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Diffstat (limited to 'source/application/api/use_case/kws/src/KwsClassifier.cc')
| -rw-r--r-- | source/application/api/use_case/kws/src/KwsClassifier.cc | 142 |
1 files changed, 142 insertions, 0 deletions
diff --git a/source/application/api/use_case/kws/src/KwsClassifier.cc b/source/application/api/use_case/kws/src/KwsClassifier.cc new file mode 100644 index 0000000..fe409b1 --- /dev/null +++ b/source/application/api/use_case/kws/src/KwsClassifier.cc @@ -0,0 +1,142 @@ +/* + * Copyright (c) 2022 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 "KwsClassifier.hpp" + +#include "TensorFlowLiteMicro.hpp" +#include "PlatformMath.hpp" +#include "log_macros.h" +#include "../include/KwsClassifier.hpp" + + +#include <vector> +#include <algorithm> +#include <string> +#include <set> +#include <cstdint> +#include <cinttypes> + + +namespace arm { +namespace app { + + bool KwsClassifier::GetClassificationResults(TfLiteTensor* outputTensor, + std::vector<ClassificationResult>& vecResults, const std::vector <std::string>& labels, + uint32_t topNCount, bool useSoftmax, std::vector<std::vector<float>>& resultHistory) + { + 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> resultData(totalOutputSize); + resultData.resize(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) { + resultData[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) { + resultData[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) { + resultData[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(resultData); + } + + /* If keeping track of recent results, update and take an average. */ + if (resultHistory.size() > 1) { + std::rotate(resultHistory.begin(), resultHistory.begin() + 1, resultHistory.end()); + resultHistory.back() = resultData; + AveragResults(resultHistory, resultData); + } + + /* Get the top N results. */ + resultState = GetTopNResults(resultData, vecResults, topNCount, labels); + + if (!resultState) { + printf_err("Failed to get top N results set\n"); + return false; + } + + return true; + } + + void app::KwsClassifier::AveragResults(const std::vector<std::vector<float>>& resultHistory, + std::vector<float>& averageResult) + { + /* Compute averages of each class across the window length. */ + float sum; + for (size_t j = 0; j < averageResult.size(); j++) { + sum = 0; + for (size_t i = 0; i < resultHistory.size(); i++) { + sum += resultHistory[i][j]; + } + averageResult[j] = (sum / resultHistory.size()); + } + } + +} /* namespace app */ +} /* namespace arm */
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