forked from masonhuang/cluster4npu
abin
55040733fe
Phase 1 — Performance Benchmarking: - PerformanceBenchmarker: sequential vs parallel benchmark with injectable runner - PerformanceHistory: JSON-backed benchmark history with regression support - PerformanceDashboard: real-time FPS/latency display widget - BenchmarkDialog: one-click benchmark with 3-phase progress bar Phase 2 — Device Management: - DeviceManager: NPU dongle scan, assign/unassign, load balance recommendation - DeviceManagementPanel: live device status cards with auto-refresh - BottleneckAlert: dataclass for pipeline bottleneck detection Phase 3 — Advanced Features: - OptimizationEngine: 3 optimization rules (rebalance/adjust_queue/add_devices) - TemplateManager: 3 built-in pipeline templates (YOLOv5, fire detection, dual-model) Phase 4 — Report Export: - ReportExporter: PDF (reportlab, optional) and CSV export - ExportReportDialog: format selection + path picker UI 192 unit tests, all passing. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
283 lines
10 KiB
Python
283 lines
10 KiB
Python
"""
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PerformanceBenchmarker 的單元測試。
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測試策略：
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- BenchmarkConfig / BenchmarkResult 資料結構驗證
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- calculate_speedup() 純計算邏輯
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- run_sequential_benchmark() / run_parallel_benchmark() 透過注入的
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inference_runner callable 進行 Mock，不需要實際硬體
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- run_full_benchmark() 整合流程
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"""
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import time
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import pytest
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from unittest.mock import MagicMock, patch
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from core.performance.benchmarker import (
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BenchmarkConfig,
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BenchmarkResult,
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PerformanceBenchmarker,
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)
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# ---------------------------------------------------------------------------
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# 輔助：建立測試用資料結構
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# ---------------------------------------------------------------------------
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def make_config(**kwargs) -> BenchmarkConfig:
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"""建立測試用 BenchmarkConfig，提供合理的預設值。"""
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defaults = dict(
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pipeline_config=[],
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test_duration_seconds=1.0,
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warmup_frames=2,
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test_input_source="test_video.mp4",
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)
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defaults.update(kwargs)
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return BenchmarkConfig(**defaults)
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def make_result(mode: str = "sequential", fps: float = 30.0) -> BenchmarkResult:
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"""建立測試用 BenchmarkResult。"""
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avg_latency_ms = (1000.0 / fps) if fps > 0 else 0.0
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return BenchmarkResult(
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mode=mode,
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fps=fps,
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avg_latency_ms=avg_latency_ms,
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p95_latency_ms=avg_latency_ms * 1.5,
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total_frames=int(fps * 30),
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timestamp=time.time(),
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device_config={"KL520": 1},
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)
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# ---------------------------------------------------------------------------
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# 測試：BenchmarkConfig 資料結構
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# ---------------------------------------------------------------------------
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class TestBenchmarkConfig:
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def should_have_default_duration_30_seconds(self):
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"""test_duration_seconds 預設值應為 30.0。"""
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config = BenchmarkConfig(
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pipeline_config=[],
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test_input_source="video.mp4",
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)
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assert config.test_duration_seconds == 30.0
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def should_have_default_warmup_50_frames(self):
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"""warmup_frames 預設值應為 50。"""
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config = BenchmarkConfig(
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pipeline_config=[],
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test_input_source="video.mp4",
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)
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assert config.warmup_frames == 50
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def should_allow_custom_duration(self):
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"""應可自訂 test_duration_seconds。"""
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config = BenchmarkConfig(
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pipeline_config=[],
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test_input_source="video.mp4",
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test_duration_seconds=10.0,
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)
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assert config.test_duration_seconds == 10.0
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# ---------------------------------------------------------------------------
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# 測試：BenchmarkResult 資料結構
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# ---------------------------------------------------------------------------
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class TestBenchmarkResult:
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def should_store_all_required_fields(self):
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"""BenchmarkResult 應儲存所有規格要求的欄位。"""
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ts = time.time()
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result = BenchmarkResult(
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mode="parallel",
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fps=45.2,
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avg_latency_ms=22.1,
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p95_latency_ms=35.0,
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total_frames=1356,
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timestamp=ts,
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device_config={"KL720": 2},
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)
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assert result.mode == "parallel"
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assert result.fps == pytest.approx(45.2)
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assert result.avg_latency_ms == pytest.approx(22.1)
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assert result.p95_latency_ms == pytest.approx(35.0)
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assert result.total_frames == 1356
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assert result.timestamp == pytest.approx(ts)
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assert result.device_config == {"KL720": 2}
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def should_accept_sequential_mode(self):
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"""mode 欄位應接受 'sequential'。"""
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result = make_result(mode="sequential")
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assert result.mode == "sequential"
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def should_accept_parallel_mode(self):
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"""mode 欄位應接受 'parallel'。"""
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result = make_result(mode="parallel")
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assert result.mode == "parallel"
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# ---------------------------------------------------------------------------
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# 測試：calculate_speedup（純計算，無外部依賴）
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# ---------------------------------------------------------------------------
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class TestCalculateSpeedup:
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def should_return_ratio_of_parallel_to_sequential_fps(self):
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"""calculate_speedup 應回傳 par.fps / seq.fps。"""
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benchmarker = PerformanceBenchmarker()
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seq = make_result(mode="sequential", fps=20.0)
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par = make_result(mode="parallel", fps=60.0)
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speedup = benchmarker.calculate_speedup(seq, par)
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assert speedup == pytest.approx(3.0)
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def should_return_one_when_same_fps(self):
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"""相同 FPS 時 speedup 應為 1.0。"""
