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| import numpy as np
from typing import Tuple
class AccuracyLatencyModel:
"""
精度-延迟权衡分析模型
基于 PRISMA 综述 33 篇论文数据拟合
核心发现: 精度与延迟呈对数关系
"""
def __init__(self):
self.early_stopping_bonus = 3.0
self.quantization_penalty = 2.0
def predict_tradeoff(self, model_type: str, latency_target: float) -> dict:
"""
预测给定延迟约束下的可达精度
Args:
model_type: 模型类型
latency_target: 目标延迟 (ms)
Returns:
预期精度、参数量、可行性
"""
arch_params = {
'yolov8': {'base_acc': 98.5, 'base_lat': 50, 'alpha': 0.8},
'mobilenetv2': {'base_acc': 95.0, 'base_lat': 30, 'alpha': 0.6},
'resnet50': {'base_acc': 97.0, 'base_lat': 80, 'alpha': 1.0},
'vit': {'base_acc': 98.0, 'base_lat': 120, 'alpha': 1.2},
'cnn_lstm': {'base_acc': 96.0, 'base_lat': 90, 'alpha': 0.9},
}
if model_type not in arch_params:
model_type = 'yolov8'
p = arch_params[model_type]
if latency_target > 0:
predicted_acc = p['base_acc'] + p['alpha'] * np.log10(
max(latency_target / p['base_lat'], 0.01)
)
else:
predicted_acc = 0
predicted_acc = np.clip(predicted_acc, 50, 99.5)
return {
'model_type': model_type,
'latency_target': latency_target,
'predicted_accuracy': predicted_acc,
'feasible': predicted_acc >= 95,
'meets_production': predicted_acc >= 99 and latency_target <= 100,
}
def recommend_architecture(self, latency_budget: float,
accuracy_requirement: float) -> list:
"""
根据约束推荐架构
Args:
latency_budget: 延迟预算 (ms)
accuracy_requirement: 精度要求 (%)
"""
recommendations = []
for arch in ['yolov8', 'mobilenetv2', 'resnet50', 'vit', 'cnn_lstm']:
result = self.predict_tradeoff(arch, latency_budget)
if result['predicted_accuracy'] >= accuracy_requirement:
recommendations.append({
'architecture': arch,
'predicted_acc': result['predicted_accuracy'],
'latency': latency_budget,
'margin': result['predicted_accuracy'] - accuracy_requirement,
})
recommendations.sort(key=lambda x: x['margin'], reverse=True)
return recommendations
if __name__ == "__main__":
model = AccuracyLatencyModel()
print("=== 精度-延迟权衡分析 ===\n")
constraints = [
("IMS车规 (33fps)", 30, 99),
("IMS车规 (20fps)", 50, 97),
("研究原型", 100, 95),
("边缘部署", 15, 93),
]
for name, lat, acc in constraints:
print(f"--- {name}: 延迟<{lat}ms, 精度>{acc}% ---")
recs = model.recommend_architecture(lat, acc)
if recs:
for r in recs:
print(f" ✅ {r['architecture']}: 预测精度 {r['predicted_acc']:.1f}%")
else:
print(" ❌ 无可行架构")
print()
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