227 lines
12 KiB
Python
227 lines
12 KiB
Python
import functools
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import logging
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from collections import OrderedDict
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import munch
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import torch
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import torchvision
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from munch import munchify
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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.RRDBNet_arch as RRDBNet_arch
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import models.archs.SPSR_arch as spsr
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import models.archs.SRResNet_arch as SRResNet_arch
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import models.archs.SwitchedResidualGenerator_arch as SwitchedGen_arch
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import models.archs.discriminator_vgg_arch as SRGAN_arch
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import models.archs.feature_arch as feature_arch
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import models.archs.panet.panet as panet
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import models.archs.rcan as rcan
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import models.archs.ChainedEmbeddingGen as chained
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logger = logging.getLogger('base')
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# Generator
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def define_G(opt, net_key='network_G', scale=None):
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if net_key is not None:
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opt_net = opt[net_key]
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else:
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opt_net = opt
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if scale is None:
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scale = opt['scale']
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which_model = opt_net['which_model_G']
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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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netG = RRDBNet_arch.RRDBNet(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'],
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mid_channels=opt_net['nf'], num_blocks=opt_net['nb'])
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elif which_model == 'rcan':
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#args: n_resgroups, n_resblocks, res_scale, reduction, scale, n_feats
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opt_net['rgb_range'] = 255
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opt_net['n_colors'] = 3
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args_obj = munchify(opt_net)
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netG = rcan.RCAN(args_obj)
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elif which_model == 'panet':
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#args: n_resblocks, res_scale, scale, n_feats
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opt_net['rgb_range'] = 255
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opt_net['n_colors'] = 3
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args_obj = munchify(opt_net)
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netG = panet.PANET(args_obj)
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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 == 'spsr':
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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'], upscale=opt_net['scale'])
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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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elif which_model == "spsr_switched":
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netG = spsr.SwitchedSpsr(in_nc=3, nf=opt_net['nf'], upscale=opt_net['scale'], init_temperature=opt_net['temperature'])
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elif which_model == "spsr7":
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recurrent = opt_net['recurrent'] if 'recurrent' in opt_net.keys() else False
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xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
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netG = spsr.Spsr7(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
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multiplexer_reductions=opt_net['multiplexer_reductions'] if 'multiplexer_reductions' in opt_net.keys() else 3,
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init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10, recurrent=recurrent)
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elif which_model == 'chained_gen_structured':
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rec = opt_net['recurrent'] if 'recurrent' in opt_net.keys() else False
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recnf = opt_net['recurrent_nf'] if 'recurrent_nf' in opt_net.keys() else 3
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recstd = opt_net['recurrent_stride'] if 'recurrent_stride' in opt_net.keys() else 2
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in_nc = opt_net['in_nc'] if 'in_nc' in opt_net.keys() else 3
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netG = chained.ChainedEmbeddingGenWithStructure(depth=opt_net['depth'], recurrent=rec, recurrent_nf=recnf, recurrent_stride=recstd, in_nc=in_nc)
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elif which_model == 'multifaceted_chained':
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scale = opt_net['scale'] if 'scale' in opt_net.keys() else 2
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netG = chained.MultifacetedChainedEmbeddingGen(depth=opt_net['depth'], scale=scale)
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elif which_model == "flownet2":
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from models.flownet2.models import FlowNet2
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ld = 'load_path' in opt_net.keys()
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args = munch.Munch({'fp16': False, 'rgb_max': 1.0, 'checkpoint': not ld})
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netG = FlowNet2(args)
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if ld:
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sd = torch.load(opt_net['load_path'])
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netG.load_state_dict(sd['state_dict'])
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elif which_model == "backbone_encoder":
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netG = SwitchedGen_arch.BackboneEncoder(pretrained_backbone=opt_net['pretrained_spinenet'])
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elif which_model == "backbone_encoder_no_ref":
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netG = SwitchedGen_arch.BackboneEncoderNoRef(pretrained_backbone=opt_net['pretrained_spinenet'])
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elif which_model == "backbone_encoder_no_head":
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netG = SwitchedGen_arch.BackboneSpinenetNoHead()
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elif which_model == "backbone_resnet":
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netG = SwitchedGen_arch.BackboneResnet()
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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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class GradDiscWrapper(torch.nn.Module):
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def __init__(self, m):
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super(GradDiscWrapper, self).__init__()
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logger.info("Wrapping a discriminator..")
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self.m = m
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def forward(self, x):
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return self.m(x)
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def define_D_net(opt_net, img_sz=None, wrap=False):
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which_model = opt_net['which_model_D']
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if 'image_size' in opt_net.keys():
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img_sz = opt_net['image_size']
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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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if wrap:
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netD = GradDiscWrapper(netD)
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elif which_model == 'discriminator_vgg_128_gn_checkpointed':
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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, do_checkpointing=True)
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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_50':
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netD = DiscriminatorResnet_arch.fixup_resnet50(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 == 'resnext':
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netD = torchvision.models.resnext50_32x4d(norm_layer=functools.partial(torch.nn.GroupNorm, 8))
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state_dict = torch.hub.load_state_dict_from_url('https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', progress=True)
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netD.load_state_dict(state_dict, strict=False)
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netD.fc = torch.nn.Linear(512 * 4, 1)
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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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elif which_model == "cross_compare_vgg128":
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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'])
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elif which_model == "discriminator_refvgg":
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netD = SRGAN_arch.RefDiscriminatorVgg128(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz / 128)
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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, wrap=False):
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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, wrap=wrap)
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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(which_model='vgg', use_bn=False, for_training=False, load_path=None, feature_layers=None):
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if which_model == 'vgg':
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# PyTorch pretrained VGG19-54, before ReLU.
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if feature_layers is None:
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if use_bn:
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feature_layers = [49]
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else:
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feature_layers = [34]
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if for_training:
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netF = feature_arch.TrainableVGGFeatureExtractor(feature_layers=feature_layers, use_bn=use_bn,
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use_input_norm=True)
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else:
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netF = feature_arch.VGGFeatureExtractor(feature_layers=feature_layers, use_bn=use_bn,
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use_input_norm=True)
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elif which_model == 'wide_resnet':
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netF = feature_arch.WideResnetFeatureExtractor(use_input_norm=True)
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else:
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raise NotImplementedError
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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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if not for_training:
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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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return netF
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