import functools
import logging
from collections import OrderedDict
import munch
import torch
import torchvision
from munch import munchify
import models.archs.stylegan.stylegan2 as stylegan2
import models.archs.stylegan.stylegan2_unet_disc as stylegan2_unet
import models.archs.fixup_resnet.DiscriminatorResnet_arch as DiscriminatorResnet_arch
import models.archs.RRDBNet_arch as RRDBNet_arch
import models.archs.SPSR_arch as spsr
import models.archs.SRResNet_arch as SRResNet_arch
import models.archs.SwitchedResidualGenerator_arch as SwitchedGen_arch
import models.archs.discriminator_vgg_arch as SRGAN_arch
import models.archs.feature_arch as feature_arch
import models.archs.panet.panet as panet
import models.archs.rcan as rcan
from models.archs import srg2_classic
from models.archs.biggan.biggan_discriminator import BigGanDiscriminator
from models.archs.stylegan.Discriminator_StyleGAN import StyleGanDiscriminator
from models.archs.teco_resgen import TecoGen
logger = logging.getLogger('base')
# Generator
def define_G(opt, opt_net, scale=None):
if scale is None:
scale = opt['scale']
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 'RRDBNet' in which_model:
if which_model == 'RRDBNetBypass':
block = RRDBNet_arch.RRDBWithBypass
elif which_model == 'RRDBNetLambda':
from models.archs.lambda_rrdb import LambdaRRDB
block = LambdaRRDB
else:
block = RRDBNet_arch.RRDB
additive_mode = opt_net['additive_mode'] if 'additive_mode' in opt_net.keys() else 'not'
output_mode = opt_net['output_mode'] if 'output_mode' in opt_net.keys() else 'hq_only'
gc = opt_net['gc'] if 'gc' in opt_net.keys() else 32
initial_stride = opt_net['initial_stride'] if 'initial_stride' in opt_net.keys() else 1
netG = RRDBNet_arch.RRDBNet(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'],
mid_channels=opt_net['nf'], num_blocks=opt_net['nb'], additive_mode=additive_mode,
output_mode=output_mode, body_block=block, scale=opt_net['scale'], growth_channels=gc,
initial_stride=initial_stride)
elif which_model == "multires_rrdb":
from models.archs.multi_res_rrdb import MultiResRRDBNet
netG = MultiResRRDBNet(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'],
mid_channels=opt_net['nf'], l1_blocks=opt_net['l1'],
l2_blocks=opt_net['l2'], l3_blocks=opt_net['l3'],
growth_channels=opt_net['gc'], scale=opt_net['scale'])
elif which_model == "twostep_rrdb":
from models.archs.multi_res_rrdb import PixelShufflingSteppedResRRDBNet
netG = PixelShufflingSteppedResRRDBNet(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'],
mid_channels=opt_net['nf'], l1_blocks=opt_net['l1'],
l2_blocks=opt_net['l2'],
growth_channels=opt_net['gc'], scale=opt_net['scale'])
elif which_model == 'rcan':
#args: n_resgroups, n_resblocks, res_scale, reduction, scale, n_feats
opt_net['rgb_range'] = 255
opt_net['n_colors'] = 3
args_obj = munchify(opt_net)
netG = rcan.RCAN(args_obj)
elif which_model == 'panet':
#args: n_resblocks, res_scale, scale, n_feats
opt_net['rgb_range'] = 255
opt_net['n_colors'] = 3
args_obj = munchify(opt_net)
netG = panet.PANET(args_obj)
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'],
for_video=opt_net['for_video'])
elif which_model == "srg2classic":
netG = srg2_classic.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'],
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 == 'spsr':
netG = spsr.SPSRNet(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 == 'spsr_net_improved':
netG = spsr.SPSRNetSimplified(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 == "spsr_switched":
netG = spsr.SwitchedSpsr(in_nc=3, nf=opt_net['nf'], upscale=opt_net['scale'], init_temperature=opt_net['temperature'])
