Use PytorchModelHubMixin to save models as safetensors (#125)

Co-authored-by: Remi <re.cadene@gmail.com>

lerobot/common/policies/factory.py

@@ -2,6 +2,7 @@ import inspect
 
 from omegaconf import DictConfig, OmegaConf
 
+from lerobot.common.policies.policy_protocol import Policy
 from lerobot.common.utils.utils import get_safe_torch_device
 
 
@@ -20,42 +21,49 @@ def _policy_cfg_from_hydra_cfg(policy_cfg_class, hydra_cfg):
     return policy_cfg
 
 
-def make_policy(hydra_cfg: DictConfig, dataset_stats=None):
-    if hydra_cfg.policy.name == "tdmpc":
-        from lerobot.common.policies.tdmpc.policy import TDMPCPolicy
-
-        policy = TDMPCPolicy(
-            hydra_cfg.policy,
-            n_obs_steps=hydra_cfg.n_obs_steps,
-            n_action_steps=hydra_cfg.n_action_steps,
-            device=hydra_cfg.device,
-        )
-    elif hydra_cfg.policy.name == "diffusion":
+def get_policy_and_config_classes(name: str) -> tuple[Policy, object]:
+    """Get the policy's class and config class given a name (matching the policy class' `name` attribute)."""
+    if name == "tdmpc":
+        raise NotImplementedError("Coming soon!")
+    elif name == "diffusion":
         from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
         from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
 
-        policy_cfg = _policy_cfg_from_hydra_cfg(DiffusionConfig, hydra_cfg)
-        policy = DiffusionPolicy(policy_cfg, hydra_cfg.training.offline_steps, dataset_stats)
-        policy.to(get_safe_torch_device(hydra_cfg.device))
-    elif hydra_cfg.policy.name == "act":
+        return DiffusionPolicy, DiffusionConfig
+    elif name == "act":
         from lerobot.common.policies.act.configuration_act import ACTConfig
         from lerobot.common.policies.act.modeling_act import ACTPolicy
 
-        policy_cfg = _policy_cfg_from_hydra_cfg(ACTConfig, hydra_cfg)
-        policy = ACTPolicy(policy_cfg, dataset_stats)
+        return ACTPolicy, ACTConfig
+    else:
+        raise NotImplementedError(f"Policy with name {name} is not implemented.")
+
+
+def make_policy(
+    hydra_cfg: DictConfig, pretrained_policy_name_or_path: str | None = None, dataset_stats=None
+) -> Policy:
+    """Make an instance of a policy class.
+
+    Args:
+        hydra_cfg: A parsed Hydra configuration (see scripts). If `pretrained_policy_name_or_path` is
+            provided, only `hydra_cfg.policy.name` is used while everything else is ignored.
+        pretrained_policy_name_or_path: Either the repo ID of a model hosted on the Hub or a path to a
+            directory containing weights saved using `Policy.save_pretrained`. Note that providing this
+            argument overrides everything in `hydra_cfg.policy` apart from `hydra_cfg.policy.name`.
+        dataset_stats: Dataset statistics to use for (un)normalization of inputs/outputs in the policy. Must
+            be provided when initializing a new policy, and must not be provided when loading a pretrained
+            policy. Therefore, this argument is mutually exclusive with `pretrained_policy_name_or_path`.
+    """
+    if not (pretrained_policy_name_or_path is None) ^ (dataset_stats is None):
+        raise ValueError("Only one of `pretrained_policy_name_or_path` and `dataset_stats` may be provided.")
+
+    policy_cls, policy_cfg_class = get_policy_and_config_classes(hydra_cfg.policy.name)
+
+    if pretrained_policy_name_or_path is None:
+        policy_cfg = _policy_cfg_from_hydra_cfg(policy_cfg_class, hydra_cfg)
+        policy = policy_cls(policy_cfg, dataset_stats)
         policy.to(get_safe_torch_device(hydra_cfg.device))
     else:
-        raise ValueError(hydra_cfg.policy.name)
-
-    if hydra_cfg.policy.pretrained_model_path:
-        # TODO(rcadene): hack for old pretrained models from fowm
-        if hydra_cfg.policy.name == "tdmpc" and "fowm" in hydra_cfg.policy.pretrained_model_path:
-            if "offline" in hydra_cfg.policy.pretrained_model_path:
-                policy.step[0] = 25000
-            elif "final" in hydra_cfg.policy.pretrained_model_path:
-                policy.step[0] = 100000
-            else:
-                raise NotImplementedError()
-        policy.load(hydra_cfg.policy.pretrained_model_path)
+        policy = policy_cls.from_pretrained(pretrained_policy_name_or_path)
 
     return policy