forked from mrq/DL-Art-School
285 lines
17 KiB
Python
285 lines
17 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.stylegan.stylegan2 as stylegan2
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import models.archs.stylegan.stylegan2_unet_disc as stylegan2_unet
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import models.archs.fixup_resnet.DiscriminatorResnet_arch as DiscriminatorResnet_arch
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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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from models.archs import srg2_classic
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from models.archs.biggan.biggan_discriminator import BigGanDiscriminator
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from models.archs.stylegan.Discriminator_StyleGAN import StyleGanDiscriminator
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from models.archs.rrdb_with_adain_latent import AdaRRDBNet, LinearLatentEstimator
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from models.archs.rrdb_with_latent import LatentEstimator, RRDBNetWithLatent, LatentEstimator2
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from models.archs.teco_resgen import TecoGen
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logger = logging.getLogger('base')
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# Generator
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def define_G(opt, opt_net, scale=None):
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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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additive_mode = opt_net['additive_mode'] if 'additive_mode' in opt_net.keys() else 'not_additive'
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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'], additive_mode=additive_mode)
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elif which_model == 'RRDBNetBypass':
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additive_mode = opt_net['additive_mode'] if 'additive_mode' in opt_net.keys() else 'not'
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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'], body_block=RRDBNet_arch.RRDBWithBypass,
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blocks_per_checkpoint=opt_net['blocks_per_checkpoint'], scale=opt_net['scale'],
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additive_mode=additive_mode)
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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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for_video=opt_net['for_video'])
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elif which_model == "srg2classic":
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netG = srg2_classic.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 == '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 == "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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elif which_model == "tecogen":
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netG = TecoGen(opt_net['nf'], opt_net['scale'])
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elif which_model == "basic_resampling_flow_predictor":
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netG = BasicResamplingFlowNet(opt_net['nf'], resample_scale=opt_net['resample_scale'])
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elif which_model == "rrdb_with_latent":
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netG = RRDBNetWithLatent(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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blocks_per_checkpoint=opt_net['blocks_per_checkpoint'],
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scale=opt_net['scale'],
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bottom_latent_only=opt_net['bottom_latent_only'])
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elif which_model == "adarrdb":
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netG = AdaRRDBNet(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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blocks_per_checkpoint=opt_net['blocks_per_checkpoint'],
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scale=opt_net['scale'])
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elif which_model == "latent_estimator":
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if opt_net['version'] == 2:
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netG = LatentEstimator2(in_nc=3, nf=opt_net['nf'])
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else:
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overwrite = [1,2] if opt_net['only_base_level'] else []
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netG = LatentEstimator(in_nc=3, nf=opt_net['nf'], overwrite_levels=overwrite)
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elif which_model == "linear_latent_estimator":
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netG = LinearLatentEstimator(in_nc=3, nf=opt_net['nf'])
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elif which_model == 'stylegan2':
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is_structured = opt_net['structured'] if 'structured' in opt_net.keys() else False
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attn = opt_net['attn_layers'] if 'attn_layers' in opt_net.keys() else []
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netG = stylegan2.StyleGan2GeneratorWithLatent(image_size=opt_net['image_size'], latent_dim=opt_net['latent_dim'],
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style_depth=opt_net['style_depth'], structure_input=is_structured,
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attn_layers=attn)
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elif which_model == 'srflow':
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from models.archs.srflow import SRFlow_arch
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netG = SRFlow_arch.SRFlowNet(in_nc=3, out_nc=3, nf=opt_net['nf'], nb=opt_net['nb'],
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quant=opt_net['quant'], flow_block_maps=opt_net['rrdb_block_maps'],
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noise_quant=opt_net['noise_quant'], hidden_channels=opt_net['nf'],
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K=opt_net['K'], L=opt_net['L'], train_rrdb_at_step=opt_net['rrdb_train_step'],
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hr_img_shape=opt_net['hr_shape'], scale=opt_net['scale'])
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elif which_model == 'srflow_orig':
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from models.archs.srflow_orig import SRFlowNet_arch
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netG = SRFlowNet_arch.SRFlowNet(in_nc=3, out_nc=3, nf=opt_net['nf'], nb=opt_net['nb'], scale=opt['scale'],
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K=opt_net['K'], opt=opt)
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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 == 'stylegan_vgg':
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netD = StyleGanDiscriminator(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_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 == '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 == 'biggan_resnet':
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netD = BigGanDiscriminator(D_activation=torch.nn.LeakyReLU(negative_slope=.2))
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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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elif which_model == "psnr_approximator":
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netD = SRGAN_arch.PsnrApproximator(nf=opt_net['nf'], input_img_factor=img_sz / 128)
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elif which_model == "stylegan2_discriminator":
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attn = opt_net['attn_layers'] if 'attn_layers' in opt_net.keys() else []
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disc = stylegan2.StyleGan2Discriminator(image_size=opt_net['image_size'], input_filters=opt_net['in_nc'], attn_layers=attn)
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netD = stylegan2.StyleGan2Augmentor(disc, opt_net['image_size'], types=opt_net['augmentation_types'], prob=opt_net['augmentation_probability'])
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elif which_model == "stylegan2_unet":
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disc = stylegan2_unet.StyleGan2UnetDiscriminator(image_size=opt_net['image_size'], input_filters=opt_net['in_nc'])
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netD = stylegan2.StyleGan2Augmentor(disc, opt_net['image_size'], types=opt_net['augmentation_types'], prob=opt_net['augmentation_probability'])
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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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