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Fix circular imports

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This commit is contained in:
James Betker 2020-11-15 11:32:35 -07:00
parent 99f0cfaab5
commit e587d549f7
2 changed files with 12 additions and 12 deletions
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10
codes/models/archs/stylegan/__init__.py
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@ -1,14 +1,14 @@
from models.archs.stylegan.stylegan2 import StyleGan2DivergenceLoss, StyleGan2PathLengthLoss import models.archs.stylegan.stylegan2 as stylegan2
from models.archs.stylegan.stylegan2_unet_disc import StyleGan2UnetDivergenceLoss import models.archs.stylegan.stylegan2_unet_disc as stylegan2_unet
def create_stylegan2_loss(opt_loss, env): def create_stylegan2_loss(opt_loss, env):
type = opt_loss['type'] type = opt_loss['type']
if type == 'stylegan2_divergence': if type == 'stylegan2_divergence':
return StyleGan2DivergenceLoss(opt_loss, env) return stylegan2.StyleGan2DivergenceLoss(opt_loss, env)
elif type == 'stylegan2_pathlen': elif type == 'stylegan2_pathlen':
return StyleGan2PathLengthLoss(opt_loss, env) return stylegan2.StyleGan2PathLengthLoss(opt_loss, env)
elif type == 'stylegan2_unet_divergence': elif type == 'stylegan2_unet_divergence':
return StyleGan2UnetDivergenceLoss(opt_loss, env) return stylegan2_unet.StyleGan2UnetDivergenceLoss(opt_loss, env)
else: else:
raise NotImplementedError raise NotImplementedError

14
codes/models/networks.py
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@ -6,6 +6,8 @@ import munch
import torch import torch
import torchvision import torchvision
from munch import munchify from munch import munchify
import models.archs.stylegan.stylegan2 as stylegan2
import models.archs.stylegan.stylegan2_unet_disc as stylegan2_unet
import models.archs.fixup_resnet.DiscriminatorResnet_arch as DiscriminatorResnet_arch import models.archs.fixup_resnet.DiscriminatorResnet_arch as DiscriminatorResnet_arch
import models.archs.RRDBNet_arch as RRDBNet_arch import models.archs.RRDBNet_arch as RRDBNet_arch
@ -22,8 +24,6 @@ from models.archs.stylegan.Discriminator_StyleGAN import StyleGanDiscriminator
from models.archs.pyramid_arch import BasicResamplingFlowNet from models.archs.pyramid_arch import BasicResamplingFlowNet
from models.archs.rrdb_with_adain_latent import AdaRRDBNet, LinearLatentEstimator from models.archs.rrdb_with_adain_latent import AdaRRDBNet, LinearLatentEstimator
from models.archs.rrdb_with_latent import LatentEstimator, RRDBNetWithLatent, LatentEstimator2 from models.archs.rrdb_with_latent import LatentEstimator, RRDBNetWithLatent, LatentEstimator2
from models.archs.stylegan.stylegan2 import StyleGan2GeneratorWithLatent, StyleGan2Discriminator, StyleGan2Augmentor
from models.archs.stylegan.stylegan2_unet_disc import StyleGan2UnetDiscriminator
from models.archs.teco_resgen import TecoGen from models.archs.teco_resgen import TecoGen
logger = logging.getLogger('base') logger = logging.getLogger('base')
@ -136,7 +136,7 @@ def define_G(opt, net_key='network_G', scale=None):
elif which_model == 'stylegan2': elif which_model == 'stylegan2':
is_structured = opt_net['structured'] if 'structured' in opt_net.keys() else False is_structured = opt_net['structured'] if 'structured' in opt_net.keys() else False
attn = opt_net['attn_layers'] if 'attn_layers' in opt_net.keys() else [] attn = opt_net['attn_layers'] if 'attn_layers' in opt_net.keys() else []
netG = StyleGan2GeneratorWithLatent(image_size=opt_net['image_size'], latent_dim=opt_net['latent_dim'], netG = stylegan2.StyleGan2GeneratorWithLatent(image_size=opt_net['image_size'], latent_dim=opt_net['latent_dim'],
style_depth=opt_net['style_depth'], structure_input=is_structured, style_depth=opt_net['style_depth'], structure_input=is_structured,
attn_layers=attn) attn_layers=attn)
else: else:
@ -199,11 +199,11 @@ def define_D_net(opt_net, img_sz=None, wrap=False):
netD = SRGAN_arch.PyramidDiscriminator(in_nc=3, nf=opt_net['nf']) netD = SRGAN_arch.PyramidDiscriminator(in_nc=3, nf=opt_net['nf'])
elif which_model == "stylegan2_discriminator": elif which_model == "stylegan2_discriminator":
attn = opt_net['attn_layers'] if 'attn_layers' in opt_net.keys() else [] attn = opt_net['attn_layers'] if 'attn_layers' in opt_net.keys() else []
disc = StyleGan2Discriminator(image_size=opt_net['image_size'], input_filters=opt_net['in_nc'], attn_layers=attn) disc = stylegan2.StyleGan2Discriminator(image_size=opt_net['image_size'], input_filters=opt_net['in_nc'], attn_layers=attn)
netD = StyleGan2Augmentor(disc, opt_net['image_size'], types=opt_net['augmentation_types'], prob=opt_net['augmentation_probability']) netD = stylegan2.StyleGan2Augmentor(disc, opt_net['image_size'], types=opt_net['augmentation_types'], prob=opt_net['augmentation_probability'])
elif which_model == "stylegan2_unet": elif which_model == "stylegan2_unet":
disc = StyleGan2UnetDiscriminator(image_size=opt_net['image_size'], input_filters=opt_net['in_nc']) disc = stylegan2_unet.StyleGan2UnetDiscriminator(image_size=opt_net['image_size'], input_filters=opt_net['in_nc'])
netD = StyleGan2Augmentor(disc, opt_net['image_size'], types=opt_net['augmentation_types'], prob=opt_net['augmentation_probability']) netD = stylegan2.StyleGan2Augmentor(disc, opt_net['image_size'], types=opt_net['augmentation_types'], prob=opt_net['augmentation_probability'])
else: else:
raise NotImplementedError('Discriminator model [{:s}] not recognized'.format(which_model)) raise NotImplementedError('Discriminator model [{:s}] not recognized'.format(which_model))
return netD return netD