Simplify configs (#550)

Co-authored-by: Remi <remi.cadene@huggingface.co>
Co-authored-by: HUANG TZU-CHUN <137322177+tc-huang@users.noreply.github.com>

lerobot/configs/eval.py

@@ -0,0 +1,84 @@
+import datetime as dt
+import logging
+from dataclasses import dataclass, field
+from pathlib import Path
+
+from lerobot.common import envs, policies  # noqa: F401
+from lerobot.common.utils.utils import auto_select_torch_device, is_amp_available, is_torch_device_available
+from lerobot.configs import parser
+from lerobot.configs.default import EvalConfig
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.train import TrainPipelineConfig
+
+
+@dataclass
+class EvalPipelineConfig:
+    # Either the repo ID of a model hosted on the Hub or a path to a directory containing weights
+    # saved using `Policy.save_pretrained`. If not provided, the policy is initialized from scratch
+    # (useful for debugging). This argument is mutually exclusive with `--config`.
+    env: envs.EnvConfig
+    eval: EvalConfig = field(default_factory=EvalConfig)
+    policy: PreTrainedConfig | None = None
+    output_dir: Path | None = None
+    job_name: str | None = None
+    # TODO(rcadene, aliberts): By default, use device and use_amp values from policy checkpoint.
+    device: str | None = None  # cuda | cpu | mps
+    # `use_amp` determines whether to use Automatic Mixed Precision (AMP) for training and evaluation. With AMP,
+    # automatic gradient scaling is used.
+    use_amp: bool = False
+    seed: int | None = 1000
+
+    def __post_init__(self):
+        # HACK: We parse again the cli args here to get the pretrained path if there was one.
+        policy_path = parser.get_path_arg("policy")
+        if policy_path:
+            cli_overrides = parser.get_cli_overrides("policy")
+            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
+            self.policy.pretrained_path = policy_path
+
+            # When no device or use_amp are given, use the one from training config.
+            if self.device is None or self.use_amp is None:
+                train_cfg = TrainPipelineConfig.from_pretrained(policy_path)
+                if self.device is None:
+                    self.device = train_cfg.device
+                if self.use_amp is None:
+                    self.use_amp = train_cfg.use_amp
+
+            # Automatically switch to available device if necessary
+            if not is_torch_device_available(self.device):
+                auto_device = auto_select_torch_device()
+                logging.warning(f"Device '{self.device}' is not available. Switching to '{auto_device}'.")
+                self.device = auto_device
+
+            # Automatically deactivate AMP if necessary
+            if self.use_amp and not is_amp_available(self.device):
+                logging.warning(
+                    f"Automatic Mixed Precision (amp) is not available on device '{self.device}'. Deactivating AMP."
+                )
+                self.use_amp = False
+
+        else:
+            logging.warning(
+                "No pretrained path was provided, evaluated policy will be built from scratch (random weights)."
+            )
+
+        if not self.job_name:
+            if self.env is None:
+                self.job_name = f"{self.policy.type}"
+            else:
+                self.job_name = f"{self.env.type}_{self.policy.type}"
+
+        if not self.output_dir:
+            now = dt.datetime.now()
+            eval_dir = f"{now:%Y-%m-%d}/{now:%H-%M-%S}_{self.job_name}"
+            self.output_dir = Path("outputs/eval") / eval_dir
+
+        if self.device is None:
+            raise ValueError("Set one of the following device: cuda, cpu or mps")
+        elif self.device == "cuda" and self.use_amp is None:
+            raise ValueError("Set 'use_amp' to True or False.")
+
+    @classmethod
+    def __get_path_fields__(cls) -> list[str]:
+        """This enables the parser to load config from the policy using `--policy.path=local/dir`"""
+        return ["policy"]