Opensource ML embedded evaluation kit
Change-Id: I12e807f19f5cacad7cef82572b6dd48252fd61fd
diff --git a/tests/use_case/ad/InferenceTestAD.cc b/tests/use_case/ad/InferenceTestAD.cc
new file mode 100644
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+++ b/tests/use_case/ad/InferenceTestAD.cc
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+/*
+ * 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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