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Diffstat (limited to 'source/application/api/common/source/Classifier.cc')
| -rw-r--r-- | source/application/api/common/source/Classifier.cc | 169 |
1 files changed, 169 insertions, 0 deletions
diff --git a/source/application/api/common/source/Classifier.cc b/source/application/api/common/source/Classifier.cc new file mode 100644 index 0000000..6fabebe --- /dev/null +++ b/source/application/api/common/source/Classifier.cc @@ -0,0 +1,169 @@ +/* + * 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 "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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