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atari_vqvae.py
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atari_vqvae.py
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import torch
import torch.nn as nn
import tensorflow as tf
import numpy as np
from tqdm import tqdm
import os
import logging
import csv
import random
logging.basicConfig(level=logging.INFO)
import argparse
from linear_models import (
VectorQuantizer,
Encoder,
Decoder,
VQVAEModel,
)
from utils import (
load_dataset,
set_seed_everywhere,
)
gfile = tf.io.gfile
def train(args):
device = torch.device("cuda")
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
set_seed_everywhere(args.seed)
## fixed dataset
observations, actions, data_variance = load_dataset(
args.env,
1,
args.datapath,
args.normal,
args.num_data,
args.stack,
args.num_episodes,
)
## Stage 1
logging.info("Building models..")
logging.info("Start stage 1...")
encoder = Encoder(
args.stack,
args.embedding_dim,
args.num_hiddens,
args.num_residual_layers,
args.num_residual_hiddens,
)
decoder = Decoder(
args.stack,
args.embedding_dim,
args.num_hiddens,
args.num_residual_layers,
args.num_residual_hiddens,
)
quantizer = VectorQuantizer(
args.embedding_dim, args.num_embeddings, args.commitment_cost,
)
vqvae = VQVAEModel(encoder, decoder, quantizer).to(device)
n_batch = len(observations) // args.batch_size + 1
total_idxs = list(range(len(observations)))
logging.info("Training starts..")
save_dir = "models_vqvae"
if args.num_episodes is None:
save_tag = "{}_s{}_data{}k_con{}_seed{}_ne{}_c{}".format(
args.env,
args.stack,
int(args.num_data / 1000),
1 - int(args.normal),
args.seed,
args.num_embeddings,
args.commitment_cost,
)
else:
save_tag = "{}_s{}_epi{}_con{}_seed{}_ne{}_c{}".format(
args.env,
args.stack,
int(args.num_episodes),
1 - int(args.normal),
args.seed,
args.num_embeddings,
args.commitment_cost,
)
if args.add_path is not None:
save_dir = save_dir + "_" + args.add_path
if not os.path.exists(save_dir):
os.makedirs(save_dir)
## Multi-GPU
if torch.cuda.device_count() > 1:
vqvae = nn.DataParallel(vqvae)
vqvae_optimizer = torch.optim.Adam(vqvae.parameters(), lr=args.lr)
f = open(os.path.join(save_dir, save_tag + "_vqvae_train.csv"), "w")
writer = csv.writer(f)
writer.writerow(["Epoch", "Recon Error", "VQ Loss"])
for epoch in tqdm(range(args.n_epochs)):
random.shuffle(total_idxs)
recon_errors = []
vq_losses = []
vqvae.train()
for j in range(n_batch):
batch_idxs = total_idxs[j * args.batch_size : (j + 1) * args.batch_size]
xx = torch.as_tensor(
observations[batch_idxs], device=device, dtype=torch.float32
)
xx = xx / 255.0
vqvae_optimizer.zero_grad()
z, x_recon, vq_loss, quantized, _ = vqvae(xx)
vq_loss = vq_loss.mean()
recon_error = torch.mean((x_recon - xx) ** 2) / data_variance
loss = recon_error + vq_loss
loss.backward()
vqvae_optimizer.step()
recon_errors.append(recon_error.mean().detach().cpu().item())
vq_losses.append(vq_loss.mean().detach().cpu().item())
logging.info(
"(Train) Epoch {} | Recon Error: {:.4f} | VQ Loss: {:.4f}".format(
epoch + 1, np.mean(recon_errors), np.mean(vq_losses)
)
)
writer.writerow([epoch + 1, np.mean(recon_errors), np.mean(vq_losses)])
if (epoch + 1) % args.save_interval == 0:
torch.save(
vqvae.module.state_dict()
if (torch.cuda.device_count() > 1)
else vqvae.state_dict(),
os.path.join(save_dir, save_tag + "_ep{}_vqvae.pth".format(epoch + 1)),
)
f.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Seed & Env
parser.add_argument("--seed", default=1, type=int)
parser.add_argument("--env", default="Pong", type=str)
parser.add_argument("--datapath", default="/data", type=str)
parser.add_argument("--num_data", default=50000, type=int)
parser.add_argument("--stack", default=1, type=int)
parser.add_argument("--normal", action="store_true", default=False)
# Save & Evaluation
parser.add_argument("--save_interval", default=100, type=int)
parser.add_argument("--num_episodes", default=None, type=int)
parser.add_argument("--n_epochs", default=1000, type=int)
parser.add_argument("--add_path", default=None, type=str)
# VQVAE & Hyperparams
parser.add_argument("--embedding_dim", default=64, type=int)
parser.add_argument("--num_embeddings", default=512, type=int)
parser.add_argument("--num_hiddens", default=128, type=int)
parser.add_argument("--num_residual_layers", default=2, type=int)
parser.add_argument("--num_residual_hiddens", default=32, type=int)
parser.add_argument("--commitment_cost", default=0.25, type=float)
parser.add_argument("--batch_size", default=1024, type=int)
parser.add_argument("--lr", default=3e-4, type=float)
args = parser.parse_args()
train(args)