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# SPDX-FileCopyrightText: Copyright 2022-2023, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Tests for config module."""
from contextlib import ExitStack as does_not_raise
from pathlib import Path
from typing import Any
from typing import Generator
import numpy as np
import pytest
from mlia.nn.rewrite.core.utils.numpy_tfrecord import numpytf_read
from mlia.nn.tensorflow.config import get_model
from mlia.nn.tensorflow.config import KerasModel
from mlia.nn.tensorflow.config import ModelConfiguration
from mlia.nn.tensorflow.config import TFLiteModel
from mlia.nn.tensorflow.config import TfModel
from tests.conftest import create_tfrecord
def test_model_configuration(test_keras_model: Path) -> None:
"""Test ModelConfiguration class."""
model = ModelConfiguration(model_path=test_keras_model)
assert test_keras_model.match(model.model_path)
with pytest.raises(NotImplementedError):
model.convert_to_keras("keras_model.h5")
with pytest.raises(NotImplementedError):
model.convert_to_tflite("model.tflite")
def test_convert_keras_to_tflite(tmp_path: Path, test_keras_model: Path) -> None:
"""Test Keras to TensorFlow Lite conversion."""
keras_model = KerasModel(test_keras_model)
tflite_model_path = tmp_path / "test.tflite"
keras_model.convert_to_tflite(tflite_model_path)
assert tflite_model_path.is_file()
assert tflite_model_path.stat().st_size > 0
def test_convert_tf_to_tflite(tmp_path: Path, test_tf_model: Path) -> None:
"""Test TensorFlow saved model to TensorFlow Lite conversion."""
tf_model = TfModel(test_tf_model)
tflite_model_path = tmp_path / "test.tflite"
tf_model.convert_to_tflite(tflite_model_path)
assert tflite_model_path.is_file()
assert tflite_model_path.stat().st_size > 0
@pytest.mark.parametrize(
"model_path, expected_type, expected_error",
[
("test.tflite", TFLiteModel, pytest.raises(ValueError)),
("test.h5", KerasModel, does_not_raise()),
("test.hdf5", KerasModel, does_not_raise()),
(
"test.model",
None,
pytest.raises(
ValueError,
match=(
"The input model format is not supported "
r"\(supported formats: TensorFlow Lite, Keras, "
r"TensorFlow saved model\)!"
),
),
),
],
)
def test_get_model_file(
model_path: str, expected_type: type, expected_error: Any
) -> None:
"""Test TensorFlow Lite model type."""
with expected_error:
model = get_model(model_path)
assert isinstance(model, expected_type)
@pytest.mark.parametrize(
"model_path, expected_type", [("tf_model_test_model", TfModel)]
)
def test_get_model_dir(
test_models_path: Path, model_path: str, expected_type: type
) -> None:
"""Test TensorFlow Lite model type."""
model = get_model(str(test_models_path / model_path))
assert isinstance(model, expected_type)
@pytest.fixture(scope="session", name="test_tfrecord_fp32_batch_3")
def fixture_test_tfrecord_fp32_batch_3(
tmp_path_factory: pytest.TempPathFactory,
) -> Generator[Path, None, None]:
"""Create tfrecord (same as test_tfrecord_fp32) but with batch size 3."""
def random_data() -> np.ndarray:
return np.random.rand(3, 28, 28, 1).astype(np.float32)
yield from create_tfrecord(tmp_path_factory, random_data)
def test_tflite_model_call(
test_tflite_model_fp32: Path, test_tfrecord_fp32_batch_3: Path
) -> None:
"""Test inference function of class TFLiteModel."""
model = TFLiteModel(test_tflite_model_fp32, batch_size=2)
data = numpytf_read(test_tfrecord_fp32_batch_3)
for named_input in data.as_numpy_iterator():
res = model(named_input)
assert res
def test_tflite_model_is_tensor_quantized(test_tflite_model: Path) -> None:
"""Test function TFLiteModel.is_tensor_quantized()."""
model = TFLiteModel(test_tflite_model)
input_details = model.input_details[0]
assert model.is_tensor_quantized(name=input_details["name"])
assert model.is_tensor_quantized(idx=input_details["index"])
with pytest.raises(ValueError):
assert model.is_tensor_quantized()
with pytest.raises(NameError):
assert model.is_tensor_quantized(name="NAME_DOES_NOT_EXIST")
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