def define_G(opt):
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
# image restoration
if which_model == 'MSRResNet':
netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'],
upscale=opt_net['scale'])
elif which_model == 'RRDBNet':
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'],
upscale=opt_net['scale'])
# video restoration
elif which_model == 'EDVR':
netG = EDVR_arch.EDVR(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'])
else:
raise NotImplementedError(
'Generator model [{:s}] not recognized'.format(which_model))
return netG
def define_G(opt):
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
# image restoration
if which_model == 'MSRResNet':
netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
nf=opt_net['nf'], nb=opt_net['nb'], upscale=opt_net['scale'])
elif which_model == 'RCAN':
netG = RCAN_arch.RCAN(n_resblocks=opt_net['n_resblocks'], n_feats=opt_net['n_feats'],
res_scale=opt_net['res_scale'], n_colors=opt_net['n_colors'],rgb_range=opt_net['rgb_range'],
scale=opt_net['scale'],reduction=opt_net['reduction'],n_resgroups=opt_net['n_resgroups'])
elif which_model == 'CARN_M':
netG = CARN_arch.CARN_M(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
nf=opt_net['nf'], scale=opt_net['scale'], group=opt_net['group'])
elif which_model == 'fsrcnn':
netG = FSRCNN_arch.FSRCNN_net(input_channels=opt_net['in_nc'],upscale=opt_net['scale'],d=opt_net['d'],
s=opt_net['s'],m=opt_net['m'])
elif which_model == 'classSR_3class_fsrcnn_net':
netG = classSR_3class_arch.classSR_3class_fsrcnn_net(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'])
elif which_model == 'classSR_3class_rcan':
netG = classSR_rcan_arch.classSR_3class_rcan(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'])
elif which_model == 'classSR_3class_srresnet':
netG = classSR_srresnet_arch.ClassSR(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'])
elif which_model == 'classSR_3class_carn':
netG = classSR_carn_arch.ClassSR(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'])
else:
raise NotImplementedError('Generator model [{:s}] not recognized'.format(which_model))
return netG
def define_G(opt):
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
# image restoration
if which_model == 'MSRResNet':
netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'],
upscale=opt_net['scale'])
elif which_model == 'RRDBNet':
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'])
elif which_model == 'ORDSRNet':
netG = ORDSRModel(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
N=opt_net['base_size'],
S=opt_net['stride'],
upscale=opt_net['scale'])
else:
raise NotImplementedError(
'Generator model [{:s}] not recognized'.format(which_model))
return netG
def define_G(opt):
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
# image restoration
if which_model == 'MSRResNet':
netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'],
upscale=opt_net['scale'])
elif which_model == 'RRDBNet':
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'])
# video restoration
elif which_model == 'EDVR':
import models.archs.EDVR_arch as EDVR_arch
netG = EDVR_arch.EDVR(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'],
w_GCB=opt_net['w_GCB'])
# elif which_model == 'EDVR_woDCN':
# import models.archs.EDVR_woDCN_arch as EDVR_arch
# netG = EDVR_arch.EDVR(nf=opt_net['nf'], nframes=opt_net['nframes'],
# groups=opt_net['groups'], front_RBs=opt_net['front_RBs'],
# back_RBs=opt_net['back_RBs'], center=opt_net['center'],
# predeblur=opt_net['predeblur'], HR_in=opt_net['HR_in'],
# w_TSA=opt_net['w_TSA'], w_GCB=opt_net['w_GCB'])
elif which_model == 'MGANet':
netG = Gen_Guided_UNet(input_size=opt_net['input_size'])
elif which_model == 'Unet':
import repo.CycleGAN.networks as unet_networks
netG = unet_networks.define_G(2 * 3, 1, opt_net['nf'],
opt_net['G_type'], opt_net['norm'],
opt_net['dropout'], opt_net['init_type'],
opt_net['init_gain'])
else:
raise NotImplementedError(
'Generator model [{:s}] not recognized'.format(which_model))
return netG
def define_G(opt):
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
# image restoration
if which_model == 'MSRResNet':
import models.archs.SRResNet_arch as SRResNet_arch
netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'],
upscale=opt_net['scale'])
elif which_model == 'RRDBNet':
import models.archs.RRDBNet_arch as RRDBNet_arch
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'])
# video restoration
elif which_model == 'EDVR':
import models.archs.EDVR_arch as EDVR_arch
netG = EDVR_arch.EDVR(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'],
scale=opt['scale'])
elif which_model == 'DUF':
import models.archs.DUF_arch as DUF_arch
if opt_net['layers'] == 16:
netG = DUF_arch.DUF_16L(scale=opt['scale'], adapt_official=True)
elif opt_net['layers'] == 28:
netG = DUF_arch.DUF_28L(scale=opt['scale'], adapt_official=True)
else:
