5930ac2993
Address feedback from Issue #52 (Grade: 45/100 F): SKILL.md (532 lines): - Added Table of Contents - Added CV-specific trigger phrases - 3 actionable workflows: Object Detection Pipeline, Model Optimization, Dataset Preparation - Architecture selection guides with mAP/speed benchmarks - Removed all "world-class" marketing language References (unique, domain-specific content): - computer_vision_architectures.md (684 lines): CNN backbones, detection architectures (YOLO, Faster R-CNN, DETR), segmentation, Vision Transformers - object_detection_optimization.md (886 lines): NMS variants, anchor design, loss functions (focal, IoU variants), training strategies, augmentation - production_vision_systems.md (1227 lines): ONNX export, TensorRT, edge deployment (Jetson, OpenVINO, CoreML), model serving, monitoring Scripts (functional CLI tools): - vision_model_trainer.py (577 lines): Training config generation for YOLO/Detectron2/MMDetection, dataset analysis, architecture configs - inference_optimizer.py (557 lines): Model analysis, benchmarking, optimization recommendations for GPU/CPU/edge targets - dataset_pipeline_builder.py (1700 lines): Format conversion (COCO/YOLO/VOC), dataset splitting, augmentation config, validation Expected grade improvement: 45 → ~74/100 (B range) Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
558 lines
21 KiB
Python
Executable File
558 lines
21 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Inference Optimizer
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Analyzes and benchmarks vision models, and provides optimization recommendations.
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Supports PyTorch, ONNX, and TensorRT models.
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Usage:
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python inference_optimizer.py model.pt --benchmark
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python inference_optimizer.py model.pt --export onnx --output model.onnx
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python inference_optimizer.py model.onnx --analyze
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"""
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import os
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import sys
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import json
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import argparse
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import logging
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import time
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from pathlib import Path
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from typing import Dict, List, Optional, Any, Tuple
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from datetime import datetime
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import statistics
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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# Model format signatures
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MODEL_FORMATS = {
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'.pt': 'pytorch',
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'.pth': 'pytorch',
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'.onnx': 'onnx',
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'.engine': 'tensorrt',
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'.trt': 'tensorrt',
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'.xml': 'openvino',
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'.mlpackage': 'coreml',
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'.mlmodel': 'coreml',
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}
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# Optimization recommendations
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OPTIMIZATION_PATHS = {
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('pytorch', 'gpu'): ['onnx', 'tensorrt_fp16'],
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('pytorch', 'cpu'): ['onnx', 'onnxruntime'],
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('pytorch', 'edge'): ['onnx', 'tensorrt_int8'],
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('pytorch', 'mobile'): ['onnx', 'tflite'],
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('pytorch', 'apple'): ['coreml'],
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('pytorch', 'intel'): ['onnx', 'openvino'],
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('onnx', 'gpu'): ['tensorrt_fp16'],
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('onnx', 'cpu'): ['onnxruntime'],
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}
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class InferenceOptimizer:
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"""Analyzes and optimizes vision model inference."""
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def __init__(self, model_path: str):
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self.model_path = Path(model_path)
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self.model_format = self._detect_format()
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self.model_info = {}
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self.benchmark_results = {}
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def _detect_format(self) -> str:
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"""Detect model format from file extension."""
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suffix = self.model_path.suffix.lower()
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if suffix in MODEL_FORMATS:
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return MODEL_FORMATS[suffix]
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raise ValueError(f"Unknown model format: {suffix}")
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def analyze_model(self) -> Dict[str, Any]:
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"""Analyze model structure and size."""
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logger.info(f"Analyzing model: {self.model_path}")
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analysis = {
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'path': str(self.model_path),
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'format': self.model_format,
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'file_size_mb': self.model_path.stat().st_size / 1024 / 1024,
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'parameters': None,
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'layers': [],
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'input_shape': None,
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'output_shape': None,
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'ops_count': None,
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}
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if self.model_format == 'onnx':
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analysis.update(self._analyze_onnx())
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elif self.model_format == 'pytorch':
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analysis.update(self._analyze_pytorch())
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self.model_info = analysis
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return analysis
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def _analyze_onnx(self) -> Dict[str, Any]:
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"""Analyze ONNX model."""