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benchmarker = PerformanceBenchmarker()
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result = make_result(fps=30.0)
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speedup = benchmarker.calculate_speedup(result, result)
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assert speedup == pytest.approx(1.0)
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def should_raise_when_sequential_fps_is_zero(self):
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"""seq.fps 為 0 時應引發 ValueError，避免除以零。"""
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benchmarker = PerformanceBenchmarker()
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seq = make_result(fps=0.0)
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par = make_result(fps=30.0)
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with pytest.raises(ValueError):
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benchmarker.calculate_speedup(seq, par)
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# ---------------------------------------------------------------------------
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# 測試：run_sequential_benchmark（Mock inference_runner）
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# ---------------------------------------------------------------------------
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class TestRunSequentialBenchmark:
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def should_return_benchmark_result_with_sequential_mode(self):
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"""run_sequential_benchmark() 應回傳 mode='sequential' 的 BenchmarkResult。"""
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benchmarker = PerformanceBenchmarker()
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config = make_config(warmup_frames=1, test_duration_seconds=0.1)
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# Mock inference_runner：每次呼叫模擬 10ms 推論
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def fake_runner(frame_data):
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time.sleep(0.01)
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return {"result": "ok"}
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result = benchmarker.run_sequential_benchmark(config, inference_runner=fake_runner)
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assert isinstance(result, BenchmarkResult)
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assert result.mode == "sequential"
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def should_report_positive_fps(self):
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"""FPS 應大於 0。"""
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benchmarker = PerformanceBenchmarker()
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config = make_config(warmup_frames=1, test_duration_seconds=0.1)
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def fake_runner(frame_data):
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time.sleep(0.01)
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return {}
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result = benchmarker.run_sequential_benchmark(config, inference_runner=fake_runner)
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assert result.fps > 0
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def should_report_positive_latency(self):
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"""avg_latency_ms 和 p95_latency_ms 應大於 0。"""
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benchmarker = PerformanceBenchmarker()
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config = make_config(warmup_frames=1, test_duration_seconds=0.1)
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def fake_runner(frame_data):
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time.sleep(0.01)
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return {}
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result = benchmarker.run_sequential_benchmark(config, inference_runner=fake_runner)
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assert result.avg_latency_ms > 0
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assert result.p95_latency_ms > 0
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def should_count_frames_excluding_warmup(self):
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"""total_frames 不應包含暖機幀數。"""
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benchmarker = PerformanceBenchmarker()
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call_times = []
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def fake_runner(frame_data):
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call_times.append(time.time())
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time.sleep(0.005)
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return {}
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config = make_config(warmup_frames=3, test_duration_seconds=0.1)
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result = benchmarker.run_sequential_benchmark(config, inference_runner=fake_runner)
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# warmup 幀不計入 total_frames
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assert result.total_frames < len(call_times)
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assert result.total_frames > 0
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def should_use_device_config_from_benchmarker(self):
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"""BenchmarkResult.device_config 應由 PerformanceBenchmarker 填寫。"""
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benchmarker = PerformanceBenchmarker(device_config={"KL520": 1})
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config = make_config(warmup_frames=1, test_duration_seconds=0.05)
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def fake_runner(frame_data):
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return {}
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result = benchmarker.run_sequential_benchmark(config, inference_runner=fake_runner)
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assert result.device_config == {"KL520": 1}
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# ---------------------------------------------------------------------------
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# 測試：run_parallel_benchmark（Mock inference_runner）
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# ---------------------------------------------------------------------------
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class TestRunParallelBenchmark:
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def should_return_benchmark_result_with_parallel_mode(self):
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"""run_parallel_benchmark() 應回傳 mode='parallel' 的 BenchmarkResult。"""
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benchmarker = PerformanceBenchmarker()
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config = make_config(warmup_frames=1, test_duration_seconds=0.1)
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def fake_runner(frame_data):
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time.sleep(0.01)
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return {}
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result = benchmarker.run_parallel_benchmark(config, inference_runner=fake_runner)
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assert isinstance(result, BenchmarkResult)
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assert result.mode == "parallel"
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# ---------------------------------------------------------------------------
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# 測試：run_full_benchmark
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# ---------------------------------------------------------------------------
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class TestRunFullBenchmark:
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def should_return_tuple_of_seq_par_speedup(self):
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"""run_full_benchmark() 應回傳 (BenchmarkResult, BenchmarkResult, float)。"""
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benchmarker = PerformanceBenchmarker()
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config = make_config(warmup_frames=1, test_duration_seconds=0.05)
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def fast_runner(frame_data):
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time.sleep(0.005)
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return {}
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seq_result, par_result, speedup = benchmarker.run_full_benchmark(
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config, inference_runner=fast_runner
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)
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assert isinstance(seq_result, BenchmarkResult)
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assert isinstance(par_result, BenchmarkResult)
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assert isinstance(speedup, float)
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assert seq_result.mode == "sequential"
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assert par_result.mode == "parallel"
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def should_calculate_speedup_consistently(self):
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"""speedup 應與 calculate_speedup(seq, par) 的結果一致。"""
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benchmarker = PerformanceBenchmarker()
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config = make_config(warmup_frames=1, test_duration_seconds=0.05)
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def fake_runner(frame_data):
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time.sleep(0.005)
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return {}
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seq_result, par_result, speedup = benchmarker.run_full_benchmark(
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config, inference_runner=fake_runner
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)
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expected_speedup = benchmarker.calculate_speedup(seq_result, par_result)
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assert speedup == pytest.approx(expected_speedup)
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