fix(pybot×actpred): 审计修复 7 项——Hailo 部署链语义对齐 + NaN 安全

P1-1: export_deploy.py forward() 从硬编码 3 帧改为 *inputs 动态解析
P1-2: hailo_infer.py 补 predict_delta 支持（残差模式 + --ckpt 自动检测）
P1-3: pybot _load_predictor 自动查找 pred_token.npy/action_stats.npy/
      companion checkpoint，恢复 Hailo 推理训练语义
P1-4: pretrain_mt.py random_images 每 batch 重新生成噪声，防 BN 退化
P2-1: predict.py/hailo_infer.py/robot.py 三层 NaN/Inf 防线
P2-3: recorder.py imu_dim 默认 8→0，避免外部直接使用时形状错误
P2-5: _npu_test.py imu_dim 移到函数定义前，消除执行顺序依赖

pybot/pybot/main.py

@@ -134,10 +134,57 @@ def _load_predictor(checkpoint):
     pybot 不硬依赖 actpred：仅 --loop 时才尝试 import，actpred 不在
     sys.path 时给出可操作提示。checkpoint 目录名决定后端（.hef → Hailo
     NPU，否则 → PyTorch），推理类由 actpred 提供。
+
+    HEF 后端会自动查找同目录下的 pred_token.npy 和 companion checkpoint
+    （pretrain_best_inference.pt / best_inference.pt），以恢复 pred_token
+    默认值和 predict_delta 训练配置——否则 Hailo 推理输出与训练语义不一致。
     """
+    import os
     try:
         if checkpoint.endswith(".hef"):
             from actpred.hailo_infer import HailoPredictor as _P
+            hef_dir = os.path.dirname(os.path.abspath(checkpoint))
+
+            # pred_token：export_deploy.py --pred-token-out 的产物
+            pred_token = None
+            pt_path = os.path.join(hef_dir, "pred_token.npy")
+            if os.path.exists(pt_path):
+                pred_token = pt_path
+                logging.info("found pred_token: %s", pt_path)
+
+            # stats_path：归一化模型的 action_stats.npy
+            stats_path = None
+            sp = os.path.join(hef_dir, "action_stats.npy")
+            if os.path.exists(sp):
+                stats_path = sp
+
+            # predict_delta：从同目录 checkpoint 读取训练配置
+            predict_delta = False
+            for name in ("pretrain_best_inference.pt",
+                         "best_inference.pt"):
+                ckpt_path = os.path.join(hef_dir, name)
+                if os.path.exists(ckpt_path):
+                    try:
+                        import torch
+                        ck = torch.load(ckpt_path, map_location="cpu",
+                                        weights_only=True)
+                        cfg = ck.get("config", {})
+                        predict_delta = cfg.get("train", {}).get(
+                            "predict_delta", False)
+                        if predict_delta:
+                            logging.info(
+                                "predict_delta=True from %s", ckpt_path)
+                    except Exception:
+                        pass
+                    break
+
+            try:
+                return _P(checkpoint, stats_path=stats_path,
+                          predict_delta=predict_delta,
+                          pred_token=pred_token)
+            except Exception:
+                logging.exception("加载 HEF 推理器失败: %s", checkpoint)
+                return None
         else:
             from actpred.predict import ActionPredictorInference as _P
     except ImportError:

pybot/pybot/recorder.py

@@ -34,7 +34,7 @@ class EpisodeRecorder:
     全程 None（无 IMU 硬件）则不产出 imu.npy，保持旧格式兼容。
     """
 
-    def __init__(self, output_dir, action_dim=18, imu_dim=8):
+    def __init__(self, output_dir, action_dim=18, imu_dim=0):
         self.action_dim = action_dim
         self.imu_dim = imu_dim
         self._frame_idx = 0

pybot/pybot/robot.py

@@ -368,6 +368,10 @@ class RobotStruct:
         SpeakerVar.play 强制要求 delay 键；闭环已剔除 delay 列（避免发声
         阻塞卡死节拍），此处为其补 delay=0 —— 发声但不阻塞，节拍由 hz 掌控。
         """
+        import numpy as np
+        if not np.all(np.isfinite(act)):
+            log.warning("action vector contains NaN/Inf, skipping step")
+            return
         row_sets = {}
         for j, dev, rp in exec_cols:
             row_sets.setdefault(dev, {})[rp] = float(act[j])

pybot/tests/test_recorder.py

@@ -314,7 +314,7 @@ cam0.frame;
 
 def test_imu_npy_written_with_dict(tmp_path):
     """record 传姿态 dict → flush 产出 imu.npy（T×8），键序 roll..gz"""
-    rec = EpisodeRecorder(str(tmp_path), action_dim=18)
+    rec = EpisodeRecorder(str(tmp_path), action_dim=18, imu_dim=8)
     imu = {"roll": 1.0, "pitch": 2.0, "ax": 3.0, "ay": 4.0,
            "az": 5.0, "gx": 6.0, "gy": 7.0, "gz": 8.0}
     rec.record(None, [0.0], imu_vec=imu)
@@ -328,7 +328,7 @@ def test_imu_npy_written_with_dict(tmp_path):
 
 def test_imu_mixed_none_marks_zero(tmp_path):
     """混合数据集：部分帧无姿态 → 该帧记零向量，仍产出 imu.npy"""
-    rec = EpisodeRecorder(str(tmp_path), action_dim=18)
+    rec = EpisodeRecorder(str(tmp_path), action_dim=18, imu_dim=8)
     rec.record(None, [0.0], imu_vec=None)
     rec.record(None, [0.0], imu_vec={"roll": 9.0})
     rec.flush()