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test_models.py
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test_models.py
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"""Test API.
Tests the models using API. The weight paths from the trained models are used for the rest of the tests.
"""
# Copyright (C) 2023-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
from pathlib import Path
import pytest
from anomalib import TaskType
from anomalib.data import AnomalibDataModule, MVTec
from anomalib.deploy.export import ExportType
from anomalib.engine import Engine
from anomalib.models import AnomalyModule, get_available_models, get_model
def models() -> set[str]:
"""Return all available models."""
return get_available_models()
def export_types() -> list[ExportType]:
"""Return all available export frameworks."""
return list(ExportType)
class TestAPI:
"""Do sanity check on all models."""
@pytest.mark.parametrize("model_name", models())
def test_fit(self, model_name: str, dataset_path: Path, project_path: Path) -> None:
"""Fit the model and save checkpoint.
Args:
model_name (str): Name of the model.
dataset_path (Path): Root to dataset from fixture.
project_path (Path): Path to temporary project folder from fixture.
"""
model, dataset, engine = self._get_objects(
model_name=model_name,
dataset_path=dataset_path,
project_path=project_path,
)
engine.fit(model=model, datamodule=dataset)
@pytest.mark.parametrize("model_name", models())
def test_test(self, model_name: str, dataset_path: Path, project_path: Path) -> None:
"""Test model from checkpoint.
Args:
model_name (str): Name of the model.
dataset_path (Path): Root to dataset from fixture.
project_path (Path): Path to temporary project folder from fixture.
"""
model, dataset, engine = self._get_objects(
model_name=model_name,
dataset_path=dataset_path,
project_path=project_path,
)
engine.test(
model=model,
datamodule=dataset,
ckpt_path=f"{project_path}/{model.name}/{dataset.name}/dummy/v0/weights/lightning/model.ckpt",
)
@pytest.mark.parametrize("model_name", models())
def test_train(self, model_name: str, dataset_path: Path, project_path: Path) -> None:
"""Train model from checkpoint.
Args:
model_name (str): Name of the model.
dataset_path (Path): Root to dataset from fixture.
project_path (Path): Path to temporary project folder from fixture.
"""
model, dataset, engine = self._get_objects(
model_name=model_name,
dataset_path=dataset_path,
project_path=project_path,
)
engine.train(
model=model,
datamodule=dataset,
ckpt_path=f"{project_path}/{model.name}/{dataset.name}/dummy/v0/weights/lightning/model.ckpt",
)
@pytest.mark.parametrize("model_name", models())
def test_validate(self, model_name: str, dataset_path: Path, project_path: Path) -> None:
"""Validate model from checkpoint.
Args:
model_name (str): Name of the model.
dataset_path (Path): Root to dataset from fixture.
project_path (Path): Path to temporary project folder from fixture.
"""
model, dataset, engine = self._get_objects(
model_name=model_name,
dataset_path=dataset_path,
project_path=project_path,
)
engine.validate(
model=model,
datamodule=dataset,
ckpt_path=f"{project_path}/{model.name}/{dataset.name}/dummy/v0/weights/lightning/model.ckpt",
)
@pytest.mark.parametrize("model_name", models())
def test_predict(self, model_name: str, dataset_path: Path, project_path: Path) -> None:
"""Predict using model from checkpoint.
Args:
model_name (str): Name of the model.
dataset_path (Path): Root to dataset from fixture.
project_path (Path): Path to temporary project folder from fixture.
"""
model, datamodule, engine = self._get_objects(
model_name=model_name,
dataset_path=dataset_path,
project_path=project_path,
)
engine.predict(
model=model,
ckpt_path=f"{project_path}/{model.name}/{datamodule.name}/dummy/v0/weights/lightning/model.ckpt",
datamodule=datamodule,
)
@pytest.mark.parametrize("model_name", models())
@pytest.mark.parametrize("export_type", export_types())
def test_export(
self,
model_name: str,
export_type: ExportType,
dataset_path: Path,
project_path: Path,
) -> None:
"""Export model from checkpoint.
Args:
model_name (str): Name of the model.
export_type (ExportType): Framework to export to.
dataset_path (Path): Root to dataset from fixture.
project_path (Path): Path to temporary project folder from fixture.
"""
if model_name in ("reverse_distillation", "rkde"):
# TODO(ashwinvaidya17): Restore this test after fixing the issue
# https://github.com/openvinotoolkit/anomalib/issues/1513
pytest.skip(f"{model_name} fails to convert to ONNX and OpenVINO")
model, dataset, engine = self._get_objects(
model_name=model_name,
dataset_path=dataset_path,
project_path=project_path,
)
engine.export(
model=model,
ckpt_path=f"{project_path}/{model.name}/{dataset.name}/dummy/v0/weights/lightning/model.ckpt",
export_type=export_type,
)
def _get_objects(
self,
model_name: str,
dataset_path: Path,
project_path: Path,
) -> tuple[AnomalyModule, AnomalibDataModule, Engine]:
"""Return model, dataset, and engine objects.
Args:
model_name (str): Name of the model to train
dataset_path (Path): Path to the root of dummy dataset
project_path (Path): path to the temporary project folder
Returns:
tuple[AnomalyModule, AnomalibDataModule, Engine]: Returns the created objects for model, dataset,
and engine
"""
# select task type
if model_name in ("rkde", "ai_vad"):
task_type = TaskType.DETECTION
elif model_name in ("ganomaly", "dfkde"):
task_type = TaskType.CLASSIFICATION
else:
task_type = TaskType.SEGMENTATION
# set extra model args
# TODO(ashwinvaidya17): Fix these Edge cases
# https://github.com/openvinotoolkit/anomalib/issues/1478
extra_args = {}
if model_name in ("rkde", "dfkde"):
extra_args["n_pca_components"] = 2
if model_name == "ai_vad":
pytest.skip("Revisit AI-VAD test")
# select dataset
elif model_name == "win_clip":
dataset = MVTec(root=dataset_path / "mvtec", category="dummy", image_size=240, task=task_type)
else:
# EfficientAd requires that the batch size be lesser than the number of images in the dataset.
# This is so that the LR step size is not 0.
dataset = MVTec(
root=dataset_path / "mvtec",
category="dummy",
task=task_type,
train_batch_size=2,
)
model = get_model(model_name, **extra_args)
engine = Engine(
logger=False,
default_root_dir=project_path,
max_epochs=1,
devices=1,
pixel_metrics=["F1Score", "AUROC"],
task=task_type,
# TODO(ashwinvaidya17): Fix these Edge cases
# https://github.com/openvinotoolkit/anomalib/issues/1478
max_steps=70000 if model_name == "efficient_ad" else -1,
)
return model, dataset, engine