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eval.py

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+#!/usr/bin/env python3
+import os
+import time
+import inspect
+import torch
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+
+from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.policies.utils import build_inference_frame, make_robot_action
+from lerobot.robots.so101_follower import SO101FollowerConfig, SO101Follower
+from lerobot.processor import PolicyProcessorPipeline
+from lerobot.datasets.utils import hw_to_dataset_features
+
+# -------------------------
+# CONFIG
+# -------------------------
+MODEL_ID = "lerobot/smolvla_base"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+FOLLOWER_PORT = "/dev/ttyACM3"
+
+TOP_CAM_INDEX = 4
+WRIST_CAM_INDEX = 9
+
+TASK = "Pick up the red block."
+ROBOT_TYPE = "so101_follower"
+
+FPS = 10
+EPISODE_SECONDS = 5.0
+
+BUFFER = "so100"  # or "so100-blue" / "so100-red"
+os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
+
+
+def hw_to_dataset_features_compat(hw_feats, prefix: str, use_videos: bool = True):
+    sig = inspect.signature(hw_to_dataset_features)
+    params = sig.parameters
+
+    if "use_videos" in params:
+        return hw_to_dataset_features(hw_feats, prefix, use_videos=use_videos)
+    if "use_images" in params:
+        return hw_to_dataset_features(hw_feats, prefix, use_images=use_videos)
+    if len(params) >= 3:
+        return hw_to_dataset_features(hw_feats, prefix, use_videos)
+    return hw_to_dataset_features(hw_feats, prefix)
+
+
+# -------------------------
+# Load pretrained model
+# -------------------------
+print("[INFO] Loading SmolVLA...")
+policy = SmolVLAPolicy.from_pretrained(MODEL_ID).to(DEVICE)
+policy.eval()
+
+# -------------------------
+# Load pretrained preprocessor
+# -------------------------
+print("[INFO] Loading pretrained preprocessor...")
+preprocess = PolicyProcessorPipeline.from_pretrained(
+    MODEL_ID,
+    config_filename="policy_preprocessor.json",
+    overrides={"device_processor": {"device": DEVICE}},
+)
+
+# -------------------------
+# Load pretrained action stats
+# -------------------------
+print("[INFO] Loading pretrained action stats...")
+state_path = hf_hub_download(
+    repo_id=MODEL_ID,
+    filename="policy_postprocessor_step_0_unnormalizer_processor.safetensors",
+)
+state = load_file(state_path)
+
+mean = state[f"{BUFFER}.buffer.action.mean"].to(DEVICE)
+std = state[f"{BUFFER}.buffer.action.std"].to(DEVICE)
+
+print(f"[INFO] Action dim = {mean.numel()}")
+
+
+def decode_action(action_norm: torch.Tensor) -> torch.Tensor:
+    return action_norm * std + mean
+
+
+# -------------------------
+# Setup cameras
+# -------------------------
+camera_cfg = {
+    "camera1": OpenCVCameraConfig(index_or_path=TOP_CAM_INDEX, width=640, height=480, fps=30),
+    "camera2": OpenCVCameraConfig(index_or_path=WRIST_CAM_INDEX, width=640, height=480, fps=30),
+}
+
+# -------------------------
+# Setup robot
+# -------------------------
+print("[INFO] Connecting SO101 follower...")
+robot_cfg = SO101FollowerConfig(
+    port=FOLLOWER_PORT,
+    id="so101_follower_arm",
+    cameras=camera_cfg,
+)
+robot = SO101Follower(robot_cfg)
+robot.connect()
+
+# -------------------------
+# Build ds_features (used by build_inference_frame AND make_robot_action)
+# -------------------------
+USE_VIDEOS = True
+action_features = hw_to_dataset_features_compat(robot.action_features, "action", use_videos=USE_VIDEOS)
+obs_features = hw_to_dataset_features_compat(robot.observation_features, "observation", use_videos=USE_VIDEOS)
+ds_features = {**obs_features, **action_features}
+
+# Sanity: make_robot_action expects ds_features["action"]["names"]
+assert "action" in ds_features and "names" in ds_features["action"], f"ds_features['action'] missing names: {ds_features.get('action')}"
+
+# -------------------------
+# Control loop
+# -------------------------
+dt = 1.0 / FPS
+t_end = time.time() + EPISODE_SECONDS
+
+print("[INFO] Starting evaluation...")
+policy.reset()
+
+try:
+    while time.time() < t_end:
+        t0 = time.time()
+
+        obs = robot.get_observation()
+
+        obs_frame = build_inference_frame(
+            observation=obs,
+            ds_features=ds_features,
+            device=DEVICE,
+            task=TASK,
+            robot_type=ROBOT_TYPE,
+        )
+
+        batch = preprocess(obs_frame)
+
+        with torch.no_grad():
+            action_norm = policy.select_action(batch)  # (1, A)
+
+        action_real = decode_action(action_norm).squeeze(0)
+
+        # ✅ FIX: pass ds_features, not robot.action_features
+        robot_action = make_robot_action(action_real, ds_features)
+        robot.send_action(robot_action)
+
+        time.sleep(max(0.0, dt - (time.time() - t0)))
+
+except KeyboardInterrupt:
+    print("\n[INFO] Ctrl+C received.")
+
+finally:
+    try:
+        robot.disconnect()
+    except Exception:
+        pass
+    print("[INFO] Done.")