以下为 HarmonyOS 5模型剪枝压缩后准确率验证方案，包含剪枝策略验证、精度回归测试和性能对比的完整代码实现：
1. 测试架构设计
2. 模型剪枝验证
2.1 结构化剪枝验证
# pruning-validator.py
import tensorflow as tf
from tensorflow_model_optimization.sparsity import keras as sparsity
def verify_pruning(model_path: str, target_sparsity: float) -> bool:
model = tf.keras.models.load_model(model_path)
pruning_summary = sparsity.pruning_summary(model)
# 验证各层稀疏度达标
for layer in pruning_summary.layers:
if not layer.sparsity >= target_sparsity * 0.9: # 允许10%误差
return False
# 验证模型大小缩减
original_size = os.path.getsize(model_path)
pruned_size = len(tf.keras.models.save_model(model, 'temp.h5'))
return pruned_size <= original_size * (1 - target_sparsity)
2.2 量化后模型校验
// quant-validator.ets
class QuantizationValidator {
static async verify(model: Model, originalSize: number): Promise<boolean> {
const quantized = await Quantizer.quantize(model, {
type: 'int8',
calibrationData: await DataLoader.load('calibration-set')
});
const currentSize = ModelAnalyzer.getSize(quantized);
const accuracyDrop = await this.testAccuracyDrop(model, quantized);
return currentSize <= originalSize * 0.25 && // 至少压缩75%
accuracyDrop < 0.03; // 精度损失<3%
}
private static async testAccuracyDrop(original: Model, quantized: Model) {
const [origAcc, quantAcc] = await Promise.all([
AccuracyTester.test(original),
AccuracyTester.test(quantized)
]);
return origAcc - quantAcc;
}
}
3. 精度回归测试
3.1 黄金数据集测试
// golden-test.ets
class GoldenTest {
static async run(model: Model): Promise<TestResult> {
const { inputs, outputs } = await GoldenDataset.load(model.name);
const predictions = await ModelRunner.batchPredict(model, inputs);
return {
accuracy: this.calculateAccuracy(predictions, outputs),
cosineSimilarity: this.calculateSimilarity(predictions, outputs)
};
}
private static calculateAccuracy(preds: Tensor, labels: Tensor): number {
const correct = tf.equal(tf.argMax(preds, 1), tf.argMax(labels, 1));
return tf.mean(tf.cast(correct, 'float32')).dataSync()[0];
}
}
3.2 逐层特征对比
// layer-comparison.ets
class LayerwiseComparator {
static async compare(original: Model, pruned: Model, input: Tensor) {
const origFeatures = await FeatureExtractor.extractAllLayers(original, input);
const prunedFeatures = await FeatureExtractor.extractAllLayers(pruned, input);
return origFeatures.map((feat, i) => ({
layerName: feat.layerName,
similarity: cosineSimilarity(feat.output, prunedFeatures[i].output)
}));
}
}
4. 性能基准测试
4.1 推理速度对比
// speed-benchmark.ets
class InferenceBenchmark {
static async compare(original: Model, compressed: Model): Promise<BenchmarkResult> {
const testData = await DataLoader.loadTestSet();
const [origTime, compressedTime] = await Promise.all([
this.measureInferenceTime(original, testData),
this.measureInferenceTime(compressed, testData)
]);
return {
original: origTime,
compressed: compressedTime,
speedup: origTime / compressedTime
};
}
private static async measureInferenceTime(model: Model, data: Tensor[]): Promise<number> {
const start = performance.now();
await Promise.all(data.map(input => ModelRunner.predict(model, input)));
return performance.now() - start;
}
}
4.2 内存占用分析
// memory-profiler.ets
class MemoryProfiler {
static async profile(model: Model): Promise<MemoryUsage> {
const before = DeviceMemory.getUsage();
await ModelRunner.warmUp(model);
const peak = DeviceMemory.getPeakUsage();
const after = DeviceMemory.getUsage();
return {
baseline: before.model,
peakUsage: peak - before.system,
leak: after.model - before.model
};
}
}
5. 剪枝效果验证
5.1 模型结构检查
// model-inspector.ets
class ModelInspector {
static verifyPruningRate(model: Model, targetRate: number): boolean {
const layerStats = ModelAnalyzer.getLayerStats(model);
const prunedLayers = layerStats.filter(l => l.sparsity >= targetRate * 0.8);
return prunedLayers.length / layerStats.length >= 0.9; // 90%以上层达标
}
}
5.2 权重分布分析
// weight-analyzer.ets
class WeightAnalyzer {
static analyzeSparsity(model: Model): SparsityReport {
const weights = ModelAnalyzer.getWeights(model);
const zeroCount = weights.filter(w => Math.abs(w) < 1e-6).length;
return {
totalWeights: weights.length,
zeroWeights: zeroCount,
sparsity: zeroCount / weights.length,
distribution: this.buildHistogram(weights)
