def main(args, unknown_args):
args, config = parse_args_uargs(args, unknown_args, dump_config=True)
pprint(args)
pprint(config)
set_global_seeds(args.seed)
assert args.baselogdir is not None or args.logdir is not None
if args.logdir is None:
modules_ = prepare_modules(model_dir=args.model_dir)
logdir = modules_["model"].prepare_logdir(config=config)
args.logdir = str(pathlib2.Path(args.baselogdir).joinpath(logdir))
create_if_need(args.logdir)
save_config(config=config, logdir=args.logdir)
modules = prepare_modules(model_dir=args.model_dir, dump_dir=args.logdir)
datasource = modules["data"].DataSource()
model = modules["model"].prepare_model(config)
runner = modules["model"].ModelRunner(model=model)
runner.train(datasource=datasource,
args=args,
stages_config=config["stages"],
verbose=args.verbose)
def main(args, unknown_args):
args, config = parse_args_uargs(args, unknown_args)
pprint(args)
pprint(config)
set_global_seeds(args.seed)
modules = prepare_modules(model_dir=args.model_dir)
datasource = modules["data"].DataSource()
loaders = datasource.prepare_loaders(args, **config["data_params"])
model = modules["model"].prepare_model(config)
runner = modules["model"].ModelRunner(model=model)
callbacks = runner.prepare_callbacks(
callbacks_params=config["callbacks_params"], args=args, mode="infer")
runner.infer(loaders=loaders, callbacks=callbacks, verbose=args.verbose)
def main(args, unknown_args):
args, config = parse_args_uargs(args, unknown_args)
pprint(args)
pprint(config)
set_global_seeds(args.seed)
modules = prepare_modules(model_dir=args.model_dir)
datasource = modules["data"].DataSource()
data_params = config.get("data_params", {}) or {}
loaders = datasource.prepare_loaders(mode="infer",
n_workers=args.workers,
batch_size=args.batch_size,
**data_params)
model = modules["model"].prepare_model(config)
runner = modules["model"].ModelRunner(model=model)
callbacks_params = config.get("callbacks_params", {}) or {}
callbacks = runner.prepare_callbacks(mode="infer",
resume=args.resume,
out_prefix=args.out_prefix,
**callbacks_params)
runner.infer(loaders=loaders, callbacks=callbacks, verbose=args.verbose)
def algos_by_dir(dir):
algos = []
dirs = path.Path(dir).listdir()
for logpath in dirs:
config_path = logpath + "/config.json"
checkpoints = path.Path(logpath).glob("*.pth.tar")
for checkpoint_path in checkpoints:
args = argparse.Namespace(config=config_path)
args, config = parse_args_uargs(args, [])
config.get("algorithm", {}).pop("resume", None)
config.get("algorithm", {}).pop("load_optimizer", None)
algo_module = import_module("algo_module", args.algorithm)
trainer_kwargs = algo_module.prepare_for_trainer(config)
algorithm = trainer_kwargs["algorithm"]
algorithm.load_checkpoint(checkpoint_path, load_optimizer=False)
actor_ = algorithm.actor.eval()
name = str(algorithm.__class__).lower()
if "ensemblecritic" in name:
critics_ = [x.eval() for x in algorithm.critics]
elif "td3" in name:
critics_ = [algorithm.critic.eval(), algorithm.critic2.eval()]
else:
raise NotImplemented
history_len = trainer_kwargs["history_len"]
algos.append(
AlgoWrapper(actor=actor_,
critics=critics_,
history_len=history_len,
consensus=IN_CONSENSUS))
return algos
parser = argparse.ArgumentParser()
parser.add_argument(
"--config",
type=str,
required=True)
parser.add_argument(
"--algorithm",
type=str,
default=None)
parser.add_argument(
"--logdir",
type=str,
default=None)
args, unknown_args = parser.parse_known_args()
args, config = parse_args_uargs(args, unknown_args, dump_config=True)
algo_module = import_module("algo_module", args.algorithm)
algo_kwargs = algo_module.prepare_for_trainer(config)
redis_server = StrictRedis(port=config.get("redis", {}).get("port", 12000))
redis_prefix = config.get("redis", {}).get("prefix", "")
pprint(config["trainer"])
pprint(algo_kwargs)
trainer = Trainer(
**config["trainer"],
**algo_kwargs,
logdir=args.logdir,
parser.add_argument(
"--max-noise-power",
type=float,
default=None)
parser.add_argument(
"--max-action-noise",
type=float,
default=None)
parser.add_argument(
"--max-param-noise",
type=float,
default=None)
boolean_flag(parser, "debug", default=False)
args = parser.parse_args()
args, config = parse_args_uargs(args, [])
env_module = import_module("env_module", args.environment)
algo_module = import_module("algo_module", args.algorithm)
def run_sampler(
*,
config, vis, infer,
action_noise_prob, param_noise_prob,
action_noise=None, param_noise=None,
noise_power=None, # @TODO: remove
id=None, resume=None, debug=False):
config_ = copy.deepcopy(config)
if debug:
