1 files changed, 129 insertions, 0 deletions
diff --git a/samples/ObjectDetection/test/delegate/ArmnnDelegateNetworkExecutorTest.cpp b/samples/ObjectDetection/test/delegate/ArmnnDelegateNetworkExecutorTest.cpp
new file mode 100644
index 0000000000..4700660eee
--- /dev/null
+++ b/samples/ObjectDetection/test/delegate/ArmnnDelegateNetworkExecutorTest.cpp
@@ -0,0 +1,129 @@
+//
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#include <catch.hpp>
+#include <opencv2/opencv.hpp>
+#include "ArmnnNetworkExecutor.hpp"
+#include "Types.hpp"
+#include "ImageUtils.hpp"
+#include "SSDResultDecoder.hpp"
+#include "YoloResultDecoder.hpp"
+
+using namespace std;
+
+static string GetResourceFilePath(const string& filename)
+{
+    string testResources = TEST_RESOURCE_DIR;
+
+    if(testResources.back() != '/')
+    {
+        return testResources + "/" + filename;
+    }
+    else
+    {
+        return testResources + filename;
+    }
+}
+
+TEST_CASE("Test Delegate Execution SSD_MOBILE")
+{
+    string testResources = TEST_RESOURCE_DIR;
+    REQUIRE(testResources != "");
+
+    vector<armnn::BackendId> m_backends = {"CpuRef"};
+    string file_path = GetResourceFilePath("ssd_mobilenet_v1.tflite");
+    common::InferenceResults<float> results;
+    cv::Mat processed;
+    cv::Mat cache;
+    float detectionThreshold = 0.6;
+    common::Profiling profiling(true);
+
+    profiling.ProfilingStart();
+    auto executor = make_unique<common::ArmnnNetworkExecutor<float>>(file_path, m_backends, true);
+    int width = executor->GetImageAspectRatio().m_Width;
+    int height = executor->GetImageAspectRatio().m_Height;
+    od::SSDResultDecoder ssdResult(detectionThreshold);
+
+    /* check GetInputDataType */
+    CHECK(executor->GetInputDataType() == armnn::DataType::QAsymmU8);
+    /* check GetImageAspectRatio */
+    CHECK(width == 300);
+    CHECK(height == 300);
+
+    cv::Mat inputFrame = cv::imread(GetResourceFilePath("basketball1.png"), cv::IMREAD_COLOR);
+    cv::cvtColor(inputFrame, inputFrame, cv::COLOR_BGR2RGB);
+    ResizeWithPad(inputFrame, processed, cache, common::Size(width,height));
+    CHECK(executor->Run(processed.data, processed.total() * processed.elemSize(), results) == true);
+    od::DetectedObjects detections = ssdResult.Decode(results,
+                      common::Size(inputFrame.size().width, inputFrame.size().height),
+                      common::Size(width, height), {});
+
+    /* Make sure we've found 2 persons in the image */
+    CHECK(detections.size() == 2 );
+    CHECK(detections[0].GetLabel() == "0");
+    CHECK(detections[1].GetLabel() == "0");
+    /* check GetQuantizationScale */
+    CHECK(to_string(executor->GetQuantizationScale()) == string("0.007812"));
+    /* check GetQuantizationOffset */
+    CHECK(executor->GetQuantizationOffset() == 128);
+    /* check GetQuantizationScale */
+    CHECK(executor->GetOutputQuantizationScale(0) == 0.0f);
+    /* check GetOutputQuantizationOffset */
+    CHECK(executor->GetOutputQuantizationOffset(0) == 0);
+    profiling.ProfilingStopAndPrintUs("Overall test");
+}
+
+TEST_CASE("Test Delegate Execution YOLO_V3")
+{
+    string testResources = TEST_RESOURCE_DIR;
+    REQUIRE(testResources != "");
+
+    vector<armnn::BackendId> m_backends = {"CpuRef"};
+    string file_path = GetResourceFilePath("yolo_v3_tiny_darknet_fp32.tflite");
+    common::InferenceResults<float> results;
+    cv::Mat processed;
+    cv::Mat cache;
+    float NMSThreshold = 0.3f;
+    float ClsThreshold = 0.3f;
+    float ObjectThreshold = 0.3f;
+
+
+    auto executor = make_unique<common::ArmnnNetworkExecutor<float>>(file_path, m_backends);
+    int width = executor->GetImageAspectRatio().m_Width;
+    int height = executor->GetImageAspectRatio().m_Height;
+    od::YoloResultDecoder yoloResult(NMSThreshold, ClsThreshold, ObjectThreshold);
+
+    /* check GetInputDataType */
+    CHECK(executor->GetInputDataType() == armnn::DataType::Float32);
+    /* check GetImageAspectRatio */
+    CHECK(width == 416);
+    CHECK(height == 416);
+
+    /* read the image */
+    cv::Mat inputFrame = cv::imread(GetResourceFilePath("basketball1.png"), cv::IMREAD_COLOR);
+    /* resize it according to the the input tensor requirments */
+    ResizeWithPad(inputFrame, processed, cache, common::Size(width,height));
+    /* converting to 3 channel matrix of 32 bits floats */
+    processed.convertTo(processed, CV_32FC3);
+    /* run the inference */
+    CHECK(executor->Run(processed.data, processed.total() * processed.elemSize(), results) == true);
+    /* decode the results */
+    od::DetectedObjects detections = yoloResult.Decode(results,
+                      common::Size(inputFrame.size().width, inputFrame.size().height),
+                      common::Size(width, height), {});
+
+    /* Make sure we've found 2 persons in the image */
+    CHECK(detections.size() == 2 );
+    CHECK(detections[0].GetLabel() == "0");
+    CHECK(detections[1].GetLabel() == "0");
+    /* check GetQuantizationScale */
+    CHECK(to_string(executor->GetQuantizationScale()) == string("0.000000"));
+    /* check GetQuantizationOffset */
+    CHECK(executor->GetQuantizationOffset() == 0);
+    /* check GetQuantizationScale */
+    CHECK(executor->GetOutputQuantizationScale(0) == 0.0f);
+    /* check GetOutputQuantizationOffset */
+    CHECK(executor->GetOutputQuantizationOffset(0) == 0);
+
+}