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mnistnet.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from layers.SNConv2d import SNConv2d
from layers.SNLinear import SNLinear
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
# https://github.com/sunshineatnoon/Paper-Implementations/tree/master/dcgan
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
class mnistnet_G(nn.Module):
def __init__(self, nc=1, ngf=64, nz=100, bias=False): # 256 ok
super(mnistnet_G,self).__init__()
self.layer1 = nn.Sequential(nn.ConvTranspose2d(nz,ngf*4,kernel_size=4,bias=bias),
nn.BatchNorm2d(ngf*4),
nn.ReLU())
# 4 x 4
self.layer2 = nn.Sequential(nn.ConvTranspose2d(ngf*4,ngf*2,kernel_size=4,stride=2,padding=1,bias=bias),
nn.BatchNorm2d(ngf*2),
nn.ReLU())
# 8 x 8
self.layer3 = nn.Sequential(nn.ConvTranspose2d(ngf*2,ngf,kernel_size=4,stride=2,padding=1,bias=bias),
nn.BatchNorm2d(ngf),
nn.ReLU())
# 16 x 16
self.layer4 = nn.Sequential(nn.ConvTranspose2d(ngf,nc,kernel_size=4,stride=2,padding=1,bias=bias),
# nn.Sigmoid())
nn.Tanh())
self.apply(weights_init)
self.apply(weights_init)
def forward(self,x):
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
return out
class mnistnet_D(nn.Module):
def __init__(self, nc=1, ndf=64, BN=True, bias=False): # 128 ok
super(mnistnet_D,self).__init__()
if BN:
# 32 x 32
self.layer1 = nn.Sequential(nn.Conv2d(nc,ndf,kernel_size=4,stride=2,padding=1, bias=bias),
nn.BatchNorm2d(ndf),
nn.LeakyReLU(0.2,inplace=True))
# 16 x 16
self.layer2 = nn.Sequential(nn.Conv2d(ndf,ndf*2,kernel_size=4,stride=2,padding=1, bias=bias),
nn.BatchNorm2d(ndf*2),
nn.LeakyReLU(0.2,inplace=True))
# 8 x 8
self.layer3 = nn.Sequential(nn.Conv2d(ndf*2,ndf*4,kernel_size=4,stride=2,padding=1, bias=bias),
nn.BatchNorm2d(ndf*4),
nn.LeakyReLU(0.2,inplace=True))
# 4 x 4
self.layer4 = nn.Sequential(nn.Conv2d(ndf*4,1,kernel_size=4,stride=1,padding=0, bias=bias))#,
# nn.Sigmoid())
else:
# 32 x 32
self.layer1 = nn.Sequential(nn.Conv2d(nc,ndf,kernel_size=4,stride=2,padding=1, bias=bias),
nn.LeakyReLU(0.2,inplace=True))
# 16 x 16
self.layer2 = nn.Sequential(nn.Conv2d(ndf,ndf*2,kernel_size=4,stride=2,padding=1, bias=bias),
nn.LeakyReLU(0.2,inplace=True))
# 8 x 8
self.layer3 = nn.Sequential(nn.Conv2d(ndf*2,ndf*4,kernel_size=4,stride=2,padding=1, bias=bias),
nn.LeakyReLU(0.2,inplace=True))
# 4 x 4
self.layer4 = nn.Sequential(nn.Conv2d(ndf*4,1,kernel_size=4,stride=1,padding=0))#,
self.apply(weights_init)
def forward(self,x):
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
return out.view(-1)
class mnistnet_DSN(nn.Module):
def __init__(self, nc=1, ndf=64, BN=True, bias=False): # 128 ok
super(mnistnet_DSN,self).__init__()
if BN:
# 32 x 32
self.layer1 = nn.Sequential(SNConv2d(nc,ndf,kernel_size=4,stride=2,padding=1, bias=bias),
nn.BatchNorm2d(ndf),
nn.LeakyReLU(0.2,inplace=True))
