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vae.py
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# VAE implementation
import math
import torch
from torch import Tensor
from torch import nn
from torch.nn import functional as F
from abc import abstractmethod
from typing import List, Any
from imu_utils import matmul_A
class BaseVAE(nn.Module):
def __init__(self) -> None:
super(BaseVAE, self).__init__()
def encode(self, input: Tensor) -> List[Tensor]:
raise NotImplementedError
def decode(self, input: Tensor) -> Any:
raise NotImplementedError
def sample(self, batch_size: int, current_device: int, **kwargs) -> Tensor:
raise NotImplementedError
def generate(self, x: Tensor, **kwargs) -> Tensor:
raise NotImplementedError
@abstractmethod
def forward(self, *inputs: Tensor) -> Tensor:
pass
@abstractmethod
def loss_function(self, *inputs: Any, **kwargs) -> Tensor:
pass
class DIPVAE(BaseVAE):
def __init__(self, latent_dim, h_in, h_out, eta, P_T, **kwargs) -> None:
super(DIPVAE, self).__init__()
self.latent_dim = latent_dim
self.h_in = h_in
self.h_out = h_out
self.noise_std = eta
self.P_T = P_T
self.h_dims = [64, 64]
torch.manual_seed(123)
encoder_layers = [
nn.Dropout(0.25),
nn.Linear(self.h_in, self.h_dims[0]),
nn.ReLU(),
]
self.encoder = nn.Sequential(*encoder_layers) # x -> encoder
self.fc_mu = nn.Linear(self.h_dims[0], self.latent_dim) # encoder -> fc_mu
self.fc_var = nn.Linear(self.h_dims[0], self.latent_dim) # encoder -> fc_var
decoder_layers = [
nn.Linear(self.latent_dim, self.h_dims[1]),
nn.ReLU(),
nn.Dropout(0.25),
nn.Linear(self.h_dims[1], self.h_out),
nn.Tanh()
]
self.decoder = nn.Sequential(*decoder_layers) # decoder_input -> decoder -> x_hat
def vector_reduction(self, x, positions, device):
b = x.size()[0]
x_ori = torch.reshape(x, [b, 17, 12]).to(device)
y_ori = x_ori[:, positions.tolist(), :] # [b, 6, 12]
y = torch.reshape(y_ori, [b, self.h_in]).to(device)
# Power normalize
y_norm_i = torch.linalg.vector_norm(y, ord=2, dim=1).to(device) # [1, b]
power = math.sqrt(self.h_in * self.P_T)
a = power / torch.reshape(y_norm_i, [b, 1]).to(device)
a = a.repeat(1, self.h_in).to(device)
y = a * y
return x, y
def encode(self, input: Tensor) -> List[Tensor]:
result = self.encoder(input)
mu = self.fc_mu(result)
log_var = self.fc_var(result)
return [mu, log_var]
def decode(self, z: Tensor) -> Tensor:
result = self.decoder(z)
return result
def reparameterize(self, mu: Tensor, logvar: Tensor) -> Tensor:
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return eps * std + mu
def forward(self, input: Tensor, **kwargs) -> List[Tensor]:
mu, log_var = self.encode(input)
z = self.reparameterize(mu, log_var)
x_hat = self.decode(z)
positions = kwargs['positions']
return [x_hat, input, mu, log_var, positions]
def loss_function(self,
*args,
**kwargs) -> dict:
recons = args[0] # (b, h_out)
input = args[1] # (b, h_in)
mu = args[2]
log_var = args[3]
positions = args[4]
kld_weight = kwargs['M_N']
recons, reduced_recons = self.vector_reduction(recons, positions, recons.device)
recons_loss = F.mse_loss(reduced_recons, input, reduction='sum')
kld_loss = torch.mean(-0.5 * torch.sum(1 + log_var - mu ** 2 - log_var.exp(), dim=1), dim=0)
loss = recons_loss + kld_weight * kld_loss
return {'loss': loss, 'Reconstruction_Loss': recons_loss.detach(), 'KLD': -kld_loss.detach()}
def sample(self, num_samples: int, current_device: int, **kwargs) -> Tensor:
z = torch.randn(num_samples,
self.latent_dim)
z = z.to(current_device)
samples = self.decode(z)
return samples
def generate(self, x: Tensor, **kwargs) -> Tensor:
return self.forward(x, **kwargs)[0]
class SMPLVAE(BaseVAE):
def __init__(self,
in_channels: int,
latent_dim: int,
h_in: int,
h_out: int,
**kwargs) -> None:
super(SMPLVAE, self).__init__()
self.latent_dim = latent_dim
self.in_channels = in_channels
self.h_in = h_in
self.h_out = h_out
self.h_dim = 512
# [b, 1, 204] -> encoder(x)
encoder_layers = [
nn.Dropout(0.2),
nn.Linear(self.h_in, self.h_dim),
nn.ReLU(),
nn.Linear(self.h_dim, self.h_dim),
nn.ReLU(),
]