elif which_model == "spsr7":
recurrent = opt_net['recurrent'] if 'recurrent' in opt_net.keys() else False
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
netG = spsr.Spsr7(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
multiplexer_reductions=opt_net['multiplexer_reductions'] if 'multiplexer_reductions' in opt_net.keys() else 3,
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10, recurrent=recurrent)
elif which_model == "flownet2":
from models.flownet2.models import FlowNet2
ld = 'load_path' in opt_net.keys()
args = munch.Munch({'fp16': False, 'rgb_max': 1.0, 'checkpoint': not ld})
netG = FlowNet2(args)
if ld:
sd = torch.load(opt_net['load_path'])
netG.load_state_dict(sd['state_dict'])
elif which_model == "backbone_encoder":
netG = SwitchedGen_arch.BackboneEncoder(pretrained_backbone=opt_net['pretrained_spinenet'])
elif which_model == "backbone_encoder_no_ref":
netG = SwitchedGen_arch.BackboneEncoderNoRef(pretrained_backbone=opt_net['pretrained_spinenet'])
elif which_model == "backbone_encoder_no_head":
netG = SwitchedGen_arch.BackboneSpinenetNoHead()
elif which_model == "backbone_resnet":
netG = SwitchedGen_arch.BackboneResnet()
elif which_model == "tecogen":
netG = TecoGen(opt_net['nf'], opt_net['scale'])
elif which_model == 'stylegan2':
is_structured = opt_net['structured'] if 'structured' in opt_net.keys() else False
attn = opt_net['attn_layers'] if 'attn_layers' in opt_net.keys() else []
netG = stylegan2.StyleGan2GeneratorWithLatent(image_size=opt_net['image_size'], latent_dim=opt_net['latent_dim'],
style_depth=opt_net['style_depth'], structure_input=is_structured,
attn_layers=attn)
elif which_model == 'srflow':
from models.archs.srflow import SRFlow_arch
netG = SRFlow_arch.SRFlowNet(in_nc=3, out_nc=3, nf=opt_net['nf'], nb=opt_net['nb'],
quant=opt_net['quant'], flow_block_maps=opt_net['rrdb_block_maps'],
noise_quant=opt_net['noise_quant'], hidden_channels=opt_net['nf'],
K=opt_net['K'], L=opt_net['L'], train_rrdb_at_step=opt_net['rrdb_train_step'],
hr_img_shape=opt_net['hr_shape'], scale=opt_net['scale'])
elif which_model == 'srflow_orig':
from models.archs.srflow_orig import SRFlowNet_arch
netG = SRFlowNet_arch.SRFlowNet(in_nc=3, out_nc=3, nf=opt_net['nf'], nb=opt_net['nb'], scale=opt_net['scale'],
K=opt_net['K'], opt=opt)
elif which_model == 'rrdb_latent_wrapper':
from models.archs.srflow_orig.RRDBNet_arch import RRDBLatentWrapper
netG = RRDBLatentWrapper(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
nf=opt_net['nf'], nb=opt_net['nb'], with_bypass=opt_net['with_bypass'],
blocks=opt_net['blocks_for_latent'], scale=opt_net['scale'], pretrain_rrdb_path=opt_net['pretrain_path'])
elif which_model == 'rrdb_centipede':
output_mode = opt_net['output_mode'] if 'output_mode' in opt_net.keys() else 'hq_only'
netG = RRDBNet_arch.RRDBNet(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'],
mid_channels=opt_net['nf'], num_blocks=opt_net['nb'], scale=opt_net['scale'],
headless=True, output_mode=output_mode)
elif which_model == 'rrdb_srflow':
from models.archs.srflow_orig.RRDBNet_arch import RRDBNet
netG = RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
nf=opt_net['nf'], nb=opt_net['nb'], scale=opt_net['scale'],
initial_conv_stride=opt_net['initial_stride'])
elif which_model == 'mdcn':
from models.archs.mdcn.mdcn import MDCN
args = munchify({'scale': opt_net['scale'], 'n_colors': 3, 'rgb_range': 1.0})
netG = MDCN(args)
else:
raise NotImplementedError('Generator model [{:s}] not recognized'.format(which_model))
return netG
class GradDiscWrapper(torch.nn.Module):
def __init__(self, m):
super(GradDiscWrapper, self).__init__()
logger.info("Wrapping a discriminator..")