# 16 x 16
self.layer2 = nn.Sequential(SNConv2d(ndf,ndf*2,kernel_size=4,stride=2,padding=1, bias=bias),
nn.BatchNorm2d(ndf*2),
nn.LeakyReLU(0.2,inplace=True))
# 8 x 8
self.layer3 = nn.Sequential(SNConv2d(ndf*2,ndf*4,kernel_size=4,stride=2,padding=1, bias=bias),
nn.BatchNorm2d(ndf*4),
nn.LeakyReLU(0.2,inplace=True))
# 4 x 4
self.layer4 = nn.Sequential(SNConv2d(ndf*4,1,kernel_size=4,stride=1,padding=0, bias=bias))#,
# nn.Sigmoid())
else:
# 32 x 32
self.layer1 = nn.Sequential(SNConv2d(nc,ndf,kernel_size=4,stride=2,padding=1, bias=bias),
nn.LeakyReLU(0.2,inplace=True))
# 16 x 16
self.layer2 = nn.Sequential(SNConv2d(ndf,ndf*2,kernel_size=4,stride=2,padding=1, bias=bias),
nn.LeakyReLU(0.2,inplace=True))
# 8 x 8
self.layer3 = nn.Sequential(SNConv2d(ndf*2,ndf*4,kernel_size=4,stride=2,padding=1, bias=bias),
nn.LeakyReLU(0.2,inplace=True))
# 4 x 4
self.layer4 = nn.Sequential(SNConv2d(ndf*4,1,kernel_size=4,stride=1,padding=0, bias=bias))#,
self.apply(weights_init)
def forward(self,x):
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
return out.view(-1)
# improved wgan pytorch
DIM = 64
OUTPUT_DIM = 784
class Generator(nn.Module):
def __init__(self, nz=100, BN=False):
super(Generator, self).__init__()
if BN:
preprocess = nn.Sequential(
# nn.Linear(100, 4*4*4*DIM),
nn.ConvTranspose2d(nz, 4*DIM, 4),
nn.BatchNorm2d(4*DIM),
nn.ReLU(True),
)
block1 = nn.Sequential(
nn.ConvTranspose2d(4*DIM, 2*DIM, 5),
nn.BatchNorm2d(2*DIM),
nn.ReLU(True),
)
block2 = nn.Sequential(
nn.ConvTranspose2d(2*DIM, DIM, 5),
nn.BatchNorm2d(DIM),
nn.ReLU(True),
)
else:
preprocess = nn.Sequential(
# nn.Linear(100, 4*4*4*DIM),
nn.ConvTranspose2d(nz, 4*DIM, 4),
nn.ReLU(True),
)
block1 = nn.Sequential(
nn.ConvTranspose2d(4*DIM, 2*DIM, 5),
nn.ReLU(True),
)
block2 = nn.Sequential(
nn.ConvTranspose2d(2*DIM, DIM, 5),
nn.ReLU(True),
)
deconv_out = nn.ConvTranspose2d(DIM, 1, 10, stride=2)
self.block1 = block1
self.block2 = block2
self.deconv_out = deconv_out
self.preprocess = preprocess
# self.sigmoid = nn.Sigmoid()
self.tanh = nn.Tanh()
def forward(self, input):
output = self.preprocess(input)
output = output.view(-1, 4*DIM, 4, 4)
#print output.size()
output = self.block1(output)
#print output.size()
output = output[:, :, :, :]
#print output.size()
output = self.block2(output)
#print output.size()
output = self.deconv_out(output)
output = self.tanh(output)
#print output.size()
return output#.view(-1, OUTPUT_DIM)
class Discriminator(nn.Module):
def __init__(self, nc=1, BN=False):
super(Discriminator, self).__init__()
if BN:
main = nn.Sequential(
nn.Conv2d(nc, DIM, 5, stride=2, padding=2),
# nn.Linear(OUTPUT_DIM, 4*4*4*DIM),
nn.BatchNorm2d(DIM),
nn.ReLU(True),
nn.Conv2d(DIM, 2*DIM, 5, stride=2, padding=2),
# nn.Linear(4*4*4*DIM, 4*4*4*DIM),
nn.BatchNorm2d(2*DIM),
nn.ReLU(True),
nn.Conv2d(2*DIM, 4*DIM, 5, stride=2, padding=2),
