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| import numpy as np
from dataclasses import dataclass
from typing import Tuple, List
import time
"""
IMS 边缘 AI 量化部署管线
参考 Liquid AI QAD + MIT/Intel 协同设计
策略:
1. FP32 -> INT8 量化（通用，2-4x 加速）
2. FP32 -> INT4 量化（激进，4-8x 压缩）
3. 量化感知蒸馏（恢复精度）
4. 算法-硬件协同优化
"""
@dataclass
class QuantizationResult:
"""量化结果"""
original_size_mb: float
quantized_size_mb: float
compression_ratio: float
original_accuracy: float
quantized_accuracy: float
accuracy_retention: float
inference_time_ms: float
speedup: float
class IMSModelQuantizer:
"""IMS 模型量化器"""
MODELS = {
'face_det': {'name': 'YOLOv8s-face', 'params': 11.2, 'fp32_mb': 44.8,
'task': '人脸检测', 'latency_fp32_ms': 12},
'landmark': {'name': 'PFLD-98pt', 'params': 1.8, 'fp32_mb': 7.2,
'task': '关键点', 'latency_fp32_ms': 5},
'gaze': {'name': 'Gaze360', 'params': 23.4, 'fp32_mb': 93.6,
'task': '视线估计', 'latency_fp32_ms': 15},
'fatigue': {'name': 'PERCLOS-LSTM', 'params': 0.5, 'fp32_mb': 2.0,
'task': '疲劳评估', 'latency_fp32_ms': 2},
'cpd_radar': {'name': 'PointNet-CPD', 'params': 3.6, 'fp32_mb': 14.4,
'task': 'CPD雷达', 'latency_fp32_ms': 4},
'occupant': {'name': 'YOLOv8n-occupant', 'params': 3.2, 'fp32_mb': 12.8,
'task': '乘员检测', 'latency_fp32_ms': 8},
}
def __init__(self, target_hardware: str = 'qualcomm_8255'):
"""
Args:
target_hardware: 目标硬件
- qualcomm_8255: QCS8255 Hexagon NPU 26 TOPS
- jetson_orin: Jetson Orin NX 100 TOPS
- intel_core: Intel Core Ultra NPU 11 TOPS
"""
self.hardware = target_hardware
self.hardware_config = self._get_hw_config(target_hardware)
def _get_hw_config(self, hw: str) -> dict:
configs = {
'qualcomm_8255': {
'name': 'QCS8255', 'tops': 26, 'npu': 'Hexagon',
'preferred_quant': 'INT8', 'memory_mb': 8192,
'power_w': 10, ' bandwidth_gbps': 25.6
},
'jetson_orin': {
'name': 'Jetson Orin NX', 'tops': 100, 'npu': 'Tensor Core',
'preferred_quant': 'INT8', 'memory_mb': 16384,
'power_w': 25, 'bandwidth_gbps': 102.4
},
'intel_core': {
'name': 'Intel Core Ultra', 'tops': 33, 'npu': 'Intel AI Boost',
'preferred_quant': 'INT8', 'memory_mb': 16384,
'power_w': 28, 'bandwidth_gbps': 51.2
}
}
return configs.get(hw, configs['qualcomm_8255'])
def quantize_int8(self, model_key: str) -> QuantizationResult:
"""INT8 量化"""
m = self.MODELS[model_key]
quant_size = m['fp32_mb'] / 4
acc_retention = np.random.uniform(0.96, 0.99)
orig_acc = np.random.uniform(0.88, 0.95)
quant_acc = orig_acc * acc_retention
speedup = np.random.uniform(2.5, 4.0)
quant_latency = m['latency_fp32_ms'] / speedup
return QuantizationResult(
original_size_mb=m['fp32_mb'],
quantized_size_mb=round(quant_size, 1),
compression_ratio=4.0,
original_accuracy=round(orig_acc, 4),
quantized_accuracy=round(quant_acc, 4),
accuracy_retention=round(acc_retention, 4),
inference_time_ms=round(quant_latency, 2),
speedup=round(speedup, 2)
)
def quantize_int4(self, model_key: str, use_qad: bool = True) -> QuantizationResult:
"""INT4 量化（+量化感知蒸馏）"""
m = self.MODELS[model_key]
quant_size = m['fp32_mb'] / 8
acc_retention = 0.97 if use_qad else 0.85
orig_acc = np.random.uniform(0.88, 0.95)
quant_acc = orig_acc * acc_retention
speedup = np.random.uniform(3.0, 6.0)
