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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.
59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
"""Tests for the Visualizer class."""
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# Copyright (C) 2022-2025 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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from collections.abc import Callable
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from pathlib import Path
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import numpy as np
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from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
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from torch.utils.data import DataLoader
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from anomalib.data import ImageBatch, MVTecAD, PredictDataset
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from anomalib.engine import Engine
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from anomalib.models import Padim
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from anomalib.utils.visualization.image import _ImageGrid
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def test_visualize_fully_defected_masks() -> None:
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"""Test if a fully defected anomaly mask results in a completely white image."""
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# create visualizer and add fully defected mask
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visualizer = _ImageGrid()
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mask = np.ones((256, 256)) * 255
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visualizer.add_image(image=mask, color_map="gray", title="fully defected mask")
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visualizer.generate()
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# retrieve plotted image
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canvas = FigureCanvas(visualizer.figure)
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canvas.draw()
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plotted_img = visualizer.axis.images[0].make_image(canvas.renderer)
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# assert that the plotted image is completely white
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assert np.all(plotted_img[0][..., 0] == 255)
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class TestVisualizer:
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"""Test visualization callback for test and predict with different task types."""
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@staticmethod
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def test_model_visualizer_mode(
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ckpt_path: Callable[[str], Path],
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project_path: Path,
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dataset_path: Path,
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) -> None:
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"""Test combination of model/visualizer/mode on only 1 epoch as a sanity check before merge."""
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_ckpt_path: Path = ckpt_path("Padim")
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model = Padim(evaluator=False)
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engine = Engine(
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default_root_dir=project_path,
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fast_dev_run=True,
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devices=1,
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)
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datamodule = MVTecAD(root=dataset_path / "mvtecad", category="dummy")
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engine.test(model=model, datamodule=datamodule, ckpt_path=str(_ckpt_path))
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dataset = PredictDataset(path=dataset_path / "mvtecad" / "dummy" / "test")
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datamodule = DataLoader(dataset, collate_fn=ImageBatch.collate)
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engine.predict(model=model, dataloaders=datamodule, ckpt_path=str(_ckpt_path))
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