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 import math import os import random import statistics
import numpy as np
import torch import torch.nn as nn import torch.optim as optim import torchvision
def set_random_seeds(random_seed=0):
torch.manual_seed(random_seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False np.random.seed(random_seed) random.seed(random_seed)
class VariationalEncoder(nn.Module):
def __init__(self, num_observed_dims=784, num_latent_dims=8, num_hidden_dims=512): super(VariationalEncoder, self).__init__()
self.num_observed_dims = num_observed_dims self.num_latent_dims = num_latent_dims self.num_hidden_dims = num_hidden_dims
self.fc = nn.Linear(in_features=self.num_observed_dims, out_features=self.num_hidden_dims) self.fc_mean = nn.Linear(in_features=self.num_hidden_dims, out_features=self.num_latent_dims) self.fc_log_std = nn.Linear(in_features=self.num_hidden_dims, out_features=self.num_latent_dims) self.fc_unmasked_lower_triangular_flatten = nn.Linear( in_features=self.num_hidden_dims, out_features=self.num_latent_dims * self.num_latent_dims) self.mask = torch.tril(torch.ones(self.num_latent_dims, self.num_latent_dims), diagonal=1) self.register_buffer('mask_const', self.mask)
def encode(self, x):
h = torch.relu(self.fc(x)) mu = self.fc_mean(h) log_std = self.fc_log_std(h) unmasked_lower_triangular_flatten = self.fc_unmasked_lower_triangular_flatten( h) unmasked_lower_triangular = unmasked_lower_triangular_flatten.view( 1, self.num_latent_dims, self.num_latent_dims)
return mu, log_std, unmasked_lower_triangular
def reparameterize(self, mu, log_std, unmasked_lower_triangular):
std = torch.exp(log_std) lower_triangular = unmasked_lower_triangular * self.mask_const + torch.diag_embed( std) eps = torch.randn_like(std) z = mu + torch.bmm(lower_triangular, eps.view(1, self.num_latent_dims, 1)).view( 1, self.num_latent_dims)
return z, eps
def forward(self, x):
mu, log_std, unmasked_lower_triangular = self.encode(x) z, eps = self.reparameterize(mu, log_std, unmasked_lower_triangular)
return z, eps, log_std
class Decoder(nn.Module):
def __init__(self, num_observed_dims=784, num_latent_dims=8, num_hidden_dims=512): super(Decoder, self).__init__()
self.num_observed_dims = num_observed_dims self.num_latent_dims = num_latent_dims self.num_hidden_dims = num_hidden_dims
self.fc = nn.Linear(in_features=self.num_latent_dims, out_features=self.num_hidden_dims) self.fc_out = nn.Linear(in_features=self.num_hidden_dims, out_features=self.num_observed_dims)
def decode(self, z):
h = torch.relu(self.fc(z)) x = torch.sigmoid(self.fc_out(h))
return x
def forward(self, z):
return self.decode(z)
class VAE(nn.Module):
def __init__(self, num_observed_dims=784, num_latent_dims=8, num_hidden_dims=512): super(VAE, self).__init__()
self.num_observed_dims = num_observed_dims self.num_latent_dims = num_latent_dims self.num_hidden_dims = num_hidden_dims
self.encoder = VariationalEncoder( num_observed_dims=self.num_observed_dims, num_latent_dims=self.num_latent_dims, num_hidden_dims=self.num_hidden_dims) self.decoder = Decoder(num_observed_dims=self.num_observed_dims, num_latent_dims=self.num_latent_dims, num_hidden_dims=self.num_hidden_dims)
def forward(self, x):
z, eps, log_std = self.encoder(x) x_reconstructed = self.decoder(z)
return x_reconstructed, z, eps, log_std
def compute_negative_evidence_lower_bound(x, x_reconstructed, z, eps, log_std):
pi = torch.tensor(math.pi).to(x.device)
log_px = torch.nn.functional.binary_cross_entropy( x_reconstructed, x, reduction="sum") log_qz = 0.5 * torch.sum(eps**2 + log_std + torch.log(2 * pi)) log_pz = 0.5 * torch.sum(z**2 + torch.log(2 * pi)) elbo = log_px + log_pz  log_qz negative_elbo = elbo
batch_size = x.shape[0] negative_elbo_avg = negative_elbo / batch_size
return negative_elbo_avg
class BinarizeTransform(object):
def __init__(self, threshold=0.5): self.threshold = threshold
