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