Opensource ML embedded evaluation kit
Change-Id: I12e807f19f5cacad7cef82572b6dd48252fd61fd
diff --git a/tests/use_case/img_class/InferenceTestMobilenetV2.cc b/tests/use_case/img_class/InferenceTestMobilenetV2.cc
new file mode 100644
index 0000000..698382f
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+++ b/tests/use_case/img_class/InferenceTestMobilenetV2.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 "hal.h"
+#include "ImageUtils.hpp"
+#include "MobileNetModel.hpp"
+#include "TensorFlowLiteMicro.hpp"
+#include "TestData_img_class.hpp"
+
+#include <catch.hpp>
+
+
+bool RunInference(arm::app::Model& model, const uint8_t imageData[])
+{
+ TfLiteTensor* inputTensor = model.GetInputTensor(0);
+ REQUIRE(inputTensor);
+
+ const size_t copySz = inputTensor->bytes < IFM_DATA_SIZE ?
+ inputTensor->bytes :
+ IFM_DATA_SIZE;
+ memcpy(inputTensor->data.data, imageData, copySz);
+
+ if(model.IsDataSigned()){
+ convertImgIoInt8(inputTensor->data.data, copySz);
+ }
+
+ return model.RunInference();
+}
+
+template<typename T>
+void TestInference(int imageIdx, arm::app::Model& model, T tolerance) {
+ auto image = get_ifm_data_array(imageIdx);
+ auto goldenFV = get_ofm_data_array(imageIdx);
+
+ REQUIRE(RunInference(model, image));
+
+ TfLiteTensor* outputTensor = model.GetOutputTensor(0);
+
+ REQUIRE(outputTensor);
+ REQUIRE(outputTensor->bytes == OFM_DATA_SIZE);
+ auto tensorData = tflite::GetTensorData<T>(outputTensor);
+ REQUIRE(tensorData);
+
+ for (size_t i = 0; i < outputTensor->bytes; i++) {
+ REQUIRE((int)tensorData[i] == Approx((int)((T)goldenFV[i])).epsilon(tolerance));
+ }
+}
+
+
+TEST_CASE("Running inference with TensorFlow Lite Micro and MobileNeV2 Uint8", "[MobileNetV2]")
+{
+ SECTION("Executing inferences sequentially")
+ {
+ arm::app::MobileNetModel model{};
+
+ REQUIRE_FALSE(model.IsInited());
+ REQUIRE(model.Init());
+ REQUIRE(model.IsInited());
+
+ for (uint32_t i = 0 ; i < NUMBER_OF_FM_FILES; ++i) {
+ TestInference<uint8_t>(i, model, 1);
+ }
+ }
+
+ for (uint32_t i = 0 ; i < NUMBER_OF_FM_FILES; ++i) {
+ DYNAMIC_SECTION("Executing inference with re-init")
+ {
+ arm::app::MobileNetModel model{};
+
+ REQUIRE_FALSE(model.IsInited());
+ REQUIRE(model.Init());
+ REQUIRE(model.IsInited());
+
+ TestInference<uint8_t>(i, model, 1);
+ }
+ }
+}