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dda5b2a367
The previous commit was meant to remove .github/workflows and removed every tracked path instead — 892 files, the whole repo. This puts back the 874 that were never in scope; the eighteen workflow files stay gone, for the reasons that commit gives. Content is byte-identical to 9d2aec9 outside .github/workflows.
95 lines
3.5 KiB
Python
95 lines
3.5 KiB
Python
"""Tests for the AnomalibMetric base class."""
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# Copyright (C) 2025 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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import torch
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from torchmetrics import Metric
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from anomalib.data import ImageBatch
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from anomalib.metrics import AnomalibMetric, create_anomalib_metric
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class DummyMetric(Metric):
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"""Dummy metric that does nothing."""
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def update(self, *args, **kwargs) -> None:
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"""Dummy update method."""
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def compute(self) -> None:
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"""Dummy compute method."""
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class TestMetricCreation:
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"""Test the creation of Anomalib metrics."""
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@staticmethod
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def test_create_anomalib_metric_function() -> None:
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"""Test if defining a metric using the function works."""
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metric_cls = create_anomalib_metric(DummyMetric)
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assert issubclass(metric_cls, AnomalibMetric)
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assert issubclass(metric_cls, Metric)
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@staticmethod
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def test_create_anomalib_metric_subclass() -> None:
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"""Test if defining a metric using a subclass works."""
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class AnomalibDummyMetric(AnomalibMetric, DummyMetric):
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pass
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assert issubclass(AnomalibDummyMetric, AnomalibMetric)
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assert issubclass(AnomalibDummyMetric, Metric)
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class TestStrictMode:
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"""Test the strict mode of Anomalib metrics."""
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@staticmethod
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def test_raises_error_on_missing_fields() -> None:
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"""Test that an error is raised when required fields are missing in strict mode."""
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metric_cls = create_anomalib_metric(DummyMetric)
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metric = metric_cls(fields=["non_existent_field"])
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batch = ImageBatch(image=torch.rand(4, 3, 10, 10)) # batch without field
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with pytest.raises(ValueError, match="instance is missing required field"):
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metric.update(batch)
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assert metric._update_count == 0 # noqa: SLF001
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assert metric.update_called is False
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@staticmethod
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def test_raises_error_when_field_is_none() -> None:
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"""Test that an error is raised when a required field is None in strict mode."""
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metric_cls = create_anomalib_metric(DummyMetric)
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metric = metric_cls(fields=["pred_score"])
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batch = ImageBatch(image=torch.rand(4, 3, 10, 10), pred_score=None) # batch where field is None
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with pytest.raises(ValueError, match="instance does not have a value for field with name"):
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metric.update(batch)
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assert metric._update_count == 0 # noqa: SLF001
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assert metric.update_called is False
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@staticmethod
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def test_no_error_on_missing_fields() -> None:
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"""Test that no error is raised when required fields are missing in non-strict mode."""
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metric_cls = create_anomalib_metric(DummyMetric)
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metric = metric_cls(fields=["pred_score"], strict=False)
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batch = ImageBatch(image=torch.rand(4, 3, 10, 10)) # batch without pred_score field
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metric.update(batch)
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assert metric.compute() is None
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assert metric._update_count == 0 # noqa: SLF001
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assert metric.update_called is False
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@staticmethod
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def test_no_error_when_field_is_none() -> None:
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"""Test that no error is raised when a required field is None in non-strict mode."""
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metric_cls = create_anomalib_metric(DummyMetric)
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metric = metric_cls(fields=["pred_score"], strict=False)
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batch = ImageBatch(image=torch.rand(4, 3, 10, 10), pred_score=None)
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metric.update(batch)
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assert metric.compute() is None
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assert metric._update_count == 0 # noqa: SLF001
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assert metric.update_called is False
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