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/*
* 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 */
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