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/*
* SPDX-FileCopyrightText: Copyright 2021-2023 Arm Limited and/or its affiliates <open-source-office@arm.com>
* 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 health 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 %" PRIu32", 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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