import functools import logging from collections import OrderedDict import munch import torch import torchvision from munch import munchify 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.pyramid_arch import BasicResamplingFlowNet from models.archs.rrdb_with_adain_latent import AdaRRDBNet, LinearLatentEstimator from models.archs.rrdb_with_latent import LatentEstimator, RRDBNetWithLatent, LatentEstimator2 from models.archs.teco_resgen import TecoGen logger = logging.getLogger('base') # Generator def define_G(opt, net_key='network_G', scale=None): if net_key is not None: opt_net = opt[net_key] else: opt_net = opt 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 which_model == 'RRDBNet': 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']) elif which_model == 'RRDBNetBypass': 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'], body_block=RRDBNet_arch.RRDBWithBypass, blocks_per_checkpoint=opt_net['blocks_per_checkpoint'], 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 == "basic_resampling_flow_predictor": netG = BasicResamplingFlowNet(opt_net['nf'], resample_scale=opt_net['resample_scale']) elif which_model == "rrdb_with_latent": netG = RRDBNetWithLatent(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'], mid_channels=opt_net['nf'], num_blocks=opt_net['nb'], blocks_per_checkpoint=opt_net['blocks_per_checkpoint'], scale=opt_net['scale'], bottom_latent_only=opt_net['bottom_latent_only']) elif which_model == "adarrdb": netG = AdaRRDBNet(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'], mid_channels=opt_net['nf'], num_blocks=opt_net['nb'], blocks_per_checkpoint=opt_net['blocks_per_checkpoint'], scale=opt_net['scale']) elif which_model == "latent_estimator": if opt_net['version'] == 2: netG = LatentEstimator2(in_nc=3, nf=opt_net['nf']) else: overwrite = [1,2] if opt_net['only_base_level'] else [] netG = LatentEstimator(in_nc=3, nf=opt_net['nf'], overwrite_levels=overwrite) elif which_model == "linear_latent_estimator": netG = LinearLatentEstimator(in_nc=3, nf=opt_net['nf']) 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': netD = SRGAN_arch.Discriminator_VGG_128_GN(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz / 128) 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 == "pyramid_rrdb_disc": netD = SRGAN_arch.PyramidRRDBDiscriminator(in_nc=3, nf=opt_net['nf']) 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