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Discriminator.py
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Discriminator.py
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from torch import nn
import torch.nn.functional as F
import numpy as np
from .spectral_normalization import SpectralNorm
import torch
class ResBlockDiscriminator(nn.Module):
def __init__(self, in_channels, out_channels, stride=1):
super(ResBlockDiscriminator, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, 3, 1, padding=1)
self.conv2 = nn.Conv2d(out_channels, out_channels, 3, 1, padding=1)
nn.init.xavier_uniform_(self.conv1.weight.data, 1.)
nn.init.xavier_uniform_(self.conv2.weight.data, 1.)
if stride == 1:
self.model = nn.Sequential(
nn.ReLU(),
SpectralNorm(self.conv1),
nn.ReLU(),
SpectralNorm(self.conv2)
)
else:
self.model = nn.Sequential(
nn.ReLU(),
SpectralNorm(self.conv1),
nn.ReLU(),
SpectralNorm(self.conv2),
nn.AvgPool2d(2, stride=stride, padding=0)
)
self.bypass = nn.Sequential()
if stride != 1:
self.bypass_conv = nn.Conv2d(in_channels,out_channels, 1, 1, padding=0)
nn.init.xavier_uniform_(self.bypass_conv.weight.data, np.sqrt(2))
self.bypass = nn.Sequential(
SpectralNorm(self.bypass_conv),
nn.AvgPool2d(2, stride=stride, padding=0)
)
def forward(self, x):
return self.model(x) + self.bypass(x)
class FirstResBlockDiscriminator(nn.Module):
def __init__(self, in_channels, out_channels, stride=1):
super(FirstResBlockDiscriminator, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, 3, 1, padding=1)
self.conv2 = nn.Conv2d(out_channels, out_channels, 3, 1, padding=1)
self.bypass_conv = nn.Conv2d(in_channels, out_channels, 1, 1, padding=0)
nn.init.xavier_uniform_(self.conv1.weight.data, 1.)
nn.init.xavier_uniform_(self.conv2.weight.data, 1.)
nn.init.xavier_uniform_(self.bypass_conv.weight.data, np.sqrt(2))
# we don't want to apply ReLU activation to raw image before convolution transformation.
self.model = nn.Sequential(
SpectralNorm(self.conv1),
nn.ReLU(),
SpectralNorm(self.conv2),
nn.AvgPool2d(2)
)
self.bypass = nn.Sequential(
nn.AvgPool2d(2),
SpectralNorm(self.bypass_conv),
)
def forward(self, x):
#print(self.model(x).size())
#print(self.bypass(x).size())
return self.model(x) + self.bypass(x)
channels=3
GEN_SIZE=128
DISC_SIZE=128
class Discriminator(nn.Module):
def __init__(self):
#16*16
super(Discriminator, self).__init__()
self.model = nn.Sequential(
FirstResBlockDiscriminator(channels, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE,stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
nn.ReLU(),
nn.AvgPool2d(4),
)
self.fc = nn.Linear(DISC_SIZE, 1)
nn.init.xavier_uniform_(self.fc.weight.data, 1.)
self.fc = SpectralNorm(self.fc)
def forward(self, x):
out = self.model(x)
return self.fc(out.view(-1,DISC_SIZE))
class Discriminator_32(nn.Module):
def __init__(self):
#32*32
super(Discriminator_32, self).__init__()
self.model = nn.Sequential(
FirstResBlockDiscriminator(channels, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE,stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE,stride=2),
nn.ReLU(),
nn.AvgPool2d(2),
)
self.fc = nn.Linear(DISC_SIZE, 1)
nn.init.xavier_uniform_(self.fc.weight.data, 1.)
self.fc = SpectralNorm(self.fc)
def forward(self, x):
out = self.model(x)
return self.fc(out.view(-1,DISC_SIZE))
class Discriminator_8(nn.Module):
# 8*8 discriminator
def __init__(self):
super(Discriminator_8, self).__init__()
self.model = nn.Sequential(
FirstResBlockDiscriminator(channels, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
nn.ReLU(),
nn.AvgPool2d(8),
)
self.fc = nn.Linear(DISC_SIZE, 1)
nn.init.xavier_uniform_(self.fc.weight.data, 1.)