self.m = m
def forward(self, x):
return self.m(x)
def define_D_net(opt_net, img_sz=None, wrap=False):
which_model = opt_net['which_model_D']
if 'image_size' in opt_net.keys():
img_sz = opt_net['image_size']
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':
extra_conv = opt_net['extra_conv'] if 'extra_conv' in opt_net.keys() else False
netD = SRGAN_arch.Discriminator_VGG_128_GN(in_nc=opt_net['in_nc'], nf=opt_net['nf'],
input_img_factor=img_sz / 128, extra_conv=extra_conv)
if wrap:
netD = GradDiscWrapper(netD)
elif which_model == 'discriminator_vgg_128_gn_checkpointed':
netD = SRGAN_arch.Discriminator_VGG_128_GN(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz / 128, do_checkpointing=True)
elif which_model == 'stylegan_vgg':
netD = StyleGanDiscriminator(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_50':
netD = DiscriminatorResnet_arch.fixup_resnet50(num_filters=opt_net['nf'], num_classes=1, input_img_size=img_sz)
elif which_model == 'resnext':
netD = torchvision.models.resnext50_32x4d(norm_layer=functools.partial(torch.nn.GroupNorm, 8))
#state_dict = torch.hub.load_state_dict_from_url('https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', progress=True)
#netD.load_state_dict(state_dict, strict=False)
netD.fc = torch.nn.Linear(512 * 4, 1)
elif which_model == 'biggan_resnet':
netD = BigGanDiscriminator(D_activation=torch.nn.LeakyReLU(negative_slope=.2))
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'])
elif which_model == "cross_compare_vgg128":
netD = SRGAN_arch.CrossCompareDiscriminator(in_nc=opt_net['in_nc'], ref_channels=opt_net['ref_channels'] if 'ref_channels' in opt_net.keys() else 3, nf=opt_net['nf'], scale=opt_net['scale'])
elif which_model == "discriminator_refvgg":
netD = SRGAN_arch.RefDiscriminatorVgg128(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz / 128)
elif which_model == "psnr_approximator":
netD = SRGAN_arch.PsnrApproximator(nf=opt_net['nf'], input_img_factor=img_sz / 128)
elif which_model == "stylegan2_discriminator":
attn = opt_net['attn_layers'] if 'attn_layers' in opt_net.keys() else []
disc = stylegan2.StyleGan2Discriminator(image_size=opt_net['image_size'], input_filters=opt_net['in_nc'], attn_layers=attn)
netD = stylegan2.StyleGan2Augmentor(disc, opt_net['image_size'], types=opt_net['augmentation_types'], prob=opt_net['augmentation_probability'])
elif which_model == "stylegan2_unet":
disc = stylegan2_unet.StyleGan2UnetDiscriminator(image_size=opt_net['image_size'], input_filters=opt_net['in_nc'])
netD = stylegan2.StyleGan2Augmentor(disc, opt_net['image_size'], types=opt_net['augmentation_types'], prob=opt_net['augmentation_probability'])
elif which_model == "rrdb_disc":
netD = RRDBNet_arch.RRDBDiscriminator(opt_net['in_nc'], opt_net['nf'], opt_net['nb'], blocks_per_checkpoint=3)
else:
raise NotImplementedError('Discriminator model [{:s}] not recognized'.format(which_model))
return netD
# Discriminator
def define_D(opt, wrap=False):
img_sz = opt['datasets']['train']['target_size']
opt_net = opt['network_D']
return define_D_net(opt_net, img_sz, wrap=wrap)
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(which_model='vgg', use_bn=False, for_training=False, load_path=None, feature_layers=None):
if which_model == 'vgg':
# PyTorch pretrained VGG19-54, before ReLU.
if feature_layers is None:
if use_bn:
feature_layers = [49]
else:
feature_layers = [34]
if for_training:
netF = feature_arch.TrainableVGGFeatureExtractor(feature_layers=feature_layers, use_bn=use_bn,
use_input_norm=True)
else:
netF = feature_arch.VGGFeatureExtractor(feature_layers=feature_layers, use_bn=use_bn,
use_input_norm=True)
elif which_model == 'wide_resnet':
netF = feature_arch.WideResnetFeatureExtractor(use_input_norm=True)
else:
raise NotImplementedError
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)
if not for_training:
# 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
return netF