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# SPDX-FileCopyrightText: Copyright 2022-2023, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Tests for Cortex-A operator compatibility."""
from pathlib import Path
from typing import cast
import pytest
import tensorflow as tf
from mlia.backend.armnn_tflite_delegate import compat
from mlia.nn.tensorflow.tflite_graph import TFL_OP
from mlia.nn.tensorflow.utils import convert_to_tflite
from mlia.target.cortex_a.config import CortexAConfiguration
from mlia.target.cortex_a.operators import CortexACompatibilityInfo
from mlia.target.cortex_a.operators import get_cortex_a_compatibility_info
def test_compat_data() -> None:
"""Make sure all data contains the necessary items."""
builtin_tfl_ops = {op.name for op in TFL_OP}
assert "backend" in compat.ARMNN_TFLITE_DELEGATE
assert "ops" in compat.ARMNN_TFLITE_DELEGATE
ops = cast(dict, compat.ARMNN_TFLITE_DELEGATE["ops"])
for data in ops.values():
assert "builtin_ops" in data
for comp in data["builtin_ops"]:
assert comp in builtin_tfl_ops
assert "custom_ops" in data
def check_get_cortex_a_compatibility_info(
model_path: Path,
expected_success: bool,
) -> None:
"""Check the function 'get_cortex_a_compatibility_info'."""
compat_info = get_cortex_a_compatibility_info(
model_path, CortexAConfiguration.load_profile("cortex-a")
)
assert isinstance(compat_info, CortexACompatibilityInfo)
assert expected_success == compat_info.is_cortex_a_compatible
assert compat_info.operators
for oper in compat_info.operators:
assert oper.name
assert oper.location
assert (
compat_info.get_support_type(oper) in CortexACompatibilityInfo.SupportType
)
def test_get_cortex_a_compatibility_info_compatible(
test_tflite_model: Path,
) -> None:
"""Test a fully compatible TensorFlow Lite model."""
check_get_cortex_a_compatibility_info(test_tflite_model, expected_success=True)
def test_get_cortex_a_compatibility_info_not_compatible(
monkeypatch: pytest.MonkeyPatch,
) -> None:
"""Construct and test a NOT fully compatible TensorFlow Lite model."""
keras_model = tf.keras.Sequential(
[
tf.keras.Input(shape=(28, 28, 1), batch_size=1, name="input"),
tf.keras.layers.Conv2D(
filters=12, kernel_size=(3, 3), activation="softmax", name="conv1"
),
tf.keras.layers.LeakyReLU(),
]
)
keras_model.compile(optimizer="sgd", loss="mean_squared_error")
tflite_model = convert_to_tflite(keras_model, quantized=False)
monkeypatch.setattr(
"mlia.nn.tensorflow.tflite_graph.load_tflite", lambda _p: tflite_model
)
check_get_cortex_a_compatibility_info(
Path("NOT_USED_BECAUSE_OF_MOCKING"), expected_success=False
)
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