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9d0b4d9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | import torch
import torch.nn as nn
from .pix2pixHD_model import *
from .model_util import *
from models import model_util
class UpBlock(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size=3, padding=1):
super().__init__()
self.convup = nn.Sequential(
nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
nn.ReflectionPad2d(padding),
# EqualConv2d(out_channel, out_channel, kernel_size, padding=padding),
SpectralNorm(nn.Conv2d(in_channel, out_channel, kernel_size)),
nn.LeakyReLU(0.2),
# Blur(out_channel),
)
def forward(self, input):
outup = self.convup(input)
return outup
class Encoder2d(nn.Module):
def __init__(self, input_nc, ngf=64, n_downsampling=3, activation = nn.LeakyReLU(0.2)):
super(Encoder2d, self).__init__()
model = [nn.ReflectionPad2d(3), SpectralNorm(nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0)), activation]
### downsample
for i in range(n_downsampling):
mult = 2**i
model += [ nn.ReflectionPad2d(1),
SpectralNorm(nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=0)),
activation]
self.model = nn.Sequential(*model)
def forward(self, input):
return self.model(input)
class Encoder3d(nn.Module):
def __init__(self, input_nc, ngf=64, n_downsampling=3, activation = nn.LeakyReLU(0.2)):
super(Encoder3d, self).__init__()
model = [SpectralNorm(nn.Conv3d(input_nc, ngf, kernel_size=3, padding=1)), activation]
### downsample
for i in range(n_downsampling):
mult = 2**i
model += [ SpectralNorm(nn.Conv3d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1)),
activation]
self.model = nn.Sequential(*model)
def forward(self, input):
return self.model(input)
class BVDNet(nn.Module):
def __init__(self, N=2, n_downsampling=3, n_blocks=4, input_nc=3, output_nc=3,activation=nn.LeakyReLU(0.2)):
super(BVDNet, self).__init__()
ngf = 64
padding_type = 'reflect'
self.N = N
### encoder
self.encoder3d = Encoder3d(input_nc,64,n_downsampling,activation)
self.encoder2d = Encoder2d(input_nc,64,n_downsampling,activation)
### resnet blocks
self.blocks = []
mult = 2**n_downsampling
for i in range(n_blocks):
self.blocks += [ResnetBlockSpectralNorm(ngf * mult, padding_type=padding_type, activation=activation)]
self.blocks = nn.Sequential(*self.blocks)
### decoder
self.decoder = []
for i in range(n_downsampling):
mult = 2**(n_downsampling - i)
self.decoder += [UpBlock(ngf * mult, int(ngf * mult / 2))]
self.decoder += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
self.decoder = nn.Sequential(*self.decoder)
self.limiter = nn.Tanh()
def forward(self, stream, previous):
this_shortcut = stream[:,:,self.N]
stream = self.encoder3d(stream)
stream = stream.reshape(stream.size(0),stream.size(1),stream.size(3),stream.size(4))
previous = self.encoder2d(previous)
x = stream + previous
x = self.blocks(x)
x = self.decoder(x)
x = x+this_shortcut
x = self.limiter(x)
return x
def define_G(N=2, n_blocks=1, gpu_id='-1'):
netG = BVDNet(N = N, n_blocks=n_blocks)
netG = model_util.todevice(netG,gpu_id)
netG.apply(model_util.init_weights)
return netG
################################Discriminator################################
def define_D(input_nc=6, ndf=64, n_layers_D=1, use_sigmoid=False, num_D=3, gpu_id='-1'):
netD = MultiscaleDiscriminator(input_nc, ndf, n_layers_D, use_sigmoid, num_D)
netD = model_util.todevice(netD,gpu_id)
netD.apply(model_util.init_weights)
return netD
class MultiscaleDiscriminator(nn.Module):
def __init__(self, input_nc, ndf=64, n_layers=3, use_sigmoid=False, num_D=3):
super(MultiscaleDiscriminator, self).__init__()
self.num_D = num_D
self.n_layers = n_layers
for i in range(num_D):
netD = NLayerDiscriminator(input_nc, ndf, n_layers, use_sigmoid)
setattr(self, 'layer'+str(i), netD.model)
self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)
def singleD_forward(self, model, input):
return [model(input)]
def forward(self, input):
num_D = self.num_D
result = []
input_downsampled = input
for i in range(num_D):
model = getattr(self, 'layer'+str(num_D-1-i))
result.append(self.singleD_forward(model, input_downsampled))
if i != (num_D-1):
input_downsampled = self.downsample(input_downsampled)
return result
# Defines the PatchGAN discriminator with the specified arguments.
class NLayerDiscriminator(nn.Module):
def __init__(self, input_nc, ndf=64, n_layers=3, use_sigmoid=False):
super(NLayerDiscriminator, self).__init__()
self.n_layers = n_layers
kw = 4
padw = int(np.ceil((kw-1.0)/2))
sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2)]]
nf = ndf
for n in range(1, n_layers):
nf_prev = nf
nf = min(nf * 2, 512)
sequence += [[
SpectralNorm(nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw)),
nn.LeakyReLU(0.2)
]]
nf_prev = nf
nf = min(nf * 2, 512)
sequence += [[
SpectralNorm(nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw)),
nn.LeakyReLU(0.2)
]]
sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
if use_sigmoid:
sequence += [[nn.Sigmoid()]]
sequence_stream = []
for n in range(len(sequence)):
sequence_stream += sequence[n]
self.model = nn.Sequential(*sequence_stream)
def forward(self, input):
return self.model(input)
class GANLoss(nn.Module):
def __init__(self, mode='D'):
super(GANLoss, self).__init__()
if mode == 'D':
self.lossf = model_util.HingeLossD()
elif mode == 'G':
self.lossf = model_util.HingeLossG()
self.mode = mode
def forward(self, dis_fake = None, dis_real = None):
if isinstance(dis_fake, list):
if self.mode == 'D':
loss = 0
for i in range(len(dis_fake)):
loss += self.lossf(dis_fake[i][-1],dis_real[i][-1])
elif self.mode =='G':
loss = 0
weight = 2**len(dis_fake)
for i in range(len(dis_fake)):
weight = weight/2
loss += weight*self.lossf(dis_fake[i][-1])
return loss
else:
if self.mode == 'D':
return self.lossf(dis_fake[-1],dis_real[-1])
elif self.mode =='G':
return self.lossf(dis_fake[-1])
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