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# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Pytest conf module."""
import shutil
import tarfile
from pathlib import Path
from typing import Any
from typing import Generator
import pytest
import tensorflow as tf
from mlia.backend.vela.compiler import optimize_model
from mlia.core.context import ExecutionContext
from mlia.nn.tensorflow.utils import convert_to_tflite
from mlia.nn.tensorflow.utils import save_keras_model
from mlia.nn.tensorflow.utils import save_tflite_model
from mlia.target.ethos_u.config import EthosUConfiguration
@pytest.fixture(scope="session", name="test_resources_path")
def fixture_test_resources_path() -> Path:
"""Return test resources path."""
return Path(__file__).parent / "test_resources"
@pytest.fixture(name="sample_context")
def fixture_sample_context(tmpdir: str) -> ExecutionContext:
"""Return sample context fixture."""
return ExecutionContext(working_dir=tmpdir)
@pytest.fixture(scope="session")
def test_systems_path(test_resources_path: Path) -> Path:
"""Return test systems path in a pytest fixture."""
return test_resources_path / "backends" / "systems"
@pytest.fixture(scope="session")
def test_applications_path(test_resources_path: Path) -> Path:
"""Return test applications path in a pytest fixture."""
return test_resources_path / "backends" / "applications"
@pytest.fixture(scope="session")
def non_optimised_input_model_file(test_tflite_model: Path) -> Path:
"""Provide the path to a quantized test model file."""
return test_tflite_model
@pytest.fixture(scope="session")
def optimised_input_model_file(test_tflite_vela_model: Path) -> Path:
"""Provide path to Vela-optimised test model file."""
return test_tflite_vela_model
@pytest.fixture(scope="session")
def invalid_input_model_file(test_tflite_invalid_model: Path) -> Path:
"""Provide the path to an invalid test model file."""
return test_tflite_invalid_model
@pytest.fixture(autouse=True)
def test_resources(monkeypatch: pytest.MonkeyPatch, test_resources_path: Path) -> Any:
"""Force using test resources as middleware's repository."""
def get_test_resources() -> Path:
"""Return path to the test resources."""
return test_resources_path / "backends"
monkeypatch.setattr(
"mlia.backend.executor.fs.get_backend_resources", get_test_resources
)
yield
def create_archive(
archive_name: str, source: Path, destination: Path, with_root_folder: bool = False
) -> None:
"""Create archive from directory source."""
with tarfile.open(destination / archive_name, mode="w:gz") as tar:
for item in source.iterdir():
item_name = item.name
if with_root_folder:
item_name = f"{source.name}/{item_name}"
tar.add(item, item_name)
def process_directory(source: Path, destination: Path) -> None:
"""Process resource directory."""
destination.mkdir()
for item in source.iterdir():
if item.is_dir():
create_archive(f"{item.name}.tar.gz", item, destination)
create_archive(f"{item.name}_dir.tar.gz", item, destination, True)
@pytest.fixture(scope="session", autouse=True)
def add_archives(
test_resources_path: Path, tmp_path_factory: pytest.TempPathFactory
) -> Any:
"""Generate archives of the test resources."""
tmp_path = tmp_path_factory.mktemp("archives")
archives_path = tmp_path / "archives"
archives_path.mkdir()
if (archives_path_link := test_resources_path / "archives").is_symlink():
archives_path_link.unlink()
archives_path_link.symlink_to(archives_path, target_is_directory=True)
for item in ["applications", "systems"]:
process_directory(test_resources_path / "backends" / item, archives_path / item)
yield
archives_path_link.unlink()
shutil.rmtree(tmp_path)
def get_test_keras_model() -> tf.keras.Model:
"""Return test Keras model."""
model = tf.keras.Sequential(
[
tf.keras.Input(shape=(28, 28, 1), batch_size=1, name="input"),
tf.keras.layers.Reshape((28, 28, 1)),
tf.keras.layers.Conv2D(
filters=12, kernel_size=(3, 3), activation="relu", name="conv1"
),
tf.keras.layers.Conv2D(
filters=12, kernel_size=(3, 3), activation="relu", name="conv2"
),
tf.keras.layers.MaxPool2D(2, 2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(10, name="output"),
]
)
model.compile(optimizer="sgd", loss="mean_squared_error")
return model
@pytest.fixture(scope="session", name="test_models_path")
def fixture_test_models_path(
tmp_path_factory: pytest.TempPathFactory,
) -> Generator[Path, None, None]:
"""Provide path to the test models."""
tmp_path = tmp_path_factory.mktemp("models")
keras_model = get_test_keras_model()
save_keras_model(keras_model, tmp_path / "test_model.h5")
tflite_model = convert_to_tflite(keras_model, quantized=True)
tflite_model_path = tmp_path / "test_model.tflite"
save_tflite_model(tflite_model, tflite_model_path)
tflite_vela_model = tmp_path / "test_model_vela.tflite"
device = EthosUConfiguration("ethos-u55-256")
optimize_model(tflite_model_path, device.compiler_options, tflite_vela_model)
tf.saved_model.save(keras_model, str(tmp_path / "tf_model_test_model"))
invalid_tflite_model = tmp_path / "invalid.tflite"
invalid_tflite_model.touch()
yield tmp_path
shutil.rmtree(tmp_path)
@pytest.fixture(scope="session", name="test_keras_model")
def fixture_test_keras_model(test_models_path: Path) -> Path:
"""Return test Keras model."""
return test_models_path / "test_model.h5"
@pytest.fixture(scope="session", name="test_tflite_model")
def fixture_test_tflite_model(test_models_path: Path) -> Path:
"""Return test TensorFlow Lite model."""
return test_models_path / "test_model.tflite"
@pytest.fixture(scope="session", name="test_tflite_vela_model")
def fixture_test_tflite_vela_model(test_models_path: Path) -> Path:
"""Return test Vela-optimized TensorFlow Lite model."""
return test_models_path / "test_model_vela.tflite"
@pytest.fixture(scope="session", name="test_tf_model")
def fixture_test_tf_model(test_models_path: Path) -> Path:
"""Return test TensorFlow Lite model."""
return test_models_path / "tf_model_test_model"
@pytest.fixture(scope="session", name="test_tflite_invalid_model")
def fixture_test_tflite_invalid_model(test_models_path: Path) -> Path:
"""Return test invalid TensorFlow Lite model."""
return test_models_path / "invalid.tflite"
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