| /* |
| * 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 "RNNoiseModel.hpp" |
| #include "UseCaseHandler.hpp" |
| #include "InputFiles.hpp" |
| #include "RNNUCTestCaseData.hpp" |
| #include "UseCaseCommonUtils.hpp" |
| |
| #include <catch.hpp> |
| #include <hal.h> |
| #include <Profiler.hpp> |
| |
| #define PLATFORM \ |
| hal_platform platform; \ |
| data_acq_module data_acq; \ |
| data_psn_module data_psn; \ |
| platform_timer timer; \ |
| hal_init(&platform, &data_acq, &data_psn, &timer); \ |
| hal_platform_init(&platform); |
| |
| #define CONTEXT \ |
| arm::app::ApplicationContext caseContext; \ |
| arm::app::Profiler profiler{&platform, "noise_reduction"}; \ |
| caseContext.Set<arm::app::Profiler&>("profiler", profiler); \ |
| caseContext.Set<hal_platform&>("platform", platform); \ |
| 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()); |
| 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", g_NumInputFeatures); |
| caseContext.Set<uint32_t>("frameLength", g_FrameLength); |
| caseContext.Set<uint32_t>("frameStride", g_FrameStride); |
| |
| /* Load the model. */ |
| REQUIRE(model.Init()); |
| |
| 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", g_FrameLength); |
| caseContext.Set<uint32_t>("frameStride", g_FrameStride); |
| caseContext.Set<uint32_t>("numInputFeatures", g_NumInputFeatures); |
| /* Load the model. */ |
| REQUIRE(model.Init()); |
| |
| size_t oneInferenceOutSizeBytes = 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*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] + g_FrameLength); |
| |
| size_t startOfLastInfOut = undefMemDumpBytesWritten - oneInferenceOutSizeBytes; |
| |
| /* The actual result from the usecase handler. */ |
| std::vector<int16_t> runtime(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 / g_FrameLength; |
| std::vector<uint32_t> numberOfInferences = {1, 2, totalInferences}; |
| testInfByIndex(numberOfInferences); |
| } |