forked from mrq/DL-Art-School
0d070b47a7
Basically just cleaning up the code, removing some bad conventions, and reducing complexity somewhat so that I can play around with this arch a bit more easily.
220 lines
15 KiB
Python
220 lines
15 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 models.archs.ProgressiveSrg_arch as psrg
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import models.archs.SPSR_arch as spsr
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import models.archs.arch_util as arch_util
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import functools
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from collections import OrderedDict
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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'], attention_norm=opt_net['attention_norm'],
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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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elif which_model == "ConfigurableSwitchedResidualGenerator4":
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netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator4(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'], attention_norm=opt_net['attention_norm'],
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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 == "ProgressiveSRG2":
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netG = psrg.GrowingSRGBase(progressive_step_schedule=opt_net['schedule'], switch_reductions=opt_net['reductions'],
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growth_fade_in_steps=opt_net['fade_in_steps'], switch_filters=opt_net['switch_filters'],
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switch_processing_layers=opt_net['switch_processing_layers'], trans_counts=opt_net['trans_counts'],
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trans_layers=opt_net['trans_layers'], 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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upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'],
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start_step=opt_net['start_step'])
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elif which_model == 'spsr_net':
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netG = spsr.SPSRNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], nf=opt_net['nf'],
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nb=opt_net['nb'], gc=opt_net['gc'], upscale=opt_net['scale'], norm_type=opt_net['norm_type'],
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act_type='leakyrelu', mode=opt_net['mode'], upsample_mode='upconv')
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if opt['is_train']:
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arch_util.initialize_weights(netG, scale=.1)
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elif which_model == 'spsr_net_improved':
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netG = spsr.SPSRNetSimplified(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], nf=opt_net['nf'],
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nb=opt_net['nb'], upscale=opt_net['scale'])
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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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def define_D_net(opt_net, img_sz=None):
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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_vgg_128_gn':
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netD = SRGAN_arch.Discriminator_VGG_128_GN(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz // 128)
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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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elif which_model == "discriminator_unet_fea":
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netD = SRGAN_arch.Discriminator_UNet_FeaOut(in_nc=opt_net['in_nc'], nf=opt_net['nf'], feature_mode=opt_net['feature_mode'])
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elif which_model == "discriminator_switched":
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netD = SRGAN_arch.Discriminator_switched(in_nc=opt_net['in_nc'], nf=opt_net['nf'], initial_temp=opt_net['initial_temp'],
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final_temperature_step=opt_net['final_temperature_step'])
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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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# 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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return define_D_net(opt_net, img_sz)
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def define_fixed_D(opt):
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# Note that this will not work with "old" VGG-style discriminators with dense blocks until the img_size parameter is added.
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net = define_D_net(opt)
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# Load the model parameters:
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load_net = torch.load(opt['pretrained_path'])
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load_net_clean = OrderedDict() # remove unnecessary 'module.'
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for k, v in load_net.items():
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if k.startswith('module.'):
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load_net_clean[k[7:]] = v
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else:
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load_net_clean[k] = v
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net.load_state_dict(load_net_clean)
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# Put into eval mode, freeze the parameters and set the 'weight' field.
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net.eval()
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for k, v in net.named_parameters():
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v.requires_grad = False
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net.fdisc_weight = opt['weight']
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return net
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# Define network used for perceptual loss
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def define_F(opt, use_bn=False, for_training=False, load_path=None):
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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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if for_training:
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netF = feature_arch.TrainableVGGFeatureExtractor(feature_layer=feature_layer, use_bn=use_bn,
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use_input_norm=True, device=device)
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else:
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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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if load_path:
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# Load the model parameters:
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load_net = torch.load(load_path)
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load_net_clean = OrderedDict() # remove unnecessary 'module.'
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for k, v in load_net.items():
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if k.startswith('module.'):
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load_net_clean[k[7:]] = v
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else:
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load_net_clean[k] = v
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netF.load_state_dict(load_net_clean)
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# Put into eval mode, freeze the parameters and set the 'weight' field.
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netF.eval()
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for k, v in netF.named_parameters():
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v.requires_grad = False
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netF.fdisc_weight = opt['weight']
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return netF
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