From 3c79893217bc632c9b0efa815091bef3c779490c Mon Sep 17 00:00:00 2001
From: alexander <alexander.efremov@arm.com>
Date: Fri, 26 Mar 2021 21:42:19 +0000
Subject: Opensource ML embedded evaluation kit

Change-Id: I12e807f19f5cacad7cef82572b6dd48252fd61fd
---
 tests/use_case/ad/InferenceTestAD.cc | 100 +++++++++++++++++++++++++++++++++++
 1 file changed, 100 insertions(+)
 create mode 100644 tests/use_case/ad/InferenceTestAD.cc

(limited to 'tests/use_case/ad/InferenceTestAD.cc')

diff --git a/tests/use_case/ad/InferenceTestAD.cc b/tests/use_case/ad/InferenceTestAD.cc
new file mode 100644
index 0000000..b87699d
--- /dev/null
+++ b/tests/use_case/ad/InferenceTestAD.cc
@@ -0,0 +1,100 @@
+/*
+ * 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 <catch.hpp>
+#include <random>
+
+#include "AdModel.hpp"
+#include "AdGoldenInput.hpp"
+#include "hal.h"
+#include "TensorFlowLiteMicro.hpp"
+
+#ifndef AD_FEATURE_VEC_DATA_SIZE
+#define AD_IN_FEATURE_VEC_DATA_SIZE (1024)
+#endif /* AD_FEATURE_VEC_DATA_SIZE */
+
+bool RunInference(arm::app::Model& model, const int8_t vec[])
+{
+    TfLiteTensor *inputTensor = model.GetInputTensor(0);
+    REQUIRE(inputTensor);
+
+    const size_t copySz = inputTensor->bytes < AD_IN_FEATURE_VEC_DATA_SIZE ? inputTensor->bytes : AD_IN_FEATURE_VEC_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> randomInput(inputTensor->bytes);
+    std::generate(std::begin(randomInput), std::end(randomInput), gen);
+
+    REQUIRE(RunInference(model, randomInput.data()));
+    return true;
+}
+
+template <typename T>
+void TestInference(const T *input_goldenFV, const T *output_goldenFV, arm::app::Model& model)
+{
+    REQUIRE(RunInference(model, (int8_t*)input_goldenFV));
+
+    TfLiteTensor *outputTensor = model.GetOutputTensor(0);
+
+    REQUIRE(outputTensor);
+    REQUIRE(outputTensor->bytes == AD_OUT_FEATURE_VEC_DATA_SIZE);
+    auto tensorData = tflite::GetTensorData<T>(outputTensor);
+    REQUIRE(tensorData);
+
+    for (size_t i = 0; i < outputTensor->bytes; i++)
+    {
+        REQUIRE((int)tensorData[i] == (int)((T)output_goldenFV[i]));
+    }
+}
+
+TEST_CASE("Running random inference with TensorFlow Lite Micro and AdModel Int8", "[AD][.]")
+{
+    arm::app::AdModel model{};
+
+    REQUIRE_FALSE(model.IsInited());
+    REQUIRE(model.Init());
+    REQUIRE(model.IsInited());
+
+    REQUIRE(RunInferenceRandom(model));
+}
+
+TEST_CASE("Running golden vector inference with TensorFlow Lite Micro and AdModel Int8", "[AD][.]")
+{
+    arm::app::AdModel model{};
+
+    REQUIRE_FALSE(model.IsInited());
+    REQUIRE(model.Init());
+    REQUIRE(model.IsInited());
+
+    TestInference(ad_golden_input, ad_golden_out, model);
+}
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