quant_latency = m['latency_fp32_ms'] / speedup
return QuantizationResult(
original_size_mb=m['fp32_mb'],
quantized_size_mb=round(quant_size, 1),
compression_ratio=8.0,
original_accuracy=round(orig_acc, 4),
quantized_accuracy=round(quant_acc, 4),
accuracy_retention=round(acc_retention, 4),
inference_time_ms=round(quant_latency, 2),
speedup=round(speedup, 2)
)
def optimize_pipeline(self, models: List[str],
quant_scheme: str = 'mixed') -> dict:
"""
优化整个推理管线
Args:
models: 模型列表
quant_scheme: 'int8' / 'int4' / 'mixed'
"""
results = {}
total_fp32 = 0
total_quant = 0
total_latency_fp32 = 0
total_latency_quant = 0
for mk in models:
m = self.MODELS[mk]
if quant_scheme == 'int8':
r = self.quantize_int8(mk)
elif quant_scheme == 'int4':
r = self.quantize_int4(mk, use_qad=True)
else:
if m['fp32_mb'] > 20:
r = self.quantize_int4(mk, use_qad=True)
else:
r = self.quantize_int8(mk)
results[mk] = r
total_fp32 += m['fp32_mb']
total_quant += r.quantized_size_mb
total_latency_fp32 += m['latency_fp32_ms']
total_latency_quant += r.inference_time_ms
total_tops_needed = total_latency_quant * 1e-3 * \
self.hardware_config['tops'] / \
max(total_latency_quant, 0.1)
return {
'hardware': self.hardware_config['name'],
'scheme': quant_scheme,
'per_model': results,
'total_fp32_mb': round(total_fp32, 1),
'total_quant_mb': round(total_quant, 1),
'total_compression': round(total_fp32 / max(total_quant, 0.1), 2),
'total_latency_fp32_ms': round(total_latency_fp32, 2),
'total_latency_quant_ms': round(total_latency_quant, 2),
'pipeline_fps': round(1000 / total_latency_quant, 1),
'memory_fit': total_quant < self.hardware_config['memory_mb'],
'power_budget': self.hardware_config['power_w'],
}
if __name__ == "__main__":
np.random.seed(42)
print("=" * 75)
print("IMS 边缘 AI 量化部署管线测试")
print("=" * 75)
for hw in ['qualcomm_8255', 'jetson_orin', 'intel_core']:
quantizer = IMSModelQuantizer(target_hardware=hw)
cfg = quantizer.hardware_config
print(f"\n{'='*75}")
print(f"硬件: {cfg['name']} | {cfg['tops']} TOPS | "
f"{cfg['memory_mb']}MB | {cfg['power_w']}W")
print(f"{'='*75}")
pipeline = ['face_det', 'landmark', 'gaze',
'fatigue', 'cpd_radar', 'occupant']
for scheme in ['int8', 'int4', 'mixed']:
result = quantizer.optimize_pipeline(pipeline, scheme)
print(f"\n 方案: {scheme.upper()}")
print(f" {'模型':<20} {'FP32(MB)':>10} {'量化(MB)':>10} "
f"{'精度':>8} {'延迟(ms)':>10} {'加速':>6}")
print(f" {'-'*65}")
for mk, r in result['per_model'].items():
m = IMSModelQuantizer.MODELS[mk]
print(f" {m['name']:<20} {r.original_size_mb:>10.1f} "
f"{r.quantized_size_mb:>10.1f} "
f"{r.accuracy_retention*100:>7.1f}% "
f"{r.inference_time_ms:>10.2f} "
f"{r.speedup:>5.2f}x")
print(f" {'-'*65}")
print(f" {'总计':<20} {result['total_fp32_mb']:>10.1f} "
f"{result['total_quant_mb']:>10.1f} "
f"{'':>8} "
f"{result['total_latency_quant_ms']:>10.2f} ")
print(f" 压缩比: {result['total_compression']}x | "
f"管线FPS: {result['pipeline_fps']} | "
f"内存适配: {'✅' if result['memory_fit'] else '❌'}")
print(f"\n{'='*75}")
print("与前沿对比:")
print(f" Liquid AI QAD: INT4 恢复 97% BF16 精度")
print(f" MIT/Intel: 95.24% 准确率，90% 算力节省")
print(f" IMS 模拟: INT4+QAD 97%, INT8 96-99%")
print(f"{'='*75}")
|