self.encoder = nn.Sequential(*encoder_layers) # x -> encoder
self.fc_mu = nn.Linear(self.h_dim, self.latent_dim) # encoder -> fc_mu
self.fc_var = nn.Linear(self.h_dim, self.latent_dim) # encoder -> fc_var
# Define the decoder layers as a list of tuples, where each tuple contains the
# layer type and its corresponding parameters.
self.decoder_input = nn.Linear(self.latent_dim, self.h_dim) # fc_mu, fc_var -> decoder_input
decoder_layers = [
nn.Linear(self.h_dim, self.h_dim),
nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(self.h_dim, self.h_out),
# nn.Tanh()
]
self.decoder = nn.Sequential(*decoder_layers) # decoder_input -> decoder -> x_hat
def encode(self, input: Tensor) -> List[Tensor]:
x = self.encoder(input)
mu = self.fc_mu(x)
log_var = self.fc_var(x)
return [mu, log_var]
def decode(self, z: Tensor) -> Tensor:
x_hat = self.decoder_input(z)
x_hat = self.decoder(x_hat)
return x_hat
def reparameterize(self, mu: Tensor, logvar: Tensor) -> Tensor:
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return eps * std + mu
def forward(self, input: Tensor, **kwargs) -> List[Tensor]:
mu, log_var = self.encode(input)
z = self.reparameterize(mu, log_var)
x_hat = self.decode(z)
labels = kwargs['labels']
return [x_hat, mu, log_var, labels]
def loss_function(self,
*args,
**kwargs) -> dict:
recons = args[0]
mu = args[1]
log_var = args[2]
labels = args[3]
# print('recons.shape: {}, labels.shape: {}'.format(recons.shape, labels.shape))
kld_weight = kwargs['M_N'] # Account for the minibatch samples from the dataset
recons_loss = F.mse_loss(recons, labels, reduction='mean')
kld_loss = torch.mean(-0.5 * torch.sum(1 + log_var - mu ** 2 - log_var.exp(), dim = 1), dim = 0)
loss = recons_loss + kld_weight * kld_loss
return {'loss': loss, 'Reconstruction_Loss':recons_loss.detach(), 'KLD':-kld_loss.detach()}
def sample(self,
num_samples:int,
current_device: int, **kwargs) -> Tensor:
z = torch.randn(num_samples,
self.latent_dim)
z = z.to(current_device)
samples = self.decode(z)
return samples
def generate(self, x: Tensor, **kwargs) -> Tensor:
return self.forward(x, **kwargs)[0]
class MyVAE(BaseVAE):
def __init__(self,
in_channels: int,
latent_dim: int,
h_in: int,
h_out: int,
eta: float,
P_T: float,
**kwargs) -> None:
super(MyVAE, self).__init__()
self.latent_dim = latent_dim
self.in_channels = in_channels
self.h_in = h_in
self.h_out = h_out
self.h_dims = [64, 64]
self.noise_std = eta
self.P_T = P_T
torch.manual_seed(1234)
encoder_layers = [
nn.Dropout(0.25),
nn.Linear(self.h_in, self.h_dims[0]),
nn.ReLU(),
]
self.encoder = nn.Sequential(*encoder_layers) # x -> encoder
self.fc_mu = nn.Linear(self.h_dims[0], self.latent_dim) # encoder -> fc_mu
self.fc_var = nn.Linear(self.h_dims[0], self.latent_dim) # encoder -> fc_var
decoder_layers = [
nn.Linear(self.latent_dim, self.h_dims[1]),
nn.ReLU(),
nn.Dropout(0.25),
nn.Linear(self.h_dims[1], self.h_out),
nn.Tanh()
]
self.decoder = nn.Sequential(*decoder_layers) # decoder_input -> decoder -> x_hat
def encode(self, input: Tensor) -> List[Tensor]:
result = self.encoder(input)
mu = self.fc_mu(result)
log_var = self.fc_var(result)
return [mu, log_var]
def decode(self, z: Tensor) -> Tensor:
result = self.decoder(z)
return result
def reparameterize(self, mu: Tensor, logvar: Tensor) -> Tensor:
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return eps * std + mu
def forward(self, input: Tensor, **kwargs) -> List[Tensor]:
# Encode the input data to get the mean and log-variance of the latent distribution
mu, log_var = self.encode(input)
# Reparameterize the latent distribution and get the latent code z.
z = self.reparameterize(mu, log_var)
# Decode the latent code to get the reconstructed output.
x_hat = self.decode(z)
A = kwargs['A']
# Return the reconstructed output, mean, and log-variance for use in the loss function.
return [x_hat, input, mu, log_var, A]
def loss_function(self,
*args,
**kwargs) -> dict:
recons = args[0] # (b, h_out)
input = args[1] # (b, h_in)
mu = args[2]
log_var = args[3]
A = args[4] # (h_in, h_out)
gz_loss = torch.mean(torch.linalg.vector_norm(recons, ord=1, dim=1)) # L_1 regularizer ||G(z)||_1
gz_weight = kwargs['g_z']
kld_weight = kwargs['M_N']
recons = matmul_A(recons, A)
# recons_loss = F.mse_loss(recons, input, reduction='mean')
# ||AG(z) - y_hat||_2^2
recons_loss = torch.mean(torch.square(torch.linalg.vector_norm(recons - input, ord=2, dim=1)))
kld_loss = torch.mean(-0.5 * torch.sum(1 + log_var - mu ** 2 - log_var.exp(), dim=1), dim=0)
loss = recons_loss + kld_weight * kld_loss + gz_weight * gz_loss
return {'loss': loss, 'Reconstruction_Loss': recons_loss.detach(), 'KLD': -kld_loss.detach()}
def sample(self, num_samples: int, current_device: int, **kwargs) -> Tensor:
z = torch.randn(num_samples,
self.latent_dim)
z = z.to(current_device)
samples = self.decode(z)
return samples
def generate(self, x: Tensor, **kwargs) -> Tensor:
return self.forward(x, **kwargs)[0]