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/*
* Copyright (c) 2021-2022 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 "RNNoiseModel.hpp"
#include "UseCaseHandler.hpp"
#include "InputFiles.hpp"
#include "RNNUCTestCaseData.hpp"
#include "BufAttributes.hpp"
#include "hal.h"
#include "Profiler.hpp"
#include <catch.hpp>
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 */
#define PLATFORM hal_platform_init();
#define CONTEXT \
arm::app::ApplicationContext caseContext; \
arm::app::Profiler profiler{"noise_reduction"}; \
caseContext.Set<arm::app::Profiler&>("profiler", profiler); \
caseContext.Set<arm::app::RNNoiseModel&>("model", model);
TEST_CASE("Verify output tensor memory dump")
{
constexpr size_t maxMemDumpSz = 0x100000; /* 1 MiB worth of space */
std::vector<uint8_t> memPool(maxMemDumpSz); /* Memory pool */
arm::app::RNNoiseModel model{};
REQUIRE(model.Init(arm::app::tensorArena,
sizeof(arm::app::tensorArena),
arm::app::rnn::GetModelPointer(),
arm::app::rnn::GetModelLen()));
REQUIRE(model.IsInited());
/* Populate the output tensors */
const size_t numOutputs = model.GetNumOutputs();
size_t sizeToWrite = 0;
size_t lastTensorSize = model.GetOutputTensor(numOutputs - 1)->bytes;
for (size_t i = 0; i < numOutputs; ++i) {
TfLiteTensor* tensor = model.GetOutputTensor(i);
auto* tData = tflite::GetTensorData<uint8_t>(tensor);
if (tensor->bytes > 0) {
memset(tData, static_cast<uint8_t>(i), tensor->bytes);
sizeToWrite += tensor->bytes;
}
}
SECTION("Positive use case")
{
/* Run the memory dump */
auto bytesWritten = DumpOutputTensorsToMemory(model, memPool.data(), memPool.size());
REQUIRE(sizeToWrite == bytesWritten);
/* Verify the dump */
size_t k = 0;
for (size_t i = 0; i < numOutputs && k < memPool.size(); ++i) {
TfLiteTensor* tensor = model.GetOutputTensor(i);
auto* tData = tflite::GetTensorData<uint8_t>(tensor);
for (size_t j = 0; j < tensor->bytes && k < memPool.size(); ++j) {
REQUIRE(tData[j] == memPool[k++]);
}
}
}
SECTION("Limited memory - skipping last tensor")
{
/* Run the memory dump */
auto bytesWritten = DumpOutputTensorsToMemory(model, memPool.data(), sizeToWrite - 1);
REQUIRE(lastTensorSize > 0);
REQUIRE(bytesWritten == sizeToWrite - lastTensorSize);
}
SECTION("Zero memory")
{
/* Run the memory dump */
auto bytesWritten = DumpOutputTensorsToMemory(model, memPool.data(), 0);
REQUIRE(bytesWritten == 0);
}
}
TEST_CASE("Inference run all clips", "[RNNoise]")
{
PLATFORM
arm::app::RNNoiseModel model;
CONTEXT
caseContext.Set<uint32_t>("clipIndex", 0);
caseContext.Set<uint32_t>("numInputFeatures", arm::app::rnn::g_NumInputFeatures);
caseContext.Set<uint32_t>("frameLength", arm::app::rnn::g_FrameLength);
caseContext.Set<uint32_t>("frameStride", arm::app::rnn::g_FrameStride);
/* Load the model. */
REQUIRE(model.Init(arm::app::tensorArena,
sizeof(arm::app::tensorArena),
arm::app::rnn::GetModelPointer(),
arm::app::rnn::GetModelLen()));
REQUIRE(arm::app::NoiseReductionHandler(caseContext, true));
}
std::function<uint32_t(const uint32_t)> get_golden_input_p232_208_array_size(const uint32_t numberOfFeatures) {
return [numberOfFeatures](const uint32_t) -> uint32_t{
return numberOfFeatures;
};
}
const char* get_test_filename(const uint32_t idx) {
auto name = get_filename(idx);
