diff options
Diffstat (limited to 'source/application/main/Classifier.cc')
-rw-r--r-- | source/application/main/Classifier.cc | 105 |
1 files changed, 53 insertions, 52 deletions
diff --git a/source/application/main/Classifier.cc b/source/application/main/Classifier.cc index c5519fb..a6ff532 100644 --- a/source/application/main/Classifier.cc +++ b/source/application/main/Classifier.cc @@ -24,61 +24,40 @@ #include <set> #include <cstdint> #include <inttypes.h> +#include "PlatformMath.hpp" namespace arm { namespace app { - template<typename T> - void SetVectorResults(std::set<std::pair<T, uint32_t>>& topNSet, + void Classifier::SetVectorResults(std::set<std::pair<float, uint32_t>>& topNSet, std::vector<ClassificationResult>& vecResults, - TfLiteTensor* tensor, - const std::vector <std::string>& labels) { - - /* For getting the floating point values, we need quantization parameters. */ - QuantParams quantParams = GetTensorQuantParams(tensor); + 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) { - T score = topNIter->first; - vecResults[i].m_normalisedVal = quantParams.scale * (score - quantParams.offset); - vecResults[i].m_label = labels[topNIter->second]; - vecResults[i].m_labelIdx = topNIter->second; - } - } - - template<> - void SetVectorResults<float>(std::set<std::pair<float, uint32_t>>& topNSet, - std::vector<ClassificationResult>& vecResults, - TfLiteTensor* tensor, - const std::vector <std::string>& labels) { - UNUSED(tensor); - /* 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; } - } - template<typename T> - bool Classifier::GetTopNResults(TfLiteTensor* tensor, + 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<T, uint32_t>> sortedSet; + + std::set<std::pair<float , 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}); + sortedSet.insert({tensor[i], i}); } /* Initialise iterator. */ @@ -86,33 +65,26 @@ namespace app { /* Scan through the rest of elements with compare operations. */ for (uint32_t i = topNCount; i < labels.size(); ++i) { - if (setFwdIter->first < tensorData[i]) { + if (setFwdIter->first < tensor[i]) { sortedSet.erase(*setFwdIter); - sortedSet.insert({tensorData[i], i}); + sortedSet.insert({tensor[i], i}); setFwdIter = sortedSet.begin(); } } /* Final results' container. */ vecResults = std::vector<ClassificationResult>(topNCount); - - SetVectorResults<T>(sortedSet, vecResults, tensor, labels); + SetVectorResults(sortedSet, vecResults, labels); 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) + const std::vector <std::string>& labels, + uint32_t topNCount, + bool useSoftmax) { if (outputTensor == nullptr) { printf_err("Output vector is null pointer.\n"); @@ -120,7 +92,7 @@ namespace app { } uint32_t totalOutputSize = 1; - for (int inputDim = 0; inputDim < outputTensor->dims->size; inputDim++){ + for (int inputDim = 0; inputDim < outputTensor->dims->size; inputDim++) { totalOutputSize *= outputTensor->dims->data[inputDim]; } @@ -139,22 +111,52 @@ namespace app { bool resultState; vecResults.clear(); - /* Get the top N results. */ + /* 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: - resultState = GetTopNResults<uint8_t>(outputTensor, vecResults, topNCount, labels); + 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: - resultState = GetTopNResults<int8_t>(outputTensor, vecResults, topNCount, labels); + } + 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: - resultState = GetTopNResults<float>(outputTensor, vecResults, topNCount, labels); + } + 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)); + 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; @@ -162,6 +164,5 @@ namespace app { return true; } - } /* namespace app */ } /* namespace arm */
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