| /* |
| * SPDX-FileCopyrightText: Copyright 2022-2023 Arm Limited and/or its affiliates <open-source-office@arm.com> |
| * 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]; |
| } |
| |
| /* Health check */ |
| 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 */ |