REQUIRE(std::string("p232_208.wav") == name);
return "p232_208.wav";
}
void testInfByIndex(std::vector<uint32_t>& numberOfInferences) {
PLATFORM
arm::app::RNNoiseModel model;
CONTEXT
caseContext.Set<std::function<const int16_t*(const uint32_t)>>("features", get_audio_array);
caseContext.Set<std::function<const char* (const uint32_t)>>("featureFileNames", get_test_filename);
caseContext.Set<uint32_t>("frameLength", arm::app::rnn::g_FrameLength);
caseContext.Set<uint32_t>("frameStride", arm::app::rnn::g_FrameStride);
caseContext.Set<uint32_t>("numInputFeatures", arm::app::rnn::g_NumInputFeatures);
/* Load the model. */
REQUIRE(model.Init(arm::app::tensorArena,
sizeof(arm::app::tensorArena),
arm::app::rnn::GetModelPointer(),
arm::app::rnn::GetModelLen()));
size_t oneInferenceOutSizeBytes = arm::app::rnn::g_FrameLength * sizeof(int16_t);
auto infIndex = 0;
for (auto numInf: numberOfInferences) {
DYNAMIC_SECTION("Number of features: "<< numInf) {
caseContext.Set<uint32_t>("clipIndex", 1); /* Only getting p232_208.wav for tests. */
uint32_t audioSizeInput = numInf * arm::app::rnn::g_FrameLength;
caseContext.Set<std::function<uint32_t(const uint32_t)>>("featureSizes",
get_golden_input_p232_208_array_size(audioSizeInput));
size_t headerNumBytes = 4 + 12 + 4; /* Filename length, filename (12 for p232_208.wav), dump size. */
size_t footerNumBytes = 4; /* Eof value. */
size_t memDumpMaxLenBytes = headerNumBytes + footerNumBytes + oneInferenceOutSizeBytes * numInf;
std::vector<uint8_t > memDump(memDumpMaxLenBytes);
size_t undefMemDumpBytesWritten = 0;
caseContext.Set<size_t>("MEM_DUMP_LEN", memDumpMaxLenBytes);
caseContext.Set<uint8_t*>("MEM_DUMP_BASE_ADDR", memDump.data());
caseContext.Set<size_t*>("MEM_DUMP_BYTE_WRITTEN", &undefMemDumpBytesWritten);
/* Inference. */
REQUIRE(arm::app::NoiseReductionHandler(caseContext, false));
/* The expected output after post-processing. */
std::vector<int16_t> golden(&ofms[infIndex][0],
&ofms[infIndex][0] + arm::app::rnn::g_FrameLength);
size_t startOfLastInfOut = undefMemDumpBytesWritten - oneInferenceOutSizeBytes;
/* The actual result from the usecase handler. */
std::vector<int16_t> runtime(arm::app::rnn::g_FrameLength);
std::memcpy(runtime.data(), &memDump[startOfLastInfOut], oneInferenceOutSizeBytes);
/* Margin of 43 is 0.07% error. */
REQUIRE_THAT(golden, Catch::Matchers::Approx(runtime).margin(43));
}
++infIndex;
}
}
TEST_CASE("Inference by index - one inference", "[RNNoise]")
{
auto totalAudioSize = get_audio_array_size(1);
REQUIRE(64757 == totalAudioSize); /* Checking that the input file is as expected and has not changed. */
/* Run 1 inference */
std::vector<uint32_t> numberOfInferences = {1};
testInfByIndex(numberOfInferences);
}
TEST_CASE("Inference by index - several inferences", "[RNNoise]")
{
auto totalAudioSize = get_audio_array_size(1);
REQUIRE(64757 == totalAudioSize); /* Checking that the input file is as expected and has not changed. */
/* 3 different inference amounts: 1, 2 and all inferences required to cover total feature set */
uint32_t totalInferences = totalAudioSize / arm::app::rnn::g_FrameLength;
std::vector<uint32_t> numberOfInferences = {1, 2, totalInferences};
testInfByIndex(numberOfInferences);
}
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