fix(pybot×actpred): v3 审计 P1 修复 3 项——零依赖承诺 + ONNX 元数据/残差全链路
- 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）
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@@ -1087,6 +1087,139 @@ class TestOnnxPredictor(unittest.TestCase):
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OnnxPredictor(path, stats_path=stats_path)
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@unittest.skipUnless(
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importlib.util.find_spec("onnxscript") is not None
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and importlib.util.find_spec("onnxruntime") is not None,
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"ONNX 元数据测试需要 onnxscript + onnxruntime")
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class TestOnnxMetadata(unittest.TestCase):
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"""v3-P1-2：export_onnx 写入训练配置元数据，onnx_infer 自动识别
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覆盖 normalize_mode（zscore/fixed_range/none）与 predict_delta 的
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元数据写入、自动识别、显式参数覆盖与残差加回数值语义。
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"""
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@staticmethod
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def _write_meta(path, meta):
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import onnx
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m = onnx.load(path)
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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, path)
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@staticmethod
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def _read_meta(path):
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import onnx
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return {p.key: p.value for p in onnx.load(path).metadata_props}
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def _frames(self, n):
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return [np.random.randint(0, 255, (48, 64, 3), dtype=np.uint8)
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for _ in range(n)]
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def _export_via_script(self, tmpdir, normalize_actions=False,
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normalize_mode="zscore", predict_delta=False):
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"""经 export_onnx.main 导出小模型，返回 ONNX 路径"""
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import export_onnx
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cfg = Config()
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cfg.data.image_width = 32
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cfg.data.image_height = 32
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cfg.data.action_dim = 4
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cfg.data.history_len = 3
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cfg.data.frame_len = 3
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cfg.data.normalize_actions = normalize_actions
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cfg.data.normalize_mode = normalize_mode
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cfg.train.predict_delta = predict_delta
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model = ActionPredictor(cfg.model, cfg.data)
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model.eval()
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ckpt = os.path.join(tmpdir, "best_inference.pt")
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torch.save({"model_state_dict": model.state_dict(),
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"config": cfg.to_dict()}, ckpt)
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out = os.path.join(tmpdir, "model.onnx")
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with mock.patch.object(
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sys, "argv",
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["export_onnx.py", "--model", ckpt, "--output", out]):
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with contextlib.redirect_stdout(io.StringIO()):
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export_onnx.main()
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return out
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def test_export_writes_metadata_unnormalized(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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meta = self._read_meta(self._export_via_script(tmpdir))
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self.assertEqual(meta["normalize_mode"], "none") # 未归一化训练
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self.assertEqual(meta["predict_delta"], "0")
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self.assertEqual(meta["action_dim"], "4")
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self.assertEqual(meta["imu_dim"], "0")
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def test_export_writes_metadata_fixed_range_delta(self):
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with tempfile.TemporaryDirectory() as tmpdir:
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meta = self._read_meta(self._export_via_script(
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tmpdir, normalize_actions=True,
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normalize_mode="fixed_range", predict_delta=True))
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self.assertEqual(meta["normalize_mode"], "fixed_range")
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self.assertEqual(meta["predict_delta"], "1")
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def test_export_metadata_roundtrip_predictor(self):
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"""导出的 ONNX 直接被 OnnxPredictor 自动识别，无需显式参数"""
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from onnx_infer import OnnxPredictor
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with tempfile.TemporaryDirectory() as tmpdir:
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out = self._export_via_script(
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tmpdir, normalize_actions=True,
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normalize_mode="fixed_range", predict_delta=True)
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with OnnxPredictor(out) as p:
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self.assertEqual(p.normalize_mode, "fixed_range")
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self.assertTrue(p._predict_delta)
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def test_predictor_metadata_fallback_old_model(self):
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"""旧版无元数据 ONNX：回退 zscore + 非残差"""
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from onnx_infer import OnnxPredictor
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with tempfile.TemporaryDirectory() as tmpdir:
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path, _ = TestOnnxPredictor._export_small_onnx(tmpdir)
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with OnnxPredictor(path) as p:
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self.assertEqual(p.normalize_mode, "zscore")
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self.assertFalse(p._predict_delta)
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def test_explicit_param_overrides_metadata(self):
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from onnx_infer import OnnxPredictor
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with tempfile.TemporaryDirectory() as tmpdir:
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path, _ = TestOnnxPredictor._export_small_onnx(tmpdir)
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self._write_meta(path, {"normalize_mode": "fixed_range",
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"predict_delta": "1"})
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with OnnxPredictor(path, normalize_mode="zscore",
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predict_delta=False) as p:
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self.assertEqual(p.normalize_mode, "zscore")
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self.assertFalse(p._predict_delta)
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def test_predict_delta_adds_last_history(self):
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"""残差输出 = 普通输出 + history 末步（none 模式下口径直接可比）"""
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from onnx_infer import OnnxPredictor
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with tempfile.TemporaryDirectory() as tmpdir:
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path, _ = TestOnnxPredictor._export_small_onnx(tmpdir)
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frames = self._frames(3)
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history = np.random.randn(3, 4).astype(np.float32)
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with OnnxPredictor(path, normalize_mode="none") as plain:
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base = plain.predict(frames, history)
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with OnnxPredictor(path, normalize_mode="none",
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predict_delta=True) as delta:
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out = delta.predict(frames, history)
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np.testing.assert_allclose(out, base + history[-1], atol=1e-5)
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def test_none_mode_ignores_stats(self):
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"""none 模式（训练未归一化）：即使传 stats 也不做归一化"""
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from onnx_infer import OnnxPredictor
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with tempfile.TemporaryDirectory() as tmpdir:
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path, _ = TestOnnxPredictor._export_small_onnx(tmpdir)
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stats_path = os.path.join(tmpdir, "action_stats.npy")
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np.save(stats_path, np.stack([np.full(4, 0.5), np.full(4, 2.0)]))
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frames = self._frames(3)
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history = np.random.randn(3, 4).astype(np.float32)
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with OnnxPredictor(path, stats_path=stats_path,
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normalize_mode="none") as p:
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out = p.predict(frames, history)
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with OnnxPredictor(path, normalize_mode="none") as q:
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ref = q.predict(frames, history)
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np.testing.assert_allclose(out, ref, atol=1e-6)
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# ---------------- HailoPredictor 测试用 fake hailo_platform ----------------
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class _FakeFormatType:
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