DL-Art-School/codes/models/networks.py

230 lines
12 KiB
Python
Raw Normal View History

2020-10-17 14:40:28 +00:00
import functools
import logging
from collections import OrderedDict
import munch
2019-08-23 13:42:47 +00:00
import torch
2020-10-17 14:40:28 +00:00
import torchvision
2020-09-07 23:01:48 +00:00
from munch import munchify
2020-10-17 14:40:28 +00:00
import models.archs.DiscriminatorResnet_arch as DiscriminatorResnet_arch
import models.archs.DiscriminatorResnet_arch_passthrough as DiscriminatorResnet_arch_passthrough
2019-08-23 13:42:47 +00:00
import models.archs.RRDBNet_arch as RRDBNet_arch
import models.archs.SPSR_arch as spsr
2020-10-17 14:40:28 +00:00
import models.archs.SRResNet_arch as SRResNet_arch
import models.archs.SwitchedResidualGenerator_arch as SwitchedGen_arch
import models.archs.discriminator_vgg_arch as SRGAN_arch
import models.archs.feature_arch as feature_arch
2020-10-12 16:20:55 +00:00
import models.archs.panet.panet as panet
2020-10-17 14:40:28 +00:00
import models.archs.rcan as rcan
2020-10-23 15:35:33 +00:00
import models.archs.ChainedEmbeddingGen as chained
2020-10-28 02:59:55 +00:00
from models.archs.teco_resgen import TecoGen
2019-08-23 13:42:47 +00:00
2020-08-26 00:14:45 +00:00
logger = logging.getLogger('base')
2019-08-23 13:42:47 +00:00
# Generator
2020-08-22 14:24:34 +00:00
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']
2019-08-23 13:42:47 +00:00
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':
2020-10-27 16:25:31 +00:00
netG = RRDBNet_arch.RRDBNet(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'],
mid_channels=opt_net['nf'], num_blocks=opt_net['nb'])
2020-09-27 22:00:41 +00:00
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)
2020-10-12 16:20:55 +00:00
elif which_model == 'panet':
#args: n_resblocks, res_scale, scale, n_feats
opt_net['rgb_range'] = 255
opt_net['n_colors'] = 3
args_obj = munchify(opt_net)
netG = panet.PANET(args_obj)
2020-06-25 01:49:37 +00:00
elif which_model == "ConfigurableSwitchedResidualGenerator2":
netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator2(switch_depth=opt_net['switch_depth'], switch_filters=opt_net['switch_filters'],
2020-06-25 01:49:37 +00:00
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'],
2020-07-18 13:24:02 +00:00
transformation_filters=opt_net['transformation_filters'], attention_norm=opt_net['attention_norm'],
2020-06-25 01:49:37 +00:00
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'])
2020-10-27 17:00:38 +00:00
elif which_model == 'spsr':
netG = spsr.SPSRNet(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 == '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'])
2020-10-15 16:13:06 +00:00
elif which_model == "spsr_switched":
netG = spsr.SwitchedSpsr(in_nc=3, nf=opt_net['nf'], upscale=opt_net['scale'], init_temperature=opt_net['temperature'])
elif which_model == "spsr7":
recurrent = opt_net['recurrent'] if 'recurrent' in opt_net.keys() else False
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, recurrent=recurrent)
elif which_model == 'chained_gen_structured':
rec = opt_net['recurrent'] if 'recurrent' in opt_net.keys() else False
recnf = opt_net['recurrent_nf'] if 'recurrent_nf' in opt_net.keys() else 3
recstd = opt_net['recurrent_stride'] if 'recurrent_stride' in opt_net.keys() else 2
in_nc = opt_net['in_nc'] if 'in_nc' in opt_net.keys() else 3
netG = chained.ChainedEmbeddingGenWithStructure(depth=opt_net['depth'], recurrent=rec, recurrent_nf=recnf, recurrent_stride=recstd, in_nc=in_nc)
elif which_model == 'multifaceted_chained':
2020-10-23 15:25:58 +00:00
scale = opt_net['scale'] if 'scale' in opt_net.keys() else 2
