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import torch
from torch import nn
class ConvolutionalVariationalAutoencoder(nn.Module):
def __init__(self, latent_dims, hidden_dims, image_shape, dropout=0.0):
super(ConvolutionalVariationalAutoencoder, self).__init__()
self.latent_dims = latent_dims # Size of the latent space layer
self.hidden_dims = (
hidden_dims # List of hidden layers number of filters/channels
)
self.image_shape = image_shape # Input image shape
self.last_channels = self.hidden_dims[-1]
self.in_channels = self.image_shape[0]
# Simple formula to get the number of neurons after the last convolution layer is flattened
self.flattened_channels = int(
self.last_channels
* (self.image_shape[1] / (2 ** len(self.hidden_dims))) ** 2
)
# For each hidden layer we will create a Convolution Block
modules = []
for h_dim in self.hidden_dims:
modules.append(
nn.Sequential(
nn.Conv2d(
in_channels=self.in_channels,
out_channels=h_dim,
kernel_size=3,
stride=2,
padding=1,
),
nn.BatchNorm2d(h_dim),
nn.LeakyReLU(),
nn.Dropout(p=dropout),
)
)
self.in_channels = h_dim
self.encoder = nn.Sequential(*modules)
# Here are our layers for our latent space distribution
self.fc_mu = nn.Linear(self.flattened_channels, latent_dims)
self.fc_var = nn.Linear(self.flattened_channels, latent_dims)
# Decoder input layer
self.decoder_input = nn.Linear(latent_dims, self.flattened_channels)
# For each Convolution Block created on the Encoder we will do a symmetric Decoder with the same Blocks, but using ConvTranspose
self.hidden_dims.reverse()
modules = []
for h_dim in self.hidden_dims:
modules.append(
nn.Sequential(
nn.ConvTranspose2d(
in_channels=self.in_channels,
out_channels=h_dim,
kernel_size=3,
stride=2,
padding=1,
output_padding=1,
),
nn.BatchNorm2d(h_dim),
nn.LeakyReLU(),
nn.Dropout(p=dropout),
)
)
self.in_channels = h_dim
self.decoder = nn.Sequential(*modules)
# The final layer the reconstructed image have the same dimensions as the input image
self.final_layer = nn.Sequential(
nn.Conv2d(
in_channels=self.in_channels,
out_channels=self.image_shape[0],
kernel_size=3,
padding=1,
),
nn.Sigmoid(),
)
def get_latent_dims(self):
return self.latent_dims
def encode(self, input):
"""
Encodes the input by passing through the encoder network
and returns the latent codes.
"""
result = self.encoder(input)
result = torch.flatten(result, start_dim=1)
# Split the result into mu and var componentsbof the latent Gaussian distribution
mu = self.fc_mu(result)
log_var = self.fc_var(result)
return [mu, log_var]
def decode(self, z):
"""
Maps the given latent codes onto the image space.
"""
result = self.decoder_input(z)
result = result.view(
-1,
self.last_channels,
int(self.image_shape[1] / (2 ** len(self.hidden_dims))),
int(self.image_shape[1] / (2 ** len(self.hidden_dims))),
)
result = self.decoder(result)
result = self.final_layer(result)
return result
def reparameterize(self, mu, log_var):
"""
Reparameterization trick to sample from N(mu, var) from N(0,1).
"""
std = torch.exp(0.5 * log_var)
eps = torch.randn_like(std)
return mu + eps * std
def forward(self, input):
"""
Forward method which will encode and decode our image.
"""
mu, log_var = self.encode(input)
z = self.reparameterize(mu, log_var)
return [self.decode(z), input, mu, log_var, z]
def loss_function(self, recons, input, mu, log_var):
"""
Computes VAE loss function
"""
recons_loss = nn.functional.binary_cross_entropy(
recons.reshape(recons.shape[0], -1),
input.reshape(input.shape[0], -1),
reduction="none",
).sum(dim=-1)
kld_loss = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp(), dim=-1)
loss = (recons_loss + kld_loss).mean(dim=0)
return loss
def sample(self, num_samples, device):
"""
Samples from the latent space and return the corresponding
image space map.
"""
z = torch.randn(num_samples, self.latent_dims)
z = z.to(device)
samples = self.decode(z)
return samples
def generate(self, x):
"""
Given an input image x, returns the reconstructed image
"""
return self.forward(x)[0]
def interpolate(self, starting_inputs, ending_inputs, device, granularity=10):
"""This function performs a linear interpolation in the latent space of the autoencoder
from starting inputs to ending inputs. It returns the interpolation trajectories.
"""
mu, log_var = self.encode(starting_inputs.to(device))
starting_z = self.reparameterize(mu, log_var)
mu, log_var = self.encode(ending_inputs.to(device))
ending_z = self.reparameterize(mu, log_var)
t = torch.linspace(0, 1, granularity).to(device)
intep_line = torch.kron(
starting_z.reshape(starting_z.shape[0], -1), (1 - t).unsqueeze(-1)
) + torch.kron(ending_z.reshape(ending_z.shape[0], -1), t.unsqueeze(-1))
decoded_line = self.decode(intep_line).reshape(
(starting_inputs.shape[0], t.shape[0]) + (starting_inputs.shape[1:])
)
return decoded_line
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