mirror of
https://github.com/illiumst/marl-factory-grid.git
synced 2025-10-31 12:37:27 +01:00
85 lines
2.6 KiB
Python
85 lines
2.6 KiB
Python
import torch
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import numpy as np
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import yaml
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from pathlib import Path
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def load_class(classname):
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from importlib import import_module
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module_path, class_name = classname.rsplit(".", 1)
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module = import_module(module_path)
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c = getattr(module, class_name)
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return c
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def instantiate_class(arguments):
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from importlib import import_module
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d = dict(arguments)
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classname = d["classname"]
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del d["classname"]
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module_path, class_name = classname.rsplit(".", 1)
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module = import_module(module_path)
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c = getattr(module, class_name)
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return c(**d)
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def get_class(arguments):
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from importlib import import_module
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if isinstance(arguments, dict):
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classname = arguments["classname"]
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module_path, class_name = classname.rsplit(".", 1)
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module = import_module(module_path)
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c = getattr(module, class_name)
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return c
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else:
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classname = arguments.classname
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module_path, class_name = classname.rsplit(".", 1)
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module = import_module(module_path)
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c = getattr(module, class_name)
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return c
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def get_arguments(arguments):
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from importlib import import_module
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d = dict(arguments)
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if "classname" in d:
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del d["classname"]
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return d
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def load_yaml_file(path: Path):
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with path.open() as stream:
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cfg = yaml.load(stream, Loader=yaml.FullLoader)
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return cfg
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def add_env_props(cfg):
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env = instantiate_class(cfg['env'].copy())
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cfg['agent'].update(dict(observation_size=list(env.observation_space.shape),
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n_actions=env.action_space.n))
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class Checkpointer(object):
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def __init__(self, experiment_name, root, config, total_steps, n_checkpoints):
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self.path = root / experiment_name
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self.checkpoint_indices = list(np.linspace(1, total_steps, n_checkpoints, dtype=int) - 1)
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self.__current_checkpoint = 0
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self.__current_step = 0
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self.path.mkdir(exist_ok=True, parents=True)
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with (self.path / 'config.yaml').open('w') as outfile:
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yaml.dump(config, outfile, default_flow_style=False)
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def save_experiment(self, name: str, model):
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cpt_path = self.path / f'checkpoint_{self.__current_checkpoint}'
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cpt_path.mkdir(exist_ok=True, parents=True)
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torch.save(model.state_dict(), cpt_path / f'{name}.pt')
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def step(self, to_save):
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if self.__current_step in self.checkpoint_indices:
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print(f'Checkpointing #{self.__current_checkpoint}')
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for name, model in to_save:
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self.save_experiment(name, model)
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self.__current_checkpoint += 1
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self.__current_step += 1 |