| /* |
| * 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 "AsrClassifier.hpp" |
| #include "Wav2LetterModel.hpp" |
| |
| #include <catch.hpp> |
| |
| TEST_CASE("Test invalid classifier") |
| { |
| TfLiteTensor* outputTens = nullptr; |
| std::vector <arm::app::ClassificationResult> resultVec; |
| arm::app::AsrClassifier classifier; |
| |
| REQUIRE(!classifier.GetClassificationResults(outputTens, resultVec, {}, 1)); |
| } |
| |
| |
| TEST_CASE("Test valid classifier UINT8") { |
| int dimArray[] = {4, 1, 1, 246, 29}; |
| std::vector <std::string> labels(29); |
| std::vector <uint8_t> outputVec(7134); |
| TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray); |
| TfLiteTensor tfTensor = tflite::testing::CreateQuantizedTensor( |
| outputVec.data(), dims, 1, 0); |
| TfLiteTensor* outputTensor = &tfTensor; |
| std::vector <arm::app::ClassificationResult> resultVec; |
| arm::app::AsrClassifier classifier; |
| |
| REQUIRE(classifier.GetClassificationResults(outputTensor, resultVec, labels, 1)); |
| REQUIRE(246 == resultVec.size()); |
| } |
| |
| |
| TEST_CASE("Get classification results") { |
| int dimArray[] = {4, 1, 1, 10, 15}; |
| std::vector <std::string> labels(15); |
| std::vector<uint8_t> outputVec(150, static_cast<uint8_t>(1)); |
| TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray); |
| TfLiteTensor tfTensor = tflite::testing::CreateQuantizedTensor( |
| outputVec.data(), dims, 1, 0); |
| TfLiteTensor* outputTensor = &tfTensor; |
| |
| std::vector <arm::app::ClassificationResult> resultVec(10); |
| |
| /* set the top five results: */ |
| std::vector<std::pair<uint32_t, std::pair<uint32_t, uint8_t>>> selectedResults { |
| {0, {3, 23}}, |
| {0, {9, 15}}, |
| {1, {5, 24}}, |
| {1, {7, 4}}, |
| {2, {9, 5}}, |
| {3, {8, 6}}, |
| {4, {13, 10}}, |
| {4, {6, 18}}, |
| {5, {3, 15}}, |
| {5, {4, 115}}, |
| {6, {6, 25}}, |
| {7, {1, 7}}, |
| {8, {11, 9}}, |
| {9, {1, 10}} |
| }; |
| |
| const uint32_t nCols = outputTensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx]; |
| for (size_t i = 0; i < selectedResults.size(); ++i) { |
| uint32_t rIndex = selectedResults[i].first; |
| uint32_t cIndex = selectedResults[i].second.first; |
| uint8_t value = selectedResults[i].second.second; |
| outputVec[rIndex * nCols + cIndex] = value; |
| } |
| |
| arm::app::AsrClassifier classifier; |
| |
| REQUIRE(classifier.GetClassificationResults(outputTensor, resultVec, labels, 1)); |
| REQUIRE(resultVec[0].m_labelIdx == 3); |
| REQUIRE(resultVec[1].m_labelIdx == 5); |
| REQUIRE(resultVec[2].m_labelIdx == 9); |
| REQUIRE(resultVec[3].m_labelIdx == 8); |
| REQUIRE(resultVec[4].m_labelIdx == 6); |
| REQUIRE(resultVec[5].m_labelIdx == 4); |
| REQUIRE(resultVec[6].m_labelIdx == 6); |
| REQUIRE(resultVec[7].m_labelIdx == 1); |
| REQUIRE(resultVec[8].m_labelIdx == 11); |
| REQUIRE(resultVec[9].m_labelIdx == 1); |
| } |