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main_download_pretrained_models.py
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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 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)
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'],
'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)
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)
else:
download_pretrained_model(args.model_dir, method_model)
else:
print(f'Do not find {method_model} from the pre-trained model zoo!')