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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.
101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
"""Tests for the AUPRO metric."""
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# Copyright (C) 2023-2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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import pytest
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import torch
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from anomalib.metrics.aupro import _AUPRO as AUPRO
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from .aupro_reference import calculate_au_pro
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def pytest_generate_tests(metafunc: pytest.Metafunc) -> None:
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"""Generate fixtures for the tests."""
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labels = [
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torch.tensor(
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[
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[
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[0, 0, 0, 1, 0, 0, 0],
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]
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* 400,
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],
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),
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torch.tensor(
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[
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[
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[0, 1, 0, 1, 0, 1, 0],
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]
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* 400,
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],
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),
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]
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preds = torch.arange(2800) / 2800.0
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preds = preds.view(1, 1, 400, 7)
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preds = [preds, preds]
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fpr_limit = [1 / 3, 1 / 3]
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expected_aupro = [torch.tensor(1 / 6), torch.tensor(1 / 6)]
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# Also test that per-region aupros are averaged
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labels.append(torch.cat(labels))
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preds.append(torch.cat(preds))
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fpr_limit.append(float(np.mean(fpr_limit)))
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expected_aupro.append(torch.tensor(np.mean(expected_aupro)))
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threshold_count = [
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200,
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200,
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200,
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]
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if metafunc.function is test_aupro:
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vals = list(zip(labels, preds, fpr_limit, expected_aupro, strict=True))
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metafunc.parametrize(argnames=("labels", "preds", "fpr_limit", "expected_aupro"), argvalues=vals)
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elif metafunc.function is test_binned_aupro:
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vals = list(zip(labels, preds, threshold_count, strict=True))
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metafunc.parametrize(argnames=("labels", "preds", "threshold_count"), argvalues=vals)
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def test_aupro(labels: torch.Tensor, preds: torch.Tensor, fpr_limit: float, expected_aupro: torch.Tensor) -> None:
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"""Test if the AUPRO metric is computed correctly."""
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aupro = AUPRO(fpr_limit=fpr_limit)
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aupro.update(preds, labels)
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computed_aupro = aupro.compute()
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tmp_labels = [label.squeeze().numpy() for label in labels]
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tmp_preds = [pred.squeeze().numpy() for pred in preds]
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ref_aupro = torch.tensor(calculate_au_pro(tmp_labels, tmp_preds, integration_limit=fpr_limit)[0], dtype=torch.float)
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tolerance = 0.001
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assert torch.allclose(computed_aupro, expected_aupro, atol=tolerance)
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assert torch.allclose(computed_aupro, ref_aupro, atol=tolerance)
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def test_binned_aupro(labels: torch.Tensor, preds: torch.Tensor, threshold_count: int) -> None:
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"""Test if the binned aupro is the same as the non-binned aupro."""
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aupro = AUPRO()
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computed_not_binned_aupro = aupro(preds, labels)
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binned_pro = AUPRO(num_thresholds=threshold_count)
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computed_binned_aupro = binned_pro(preds, labels)
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tolerance = 0.001
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# with threshold binning the roc curve computed within the metric is more memory efficient
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# but a bit less accurate. So we check the difference in order to validate the binning effect.
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assert computed_binned_aupro != computed_not_binned_aupro
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assert torch.allclose(computed_not_binned_aupro, computed_binned_aupro, atol=tolerance)
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# test with prediction higher than 1
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preds = preds * 2
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computed_binned_aupro = binned_pro(preds, labels)
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computed_not_binned_aupro = aupro(preds, labels)
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# with threshold binning the roc curve computed within the metric is more memory efficient
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# but a bit less accurate. So we check the difference in order to validate the binning effect.
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assert computed_binned_aupro != computed_not_binned_aupro
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assert torch.allclose(computed_not_binned_aupro, computed_binned_aupro, atol=tolerance)
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