gps-denied-onboard/f16_model_manager.py

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