nn.BatchNorm2d(4*DIM),
# nn.Linear(4*4*4*DIM, 4*4*4*DIM),
nn.ReLU(True),
# nn.Linear(4*4*4*DIM, 4*4*4*DIM),
# nn.LeakyReLU(True),
# nn.Linear(4*4*4*DIM, 4*4*4*DIM),
# nn.LeakyReLU(True),
)
else:
main = nn.Sequential(
nn.Conv2d(nc, DIM, 5, stride=2, padding=2),
# nn.Linear(OUTPUT_DIM, 4*4*4*DIM),
nn.ReLU(True),
nn.Conv2d(DIM, 2*DIM, 5, stride=2, padding=2),
# nn.Linear(4*4*4*DIM, 4*4*4*DIM),
nn.ReLU(True),
nn.Conv2d(2*DIM, 4*DIM, 5, stride=2, padding=2),
# nn.Linear(4*4*4*DIM, 4*4*4*DIM),
nn.ReLU(True),
# nn.Linear(4*4*4*DIM, 4*4*4*DIM),
# nn.LeakyReLU(True),
# nn.Linear(4*4*4*DIM, 4*4*4*DIM),
# nn.LeakyReLU(True),
)
self.main = main
self.output = nn.Linear(4*4*4*DIM, 1)
def forward(self, input):
# input = input.view(-1, 1, 28, 28)
out = self.main(input)
out = out.view(-1, 4*4*4*DIM)
out = self.output(out)
return out.view(-1)
class LINnet_G(nn.Module):
def __init__(self, nc=1, ngf=64, nz=100, bias=False): # 256 ok
super(LINnet_G,self).__init__()
self.layer1 = nn.Sequential(nn.ConvTranspose2d(nz,ngf*8,kernel_size=(3, 5), bias=bias),
nn.BatchNorm2d(ngf*8),
nn.ReLU())
# 3 x 5
self.layer2 = nn.Sequential(nn.ConvTranspose2d(ngf*8,ngf*4,kernel_size=4,stride=2,padding=1, bias=bias),
nn.BatchNorm2d(ngf*4),
nn.ReLU())
# 6 x 10
self.layer3 = nn.Sequential(nn.ConvTranspose2d(ngf*4,ngf*2,kernel_size=4,stride=2,padding=1, bias=bias),
nn.BatchNorm2d(ngf*2),
nn.ReLU())
# 12 x 20
self.layer4 = nn.Sequential(nn.ConvTranspose2d(ngf*2,ngf,kernel_size=4,stride=2,padding=1, bias=bias),
nn.BatchNorm2d(ngf),
nn.ReLU())
# 24 x 40
self.layer5 = nn.Sequential(nn.ConvTranspose2d(ngf,nc,kernel_size=4,stride=2,padding=1, bias=bias),
# nn.Sigmoid())
nn.Tanh())
self.apply(weights_init)
def forward(self,x):
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.layer5(out)
return out
class LINnet_D(nn.Module):
def __init__(self,nc=1,ndf=64,BN=True,bias=False): # 128 ok
super(LINnet_D,self).__init__()
# 48 x 80
if BN:
self.layer1 = nn.Sequential(nn.Conv2d(nc,ndf,kernel_size=4,stride=2,padding=1,bias=bias),
nn.BatchNorm2d(ndf),
nn.LeakyReLU(0.2,inplace=True))
# 24 x 40
self.layer2 = nn.Sequential(nn.Conv2d(ndf,ndf*2,kernel_size=4,stride=2,padding=1,bias=bias),
nn.BatchNorm2d(ndf*2),
nn.LeakyReLU(0.2,inplace=True))
# 12 x 20
self.layer3 = nn.Sequential(nn.Conv2d(ndf*2,ndf*4,kernel_size=4,stride=2,padding=1,bias=bias),
nn.BatchNorm2d(ndf*4),
nn.LeakyReLU(0.2,inplace=True))
# 6 x 10
self.layer4 = nn.Sequential(nn.Conv2d(ndf*4,ndf*8,kernel_size=4,stride=2,padding=1,bias=bias),
nn.BatchNorm2d(ndf*8),
nn.LeakyReLU(0.2,inplace=True))
# 3 x 5
self.layer5 = nn.Sequential(nn.Conv2d(ndf*8,1,kernel_size=(3, 5),stride=1,padding=0,bias=bias))#,
# nn.Sigmoid())
else:
self.layer1 = nn.Sequential(nn.Conv2d(nc,ndf,kernel_size=4,stride=2,padding=1,bias=bias),
nn.LeakyReLU(0.2,inplace=True))
