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/*
* 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 "TensorFlowLiteMicro.hpp"
#include "RNNoiseModel.hpp"
#include "TestData_noise_reduction.hpp"
#include <catch.hpp>
#include <random>
namespace test {
namespace rnnoise {
bool RunInference(arm::app::Model& model, const std::vector<std::vector<int8_t>> inData)
{
for (size_t i = 0; i < model.GetNumInputs(); ++i) {
TfLiteTensor* inputTensor = model.GetInputTensor(i);
REQUIRE(inputTensor);
memcpy(inputTensor->data.data, inData[i].data(), inData[i].size());
}
return model.RunInference();
}
bool RunInferenceRandom(arm::app::Model& model)
{
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<std::vector<int8_t>> randomInput{NUMBER_OF_IFM_FILES};
for (size_t i = 0; i < model.GetNumInputs(); ++i) {
TfLiteTensor *inputTensor = model.GetInputTensor(i);
REQUIRE(inputTensor);
randomInput[i].resize(inputTensor->bytes);
std::generate(std::begin(randomInput[i]), std::end(randomInput[i]), gen);
}
REQUIRE(RunInference(model, randomInput));
return true;
}
TEST_CASE("Running random inference with Tflu and RNNoise Int8", "[RNNoise]")
{
arm::app::RNNoiseModel model{};
REQUIRE_FALSE(model.IsInited());
REQUIRE(model.Init());
REQUIRE(model.IsInited());
REQUIRE(RunInferenceRandom(model));
}
template<typename T>
void TestInference(const std::vector<std::vector<T>> input_goldenFV, const std::vector<std::vector<T>> output_goldenFV, arm::app::Model& model)
{
for (size_t i = 0; i < model.GetNumInputs(); ++i) {
TfLiteTensor* inputTensor = model.GetInputTensor(i);
REQUIRE(inputTensor);
}
REQUIRE(RunInference(model, input_goldenFV));
for (size_t i = 0; i < model.GetNumOutputs(); ++i) {
TfLiteTensor *outputTensor = model.GetOutputTensor(i);
REQUIRE(outputTensor);
auto tensorData = tflite::GetTensorData<T>(outputTensor);
REQUIRE(tensorData);
for (size_t j = 0; j < outputTensor->bytes; j++) {
REQUIRE(static_cast<int>(tensorData[j]) == static_cast<int>((output_goldenFV[i][j])));
}
}
}
TEST_CASE("Running inference with Tflu and RNNoise Int8", "[RNNoise]")
{
std::vector<std::vector<int8_t>> goldenInputFV {NUMBER_OF_IFM_FILES};
std::vector<std::vector<int8_t>> goldenOutputFV {NUMBER_OF_OFM_FILES};
std::array<size_t, NUMBER_OF_IFM_FILES> inputSizes = {IFM_0_DATA_SIZE,
IFM_1_DATA_SIZE,
IFM_2_DATA_SIZE,
IFM_3_DATA_SIZE};
std::array<size_t, NUMBER_OF_OFM_FILES> outputSizes = {OFM_0_DATA_SIZE,
OFM_1_DATA_SIZE,
OFM_2_DATA_SIZE,
OFM_3_DATA_SIZE,
OFM_4_DATA_SIZE};
for (uint32_t i = 0 ; i < NUMBER_OF_IFM_FILES; ++i) {
goldenInputFV[i].resize(inputSizes[i]);
std::memcpy(goldenInputFV[i].data(), get_ifm_data_array(i), inputSizes[i]);
}
for (uint32_t i = 0 ; i < NUMBER_OF_OFM_FILES; ++i) {
goldenOutputFV[i].resize(outputSizes[i]);
std::memcpy(goldenOutputFV[i].data(), get_ofm_data_array(i), outputSizes[i]);
}
DYNAMIC_SECTION("Executing inference with re-init")
{
arm::app::RNNoiseModel model{};
REQUIRE_FALSE(model.IsInited());
REQUIRE(model.Init());
REQUIRE(model.IsInited());
TestInference<int8_t>(goldenInputFV, goldenOutputFV, model);
}
}
} /* namespace rnnoise */
} /* namespace test */
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