def main():
parser = argparse.ArgumentParser(description='VAE')
parser.add_argument('--batch_size',
type=int,
default=100,
help='Batch size for training (default=100)')
parser.add_argument('--n_epochs',
type=int,
default=100,
help='Number of epochs to train (default=100)')
parser.add_argument('--latent_dim',
type=int,
default=32,
help='Dimension of latent space (default=32)')
parser.add_argument('--episode_len',
type=int,
default=1000,
help='Length of rollout (default=1000)')
parser.add_argument(
'--kl_bound',
type=float,
default=0.5,
help='Clamp KL loss by kl_bound*latent_dim from below (default=0.5)')
parser.add_argument('--learning_rate',
type=float,
default=1e-4,
help='Learning rate for optimizer (default=1e-4)')
parser.add_argument('--cuda',
action='store_true',
default=False,
help='enables CUDA training (default=False)')
parser.add_argument('--dir_name', help='Rollouts directory name')
parser.add_argument('--log_interval',
nargs='?',
default='2',
type=int,
help='After how many batches to log (default=2)')
args = parser.parse_args()
# Read in and prepare the data.
dataset = RolloutDataset(
path_to_dir=os.path.join(DATA_DIR, 'rollouts', args.dir_name),
size=int(args.dir_name.split('_')[-1])) # TODO: hack. fix?
data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True)
# Use GPU if available.
use_cuda = args.cuda and torch.cuda.is_available()
device = torch.device('cuda' if use_cuda else 'cpu')
# Set up the model and the optimizer.
vae = VAE(latent_dim=args.latent_dim).to(device)
optimizer = optim.Adam(params=vae.parameters(), lr=args.learning_rate)
# Training procedure.
def train(epoch):
vae.train()
train_loss = 0
start_time = datetime.datetime.now()
# for rollout_id, rollout in enumerate(data_loader):
# n_batches = len(rollout.squeeze()['obs']) // args.batch_size
# for batch_id in range(n_batches):
# start, stop = args.batch_size * batch_id, args.batch_size * (batch_id + 1)
# batch = rollout.squeeze()['obs'][start:stop]
# batch = batch.to(device)
#
# optimizer.zero_grad()
#
# recon_batch, mu, logvar = vae(batch)
# rec_loss, kl_loss = vae_loss(recon_batch, batch, mu, logvar, kl_bound=args.kl_bound)
# loss = rec_loss + kl_loss
# loss.backward()
# train_loss += loss.item()
#
# optimizer.step()
#
# if batch_id % args.log_interval == 0:
# print(
# 'Epoch: {0:}\t| Examples: {1:} / {2:}({3:.0f}%)\t| Rec Loss: {4: .4f}\t| KL Loss: {5:.4f}'
# .format(epoch, (batch_id + 1) * len(batch), len(data_loader.dataset),
# 100. * (batch_id + 1) / len(data_loader),
# rec_loss.item() / len(batch),
# kl_loss.item() / len(batch)))
for batch_id, batch in enumerate(data_loader):
batch = batch['obs']
# Take a random observation from each rollout.
batch = batch[
torch.arange(args.batch_size, dtype=torch.long),
torch.
randint(high=1000, size=(args.batch_size, ), dtype=torch.long)]
# TODO: use all obs from the rollout (from the randomized start)?
batch = batch.to(device)
optimizer.zero_grad()
recon_batch, mu, logvar = vae(batch)
rec_loss, kl_loss = vae_loss(recon_batch,
batch,
mu,
logvar,
kl_bound=args.kl_bound)
loss = rec_loss + kl_loss
loss.backward()
train_loss += loss.item()
optimizer.step()
if batch_id % args.log_interval == 0:
print(
'Epoch: {0:}\t| Examples: {1:} / {2:}({3:.0f}%)\t| Rec Loss: {4: .4f}\t| KL Loss: {5:.4f}'
.format(epoch, (batch_id + 1) * len(batch),
len(data_loader.dataset),
100. * (batch_id + 1) / len(data_loader),
rec_loss.item() / len(batch),
kl_loss.item() / len(batch)))
duration = datetime.datetime.now() - start_time
print(
'Epoch {} average train loss was {:.4f} after {}m{}s of training.'.
