finish examples 2 and 3

lerobot/scripts/eval.py

@@ -30,14 +30,12 @@ python lerobot/scripts/eval.py --hub-id HUB/ID --revision v1.0 eval_episodes=10
 import argparse
 import json
 import logging
-import os.path as osp
 import threading
 import time
 from datetime import datetime as dt
 from pathlib import Path
 
 import einops
-import hydra
 import imageio
 import numpy as np
 import torch
@@ -52,7 +50,7 @@ from lerobot.common.envs.factory import make_env
 from lerobot.common.logger import log_output_dir
 from lerobot.common.policies.abstract import AbstractPolicy
 from lerobot.common.policies.factory import make_policy
-from lerobot.common.utils import get_safe_torch_device, init_logging, set_global_seed
+from lerobot.common.utils import get_safe_torch_device, init_hydra_config, init_logging, set_global_seed
 
 
 def write_video(video_path, stacked_frames, fps):
@@ -195,7 +193,8 @@ def eval(cfg: dict, out_dir=None, stats_path=None):
     log_output_dir(out_dir)
 
     logging.info("Making transforms.")
-    offline_buffer = make_offline_buffer(cfg, stats_path=stats_path)
+    # TODO(alexander-soare): Completely decouple datasets from evaluation.
+    offline_buffer = make_offline_buffer(cfg, stats_path=stats_path, dummy=stats_path is not None)
 
     logging.info("Making environment.")
     env = make_env(cfg, transform=offline_buffer.transform)
@@ -229,19 +228,6 @@ def eval(cfg: dict, out_dir=None, stats_path=None):
     logging.info("End of eval")
 
 
-def _relative_path_between(path1: Path, path2: Path) -> Path:
-    """Returns path1 relative to path2."""
-    path1 = path1.absolute()
-    path2 = path2.absolute()
-    try:
-        return path1.relative_to(path2)
-    except ValueError:  # most likely because path1 is not a subpath of path2
-        common_parts = Path(osp.commonpath([path1, path2])).parts
-        return Path(
-            "/".join([".."] * (len(path2.parts) - len(common_parts)) + list(path1.parts[len(common_parts) :]))
-        )
-
-
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
         description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
@@ -259,19 +245,14 @@ if __name__ == "__main__":
 
     if args.config is not None:
         # Note: For the config_path, Hydra wants a path relative to this script file.
-        hydra.initialize(
-            config_path=str(
-                _relative_path_between(Path(args.config).absolute().parent, Path(__file__).parent)
-            )
-        )
-        cfg = hydra.compose(Path(args.config).stem, args.overrides)
+        cfg = init_hydra_config(args.config, args.overrides)
         # TODO(alexander-soare): Save and load stats in trained model directory.
         stats_path = None
     elif args.hub_id is not None:
         folder = Path(snapshot_download(args.hub_id, revision="v1.0"))
-        cfg = hydra.initialize(config_path=str(_relative_path_between(folder, Path(__file__).parent)))
-        cfg = hydra.compose("config", args.overrides)
-        cfg.policy.pretrained_model_path = folder / "model.pt"
+        cfg = init_hydra_config(
+            folder / "config.yaml", [f"policy.pretrained_model_path={folder / 'model.pt'}", *args.overrides]
+        )
         stats_path = folder / "stats.pth"
 
     eval(