1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
|
# 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 pytest
import tensorflow as tf
from mlia.nn.rewrite.core.graph_edit.record import record_model
from mlia.nn.rewrite.core.utils.numpy_tfrecord import NumpyTFReader
@pytest.mark.parametrize("batch_size", (None, 1, 2))
def test_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 = NumpyTFReader(str(output_file))
for data in dataset:
for name, tensor in data.items():
assert data_matches_outputs(name, tensor, model_outputs)
|
