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
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
|
/*
* Copyright (c) 2021 Arm Limited. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "hal.h"
#include "TensorFlowLiteMicro.hpp"
#include "Wav2LetterModel.hpp"
#include "TestData_asr.hpp"
#include <catch.hpp>
#include <random>
bool RunInference(arm::app::Model& model, const int8_t vec[], const size_t copySz)
{
TfLiteTensor* inputTensor = model.GetInputTensor(0);
REQUIRE(inputTensor);
memcpy(inputTensor->data.data, vec, copySz);
return model.RunInference();
}
bool RunInferenceRandom(arm::app::Model& model)
{
TfLiteTensor* inputTensor = model.GetInputTensor(0);
REQUIRE(inputTensor);
std::random_device rndDevice;
std::mt19937 mersenneGen{rndDevice()};
std::uniform_int_distribution<short> dist {-128, 127};
auto gen = [&dist, &mersenneGen](){
return dist(mersenneGen);
};
std::vector<int8_t> randomAudio(inputTensor->bytes);
std::generate(std::begin(randomAudio), std::end(randomAudio), gen);
REQUIRE(RunInference(model, randomAudio.data(), inputTensor->bytes));
return true;
}
/* Skip this test, Wav2LetterModel if not Vela optimized but only from ML-zoo will fail. */
TEST_CASE("Running random inference with TensorFlow Lite Micro and Wav2LetterModel Int8", "[Wav2Letter][.]")
{
arm::app::Wav2LetterModel model{};
REQUIRE_FALSE(model.IsInited());
REQUIRE(model.Init());
REQUIRE(model.IsInited());
REQUIRE(RunInferenceRandom(model));
}
template<typename T>
void TestInference(const T* input_goldenFV, const T* output_goldenFV, arm::app::Model& model)
{
TfLiteTensor* inputTensor = model.GetInputTensor(0);
REQUIRE(inputTensor);
REQUIRE(RunInference(model, input_goldenFV, inputTensor->bytes));
TfLiteTensor* outputTensor = model.GetOutputTensor(0);
REQUIRE(outputTensor);
REQUIRE(outputTensor->bytes == OFM_DATA_SIZE);
auto tensorData = tflite::GetTensorData<T>(outputTensor);
REQUIRE(tensorData);
for (size_t i = 0; i < outputTensor->bytes; i++) {
REQUIRE((int)tensorData[i] == (int)((T)output_goldenFV[i]));
}
}
TEST_CASE("Running inference with Tflu and Wav2LetterModel Int8", "[Wav2Letter][.]")
{
for (uint32_t i = 0 ; i < NUMBER_OF_FM_FILES; ++i) {
auto input_goldenFV = get_ifm_data_array(i);;
auto output_goldenFV = get_ofm_data_array(i);
DYNAMIC_SECTION("Executing inference with re-init")
{
arm::app::Wav2LetterModel model{};
REQUIRE_FALSE(model.IsInited());
REQUIRE(model.Init());
REQUIRE(model.IsInited());
TestInference<int8_t>(input_goldenFV, output_goldenFV, model);
}
}
}
|
