0b7193392f
The latest discriminator architecture was already pretty much a unet. This one makes that official and uses shared layers. It also upsamples one additional time and throws out the lowest upsampling result. The intent is to delete the old vgg pixdisc, but I'll keep it around for a bit since I'm still trying out a few models with it.
138 lines
10 KiB
Python
138 lines
10 KiB
Python
import torch
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import models.archs.SRResNet_arch as SRResNet_arch
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import models.archs.discriminator_vgg_arch as SRGAN_arch
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import models.archs.DiscriminatorResnet_arch as DiscriminatorResnet_arch
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import models.archs.DiscriminatorResnet_arch_passthrough as DiscriminatorResnet_arch_passthrough
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import models.archs.FlatProcessorNetNew_arch as FlatProcessorNetNew_arch
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import models.archs.RRDBNet_arch as RRDBNet_arch
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import models.archs.HighToLowResNet as HighToLowResNet
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import models.archs.NestedSwitchGenerator as ng
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import models.archs.feature_arch as feature_arch
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import models.archs.SwitchedResidualGenerator_arch as SwitchedGen_arch
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import models.archs.SRG1_arch as srg1
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import functools
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# Generator
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def define_G(opt, net_key='network_G'):
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opt_net = opt[net_key]
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which_model = opt_net['which_model_G']
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scale = opt['scale']
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# image restoration
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if which_model == 'MSRResNet':
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netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
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nf=opt_net['nf'], nb=opt_net['nb'], upscale=opt_net['scale'])
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elif which_model == 'RRDBNet':
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# RRDB does scaling in two steps, so take the sqrt of the scale we actually want to achieve and feed it to RRDB.
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initial_stride = 1 if 'initial_stride' not in opt_net else opt_net['initial_stride']
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assert initial_stride == 1 or initial_stride == 2
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# Need to adjust the scale the generator sees by the stride since the stride causes a down-sample.
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gen_scale = scale * initial_stride
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netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
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nf=opt_net['nf'], nb=opt_net['nb'], scale=gen_scale, initial_stride=initial_stride)
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elif which_model == 'AssistedRRDBNet':
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netG = RRDBNet_arch.AssistedRRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
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nf=opt_net['nf'], nb=opt_net['nb'], scale=scale)
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elif which_model == 'LowDimRRDBNet':
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gen_scale = scale * opt_net['initial_stride']
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rrdb = functools.partial(RRDBNet_arch.LowDimRRDB, nf=opt_net['nf'], gc=opt_net['gc'], dimensional_adjustment=opt_net['dim'])
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netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
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nf=opt_net['nf'], nb=opt_net['nb'], scale=gen_scale, rrdb_block_f=rrdb, initial_stride=opt_net['initial_stride'])
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elif which_model == 'PixRRDBNet':
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block_f = None
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if opt_net['attention']:
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block_f = functools.partial(RRDBNet_arch.SwitchedRRDB, nf=opt_net['nf'], gc=opt_net['gc'],
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init_temperature=opt_net['temperature'],
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final_temperature_step=opt_net['temperature_final_step'])
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if opt_net['mhattention']:
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block_f = functools.partial(RRDBNet_arch.SwitchedMultiHeadRRDB, num_convs=8, num_heads=2, nf=opt_net['nf'], gc=opt_net['gc'],
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init_temperature=opt_net['temperature'],
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final_temperature_step=opt_net['temperature_final_step'])
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netG = RRDBNet_arch.PixShuffleRRDB(nf=opt_net['nf'], nb=opt_net['nb'], gc=opt_net['gc'], scale=scale, rrdb_block_f=block_f)
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elif which_model == "ConfigurableSwitchedResidualGenerator":
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netG = srg1.ConfigurableSwitchedResidualGenerator(switch_filters=opt_net['switch_filters'], switch_growths=opt_net['switch_growths'],
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switch_reductions=opt_net['switch_reductions'],
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switch_processing_layers=opt_net['switch_processing_layers'], trans_counts=opt_net['trans_counts'],
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trans_kernel_sizes=opt_net['trans_kernel_sizes'], trans_layers=opt_net['trans_layers'],
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trans_filters_mid=opt_net['trans_filters_mid'],
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initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
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heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
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upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
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elif which_model == "ConfigurableSwitchedResidualGenerator2":
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netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator2(switch_depth=opt_net['switch_depth'], switch_filters=opt_net['switch_filters'],
