renaming

marl_factory_grid/algorithms/utils.py

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