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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.
68 lines
2.3 KiB
Python
68 lines
2.3 KiB
Python
"""Test MinMax metric."""
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# Copyright (C) 2025 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import torch
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from anomalib.metrics.min_max import _MinMax # Assuming the metric is part of `anomalib`
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def test_initialization() -> None:
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"""Test if the metric initializes with correct default values."""
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metric = _MinMax()
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assert torch.isinf(metric.min), "Initial min should be positive infinity."
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assert metric.min > 0, "Initial min should be positive infinity."
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assert torch.isinf(metric.max), "Initial max should be negative infinity."
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assert metric.max < 0, "Initial max should be negative infinity."
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def test_update_single_batch() -> None:
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"""Test updating the metric with a single batch."""
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metric = _MinMax()
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batch = torch.tensor([1.0, 2.0, 3.0, -1.0])
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metric.update(batch)
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assert metric.min.item() == -1.0, "Min should be -1.0 after single batch update."
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assert metric.max.item() == 3.0, "Max should be 3.0 after single batch update."
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def test_update_multiple_batches() -> None:
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"""Test updating the metric with multiple batches."""
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metric = _MinMax()
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batch1 = torch.tensor([0.5, 1.5, 3.0])
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batch2 = torch.tensor([-0.5, 0.0, 2.5])
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metric.update(batch1)
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metric.update(batch2)
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assert metric.min.item() == -0.5, "Min should be -0.5 after multiple batch updates."
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assert metric.max.item() == 3.0, "Max should be 3.0 after multiple batch updates."
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def test_compute() -> None:
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"""Test computation of the min and max values after updates."""
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metric = _MinMax()
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batch1 = torch.tensor([1.0, 2.0])
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batch2 = torch.tensor([-1.0, 0.0])
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metric.update(batch1)
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metric.update(batch2)
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min_val, max_val = metric.compute()
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assert min_val.item() == -1.0, "Computed min should be -1.0."
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assert max_val.item() == 2.0, "Computed max should be 2.0."
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def test_no_updates() -> None:
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"""Test behavior when no updates are made to the metric."""
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metric = _MinMax()
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min_val, max_val = metric.compute()
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assert torch.isinf(min_val), "Min should remain positive infinity with no updates."
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assert min_val > 0, "Min should remain positive infinity with no updates."
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assert torch.isinf(max_val), "Max should remain negative infinity with no updates."
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assert max_val < 0, "Max should remain negative infinity with no updates."
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