qing
35ee614ca9
- robot.py numpy 恢复函数级 lazy import，纯 .mt 脚本执行不再需要 numpy（v3-P1-1） - export_onnx 写入训练配置元数据；onnx_infer 自动识别 normalize_mode/predict_delta 并支持残差加回（v3-P1-2） - pybot --loop 补 .onnx 后端分支，zscore 缺 action_stats.npy 时告警（P2-a 闭环） - 回归测试：actpred TestOnnxMetadata +7、pybot 零依赖导入 +1（88/148 全绿） - 附 v3 审计报告：13 项 v2 修复回归核对 + 7 项新发现（P0-2 遗留 + P2/P3 六项）
85 lines
3.3 KiB
Python
85 lines
3.3 KiB
Python
import argparse
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import torch
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from torch.export import Dim
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from config import parse_checkpoint_config
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from model import ActionPredictor
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def main():
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parser = argparse.ArgumentParser(description="Export actpred model to ONNX")
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parser.add_argument("--model", required=True, help="checkpoint path (best_inference.pt)")
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parser.add_argument("--output", default="model.onnx", help="output ONNX file path")
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args = parser.parse_args()
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checkpoint = torch.load(args.model, map_location="cpu", weights_only=True)
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cfg = parse_checkpoint_config(checkpoint["config"])
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data_cfg = cfg.data
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model_cfg = cfg.model
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model = ActionPredictor(model_cfg, data_cfg, pretrained=False)
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model.load_state_dict(checkpoint["model_state_dict"])
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model.eval()
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dummy_images = torch.randn(
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1, data_cfg.frame_len, 3, data_cfg.image_height, data_cfg.image_width)
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dummy_history = torch.randn(1, data_cfg.history_len, data_cfg.action_dim)
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# R2 方案一：imu_dim>0 时追加归一化姿态输入 (1, imu_dim)
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imu_dim = data_cfg.imu_dim
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extra_inputs = (torch.randn(1, imu_dim),) if imu_dim > 0 else ()
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extra_names = ["imu"] if imu_dim > 0 else []
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batch = Dim("batch")
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dynamic = {"images": {0: batch}, "action_history": {0: batch}}
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if imu_dim > 0:
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dynamic["imu"] = {0: batch}
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torch.onnx.export(
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model, (dummy_images, dummy_history, *extra_inputs),
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args.output,
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input_names=["images", "action_history"] + extra_names,
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output_names=["predicted_action"],
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dynamic_shapes=dynamic,
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opset_version=18,
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)
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print(f"ONNX model exported to: {args.output}"
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f"{'（含 imu 输入）' if imu_dim > 0 else ''}")
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# 写入训练配置元数据：onnx_infer 据此自动识别归一化方式与残差模式，
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# 避免推理端配置与训练不一致导致静默错误（normalize_actions=False 时
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# 记为 "none"，推理端不做任何归一化）
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import onnx
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m = onnx.load(args.output)
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meta = {
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"normalize_mode": (data_cfg.normalize_mode
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if data_cfg.normalize_actions else "none"),
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"predict_delta": ("1" if getattr(cfg.train, "predict_delta", False)
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else "0"),
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"imu_dim": str(imu_dim),
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"action_dim": str(data_cfg.action_dim),
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}
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for k, v in meta.items():
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p = m.metadata_props.add()
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p.key, p.value = k, v
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onnx.save(m, args.output)
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print(f"ONNX 元数据已写入: {meta}")
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# 可选：验证导出的 ONNX 模型一致性
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try:
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import onnxruntime as ort
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import numpy as np
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sess = ort.InferenceSession(args.output)
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feeds = {"images": dummy_images.numpy(),
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"action_history": dummy_history.numpy()}
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for name, t in zip(extra_names, extra_inputs):
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feeds[name] = t.numpy()
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ort_out = sess.run(None, feeds)[0]
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with torch.no_grad():
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torch_out = model(dummy_images, dummy_history, *extra_inputs).numpy()
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max_diff = np.abs(ort_out - torch_out).max()
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print(f"ONNX 一致性验证通过（最大差异: {max_diff:.6e}）")
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except ImportError:
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print("提示: 安装 onnxruntime 可验证导出模型一致性 (pip install onnxruntime)")
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if __name__ == "__main__":
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main()
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