| /* |
| * 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 "Wav2LetterModel.hpp" |
| #include "TestData_asr.hpp" |
| |
| #include <catch.hpp> |
| #include <random> |
| |
| using namespace test; |
| |
| bool RunInference(arm::app::Model& model, const int8_t vec[], const size_t copySz) |
| { |
| TfLiteTensor* inputTensor = model.GetInputTensor(0); |
| REQUIRE(inputTensor); |
| |
| memcpy(inputTensor->data.data, vec, copySz); |
| |
| return model.RunInference(); |
| } |
| |
| bool RunInferenceRandom(arm::app::Model& model) |
| { |
| TfLiteTensor* inputTensor = model.GetInputTensor(0); |
| REQUIRE(inputTensor); |
| |
| 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<int8_t> randomAudio(inputTensor->bytes); |
| std::generate(std::begin(randomAudio), std::end(randomAudio), gen); |
| |
| REQUIRE(RunInference(model, randomAudio.data(), inputTensor->bytes)); |
| return true; |
| } |
| |
| TEST_CASE("Running random inference with TensorFlow Lite Micro and Wav2LetterModel Int8", "[Wav2Letter]") |
| { |
| arm::app::Wav2LetterModel model{}; |
| |
| REQUIRE_FALSE(model.IsInited()); |
| REQUIRE(model.Init()); |
| REQUIRE(model.IsInited()); |
| |
| REQUIRE(RunInferenceRandom(model)); |
| } |
| |
| template<typename T> |
| void TestInference(const T* input_goldenFV, const T* output_goldenFV, arm::app::Model& model) |
| { |
| TfLiteTensor* inputTensor = model.GetInputTensor(0); |
| REQUIRE(inputTensor); |
| |
| REQUIRE(RunInference(model, input_goldenFV, inputTensor->bytes)); |
| |
| TfLiteTensor* outputTensor = model.GetOutputTensor(0); |
| |
| REQUIRE(outputTensor); |
| REQUIRE(outputTensor->bytes == OFM_0_DATA_SIZE); |
| auto tensorData = tflite::GetTensorData<T>(outputTensor); |
| REQUIRE(tensorData); |
| |
| for (size_t i = 0; i < outputTensor->bytes; i++) { |
| REQUIRE(static_cast<int>(tensorData[i]) == static_cast<int>(((T)output_goldenFV[i]))); |
| } |
| } |
| |
| TEST_CASE("Running inference with Tflu and Wav2LetterModel Int8", "[Wav2Letter]") |
| { |
| REQUIRE(NUMBER_OF_IFM_FILES == NUMBER_OF_IFM_FILES); |
| for (uint32_t i = 0 ; i < NUMBER_OF_IFM_FILES; ++i) { |
| auto input_goldenFV = get_ifm_data_array(i);; |
| auto output_goldenFV = get_ofm_data_array(i); |
| |
| DYNAMIC_SECTION("Executing inference with re-init") |
| { |
| arm::app::Wav2LetterModel model{}; |
| |
| REQUIRE_FALSE(model.IsInited()); |
| REQUIRE(model.Init()); |
| REQUIRE(model.IsInited()); |
| |
| TestInference<int8_t>(input_goldenFV, output_goldenFV, model); |
| |
| } |
| } |
| } |