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pixelcnn_prior.py
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pixelcnn_prior.py
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import numpy as np
import torch
import torch.nn.functional as F
import json
from torchvision import transforms
from torchvision.utils import save_image, make_grid
from modules import VectorQuantizedVAE, GatedPixelCNN
from datasets import MiniImagenet
from tensorboardX import SummaryWriter
def train(data_loader, model, prior, optimizer, args, writer):
for images, labels in data_loader:
with torch.no_grad():
images = images.to(args.device)
latents = model.encode(images)
latents = latents.detach()
labels = labels.to(args.device)
logits = prior(latents, labels)
logits = logits.permute(0, 2, 3, 1).contiguous()
optimizer.zero_grad()
loss = F.cross_entropy(logits.view(-1, args.k),
latents.view(-1))
loss.backward()
# Logs
writer.add_scalar('loss/train', loss.item(), args.steps)
optimizer.step()
args.steps += 1
def test(data_loader, model, prior, args, writer):
with torch.no_grad():
loss = 0.
for images, labels in data_loader:
images = images.to(args.device)
labels = labels.to(args.device)
latents = model.encode(images)
latents = latents.detach()
logits = prior(latents, labels)
logits = logits.permute(0, 2, 3, 1).contiguous()
loss += F.cross_entropy(logits.view(-1, args.k),
latents.view(-1))
loss /= len(data_loader)
# Logs
writer.add_scalar('loss/valid', loss.item(), args.steps)
return loss.item()
def main(args):
writer = SummaryWriter('./logs/{0}'.format(args.output_folder))
save_filename = './models/{0}/prior.pt'.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)
# Save the label encoder
with open('./models/{0}/labels.json'.format(args.output_folder), 'w') as f:
json.dump(train_dataset._label_encoder, f)
# 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_vae, args.k).to(args.device)
with open(args.model, 'rb') as f:
state_dict = torch.load(f)
model.load_state_dict(state_dict)
model.eval()
prior = GatedPixelCNN(args.k, args.hidden_size_prior,
args.num_layers, n_classes=len(train_dataset._label_encoder)).to(args.device)
optimizer = torch.optim.Adam(prior.parameters(), lr=args.lr)
best_loss = -1.
for epoch in range(args.num_epochs):
train(train_loader, model, prior, optimizer, args, writer)
# The validation loss is not properly computed since
# the classes in the train and valid splits of Mini-Imagenet
# do not overlap.
loss = test(valid_loader, model, prior, args, writer)
if (epoch == 0) or (loss < best_loss):
best_loss = loss
with open(save_filename, 'wb') as f:
torch.save(prior.state_dict(), f)
if __name__ == '__main__':
import argparse
import os
import multiprocessing as mp
parser = argparse.ArgumentParser(description='PixelCNN Prior for 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)')
parser.add_argument('--model', type=str,
help='filename containing the model')
# Latent space
parser.add_argument('--hidden-size-vae', type=int, default=256,
help='size of the latent vectors (default: 256)')
parser.add_argument('--hidden-size-prior', type=int, default=64,
help='hidden size for the PixelCNN prior (default: 64)')
parser.add_argument('--k', type=int, default=512,
help='number of latent vectors (default: 512)')
parser.add_argument('--num-layers', type=int, default=15,
help='number of layers for the PixelCNN prior (default: 15)')
# 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=3e-4,
help='learning rate for Adam optimizer (default: 3e-4)')
# Miscellaneous
parser.add_argument('--output-folder', type=str, default='prior',
help='name of the output folder (default: prior)')
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)