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/*
* SPDX-FileCopyrightText: Copyright 2021-2022 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 "Classifier.hpp"
#include "TensorFlowLiteMicro.hpp"
#include "PlatformMath.hpp"
#include "log_macros.h"
#include <vector>
#include <string>
#include <set>
#include <cstdint>
#include <cinttypes>
namespace arm {
namespace app {
void Classifier::SetVectorResults(std::set<std::pair<float, uint32_t>>& topNSet,
std::vector<ClassificationResult>& vecResults,
const std::vector <std::string>& labels)
{
/* Reset the iterator to the largest element - use reverse iterator. */
auto topNIter = topNSet.rbegin();
for (size_t i = 0; i < vecResults.size() && topNIter != topNSet.rend(); ++i, ++topNIter) {
vecResults[i].m_normalisedVal = topNIter->first;
vecResults[i].m_label = labels[topNIter->second];
vecResults[i].m_labelIdx = topNIter->second;
}
}
bool Classifier::GetTopNResults(const std::vector<float>& tensor,
std::vector<ClassificationResult>& vecResults,
uint32_t topNCount, const std::vector <std::string>& labels)
{
std::set<std::pair<float , uint32_t>> sortedSet;
/* NOTE: inputVec's size verification against labels should be
* checked by the calling/public function. */
/* Set initial elements. */
for (uint32_t i = 0; i < topNCount; ++i) {
sortedSet.insert({tensor[i], i});
}
/* Initialise iterator. */
auto setFwdIter = sortedSet.begin();
/* Scan through the rest of elements with compare operations. */
for (uint32_t i = topNCount; i < labels.size(); ++i) {
if (setFwdIter->first < tensor[i]) {
sortedSet.erase(*setFwdIter);
sortedSet.insert({tensor[i], i});
setFwdIter = sortedSet.begin();
}
}
/* Final results' container. */
vecResults = std::vector<ClassificationResult>(topNCount);
SetVectorResults(sortedSet, vecResults, labels);
return true;
}
bool Classifier::GetClassificationResults(TfLiteTensor* outputTensor,
std::vector<ClassificationResult>& vecResults, const std::vector <std::string>& labels,
uint32_t topNCount, bool useSoftmax)
{
if (outputTensor == nullptr) {
printf_err("Output vector is null pointer.\n");
return false;
}
uint32_t totalOutputSize = 1;
for (int inputDim = 0; inputDim < outputTensor->dims->size; inputDim++) {
totalOutputSize *= outputTensor->dims->data[inputDim];
}
/* Sanity checks. */
if (totalOutputSize < topNCount) {
printf_err("Output vector is smaller than %" PRIu32 "\n", topNCount);
return false;
} else if (totalOutputSize != labels.size()) {
printf_err("Output size doesn't match the labels' size\n");
return false;
} else if (topNCount == 0) {
printf_err("Top N results cannot be zero\n");
return false;
}
bool resultState;
vecResults.clear();
/* De-Quantize Output Tensor */
QuantParams quantParams = GetTensorQuantParams(outputTensor);
/* Floating point tensor data to be populated
* NOTE: The assumption here is that the output tensor size isn't too
* big and therefore, there's neglibible impact on heap usage. */
std::vector<float> tensorData(totalOutputSize);
/* Populate the floating point buffer */
switch (outputTensor->type) {
case kTfLiteUInt8: {
uint8_t *tensor_buffer = tflite::GetTensorData<uint8_t>(outputTensor);
for (size_t i = 0; i < totalOutputSize; ++i) {
tensorData[i] = quantParams.scale *
(static_cast<float>(tensor_buffer[i]) - quantParams.offset);
}
break;
}
case kTfLiteInt8: {
int8_t *tensor_buffer = tflite::GetTensorData<int8_t>(outputTensor);
for (size_t i = 0; i < totalOutputSize; ++i) {
tensorData[i] = quantParams.scale *
(static_cast<float>(tensor_buffer[i]) - quantParams.offset);
}
break;
}
case kTfLiteFloat32: {
float *tensor_buffer = tflite::GetTensorData<float>(outputTensor);
for (size_t i = 0; i < totalOutputSize; ++i) {
tensorData[i] = tensor_buffer[i];
}
break;
}
default:
printf_err("Tensor type %s not supported by classifier\n",
TfLiteTypeGetName(outputTensor->type));
return false;
}
if (useSoftmax) {
math::MathUtils::SoftmaxF32(tensorData);
}
/* Get the top N results. */
resultState = GetTopNResults(tensorData, vecResults, topNCount, labels);
if (!resultState) {
printf_err("Failed to get top N results set\n");
return false;
}
return true;
}
} /* namespace app */
} /* namespace arm */
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