diff options
Diffstat (limited to 'source/application/api/use_case/asr/src/AsrClassifier.cc')
| -rw-r--r-- | source/application/api/use_case/asr/src/AsrClassifier.cc | 144 |
1 files changed, 144 insertions, 0 deletions
diff --git a/source/application/api/use_case/asr/src/AsrClassifier.cc b/source/application/api/use_case/asr/src/AsrClassifier.cc new file mode 100644 index 0000000..4ba8c7b --- /dev/null +++ b/source/application/api/use_case/asr/src/AsrClassifier.cc @@ -0,0 +1,144 @@ +/* + * 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 "log_macros.h" +#include "TensorFlowLiteMicro.hpp" +#include "Wav2LetterModel.hpp" + +namespace arm { +namespace app { + + template<typename T> + bool AsrClassifier::GetTopResults(TfLiteTensor* tensor, + std::vector<ClassificationResult>& vecResults, + const std::vector <std::string>& labels, double scale, double zeroPoint) + { + const uint32_t nElems = tensor->dims->data[Wav2LetterModel::ms_outputRowsIdx]; + const uint32_t nLetters = tensor->dims->data[Wav2LetterModel::ms_outputColsIdx]; + + if (nLetters != labels.size()) { + printf("Output size doesn't match the labels' size\n"); + return false; + } + + /* NOTE: tensor's size verification against labels should be + * checked by the calling/public function. */ + if (nLetters < 1) { + return false; + } + + /* Final results' container. */ + vecResults = std::vector<ClassificationResult>(nElems); + + T* tensorData = tflite::GetTensorData<T>(tensor); + + /* Get the top 1 results. */ + for (uint32_t i = 0, row = 0; i < nElems; ++i, row+=nLetters) { + std::pair<T, uint32_t> top_1 = std::make_pair(tensorData[row + 0], 0); + + for (uint32_t j = 1; j < nLetters; ++j) { + if (top_1.first < tensorData[row + j]) { + top_1.first = tensorData[row + j]; + top_1.second = j; + } + } + + double score = static_cast<int> (top_1.first); + vecResults[i].m_normalisedVal = scale * (score - zeroPoint); + vecResults[i].m_label = labels[top_1.second]; + vecResults[i].m_labelIdx = top_1.second; + } + + return true; + } + template bool AsrClassifier::GetTopResults<uint8_t>(TfLiteTensor* tensor, + std::vector<ClassificationResult>& vecResults, + const std::vector <std::string>& labels, + double scale, double zeroPoint); + template bool AsrClassifier::GetTopResults<int8_t>(TfLiteTensor* tensor, + std::vector<ClassificationResult>& vecResults, + const std::vector <std::string>& labels, + double scale, double zeroPoint); + + bool AsrClassifier::GetClassificationResults( + TfLiteTensor* outputTensor, + std::vector<ClassificationResult>& vecResults, + const std::vector <std::string>& labels, uint32_t topNCount, bool use_softmax) + { + UNUSED(use_softmax); + vecResults.clear(); + + constexpr int minTensorDims = static_cast<int>( + (Wav2LetterModel::ms_outputRowsIdx > Wav2LetterModel::ms_outputColsIdx)? + Wav2LetterModel::ms_outputRowsIdx : Wav2LetterModel::ms_outputColsIdx); + + constexpr uint32_t outColsIdx = Wav2LetterModel::ms_outputColsIdx; + + /* Sanity checks. */ + if (outputTensor == nullptr) { + printf_err("Output vector is null pointer.\n"); + return false; + } else if (outputTensor->dims->size < minTensorDims) { + printf_err("Output tensor expected to be %dD\n", minTensorDims); + return false; + } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) < topNCount) { + printf_err("Output vectors are smaller than %" PRIu32 "\n", topNCount); + return false; + } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) != labels.size()) { + printf("Output size doesn't match the labels' size\n"); + return false; + } + + if (topNCount != 1) { + warn("TopNCount value ignored in this implementation\n"); + } + + /* To return the floating point values, we need quantization parameters. */ + QuantParams quantParams = GetTensorQuantParams(outputTensor); + + bool resultState; + + switch (outputTensor->type) { + case kTfLiteUInt8: + resultState = this->GetTopResults<uint8_t>( + outputTensor, vecResults, + labels, quantParams.scale, + quantParams.offset); + break; + case kTfLiteInt8: + resultState = this->GetTopResults<int8_t>( + outputTensor, vecResults, + labels, quantParams.scale, + quantParams.offset); + break; + default: + printf_err("Tensor type %s not supported by classifier\n", + TfLiteTypeGetName(outputTensor->type)); + return false; + } + + if (!resultState) { + printf_err("Failed to get sorted set\n"); + return false; + } + + return true; + } + +} /* namespace app */ +} /* namespace arm */
\ No newline at end of file |