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>
diff --git a/source/application/api/use_case/noise_reduction/src/RNNoiseModel.cc b/source/application/api/use_case/noise_reduction/src/RNNoiseModel.cc
new file mode 100644
index 0000000..457cda9
--- /dev/null
+++ b/source/application/api/use_case/noise_reduction/src/RNNoiseModel.cc
@@ -0,0 +1,96 @@
+/*
+ * 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 "RNNoiseModel.hpp"
+#include "log_macros.h"
+
+const tflite::MicroOpResolver& arm::app::RNNoiseModel::GetOpResolver()
+{
+ return this->m_opResolver;
+}
+
+bool arm::app::RNNoiseModel::EnlistOperations()
+{
+ this->m_opResolver.AddUnpack();
+ this->m_opResolver.AddFullyConnected();
+ this->m_opResolver.AddSplit();
+ this->m_opResolver.AddSplitV();
+ this->m_opResolver.AddAdd();
+ this->m_opResolver.AddLogistic();
+ this->m_opResolver.AddMul();
+ this->m_opResolver.AddSub();
+ this->m_opResolver.AddTanh();
+ this->m_opResolver.AddPack();
+ this->m_opResolver.AddReshape();
+ this->m_opResolver.AddQuantize();
+ this->m_opResolver.AddConcatenation();
+ this->m_opResolver.AddRelu();
+
+ if (kTfLiteOk == this->m_opResolver.AddEthosU()) {
+ info("Added %s support to op resolver\n",
+ tflite::GetString_ETHOSU());
+ } else {
+ printf_err("Failed to add Arm NPU support to op resolver.");
+ return false;
+ }
+ return true;
+}
+
+bool arm::app::RNNoiseModel::RunInference()
+{
+ return Model::RunInference();
+}
+
+void arm::app::RNNoiseModel::ResetGruState()
+{
+ for (auto& stateMapping: this->m_gruStateMap) {
+ TfLiteTensor* inputGruStateTensor = this->GetInputTensor(stateMapping.second);
+ auto* inputGruState = tflite::GetTensorData<int8_t>(inputGruStateTensor);
+ /* Initial value of states is 0, but this is affected by quantization zero point. */
+ auto quantParams = arm::app::GetTensorQuantParams(inputGruStateTensor);
+ memset(inputGruState, quantParams.offset, inputGruStateTensor->bytes);
+ }
+}
+
+bool arm::app::RNNoiseModel::CopyGruStates()
+{
+ std::vector<std::pair<size_t, std::vector<int8_t>>> tempOutGruStates;
+ /* Saving output states before copying them to input states to avoid output states modification in the tensor.
+ * tflu shares input and output tensors memory, thus writing to input tensor can change output tensor values. */
+ for (auto& stateMapping: this->m_gruStateMap) {
+ TfLiteTensor* outputGruStateTensor = this->GetOutputTensor(stateMapping.first);
+ std::vector<int8_t> tempOutGruState(outputGruStateTensor->bytes);
+ auto* outGruState = tflite::GetTensorData<int8_t>(outputGruStateTensor);
+ memcpy(tempOutGruState.data(), outGruState, outputGruStateTensor->bytes);
+ /* Index of the input tensor and the data to copy. */
+ tempOutGruStates.emplace_back(stateMapping.second, std::move(tempOutGruState));
+ }
+ /* Updating input GRU states with saved GRU output states. */
+ for (auto& stateMapping: tempOutGruStates) {
+ auto outputGruStateTensorData = stateMapping.second;
+ TfLiteTensor* inputGruStateTensor = this->GetInputTensor(stateMapping.first);
+ if (outputGruStateTensorData.size() != inputGruStateTensor->bytes) {
+ printf_err("Unexpected number of bytes for GRU state mapping. Input = %zuz, output = %zuz.\n",
+ inputGruStateTensor->bytes,
+ outputGruStateTensorData.size());
+ return false;
+ }
+ auto* inputGruState = tflite::GetTensorData<int8_t>(inputGruStateTensor);
+ auto* outGruState = outputGruStateTensorData.data();
+ memcpy(inputGruState, outGruState, inputGruStateTensor->bytes);
+ }
+ return true;
+}