mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-20 11:09:59 +00:00
Khalil Meftah
227 lines
8.7 KiB
Python
227 lines
8.7 KiB
Python
#!/usr/bin/env python
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# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
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"""Run LeRobot Robometer parity against upstream Robometer's bundled examples.
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Upstream Robometer ships three reference videos with their pre-computed
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progress / success outputs at
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``third_party/robometer/scripts/example_videos/``::
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soar_put_green_stick_in_brown_bowl.mp4
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+ soar_put_green_stick_in_brown_bowl_rewards.npy (progress)
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+ soar_put_green_stick_in_brown_bowl_rewards_success_probs.npy (success)
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berkeley_rpt_stack_cup.mp4
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+ berkeley_rpt_stack_cup_rewards.npy
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+ berkeley_rpt_stack_cup_rewards_success_probs.npy
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jaco_play_pick_up_green_cup.mp4
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+ pick_up_green_cup_rewards.npy
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+ pick_up_green_cup_rewards_success_probs.npy
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This script:
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1. Decodes each video at upstream's sampling fps using ``av`` (PyAV), with the
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same linspace-over-total-frames logic as upstream's ``extract_frames``.
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2. Runs the LeRobot ``RobometerRewardModel`` on those frames + the task from
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upstream's README.
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3. Compares per-frame progress / success to the pre-saved upstream outputs.
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This means you do **not** need to install upstream Robometer to confirm parity.
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Run::
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uv run python scripts/parity_robometer_upstream_examples.py \\
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--lerobot-model lilkm/robometer-4b \\
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--device cuda \\
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--fps 3
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"""
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from __future__ import annotations
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import argparse
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import sys
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from pathlib import Path
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import av
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import numpy as np
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import torch
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from lerobot.configs.rewards import RewardModelConfig
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from lerobot.rewards.robometer import RobometerConfig, RobometerRewardModel
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from lerobot.rewards.robometer.modeling_robometer import decode_progress_outputs
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from lerobot.rewards.robometer.processor_robometer import RobometerEncoderProcessorStep
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EXAMPLES = [
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{
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"name": "soar_put_green_stick_in_brown_bowl",
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"video": "soar_put_green_stick_in_brown_bowl.mp4",
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"task": "Put green stick in brown bowl",
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"progress_npy": "soar_put_green_stick_in_brown_bowl_rewards.npy",
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"success_npy": "soar_put_green_stick_in_brown_bowl_rewards_success_probs.npy",
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},
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{
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"name": "berkeley_rpt_stack_cup",
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"video": "berkeley_rpt_stack_cup.mp4",
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"task": "Pick up the yellow cup and stack it on the other cup",
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"progress_npy": "berkeley_rpt_stack_cup_rewards.npy",
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"success_npy": "berkeley_rpt_stack_cup_rewards_success_probs.npy",
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},
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{
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"name": "jaco_play_pick_up_green_cup",
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"video": "jaco_play_pick_up_green_cup.mp4",
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"task": "Pick up the green cup",
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"progress_npy": "pick_up_green_cup_rewards.npy",
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"success_npy": "pick_up_green_cup_rewards_success_probs.npy",
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},
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]
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def _extract_frames_av(video_path: Path, fps: float) -> np.ndarray:
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"""Mirror upstream's ``extract_frames`` sampling logic using PyAV.
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Upstream uses ``decord`` to read all frames, then samples
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``np.linspace(0, total_frames - 1, desired_frames, dtype=int)`` where
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``desired_frames = round(total_frames * (fps / native_fps))``. We do the
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same here so the per-frame outputs are directly comparable.
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"""
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container = av.open(str(video_path))
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stream = container.streams.video[0]
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native_fps = float(stream.average_rate) if stream.average_rate else float(stream.guessed_rate or 30.0)
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rgb_frames: list[np.ndarray] = []
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for frame in container.decode(stream):
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rgb_frames.append(frame.to_ndarray(format="rgb24"))
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container.close()
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total_frames = len(rgb_frames)
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if total_frames == 0:
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raise RuntimeError(f"No decodable frames in {video_path}.")
