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alphagan_models.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from spectral import SpectralNorm
import numpy as np
class Self_Attn(nn.Module):
def __init__(self,in_dim,activation):
super(Self_Attn,self).__init__()
self.chanel_in = in_dim
self.activation = activation
self.query_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)
self.key_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1)
self.value_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim , kernel_size= 1)
self.gamma = nn.Parameter(torch.zeros(1))
self.softmax = nn.Softmax(dim=-1) #
def forward(self,x):
"""
inputs :
x : input feature maps( B X C X W X H)
returns :
out : self attention value + input feature
attention: B X N X N (N is Width*Height)
"""
m_batchsize,C,width ,height = x.size()
proj_query = self.query_conv(x).view(m_batchsize,-1,width*height).permute(0,2,1) # B X CX(N)
proj_key = self.key_conv(x).view(m_batchsize,-1,width*height) # B X C x (*W*H)
energy = torch.bmm(proj_query,proj_key) # transpose check
attention = self.softmax(energy) # BX (N) X (N)
proj_value = self.value_conv(x).view(m_batchsize,-1,width*height) # B X C X N
out = torch.bmm(proj_value,attention.permute(0,2,1) )
out = out.view(m_batchsize,C,width,height)
out = self.gamma*out + x
return out,attention
class Generator(nn.Module):
def __init__(self, batch_size, image_size=64, z_dim=100, conv_dim=64):
super(Generator, self).__init__()
self.imsize = image_size
layer1 = []
layer2 = []
layer3 = []
layer4 = []
last = []
repeat_num = int(np.log2(self.imsize)) - 3
mult = 2 ** repeat_num # 8
layer1.append(SpectralNorm(nn.ConvTranspose2d(z_dim, conv_dim * mult, 4)))
layer1.append(nn.BatchNorm2d(conv_dim * mult))
layer1.append(nn.ReLU())
curr_dim = conv_dim * mult
layer2.append(SpectralNorm(nn.ConvTranspose2d(curr_dim, int(curr_dim / 2), 4, 2, 1)))
layer2.append(nn.BatchNorm2d(int(curr_dim / 2)))
layer2.append(nn.ReLU())
curr_dim = int(curr_dim / 2)
layer3.append(SpectralNorm(nn.ConvTranspose2d(curr_dim, int(curr_dim / 2), 4, 2, 1)))
layer3.append(nn.BatchNorm2d(int(curr_dim / 2)))
layer3.append(nn.ReLU())
curr_dim = int(curr_dim / 2)
layer4.append(SpectralNorm(nn.ConvTranspose2d(curr_dim, int(curr_dim / 2), 4, 2, 1)))
layer4.append(nn.BatchNorm2d(int(curr_dim / 2)))
layer4.append(nn.ReLU())
curr_dim = int(curr_dim / 2)
self.l1 = nn.Sequential(*layer1)
self.l2 = nn.Sequential(*layer2)
self.l3 = nn.Sequential(*layer3)
self.l4 = nn.Sequential(*layer4)
last.append(nn.ConvTranspose2d(curr_dim, 3, 4, 2, 1))
last.append(nn.Tanh())
self.last = nn.Sequential(*last)
self.attn1 = Self_Attn( 128, 'relu')
self.attn2 = Self_Attn( 64, 'relu')
def forward(self, z):
z = z.view(z.size(0), z.size(1), 1, 1)
out=self.l1(z)
out=self.l2(out)
out=self.l3(out)
out,p1 = self.attn1(out)
out=self.l4(out)
out,p2 = self.attn2(out)
out=self.last(out)
return out, p1, p2
class Encoder(nn.Module):
def __init__(self, batch_size=64, image_size=64, z_dim=100, conv_dim=64):
super(Encoder, self).__init__()
self.imsize = image_size