2020-10-23 15:35:33 +00:00
netG = chained.MultifacetedChainedEmbeddingGen(depth=opt_net['depth'], scale=scale)
elif which_model == "flownet2":
from models.flownet2.models import FlowNet2
ld = 'load_path' in opt_net.keys()
args = munch.Munch({'fp16': False, 'rgb_max': 1.0, 'checkpoint': not ld})
netG = FlowNet2(args)
if ld:
sd = torch.load(opt_net['load_path'])
netG.load_state_dict(sd['state_dict'])
elif which_model == "backbone_encoder":
netG = SwitchedGen_arch.BackboneEncoder(pretrained_backbone=opt_net['pretrained_spinenet'])
2020-09-15 22:55:38 +00:00
elif which_model == "backbone_encoder_no_ref":
netG = SwitchedGen_arch.BackboneEncoderNoRef(pretrained_backbone=opt_net['pretrained_spinenet'])
2020-09-26 04:45:57 +00:00
elif which_model == "backbone_encoder_no_head":
netG = SwitchedGen_arch.BackboneSpinenetNoHead()
2020-09-21 18:36:49 +00:00
elif which_model == "backbone_resnet":
netG = SwitchedGen_arch.BackboneResnet()
2020-10-28 02:59:55 +00:00
elif which_model == "tecogen":
netG = TecoGen(opt_net['nf'], opt_net['scale'])
2019-08-23 13:42:47 +00:00
else:
raise NotImplementedError('Generator model [{:s}] not recognized'.format(which_model))
return netG
2020-08-25 23:58:20 +00:00
class GradDiscWrapper(torch.nn.Module):
def __init__(self, m):
super(GradDiscWrapper, self).__init__()
2020-08-26 00:14:45 +00:00
logger.info("Wrapping a discriminator..")
2020-08-25 23:58:20 +00:00
self.m = m
2020-08-26 00:14:45 +00:00
def forward(self, x):
return self.m(x)
2020-08-25 23:58:20 +00:00
def define_D_net(opt_net, img_sz=None, wrap=False):
2019-08-23 13:42:47 +00:00
which_model = opt_net['which_model_D']
2020-09-20 03:47:10 +00:00
if 'image_size' in opt_net.keys():
img_sz = opt_net['image_size']
2019-08-23 13:42:47 +00:00
if which_model == 'discriminator_vgg_128':
2020-08-31 15:50:30 +00:00
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':
2020-08-31 15:50:30 +00:00
netD = SRGAN_arch.Discriminator_VGG_128_GN(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz / 128)
2020-08-26 00:14:45 +00:00
if wrap:
netD = GradDiscWrapper(netD)
2020-10-18 15:57:47 +00:00
elif which_model == 'discriminator_vgg_128_gn_checkpointed':
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)
elif which_model == 'discriminator_resnet':
2020-05-02 01:56:14 +00:00
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'])
2020-10-01 17:48:14 +00:00
elif which_model == 'resnext':
2020-10-01 21:49:28 +00:00
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)
2020-10-01 17:48:14 +00:00
netD.fc = torch.nn.Linear(512 * 4, 1)
2020-07-06 03:49:09 +00:00
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'])
2020-08-06 14:56:21 +00:00
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)
2019-08-23 13:42:47 +00:00
else:
raise NotImplementedError('Discriminator model [{:s}] not recognized'.format(which_model))
return netD
# Discriminator
2020-08-25 23:58:20 +00:00
def define_D(opt, wrap=False):
img_sz = opt['datasets']['train']['target_size']
opt_net = opt['network_D']
2020-08-25 23:58:20 +00:00
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']
2020-07-31 21:07:10 +00:00
return net
2019-08-23 13:42:47 +00:00
# Define network used for perceptual loss
def define_F(which_model='vgg', use_bn=False, for_training=False, load_path=None, feature_layers=None):
2020-08-22 19:08:33 +00:00
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,
2020-08-22 19:08:33 +00:00
use_input_norm=True)
else:
netF = feature_arch.VGGFeatureExtractor(feature_layers=feature_layers, use_bn=use_bn,
2020-08-22 19:08:33 +00:00
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
2019-08-23 13:42:47 +00:00
return netF