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desired_frames = max(1, int(round(total_frames * (fps / max(native_fps, 1e-6)))))
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desired_frames = min(desired_frames, total_frames)
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indices = np.linspace(0, total_frames - 1, desired_frames, dtype=int)
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return np.stack([rgb_frames[i] for i in indices])
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def _run_lerobot(
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model: RobometerRewardModel,
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encoder: RobometerEncoderProcessorStep,
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frames: np.ndarray,
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task: str,
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) -> tuple[np.ndarray, np.ndarray]:
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batch = encoder.encode_samples([(frames, task)])
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device = next(model.model.parameters()).device
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inputs = {key: value.to(device) if hasattr(value, "to") else value for key, value in batch.items()}
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model.eval()
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with torch.no_grad():
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progress_logits, success_logits = model._compute_rbm_logits(inputs)
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decoded = decode_progress_outputs(
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progress_logits, success_logits, is_discrete_mode=model.config.use_discrete_progress
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)
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progress = np.asarray(decoded["progress_pred"][0], dtype=np.float32)
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success = (
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np.asarray(decoded["success_probs"][0], dtype=np.float32)
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if decoded["success_probs"]
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else np.array([], dtype=np.float32)
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)
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return progress, success
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def _compare(name: str, lerobot: np.ndarray, upstream: np.ndarray, atol: float, rtol: float) -> bool:
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if lerobot.shape != upstream.shape:
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print(f" {name}: shape mismatch lerobot={lerobot.shape} upstream={upstream.shape}")
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return False
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abs_diff = np.abs(lerobot - upstream)
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print(f" {name:16s} shape={lerobot.shape} max|Δ|={abs_diff.max():.3e} mean|Δ|={abs_diff.mean():.3e}")
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return bool(np.allclose(lerobot, upstream, atol=atol, rtol=rtol))
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def main() -> int:
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parser = argparse.ArgumentParser(
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description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter,
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)
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parser.add_argument(
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"--examples-dir",
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type=Path,
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default=Path("third_party/robometer/scripts/example_videos"),
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help="Directory containing the upstream Robometer example mp4s + .npy outputs.",
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)
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parser.add_argument(
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"--lerobot-model",
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default="lilkm/robometer-4b",
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help="LeRobot-format Robometer Hub repo id or local path.",
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)
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parser.add_argument(
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"--device",
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default="cuda" if torch.cuda.is_available() else "cpu",
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help="Device for the LeRobot model.",
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)
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parser.add_argument(
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"--fps",
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type=float,
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default=3.0,
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help="Sampling fps (default: 3, matching the upstream README).",
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)
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parser.add_argument("--atol", type=float, default=1e-3)
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parser.add_argument("--rtol", type=float, default=1e-2)
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args = parser.parse_args()
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examples_dir = args.examples_dir.resolve()
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if not examples_dir.is_dir():
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print(f"ERROR: examples dir {examples_dir} does not exist.", file=sys.stderr)
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return 2
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# Sanity-check the LeRobot config is a RobometerConfig before loading weights.
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cfg = RewardModelConfig.from_pretrained(args.lerobot_model)
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if not isinstance(cfg, RobometerConfig):
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print(f"ERROR: {args.lerobot_model!r} did not resolve to a RobometerConfig.", file=sys.stderr)
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return 2
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print(f"Loading LeRobot Robometer from {args.lerobot_model} on {args.device}...")
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cfg.pretrained_path = args.lerobot_model
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cfg.device = args.device
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model = RobometerRewardModel.from_pretrained(args.lerobot_model, config=cfg)
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encoder = RobometerEncoderProcessorStep(
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base_model_id=model.config.base_model_id,
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use_multi_image=model.config.use_multi_image,
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use_per_frame_progress_token=model.config.use_per_frame_progress_token,
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max_frames=None,
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)
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all_ok = True
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for ex in EXAMPLES:
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video_path = examples_dir / ex["video"]
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upstream_progress_path = examples_dir / ex["progress_npy"]
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upstream_success_path = examples_dir / ex["success_npy"]
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missing = [p for p in (video_path, upstream_progress_path, upstream_success_path) if not p.exists()]
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if missing:
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print(f"[skip] {ex['name']}: missing {[str(m) for m in missing]}")
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all_ok = False
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continue
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print(f"\n=== {ex['name']} ===")
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print(f" task: {ex['task']!r}")
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frames = _extract_frames_av(video_path, fps=args.fps)
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print(f" decoded {frames.shape[0]} frames @ fps={args.fps}; shape={frames.shape}")
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progress, success = _run_lerobot(model, encoder, frames, ex["task"])
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upstream_progress = np.load(upstream_progress_path).astype(np.float32)
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upstream_success = np.load(upstream_success_path).astype(np.float32)
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progress_ok = _compare("progress", progress, upstream_progress, args.atol, args.rtol)
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success_ok = _compare("success", success, upstream_success, args.atol, args.rtol)
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verdict = "PASS" if (progress_ok and success_ok) else "FAIL"
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print(f" -> {verdict}")
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all_ok = all_ok and progress_ok and success_ok
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print()
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if all_ok:
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print("All upstream example parity checks passed.")
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return 0
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print("Some upstream example parity checks FAILED.")
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return 1
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if __name__ == "__main__":
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sys.exit(main())
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