self.fc = SpectralNorm(self.fc)
def forward(self, x):
out = self.model(x)
return self.fc(out.view(-1,DISC_SIZE))
class Discriminator_16(nn.Module):
def __init__(self,add_n=False):
super(Discriminator_16, self).__init__()
self.add_noise = add_n
if self.add_noise:
self.linear = nn.Linear(64,16*16)
self.model = nn.Sequential(
FirstResBlockDiscriminator(channels+1, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
nn.ReLU(),
nn.AvgPool2d(4),
)
else:
self.model = nn.Sequential(
FirstResBlockDiscriminator(channels, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
nn.ReLU(),
nn.AvgPool2d(4),
)
self.fc = nn.Linear(DISC_SIZE, 1)
nn.init.xavier_uniform_(self.fc.weight.data, 1.)
self.fc = SpectralNorm(self.fc)
def forward(self, x,noise=None):
if self.add_noise:
noise = self.linear(noise)
noise = noise.view(x.size(0), 1, x.size(2), x.size(3))
x = torch.cat((x, noise), 1)
out = self.model(x)
return self.fc(out.view(-1,DISC_SIZE))
class Discriminator_32_(nn.Module):
def __init__(self,add_n=False):
super(Discriminator_32_, self).__init__()
self.add_noise = add_n
if self.add_noise:
self.linear = nn.Linear(64,16*16)
self.model = nn.Sequential(
FirstResBlockDiscriminator(channels+1, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE,stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
nn.ReLU(),
nn.AvgPool2d(4),
)
else:
self.model = nn.Sequential(
FirstResBlockDiscriminator(channels, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE,stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
nn.ReLU(),
nn.AvgPool2d(4),
)
self.fc = nn.Linear(DISC_SIZE, 1)
nn.init.xavier_uniform_(self.fc.weight.data, 1.)
self.fc = SpectralNorm(self.fc)
def forward(self, x,noise=None):
if self.add_noise:
noise = self.linear(noise)
noise = noise.view(x.size(0), 1, x.size(2), x.size(3))
x = torch.cat((x, noise), 1)
out = self.model(x)
return self.fc(out.view(-1,DISC_SIZE))
class Discriminator_4(nn.Module):
def __init__(self):
super(Discriminator_4, self).__init__()
self.model = nn.Sequential(
FirstResBlockDiscriminator(64, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
nn.ReLU()
)
self.fc = nn.Linear(DISC_SIZE, 1)
nn.init.xavier_uniform_(self.fc.weight.data, 1.)
self.fc = SpectralNorm(self.fc)
def forward(self, x):
out = self.model(x)
return self.fc(out.view(-1,DISC_SIZE))
class Discriminator_16_c(nn.Module):
def __init__(self,add_n=False):
super(Discriminator_16_c, self).__init__()
self.add_noise = add_n
if add_n:
self.model = nn.Sequential(
FirstResBlockDiscriminator(channels+8, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE,stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
nn.ReLU(),
nn.AvgPool2d(2),
)
else:
self.model = nn.Sequential(
FirstResBlockDiscriminator(channels, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE,stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE),
nn.ReLU(),
nn.AvgPool2d(2),
)
self.fc = nn.Linear(DISC_SIZE, 1)
nn.init.xavier_uniform(self.fc.weight.data, 1.)
self.fc = SpectralNorm(self.fc)
def forward(self, x,noise=None):
if self.add_noise:
noise = noise.view(noise.size(0),noise.size(1),1, 1).expand(noise.size(0),noise.size(1), x.size(2), x.size(3))
x = torch.cat((x, noise), 1)
out = self.model(x)
return self.fc(out.view(-1,DISC_SIZE))
class Discriminator_3D(nn.Module):
def __init__(self):
super(Discriminator_3D, self).__init__()
self.model = nn.Sequential(
FirstResBlockDiscriminator(channels, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE, stride=2),
ResBlockDiscriminator(DISC_SIZE, DISC_SIZE,stride=2),
nn.ReLU(),
nn.AvgPool2d(7),
)
self.fc = nn.Linear(DISC_SIZE, 1)
nn.init.xavier_uniform_(self.fc.weight.data, 1.)
self.fc = SpectralNorm(self.fc)
def forward(self, x):
out = self.model(x)
#print(out.size())
#exit()
return self.fc(out.view(-1,DISC_SIZE))
#net = Discriminator_ms().cuda()
#output = net(torch.ones(1,3,64,64).cuda())
#print(len(output))