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Diffstat (limited to 'source/use_case/kws_asr/src/Wav2LetterPostprocess.cc')
| -rw-r--r-- | source/use_case/kws_asr/src/Wav2LetterPostprocess.cc | 214 |
1 files changed, 0 insertions, 214 deletions
diff --git a/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc b/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc deleted file mode 100644 index 42f434e..0000000 --- a/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc +++ /dev/null @@ -1,214 +0,0 @@ -/* - * Copyright (c) 2021-2022 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 "Wav2LetterPostprocess.hpp" - -#include "Wav2LetterModel.hpp" -#include "log_macros.h" - -#include <cmath> - -namespace arm { -namespace app { - - AsrPostProcess::AsrPostProcess(TfLiteTensor* outputTensor, AsrClassifier& classifier, - const std::vector<std::string>& labels, std::vector<ClassificationResult>& results, - const uint32_t outputContextLen, - const uint32_t blankTokenIdx, const uint32_t reductionAxisIdx - ): - m_classifier(classifier), - m_outputTensor(outputTensor), - m_labels{labels}, - m_results(results), - m_outputContextLen(outputContextLen), - m_countIterations(0), - m_blankTokenIdx(blankTokenIdx), - m_reductionAxisIdx(reductionAxisIdx) - { - this->m_outputInnerLen = AsrPostProcess::GetOutputInnerLen(this->m_outputTensor, this->m_outputContextLen); - this->m_totalLen = (2 * this->m_outputContextLen + this->m_outputInnerLen); - } - - bool AsrPostProcess::DoPostProcess() - { - /* Basic checks. */ - if (!this->IsInputValid(this->m_outputTensor, this->m_reductionAxisIdx)) { - return false; - } - - /* Irrespective of tensor type, we use unsigned "byte" */ - auto* ptrData = tflite::GetTensorData<uint8_t>(this->m_outputTensor); - const uint32_t elemSz = AsrPostProcess::GetTensorElementSize(this->m_outputTensor); - - /* Other sanity checks. */ - if (0 == elemSz) { - printf_err("Tensor type not supported for post processing\n"); - return false; - } else if (elemSz * this->m_totalLen > this->m_outputTensor->bytes) { - printf_err("Insufficient number of tensor bytes\n"); - return false; - } - - /* Which axis do we need to process? */ - switch (this->m_reductionAxisIdx) { - case Wav2LetterModel::ms_outputRowsIdx: - this->EraseSectionsRowWise( - ptrData, elemSz * this->m_outputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx], - this->m_lastIteration); - break; - default: - printf_err("Unsupported axis index: %" PRIu32 "\n", this->m_reductionAxisIdx); - return false; - } - this->m_classifier.GetClassificationResults(this->m_outputTensor, - this->m_results, this->m_labels, 1); - - return true; - } - - bool AsrPostProcess::IsInputValid(TfLiteTensor* tensor, const uint32_t axisIdx) const - { - if (nullptr == tensor) { - return false; - } - - if (static_cast<int>(axisIdx) >= tensor->dims->size) { - printf_err("Invalid axis index: %" PRIu32 "; Max: %d\n", - axisIdx, tensor->dims->size); - return false; - } - - if (static_cast<int>(this->m_totalLen) != - tensor->dims->data[axisIdx]) { - printf_err("Unexpected tensor dimension for axis %d, got %d, \n", - axisIdx, tensor->dims->data[axisIdx]); - return false; - } - - return true; - } - - uint32_t AsrPostProcess::GetTensorElementSize(TfLiteTensor* tensor) - { - switch(tensor->type) { - case kTfLiteUInt8: - case kTfLiteInt8: - return 1; - case kTfLiteInt16: - return 2; - case kTfLiteInt32: - case kTfLiteFloat32: - return 4; - default: - printf_err("Unsupported tensor type %s\n", - TfLiteTypeGetName(tensor->type)); - } - - return 0; - } - - bool AsrPostProcess::EraseSectionsRowWise( - uint8_t* ptrData, - const uint32_t strideSzBytes, - const bool lastIteration) - { - /* In this case, the "zero-ing" is quite simple as the region - * to be zeroed sits in contiguous memory (row-major). */ - const uint32_t eraseLen = strideSzBytes * this->m_outputContextLen; - - /* Erase left context? */ - if (this->m_countIterations > 0) { - /* Set output of each classification window to the blank token. */ - std::memset(ptrData, 0, eraseLen); - for (size_t windowIdx = 0; windowIdx < this->m_outputContextLen; windowIdx++) { - ptrData[windowIdx*strideSzBytes + this->m_blankTokenIdx] = 1; - } - } - - /* Erase right context? */ - if (false == lastIteration) { - uint8_t* rightCtxPtr = ptrData + (strideSzBytes * (this->m_outputContextLen + this->m_outputInnerLen)); - /* Set output of each classification window to the blank token. */ - std::memset(rightCtxPtr, 0, eraseLen); - for (size_t windowIdx = 0; windowIdx < this->m_outputContextLen; windowIdx++) { - rightCtxPtr[windowIdx*strideSzBytes + this->m_blankTokenIdx] = 1; - } - } - - if (lastIteration) { - this->m_countIterations = 0; - } else { - ++this->m_countIterations; - } - - return true; - } - - uint32_t AsrPostProcess::GetNumFeatureVectors(const Model& model) - { - TfLiteTensor* inputTensor = model.GetInputTensor(0); - const int inputRows = std::max(inputTensor->dims->data[Wav2LetterModel::ms_inputRowsIdx], 0); - if (inputRows == 0) { - printf_err("Error getting number of input rows for axis: %" PRIu32 "\n", - Wav2LetterModel::ms_inputRowsIdx); - } - return inputRows; - } - - uint32_t AsrPostProcess::GetOutputInnerLen(const TfLiteTensor* outputTensor, const uint32_t outputCtxLen) - { - const uint32_t outputRows = std::max(outputTensor->dims->data[Wav2LetterModel::ms_outputRowsIdx], 0); - if (outputRows == 0) { - printf_err("Error getting number of output rows for axis: %" PRIu32 "\n", - Wav2LetterModel::ms_outputRowsIdx); - } - - /* Watching for underflow. */ - int innerLen = (outputRows - (2 * outputCtxLen)); - - return std::max(innerLen, 0); - } - - uint32_t AsrPostProcess::GetOutputContextLen(const Model& model, const uint32_t inputCtxLen) - { - const uint32_t inputRows = AsrPostProcess::GetNumFeatureVectors(model); - const uint32_t inputInnerLen = inputRows - (2 * inputCtxLen); - constexpr uint32_t ms_outputRowsIdx = Wav2LetterModel::ms_outputRowsIdx; - - /* Check to make sure that the input tensor supports the above - * context and inner lengths. */ - if (inputRows <= 2 * inputCtxLen || inputRows <= inputInnerLen) { - printf_err("Input rows not compatible with ctx of %" PRIu32 "\n", - inputCtxLen); - return 0; - } - - TfLiteTensor* outputTensor = model.GetOutputTensor(0); - const uint32_t outputRows = std::max(outputTensor->dims->data[ms_outputRowsIdx], 0); - if (outputRows == 0) { - printf_err("Error getting number of output rows for axis: %" PRIu32 "\n", - Wav2LetterModel::ms_outputRowsIdx); - return 0; - } - - const float inOutRowRatio = static_cast<float>(inputRows) / - static_cast<float>(outputRows); - - return std::round(static_cast<float>(inputCtxLen) / inOutRowRatio); - } - -} /* namespace app */ -} /* namespace arm */
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