Opensource ML embedded evaluation kit
Change-Id: I12e807f19f5cacad7cef82572b6dd48252fd61fd
diff --git a/source/application/main/Classifier.cc b/source/application/main/Classifier.cc
new file mode 100644
index 0000000..bc2c378
--- /dev/null
+++ b/source/application/main/Classifier.cc
@@ -0,0 +1,191 @@
+/*
+ * 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 "hal.h"
+#include "TensorFlowLiteMicro.hpp"
+
+#include <vector>
+#include <string>
+#include <set>
+#include <cstdint>
+
+namespace arm {
+namespace app {
+
+ template<typename T>
+ bool Classifier::_GetTopNResults(TfLiteTensor* tensor,
+ std::vector<ClassificationResult>& vecResults,
+ uint32_t topNCount,
+ const std::vector <std::string>& labels)
+ {
+ std::set<std::pair<T, 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});
+ }
+
+ /* 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 < tensorData[i]) {
+ sortedSet.erase(*setFwdIter);
+ sortedSet.insert({tensorData[i], i});
+ setFwdIter = sortedSet.begin();
+ }
+ }
+
+ /* Final results' container. */
+ vecResults = std::vector<ClassificationResult>(topNCount);
+
+ /* For getting the floating point values, we need quantization parameters. */
+ QuantParams quantParams = GetTensorQuantParams(tensor);
+
+ /* Reset the iterator to the largest element - use reverse iterator. */
+ auto setRevIter = sortedSet.rbegin();
+
+ /* Populate results
+ * Note: we could combine this loop with the loop above, but that
+ * would, involve more multiplications and other operations.
+ **/
+ for (size_t i = 0; i < vecResults.size(); ++i, ++setRevIter) {
+ double score = static_cast<int> (setRevIter->first);
+ vecResults[i].m_normalisedVal = quantParams.scale *
+ (score - quantParams.offset);
+ vecResults[i].m_label = labels[setRevIter->second];
+ vecResults[i].m_labelIdx = setRevIter->second;
+ }
+
+ return true;
+ }
+
+ template<>
+ bool Classifier::_GetTopNResults<float>(TfLiteTensor* 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. */
+ float* tensorData = tflite::GetTensorData<float>(tensor);
+
+ /* Set initial elements. */
+ for (uint32_t i = 0; i < topNCount; ++i) {
+ sortedSet.insert({tensorData[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 < tensorData[i]) {
+ sortedSet.erase(*setFwdIter);
+ sortedSet.insert({tensorData[i], i});
+ setFwdIter = sortedSet.begin();
+ }
+ }
+
+ /* Final results' container. */
+ vecResults = std::vector<ClassificationResult>(topNCount);
+
+ /* Reset the iterator to the largest element - use reverse iterator. */
+ auto setRevIter = sortedSet.rbegin();
+
+ /* Populate results
+ * Note: we could combine this loop with the loop above, but that
+ * would, involve more multiplications and other operations.
+ **/
+ for (size_t i = 0; i < vecResults.size(); ++i, ++setRevIter) {
+ vecResults[i].m_normalisedVal = setRevIter->first;
+ vecResults[i].m_label = labels[setRevIter->second];
+ vecResults[i].m_labelIdx = setRevIter->second;
+ }
+
+ 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)
+ {
+ 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 %u\n", topNCount);
+ return false;
+ } else if (totalOutputSize != labels.size()) {
+ printf_err("Output size doesn't match the labels' size\n");
+ return false;
+ }
+
+ bool resultState;
+ vecResults.clear();
+
+ /* Get the top N results. */
+ switch (outputTensor->type) {
+ case kTfLiteUInt8:
+ resultState = _GetTopNResults<uint8_t>(outputTensor, vecResults, topNCount, labels);
+ break;
+ case kTfLiteInt8:
+ resultState = _GetTopNResults<int8_t>(outputTensor, vecResults, topNCount, labels);
+ break;
+ case kTfLiteFloat32:
+ resultState = _GetTopNResults<float>(outputTensor, vecResults, topNCount, labels);
+ break;
+ default:
+ printf_err("Tensor type %s not supported by classifier\n", TfLiteTypeGetName(outputTensor->type));
+ return false;
+ }
+
+ if (!resultState) {
+ printf_err("Failed to get sorted set\n");
+ return false;
+ }
+
+ return true;
+ }
+
+} /* namespace app */
+} /* namespace arm */
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