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trainColab.py
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trainColab.py
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"""Train Glow on CIFAR-10.
Train script adapted from: https://github.com/kuangliu/pytorch-cifar/
"""
import argparse
import numpy as np
import os
import random
import torch
import torch.optim as optim
import torch.optim.lr_scheduler as sched
import torch.backends.cudnn as cudnn
import torch.utils.data as data
import torchvision
import torchvision.transforms as transforms
import util
from models import Glow
from tqdm import tqdm
root = "./"
def main(args):
# Set up main device and scale batch size
device = 'cuda' if torch.cuda.is_available() and args.gpu_ids else 'cpu'
args.batch_size *= max(1, len(args.gpu_ids))
# Set random seeds
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# No normalization applied, since Glow expects inputs in (0, 1)
transform_train = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ToTensor()
])
transform_test = transforms.Compose([
transforms.ToTensor()
])
trainset = torchvision.datasets.CIFAR10(root='data', train=True, download=True, transform=transform_train)
trainloader = data.DataLoader(trainset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers)
testset = torchvision.datasets.CIFAR10(root='data', train=False, download=True, transform=transform_test)
testloader = data.DataLoader(testset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers)
# Model
print('Building model..')
net = Glow(num_channels=args.num_channels,
num_levels=args.num_levels,
num_steps=args.num_steps)
net = net.to(device)
if device == 'cuda':
net = torch.nn.DataParallel(net, args.gpu_ids)
cudnn.benchmark = args.benchmark
start_epoch = 0
if args.resume:
# Load checkpoint.
print('Resuming from checkpoint at ckpts/best.pth.tar...')
assert os.path.isdir('ckpts'), 'Error: no checkpoint directory found!'
checkpoint = torch.load('ckpts/best.pth.tar')
net.load_state_dict(checkpoint['net'])
global best_loss
global global_step
best_loss = checkpoint['test_loss']
start_epoch = checkpoint['epoch']
global_step = start_epoch * len(trainset)
loss_fn = util.NLLLoss().to(device)
optimizer = optim.Adam(net.parameters(), lr=args.lr)
scheduler = sched.LambdaLR(optimizer, lambda s: min(1., s / args.warm_up))
for epoch in range(start_epoch, start_epoch + args.num_epochs):
train(epoch, net, trainloader, device, optimizer, scheduler,
loss_fn, args.max_grad_norm)
test(epoch, net, testloader, device, loss_fn, args.num_samples)
@torch.enable_grad()
def train(epoch, net, trainloader, device, optimizer, scheduler, loss_fn, max_grad_norm):
global global_step
print('\nEpoch: %d' % epoch)
net.train()
loss_meter = util.AverageMeter()
with tqdm(total=len(trainloader.dataset)) as progress_bar:
for x, _ in trainloader:
x = x.to(device)
optimizer.zero_grad()
z, sldj = net(x, reverse=False)
loss = loss_fn(z, sldj)
loss_meter.update(loss.item(), x.size(0))
loss.backward()
if max_grad_norm > 0:
util.clip_grad_norm(optimizer, max_grad_norm)
optimizer.step()
scheduler.step(global_step)
progress_bar.set_postfix(nll=loss_meter.avg,
bpd=util.bits_per_dim(x, loss_meter.avg),
lr=optimizer.param_groups[0]['lr'])
progress_bar.update(x.size(0))
global_step += x.size(0)
@torch.no_grad()
def sample(net, batch_size, device):
"""Sample from RealNVP model.
Args:
net (torch.nn.DataParallel): The RealNVP model wrapped in DataParallel.
batch_size (int): Number of samples to generate.
device (torch.device): Device to use.
"""
z = torch.randn((batch_size, 3, 32, 32), dtype=torch.float32, device=device)
x, _ = net(z, reverse=True)
x = torch.sigmoid(x)
return x
@torch.no_grad()
def test(epoch, net, testloader, device, loss_fn, num_samples):
global best_loss
net.eval()
loss_meter = util.AverageMeter()
with tqdm(total=len(testloader.dataset)) as progress_bar:
for x, _ in testloader:
x = x.to(device)
z, sldj = net(x, reverse=False)
loss = loss_fn(z, sldj)
loss_meter.update(loss.item(), x.size(0))
progress_bar.set_postfix(nll=loss_meter.avg,
bpd=util.bits_per_dim(x, loss_meter.avg))
progress_bar.update(x.size(0))
# Save checkpoint
if loss_meter.avg < best_loss:
print('Saving...')
state = {
'net': net.state_dict(),
'test_loss': loss_meter.avg,
'epoch': epoch,
}
os.makedirs('ckpts', exist_ok=True)
torch.save(state, 'ckpts/best.pth.tar')
best_loss = loss_meter.avg
# Save samples and data
images = sample(net, num_samples, device)
os.makedirs('samples', exist_ok=True)
images_concat = torchvision.utils.make_grid(images, nrow=int(num_samples ** 0.5), padding=2, pad_value=255)
torchvision.utils.save_image(images_concat, 'samples/epoch_{}.png'.format(epoch))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Glow on CIFAR-10')
def str2bool(s):
return s.lower().startswith('t')
parser.add_argument('--batch_size', default=64, type=int, help='Batch size per GPU')
parser.add_argument('--benchmark', type=str2bool, default=True, help='Turn on CUDNN benchmarking')
parser.add_argument('--gpu_ids', default=[0], type=eval, help='IDs of GPUs to use')
parser.add_argument('--lr', default=1e-3, type=float, help='Learning rate')
parser.add_argument('--max_grad_norm', type=float, default=-1., help='Max gradient norm for clipping')
parser.add_argument('--num_channels', '-C', default=512, type=int, help='Number of channels in hidden layers')
parser.add_argument('--num_levels', '-L', default=3, type=int, help='Number of levels in the Glow model')
parser.add_argument('--num_steps', '-K', default=32, type=int, help='Number of steps of flow in each level')
parser.add_argument('--num_epochs', default=100, type=int, help='Number of epochs to train')
parser.add_argument('--num_samples', default=64, type=int, help='Number of samples at test time')
parser.add_argument('--num_workers', default=8, type=int, help='Number of data loader threads')
parser.add_argument('--resume', type=str2bool, default=False, help='Resume from checkpoint')
parser.add_argument('--seed', type=int, default=0, help='Random seed for reproducibility')
parser.add_argument('--warm_up', default=500000, type=int, help='Number of steps for lr warm-up')
best_loss = 0
global_step = 0
main(parser.parse_args())