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/*
* Copyright (c) 2022 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 "YoloFastestModel.hpp"
#include "TensorFlowLiteMicro.hpp"
#include "DetectorPostProcessing.hpp"
#include "InputFiles.hpp"
#include "UseCaseCommonUtils.hpp"
#include "DetectionUseCaseUtils.hpp"
#include "ExpectedObjectDetectionResults.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 < (INPUT_IMAGE_WIDTH*INPUT_IMAGE_HEIGHT) ?
inputTensor->bytes :
(INPUT_IMAGE_WIDTH*INPUT_IMAGE_HEIGHT);
arm::app::RgbToGrayscale(imageData,inputTensor->data.uint8,INPUT_IMAGE_WIDTH,INPUT_IMAGE_HEIGHT);
if(model.IsDataSigned()){
convertImgIoInt8(inputTensor->data.data, copySz);
}
return model.RunInference();
}
template<typename T>
void TestInference(int imageIdx, arm::app::Model& model, T tolerance) {
info("Entering TestInference for image %d \n", imageIdx);
std::vector<arm::app::DetectionResult> results;
auto image = get_img_array(imageIdx);
REQUIRE(RunInference(model, image));
TfLiteTensor* output_arr[2] = {nullptr,nullptr};
output_arr[0] = model.GetOutputTensor(0);
output_arr[1] = model.GetOutputTensor(1);
for (int i =0; i < 2; i++) {
REQUIRE(output_arr[i]);
REQUIRE(tflite::GetTensorData<T>(output_arr[i]));
}
RunPostProcessing(NULL,output_arr,results);
info("Got %ld boxes \n",results.size());
std::vector<std::vector<arm::app::DetectionResult>> expected_results;
get_expected_ut_results(expected_results);
/*validate got the same number of boxes */
REQUIRE(results.size() == expected_results[imageIdx].size());
for (int i=0; i < (int)results.size(); i++) {
info("%" PRIu32 ") (%f) -> %s {x=%d,y=%d,w=%d,h=%d}\n", (int)i,
results[i].m_normalisedVal, "Detection box:",
results[i].m_x0, results[i].m_y0, results[i].m_w, results[i].m_h );
/*validate confidence and box dimensions */
REQUIRE(fabs(results[i].m_normalisedVal - expected_results[imageIdx][i].m_normalisedVal) < 0.1);
REQUIRE(static_cast<int>(results[i].m_x0) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_x0)).epsilon(tolerance));
REQUIRE(static_cast<int>(results[i].m_y0) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_y0)).epsilon(tolerance));
REQUIRE(static_cast<int>(results[i].m_w) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_w)).epsilon(tolerance));
REQUIRE(static_cast<int>(results[i].m_h) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_h)).epsilon(tolerance));
}
}
TEST_CASE("Running inference with TensorFlow Lite Micro and YoloFastest", "[YoloFastest]")
{
SECTION("Executing inferences sequentially")
{
arm::app::YoloFastestModel model{};
REQUIRE_FALSE(model.IsInited());
REQUIRE(model.Init());
REQUIRE(model.IsInited());
for (uint32_t i = 0 ; i < NUMBER_OF_FILES; ++i) {
TestInference<uint8_t>(i, model, 1);
}
}
for (uint32_t i = 0 ; i < NUMBER_OF_FILES; ++i) {
DYNAMIC_SECTION("Executing inference with re-init")
{
arm::app::YoloFastestModel model{};
REQUIRE_FALSE(model.IsInited());
REQUIRE(model.Init());
REQUIRE(model.IsInited());
TestInference<uint8_t>(i, model, 1);
}
}
}
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