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/*
* SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates
* <open-source-office@arm.com> 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 "BufAttributes.hpp"
#include "TensorFlowLiteMicro.hpp"
#include "TestData_asr.hpp"
#include "Wav2LetterModel.hpp"
#include <catch.hpp>
#include <random>
namespace arm {
namespace app {
static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE;
namespace asr {
extern uint8_t* GetModelPointer();
extern size_t GetModelLen();
} /* namespace asr */
} /* namespace app */
} /* namespace arm */
using namespace test;
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;
}
TEST_CASE("Running random inference with TensorFlow Lite Micro and Wav2LetterModel Int8",
"[Wav2Letter]")
{
arm::app::Wav2LetterModel model{};
REQUIRE_FALSE(model.IsInited());
REQUIRE(model.Init(arm::app::tensorArena,
sizeof(arm::app::tensorArena),
arm::app::asr::GetModelPointer(),
arm::app::asr::GetModelLen()));
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_0_DATA_SIZE);
auto tensorData = tflite::GetTensorData<T>(outputTensor);
REQUIRE(tensorData);
for (size_t i = 0; i < outputTensor->bytes; i++) {
REQUIRE(static_cast<int>(tensorData[i]) == static_cast<int>(((T)output_goldenFV[i])));
}
}
TEST_CASE("Running inference with Tflu and Wav2LetterModel Int8", "[Wav2Letter]")
{
REQUIRE(NUMBER_OF_IFM_FILES == NUMBER_OF_IFM_FILES);
for (uint32_t i = 0; i < NUMBER_OF_IFM_FILES; ++i) {
auto input_goldenFV = GetIfmDataArray(i);
;
auto output_goldenFV = GetOfmDataArray(i);
DYNAMIC_SECTION("Executing inference with re-init")
{
arm::app::Wav2LetterModel model{};
REQUIRE_FALSE(model.IsInited());
REQUIRE(model.Init(arm::app::tensorArena,
sizeof(arm::app::tensorArena),
arm::app::asr::GetModelPointer(),
arm::app::asr::GetModelLen()));
REQUIRE(model.IsInited());
TestInference<int8_t>(input_goldenFV, output_goldenFV, model);
}
}
}
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