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>
89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
"""
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Tests for ResultSerializer — JSON serialization of inference result objects.
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"""
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import dataclasses
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import pytest
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from unittest.mock import MagicMock
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from core.functions.result_handler import ResultSerializer
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# Minimal stand-ins for the SDK dataclasses (no kp import needed)
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@dataclasses.dataclass
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class FakeBoundingBox:
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x1: int = 0
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y1: int = 0
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x2: int = 100
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y2: int = 100
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class_name: str = "fire"
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score: float = 0.9
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@dataclasses.dataclass
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class FakeObjectDetectionResult:
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class_count: int = 1
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box_count: int = 1
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box_list: list = dataclasses.field(default_factory=list)
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@dataclasses.dataclass
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class FakeClassificationResult:
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probability: float = 0.85
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class_name: str = "fire"
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class_num: int = 0
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class TestResultSerializerToJson:
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def setup_method(self):
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self.serializer = ResultSerializer()
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def should_serialize_plain_dict(self):
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data = {"fps": 30.0, "pipeline_id": "p1"}
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result = self.serializer.to_json(data)
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assert '"fps"' in result
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assert "30.0" in result
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def should_serialize_dict_containing_dataclass_object(self):
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"""Bug reproduction: ObjectDetectionResult in result dict caused TypeError."""
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det = FakeObjectDetectionResult(
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class_count=1,
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box_count=1,
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box_list=[FakeBoundingBox()]
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)
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data = {"stage_results": {"stage_0": det}}
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# Should NOT raise TypeError: Object of type FakeObjectDetectionResult is not JSON serializable
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result = self.serializer.to_json(data)
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assert result is not None
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assert "stage_0" in result
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def should_serialize_dict_containing_classification_result(self):
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"""ClassificationResult must also be handled."""
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clf = FakeClassificationResult(probability=0.85, class_name="fire")
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data = {"stage_results": {"stage_0": clf}}
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result = self.serializer.to_json(data)
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assert "stage_0" in result
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def should_serialize_nested_dataclass_in_list(self):
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"""box_list inside ObjectDetectionResult contains BoundingBox dataclasses."""
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det = FakeObjectDetectionResult(
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box_count=1,
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box_list=[FakeBoundingBox(x1=10, y1=20, x2=110, y2=120, class_name="fire")]
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)
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data = {"detections": det}
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result = self.serializer.to_json(data)
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assert "fire" in result
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def should_preserve_primitive_values_unchanged(self):
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data = {"fps": 45.2, "count": 3, "name": "test", "flag": True}
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import json
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result = json.loads(self.serializer.to_json(data))
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assert result["fps"] == 45.2
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assert result["count"] == 3
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assert result["name"] == "test"
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assert result["flag"] is True
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def should_handle_none_values(self):
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data = {"result": None, "stage": "stage_0"}
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result = self.serializer.to_json(data)
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assert "null" in result
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