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/*
* SPDX-FileCopyrightText: Copyright 2021,2023 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 "RNNoiseModel.hpp"
#include "TensorFlowLiteMicro.hpp"
#include "TestData_noise_reduction.hpp"
#include "BufAttributes.hpp"
#include <catch.hpp>
#include <random>
namespace arm {
namespace app {
static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE;
namespace rnn {
extern uint8_t* GetModelPointer();
extern size_t GetModelLen();
} /* namespace rnn */
} /* namespace app */
} /* namespace arm */
bool RunInference(arm::app::Model& model, std::vector<int8_t> vec,
const size_t sizeRequired, const size_t dataInputIndex)
{
TfLiteTensor* inputTensor = model.GetInputTensor(dataInputIndex);
REQUIRE(inputTensor);
size_t copySz = inputTensor->bytes < sizeRequired ? inputTensor->bytes : sizeRequired;
const int8_t* vecData = vec.data();
memcpy(inputTensor->data.data, vecData, copySz);
return model.RunInference();
}
void genRandom(size_t bytes, std::vector<int8_t>& randomAudio)
{
randomAudio.resize(bytes);
std::random_device rndDevice;
std::mt19937 mersenneGen{rndDevice()};
std::uniform_int_distribution<short> dist {-128, 127};
auto gen = [&dist, &mersenneGen](){
return dist(mersenneGen);
};
std::generate(std::begin(randomAudio), std::end(randomAudio), gen);
}
bool RunInferenceRandom(arm::app::Model& model, const size_t dataInputIndex)
{
std::array<size_t, 4> inputSizes = {IFM_0_DATA_SIZE, IFM_1_DATA_SIZE, IFM_2_DATA_SIZE, IFM_3_DATA_SIZE};
std::vector<int8_t> randomAudio;
TfLiteTensor* inputTensor = model.GetInputTensor(dataInputIndex);
REQUIRE(inputTensor);
genRandom(inputTensor->bytes, randomAudio);
REQUIRE(RunInference(model, randomAudio, inputSizes[dataInputIndex], dataInputIndex));
return true;
}
TEST_CASE("Running random inference with TensorFlow Lite Micro and RNNoiseModel Int8", "[RNNoise]")
{
arm::app::RNNoiseModel model{};
REQUIRE_FALSE(model.IsInited());
REQUIRE(model.Init(arm::app::tensorArena,
sizeof(arm::app::tensorArena),
arm::app::rnn::GetModelPointer(),
arm::app::rnn::GetModelLen()));
REQUIRE(model.IsInited());
model.ResetGruState();
for (int i = 1; i < 4; i++ ) {
TfLiteTensor* inputGruStateTensor = model.GetInputTensor(i);
auto* inputGruState = tflite::GetTensorData<int8_t>(inputGruStateTensor);
for (size_t tIndex = 0; tIndex < inputGruStateTensor->bytes; tIndex++) {
REQUIRE(inputGruState[tIndex] == arm::app::GetTensorQuantParams(inputGruStateTensor).offset);
}
}
REQUIRE(RunInferenceRandom(model, 0));
}
class TestRNNoiseModel : public arm::app::RNNoiseModel
{
public:
bool CopyGruStatesTest() {
return RNNoiseModel::CopyGruStates();
}
std::vector<std::pair<size_t, size_t>> GetStateMap() {
return m_gruStateMap;
}
};
/* This is true for gcc x86 platform, not guaranteed for other compilers and platforms. */
TEST_CASE("Test initial GRU out state is 0", "[RNNoise]")
{
TestRNNoiseModel model{};
model.Init(arm::app::tensorArena,
sizeof(arm::app::tensorArena),
arm::app::rnn::GetModelPointer(),
arm::app::rnn::GetModelLen());
auto map = model.GetStateMap();
for(auto& mapping: map) {
TfLiteTensor* gruOut = model.GetOutputTensor(mapping.first);
auto* outGruState = tflite::GetTensorData<uint8_t>(gruOut);
for (size_t tIndex = 0; tIndex < gruOut->bytes; tIndex++) {
REQUIRE(outGruState[tIndex] == 0);
}
}
}
TEST_CASE("Test GRU state copy", "[RNNoise]")
{
TestRNNoiseModel model{};
model.Init(arm::app::tensorArena,
sizeof(arm::app::tensorArena),
arm::app::rnn::GetModelPointer(),
arm::app::rnn::GetModelLen());
REQUIRE(RunInferenceRandom(model, 0));
auto map = model.GetStateMap();
std::vector<std::vector<uint8_t>> oldStates;
for(auto& mapping: map) {
TfLiteTensor* gruOut = model.GetOutputTensor(mapping.first);
auto* outGruState = tflite::GetTensorData<uint8_t>(gruOut);
/* Save old output state. */
std::vector<uint8_t> oldState(gruOut->bytes);
memcpy(oldState.data(), outGruState, gruOut->bytes);
oldStates.push_back(oldState);
}
model.CopyGruStatesTest();
auto statesIter = oldStates.begin();
for(auto& mapping: map) {
TfLiteTensor* gruInput = model.GetInputTensor(mapping.second);
auto* inGruState = tflite::GetTensorData<uint8_t>(gruInput);
for (size_t tIndex = 0; tIndex < gruInput->bytes; tIndex++) {
REQUIRE((*statesIter)[tIndex] == inGruState[tIndex]);
}
statesIter++;
}
}
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