qing
36feeb4b9f
维度从 ONNX session 元数据自动推导，预处理复现训练流水线；Pi 5 实测 12ms/次含预处理（纯推理 5.9ms），与 torch 前向差异 <1e-5。新增 6 个 单测（真实导出 ONNX 对齐验证），全量 71 测试通过。
176 lines
7.2 KiB
Python
176 lines
7.2 KiB
Python
"""actpred ONNX Runtime 推理封装 —— CPU 后端（Pi 5 实测 5.9ms/次，PyTorch 的 ~5 倍）
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接口对齐 predict.ActionPredictorInference：predict(images, action_history)。
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模型维度从 ONNX session 输入元数据动态推导（images: [1,F,3,S,S]，
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action_history: [1,H,D]），无需 checkpoint。
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图像预处理复现训练时的 dataset.py 流水线：Resize → /255 → ImageNet 归一化。
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背景见 docs/reports/actpred_inference_speed_benchmark_20260813.md。
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"""
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import argparse
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import json
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import os
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import numpy as np
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from PIL import Image
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IMAGENET_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32)
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IMAGENET_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32)
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def parse_action_history(s: str, action_dim: int) -> np.ndarray:
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steps = s.split(":")
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history = []
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for step in steps:
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values = [float(v) for v in step.split(",")]
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if len(values) != action_dim:
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raise ValueError(
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f"Expected {action_dim} values per step, got {len(values)}")
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history.append(values)
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return np.array(history, dtype=np.float32)
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class OnnxPredictor:
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"""ONNX Runtime 动作预测推理器（CPU 后端）
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用法：
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with OnnxPredictor("model.onnx") as predictor:
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action = predictor.predict(images, action_history)
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"""
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def __init__(self, onnx_path: str, stats_path: str = None,
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providers=None):
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import onnxruntime as ort
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if providers is None:
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providers = ["CPUExecutionProvider"]
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ort.set_default_logger_severity(3) # 抑制 GPU 探测等无害警告
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opts = ort.SessionOptions()
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opts.log_severity_level = 3
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self._session = ort.InferenceSession(
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str(onnx_path), sess_options=opts, providers=providers)
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inputs = {i.name: [d if isinstance(d, int) else -1
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for d in i.shape] for i in self._session.get_inputs()}
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img_shape, hist_shape = self._locate_inputs(inputs)
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_, self.frame_len, _, self.image_size, _ = img_shape
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_, self.history_len, self.action_dim = hist_shape
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self._image_name = next(n for n, s in inputs.items() if len(s) == 5)
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self._history_name = next(n for n, s in inputs.items() if len(s) == 3)
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self._action_mean = None
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self._action_std = None
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if stats_path is not None:
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if not os.path.exists(stats_path):
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raise FileNotFoundError(f"统计文件不存在: {stats_path}")
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stats = np.load(stats_path)
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if stats.shape != (2, self.action_dim):
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raise ValueError(
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f"action_stats.npy 形状应为 (2, {self.action_dim})，"
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f"实际为 {stats.shape}: {stats_path}")
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self._action_mean = stats[0].astype(np.float32)
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self._action_std = stats[1].astype(np.float32)
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@staticmethod
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def _locate_inputs(inputs):
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"""按维度数识别图像(5维)/动作历史(3维)输入，返回各自形状"""
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img = [s for s in inputs.values() if len(s) == 5]
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hist = [s for s in inputs.values() if len(s) == 3]
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if len(img) != 1 or len(hist) != 1:
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raise ValueError(
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"ONNX 输入应为 1 个 5 维图像 + 1 个 3 维动作历史，"
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f"实际: {inputs}")
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if img[0][2] != 3 or img[0][3] != img[0][4]:
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raise ValueError(
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f"图像输入形状应为 [1,F,3,S,S]，实际为 {img[0]}")
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return img[0], hist[0]
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# ---------------- 生命周期 ----------------
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def close(self):
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self._session = None
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc, tb):
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self.close()
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return False
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# ---------------- 推理 ----------------
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def predict(self, images, action_history: np.ndarray) -> np.ndarray:
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"""images: 长度为 frame_len 的 RGB 图像列表（旧→新，末帧为当前帧）
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返回 (action_dim,) float32 动作向量。
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"""
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if self._session is None:
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raise RuntimeError("predictor 已关闭")
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if not isinstance(images, (list, tuple)):
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images = [images]
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if len(images) != self.frame_len:
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raise ValueError(
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f"images 应为 {self.frame_len} 帧，实际为 {len(images)} 帧。"
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"冷启动时可重复传入同一帧。")
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action_history = np.asarray(action_history, dtype=np.float32)
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expected = (self.history_len, self.action_dim)
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if action_history.shape != expected:
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raise ValueError(
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f"action_history 形状应为 {expected}，实际为 {action_history.shape}")
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if self._action_mean is not None:
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action_history = (action_history - self._action_mean) / self._action_std
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frames = np.stack([self._preprocess(img) for img in images])
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feeds = {
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self._image_name: frames[np.newaxis], # (1,F,3,S,S)
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self._history_name: action_history[np.newaxis], # (1,H,D)
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}
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result = self._session.run(None, feeds)[0].reshape(-1).astype(np.float32)
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if self._action_mean is not None:
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result = result * self._action_std + self._action_mean
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return result
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def _preprocess(self, image) -> np.ndarray:
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"""Resize → /255 → ImageNet 归一化 → (3, S, S) float32"""
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img = Image.fromarray(image) if isinstance(image, np.ndarray) else image
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img = img.convert("RGB").resize(
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(self.image_size, self.image_size), Image.BILINEAR)
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x = np.asarray(img, dtype=np.float32) / 255.0 # HWC
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x = x.transpose(2, 0, 1) # CHW
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return (x - IMAGENET_MEAN[:, None, None]) / IMAGENET_STD[:, None, None]
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def main():
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parser = argparse.ArgumentParser(description="actpred ONNX Runtime inference")
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parser.add_argument("--model", required=True, help="ONNX 模型路径")
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parser.add_argument("--images", required=True, nargs="+",
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help="按时间顺序（旧→新）传入 frame_len 个图像路径")
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parser.add_argument("--action-history", required=True,
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help="冒号分隔各步，每步 action_dim 个值")
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parser.add_argument("--stats", default=None,
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help="action_stats.npy 路径（训练时启用归一化才需要）")
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parser.add_argument("--output-format", choices=["json", "raw"],
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default="json")
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args = parser.parse_args()
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with OnnxPredictor(args.model, stats_path=args.stats) as predictor:
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if len(args.images) != predictor.frame_len:
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raise SystemExit(
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f"--images 需传入 {predictor.frame_len} 个路径（旧→新），"
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f"实际为 {len(args.images)} 个")
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images = [np.array(Image.open(p).convert("RGB")) for p in args.images]
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history = parse_action_history(args.action_history, predictor.action_dim)
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predicted = predictor.predict(images, history)
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if args.output_format == "json":
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print(json.dumps({"predicted_action": predicted.tolist()}, indent=2))
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else:
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print(" ".join(f"{v:.4f}" for v in predicted))
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if __name__ == "__main__":
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main()
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