"""actpred ONNX Runtime 推理封装 —— CPU 后端（Pi 5 实测 5.9ms/次，PyTorch 的 ~5 倍） 接口对齐 predict.ActionPredictorInference：predict(images, action_history, imu=None)。 模型维度从 ONNX session 输入元数据动态推导（images: [1,F,3,H,W]， action_history: [1,H,D]；R2 imu_dim>0 的模型另有 imu: [1,imu_dim]）， 无需 checkpoint。图像预处理复现训练时的 dataset.py 流水线： Resize → /255 → ImageNet 归一化。 背景见 docs/reports/actpred_inference_speed_benchmark_20260813.md。 """ import argparse import json import os import numpy as np from PIL import Image from imu_norm import normalize_imu from action_norm import (normalize_action, denormalize_action, is_range_mode) IMAGENET_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32) IMAGENET_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32) from utils import parse_action_history class OnnxPredictor: """ONNX Runtime 动作预测推理器（CPU 后端） 用法： with OnnxPredictor("model.onnx") as predictor: action = predictor.predict(images, action_history) """ def __init__(self, onnx_path: str, stats_path: str = None, providers=None, normalize_mode: str = None, predict_delta: bool = None): """ONNX 推理器。 normalize_mode: 与训练时 DataConfig.normalize_mode 一致。 "zscore" 需传 stats_path；"fixed_range" 免 stats 文件， 按固定物理范围 min-max 归一化（action_norm.py）； "none" 表示训练未归一化，输入输出均不处理。 缺省 None 时从 ONNX 元数据自动识别（export_onnx 写入）， 旧模型无元数据回退 "zscore"。 predict_delta: 残差模式（输出 Δ̂ 加回 history 末步得绝对动作）。 缺省 None 时同样从 ONNX 元数据识别，无元数据回退 False。 """ import onnxruntime as ort if providers is None: providers = ["CPUExecutionProvider"] ort.set_default_logger_severity(3) # 抑制 GPU 探测等无害警告 opts = ort.SessionOptions() opts.log_severity_level = 3 self._session = ort.InferenceSession( str(onnx_path), sess_options=opts, providers=providers) inputs = {i.name: [d if isinstance(d, int) else -1 for d in i.shape] for i in self._session.get_inputs()} img_shape, hist_shape, imu_shape = self._locate_inputs(inputs) _, self.frame_len, _, self.image_height, self.image_width = img_shape _, self.history_len, self.action_dim = hist_shape self._image_name = next(n for n, s in inputs.items() if len(s) == 5) self._history_name = next(n for n, s in inputs.items() if len(s) == 3) # R2 方案一：2 维输入 [1, imu_dim] 为姿态（无则 None）。 # pybot 闭环鸭子类型约定：None 时按旧两参调用。 self.imu_dim = imu_shape[-1] if imu_shape is not None else None self._imu_name = (next(n for n, s in inputs.items() if len(s) == 2) if imu_shape is not None else None) meta = self._read_metadata() if normalize_mode is None: normalize_mode = meta.get("normalize_mode") or "zscore" if normalize_mode not in ("zscore", "fixed_range", "none", "log_range"): raise ValueError(f"normalize_mode={normalize_mode!r} 非法，" "应为 zscore/fixed_range/none/log_range") self._range_mode = (normalize_mode if is_range_mode(normalize_mode) else None) self._no_norm = normalize_mode == "none" self.normalize_mode = normalize_mode # 解析后的最终模式（供调用方判断） # R1-C 残差预测：元数据 predict_delta=1 时模型输出 Δ̂， # 需在归一化空间加回 a_{t-1}（与 predict.py/hailo_infer 同语义） self._predict_delta = (predict_delta if predict_delta is not None else meta.get("predict_delta") == "1") self._action_mean = None self._action_std = None if stats_path is not None and not (self._range_mode or self._no_norm): if not os.path.exists(stats_path): raise FileNotFoundError(f"统计文件不存在: {stats_path}") stats = np.load(stats_path) if stats.shape != (2, self.action_dim): raise ValueError( f"action_stats.npy 形状应为 (2, {self.action_dim})，" f"实际为 {stats.shape}: {stats_path}") self._action_mean = stats[0].astype(np.float32) self._action_std = stats[1].astype(np.float32) def _read_metadata(self) -> dict: """读 ONNX custom metadata（export_onnx 写入）；旧模型无则空 dict""" try: return dict(self._session.get_modelmeta().custom_metadata_map) except Exception: return {} @staticmethod def _locate_inputs(inputs): """按维度数识别图像(5维)/动作历史(3维)/姿态(2维,可选)输入，返回各自形状""" img = [s for s in inputs.values() if len(s) == 5] hist = [s for s in inputs.values() if len(s) == 3] imu = [s for s in inputs.values() if len(s) == 2] if len(img) != 1 or len(hist) != 1 or len(imu) > 1: raise ValueError( "ONNX 输入应为 1 个 5 维图像 + 1 个 3 维动作历史" "（R2 可再加 1 个 2 维 imu），" f"实际: {inputs}") if img[0][2] != 3: raise ValueError( f"图像输入形状应为 [1,F,3,H,W]，实际为 {img[0]}") return img[0], hist[0], (imu[0] if imu else None) # ---------------- 生命周期 ---------------- def close(self): self._session = None def __enter__(self): return self def __exit__(self, exc_type, exc, tb): self.close() return False # ---------------- 推理 ---------------- def predict(self, images, action_history: np.ndarray, imu: np.ndarray = None) -> np.ndarray: """images: 长度为 frame_len 的 RGB 图像列表（旧→新，末帧为当前帧） imu: 可选原始姿态向量（roll,pitch,ax,ay,az,gx,gy,gz，模型含 imu 输入时生效，内部按物理量程归一化；None 等价零向量） 返回 (action_dim,) float32 动作向量。 """ if self._session is None: raise RuntimeError("predictor 已关闭") if not isinstance(images, (list, tuple)): images = [images] if len(images) != self.frame_len: raise ValueError( f"images 应为 {self.frame_len} 帧，实际为 {len(images)} 帧。" "冷启动时可重复传入同一帧。") action_history = np.asarray(action_history, dtype=np.float32) expected = (self.history_len, self.action_dim) if action_history.shape != expected: raise ValueError( f"action_history 形状应为 {expected}，实际为 {action_history.shape}") if self._range_mode: action_history = normalize_action(action_history, self.action_dim, mode=self._range_mode) elif self._action_mean is not None: action_history = (action_history - self._action_mean) / self._action_std frames = np.stack([self._preprocess(img) for img in images]) feeds = { self._image_name: frames[np.newaxis], # (1,F,3,S,S) self._history_name: action_history[np.newaxis], # (1,H,D) } if self._imu_name is not None: feeds[self._imu_name] = normalize_imu( imu, self.imu_dim)[np.newaxis] # (1,imu_dim) result = self._session.run(None, feeds)[0].reshape(-1).astype(np.float32) if self._predict_delta: # 残差：Δ̂ + a_{t-1}（归一化空间，与 predict.py 对齐） result = result + action_history[-1] if self._range_mode: result = denormalize_action(result, self.action_dim, mode=self._range_mode) elif self._action_mean is not None: result = result * self._action_std + self._action_mean return result def _preprocess(self, image) -> np.ndarray: """Resize → /255 → ImageNet 归一化 → (3, H, W) float32""" img = Image.fromarray(image) if isinstance(image, np.ndarray) else image img = img.convert("RGB").resize( (self.image_width, self.image_height), Image.BILINEAR) x = np.asarray(img, dtype=np.float32) / 255.0 # HWC x = x.transpose(2, 0, 1) # CHW return (x - IMAGENET_MEAN[:, None, None]) / IMAGENET_STD[:, None, None] def main(): parser = argparse.ArgumentParser(description="actpred ONNX Runtime inference") parser.add_argument("--model", required=True, help="ONNX 模型路径") parser.add_argument("--images", required=True, nargs="+", help="按时间顺序（旧→新）传入 frame_len 个图像路径") parser.add_argument("--action-history", required=True, help="冒号分隔各步，每步 action_dim 个值") parser.add_argument("--stats", default=None, help="action_stats.npy 路径（zscore 归一化训练才需要）") parser.add_argument("--normalize-mode", choices=["zscore", "fixed_range", "none", "log_range"], default=None, help="与训练时 normalize_mode 一致；缺省从 ONNX 元数据" "自动识别（旧模型回退 zscore）") parser.add_argument("--predict-delta", action="store_true", default=None, help="残差模式（输出加回 history 末步）；缺省从 ONNX " "元数据自动识别（旧模型回退关闭）") parser.add_argument("--imu", type=str, default=None, help="可选姿态向量（逗号分隔，原始量纲：roll,pitch 度 / " "ax..gz int16 原始值）；模型含 imu 输入且缺省时按零向量") parser.add_argument("--output-format", choices=["json", "raw"], default="json") args = parser.parse_args() with OnnxPredictor(args.model, stats_path=args.stats, normalize_mode=args.normalize_mode, predict_delta=args.predict_delta) as predictor: if len(args.images) != predictor.frame_len: raise SystemExit( f"--images 需传入 {predictor.frame_len} 个路径（旧→新），" f"实际为 {len(args.images)} 个") images = [np.array(Image.open(p).convert("RGB")) for p in args.images] history = parse_action_history(args.action_history, predictor.action_dim) imu = None if args.imu is not None: imu = np.array([float(v) for v in args.imu.split(",")], dtype=np.float32) predicted = predictor.predict(images, history, imu=imu) if args.output_format == "json": print(json.dumps({"predicted_action": predicted.tolist()}, indent=2)) else: print(" ".join(f"{v:.4f}" for v in predicted)) if __name__ == "__main__": main()