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 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']) # 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_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