From 76a1580861210e0310db23acbc29e1064ae30ead Mon Sep 17 00:00:00 2001
From: Kshitij Sisodia <kshitij.sisodia@arm.com>
Date: Fri, 24 Dec 2021 11:05:11 +0000
Subject: MLECO-2599: Replace DSCNN with MicroNet for KWS

Added SoftMax function to Mathutils to allow MicroNet
to output probability as it does not nativelu have this layer.
Minor refactoring to accommodate Softmax Calculations
Extensive renaming and updating of documentation and resource download script.
Added SoftMax function to Mathutils to allow MicroNet
to output probability.

Change-Id: I7cbbda1024d14b85c9ac1beea7ca8fbffd0b6eb5
Signed-off-by: Liam Barry <liam.barry@arm.com>
---
 tests/use_case/kws/InferenceTestMicroNetKws.cc | 107 +++++++++++++++++++++++++
 1 file changed, 107 insertions(+)
 create mode 100644 tests/use_case/kws/InferenceTestMicroNetKws.cc

(limited to 'tests/use_case/kws/InferenceTestMicroNetKws.cc')

diff --git a/tests/use_case/kws/InferenceTestMicroNetKws.cc b/tests/use_case/kws/InferenceTestMicroNetKws.cc
new file mode 100644
index 0000000..e6e7753
--- /dev/null
+++ b/tests/use_case/kws/InferenceTestMicroNetKws.cc
@@ -0,0 +1,107 @@
+/*
+ * 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 "MicroNetKwsModel.hpp"
+#include "hal.h"
+#include "TestData_kws.hpp"
+#include "TensorFlowLiteMicro.hpp"
+
+#include <catch.hpp>
+#include <random>
+
+using namespace test;
+
+bool RunInference(arm::app::Model& model, const int8_t vec[])
+{
+    TfLiteTensor* inputTensor = model.GetInputTensor(0);
+    REQUIRE(inputTensor);
+
+    const size_t copySz = inputTensor->bytes < IFM_0_DATA_SIZE ?
+                            inputTensor->bytes :
+                            IFM_0_DATA_SIZE;
+    memcpy(inputTensor->data.data, vec, copySz);
+
+    return model.RunInference();
+}
+
+bool RunInferenceRandom(arm::app::Model& model)
+{
+    TfLiteTensor* inputTensor = model.GetInputTensor(0);
+    REQUIRE(inputTensor);
+
+    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<int8_t> randomAudio(inputTensor->bytes);
+    std::generate(std::begin(randomAudio), std::end(randomAudio), gen);
+
+    REQUIRE(RunInference(model, randomAudio.data()));
+    return true;
+}
+
+template<typename T>
+void TestInference(const T* input_goldenFV, const T* output_goldenFV, arm::app::Model& model)
+{
+    REQUIRE(RunInference(model, input_goldenFV));
+
+    TfLiteTensor* outputTensor = model.GetOutputTensor(0);
+
+    REQUIRE(outputTensor);
+    REQUIRE(outputTensor->bytes == OFM_0_DATA_SIZE);
+    auto tensorData = tflite::GetTensorData<T>(outputTensor);
+    REQUIRE(tensorData);
+
+    for (size_t i = 0; i < outputTensor->bytes; i++) {
+        REQUIRE(static_cast<int>(tensorData[i]) == static_cast<int>(((T)output_goldenFV[i])));
+    }
+}
+
+TEST_CASE("Running random inference with TensorFlow Lite Micro and MicroNetKwsModel Int8", "[MicroNetKws]")
+{
+    arm::app::MicroNetKwsModel model{};
+
+    REQUIRE_FALSE(model.IsInited());
+    REQUIRE(model.Init());
+    REQUIRE(model.IsInited());
+
+    REQUIRE(RunInferenceRandom(model));
+}
+
+TEST_CASE("Running inference with TensorFlow Lite Micro and MicroNetKwsModel int8", "[MicroNetKws]")
+{
+    REQUIRE(NUMBER_OF_IFM_FILES == NUMBER_OF_OFM_FILES);
+    for (uint32_t i = 0 ; i < NUMBER_OF_IFM_FILES; ++i) {
+        const int8_t* input_goldenFV = get_ifm_data_array(i);;
+        const int8_t* output_goldenFV = get_ofm_data_array(i);
+
+        DYNAMIC_SECTION("Executing inference with re-init")
+        {
+            arm::app::MicroNetKwsModel model{};
+
+            REQUIRE_FALSE(model.IsInited());
+            REQUIRE(model.Init());
+            REQUIRE(model.IsInited());
+
+            TestInference<int8_t>(input_goldenFV, output_goldenFV, model);
+
+        }
+    }
+}
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