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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.
109 lines
5.0 KiB
Python
109 lines
5.0 KiB
Python
"""Test the PostProcessor 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 anomalib.data import ImageBatch
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from anomalib.post_processing import PostProcessor
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class TestPostProcessor:
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"""Test the PreProcessor class."""
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@staticmethod
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@pytest.mark.parametrize(
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("preds", "min_val", "max_val", "thresh", "target"),
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[
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(torch.tensor([20, 40, 60, 80]), 0, 100, 50, torch.tensor([0.2, 0.4, 0.6, 0.8])),
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(torch.tensor([20, 40, 60, 80]), 0, 100, 40, torch.tensor([0.3, 0.5, 0.7, 0.9])), # lower threshold
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(torch.tensor([20, 40, 60, 80]), 0, 100, 60, torch.tensor([0.1, 0.3, 0.5, 0.7])), # higher threshold
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(torch.tensor([0, 40, 80, 120]), 20, 100, 50, torch.tensor([0.0, 0.375, 0.875, 1.0])), # out of bounds
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(torch.tensor([-80, -60, -40, -20]), -100, 0, -50, torch.tensor([0.2, 0.4, 0.6, 0.8])), # negative values
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(torch.tensor([20, 40, 60, 80]), 0, 100, -50, torch.tensor([1.0, 1.0, 1.0, 1.0])), # threshold below range
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(torch.tensor([20, 40, 60, 80]), 0, 100, 150, torch.tensor([0.0, 0.0, 0.0, 0.0])), # threshold above range
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(torch.tensor([20, 40, 60, 80]), 50, 50, 50, torch.tensor([0.0, 0.0, 1.0, 1.0])), # all same
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(torch.tensor(60), 0, 100, 50, torch.tensor(0.6)), # scalar tensor
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(torch.tensor([[20, 40], [60, 80]]), 0, 100, 50, torch.tensor([[0.2, 0.4], [0.6, 0.8]])), # 2D tensor
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],
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)
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def test_normalize(
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preds: torch.Tensor,
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min_val: float,
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max_val: float,
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thresh: float,
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target: torch.Tensor,
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) -> None:
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"""Test the normalize method."""
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pre_processor = PostProcessor()
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normalized = pre_processor._normalize( # noqa: SLF001
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preds,
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torch.tensor(min_val),
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torch.tensor(max_val),
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torch.tensor(thresh),
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)
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assert torch.allclose(normalized, target)
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@staticmethod
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@pytest.mark.parametrize(
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("preds", "thresh", "target"),
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[
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(torch.tensor(20), 50, torch.tensor(0).bool()), # test scalar
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(torch.tensor([20, 40, 60, 80]), 50, torch.tensor([0, 0, 1, 1]).bool()), # test 1d tensor
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(torch.tensor([[20, 40], [60, 80]]), 50, torch.tensor([[0, 0], [1, 1]]).bool()), # test 2d tensor
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(torch.tensor(50), 50, torch.tensor(0).bool()), # test on threshold labeled as normal
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(torch.tensor([-80, -60, -40, -20]), -50, torch.tensor([0, 0, 1, 1]).bool()), # test negative
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],
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)
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def test_apply_threshold(preds: torch.Tensor, thresh: float, target: torch.Tensor) -> None:
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"""Test the apply_threshold method."""
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pre_processor = PostProcessor()
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binary_preds = pre_processor._apply_threshold(preds, torch.tensor(thresh)) # noqa: SLF001
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assert torch.allclose(binary_preds, target)
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@staticmethod
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def test_thresholds_computed() -> None:
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"""Test that both image and pixel threshold are computed correctly."""
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batch = ImageBatch(
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image=torch.rand(4, 3, 3, 3),
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anomaly_map=torch.tensor([[10, 20, 30], [40, 50, 60], [70, 80, 90]]),
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gt_mask=torch.tensor([[0, 0, 0], [0, 0, 0], [0, 1, 1]]),
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pred_score=torch.tensor([20, 40, 60, 80]),
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gt_label=torch.tensor([0, 0, 1, 1]),
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)
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pre_processor = PostProcessor()
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pre_processor.on_validation_batch_end(None, None, batch)
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pre_processor.on_validation_epoch_end(None, None)
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assert pre_processor.image_threshold == 60
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assert pre_processor.pixel_threshold == 80
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@staticmethod
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def test_pixel_threshold_matching() -> None:
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"""Test that pixel_threshold is used as image threshold when no gt masks are available."""
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batch = ImageBatch(
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image=torch.rand(4, 3, 10, 10),
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anomaly_map=torch.rand(4, 10, 10),
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pred_score=torch.tensor([20, 40, 60, 80]),
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gt_label=torch.tensor([0, 0, 1, 1]),
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)
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pre_processor = PostProcessor(enable_threshold_matching=True)
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pre_processor.on_validation_batch_end(None, None, batch)
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pre_processor.on_validation_epoch_end(None, None)
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assert pre_processor.image_threshold == pre_processor.pixel_threshold
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@staticmethod
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def test_image_threshold_matching() -> None:
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"""Test that pixel_threshold is used as image threshold when no gt masks are available."""
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batch = ImageBatch(
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image=torch.rand(4, 3, 3, 3),
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anomaly_map=torch.tensor([[10, 20, 30], [40, 50, 60], [70, 80, 90]]),
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gt_mask=torch.tensor([[0, 0, 0], [0, 0, 0], [0, 1, 1]]),
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pred_score=torch.tensor([20, 40, 60, 80]),
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)
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pre_processor = PostProcessor(enable_threshold_matching=True)
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pre_processor.on_validation_batch_end(None, None, batch)
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pre_processor.on_validation_epoch_end(None, None)
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assert pre_processor.image_threshold == pre_processor.pixel_threshold
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