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train_vae.py
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import copy
import wandb
import datetime
import importlib
from pathlib import Path
import torch
import argparse
import numpy as np
from torch import nn
from math import sqrt
from torch.optim import Adam
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import MultiStepLR, StepLR
from vae import VAE
from arguments import add_train_args, get_initial_parser
def init_weights(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1 or classname.find('Linear') != -1:
nn.init.normal_(m.weight, 0.0, 0.02)
# nn.init.constant_(m.bias, 0)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight, 1.0, 0.02)
nn.init.constant_(m.bias, 0)
def train(epoch, args, train_loader, vae, optimizer):
n_data = 0
train_elbo, train_recon, train_kl = 0., 0., 0.
for x in train_loader:
for param in vae.parameters():
param.grad = None
x = x.to(args.device)
loss, elbo, _, _, recon_loss, kl_loss, _ = vae(
x,
args.train_samples,
args.beta,
args.iwae
)
loss.backward()
optimizer.step()
n_data += x.size(0)
train_elbo += elbo.item()
train_recon += recon_loss.item()
train_kl += kl_loss.item()
if epoch % args.log_interval == 0 or epoch == args.n_epochs:
train_elbo /= n_data
train_recon /= n_data
train_kl /= n_data
print(f'Epoch: {epoch:6d} | ELBO: {train_elbo:.2f} | Recon Loss: {train_recon:.2f} | KL: {train_kl:.3f}')
wandb.log({
'epoch': epoch,
'train_elbo': train_elbo,
'train_recon': train_recon,
'train_kl': train_kl
})
return train_elbo
def eval(prefix, epoch, args, test_loader, vae, root_dir, test_data, eval_fn):
log_dir = root_dir / str(epoch)
log_dir.mkdir(parents=True, exist_ok=True)
vae.eval()
with torch.no_grad():
n_data = 0
means, kls = None, None
total_elbo, total_recon, total_kl = 0., 0., 0.
for x in test_loader:
x = x.to(args.device)
_, elbo, _, means_, recon_loss, kl_loss, kls_ = vae(x, (args.test_samples if prefix == 'test' else 1), iwae=args.iwae)
n_data += x.size(0)
total_elbo += elbo.item()
total_recon += recon_loss.item()
total_kl += kl_loss.item()
if means is None:
means = means_
kls = kls_
else:
means = torch.concat((means, means_), dim=0)
kls = torch.concat((kls, kls_), dim=0)
if eval_fn is not None:
eval_fn(args, vae, test_data, means, kls)
total_elbo /= n_data
total_recon /= n_data
total_kl /= n_data
print(f'===========> {prefix} ELBO: {total_elbo:.2f} | Recon: {total_recon:.2f} | KL: {total_kl:.2f}')
wandb.log({
'epoch': epoch,
f'{prefix}_elbo': total_elbo,
f'{prefix}_recon': total_recon,
f'{prefix}_kl': total_kl
})
return total_elbo
if __name__ == "__main__":
init_parser = get_initial_parser()
task_name = init_parser.parse_known_args()[0].task
task_module = importlib.import_module(f'tasks.{task_name}')
dist_name = init_parser.parse_known_args()[0].dist
dist_module = importlib.import_module(f'distributions.{dist_name}')
parser = argparse.ArgumentParser()
add_train_args(parser)
getattr(task_module, 'add_task_args')(parser)
getattr(dist_module, 'add_distribution_args')(parser)
args = parser.parse_args()
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
torch.set_default_tensor_type(torch.DoubleTensor)
torch.set_num_threads(1)
runId = datetime.datetime.now().isoformat().replace(':', '_')
root_dir = Path(args.log_dir) / runId
train_data = getattr(task_module, 'Dataset')(args, split='train')
train_loader = DataLoader(train_data, batch_size=args.train_batch_size, shuffle=True, num_workers=1)
valid_data = getattr(task_module, 'Dataset')(args, split='valid')
valid_loader = DataLoader(valid_data, batch_size=args.train_batch_size, shuffle=False, num_workers=1)
test_data = getattr(task_module, 'Dataset')(args, split='test')
test_loader = DataLoader(test_data, batch_size=args.test_batch_size, shuffle=False, num_workers=1)
eval_fn = getattr(task_module, 'evaluation')
variational_fn = getattr(dist_module, 'Distribution')
prior = getattr(dist_module, 'get_prior')(args)
encoder = getattr(task_module, 'Encoder')(args)
encoder_layer = getattr(
dist_module,
f'{args.layer}EncoderLayer'
)(args, encoder.output_dim)
decoder = getattr(task_module, 'Decoder')(args)
decoder_layer = getattr(
dist_module,
f'{args.layer}DecoderLayer'
)(args)
recon_loss_type = getattr(task_module, 'recon_loss_type')
vae = VAE(
args,
prior,
variational_fn,
encoder,
encoder_layer,
decoder,
decoder_layer,
recon_loss_type
)
vae = vae.to(args.device)
optimizer = Adam(
list(encoder.parameters()) + list(decoder.parameters()) + list(decoder_layer.parameters()) + list(encoder_layer.parameters()),
lr=args.lr
)
wandb.init(project='GM-VAE')
wandb.run.name = args.exp_name
wandb.config.update(args)
best_model = copy.deepcopy(vae)
best_elbo = -1e9
print(root_dir)
for epoch in range(1, args.n_epochs + 1):
vae.train()
train_elbo = train(epoch, args, train_loader, vae, optimizer)
if epoch % args.eval_interval == 0 or epoch == args.n_epochs:
elbo = eval('valid', epoch, args, valid_loader, vae, root_dir, valid_data, None)
if best_elbo < elbo:
best_elbo = elbo
best_model = copy.deepcopy(vae)
torch.save(best_model.state_dict(), root_dir / 'model.pt')
_ = eval('test', epoch, args, test_loader, best_model, root_dir, test_data, eval_fn)
print(root_dir)