diff options
Diffstat (limited to 'source/application/main/Classifier.cc')
| -rw-r--r-- | source/application/main/Classifier.cc | 191 |
1 files changed, 191 insertions, 0 deletions
diff --git a/source/application/main/Classifier.cc b/source/application/main/Classifier.cc new file mode 100644 index 0000000..bc2c378 --- /dev/null +++ b/source/application/main/Classifier.cc @@ -0,0 +1,191 @@ +/* + * 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 */
\ No newline at end of file |