qing
85bf68f505
新增 action_norm.py 固定物理范围 min-max 归一化，无 stats 文件零状态部署；train.py 引入 _weighted_mse 逐列加权（舵机 1.0/spk 0.2/保留位 0.0）。审计修复: P0-1 pybot 读 normalize_mode, R2-1 NaN 阻断 best, P2-a 后端大小写不敏感, P1-3 utf-8-sig BOM, R1-1 data_dir 覆盖, R2-7 _run.sh 报错, R2-3 空 val 存 best.pt, P2-a2 误报警, R1-2 合并 parse_action_history, P2-b 合并 numpy import。评估修正 oob 物理界+error_growth 按 episode 中位数。推理部署全链支持 fixed_range。
206 lines
9.2 KiB
Python
206 lines
9.2 KiB
Python
"""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
|
||
|
||
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 = "zscore"):
|
||
"""ONNX 推理器。
|
||
|
||
normalize_mode: 与训练时 DataConfig.normalize_mode 一致。
|
||
"zscore"（默认）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）。
|
||
"""
|
||
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)
|
||
|
||
self._action_mean = None
|
||
self._action_std = None
|
||
self._fixed_range = normalize_mode == "fixed_range"
|
||
if stats_path is not None and not self._fixed_range:
|
||
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)
|
||
|
||
@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._fixed_range:
|
||
action_history = normalize_action(action_history, self.action_dim)
|
||
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._fixed_range:
|
||
result = denormalize_action(result, self.action_dim)
|
||
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"],
|
||
default="zscore",
|
||
help="与训练时 normalize_mode 一致；fixed_range 免 --stats")
|
||
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) 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()
|