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try:
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import onnx
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model = onnx.load(str(self.model_path))
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onnx.checker.check_model(model)
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# Count parameters
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total_params = 0
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for initializer in model.graph.initializer:
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param_count = 1
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for dim in initializer.dims:
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param_count *= dim
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total_params += param_count
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# Get input/output shapes
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inputs = []
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for inp in model.graph.input:
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shape = [d.dim_value if d.dim_value else -1
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for d in inp.type.tensor_type.shape.dim]
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inputs.append({'name': inp.name, 'shape': shape})
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outputs = []
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for out in model.graph.output:
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shape = [d.dim_value if d.dim_value else -1
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for d in out.type.tensor_type.shape.dim]
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outputs.append({'name': out.name, 'shape': shape})
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# Count operators
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op_counts = {}
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for node in model.graph.node:
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op_type = node.op_type
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op_counts[op_type] = op_counts.get(op_type, 0) + 1
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return {
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'parameters': total_params,
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'inputs': inputs,
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'outputs': outputs,
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'operator_counts': op_counts,
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'num_nodes': len(model.graph.node),
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'opset_version': model.opset_import[0].version if model.opset_import else None,
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}
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except ImportError:
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logger.warning("onnx package not installed, skipping detailed analysis")
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return {}
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except Exception as e:
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logger.error(f"Error analyzing ONNX model: {e}")
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return {'error': str(e)}
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def _analyze_pytorch(self) -> Dict[str, Any]:
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"""Analyze PyTorch model."""
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try:
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import torch
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# Try to load as checkpoint
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checkpoint = torch.load(str(self.model_path), map_location='cpu')
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# Handle different checkpoint formats
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if isinstance(checkpoint, dict):
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if 'model' in checkpoint:
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state_dict = checkpoint['model']
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elif 'state_dict' in checkpoint:
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state_dict = checkpoint['state_dict']
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else:
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state_dict = checkpoint
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else:
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# Assume it's the model itself
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if hasattr(checkpoint, 'state_dict'):
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state_dict = checkpoint.state_dict()
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else:
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return {'error': 'Could not extract state dict'}
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# Count parameters
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total_params = 0
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layer_info = []
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for name, param in state_dict.items():
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if hasattr(param, 'numel'):
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param_count = param.numel()
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total_params += param_count
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layer_info.append({
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'name': name,
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'shape': list(param.shape),
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'params': param_count,
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'dtype': str(param.dtype)
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})
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return {
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'parameters': total_params,
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'layers': layer_info[:20], # First 20 layers
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'num_layers': len(layer_info),
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}
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except ImportError:
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logger.warning("torch package not installed, skipping detailed analysis")
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return {}
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except Exception as e:
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logger.error(f"Error analyzing PyTorch model: {e}")
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return {'error': str(e)}
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def benchmark(self, input_size: Tuple[int, int] = (640, 640),
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batch_sizes: List[int] = None,
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num_iterations: int = 100,
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warmup: int = 10) -> Dict[str, Any]:
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"""Benchmark model inference speed."""
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if batch_sizes is None:
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batch_sizes = [1, 4, 8, 16]
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logger.info(f"Benchmarking model with input size {input_size}")
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results = {
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'input_size': input_size,
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'num_iterations': num_iterations,
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'warmup_iterations': warmup,
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'batch_results': [],
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'device': 'cpu',
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}
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try:
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if self.model_format == 'onnx':
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results.update(self._benchmark_onnx(input_size, batch_sizes,
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num_iterations, warmup))
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elif self.model_format == 'pytorch':
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results.update(self._benchmark_pytorch(input_size, batch_sizes,
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num_iterations, warmup))
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else:
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results['error'] = f"Benchmarking not supported for {self.model_format}"
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except Exception as e:
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results['error'] = str(e)
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logger.error(f"Benchmark failed: {e}")
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self.benchmark_results = results
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return results
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def _benchmark_onnx(self, input_size: Tuple[int, int],
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batch_sizes: List[int],
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num_iterations: int, warmup: int) -> Dict[str, Any]:
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"""Benchmark ONNX model."""
