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110 行
4.2 KiB
110 行
4.2 KiB
import attr
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import cattr
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import yaml
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from typing import Dict, Any
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import argparse
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from mlagents.trainers.settings import TrainerSettings, NetworkSettings, TrainerType
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from mlagents.trainers.cli_utils import load_config
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# Take an existing trainer config (e.g. trainer_config.yaml) and turn it into the new format.
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def convert_behaviors(old_trainer_config: Dict[str, Any]) -> Dict[str, Any]:
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all_behavior_config_dict = {}
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default_config = old_trainer_config.get("default", {})
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for behavior_name, config in old_trainer_config.items():
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if behavior_name != "default":
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config = default_config.copy()
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config.update(old_trainer_config[behavior_name])
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# Convert to split TrainerSettings, Hyperparameters, NetworkSettings
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# Set trainer_type and get appropriate hyperparameter settings
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trainer_type = config["trainer"]
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new_config = {}
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new_config["trainer_type"] = trainer_type
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hyperparam_cls = TrainerType(trainer_type).to_settings()
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# Try to absorb as much as possible into the hyperparam_cls
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new_config["hyperparameters"] = cattr.structure(config, hyperparam_cls)
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# Try to absorb as much as possible into the network settings
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new_config["network_settings"] = cattr.structure(config, NetworkSettings)
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# Deal with recurrent
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if config["use_recurrent"]:
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new_config["network_settings"].memory = NetworkSettings.MemorySettings(
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sequence_length=config["sequence_length"],
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memory_size=config["memory_size"],
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)
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# Absorb the rest into the base TrainerSettings
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for key, val in config.items():
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if key in attr.fields_dict(TrainerSettings):
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new_config[key] = val
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# Structure the whole thing
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all_behavior_config_dict[behavior_name] = cattr.structure(
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new_config, TrainerSettings
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)
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return all_behavior_config_dict
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def write_to_yaml_file(config: Dict[str, Any], output_config: str):
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unstructed_config = cattr.unstructure(config)
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unstructed_config = remove_nones(unstructed_config)
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with open(output_config, "w") as f:
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try:
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yaml.dump(unstructed_config, f, sort_keys=False)
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except TypeError: # Older versions of pyyaml don't support sort_keys
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yaml.dump(unstructed_config, f)
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def remove_nones(config: Dict[Any, Any]):
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new_config = {}
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for key, val in config.items():
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if isinstance(val, dict):
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new_config[key] = remove_nones(val)
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elif val is not None:
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new_config[key] = val
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return new_config
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if __name__ == "__main__":
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argparser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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argparser.add_argument(
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"trainer_config_path",
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help="Path to old format (<=0.16.X) trainer configuration YAML.",
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)
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argparser.add_argument(
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"--curriculum",
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help="Path to old format (<=0.16.X) curriculum configuration YAML.",
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default=None,
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)
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argparser.add_argument(
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"--sampler",
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help="Path to old format (<=0.16.X) parameter randomization configuration YAML.",
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default=None,
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)
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argparser.add_argument(
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"output_config_path", help="Path to write converted YAML file."
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)
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args = argparser.parse_args()
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print(
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f"Converting {args.trainer_config_path} and saving to {args.output_config_path}."
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)
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old_config = load_config(args.trainer_config_path)
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behavior_config_dict = convert_behaviors(old_config)
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full_config = {"behaviors": behavior_config_dict}
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# Convert curriculum and sampler. note that we don't validate these; if it was correct
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# before it should be correct now.
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if args.curriculum is not None:
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curriculum_config_dict = load_config(args.curriculum)
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full_config["curriculum"] = curriculum_config_dict
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if args.sampler is not None:
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sampler_config_dict = load_config(args.curriculum)
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full_config["parameter_randomization"] = sampler_config_dict
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write_to_yaml_file(full_config, args.output_config_path)
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