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import collections
import functools
import logging
import os
import pathlib
import re
import warnings
try:
import rich.traceback
rich.traceback.install()
except ImportError:
pass
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
logging.getLogger().setLevel('ERROR')
warnings.filterwarnings('ignore', '.*box bound precision lowered.*')
import numpy as np
import ruamel.yaml as yaml
from dreamerv2 import agent
from dreamerv2 import common
def make_env(mode, config, logdir):
suite, task = config.task.split('_', 1)
if suite == 'dmc':
env = common.DMC(
task, config.action_repeat, config.render_size, config.dmc_camera)
env = common.NormalizeAction(env)
elif suite == 'dmcmt':
env = common.DMCMultitask(
task, config.action_repeat, config.render_size, config.dmc_camera)
env = common.NormalizeAction(env)
elif suite == 'atari':
env = common.Atari(
task, config.action_repeat, config.render_size,
config.atari_grayscale)
env = common.OneHotAction(env)
elif suite == 'crafter':
assert config.action_repeat == 1
outdir = logdir / 'crafter' if mode == 'train' else None
reward = bool(['noreward', 'reward'].index(task)) or mode == 'eval'
env = common.Crafter(outdir, reward)
env = common.OneHotAction(env)
else:
raise NotImplementedError(suite)
env = common.TimeLimit(env, config.time_limit)
return env
def main():
config_path = pathlib.Path('offlinedv2/dreamerv2') / 'configs.yaml'
configs = yaml.safe_load(config_path.read_text())
parsed, remaining = common.Flags(configs=['defaults']).parse(known_only=True)
config = common.Config(configs['defaults'])
for name in parsed.configs:
config = config.update(configs[name])
config = common.Flags(config).parse(remaining)
logdir = pathlib.Path(config.logdir).expanduser()
logdir.mkdir(parents=True, exist_ok=True)
config.save(logdir / 'config.yaml')
print(config, '\n')
print('Logdir', logdir)
import tensorflow as tf
tf.config.experimental_run_functions_eagerly(not config.jit)
message = 'No GPU found. To actually train on CPU remove this assert.'
assert tf.config.experimental.list_physical_devices('GPU'), message
for gpu in tf.config.experimental.list_physical_devices('GPU'):
tf.config.experimental.set_memory_growth(gpu, True)
assert config.precision in (16, 32), config.precision
if config.precision == 16:
from tensorflow.keras.mixed_precision import experimental as prec
prec.set_policy(prec.Policy('mixed_float16'))
print('Offline Datadir', config.offline_dir)
train_replay = common.OfflineReplay(list(config.offline_dir), split_val=config.offline_split_val, **config.replay)
eval_replay = common.Replay(
logdir / 'eval_episodes',
capacity=config.replay.capacity // 10,
minlen=config.dataset.length,
maxlen=config.dataset.length,
disable_save=True,
)
step = common.Counter(0)
outputs = [
common.TerminalOutput(),
common.JSONLOutput(logdir),
common.TensorBoardOutput(logdir),
]
logger = common.Logger(step, outputs)
metrics = collections.defaultdict(list)
should_video_eval = common.Every(config.eval_every)
def per_episode(ep, mode):
length = len(ep['reward']) - 1
score = float(ep['reward'].astype(np.float64).sum())
print(f'{mode.title()} episode has {length} steps and return {score:.1f}.')
logger.scalar(f'{mode}_return', score)
logger.scalar(f'{mode}_length', length)
for key, value in ep.items():
if re.match(config.log_keys_sum, key):
logger.scalar(f'sum_{mode}_{key}', ep[key].sum())
if re.match(config.log_keys_mean, key):
logger.scalar(f'mean_{mode}_{key}', ep[key].mean())
if re.match(config.log_keys_max, key):
logger.scalar(f'max_{mode}_{key}', ep[key].max(0).mean())
# if should_video_eval(step):
# for key in config.log_keys_video:
# logger.video(f'{mode}_policy_{key}', ep[key])
replay = dict(train=train_replay, eval=eval_replay)[mode]
logger.add(replay.stats, prefix=mode)
logger.write()
print('Create envs.')
num_eval_envs = min(config.envs, config.eval_eps)
if config.envs_parallel == 'none':
eval_envs = [make_env('eval', config, logdir) for _ in range(num_eval_envs)]
else:
make_async_env = lambda mode: common.Async(
functools.partial(make_env, mode, config, logdir), config.envs_parallel)
eval_envs = [make_async_env('eval') for _ in range(num_eval_envs)]
act_space = eval_envs[0].act_space
obs_space = eval_envs[0].obs_space
eval_driver = common.Driver(eval_envs)
eval_driver.on_episode(lambda ep: per_episode(ep, mode='eval'))
eval_driver.on_episode(eval_replay.add_episode)
print(f'Precollecting eval steps.')
random_agent = common.RandomAgent(act_space)
eval_driver(random_agent, episodes=1)
eval_driver.reset()
print('Create agent.')
train_dataset = iter(train_replay.dataset(**config.offline_model_dataset))
if config.offline_split_val:
val_dataset = iter(train_replay.dataset(validation=True, **config.offline_model_dataset))
agnt = agent.Agent(config, obs_space, act_space, step)
agnt.train(next(train_dataset))
eval_policy = lambda *args: agnt.policy(*args, mode='eval')
if config.offline_model_loaddir != 'none':
print('Loading model', config.offline_model_loaddir)
agnt.wm.load(config.offline_model_loaddir)
else:
print('Start model training.')
model_step = common.Counter(0)
model_logger = common.Logger(model_step, outputs)
while model_step < config.offline_model_train_steps:
if config.offline_split_val:
# Compute model validation loss as average over chunks of data
val_losses = []
for _ in range(10):
val_loss, _, _, val_mets = agnt.wm.loss(next(val_dataset))
val_losses.append(val_loss)
model_logger.scalar(f'validation_model_loss', np.array(val_losses, np.float64).mean())
for it in range(100):
model_step.increment()
mets = agnt.model_train(next(train_dataset))
for key, value in mets.items():
metrics[key].append(value)
for name, values in metrics.items():
model_logger.scalar(name, np.array(values, np.float64).mean())
metrics[name].clear()
model_logger.write()
if model_step % config.offline_model_save_every == 0:
print('Saving model')
agnt.wm.save(logdir / f'model_{model_step.value}.pkl')
agnt.wm.save(logdir / f'final_model.pkl')
# Begin training
metrics = collections.defaultdict(list)
train_dataset = iter(train_replay.dataset(**config.offline_train_dataset))
eval_dataset = iter(eval_replay.dataset(**config.offline_train_dataset))
while step < config.steps:
logger.write()
print('Start evaluation.')
if should_video_eval(step):
logger.add(agnt.report(next(eval_dataset)), prefix='eval')
eval_driver(eval_policy, episodes=config.eval_eps)
print('Start training.')
for it in range(200):
step.increment()
mets = agnt.agent_train(next(train_dataset))
for key, value in mets.items():
metrics[key].append(value)
for name, values in metrics.items():
logger.scalar(name, np.array(values, np.float64).mean())
metrics[name].clear()
logger.write(fps=True)
agnt.save(logdir / 'variables.pkl')
for env in eval_envs:
try:
env.close()
except Exception:
pass
if __name__ == '__main__':
main()
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