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vqvae.py
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vqvae.py
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import numpy as np
import torch
import torch.nn.functional as F
from torchvision import transforms, datasets
from torchvision.utils import save_image, make_grid
from modules import VectorQuantizedVAE, to_scalar
from datasets import MiniImagenet
from tensorboardX import SummaryWriter
def train(data_loader, model, optimizer, args, writer):
for images, _ in data_loader:
images = images.to(args.device)
optimizer.zero_grad()
x_tilde, z_e_x, z_q_x = model(images)
# Reconstruction loss
loss_recons = F.mse_loss(x_tilde, images)
# Vector quantization objective
loss_vq = F.mse_loss(z_q_x, z_e_x.detach())
# Commitment objective
loss_commit = F.mse_loss(z_e_x, z_q_x.detach())
loss = loss_recons + loss_vq + args.beta * loss_commit
loss.backward()
# Logs
writer.add_scalar('loss/train/reconstruction', loss_recons.item(), args.steps)
writer.add_scalar('loss/train/quantization', loss_vq.item(), args.steps)
optimizer.step()
args.steps += 1
def test(data_loader, model, args, writer):
with torch.no_grad():
loss_recons, loss_vq = 0., 0.
for images, _ in data_loader:
images = images.to(args.device)
x_tilde, z_e_x, z_q_x = model(images)
loss_recons += F.mse_loss(x_tilde, images)
loss_vq += F.mse_loss(z_q_x, z_e_x)
loss_recons /= len(data_loader)
loss_vq /= len(data_loader)
# Logs
writer.add_scalar('loss/test/reconstruction', loss_recons.item(), args.steps)
writer.add_scalar('loss/test/quantization', loss_vq.item(), args.steps)
return loss_recons.item(), loss_vq.item()
def generate_samples(images, model, args):
with torch.no_grad():
images = images.to(args.device)
x_tilde, _, _ = model(images)
return x_tilde
def main(args):
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_filename = './models/{0}'.format(args.output_folder)
if args.dataset in ['mnist', 'fashion-mnist', 'cifar10']:
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
if args.dataset == 'mnist':
# Define the train & test datasets
train_dataset = datasets.MNIST(args.data_folder, train=True,
download=True, transform=transform)
test_dataset = datasets.MNIST(args.data_folder, train=False,
transform=transform)
num_channels = 1
elif args.dataset == 'fashion-mnist':
# Define the train & test datasets
train_dataset = datasets.FashionMNIST(args.data_folder,
train=True, download=True, transform=transform)
test_dataset = datasets.FashionMNIST(args.data_folder,
train=False, transform=transform)
num_channels = 1
elif args.dataset == 'cifar10':
# Define the train & test datasets
train_dataset = datasets.CIFAR10(args.data_folder,
train=True, download=True, transform=transform)
test_dataset = datasets.CIFAR10(args.data_folder,
train=False, transform=transform)
num_channels = 3
valid_dataset = test_dataset
elif args.dataset == 'miniimagenet':
transform = transforms.Compose([
transforms.RandomResizedCrop(128),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# Define the train, valid & test datasets
train_dataset = MiniImagenet(args.data_folder, train=True,
download=True, transform=transform)
valid_dataset = MiniImagenet(args.data_folder, valid=True,
download=True, transform=transform)
test_dataset = MiniImagenet(args.data_folder, test=True,
download=True, transform=transform)
num_channels = 3
# Define the data loaders
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=args.batch_size, shuffle=False, drop_last=True,
num_workers=args.num_workers, pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=16, shuffle=True)
# Fixed images for Tensorboard
fixed_images, _ = next(iter(test_loader))
fixed_grid = make_grid(fixed_images, nrow=8, range=(-1, 1), normalize=True)
writer.add_image('original', fixed_grid, 0)
model = VectorQuantizedVAE(num_channels, args.hidden_size, args.k).to(args.device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
# Generate the samples first once
reconstruction = generate_samples(fixed_images, model, args)
grid = make_grid(reconstruction.cpu(), nrow=8, range=(-1, 1), normalize=True)
writer.add_image('reconstruction', grid, 0)
best_loss = -1.
for epoch in range(args.num_epochs):
train(train_loader, model, optimizer, args, writer)
loss, _ = test(valid_loader, model, args, writer)
reconstruction = generate_samples(fixed_images, model, args)
grid = make_grid(reconstruction.cpu(), nrow=8, range=(-1, 1), normalize=True)
writer.add_image('reconstruction', grid, epoch + 1)
if (epoch == 0) or (loss < best_loss):
best_loss = loss
with open('{0}/best.pt'.format(save_filename), 'wb') as f:
torch.save(model.state_dict(), f)
with open('{0}/model_{1}.pt'.format(save_filename, epoch + 1), 'wb') as f:
torch.save(model.state_dict(), f)
if __name__ == '__main__':
import argparse
import os
import multiprocessing as mp
parser = argparse.ArgumentParser(description='VQ-VAE')
# General
parser.add_argument('--data-folder', type=str,
help='name of the data folder')
parser.add_argument('--dataset', type=str,
help='name of the dataset (mnist, fashion-mnist, cifar10, miniimagenet)')
# Latent space
parser.add_argument('--hidden-size', type=int, default=256,
help='size of the latent vectors (default: 256)')
parser.add_argument('--k', type=int, default=512,
help='number of latent vectors (default: 512)')
# Optimization
parser.add_argument('--batch-size', type=int, default=128,
help='batch size (default: 128)')
parser.add_argument('--num-epochs', type=int, default=100,
help='number of epochs (default: 100)')
parser.add_argument('--lr', type=float, default=2e-4,
help='learning rate for Adam optimizer (default: 2e-4)')
parser.add_argument('--beta', type=float, default=1.0,
help='contribution of commitment loss, between 0.1 and 2.0 (default: 1.0)')
# Miscellaneous
parser.add_argument('--output-folder', type=str, default='vqvae',
help='name of the output folder (default: vqvae)')
parser.add_argument('--num-workers', type=int, default=mp.cpu_count() - 1,
help='number of workers for trajectories sampling (default: {0})'.format(mp.cpu_count() - 1))
parser.add_argument('--device', type=str, default='cpu',
help='set the device (cpu or cuda, default: cpu)')
args = parser.parse_args()
# Create logs and models folder if they don't exist
if not os.path.exists('./logs'):
os.makedirs('./logs')
if not os.path.exists('./models'):
os.makedirs('./models')
# Device
args.device = torch.device(args.device
if torch.cuda.is_available() else 'cpu')
# Slurm
if 'SLURM_JOB_ID' in os.environ:
args.output_folder += '-{0}'.format(os.environ['SLURM_JOB_ID'])
if not os.path.exists('./models/{0}'.format(args.output_folder)):
os.makedirs('./models/{0}'.format(args.output_folder))
args.steps = 0
main(args)