def train(unused_argv):
# image size must be divisible by 2 ** L
if FLAGS.dataset == "CIFAR":
K = 32
L = 3
num_channels = 3
elif FLAGS.dataset == "COCO":
K = 32
L = 3
num_channels = 3
flow = Flow(K, L, num_channels).cuda()
optimizer = optim.Adam(flow.parameters(), lr=FLAGS.lr)
scheduler = optim.lr_scheduler.LambdaLR(optimizer,
lambda step: min(1.0, step / 100))
epochs_done = 1
if FLAGS.resume_from:
ckpt = torch.load(FLAGS.resume_from)
flow.load_state_dict(ckpt["flow_state_dict"])
optimizer.load_state_dict(ckpt["optimizer_state_dict"])
epochs_done = ckpt["epoch"]
flow.eval()
timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
log_folder = Path(FLAGS.logs) / timestamp
writer = SummaryWriter(str(log_folder))
zs_shapes = None
bpd = None
for epoch in range(epochs_done, FLAGS.num_epochs + epochs_done):
trainloader = get_trainloader(FLAGS.crop_size)
pbar = tqdm(trainloader, desc="epoch %d" % epoch, leave=False)
for n, image_batch in enumerate(pbar):
if not isinstance(image_batch, torch.Tensor):
image_batch = image_batch[0]
image_batch = image_batch.to(cuda_device)
batch_size = image_batch.shape[0]
optimizer.zero_grad()
zs, log_det = flow(image_batch)
image_dim = image_batch.numel() / batch_size
nll = -image_dim * torch.log(1 / 256 * one)
nll = nll + nll_loss(zs, log_det)
bpd = nll / (image_dim * torch.log(2 * one))
pbar.set_postfix(nll=nll.item(), bpd=bpd.item())
nll.backward()
optimizer.step()
if zs_shapes is None:
zs_shapes = [z.size() for z in zs]
step = (epoch - 1) * len(trainloader) + n
writer.add_scalar("nll", nll, step)
writer.add_scalar("bpd", bpd, step)
scheduler.step(step)
pbar.close()
if epoch % FLAGS.test_every == 0:
torch.save(
{
"epoch": epoch,
"flow_state_dict": flow.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
"zs_shapes": zs_shapes,
"bpd": bpd,
}, str(log_folder / ("ckpt-%d.pth" % epoch)))
image_samples, _ = sample(flow, zs_shapes)
image_samples = make_grid(image_samples,
nrow=10,
normalize=True,
range=(0, 1),
pad_value=10)
writer.add_image("samples", image_samples, epoch)
writer.close()
default=False,
help='Resume from checkpoint')
args = parser.parse_args()
# ------------------------------
# I. MODEL
# ------------------------------
flow = Flow(width=args.width,
depth=args.depth,
n_levels=args.n_levels,
data_dim=2).to(device)
# ------------------------------
# II. OPTIMIZER
# ------------------------------
optim_flow = torch.optim.Adam(flow.parameters(),
lr=args.lr,
betas=(args.b1, args.b2))
# ------------------------------
# III. DATA LOADER
# ------------------------------
dataset, dataloader = util.get_data(args)
# ------------------------------
# IV. TRAINING
# ------------------------------
def main(args):
start_epoch = 0
if args.resume:
# transform = transforms.Compose([transforms.Resize((img_sz, img_sz)), transforms.ToTensor()])
# train_dataset = MNIST(root=opt.root, train=True, transform=transform, \
# download=True)
# train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, \
# **kwargs)
dataset = CustomDataset(data_root, img_sz)
train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
model = Flow(in_channel, 64, n_block, n_flows, img_sz, num_scales)
if test_model:
test(os.path.join(root, model_path), model)
else:
optimizer = optim.Adam(model.parameters(), lr=lr)
loss_fn = RealNVPLoss()
if device=="cuda" and torch.cuda.device_count()>1:
model = DataParallel(model, device_ids=[0, 1, 2, 3])
model = model.to(device)
old_loss = 1e6
best_loss = 0
is_best = 0
for epoch in range(epochs):
for b, x in enumerate(train_loader, 0):
model.train()
optimizer.zero_grad()
x = x.to(device)