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# SPDX-FileCopyrightText: Copyright 2023, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Tests for module mlia.nn.rewrite.graph_edit.record."""
from pathlib import Path
import tensorflow as tf
from mlia.nn.rewrite.core.graph_edit.record import record_model
from mlia.nn.rewrite.core.utils.numpy_tfrecord import numpytf_read
def check_record_model(
test_tflite_model: Path,
tmp_path: Path,
test_tfrecord: Path,
batch_size: int,
) -> None:
"""Test the function record_model()."""
output_file = tmp_path / "out.tfrecord"
record_model(
input_filename=str(test_tfrecord),
model_filename=str(test_tflite_model),
output_filename=str(output_file),
batch_size=batch_size,
)
assert output_file.is_file()
def data_matches_outputs(name: str, tensor: tf.Tensor, model_outputs: list) -> bool:
"""Check that the name and the tensor match any of the model outputs."""
for model_output in model_outputs:
if model_output["name"] == name:
# If the name is a match, tensor shape and type have to match!
tensor_shape = tensor.shape.as_list()
tensor_type = tensor.dtype.as_numpy_dtype
return all(
(
tensor_shape == model_output["shape"].tolist(),
tensor_type == model_output["dtype"],
)
)
return False
# Now load model and the data and make sure that the written data matches
# any of the model outputs
interpreter = tf.lite.Interpreter(str(test_tflite_model))
model_outputs = interpreter.get_output_details()
dataset = numpytf_read(str(output_file))
for data in dataset:
for name, tensor in data.items():
assert data_matches_outputs(name, tensor, model_outputs)
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