# 24 x 40
self.layer2 = nn.Sequential(nn.Conv2d(ndf,ndf*2,kernel_size=4,stride=2,padding=1,bias=bias),
nn.LeakyReLU(0.2,inplace=True))
# 12 x 20
self.layer3 = nn.Sequential(nn.Conv2d(ndf*2,ndf*4,kernel_size=4,stride=2,padding=1,bias=bias),
nn.LeakyReLU(0.2,inplace=True))
# 6 x 10
self.layer4 = nn.Sequential(nn.Conv2d(ndf*4,ndf*8,kernel_size=4,stride=2,padding=1,bias=bias),
nn.LeakyReLU(0.2,inplace=True))
# 3 x 5
self.layer5 = nn.Sequential(nn.Conv2d(ndf*8,1,kernel_size=(3, 5),stride=1,padding=0,bias=bias))#,
# nn.Sigmoid())
self.apply(weights_init)
def forward(self,x):
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.layer5(out)
return out.view(-1)
class LINnet_DSN(nn.Module):
def __init__(self,nc=1,ndf=64,spec_norm=True,bias=False): # 128 ok
super(LINnet_DSN,self).__init__()
# 48 x 80
self.layer1 = nn.Sequential(SNConv2d(nc,ndf,kernel_size=4,stride=2,padding=1,bias=bias,spec_norm=spec_norm),
nn.BatchNorm2d(ndf),
nn.LeakyReLU(0.2,inplace=True))
# 24 x 40
self.layer2 = nn.Sequential(SNConv2d(ndf,ndf*2,kernel_size=4,stride=2,padding=1,bias=bias,spec_norm=spec_norm),
nn.BatchNorm2d(ndf*2),
nn.LeakyReLU(0.2,inplace=True))
# 12 x 20
self.layer3 = nn.Sequential(SNConv2d(ndf*2,ndf*4,kernel_size=4,stride=2,padding=1,bias=bias,spec_norm=spec_norm),
nn.BatchNorm2d(ndf*4),
nn.LeakyReLU(0.2,inplace=True))
# 6 x 10
self.layer4 = nn.Sequential(SNConv2d(ndf*4,ndf*8,kernel_size=4,stride=2,padding=1,bias=bias,spec_norm=spec_norm),
nn.BatchNorm2d(ndf*8),
nn.LeakyReLU(0.2,inplace=True))
# 3 x 5
self.layer5 = nn.Sequential(SNConv2d(ndf*8,1,kernel_size=(3, 5),stride=1,padding=0,bias=bias,spec_norm=spec_norm))#,
# nn.Sigmoid())
self.apply(weights_init)
def forward(self,x):
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.layer5(out)
return out.view(-1)
class netG(nn.Module):
def __init__(self, nc=1, ngf=64, nz=100): # 256 ok
super(netG,self).__init__()
self.layer1 = nn.Sequential(nn.ConvTranspose2d(nz,ngf*8,kernel_size=4),
nn.BatchNorm2d(ngf*8),
nn.ReLU())
# 4 x 4
self.layer2 = nn.Sequential(nn.ConvTranspose2d(ngf*8,ngf*4,kernel_size=4,stride=2,padding=1),
nn.BatchNorm2d(ngf*4),
nn.ReLU())
# 8 x 8
self.layer3 = nn.Sequential(nn.ConvTranspose2d(ngf*4,ngf*2,kernel_size=4,stride=2,padding=1),
nn.BatchNorm2d(ngf*2),
nn.ReLU())
# 16 x 16
self.layer4 = nn.Sequential(nn.ConvTranspose2d(ngf*2,ngf,kernel_size=4,stride=2,padding=1),
nn.BatchNorm2d(ngf),
nn.ReLU())
# 16 x 16
self.layer5 = nn.Sequential(nn.ConvTranspose2d(ngf,nc,kernel_size=3,stride=1,padding=1),
# nn.Sigmoid())
nn.Tanh())
self.apply(weights_init)
def forward(self,x):
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.layer5(out)
return out
class netD(nn.Module):
def __init__(self,nc=1,ndf=64,spec_norm=True,detach=False,n_classes=10): # 128 ok
super(netD,self).__init__()
self.n_classes = n_classes
# 32 x 32
self.layer0_0 = nn.Sequential(SNConv2d(nc,ndf,kernel_size=3,stride=1,padding=1,spec_norm=spec_norm,detach=detach),
