import torch import logging from munch import munchify 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.RRDBNet_arch as RRDBNet_arch import models.archs.feature_arch as feature_arch import models.archs.SwitchedResidualGenerator_arch as SwitchedGen_arch import models.archs.SPSR_arch as spsr import models.archs.StructuredSwitchedGenerator as ssg import models.archs.rcan as rcan from collections import OrderedDict import torchvision import functools 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': # 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=opt_net['scale'] if 'scale' in opt_net.keys() else gen_scale, initial_stride=initial_stride) 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 == "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 == '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 == "spsr5": xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8 netG = spsr.Spsr5(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 2, init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10) elif which_model == "spsr6": xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8 netG = spsr.Spsr6(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) elif which_model == "spsr7": 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) elif which_model == "spsr9": xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8 netG = spsr.Spsr9(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) elif which_model == "ssgr1": xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8 netG = ssg.SSGr1(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'], init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10) elif which_model == 'stacked_switches': xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8 netG = ssg.StackedSwitchGenerator(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'], init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10) elif which_model == 'ssg_deep': xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8 netG = ssg.SSGDeep(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'], init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10) 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() 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_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 == '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 == '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 == '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) 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