Initial commit

f16_model_manager.py

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+import os
+import time
+import logging
+import numpy as np
+from typing import Dict, Optional, Any, Tuple
+from pydantic import BaseModel
+from abc import ABC, abstractmethod
+
+logger = logging.getLogger(__name__)
+
+# Optional imports for hardware acceleration (graceful degradation if missing)
+try:
+    import onnxruntime as ort
+    ONNX_AVAILABLE = True
+except ImportError:
+    ONNX_AVAILABLE = False
+
+try:
+    import tensorrt as trt
+    TRT_AVAILABLE = True
+except ImportError:
+    TRT_AVAILABLE = False
+
+# --- Data Models ---
+
+class ModelConfig(BaseModel):
+    model_name: str
+    model_path: str
+    format: str
+    precision: str = "fp16"
+    warmup_iterations: int = 3
+
+class InferenceEngine(ABC):
+    model_name: str
+    format: str
+    
+    @abstractmethod
+    def infer(self, *args, **kwargs) -> Any:
+        """Unified inference interface for all models."""
+        pass
+
+# --- Interfaces ---
+
+class IModelManager(ABC):
+    @abstractmethod
+    def load_model(self, model_name: str, model_format: str, model_path: Optional[str] = None) -> bool: pass
+    
+    @abstractmethod
+    def get_inference_engine(self, model_name: str) -> Optional[InferenceEngine]: pass
+    
+    @abstractmethod
+    def optimize_to_tensorrt(self, model_name: str, onnx_path: str) -> str: pass
+    
+    @abstractmethod
+    def fallback_to_onnx(self, model_name: str, onnx_path: str) -> bool: pass
+    
+    @abstractmethod
+    def warmup_model(self, model_name: str) -> bool: pass
+
+# --- Engine Implementations ---
+
+class ONNXInferenceEngine(InferenceEngine):
+    def __init__(self, model_name: str, path: str):
+        self.model_name = model_name
+        self.format = "onnx"
+        self.path = path
+        self.session = None
+        
+        if ONNX_AVAILABLE and os.path.exists(path):
+            providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
+            self.session = ort.InferenceSession(path, providers=providers)
+        else:
+            logger.warning(f"ONNX Runtime not available or path missing for {model_name}. Using mock inference.")
+            
+    def infer(self, *args, **kwargs) -> Any:
+        if self.session:
+            # Real ONNX inference logic would map args to session.run()
+            pass
+            
+        # Mock execution for fallback / testing
+        time.sleep(0.05)  # Simulate ~50ms ONNX latency
+        return np.random.rand(1, 256).astype(np.float32)
+
+class TensorRTInferenceEngine(InferenceEngine):
+    def __init__(self, model_name: str, path: str):
+        self.model_name = model_name
+        self.format = "tensorrt"
+        self.path = path
+        self.engine = None
+        self.context = None
+        
+        if TRT_AVAILABLE and os.path.exists(path):
+            # Real TensorRT deserialization logic
+            pass
+        else:
+            logger.warning(f"TensorRT not available or path missing for {model_name}. Using mock inference.")
+            
+    def infer(self, *args, **kwargs) -> Any:
+        if self.context:
+            # Real TensorRT execution logic
+            pass
+            
+        # Mock execution for fallback / testing
+        time.sleep(0.015)  # Simulate ~15ms TensorRT latency
+        return np.random.rand(1, 256).astype(np.float32)
+
+# --- Manager Implementation ---
+
+class ModelManager(IModelManager):
+    """
+    F16: Model Manager
+    Provisions inference engines (SuperPoint, LightGlue, DINOv2, LiteSAM) and handles
+    hardware acceleration, TensorRT compilation, and ONNX fallbacks.
+    """
+    def __init__(self, models_dir: str = "./models"):
+        self.models_dir = models_dir
+        self._engines: Dict[str, InferenceEngine] = {}
+        
+        # Pre-defined mock paths/configurations
+        self.model_registry = {
+            "SuperPoint": "superpoint",
+            "LightGlue": "lightglue",
+            "DINOv2": "dinov2",
+            "LiteSAM": "litesam"
+        }
+        os.makedirs(self.models_dir, exist_ok=True)
+
+    def _get_default_path(self, model_name: str, format: str) -> str:
+        base = self.model_registry.get(model_name, model_name.lower())
+        ext = ".engine" if format == "tensorrt" else ".onnx"
+        return os.path.join(self.models_dir, f"{base}{ext}")
+
+    def load_model(self, model_name: str, model_format: str, model_path: Optional[str] = None) -> bool:
+        if model_name in self._engines and self._engines[model_name].format == model_format:
+            logger.info(f"Model {model_name} already loaded in {model_format} format. Cache hit.")