nn.LeakyReLU(0.1,inplace=True))
self.layer0_1 = nn.Sequential(SNConv2d(ndf,ndf*2,kernel_size=4,stride=2,padding=1,spec_norm=spec_norm,detach=detach),
nn.LeakyReLU(0.1,inplace=True))
self.layer1_0 = nn.Sequential(SNConv2d(ndf*2,ndf*2,kernel_size=3,stride=1,padding=1,spec_norm=spec_norm,detach=detach),
nn.LeakyReLU(0.1,inplace=True))
self.layer1_1 = nn.Sequential(SNConv2d(ndf*2,ndf*4,kernel_size=4,stride=2,padding=1,spec_norm=spec_norm,detach=detach),
nn.LeakyReLU(0.1,inplace=True))
self.layer2_0 = nn.Sequential(SNConv2d(ndf*4,ndf*4,kernel_size=3,stride=1,padding=1,spec_norm=spec_norm,detach=detach),
nn.LeakyReLU(0.1,inplace=True))
self.layer2_1 = nn.Sequential(SNConv2d(ndf*4,ndf*8,kernel_size=4,stride=2,padding=1,spec_norm=spec_norm,detach=detach),
nn.LeakyReLU(0.1,inplace=True))
self.layer3_0 = nn.Sequential(SNConv2d(ndf*8,ndf*8,kernel_size=3,stride=1,padding=1,spec_norm=spec_norm,detach=detach),
nn.LeakyReLU(0.1,inplace=True))
self.layer3_1 = nn.Sequential(SNConv2d(ndf*8,1,kernel_size=4,stride=1,padding=0,spec_norm=spec_norm,detach=detach),
)
# self.layer0_0 = nn.Sequential(nn.Conv2d(nc,ndf,kernel_size=3,stride=1,padding=1),
# nn.LeakyReLU(0.1,inplace=True))
# self.layer0_1 = nn.Sequential(nn.Conv2d(ndf,ndf*2,kernel_size=4,stride=2,padding=1),
# nn.LeakyReLU(0.1,inplace=True))
# self.layer1_0 = nn.Sequential(nn.Conv2d(ndf*2,ndf*2,kernel_size=3,stride=1,padding=1),
# nn.LeakyReLU(0.1,inplace=True))
# self.layer1_1 = nn.Sequential(nn.Conv2d(ndf*2,ndf*4,kernel_size=4,stride=2,padding=1),
# nn.LeakyReLU(0.1,inplace=True))
# self.layer2_0 = nn.Sequential(nn.Conv2d(ndf*4,ndf*4,kernel_size=3,stride=1,padding=1),
# nn.LeakyReLU(0.1,inplace=True))
# self.layer2_1 = nn.Sequential(nn.Conv2d(ndf*4,ndf*8,kernel_size=4,stride=2,padding=1),
# nn.LeakyReLU(0.1,inplace=True))
# self.layer3_0 = nn.Sequential(nn.Conv2d(ndf*8,ndf*8,kernel_size=3,stride=1,padding=1),
# nn.LeakyReLU(0.1,inplace=True))
# self.layer3_1 = nn.Sequential(nn.Conv2d(ndf*8,1,kernel_size=4,stride=1,padding=0),
# )
self.embedding = SNLinear(n_classes, ndf*8)
self.linear = SNLinear(ndf*8, 1)
self.apply(weights_init)
def forward(self,input):
x, y = input
out = x
out = self.layer0_0(out)
out = self.layer0_1(out)
out = self.layer1_0(out)
out = self.layer1_1(out)
out = self.layer2_0(out)
out = self.layer2_1(out)
h = self.layer3_0(out)
# h = self.layer3_1(out)
h = torch.sum(h, dim=2).sum(dim=2) # Global pooling
output = self.linear(h)
th = torch.cuda if h.is_cuda else torch
y_onehot = th.FloatTensor(y.size()[0], self.n_classes)
y_onehot.zero_()
y_onehot.scatter_(1, y.data.view(-1,1), 1)
y_onehot = Variable(y_onehot)
w_y = self.embedding(y_onehot.float())
output += torch.sum(w_y * h, dim=1).view(-1, 1)
# out = self.layer4(out)
return output.view(-1)
return out.view(-1)