MLECO-2354 MLECO-2355 MLECO-2356: Moving noise reduction to public repository
* Use RNNoise model from PMZ
* Add Noise reduction use-case
Signed-off-by: Richard burton <richard.burton@arm.com>
Change-Id: Ia8cc7ef102e22a5ff8bfbd3833594a4905a66057
diff --git a/tests/use_case/noise_reduction/InferenceTestRNNoise.cc b/tests/use_case/noise_reduction/InferenceTestRNNoise.cc
new file mode 100644
index 0000000..f32a460
--- /dev/null
+++ b/tests/use_case/noise_reduction/InferenceTestRNNoise.cc
@@ -0,0 +1,133 @@
+/*
+ * 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 "hal.h"
+#include "TensorFlowLiteMicro.hpp"
+#include "RNNoiseModel.hpp"
+#include "TestData_noise_reduction.hpp"
+
+#include <catch.hpp>
+#include <random>
+
+namespace test {
+namespace rnnoise {
+
+ bool RunInference(arm::app::Model& model, const std::vector<std::vector<int8_t>> inData)
+ {
+ for (size_t i = 0; i < model.GetNumInputs(); ++i) {
+ TfLiteTensor* inputTensor = model.GetInputTensor(i);
+ REQUIRE(inputTensor);
+ memcpy(inputTensor->data.data, inData[i].data(), inData[i].size());
+ }
+
+ return model.RunInference();
+ }
+
+ bool RunInferenceRandom(arm::app::Model& model)
+ {
+ std::random_device rndDevice;
+ std::mt19937 mersenneGen{rndDevice()};
+ std::uniform_int_distribution<short> dist {-128, 127};
+
+ auto gen = [&dist, &mersenneGen](){
+ return dist(mersenneGen);
+ };
+
+ std::vector<std::vector<int8_t>> randomInput{NUMBER_OF_IFM_FILES};
+ for (size_t i = 0; i < model.GetNumInputs(); ++i) {
+ TfLiteTensor *inputTensor = model.GetInputTensor(i);
+ REQUIRE(inputTensor);
+ randomInput[i].resize(inputTensor->bytes);
+ std::generate(std::begin(randomInput[i]), std::end(randomInput[i]), gen);
+ }
+
+ REQUIRE(RunInference(model, randomInput));
+ return true;
+ }
+
+ TEST_CASE("Running random inference with Tflu and RNNoise Int8", "[RNNoise]")
+ {
+ arm::app::RNNoiseModel model{};
+
+ REQUIRE_FALSE(model.IsInited());
+ REQUIRE(model.Init());
+ REQUIRE(model.IsInited());
+
+ REQUIRE(RunInferenceRandom(model));
+ }
+
+ template<typename T>
+ void TestInference(const std::vector<std::vector<T>> input_goldenFV, const std::vector<std::vector<T>> output_goldenFV, arm::app::Model& model)
+ {
+ for (size_t i = 0; i < model.GetNumInputs(); ++i) {
+ TfLiteTensor* inputTensor = model.GetInputTensor(i);
+ REQUIRE(inputTensor);
+ }
+
+ REQUIRE(RunInference(model, input_goldenFV));
+
+ for (size_t i = 0; i < model.GetNumOutputs(); ++i) {
+ TfLiteTensor *outputTensor = model.GetOutputTensor(i);
+
+ REQUIRE(outputTensor);
+ auto tensorData = tflite::GetTensorData<T>(outputTensor);
+ REQUIRE(tensorData);
+
+ for (size_t j = 0; j < outputTensor->bytes; j++) {
+ REQUIRE(static_cast<int>(tensorData[j]) == static_cast<int>((output_goldenFV[i][j])));
+ }
+ }
+ }
+
+ TEST_CASE("Running inference with Tflu and RNNoise Int8", "[RNNoise]")
+ {
+ std::vector<std::vector<int8_t>> goldenInputFV {NUMBER_OF_IFM_FILES};
+ std::vector<std::vector<int8_t>> goldenOutputFV {NUMBER_OF_OFM_FILES};
+
+ std::array<size_t, NUMBER_OF_IFM_FILES> inputSizes = {IFM_0_DATA_SIZE,
+ IFM_1_DATA_SIZE,
+ IFM_2_DATA_SIZE,
+ IFM_3_DATA_SIZE};
+
+ std::array<size_t, NUMBER_OF_OFM_FILES> outputSizes = {OFM_0_DATA_SIZE,
+ OFM_1_DATA_SIZE,
+ OFM_2_DATA_SIZE,
+ OFM_3_DATA_SIZE,
+ OFM_4_DATA_SIZE};
+
+ for (uint32_t i = 0 ; i < NUMBER_OF_IFM_FILES; ++i) {
+ goldenInputFV[i].resize(inputSizes[i]);
+ std::memcpy(goldenInputFV[i].data(), get_ifm_data_array(i), inputSizes[i]);
+ }
+ for (uint32_t i = 0 ; i < NUMBER_OF_OFM_FILES; ++i) {
+ goldenOutputFV[i].resize(outputSizes[i]);
+ std::memcpy(goldenOutputFV[i].data(), get_ofm_data_array(i), outputSizes[i]);
+ }
+
+ DYNAMIC_SECTION("Executing inference with re-init")
+ {
+ arm::app::RNNoiseModel model{};
+
+ REQUIRE_FALSE(model.IsInited());
+ REQUIRE(model.Init());
+ REQUIRE(model.IsInited());
+
+ TestInference<int8_t>(goldenInputFV, goldenOutputFV, model);
+ }
+ }
+
+} /* namespace rnnoise */
+} /* namespace test */