format(epoch, train_loss / len(data_loader.dataset),
*divmod(int(duration.total_seconds()), 60)))
# TODO: add test for VAE?
# Train loop.
for i in range(1, args.n_epochs + 1):
train(i)
# Save the learned model.
if not os.path.exists(os.path.join(DATA_DIR, 'vae')):
os.makedirs(os.path.join(DATA_DIR, 'vae'))
torch.save(
vae.state_dict(),
os.path.join(
DATA_DIR, 'vae',
datetime.datetime.today().isoformat() + '_' + str(args.n_epochs)))
def run(batch_size, max_batch_steps, epochs, annealing_epochs, temp, min_af, loader_workers, eval_freq, _run: "Run"):
pyro.clear_param_store()
_run.add_artifact(_run.config["config_file"])
# Seed randomness for repeatability
seed_random()
# dataset
wildfire_dataset = WildFireDataset(train=True, config_file="config.ini")
data_loader = DataLoader(wildfire_dataset, batch_size=batch_size, shuffle=True, num_workers=loader_workers)
expected_batch_size = np.ceil(len(wildfire_dataset) / batch_size)
expected_batch_size = max_batch_steps if max_batch_steps > 0 else expected_batch_size
vae_config = get_vae_config()
with open(temp / "vae_config.json", "w") as fptr:
json.dump(vae_config.__dict__, fptr, indent=1)
_run.add_artifact(temp / "vae_config.json")
vae = VAE(vae_config)
svi = SVI(vae.model, vae.guide, vae.optimizer, loss=Trace_ELBO())
from src.data.dataset import _ct
for step in trange(epochs, desc="Epoch: ", ascii=False, dynamic_ncols=True,
bar_format='{desc:<8.5}{percentage:3.0f}%|{bar:40}{r_bar}'):
if step < annealing_epochs:
annealing_factor = min_af + (1.0 - min_af) * step / annealing_epochs
else:
annealing_factor = 1.0
_run.log_scalar("annealing_factor", annealing_factor, step=step)
epoch_elbo = 0.0
epoch_time_slices = 0
for batch_steps_i, d in tqdm(enumerate(data_loader), desc="Batch: ", ascii=False, dynamic_ncols=True,
bar_format='{desc:<8.5}{percentage:3.0f}%|{bar:40}{r_bar}',
total=expected_batch_size,
leave=False):
epoch_elbo += svi.step(_ct(d.diurnality), _ct(d.viirs), _ct(d.land_cover), _ct(d.latitude),
_ct(d.longitude), _ct(d.meteorology), annealing_factor)
epoch_time_slices += d.viirs.shape[0] * d.viirs.shape[0]
if 0 < max_batch_steps == batch_steps_i:
break
elbo = -epoch_elbo / epoch_time_slices
print(f" [{step:05d}] ELBO: {elbo:.3f}", end="")
_run.log_scalar("elbo", elbo, step=step)
alpha = pyro.param("alpha").item()
beta = pyro.param("beta").item()
_run.log_scalar("alpha", alpha, step=step)
_run.log_scalar("beta", beta, step=step)
inferred_mean, inferred_std = beta_to_mean_std(alpha, beta)
_run.log_scalar("inferred_mean", inferred_mean, step=step)
_run.log_scalar("inferred_std", inferred_std, step=step)
if eval_freq > 0 and step > 0 and step % eval_freq == 0:
logger.info("Evaluating")
eval_light(Path(_run.observers[0].dir), vae, data_loader, wildfire_dataset, step)
vae.train()
torch.save(vae.state_dict(), temp / "model_final.pt")
_run.add_artifact(temp / "model_final.pt")
vae.optimizer.save(temp / "optimizer.pt")
_run.add_artifact(temp / "optimizer.pt")