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/*
* SPDX-FileCopyrightText: Copyright 2022 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 "AdProcessing.hpp"
#include "AdModel.hpp"
namespace arm {
namespace app {
AdPreProcess::AdPreProcess(TfLiteTensor* inputTensor,
uint32_t melSpectrogramFrameLen,
uint32_t melSpectrogramFrameStride,
float adModelTrainingMean):
m_validInstance{false},
m_melSpectrogramFrameLen{melSpectrogramFrameLen},
m_melSpectrogramFrameStride{melSpectrogramFrameStride},
/**< Model is trained on features downsampled 2x */
m_inputResizeScale{2},
/**< We are choosing to move by 20 frames across the audio for each inference. */
m_numMelSpecVectorsInAudioStride{20},
m_audioDataStride{m_numMelSpecVectorsInAudioStride * melSpectrogramFrameStride},
m_melSpec{melSpectrogramFrameLen}
{
UNUSED(this->m_melSpectrogramFrameStride);
if (!inputTensor) {
printf_err("Invalid input tensor provided to pre-process\n");
return;
}
TfLiteIntArray* inputShape = inputTensor->dims;
if (!inputShape) {
printf_err("Invalid input tensor dims\n");
return;
}
const uint32_t kNumRows = inputShape->data[AdModel::ms_inputRowsIdx];
const uint32_t kNumCols = inputShape->data[AdModel::ms_inputColsIdx];
/* Deduce the data length required for 1 inference from the network parameters. */
this->m_audioDataWindowSize = (((this->m_inputResizeScale * kNumCols) - 1) *
melSpectrogramFrameStride) +
melSpectrogramFrameLen;
this->m_numReusedFeatureVectors = kNumRows -
(this->m_numMelSpecVectorsInAudioStride /
this->m_inputResizeScale);
this->m_melSpec.Init();
/* Creating a Mel Spectrogram sliding window for the data required for 1 inference.
* "resizing" done here by multiplying stride by resize scale. */
this->m_melWindowSlider = audio::SlidingWindow<const int16_t>(
nullptr, /* to be populated later. */
this->m_audioDataWindowSize,
melSpectrogramFrameLen,
melSpectrogramFrameStride * this->m_inputResizeScale);
/* Construct feature calculation function. */
this->m_featureCalc = GetFeatureCalculator(this->m_melSpec, inputTensor,
this->m_numReusedFeatureVectors,
adModelTrainingMean);
this->m_validInstance = true;
}
bool AdPreProcess::DoPreProcess(const void* input, size_t inputSize)
{
/* Check that we have a valid instance. */
if (!this->m_validInstance) {
printf_err("Invalid pre-processor instance\n");
return false;
}
/* We expect that we can traverse the size with which the MEL spectrogram
* sliding window was initialised with. */
if (!input || inputSize < this->m_audioDataWindowSize) {
printf_err("Invalid input provided for pre-processing\n");
return false;
}
/* We moved to the next window - set the features sliding to the new address. */
this->m_melWindowSlider.Reset(static_cast<const int16_t*>(input));
/* The first window does not have cache ready. */
const bool useCache = this->m_audioWindowIndex > 0 && this->m_numReusedFeatureVectors > 0;
/* Start calculating features inside one audio sliding window. */
while (this->m_melWindowSlider.HasNext()) {
const int16_t* melSpecWindow = this->m_melWindowSlider.Next();
std::vector<int16_t> melSpecAudioData = std::vector<int16_t>(
melSpecWindow,
melSpecWindow + this->m_melSpectrogramFrameLen);
/* Compute features for this window and write them to input tensor. */
this->m_featureCalc(melSpecAudioData,
this->m_melWindowSlider.Index(),
useCache,
this->m_numMelSpecVectorsInAudioStride,
this->m_inputResizeScale);
}
return true;
}
uint32_t AdPreProcess::GetAudioWindowSize()
{
return this->m_audioDataWindowSize;
}
uint32_t AdPreProcess::GetAudioDataStride()
{
return this->m_audioDataStride;
}
void AdPreProcess::SetAudioWindowIndex(uint32_t idx)
{
this->m_audioWindowIndex = idx;
}
AdPostProcess::AdPostProcess(TfLiteTensor* outputTensor) :
m_outputTensor {outputTensor}
{}
bool AdPostProcess::DoPostProcess()
{
switch (this->m_outputTensor->type) {
case kTfLiteInt8:
this->Dequantize<int8_t>();
break;
default:
printf_err("Unsupported tensor type");
return false;
}
math::MathUtils::SoftmaxF32(this->m_dequantizedOutputVec);
return true;
}
float AdPostProcess::GetOutputValue(uint32_t index)
{
if (index < this->m_dequantizedOutputVec.size()) {
return this->m_dequantizedOutputVec[index];
}
printf_err("Invalid index for output\n");
return 0.0;
}
std::function<void (std::vector<int16_t>&, int, bool, size_t, size_t)>
GetFeatureCalculator(audio::AdMelSpectrogram& melSpec,
TfLiteTensor* inputTensor,
size_t cacheSize,
float trainingMean)
{
std::function<void (std::vector<int16_t>&, size_t, bool, size_t, size_t)> melSpecFeatureCalc = nullptr;
TfLiteQuantization quant = inputTensor->quantization;
if (kTfLiteAffineQuantization == quant.type) {
auto* quantParams = static_cast<TfLiteAffineQuantization*>(quant.params);
const float quantScale = quantParams->scale->data[0];
const int quantOffset = quantParams->zero_point->data[0];
switch (inputTensor->type) {
case kTfLiteInt8: {
melSpecFeatureCalc = FeatureCalc<int8_t>(
inputTensor,
cacheSize,
[=, &melSpec](std::vector<int16_t>& audioDataWindow) {
return melSpec.MelSpecComputeQuant<int8_t>(
audioDataWindow,
quantScale,
quantOffset,
trainingMean);
}
);
break;
}
default:
printf_err("Tensor type %s not supported\n", TfLiteTypeGetName(inputTensor->type));
}
} else {
melSpecFeatureCalc = FeatureCalc<float>(
inputTensor,
cacheSize,
[=, &melSpec](
std::vector<int16_t>& audioDataWindow) {
return melSpec.ComputeMelSpec(
audioDataWindow,
trainingMean);
});
}
return melSpecFeatureCalc;
}
} /* namespace app */
} /* namespace arm */
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