MLECO-3164: Additional refactoring of KWS API
Part 1
* Add KwsClassifier
* KwsPostProcess can now be told to average results
* Averaging is handlded by KwsClassifier
* Current sliding window index is now an argument of DoPreProcess
Change-Id: I07626da595ad1cbd982e8366f0d1bb56d1040459
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 */
\ No newline at end of file