feat: initial HSAP platform

Huaxu Sentinel Active Safety Platform with embedded algorithm code,
Docker Compose setup, and vendored dataset scaffolds for clone-and-run.

Co-authored-by: Cursor <cursoragent@cursor.com>

algorithms/dms_yolo/code/tests/test_engine.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import sys
+from unittest import mock
+
+import torch
+
+from tests import MODEL, SOURCE
+from ultralytics import YOLO
+from ultralytics.cfg import get_cfg
+from ultralytics.engine.exporter import Exporter
+from ultralytics.models.yolo import classify, detect, segment
+from ultralytics.utils import ASSETS, DEFAULT_CFG, WEIGHTS_DIR
+
+
+def test_func(*args, **kwargs):
+    """Test function callback for evaluating YOLO model performance metrics."""
+    print("callback test passed")
+
+
+def test_export():
+    """Test model exporting functionality by adding a callback and verifying its execution."""
+    exporter = Exporter()
+    exporter.add_callback("on_export_start", test_func)
+    assert test_func in exporter.callbacks["on_export_start"], "callback test failed"
+    f = exporter(model=YOLO("yolo26n.yaml").model)
+    YOLO(f)(SOURCE)  # exported model inference
+
+
+def test_detect():
+    """Test YOLO object detection training, validation, and prediction functionality."""
+    overrides = {"data": "coco8.yaml", "model": "yolo26n.yaml", "imgsz": 32, "epochs": 1, "save": False}
+    cfg = get_cfg(DEFAULT_CFG)
+    cfg.data = "coco8.yaml"
+    cfg.imgsz = 32
+
+    # Trainer
+    trainer = detect.DetectionTrainer(overrides=overrides)
+    trainer.add_callback("on_train_start", test_func)
+    assert test_func in trainer.callbacks["on_train_start"], "callback test failed"
+    trainer.train()
+
+    # Validator
+    val = detect.DetectionValidator(args=cfg)
+    val.add_callback("on_val_start", test_func)
+    assert test_func in val.callbacks["on_val_start"], "callback test failed"
+    val(model=trainer.best)  # validate best.pt
+
+    # Predictor
+    pred = detect.DetectionPredictor(overrides={"imgsz": [64, 64]})
+    pred.add_callback("on_predict_start", test_func)
+    assert test_func in pred.callbacks["on_predict_start"], "callback test failed"
+    # Confirm there is no issue with sys.argv being empty
+    with mock.patch.object(sys, "argv", []):
+        result = pred(source=ASSETS, model=MODEL)
+        assert len(result), "predictor test failed"
+
+    # Test resume functionality
+    overrides["resume"] = trainer.last
+    trainer = detect.DetectionTrainer(overrides=overrides)
+    try:
+        trainer.train()
+    except Exception as e:
+        print(f"Expected exception caught: {e}")
+        return
+
+    raise Exception("Resume test failed!")
+
+
+def test_segment():
+    """Test image segmentation training, validation, and prediction pipelines using YOLO models."""
+    overrides = {
+        "data": "coco8-seg.yaml",
+        "model": "yolo26n-seg.yaml",
+        "imgsz": 32,
+        "epochs": 1,
+        "save": False,
+        "mask_ratio": 1,
+        "overlap_mask": False,
+    }
+    cfg = get_cfg(DEFAULT_CFG)
+    cfg.data = "coco8-seg.yaml"
+    cfg.imgsz = 32
+
+    # Trainer
+    trainer = segment.SegmentationTrainer(overrides=overrides)
+    trainer.add_callback("on_train_start", test_func)
+    assert test_func in trainer.callbacks["on_train_start"], "callback test failed"
+    trainer.train()
+
+    # Validator
+    val = segment.SegmentationValidator(args=cfg)
+    val.add_callback("on_val_start", test_func)
+    assert test_func in val.callbacks["on_val_start"], "callback test failed"
+    val(model=trainer.best)  # validate best.pt
+
+    # Predictor
+    pred = segment.SegmentationPredictor(overrides={"imgsz": [64, 64]})
+    pred.add_callback("on_predict_start", test_func)
+    assert test_func in pred.callbacks["on_predict_start"], "callback test failed"
+    result = pred(source=ASSETS, model=WEIGHTS_DIR / "yolo26n-seg.pt")
+    assert len(result), "predictor test failed"
+
+    # Test resume functionality
+    overrides["resume"] = trainer.last
+    trainer = segment.SegmentationTrainer(overrides=overrides)
+    try:
+        trainer.train()
+    except Exception as e:
+        print(f"Expected exception caught: {e}")
+        return
+
+    raise Exception("Resume test failed!")
+
+
+def test_classify():
+    """Test image classification including training, validation, and prediction phases."""
+    overrides = {"data": "imagenet10", "model": "yolo26n-cls.yaml", "imgsz": 32, "epochs": 1, "save": False}
+    cfg = get_cfg(DEFAULT_CFG)
+    cfg.data = "imagenet10"
+    cfg.imgsz = 32
+
+    # Trainer
+    trainer = classify.ClassificationTrainer(overrides=overrides)
+    trainer.add_callback("on_train_start", test_func)
+    assert test_func in trainer.callbacks["on_train_start"], "callback test failed"
+    trainer.train()
+
+    # Validator
+    val = classify.ClassificationValidator(args=cfg)
+    val.add_callback("on_val_start", test_func)
+    assert test_func in val.callbacks["on_val_start"], "callback test failed"
+    val(model=trainer.best)
+
+    # Predictor
+    pred = classify.ClassificationPredictor(overrides={"imgsz": [64, 64]})
+    pred.add_callback("on_predict_start", test_func)
+    assert test_func in pred.callbacks["on_predict_start"], "callback test failed"
+    result = pred(source=ASSETS, model=trainer.best)
+    assert len(result), "predictor test failed"
+
+
+def test_nan_recovery():
+    """Test NaN loss detection and recovery during training."""
+    nan_injected = [False]
+
+    def inject_nan(trainer):
+        """Inject NaN into loss during batch processing to test recovery mechanism."""
+        if trainer.epoch == 1 and trainer.tloss is not None and not nan_injected[0]:
+            trainer.tloss *= torch.tensor(float("nan"))
+            nan_injected[0] = True
+
+    overrides = {"data": "coco8.yaml", "model": "yolo26n.yaml", "imgsz": 32, "epochs": 3}
+    trainer = detect.DetectionTrainer(overrides=overrides)
+    trainer.add_callback("on_train_batch_end", inject_nan)
+    trainer.train()
+    assert nan_injected[0], "NaN injection failed"