def __call__(self, x): return (x > self.threshold).float()
def prepare_cifar10_dataset(root="data"):
train_transform = torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), BinarizeTransform(threshold=0.5), ])
test_transform = torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), BinarizeTransform(threshold=0.5), ])
train_set = torchvision.datasets.MNIST(root="data", train=True, download=True, transform=train_transform)
test_set = torchvision.datasets.MNIST(root="data", train=False, download=True, transform=test_transform)
class_names = train_set.classes
return train_set, test_set, class_names
def prepare_cifar10_dataloader(train_set, test_set, train_batch_size=128, eval_batch_size=256, num_workers=2):
train_sampler = torch.utils.data.RandomSampler(train_set) test_sampler = torch.utils.data.SequentialSampler(test_set)
train_loader = torch.utils.data.DataLoader(dataset=train_set, batch_size=train_batch_size, sampler=train_sampler, num_workers=num_workers)
test_loader = torch.utils.data.DataLoader(dataset=test_set, batch_size=eval_batch_size, sampler=test_sampler, num_workers=num_workers)
return train_loader, test_loader
def train(model, device, train_loader, loss_func, optimizer, epoch, log_interval=10):
model.train() train_loss = 0 for batch_idx, (x, _) in enumerate(train_loader): image_height = x.shape[2] image_width = x.shape[3] x = x.to(device) x = x.view(1, image_height * image_width) optimizer.zero_grad() x_reconstructed, z, eps, log_std = model(x) loss = loss_func(x, x_reconstructed, z, eps, log_std) loss.backward() train_loss += loss.item() * len(x) optimizer.step() if batch_idx % log_interval == 0: print( f"Train Epoch: {epoch} [{batch_idx * len(x)}/{len(train_loader.dataset)} " f"({100. * batch_idx / len(train_loader):.0f}%)]\tLoss: {loss.item():.6f}" ) avg_train_loss = train_loss / len(train_loader.dataset) print(f"====> Epoch: {epoch} Average Loss: {avg_train_loss:.4f}")
def test(model, device, num_samples, test_loader, loss_func, epoch, results_dir):
image_dir = os.path.join(results_dir, "reconstruction") if not os.path.exists(image_dir): os.makedirs(image_dir)
model.eval() test_loss = 0 with torch.no_grad(): for i, (x, _) in enumerate(test_loader): x = x.to(device) x = x.view(1, model.num_observed_dims) x_reconstructed, z, eps, log_std = model(x) loss = loss_func(x, x_reconstructed, z, eps, log_std) test_loss += loss.item() * len(x) if i == 0: n = min(x.size(0), num_samples) comparison = torch.cat([ x.view(x.size(0), 1, 28, 28)[:n], x_reconstructed.view(x.size(0), 1, 28, 28)[:n] ]) torchvision.utils.save_image( comparison.cpu(), os.path.join(image_dir, f"reconstruction_{epoch}.png"), nrow=n) avg_test_loss = test_loss / len(test_loader.dataset) print(f"====> Test set loss: {avg_test_loss:.4f}")
def sample_random_images_using_std_normal_prior(model, device, num_samples, epoch, results_dir):
image_dir = os.path.join(results_dir, "sample_using_std_normal_prior") if not os.path.exists(image_dir): os.makedirs(image_dir)
model.eval() with torch.no_grad(): sample = torch.randn(num_samples, model.num_latent_dims).to(device) sample = model.decoder(sample).cpu() torchvision.utils.save_image( sample.view(num_samples, 1, 28, 28), os.path.join(image_dir, f"sample_using_std_normal_prior_{epoch}.png"))
def sample_random_images_using_2d_std_normal_prior_inverse_cdf( model, device, num_samples, epoch, results_dir):
image_dir = os.path.join(results_dir, "sample_using_2d_std_normal_prior_inverse_cdf") if not os.path.exists(image_dir): os.makedirs(image_dir)
num_samples_per_dimension = int(math.sqrt(num_samples)) cumulative_probability_samples = np.linspace(start=0.0001, stop=0.9999, num=num_samples_per_dimension) quantile_samples = torch.tensor([ statistics.NormalDist(mu=0.0, sigma=1.0).inv_cdf(cp) for cp in cumulative_probability_samples ], dtype=torch.float32)
