125 lines
7.3 KiB
Python
125 lines
7.3 KiB
Python
import torch
|
|
import models.archs.SRResNet_arch as SRResNet_arch
|
|
import models.archs.discriminator_vgg_arch as SRGAN_arch
|
|
import models.archs.DiscriminatorResnet_arch as DiscriminatorResnet_arch
|
|
import models.archs.DiscriminatorResnet_arch_passthrough as DiscriminatorResnet_arch_passthrough
|
|
import models.archs.FlatProcessorNetNew_arch as FlatProcessorNetNew_arch
|
|
import models.archs.RRDBNet_arch as RRDBNet_arch
|
|
import models.archs.HighToLowResNet as HighToLowResNet
|
|
import models.archs.ResGen_arch as ResGen_arch
|
|
import models.archs.biggan_gen_arch as biggan_arch
|
|
import models.archs.feature_arch as feature_arch
|
|
import functools
|
|
|
|
# Generator
|
|
def define_G(opt, net_key='network_G'):
|
|
opt_net = opt[net_key]
|
|
which_model = opt_net['which_model_G']
|
|
scale = opt['scale']
|
|
|
|
# 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':
|
|
# RRDB does scaling in two steps, so take the sqrt of the scale we actually want to achieve and feed it to RRDB.
|
|
initial_stride = 1 if 'initial_stride' not in opt_net else opt_net['initial_stride']
|
|
assert initial_stride == 1 or initial_stride == 2
|
|
# Need to adjust the scale the generator sees by the stride since the stride causes a down-sample.
|
|
gen_scale = scale * initial_stride
|
|
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
|
|
nf=opt_net['nf'], nb=opt_net['nb'], scale=gen_scale, initial_stride=initial_stride)
|
|
elif which_model == 'AssistedRRDBNet':
|
|
netG = RRDBNet_arch.AssistedRRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
|
|
nf=opt_net['nf'], nb=opt_net['nb'], scale=scale)
|
|
elif which_model == 'AttentiveRRDBNet':
|
|
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
|
|
nf=opt_net['nf'], nb=opt_net['nb'], scale=scale,
|
|
rrdb_block_f=functools.partial(RRDBNet_arch.AttentiveRRDB, nf=opt_net['nf'], gc=opt_net['gc'],
|
|
init_temperature=opt_net['temperature'],
|
|
final_temperature_step=opt_net['temperature_final_step']))
|
|
elif which_model == 'MultiRRDBNet':
|
|
block_f = None
|
|
if opt_net['attention']:
|
|
block_f = functools.partial(RRDBNet_arch.AttentiveRRDB, nf=opt_net['nf'], gc=opt_net['gc'],
|
|
init_temperature=opt_net['temperature'],
|
|
final_temperature_step=opt_net['temperature_final_step'])
|
|
netG = RRDBNet_arch.MultiRRDBNet(nf_base=opt_net['nf'], gc_base=opt_net['gc'], lo_blocks=opt_net['lo_blocks'],
|
|
hi_blocks=opt_net['hi_blocks'], scale=scale, rrdb_block_f=block_f)
|
|
elif which_model == 'PixRRDBNet':
|
|
block_f = None
|
|
if opt_net['attention']:
|
|
block_f = functools.partial(RRDBNet_arch.AttentiveRRDB, nf=opt_net['nf'], gc=opt_net['gc'],
|
|
init_temperature=opt_net['temperature'],
|
|
final_temperature_step=opt_net['temperature_final_step'])
|
|
netG = RRDBNet_arch.PixShuffleRRDB(nf=opt_net['nf'], nb=opt_net['nb'], gc=opt_net['gc'], scale=scale, rrdb_block_f=block_f)
|
|
elif which_model == 'ResGen':
|
|
netG = ResGen_arch.fixup_resnet34(nb_denoiser=opt_net['nb_denoiser'], nb_upsampler=opt_net['nb_upsampler'],
|
|
upscale_applications=opt_net['upscale_applications'], num_filters=opt_net['nf'])
|
|
elif which_model == 'ResGenV2':
|
|
netG = ResGen_arch.fixup_resnet34_v2(nb_denoiser=opt_net['nb_denoiser'], nb_upsampler=opt_net['nb_upsampler'],
|
|
upscale_applications=opt_net['upscale_applications'], num_filters=opt_net['nf'],
|
|
inject_noise=opt_net['inject_noise'])
|
|
elif which_model == "BigGan":
|
|
netG = biggan_arch.biggan_medium(num_filters=opt_net['nf'])
|
|
|
|
# image corruption
|
|
elif which_model == 'HighToLowResNet':
|
|
netG = HighToLowResNet.HighToLowResNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
|
|
nf=opt_net['nf'], nb=opt_net['nb'], downscale=opt_net['scale'])
|
|
elif which_model == 'FlatProcessorNet':
|
|
'''netG = FlatProcessorNet_arch.FlatProcessorNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
|
|
nf=opt_net['nf'], downscale=opt_net['scale'], reduce_anneal_blocks=opt_net['ra_blocks'],
|
|
assembler_blocks=opt_net['assembler_blocks'])'''
|
|
netG = FlatProcessorNetNew_arch.fixup_resnet34(num_filters=opt_net['nf'])
|
|
# 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
|
|
|
|
|
|
# Discriminator
|
|
def define_D(opt):
|
|
img_sz = opt['datasets']['train']['target_size']
|
|
opt_net = opt['network_D']
|
|
which_model = opt_net['which_model_D']
|
|
|
|
if which_model == 'discriminator_vgg_128':
|
|
netD = SRGAN_arch.Discriminator_VGG_128(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz / 128)
|
|
elif which_model == 'discriminator_resnet':
|
|
netD = DiscriminatorResnet_arch.fixup_resnet34(num_filters=opt_net['nf'], num_classes=1, input_img_size=img_sz)
|
|
elif which_model == 'discriminator_resnet_passthrough':
|
|
netD = DiscriminatorResnet_arch_passthrough.fixup_resnet34(num_filters=opt_net['nf'], num_classes=1, input_img_size=img_sz,
|
|
number_skips=opt_net['number_skips'], use_bn=True,
|
|
disable_passthrough=opt_net['disable_passthrough'])
|
|
else:
|
|
raise NotImplementedError('Discriminator model [{:s}] not recognized'.format(which_model))
|
|
return netD
|
|
|
|
|
|
# Define network used for perceptual loss
|
|
def define_F(opt, use_bn=False):
|
|
gpu_ids = opt['gpu_ids']
|
|
device = torch.device('cuda' if gpu_ids else 'cpu')
|
|
if 'which_model_F' not in opt['train'].keys() or opt['train']['which_model_F'] == 'vgg':
|
|
# PyTorch pretrained VGG19-54, before ReLU.
|
|
if use_bn:
|
|
feature_layer = 49
|
|
else:
|
|
feature_layer = 34
|
|
netF = feature_arch.VGGFeatureExtractor(feature_layer=feature_layer, use_bn=use_bn,
|
|
use_input_norm=True, device=device)
|
|
elif opt['train']['which_model_F'] == 'wide_resnet':
|
|
netF = feature_arch.WideResnetFeatureExtractor(use_input_norm=True, device=device)
|
|
|
|
netF.eval() # No need to train
|
|
return netF
|