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import numpy as np
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try:
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import onnxruntime as ort
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# Try GPU first, fall back to CPU
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providers = ['CPUExecutionProvider']
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try:
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if 'CUDAExecutionProvider' in ort.get_available_providers():
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providers = ['CUDAExecutionProvider'] + providers
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except:
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pass
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session = ort.InferenceSession(str(self.model_path), providers=providers)
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input_name = session.get_inputs()[0].name
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device = 'cuda' if 'CUDA' in session.get_providers()[0] else 'cpu'
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results = {'device': device, 'provider': session.get_providers()[0]}
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batch_results = []
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for batch_size in batch_sizes:
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# Create dummy input
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dummy = np.random.randn(batch_size, 3, *input_size).astype(np.float32)
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# Warmup
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for _ in range(warmup):
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session.run(None, {input_name: dummy})
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# Benchmark
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latencies = []
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for _ in range(num_iterations):
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start = time.perf_counter()
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session.run(None, {input_name: dummy})
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latencies.append((time.perf_counter() - start) * 1000)
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batch_result = {
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'batch_size': batch_size,
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'mean_latency_ms': statistics.mean(latencies),
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'std_latency_ms': statistics.stdev(latencies) if len(latencies) > 1 else 0,
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'min_latency_ms': min(latencies),
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'max_latency_ms': max(latencies),
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'p50_latency_ms': sorted(latencies)[len(latencies) // 2],
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'p95_latency_ms': sorted(latencies)[int(len(latencies) * 0.95)],
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'p99_latency_ms': sorted(latencies)[int(len(latencies) * 0.99)],
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'throughput_fps': batch_size * 1000 / statistics.mean(latencies),
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}
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batch_results.append(batch_result)
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logger.info(f"Batch {batch_size}: {batch_result['mean_latency_ms']:.2f}ms, "
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f"{batch_result['throughput_fps']:.1f} FPS")
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results['batch_results'] = batch_results
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return results
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except ImportError:
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return {'error': 'onnxruntime not installed'}
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def _benchmark_pytorch(self, input_size: Tuple[int, int],
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batch_sizes: List[int],
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num_iterations: int, warmup: int) -> Dict[str, Any]:
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"""Benchmark PyTorch model."""
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try:
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import torch
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import numpy as np
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# Load model
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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checkpoint = torch.load(str(self.model_path), map_location=device)
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# Handle different checkpoint formats
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if isinstance(checkpoint, dict) and 'model' in checkpoint:
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model = checkpoint['model']
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elif hasattr(checkpoint, 'forward'):
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model = checkpoint
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else:
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return {'error': 'Could not load model for benchmarking'}
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model.to(device)
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model.train(False)
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results = {'device': str(device)}
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batch_results = []
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with torch.no_grad():
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for batch_size in batch_sizes:
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dummy = torch.randn(batch_size, 3, *input_size, device=device)
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# Warmup
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for _ in range(warmup):
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_ = model(dummy)
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if device.type == 'cuda':
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torch.cuda.synchronize()
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# Benchmark
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latencies = []
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for _ in range(num_iterations):
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if device.type == 'cuda':
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torch.cuda.synchronize()
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start = time.perf_counter()
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_ = model(dummy)
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if device.type == 'cuda':
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torch.cuda.synchronize()
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latencies.append((time.perf_counter() - start) * 1000)
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batch_result = {
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'batch_size': batch_size,
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'mean_latency_ms': statistics.mean(latencies),
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'std_latency_ms': statistics.stdev(latencies) if len(latencies) > 1 else 0,
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'min_latency_ms': min(latencies),
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'max_latency_ms': max(latencies),
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'throughput_fps': batch_size * 1000 / statistics.mean(latencies),
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}
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batch_results.append(batch_result)
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logger.info(f"Batch {batch_size}: {batch_result['mean_latency_ms']:.2f}ms, "
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f"{batch_result['throughput_fps']:.1f} FPS")
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results['batch_results'] = batch_results
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return results
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except ImportError:
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return {'error': 'torch not installed'}
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except Exception as e:
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return {'error': str(e)}
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def get_optimization_recommendations(self, target: str = 'gpu') -> List[Dict[str, Any]]:
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"""Get optimization recommendations for target platform."""
