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.NestedSwitchGenerator as ng
import models.archs.feature_arch as feature_arch
import models.archs.SwitchedResidualGenerator_arch as SwitchedGen_arch
import models.archs.SRG1_arch as srg1
import models.archs.ProgressiveSrg_arch as psrg
import models.archs.SPSR_arch as spsr
import models.archs.arch_util as arch_util
import functools
from collections import OrderedDict
# 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 == 'LowDimRRDBNet':
gen_scale = scale * opt_net['initial_stride']
rrdb = functools.partial(RRDBNet_arch.LowDimRRDB, nf=opt_net['nf'], gc=opt_net['gc'], dimensional_adjustment=opt_net['dim'])
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, rrdb_block_f=rrdb, initial_stride=opt_net['initial_stride'])
elif which_model == 'PixRRDBNet':
block_f = None
if opt_net['attention']:
block_f = functools.partial(RRDBNet_arch.SwitchedRRDB, nf=opt_net['nf'], gc=opt_net['gc'],
init_temperature=opt_net['temperature'],
final_temperature_step=opt_net['temperature_final_step'])
if opt_net['mhattention']:
block_f = functools.partial(RRDBNet_arch.SwitchedMultiHeadRRDB, num_convs=8, num_heads=2, 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 == "ConfigurableSwitchedResidualGenerator":
netG = srg1.ConfigurableSwitchedResidualGenerator(switch_filters=opt_net['switch_filters'], switch_growths=opt_net['switch_growths'],
switch_reductions=opt_net['switch_reductions'],
switch_processing_layers=opt_net['switch_processing_layers'], trans_counts=opt_net['trans_counts'],
trans_kernel_sizes=opt_net['trans_kernel_sizes'], trans_layers=opt_net['trans_layers'],
trans_filters_mid=opt_net['trans_filters_mid'],
initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
elif which_model == "ConfigurableSwitchedResidualGenerator2":
netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator2(switch_depth=opt_net['switch_depth'], switch_filters=opt_net['switch_filters'],
switch_reductions=opt_net['switch_reductions'],
switch_processing_layers=opt_net['switch_processing_layers'], trans_counts=opt_net['trans_counts'],
trans_kernel_sizes=opt_net['trans_kernel_sizes'], trans_layers=opt_net['trans_layers'],
transformation_filters=opt_net['transformation_filters'], attention_norm=opt_net['attention_norm'],
initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
elif which_model == "ConfigurableSwitchedResidualGenerator3":
netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator3(base_filters=opt_net['base_filters'], trans_count=opt_net['trans_count'])
elif which_model == "NestedSwitchGenerator":
netG = ng.NestedSwitchedGenerator(switch_filters=opt_net['switch_filters'],
switch_reductions=opt_net['switch_reductions'],
switch_processing_layers=opt_net['switch_processing_layers'], trans_counts=opt_net['trans_counts'],
trans_kernel_sizes=opt_net['trans_kernel_sizes'], trans_layers=opt_net['trans_layers'],
transformation_filters=opt_net['transformation_filters'],
initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
elif which_model == "ConfigurableSwitchedResidualGenerator4":
netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator4(switch_filters=opt_net['switch_filters'],
switch_reductions=opt_net['switch_reductions'],
switch_processing_layers=opt_net['switch_processing_layers'], trans_counts=opt_net['trans_counts'],
trans_kernel_sizes=opt_net['trans_kernel_sizes'], trans_layers=opt_net['trans_layers'],
transformation_filters=opt_net['transformation_filters'], attention_norm=opt_net['attention_norm'],
initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
elif which_model == "ProgressiveSRG2":
netG = psrg.GrowingSRGBase(progressive_step_schedule=opt_net['schedule'], switch_reductions=opt_net['reductions'],
growth_fade_in_steps=opt_net['fade_in_steps'], switch_filters=opt_net['switch_filters'],
switch_processing_layers=opt_net['switch_processing_layers'], trans_counts=opt_net['trans_counts'],
trans_layers=opt_net['trans_layers'], transformation_filters=opt_net['transformation_filters'],
initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'],
start_step=opt_net['start_step'])
elif which_model == 'spsr_net':
netG = spsr.SPSRNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], nf=opt_net['nf'],
nb=opt_net['nb'], gc=opt_net['gc'], upscale=opt_net['scale'], norm_type=opt_net['norm_type'],
act_type='leakyrelu', mode=opt_net['mode'], upsample_mode='upconv')
if opt['is_train']:
arch_util.initialize_weights(netG, scale=.1)
# 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'])\
else:
raise NotImplementedError('Generator model [{:s}] not recognized'.format(which_model))
return netG
def define_D_net(opt_net, img_sz=None):
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, extra_conv=opt_net['extra_conv'])
elif which_model == 'discriminator_vgg_128_gn':
netD = SRGAN_arch.Discriminator_VGG_128_GN(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'])
elif which_model == 'discriminator_pix':
netD = SRGAN_arch.Discriminator_VGG_PixLoss(in_nc=opt_net['in_nc'], nf=opt_net['nf'])
elif which_model == "discriminator_unet":
netD = SRGAN_arch.Discriminator_UNet(in_nc=opt_net['in_nc'], nf=opt_net['nf'])
elif which_model == "discriminator_unet_fea":
netD = SRGAN_arch.Discriminator_UNet_FeaOut(in_nc=opt_net['in_nc'], nf=opt_net['nf'], feature_mode=opt_net['feature_mode'])
elif which_model == "discriminator_switched":
netD = SRGAN_arch.Discriminator_switched(in_nc=opt_net['in_nc'], nf=opt_net['nf'], initial_temp=opt_net['initial_temp'],
final_temperature_step=opt_net['final_temperature_step'])
else:
raise NotImplementedError('Discriminator model [{:s}] not recognized'.format(which_model))
return netD
# Discriminator
def define_D(opt):
img_sz = opt['datasets']['train']['target_size']
opt_net = opt['network_D']
return define_D_net(opt_net, img_sz)
def define_fixed_D(opt):
# Note that this will not work with "old" VGG-style discriminators with dense blocks until the img_size parameter is added.
net = define_D_net(opt)
# Load the model parameters:
load_net = torch.load(opt['pretrained_path'])
load_net_clean = OrderedDict() # remove unnecessary 'module.'
for k, v in load_net.items():
if k.startswith('module.'):
load_net_clean[k[7:]] = v
else:
load_net_clean[k] = v
net.load_state_dict(load_net_clean)
# Put into eval mode, freeze the parameters and set the 'weight' field.
net.eval()
for k, v in net.named_parameters():
v.requires_grad = False
net.fdisc_weight = opt['weight']
return net
# Define network used for perceptual loss
def define_F(opt, use_bn=False, for_training=False, load_path=None):
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
if for_training:
netF = feature_arch.TrainableVGGFeatureExtractor(feature_layer=feature_layer, use_bn=use_bn,
use_input_norm=True, device=device)
else:
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)
if load_path:
# Load the model parameters:
load_net = torch.load(load_path)
load_net_clean = OrderedDict() # remove unnecessary 'module.'
for k, v in load_net.items():
if k.startswith('module.'):
load_net_clean[k[7:]] = v
else:
load_net_clean[k] = v
netF.load_state_dict(load_net_clean)
# Put into eval mode, freeze the parameters and set the 'weight' field.
netF.eval()
for k, v in netF.named_parameters():
v.requires_grad = False
netF.fdisc_weight = opt['weight']
return netF