet_x4_psnr.pth', 'msrresnet_x4_gan.pth', 'imdn_x4.pth', 'RRDB.pth', 'ESRGAN.pth', 'FSSR_DPED.pth', 'FSSR_JPEG.pth', 'RealSR_DPED.pth', 'RealSR_JPEG.pth'] } method_zoo = list(method_model_zoo.keys()) model_zoo = [] for b in list(method_model_zoo.values()): model_zoo += b if 'all' in args.models: for method in method_zoo: for model_name in method_model_zoo[method]: download_pretrained_model(args.model_dir, model_name) else: for method_model in args.models: if method_model in method_zoo: # method, need for loop for model_name in method_model_zoo[method_model]: if 'SwinIR' in model_name: download_pretrained_model(os.path.join(args.model_dir, 'swinir'), model_name) elif 'VRT' in model_name: download_pretrained_model(os.path.join(args.model_dir, 'vrt'), model_name) else: download_pretrained_model(args.model_dir, model_name) elif method_model in model_zoo: # model, do not need for loop if 'SwinIR' in method_model: download_pretrained_model(os.path.join(args.model_dir, 'swinir'), method_model) elif 'VRT' in method_model: download_pretrained_model(os.path.join(args.model_dir, 'vrt'), method_model) else: download_pretrained_model(args.model_dir, method_model) else: print(f'Do not find {method_model} from the pre-trained model zoo!')