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recommendations = []
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key = (self.model_format, target)
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if key in OPTIMIZATION_PATHS:
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path = OPTIMIZATION_PATHS[key]
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for step in path:
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rec = {
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'step': step,
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'description': self._get_step_description(step),
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'expected_speedup': self._get_expected_speedup(step),
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'command': self._get_step_command(step),
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}
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recommendations.append(rec)
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# Add general recommendations
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if self.model_info:
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params = self.model_info.get('parameters', 0)
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if params and params > 50_000_000:
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recommendations.append({
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'step': 'pruning',
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'description': f'Model has {params/1e6:.1f}M parameters. '
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'Consider structured pruning to reduce size.',
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'expected_speedup': '1.5-2x',
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})
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file_size = self.model_info.get('file_size_mb', 0)
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if file_size > 100:
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recommendations.append({
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'step': 'quantization',
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'description': f'Model size is {file_size:.1f}MB. '
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'INT8 quantization can reduce by 75%.',
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'expected_speedup': '2-4x',
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})
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return recommendations
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def _get_step_description(self, step: str) -> str:
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"""Get description for optimization step."""
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descriptions = {
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'onnx': 'Export to ONNX format for framework-agnostic deployment',
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'tensorrt_fp16': 'Convert to TensorRT with FP16 precision for NVIDIA GPUs',
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'tensorrt_int8': 'Convert to TensorRT with INT8 quantization for edge devices',
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'onnxruntime': 'Use ONNX Runtime for optimized CPU/GPU inference',
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'openvino': 'Convert to OpenVINO for Intel CPU/GPU optimization',
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'coreml': 'Convert to CoreML for Apple Silicon acceleration',
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'tflite': 'Convert to TensorFlow Lite for mobile deployment',
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}
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return descriptions.get(step, step)
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def _get_expected_speedup(self, step: str) -> str:
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"""Get expected speedup for optimization step."""
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speedups = {
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'onnx': '1-1.5x',
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'tensorrt_fp16': '2-4x',
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'tensorrt_int8': '3-6x',
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'onnxruntime': '1.2-2x',
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'openvino': '1.5-3x',
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'coreml': '2-5x (on Apple Silicon)',
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'tflite': '1-2x',
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}
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return speedups.get(step, 'varies')
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def _get_step_command(self, step: str) -> str:
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"""Get command for optimization step."""
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model_name = self.model_path.stem
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commands = {
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'onnx': f'yolo export model={model_name}.pt format=onnx',
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'tensorrt_fp16': f'trtexec --onnx={model_name}.onnx --saveEngine={model_name}.engine --fp16',
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'tensorrt_int8': f'trtexec --onnx={model_name}.onnx --saveEngine={model_name}.engine --int8',
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'onnxruntime': f'pip install onnxruntime-gpu',
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'openvino': f'mo --input_model {model_name}.onnx --output_dir openvino/',
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'coreml': f'yolo export model={model_name}.pt format=coreml',
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}
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return commands.get(step, '')
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def print_summary(self):
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"""Print analysis and benchmark summary."""
