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Remove pyramid_disc hard dependencies

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James Betker 2020-11-17 18:34:11 -07:00
parent 6b679e2b51
commit 5c10264538
2 changed files with 0 additions and 30 deletions
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27
codes/models/archs/discriminator_vgg_arch.py
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@ -5,7 +5,6 @@ from models.archs.RRDBNet_arch import RRDB, RRDBWithBypass
from models.archs.arch_util import ConvBnLelu, ConvGnLelu, ExpansionBlock, ConvGnSilu, ResidualBlockGN from models.archs.arch_util import ConvBnLelu, ConvGnLelu, ExpansionBlock, ConvGnSilu, ResidualBlockGN
import torch.nn.functional as F import torch.nn.functional as F
from models.archs.SwitchedResidualGenerator_arch import gather_2d from models.archs.SwitchedResidualGenerator_arch import gather_2d
from models.archs.pyramid_arch import Pyramid
from utils.util import checkpoint from utils.util import checkpoint
@ -660,29 +659,3 @@ class SingleImageQualityEstimator(nn.Module):
fea = self.lrelu(self.conv4_2(fea)) fea = self.lrelu(self.conv4_2(fea))
fea = self.sigmoid(self.conv4_3(fea)) fea = self.sigmoid(self.conv4_3(fea))
return fea return fea
class PyramidDiscriminator(nn.Module):
def __init__(self, in_nc, nf, block=ConvGnLelu):
super(PyramidDiscriminator, self).__init__()
self.initial_conv = block(in_nc, nf, kernel_size=3, stride=2, bias=True, norm=False, activation=True)
self.top_proc = nn.Sequential(*[ResidualBlockGN(nf),
ResidualBlockGN(nf),
ResidualBlockGN(nf)])
self.pyramid = Pyramid(nf, depth=3, processing_convs_per_layer=2, processing_at_point=2,
scale_per_level=1.5, norm=True, return_outlevels=False)
self.bottom_proc = nn.Sequential(*[ResidualBlockGN(nf),
ResidualBlockGN(nf),
ResidualBlockGN(nf),
ResidualBlockGN(nf),
ConvGnLelu(nf, nf // 2, kernel_size=1, activation=True, norm=False, bias=True),
ConvGnLelu(nf // 2, nf // 4, kernel_size=1, activation=True, norm=False, bias=True),
ConvGnLelu(nf // 4, 1, kernel_size=1, activation=False, norm=False, bias=True)])
def forward(self, x):
fea = self.initial_conv(x)
fea = checkpoint(self.top_proc, fea)
fea = checkpoint(self.pyramid, fea)
fea = checkpoint(self.bottom_proc, fea)
return torch.mean(fea, dim=[1,2,3])

3
codes/models/networks.py
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@ -21,7 +21,6 @@ import models.archs.rcan as rcan
from models.archs import srg2_classic from models.archs import srg2_classic
from models.archs.biggan.biggan_discriminator import BigGanDiscriminator from models.archs.biggan.biggan_discriminator import BigGanDiscriminator
from models.archs.stylegan.Discriminator_StyleGAN import StyleGanDiscriminator from models.archs.stylegan.Discriminator_StyleGAN import StyleGanDiscriminator
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.teco_resgen import TecoGen from models.archs.teco_resgen import TecoGen
@ -198,8 +197,6 @@ def define_D_net(opt_net, img_sz=None, wrap=False):
netD = SRGAN_arch.RefDiscriminatorVgg128(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz / 128) netD = SRGAN_arch.RefDiscriminatorVgg128(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz / 128)
elif which_model == "psnr_approximator": elif which_model == "psnr_approximator":
netD = SRGAN_arch.PsnrApproximator(nf=opt_net['nf'], input_img_factor=img_sz / 128) netD = SRGAN_arch.PsnrApproximator(nf=opt_net['nf'], input_img_factor=img_sz / 128)
elif which_model == "pyramid_disc":
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 = stylegan2.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)