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switch_reductions=opt_net['switch_reductions'],
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switch_processing_layers=opt_net['switch_processing_layers'], trans_counts=opt_net['trans_counts'],
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trans_kernel_sizes=opt_net['trans_kernel_sizes'], trans_layers=opt_net['trans_layers'],
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transformation_filters=opt_net['transformation_filters'],
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initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
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heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
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upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
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elif which_model == "ConfigurableSwitchedResidualGenerator3":
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netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator3(base_filters=opt_net['base_filters'], trans_count=opt_net['trans_count'])
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elif which_model == "NestedSwitchGenerator":
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netG = ng.NestedSwitchedGenerator(switch_filters=opt_net['switch_filters'],
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switch_reductions=opt_net['switch_reductions'],
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switch_processing_layers=opt_net['switch_processing_layers'], trans_counts=opt_net['trans_counts'],
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trans_kernel_sizes=opt_net['trans_kernel_sizes'], trans_layers=opt_net['trans_layers'],
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transformation_filters=opt_net['transformation_filters'],
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initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
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heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
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upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
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# image corruption
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elif which_model == 'HighToLowResNet':
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netG = HighToLowResNet.HighToLowResNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
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nf=opt_net['nf'], nb=opt_net['nb'], downscale=opt_net['scale'])
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elif which_model == 'FlatProcessorNet':
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'''netG = FlatProcessorNet_arch.FlatProcessorNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
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nf=opt_net['nf'], downscale=opt_net['scale'], reduce_anneal_blocks=opt_net['ra_blocks'],
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assembler_blocks=opt_net['assembler_blocks'])'''
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netG = FlatProcessorNetNew_arch.fixup_resnet34(num_filters=opt_net['nf'])\
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else:
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raise NotImplementedError('Generator model [{:s}] not recognized'.format(which_model))
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return netG
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# Discriminator
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def define_D(opt):
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img_sz = opt['datasets']['train']['target_size']
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opt_net = opt['network_D']
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which_model = opt_net['which_model_D']
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if which_model == 'discriminator_vgg_128':
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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'])
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elif which_model == 'discriminator_resnet':
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netD = DiscriminatorResnet_arch.fixup_resnet34(num_filters=opt_net['nf'], num_classes=1, input_img_size=img_sz)
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elif which_model == 'discriminator_resnet_passthrough':
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netD = DiscriminatorResnet_arch_passthrough.fixup_resnet34(num_filters=opt_net['nf'], num_classes=1, input_img_size=img_sz,
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number_skips=opt_net['number_skips'], use_bn=True,
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disable_passthrough=opt_net['disable_passthrough'])
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elif which_model == 'discriminator_pix':
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netD = SRGAN_arch.Discriminator_VGG_PixLoss(in_nc=opt_net['in_nc'], nf=opt_net['nf'])
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elif which_model == "discriminator_unet":
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netD = SRGAN_arch.Discriminator_UNet(in_nc=opt_net['in_nc'], nf=opt_net['nf'])
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else:
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raise NotImplementedError('Discriminator model [{:s}] not recognized'.format(which_model))
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return netD
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# Define network used for perceptual loss
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def define_F(opt, use_bn=False):
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gpu_ids = opt['gpu_ids']
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device = torch.device('cuda' if gpu_ids else 'cpu')
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if 'which_model_F' not in opt['train'].keys() or opt['train']['which_model_F'] == 'vgg':
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# PyTorch pretrained VGG19-54, before ReLU.
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if use_bn:
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feature_layer = 49
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else:
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feature_layer = 34
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netF = feature_arch.VGGFeatureExtractor(feature_layer=feature_layer, use_bn=use_bn,
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use_input_norm=True, device=device)
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elif opt['train']['which_model_F'] == 'wide_resnet':
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netF = feature_arch.WideResnetFeatureExtractor(use_input_norm=True, device=device)
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netF.eval() # No need to train
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return netF
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