| /* |
| * 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 "TensorFlowLiteMicro.hpp" |
| #include "RNNoiseModel.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 */ |
| |
| namespace test { |
| namespace noise_reduction { |
| |
| 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(arm::app::tensorArena, |
| sizeof(arm::app::tensorArena), |
| arm::app::rnn::GetModelPointer(), |
| arm::app::rnn::GetModelLen())); |
| 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(arm::app::tensorArena, |
| sizeof(arm::app::tensorArena), |
| arm::app::rnn::GetModelPointer(), |
| arm::app::rnn::GetModelLen())); |
| REQUIRE(model.IsInited()); |
| |
| TestInference<int8_t>(goldenInputFV, goldenOutputFV, model); |
| } |
| } |
| |
| } /* namespace noise_reduction */ |
| } /* namespace test */ |