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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 "Classifier.hpp"
#include "hal.h"
#include "TensorFlowLiteMicro.hpp"
#include <vector>
#include <string>
#include <set>
#include <cstdint>
namespace arm {
namespace app {
template<typename T>
bool Classifier::_GetTopNResults(TfLiteTensor* tensor,
std::vector<ClassificationResult>& vecResults,
uint32_t topNCount,
const std::vector <std::string>& labels)
{
std::set<std::pair<T, uint32_t>> sortedSet;
/* NOTE: inputVec's size verification against labels should be
* checked by the calling/public function. */
T* tensorData = tflite::GetTensorData<T>(tensor);
/* Set initial elements. */
for (uint32_t i = 0; i < topNCount; ++i) {
sortedSet.insert({tensorData[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 < tensorData[i]) {
sortedSet.erase(*setFwdIter);
sortedSet.insert({tensorData[i], i});
setFwdIter = sortedSet.begin();
}
}
/* Final results' container. */
vecResults = std::vector<ClassificationResult>(topNCount);
/* For getting the floating point values, we need quantization parameters. */
QuantParams quantParams = GetTensorQuantParams(tensor);
/* Reset the iterator to the largest element - use reverse iterator. */
auto setRevIter = sortedSet.rbegin();
/* Populate results
* Note: we could combine this loop with the loop above, but that
* would, involve more multiplications and other operations.
**/
for (size_t i = 0; i < vecResults.size(); ++i, ++setRevIter) {
double score = static_cast<int> (setRevIter->first);
vecResults[i].m_normalisedVal = quantParams.scale *
(score - quantParams.offset);
vecResults[i].m_label = labels[setRevIter->second];
vecResults[i].m_labelIdx = setRevIter->second;
}
return true;
}
template<>
bool Classifier::_GetTopNResults<float>(TfLiteTensor* 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. */
float* tensorData = tflite::GetTensorData<float>(tensor);
/* Set initial elements. */
for (uint32_t i = 0; i < topNCount; ++i) {
sortedSet.insert({tensorData[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 < tensorData[i]) {
sortedSet.erase(*setFwdIter);
sortedSet.insert({tensorData[i], i});
setFwdIter = sortedSet.begin();
}
}
/* Final results' container. */
vecResults = std::vector<ClassificationResult>(topNCount);
/* Reset the iterator to the largest element - use reverse iterator. */
auto setRevIter = sortedSet.rbegin();
/* Populate results
* Note: we could combine this loop with the loop above, but that
* would, involve more multiplications and other operations.
**/
for (size_t i = 0; i < vecResults.size(); ++i, ++setRevIter) {
vecResults[i].m_normalisedVal = setRevIter->first;
vecResults[i].m_label = labels[setRevIter->second];
vecResults[i].m_labelIdx = setRevIter->second;
}
return true;
}
template bool Classifier::_GetTopNResults<uint8_t>(TfLiteTensor* tensor,
std::vector<ClassificationResult>& vecResults,
uint32_t topNCount, const std::vector <std::string>& labels);
template bool Classifier::_GetTopNResults<int8_t>(TfLiteTensor* tensor,
std::vector<ClassificationResult>& vecResults,
uint32_t topNCount, const std::vector <std::string>& labels);
bool Classifier::GetClassificationResults(
TfLiteTensor* outputTensor,
std::vector<ClassificationResult>& vecResults,
const std::vector <std::string>& labels, uint32_t topNCount)
{
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 %u\n", topNCount);
return false;
} else if (totalOutputSize != labels.size()) {
printf_err("Output size doesn't match the labels' size\n");
return false;
}
bool resultState;
vecResults.clear();
/* Get the top N results. */
switch (outputTensor->type) {
case kTfLiteUInt8:
resultState = _GetTopNResults<uint8_t>(outputTensor, vecResults, topNCount, labels);
break;
case kTfLiteInt8:
resultState = _GetTopNResults<int8_t>(outputTensor, vecResults, topNCount, labels);
break;
case kTfLiteFloat32:
resultState = _GetTopNResults<float>(outputTensor, vecResults, topNCount, labels);
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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