netG = DUF_arch.DUF_52L(scale=opt['scale'], adapt_official=True)
elif which_model == 'TOF':
import models.archs.TOF_arch as TOF_arch
netG = TOF_arch.TOFlow(adapt_official=True)
else:
raise NotImplementedError(
'Generator model [{:s}] not recognized'.format(which_model))
return netG
import os.path as osp
import sys
import torch
try:
sys.path.append(osp.dirname(osp.dirname(osp.abspath(__file__))))
import models.archs.SRResNet_arch as SRResNet_arch
except ImportError:
pass
pretrained_net = torch.load(
'../../experiments/pretrained_models/MSRResNetx4.pth')
crt_model = SRResNet_arch.MSRResNet(in_nc=3, out_nc=3, nf=64, nb=16, upscale=3)
crt_net = crt_model.state_dict()
for k, v in crt_net.items():
if k in pretrained_net and 'upconv1' not in k:
crt_net[k] = pretrained_net[k]
print('replace ... ', k)
# x4 -> x3
crt_net['upconv1.weight'][
0:256, :, :, :] = pretrained_net['upconv1.weight'] / 2
crt_net['upconv1.weight'][
256:512, :, :, :] = pretrained_net['upconv1.weight'] / 2
crt_net['upconv1.weight'][
512:576, :, :, :] = pretrained_net['upconv1.weight'][0:64, :, :, :] / 2
crt_net['upconv1.bias'][0:256] = pretrained_net['upconv1.bias'] / 2
crt_net['upconv1.bias'][256:512] = pretrained_net['upconv1.bias'] / 2
crt_net['upconv1.bias'][512:576] = pretrained_net['upconv1.bias'][0:64] / 2
torch.save(crt_net, '../../experiments/pretrained_models/MSRResNetx3_ini.pth')
def define_G(opt):
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
# image restoration
if which_model == 'MSRResNet':
netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'],
upscale=opt_net['scale'])
elif which_model == 'RRDBNet':
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'])
# video restoration
elif which_model == 'EDVR':
netG = EDVR_arch.EDVR(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'])
elif which_model == 'EDVR2X':
netG = EDVR_arch.EDVR2X(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'])
elif which_model == 'EDVRImg':
netG = EDVR_arch.EDVRImage(nf=opt_net['nf'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
down_scale=opt_net['down_scale'])
elif which_model == 'EDVR3D':
netG = EDVR_arch.EDVR3D(nf=opt_net['nf'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
down_scale=opt_net['down_scale'])
elif which_model == 'UPEDVR':
netG = EDVR_arch.UPEDVR(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
w_TSA=opt_net['w_TSA'],
down_scale=opt_net['down_scale'],
align_target=opt_net['align_target'],
ret_valid=opt_net['ret_valid'])
elif which_model == 'UPContEDVR':
netG = EDVR_arch.UPControlEDVR(
nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
w_TSA=opt_net['w_TSA'],
down_scale=opt_net['down_scale'],
align_target=opt_net['align_target'],
ret_valid=opt_net['ret_valid'],
multi_scale_cont=opt_net['multi_scale_cont'])
elif which_model == 'FlowUPContEDVR':
netG = EDVR_arch.FlowUPControlEDVR(
nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
w_TSA=opt_net['w_TSA'],
down_scale=opt_net['down_scale'],
align_target=opt_net['align_target'],
ret_valid=opt_net['ret_valid'],
multi_scale_cont=opt_net['multi_scale_cont'])
# video SR for multiple target frames
elif which_model == 'MultiEDVR':
netG = EDVR_arch.MultiEDVR(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'])
# arbitrary magnification video super-resolution
elif which_model == 'MetaEDVR':
netG = EDVR_arch.MetaEDVR(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'],
fix_edvr=opt_net['fix_edvr'])
else:
raise NotImplementedError(
'Generator model [{:s}] not recognized'.format(which_model))
return netG
def define_G(opt):
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
# image restoration
if which_model == 'MSRResNet':
netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'],
upscale=opt_net['scale'])
elif which_model == 'RRDBNet':
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'],
out_nc=opt_net['out_nc'],
nf=opt_net['nf'],
nb=opt_net['nb'])
# video restoration
elif which_model == 'EDVR':
netG = EDVR_arch.EDVR(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'])
elif which_model == 'MY_EDVR_FusionDenoise':
netG = my_EDVR_arch.MYEDVR_FusionDenoise(
nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'])
elif which_model == 'MY_EDVR_RES':
netG = my_EDVR_arch.MYEDVR_RES(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'])
elif which_model == 'MY_EDVR_PreEnhance':
netG = my_EDVR_arch.MYEDVR_PreEnhance(nf=opt_net['nf'],
nframes=opt_net['nframes'],
groups=opt_net['groups'],
front_RBs=opt_net['front_RBs'],
back_RBs=opt_net['back_RBs'],
center=opt_net['center'],
predeblur=opt_net['predeblur'],
HR_in=opt_net['HR_in'],
w_TSA=opt_net['w_TSA'])
elif which_model == 'Recurr_ResBlocks':
netG = Recurr_arch.Recurr_ResBlocks(
nf=opt_net['nf'],
N_RBs=opt_net['N_RBs'],
N_flow_lv=opt_net['N_flow_lv'],
pretrain_flow=opt_net['pretrain_flow'])
else:
raise NotImplementedError(
'Generator model [{:s}] not recognized'.format(which_model))
return netG
