alexander | 3c79893 | 2021-03-26 21:42:19 +0000 | [diff] [blame^] | 1 | /* |
| 2 | * Copyright (c) 2021 Arm Limited. All rights reserved. |
| 3 | * SPDX-License-Identifier: Apache-2.0 |
| 4 | * |
| 5 | * Licensed under the Apache License, Version 2.0 (the "License"); |
| 6 | * you may not use this file except in compliance with the License. |
| 7 | * You may obtain a copy of the License at |
| 8 | * |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 |
| 10 | * |
| 11 | * Unless required by applicable law or agreed to in writing, software |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 14 | * See the License for the specific language governing permissions and |
| 15 | * limitations under the License. |
| 16 | */ |
| 17 | #include "AsrClassifier.hpp" |
| 18 | |
| 19 | #include "hal.h" |
| 20 | #include "TensorFlowLiteMicro.hpp" |
| 21 | #include "Wav2LetterModel.hpp" |
| 22 | |
| 23 | template<typename T> |
| 24 | bool arm::app::AsrClassifier::_GetTopResults(TfLiteTensor* tensor, |
| 25 | std::vector<ClassificationResult>& vecResults, |
| 26 | const std::vector <std::string>& labels, double scale, double zeroPoint) |
| 27 | { |
| 28 | const uint32_t nElems = tensor->dims->data[arm::app::Wav2LetterModel::ms_outputRowsIdx]; |
| 29 | const uint32_t nLetters = tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx]; |
| 30 | |
| 31 | |
| 32 | /* NOTE: tensor's size verification against labels should be |
| 33 | * checked by the calling/public function. */ |
| 34 | if (nLetters < 1) { |
| 35 | return false; |
| 36 | } |
| 37 | |
| 38 | /* Final results' container. */ |
| 39 | vecResults = std::vector<ClassificationResult>(nElems); |
| 40 | |
| 41 | T* tensorData = tflite::GetTensorData<T>(tensor); |
| 42 | |
| 43 | /* Get the top 1 results. */ |
| 44 | for (uint32_t i = 0, row = 0; i < nElems; ++i, row+=nLetters) { |
| 45 | std::pair<T, uint32_t> top_1 = std::make_pair(tensorData[row + 0], 0); |
| 46 | |
| 47 | for (uint32_t j = 1; j < nLetters; ++j) { |
| 48 | if (top_1.first < tensorData[row + j]) { |
| 49 | top_1.first = tensorData[row + j]; |
| 50 | top_1.second = j; |
| 51 | } |
| 52 | } |
| 53 | |
| 54 | double score = static_cast<int> (top_1.first); |
| 55 | vecResults[i].m_normalisedVal = scale * (score - zeroPoint); |
| 56 | vecResults[i].m_label = labels[top_1.second]; |
| 57 | vecResults[i].m_labelIdx = top_1.second; |
| 58 | } |
| 59 | |
| 60 | return true; |
| 61 | } |
| 62 | template bool arm::app::AsrClassifier::_GetTopResults<uint8_t>(TfLiteTensor* tensor, |
| 63 | std::vector<ClassificationResult>& vecResults, |
| 64 | const std::vector <std::string>& labels, double scale, double zeroPoint); |
| 65 | template bool arm::app::AsrClassifier::_GetTopResults<int8_t>(TfLiteTensor* tensor, |
| 66 | std::vector<ClassificationResult>& vecResults, |
| 67 | const std::vector <std::string>& labels, double scale, double zeroPoint); |
| 68 | |
| 69 | bool arm::app::AsrClassifier::GetClassificationResults( |
| 70 | TfLiteTensor* outputTensor, |
| 71 | std::vector<ClassificationResult>& vecResults, |
| 72 | const std::vector <std::string>& labels, uint32_t topNCount) |
| 73 | { |
| 74 | vecResults.clear(); |
| 75 | |
| 76 | constexpr int minTensorDims = static_cast<int>( |
| 77 | (arm::app::Wav2LetterModel::ms_outputRowsIdx > arm::app::Wav2LetterModel::ms_outputColsIdx)? |
| 78 | arm::app::Wav2LetterModel::ms_outputRowsIdx : arm::app::Wav2LetterModel::ms_outputColsIdx); |
| 79 | |
| 80 | constexpr uint32_t outColsIdx = arm::app::Wav2LetterModel::ms_outputColsIdx; |
| 81 | |
| 82 | /* Sanity checks. */ |
| 83 | if (outputTensor == nullptr) { |
| 84 | printf_err("Output vector is null pointer.\n"); |
| 85 | return false; |
| 86 | } else if (outputTensor->dims->size < minTensorDims) { |
| 87 | printf_err("Output tensor expected to be 3D (1, m, n)\n"); |
| 88 | return false; |
| 89 | } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) < topNCount) { |
| 90 | printf_err("Output vectors are smaller than %u\n", topNCount); |
| 91 | return false; |
| 92 | } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) != labels.size()) { |
| 93 | printf("Output size doesn't match the labels' size\n"); |
| 94 | return false; |
| 95 | } |
| 96 | |
| 97 | if (topNCount != 1) { |
| 98 | warn("TopNCount value ignored in this implementation\n"); |
| 99 | } |
| 100 | |
| 101 | /* To return the floating point values, we need quantization parameters. */ |
| 102 | QuantParams quantParams = GetTensorQuantParams(outputTensor); |
| 103 | |
| 104 | bool resultState; |
| 105 | |
| 106 | switch (outputTensor->type) { |
| 107 | case kTfLiteUInt8: |
| 108 | resultState = this->_GetTopResults<uint8_t>( |
| 109 | outputTensor, vecResults, |
| 110 | labels, quantParams.scale, |
| 111 | quantParams.offset); |
| 112 | break; |
| 113 | case kTfLiteInt8: |
| 114 | resultState = this->_GetTopResults<int8_t>( |
| 115 | outputTensor, vecResults, |
| 116 | labels, quantParams.scale, |
| 117 | quantParams.offset); |
| 118 | break; |
| 119 | default: |
| 120 | printf_err("Tensor type %s not supported by classifier\n", |
| 121 | TfLiteTypeGetName(outputTensor->type)); |
| 122 | return false; |
| 123 | } |
| 124 | |
| 125 | if (!resultState) { |
| 126 | printf_err("Failed to get sorted set\n"); |
| 127 | return false; |
| 128 | } |
| 129 | |
| 130 | return true; |
| 131 | } |