MLECO-3183: Refactoring application sources

Platform agnostic application sources are moved into application
api module with their own independent CMake projects.

Changes for MLECO-3080 also included - they create CMake projects
individial API's (again, platform agnostic) that dependent on the
common logic. The API for KWS_API "joint" API has been removed and
now the use case relies on individual KWS, and ASR API libraries.

Change-Id: I1f7748dc767abb3904634a04e0991b74ac7b756d
Signed-off-by: Kshitij Sisodia <kshitij.sisodia@arm.com>

1 files changed, 0 insertions, 169 deletions

diff --git a/source/application/main/Classifier.cc b/source/application/main/Classifier.cc
deleted file mode 100644
index 6fabebe..0000000
--- a/source/application/main/Classifier.cc
+++ /dev/null
@@ -1,169 +0,0 @@
-/*
- * 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 "TensorFlowLiteMicro.hpp"
-#include "PlatformMath.hpp"
-#include "log_macros.h"
-
-#include <vector>
-#include <string>
-#include <set>
-#include <cstdint>
-#include <cinttypes>
-
-
-namespace arm {
-namespace app {
-
-    void Classifier::SetVectorResults(std::set<std::pair<float, uint32_t>>& topNSet,
-                          std::vector<ClassificationResult>& vecResults,
-                          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) {
-            vecResults[i].m_normalisedVal = topNIter->first;
-            vecResults[i].m_label = labels[topNIter->second];
-            vecResults[i].m_labelIdx = topNIter->second;
-        }
-    }
-
-    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<float , uint32_t>> sortedSet;
-
-        /* NOTE: inputVec's size verification against labels should be
-         *       checked by the calling/public function. */
-
-        /* Set initial elements. */
-        for (uint32_t i = 0; i < topNCount; ++i) {
-            sortedSet.insert({tensor[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 < tensor[i]) {
-                sortedSet.erase(*setFwdIter);
-                sortedSet.insert({tensor[i], i});
-                setFwdIter = sortedSet.begin();
-            }
-        }
-
-        /* Final results' container. */
-        vecResults = std::vector<ClassificationResult>(topNCount);
-        SetVectorResults(sortedSet, vecResults, labels);
-
-        return true;
-    }
-
-    bool  Classifier::GetClassificationResults(
-        TfLiteTensor* outputTensor,
-        std::vector<ClassificationResult>& vecResults,
-        const std::vector <std::string>& labels,
-        uint32_t topNCount,
-        bool useSoftmax)
-    {
-        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> tensorData(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) {
-                    tensorData[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) {
-                    tensorData[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) {
-                    tensorData[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(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;
-        }
-
-        return true;
-    }
-} /* namespace app */
-} /* namespace arm */
\ No newline at end of file