+            return True
+            
+        path = model_path or self._get_default_path(model_name, model_format)
+        
+        try:
+            if model_format == "tensorrt":
+                # Attempt TensorRT load
+                engine = TensorRTInferenceEngine(model_name, path)
+                self._engines[model_name] = engine
+                # If we lack the actual TRT file but requested it, attempt compilation or fallback
+                if not os.path.exists(path) and not TRT_AVAILABLE:
+                    raise RuntimeError("TensorRT engine file missing or TRT unavailable.")
+            elif model_format == "onnx":
+                engine = ONNXInferenceEngine(model_name, path)
+                self._engines[model_name] = engine
+            else:
+                logger.error(f"Unsupported format: {model_format}")
+                return False
+                
+            logger.info(f"Loaded {model_name} ({model_format}).")
+            self.warmup_model(model_name)
+            return True
+            
+        except Exception as e:
+            logger.warning(f"Failed to load {model_name} as {model_format}: {e}")
+            if model_format == "tensorrt":
+                onnx_path = self._get_default_path(model_name, "onnx")
+                return self.fallback_to_onnx(model_name, onnx_path)
+            return False
+
+    def get_inference_engine(self, model_name: str) -> Optional[InferenceEngine]:
+        return self._engines.get(model_name)
+
+    def optimize_to_tensorrt(self, model_name: str, onnx_path: str) -> str:
+        """Compiles ONNX to TensorRT with FP16 precision."""
+        trt_path = self._get_default_path(model_name, "tensorrt")
+        
+        if not os.path.exists(onnx_path):
+            logger.error(f"Source ONNX model not found for optimization: {onnx_path}")
+            return ""
+            
+        logger.info(f"Optimizing {model_name} to TensorRT (FP16)...")
+        if TRT_AVAILABLE:
+            # Real TRT Builder logic:
+            # builder = trt.Builder(TRT_LOGGER)
+            # config = builder.create_builder_config()
+            # config.set_flag(trt.BuilderFlag.FP16)
+            pass
+        else:
+            # Mock compilation
+            time.sleep(0.5)
+            with open(trt_path, "wb") as f:
+                f.write(b"mock_tensorrt_engine_data")
+                
+        logger.info(f"Optimization complete: {trt_path}")
+        return trt_path
+
+    def fallback_to_onnx(self, model_name: str, onnx_path: str) -> bool:
+        logger.warning(f"Falling back to ONNX for model: {model_name}")
+        engine = ONNXInferenceEngine(model_name, onnx_path)
+        self._engines[model_name] = engine
+        return True
+
+    def _create_dummy_input(self, model_name: str) -> Any:
+        if model_name == "SuperPoint":
+            return np.random.rand(480, 640).astype(np.float32)
+        elif model_name == "LightGlue":
+            return {
+                "keypoints0": np.random.rand(1, 100, 2).astype(np.float32),
+                "keypoints1": np.random.rand(1, 100, 2).astype(np.float32),
+                "descriptors0": np.random.rand(1, 100, 256).astype(np.float32),
+                "descriptors1": np.random.rand(1, 100, 256).astype(np.float32)
+            }
+        elif model_name == "DINOv2":
+            return np.random.rand(1, 3, 224, 224).astype(np.float32)
+        elif model_name == "LiteSAM":
+            return {
+                "uav_feat": np.random.rand(1, 256, 64, 64).astype(np.float32),
+                "sat_feat": np.random.rand(1, 256, 64, 64).astype(np.float32)
+            }
+        return np.random.rand(1, 3, 224, 224).astype(np.float32)
+
+    def warmup_model(self, model_name: str) -> bool:
+        engine = self.get_inference_engine(model_name)
+        if not engine:
+            logger.error(f"Cannot warmup {model_name}: Engine not loaded.")
+            return False
+            
+        logger.info(f"Warming up {model_name}...")
+        dummy_input = self._create_dummy_input(model_name)
+        
+        try:
+            for _ in range(3):
+                if isinstance(dummy_input, dict):
+                    engine.infer(**dummy_input)
+                else:
+                    engine.infer(dummy_input)
+            logger.info(f"{model_name} warmup complete.")
+            return True
+        except Exception as e:
+            logger.error(f"Warmup failed for {model_name}: {e}")
+            return False
+            
+    def initialize_models(self) -> bool:
+        """Convenience method to provision the core baseline models."""
+        models = ["SuperPoint", "LightGlue", "DINOv2", "LiteSAM"]
+        success = True
+        for m in models:
+            if not self.load_model(m, "tensorrt"):
+                success = False
+        return success