layer1 = []
layer2 = []
layer3 = []
layer4 = []
layer1.append(SpectralNorm(nn.Conv2d(3, conv_dim, 4, 2, 1)))
layer1.append(nn.LeakyReLU(0.1))
curr_dim = conv_dim
layer2.append(SpectralNorm(nn.Conv2d(curr_dim, curr_dim * 2, 4, 2, 1)))
layer2.append(nn.LeakyReLU(0.1))
curr_dim = curr_dim * 2
layer3.append(SpectralNorm(nn.Conv2d(curr_dim, curr_dim * 2, 4, 2, 1)))
layer3.append(nn.LeakyReLU(0.1))
curr_dim = curr_dim * 2
layer4.append(SpectralNorm(nn.Conv2d(curr_dim, curr_dim * 2, 4, 2, 1)))
layer4.append(nn.LeakyReLU(0.1))
curr_dim = curr_dim*2
self.l1 = nn.Sequential(*layer1)
self.l2 = nn.Sequential(*layer2)
self.l3 = nn.Sequential(*layer3)
self.l4 = nn.Sequential(*layer4)
self.mean = nn.Sequential(
nn.Conv2d(curr_dim, z_dim, 4),
nn.Tanh(),
)
self.log_var = nn.Sequential(
nn.Conv2d(curr_dim, z_dim, 4),
nn.Tanh(),
)
self.attn1 = Self_Attn(256, 'relu')
self.attn2 = Self_Attn(512, 'relu')
def forward(self, x):
out = self.l1(x)
out = self.l2(out)
out = self.l3(out)
out,p1 = self.attn1(out)
out = self.l4(out)
out,p2 = self.attn2(out)
mean = self.mean(out)
log_var = self.log_var(out)
return mean.squeeze(), log_var.squeeze(), p1, p2
class Discriminator(nn.Module):
def __init__(self, batch_size=64, image_size=64, conv_dim=64):
super(Discriminator, self).__init__()
self.imsize = image_size
layer1 = []
layer2 = []
layer3 = []
layer4 = []
last = []
## For inference x
layer1.append(SpectralNorm(nn.Conv2d(3, conv_dim, 4, 2, 1)))
layer1.append(nn.LeakyReLU(0.1))
curr_dim = conv_dim
layer2.append(SpectralNorm(nn.Conv2d(curr_dim, curr_dim * 2, 4, 2, 1)))
layer2.append(nn.LeakyReLU(0.1))
curr_dim = curr_dim * 2
layer3.append(SpectralNorm(nn.Conv2d(curr_dim, curr_dim * 2, 4, 2, 1)))
layer3.append(nn.LeakyReLU(0.1))
curr_dim = curr_dim * 2
layer4.append(SpectralNorm(nn.Conv2d(curr_dim, curr_dim * 2, 4, 2, 1)))
layer4.append(nn.LeakyReLU(0.1))
curr_dim = curr_dim*2
self.l1 = nn.Sequential(*layer1)
self.l2 = nn.Sequential(*layer2)
self.l3 = nn.Sequential(*layer3)
self.l4 = nn.Sequential(*layer4)
last.append(SpectralNorm(nn.Conv2d(curr_dim, 1, 4)))
last.append(nn.Sigmoid())
self.last = nn.Sequential(*last)
self.attn1 = Self_Attn(256, 'relu')
self.attn2 = Self_Attn(512, 'relu')
def forward(self, x):
x = self.l1(x)
x = self.l2(x)
x = self.l3(x)
x3, p1 = self.attn1(x)
x = self.l4(x3)
x4, p2 = self.attn2(x)
x5 = self.last(x4)
return x5.squeeze(), x4, x3
class Codecriminator(nn.Module):
def __init__(self, batch_size=64, z_dim=100):
super(Codecriminator, self).__init__()
self.z_dim = z_dim
layer1 = []
layer2 = []
last = []
## For inference x
curr_dim = int(self.z_dim / 2)
layer1.append(nn.Linear(self.z_dim, curr_dim))
layer1.append(nn.LeakyReLU(0.1))
curr_dim = int(curr_dim / 2)
layer2.append(nn.Linear(curr_dim * 2, curr_dim))
layer2.append(nn.LeakyReLU(0.1))
last.append(nn.Linear(curr_dim, 1))
last.append(nn.Sigmoid())
self.l1 = nn.Sequential(*layer1)
self.l2 = nn.Sequential(*layer2)
self.last = nn.Sequential(*last)
def forward(self, x):
x = self.l1(x.squeeze())
x = self.l2(x)
x = self.last(x)
return x.squeeze()