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author | alexander <alexander.efremov@arm.com> | 2021-03-26 21:42:19 +0000 |
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committer | Kshitij Sisodia <kshitij.sisodia@arm.com> | 2021-03-29 16:29:55 +0100 |
commit | 3c79893217bc632c9b0efa815091bef3c779490c (patch) | |
tree | ad06b444557eb8124652b45621d736fa1b92f65d /tests/use_case/img_class/InferenceTestMobilenetV2.cc | |
parent | 6ad6d55715928de72979b04194da1bdf04a4c51b (diff) | |
download | ml-embedded-evaluation-kit-3c79893217bc632c9b0efa815091bef3c779490c.tar.gz |
Opensource ML embedded evaluation kit21.03
Change-Id: I12e807f19f5cacad7cef82572b6dd48252fd61fd
Diffstat (limited to 'tests/use_case/img_class/InferenceTestMobilenetV2.cc')
-rw-r--r-- | tests/use_case/img_class/InferenceTestMobilenetV2.cc | 90 |
1 files changed, 90 insertions, 0 deletions
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 --- /dev/null +++ b/tests/use_case/img_class/InferenceTestMobilenetV2.cc @@ -0,0 +1,90 @@ +/* + * 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); + } + } +} |