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RES_VAE4.py
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# no condition in encoder, only in latent space before encoding
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
use_cuda = torch.cuda.is_available()
GPU_indx = 0
device = torch.device(GPU_indx if use_cuda else "cpu")
class ResDown(nn.Module):
"""
Residual down sampling block for the encoder
"""
def __init__(self, channel_in, channel_out, scale=2):
super(ResDown, self).__init__()
self.conv1 = nn.Conv2d(channel_in, channel_out // 2, 3, 1, 1)
self.BN1 = nn.BatchNorm2d(channel_out // 2)
self.conv2 = nn.Conv2d(channel_out // 2, channel_out, 3, 1, 1)
self.BN2 = nn.BatchNorm2d(channel_out)
self.conv3 = nn.Conv2d(channel_in, channel_out, 3, 1, 1)
self.AvePool = nn.AvgPool2d(scale, scale)
def forward(self, x):
skip = self.conv3(self.AvePool(x))
x = F.rrelu(self.BN1(self.conv1(x)))
x = self.AvePool(x)
x = self.BN2(self.conv2(x))
x = F.rrelu(x + skip)
return x
class ResUp(nn.Module):
"""
Residual up sampling block for the decoder
"""
def __init__(self, channel_in, channel_out, scale=2):
super(ResUp, self).__init__()
self.conv1 = nn.Conv2d(channel_in, channel_out // 2, 3, 1, 1)
self.BN1 = nn.BatchNorm2d(channel_out // 2)
self.conv2 = nn.Conv2d(channel_out // 2, channel_out, 3, 1, 1)
self.BN2 = nn.BatchNorm2d(channel_out)
self.conv3 = nn.Conv2d(channel_in, channel_out, 3, 1, 1)
self.UpNN = nn.Upsample(scale_factor=scale, mode="nearest")
def forward(self, x):
skip = self.conv3(self.UpNN(x))
x = F.rrelu(self.BN1(self.conv1(x)))
x = self.UpNN(x)
x = self.BN2(self.conv2(x))
x = F.rrelu(x + skip)
return x
class Encoder(nn.Module):
"""
Encoder block
Built for a 3x64x64 image and will result in a latent vector of size z x 1 x 1
As the network is fully convolutional it will work for images LARGER than 64
For images sized 64 * n where n is a power of 2, (1, 2, 4, 8 etc) the latent feature map size will be z x n x n
When in .eval() the Encoder will not sample from the distribution and will instead output mu as the encoding vector
and log_var will be None
"""
def __init__(self, channels, ch=64, z=512):
super(Encoder, self).__init__()
self.conv1 = ResDown(channels, ch) # 64
self.conv2 = ResDown(ch, 2 * ch) # 32
self.conv3 = ResDown(2 * ch, 4 * ch) # 16
self.conv4 = ResDown(4 * ch, 8 * ch) # 8
self.conv5 = ResDown(8 * ch, 8 * ch) # 4
self.conv_mu = nn.Conv2d(8 * ch, z, 2, 2) # 2
self.conv_log_var = nn.Conv2d(8 * ch, z, 2, 2) # 2
def sample(self, mu, log_var):
std = torch.exp(0.5 * log_var)
eps = torch.randn_like(std)
return mu + eps * std
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = self.conv5(x)
if self.training:
mu = self.conv_mu(x)
log_var = self.conv_log_var(x)
x = self.sample(mu, log_var)
else:
mu = self.conv_mu(x)
x = mu
log_var = None
return x, mu, log_var
class Decoder(nn.Module):
"""
Decoder block
Built to be a mirror of the encoder block
"""
def __init__(self, channels, ch=64, z=512):
super(Decoder, self).__init__()
self.conv1 = ResUp(z, ch * 8)
self.conv2 = ResUp(ch * 8, ch * 8)
self.conv3 = ResUp(ch * 8, ch * 4)
self.conv4 = ResUp(ch * 4, ch * 2)
self.conv5 = ResUp(ch * 2, ch)
self.conv6 = ResUp(ch, ch // 2)
self.conv7 = nn.Conv2d(ch // 2, 3, 3, 1, 1) # the second dim is output image channel = 3
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = self.conv5(x)
x = self.conv6(x)
x = self.conv7(x)
return x
class VAE(nn.Module):
"""
VAE network, uses the above encoder and decoder blocks
"""
def __init__(self, channel_in, ch=64, z=512, condition_dim=512):
super(VAE, self).__init__()
"""Res VAE Network
channel_in = number of channels of the image
z = the number of channels of the latent representation
(for a 64x64 image this is the size of the latent vector)
"""
self.latent_dim = z
channel_in = 3
self.condition_dim = condition_dim
self.encoder = Encoder(channel_in, ch=ch, z=z)
self.decoder = Decoder(channel_in, ch=ch, z=z + self.condition_dim)
def forward(self, x, image_embed):
# image_embed = [batch, embed dim]
#image_embed = torch.randn(x.shape[0], self.condition_dim).to(device)
image_embed = torch.reshape(image_embed, [-1, self.condition_dim, 1, 1])
# ones = torch.ones(x.shape[0], self.condition_dim, 64, 64).to(device)
# condition = ones * image_embed # [16, 32, 64, 64]
# # x = torch.Size([128, 3, 64, 64])
# x = torch.cat((x, condition), dim=1)
# encoding =[batch, latent=128, 1, 1]
encoding, mu, log_var = self.encoder(x)
# encoding = [batch, latent + image_embed, 1, 1]
encoding = torch.cat((encoding, image_embed), dim=1)
recon = self.decoder(encoding)
return recon, mu, log_var
def sample(self, mu, log_var):
std = torch.exp(0.5 * log_var)
eps = torch.randn_like(std)
return mu + eps * std
def reparameterization(self, z_mean, z_log_var):
""" Performs the reparameterization trick"""
eps = torch.randn(z_mean.shape[0], self.latent_dim, 1, 1).to(device)
# z_mean = torch.randn(32, 128,1,1)
# z_log_var = torch.randn(32, 128,1,1)
z = z_mean + torch.exp(z_log_var * .5) * eps
# z_cond = torch.cat([z, input_label], dim=1)
return z