model.eval() samples = [] with torch.no_grad(): for i in range(num_samples_per_dimension): for j in range(num_samples_per_dimension): sample = torch.tensor( [quantile_samples[i], quantile_samples[j]]).to(device) sample = sample.view(1, model.num_latent_dims) sample = model.decoder(sample).cpu() samples.append(sample) samples = torch.cat(samples) torchvision.utils.save_image( samples.view(num_samples, 1, 28, 28), os.path.join( image_dir, f"sample_using_2d_std_normal_prior_inverse_cdf_{epoch}.png"), nrow=num_samples_per_dimension)
def sample_random_images_using_reference_images(model, device, data_set, num_samples, epoch, results_dir):
image_dir = os.path.join(results_dir, "sample_using_reference") if not os.path.exists(image_dir): os.makedirs(image_dir) reference_image_dir = os.path.join(results_dir, "reference") if not os.path.exists(reference_image_dir): os.makedirs(reference_image_dir)
model.eval() with torch.no_grad(): indices = np.random.choice(len(data_set), num_samples, replace=False) reference = torch.stack([data_set[i][0] for i in indices]) reference = reference.to(device) reference = reference.view(1, model.num_observed_dims) sample, _, _, _ = model(reference) torchvision.utils.save_image( sample.view(num_samples, 1, 28, 28), os.path.join(image_dir, f"sample_using_reference_images_{epoch}.png")) torchvision.utils.save_image( reference.view(num_samples, 1, 28, 28), os.path.join(reference_image_dir, f"reference_images_{epoch}.png"))
def sample_ground_truth_images(data_set, num_samples, results_dir):
indices = np.random.choice(len(data_set), num_samples, replace=False) sample = torch.stack([data_set[i][0] for i in indices]) torchvision.utils.save_image( sample, os.path.join(results_dir, "ground_truth_sample.png"))
def main():
cuda_device = torch.device("cuda:0")
results_dir = "results" if not os.path.exists(results_dir): os.makedirs(results_dir) model_dir = "models" if not os.path.exists(model_dir): os.makedirs(model_dir) data_dir = "data" if not os.path.exists(data_dir): os.makedirs(data_dir)
random_seed = 0 set_random_seeds(random_seed=random_seed)
mnist_image_height = 28 mnist_image_width = 28
num_observed_dims = mnist_image_height * mnist_image_width num_latent_dims = 2 num_hidden_dims = 1024
num_epochs = 30 learning_rate = 1e3 log_interval = 10
train_set, test_set, class_names = prepare_cifar10_dataset(root=data_dir)
sample_ground_truth_images(data_set=train_set, num_samples=64, results_dir=results_dir)
train_loader, test_loader = prepare_cifar10_dataloader( train_set=train_set, test_set=test_set, train_batch_size=128, eval_batch_size=256, num_workers=2)
model = VAE(num_observed_dims=num_observed_dims, num_latent_dims=num_latent_dims, num_hidden_dims=num_hidden_dims) model.to(cuda_device) optimizer = optim.Adam(model.parameters(), lr=learning_rate)
for epoch in range(num_epochs):
train(model=model, device=cuda_device, train_loader=train_loader, loss_func=compute_negative_evidence_lower_bound, optimizer=optimizer, epoch=epoch, log_interval=log_interval) test(model=model, device=cuda_device, num_samples=16, test_loader=test_loader, loss_func=compute_negative_evidence_lower_bound, epoch=epoch, results_dir=results_dir) sample_random_images_using_std_normal_prior(model=model, device=cuda_device, num_samples=64, epoch=epoch, results_dir=results_dir) sample_random_images_using_reference_images(model=model, device=cuda_device, data_set=train_set, num_samples=64, epoch=epoch, results_dir=results_dir) if num_latent_dims == 2: sample_random_images_using_2d_std_normal_prior_inverse_cdf( model=model, device=cuda_device, num_samples=400, epoch=epoch, results_dir=results_dir)
torch.save(model.state_dict(), os.path.join(model_dir, "model.pth")) z = torch.randn(1, num_latent_dims).to(cuda_device) model.decoder.eval() torch.onnx.export(model.decoder, z, os.path.join(model_dir, "decoder.onnx"), input_names=["input"], output_names=["output"], opset_version=13)
if __name__ == "__main__":
main()