};
}
}
6. 完整测试流程
6.1 自动化测试套件
// test-suite.ets
describe('模型剪枝验证', () => {
let originalModel: Model;
let prunedModel: Model;
beforeAll(async () => {
originalModel = await ModelLoader.load('original.h5');
prunedModel = await ModelLoader.load('pruned.h5');
});
it('模型大小应缩减至5MB以内', () => {
expect(ModelAnalyzer.getSize(prunedModel)).toBeLessThan(5 * 1024 * 1024);
});
it('精度损失应<5%', async () => {
const originalAcc = await GoldenTest.run(originalModel);
const prunedAcc = await GoldenTest.run(prunedModel);
expect(originalAcc.accuracy - prunedAcc.accuracy).toBeLessThan(0.05);
});
it('推理速度应提升2倍以上', async () => {
const { speedup } = await InferenceBenchmark.compare(originalModel, prunedModel);
expect(speedup).toBeGreaterThan(2);
});
});
6.2 剪枝质量报告
// pruning-report.ets
class PruningReport {
static async generate(original: Model, pruned: Model) {
const [originalSize, prunedSize] = [
ModelAnalyzer.getSize(original),
ModelAnalyzer.getSize(pruned)
];
const [originalAcc, prunedAcc] = await Promise.all([
GoldenTest.run(original),
GoldenTest.run(pruned)
]);
const { speedup } = await InferenceBenchmark.compare(original, pruned);
const sparsity = WeightAnalyzer.analyzeSparsity(pruned);
return {
compressionRate: (originalSize - prunedSize) / originalSize,
accuracyDrop: originalAcc.accuracy - prunedAcc.accuracy,
speedupRatio: speedup,
sparsity: sparsity.sparsity,
layerSimilarities: await LayerwiseComparator.compare(original, pruned)
};
}
}
7. 可视化分析工具
7.1 权重分布对比
// weight-visualizer.ets
@Component
struct WeightComparison {
@Prop original: number[];
@Prop pruned: number[];
build() {
Column() {
LineChart({
title: '权重分布对比',
series: [
{ name: '原始', data: this.original },
{ name: '剪枝后', data: this.pruned }
]
})
}
}
}
7.2 精度损失热力图
// heatmap-visualizer.ets
@Component
struct AccuracyHeatmap {
@Prop similarities: LayerSimilarity[];
build() {
Grid() {
ForEach(this.similarities, item => {
GridItem() {
Text(item.layerName)
Progress({
value: item.similarity * 100,
style: {
color: this.getColor(item.similarity)
}
})
}
})
}
}
private getColor(sim: number): string {
return sim > 0.9 ? '#00ff00' :
sim > 0.7 ? '#ffff00' : '#ff0000';
}
}
8. 关键验证指标
| 指标 | 目标值 | 测量工具 |
|---|---|---|
| 模型大小 | ≤5MB | ModelAnalyzer |
| 精度损失 | ≤5% | GoldenTest |
| 推理加速比 | ≥2x | InferenceBenchmark |
| 权重稀疏度 | ≥70% | WeightAnalyzer |
9. 高级验证场景
9.1 对抗样本鲁棒性
// adversarial-test.ets
class AdversarialTest {
static async compareRobustness(original: Model, pruned: Model) {
const attacks = ['FGSM', 'PGD', 'CW'];
const results = [];
for (const attack of attacks) {
const dataset = await AdversarialLoader.load(attack);
const [origAcc, prunedAcc] = await Promise.all([
AccuracyTester.test(original, dataset),
AccuracyTester.test(pruned, dataset)
]);
results.push({
attack,
originalAcc,
prunedAcc,
drop: origAcc - prunedAcc
});
}
return results;
}
}
9.2 边缘设备部署测试
// edge-deployment.ets
class EdgeDeployTest {
static async testOnDevice(model: Model) {
const devices = await EdgeDeviceManager.getDevices();
const results = [];
for (const device of devices) {
const result = await device.deployAndTest(model);
results.push({
device: device.model,
fps: result.fps,
memory: result.memoryUsage,
temperature: result.maxTemp
});
}
return results;
}
}
10. 完整工作流示例
10.1 模型压缩验证
// compression-workflow.ets
async function validateModelCompression() {
// 1. 加载原始模型
const original = await ModelLoader.load('original.h5');
// 2. 执行剪枝
const pruned = await Pruner.prune(original, {
targetSparsity: 0.8,
preserveAccuracy: true
});
// 3. 量化压缩
const quantized = await Quantizer.quantize(pruned);
// 4. 运行测试套件
const report = await PruningReport.generate(original, quantized);
// 5. 生成可视化报告
ReportVisualizer.show(report);
return {
passed: report.accuracyDrop < 0.05 &&
report.compressionRate >= 0.95,
report
};
}
10.2 CI/CD集成
# .github/workflows/model-test.yml
jobs:
pruning-validation:
runs-on: harmonyos-latest
steps:
- uses: harmonyos/model-compression-test@v1
with:
original_model: models/original.h5
target_size: 5MB
accuracy_threshold: 0.95
- name: Upload Report
uses: actions/upload-artifact@v3
with:
name: pruning-report
path: report.html
通过本方案可实现：
- 95%+ 模型体积压缩
- 可验证 的精度损失控制
- 多维度 性能对比
- 自动化 部署验证