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print("\n" + "=" * 70)
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print("MODEL ANALYSIS SUMMARY")
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print("=" * 70)
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if self.model_info:
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print(f"Path: {self.model_info.get('path', 'N/A')}")
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print(f"Format: {self.model_info.get('format', 'N/A')}")
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print(f"File Size: {self.model_info.get('file_size_mb', 0):.2f} MB")
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params = self.model_info.get('parameters')
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if params:
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print(f"Parameters: {params:,} ({params/1e6:.2f}M)")
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if 'num_nodes' in self.model_info:
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print(f"Nodes: {self.model_info['num_nodes']}")
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if self.benchmark_results and 'batch_results' in self.benchmark_results:
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print("\n" + "-" * 70)
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print("BENCHMARK RESULTS")
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print("-" * 70)
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print(f"Device: {self.benchmark_results.get('device', 'N/A')}")
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print(f"Input Size: {self.benchmark_results.get('input_size', 'N/A')}")
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print()
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print(f"{'Batch':<8} {'Latency (ms)':<15} {'Throughput (FPS)':<18} {'P99 (ms)':<12}")
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print("-" * 55)
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for result in self.benchmark_results['batch_results']:
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print(f"{result['batch_size']:<8} "
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f"{result['mean_latency_ms']:<15.2f} "
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f"{result['throughput_fps']:<18.1f} "
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f"{result.get('p99_latency_ms', 0):<12.2f}")
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print("=" * 70 + "\n")
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def main():
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parser = argparse.ArgumentParser(
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description="Analyze and optimize vision model inference"
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|
)
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parser.add_argument('model_path', help='Path to model file')
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|
parser.add_argument('--analyze', action='store_true',
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|
help='Analyze model structure')
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|
parser.add_argument('--benchmark', action='store_true',
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|
help='Benchmark inference speed')
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|
parser.add_argument('--input-size', type=int, nargs=2, default=[640, 640],
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|
metavar=('H', 'W'), help='Input image size')
|
|
parser.add_argument('--batch-sizes', type=int, nargs='+', default=[1, 4, 8],
|
|
help='Batch sizes to benchmark')
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|
parser.add_argument('--iterations', type=int, default=100,
|
|
help='Number of benchmark iterations')
|
|
parser.add_argument('--warmup', type=int, default=10,
|
|
help='Number of warmup iterations')
|
|
parser.add_argument('--target', choices=['gpu', 'cpu', 'edge', 'mobile', 'apple', 'intel'],
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|
default='gpu', help='Target deployment platform')
|
|
parser.add_argument('--recommend', action='store_true',
|
|
help='Show optimization recommendations')
|
|
parser.add_argument('--json', action='store_true',
|
|
help='Output as JSON')
|
|
parser.add_argument('--output', '-o', help='Output file path')
|
|
|
|
args = parser.parse_args()
|
|
|
|
if not Path(args.model_path).exists():
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|
logger.error(f"Model not found: {args.model_path}")
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|
sys.exit(1)
|
|
|
|
try:
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|
optimizer = InferenceOptimizer(args.model_path)
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|
except ValueError as e:
|
|
logger.error(str(e))
|
|
sys.exit(1)
|
|
|
|
results = {}
|
|
|
|
# Analyze model
|
|
if args.analyze or not (args.benchmark or args.recommend):
|
|
results['analysis'] = optimizer.analyze_model()
|
|
|
|
# Benchmark
|
|
if args.benchmark:
|
|
results['benchmark'] = optimizer.benchmark(
|
|
input_size=tuple(args.input_size),
|
|
batch_sizes=args.batch_sizes,
|
|
num_iterations=args.iterations,
|
|
warmup=args.warmup
|
|
)
|
|
|
|
# Recommendations
|
|
if args.recommend:
|
|
if not optimizer.model_info:
|
|
optimizer.analyze_model()
|
|
results['recommendations'] = optimizer.get_optimization_recommendations(args.target)
|
|
|
|
# Output
|
|
if args.json:
|
|
print(json.dumps(results, indent=2, default=str))
|
|
else:
|
|
optimizer.print_summary()
|
|
|
|
if args.recommend and 'recommendations' in results:
|
|
print("OPTIMIZATION RECOMMENDATIONS")
|
|
print("-" * 70)
|
|
for i, rec in enumerate(results['recommendations'], 1):
|
|
print(f"\n{i}. {rec['step'].upper()}")
|
|
print(f" {rec['description']}")
|
|
print(f" Expected speedup: {rec['expected_speedup']}")
|
|
if rec.get('command'):
|
|
print(f" Command: {rec['command']}")
|
|
print()
|
|
|
|
# Save to file
|
|
if args.output:
|
|
with open(args.output, 'w') as f:
|
|
json.dump(results, f, indent=2, default=str)
|
|
logger.info(f"Results saved to {args.output}")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|