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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.
216 lines
6.8 KiB
Python
216 lines
6.8 KiB
Python
"""Test CLI entrypoints on Padim model.
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This just checks if one of the model works end-to-end. The rest of the models are checked using the API.
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"""
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# Copyright (C) 2023-2025 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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from pathlib import Path
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import pytest
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import torch
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from anomalib.cli import AnomalibCLI
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from anomalib.deploy import ExportType
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class TestCLI:
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"""Do sanity check on all models."""
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def test_fit(self, dataset_path: Path, project_path: Path) -> None:
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"""Test fit CLI.
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Args:
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dataset_path (Path): Root of the synthetic/original dataset.
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project_path (Path): Path to temporary project folder.
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"""
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AnomalibCLI(
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args=[
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"fit",
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*self._get_common_cli_args(dataset_path, project_path),
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],
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)
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torch.cuda.empty_cache()
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def test_test(self, dataset_path: Path, project_path: Path) -> None:
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"""Test the test method of the CLI.
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Args:
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dataset_path (Path): Root of the synthetic/original dataset.
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project_path (Path): Path to temporary project folder.
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"""
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AnomalibCLI(
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args=[
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"test",
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*self._get_common_cli_args(dataset_path, project_path),
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"--ckpt_path",
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f"{project_path}/Padim/MVTecAD/dummy/v0/weights/lightning/model.ckpt",
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],
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)
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torch.cuda.empty_cache()
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def test_train(self, dataset_path: Path, project_path: Path) -> None:
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"""Test the train method of the CLI.
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Args:
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dataset_path (Path): Root of the synthetic/original dataset.
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project_path (Path): Path to temporary project folder.
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"""
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AnomalibCLI(
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args=[
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"train",
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*self._get_common_cli_args(dataset_path, project_path),
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"--ckpt_path",
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f"{project_path}/Padim/MVTecAD/dummy/v0/weights/lightning/model.ckpt",
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],
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)
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torch.cuda.empty_cache()
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def test_validate(self, dataset_path: Path, project_path: Path) -> None:
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"""Test the validate method of the CLI.
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Args:
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dataset_path (Path): Root of the synthetic/original dataset.
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project_path (Path): Path to temporary project folder.
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"""
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AnomalibCLI(
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args=[
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"validate",
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*self._get_common_cli_args(dataset_path, project_path),
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"--ckpt_path",
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f"{project_path}/Padim/MVTecAD/dummy/v0/weights/lightning/model.ckpt",
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],
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)
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torch.cuda.empty_cache()
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def test_predict_with_dataloader(self, dataset_path: Path, project_path: Path) -> None:
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"""Test the predict method of the CLI.
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This test uses the MVTec AD dataloader for predict test.
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Args:
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dataset_path (Path): Root of the synthetic/original dataset.
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project_path (Path): Path to temporary project folder.
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"""
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# Test with MVTec AD Dataset
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AnomalibCLI(
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args=[
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"predict",
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*self._get_common_cli_args(
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dataset_path,
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project_path,
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),
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"--ckpt_path",
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f"{project_path}/Padim/MVTecAD/dummy/v0/weights/lightning/model.ckpt",
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],
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)
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torch.cuda.empty_cache()
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def test_predict_with_image_folder(self, project_path: Path) -> None:
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"""Test the predict method of the CLI.
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This test uses the path to image folder for predict test.
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Args:
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project_path (Path): Path to temporary project folder.
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"""
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# Test with image path
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AnomalibCLI(
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args=[
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"predict",
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"--data",
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f"{project_path}/datasets/visa_pytorch/dummy/test/bad",
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*self._get_common_cli_args(
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None,
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project_path,
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),
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"--ckpt_path",
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f"{project_path}/Padim/MVTecAD/dummy/v0/weights/lightning/model.ckpt",
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],
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)
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torch.cuda.empty_cache()
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def test_predict_with_image_path(self, project_path: Path) -> None:
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"""Test the predict method of the CLI.
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This test uses the path to image for predict test.
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Args:
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project_path (Path): Path to temporary project folder.
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"""
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# Test with image path
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AnomalibCLI(
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args=[
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"predict",
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"--data",
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f"{project_path}/datasets/visa_pytorch/dummy/test/bad/000.jpg",
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*self._get_common_cli_args(
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None,
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project_path,
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),
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"--ckpt_path",
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f"{project_path}/Padim/MVTecAD/dummy/v0/weights/lightning/model.ckpt",
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],
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)
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torch.cuda.empty_cache()
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@pytest.mark.parametrize("export_type", [ExportType.TORCH, ExportType.ONNX, ExportType.OPENVINO])
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def test_export(
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self,
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project_path: Path,
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export_type: ExportType,
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) -> None:
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"""Test the export method of the CLI.
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Args:
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dataset_path (Path): Root of the synthetic/original dataset.
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project_path (Path): Path to temporary project folder.
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export_type (ExportType): Export type.
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"""
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AnomalibCLI(
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args=[
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"export",
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"--export_type",
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export_type,
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*self._get_common_cli_args(None, project_path),
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"--ckpt_path",
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f"{project_path}/Padim/MVTecAD/dummy/v0/weights/lightning/model.ckpt",
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],
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)
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@staticmethod
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def _get_common_cli_args(dataset_path: Path | None, project_path: Path) -> list[str]:
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"""Return common CLI args for all models.
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Args:
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dataset_path (Path): Path to the dataset. If the path is None, data arguments are not appended to the args.
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project_path (Path): Path to the project folder.
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model_name (str): Name of the model. Defaults to None.
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"""
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# We need to set the predict dataloader as MVTec AD and UCSDped do not
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# have predict_dataloader attribute defined.
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if dataset_path:
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data_root = f"{dataset_path}/mvtecad"
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dataclass = "MVTecAD"
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data_args = [
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"--data",
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dataclass,
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"--data.root",
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data_root,
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"--data.category",
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"dummy",
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]
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else:
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data_args = []
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return [
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"--model",
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"Padim",
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*data_args,
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"--default_root_dir",
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str(project_path),
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"--trainer.max_epochs",
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"1",
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]
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