processNet("dnn/yolov4.weights", "dnn/yolov4.cfg", "", inp);
}
+PERF_TEST_P_(DNNTestNetwork, YOLOv4_tiny)
+{
+ if (backend == DNN_BACKEND_HALIDE)
+ throw SkipTestException("");
+ if (target == DNN_TARGET_MYRIAD)
+ throw SkipTestException("");
+ Mat sample = imread(findDataFile("dnn/dog416.png"));
+ cvtColor(sample, sample, COLOR_BGR2RGB);
+ Mat inp;
+ sample.convertTo(inp, CV_32FC3, 1.0f / 255, 0);
+ processNet("dnn/yolov4-tiny.weights", "dnn/yolov4-tiny.cfg", "", inp);
+}
+
PERF_TEST_P_(DNNTestNetwork, EAST_text_detection)
{
if (backend == DNN_BACKEND_HALIDE)
const char *comment /*= ""*/, double confThreshold /*= 0.0*/,
double scores_diff /*= 1e-5*/, double boxes_iou_diff /*= 1e-4*/)
{
+ ASSERT_FALSE(testClassIds.empty()) << "No detections";
std::vector<bool> matchedRefBoxes(refBoxes.size(), false);
+ std::vector<double> refBoxesIoUDiff(refBoxes.size(), 1.0);
for (int i = 0; i < testBoxes.size(); ++i)
{
+ //cout << "Test[i=" << i << "]: score=" << testScores[i] << " id=" << testClassIds[i] << " box " << testBoxes[i] << endl;
double testScore = testScores[i];
if (testScore < confThreshold)
continue;
double interArea = (testBox & refBoxes[j]).area();
double iou = interArea / (testBox.area() + refBoxes[j].area() - interArea);
topIoU = std::max(topIoU, iou);
+ refBoxesIoUDiff[j] = std::min(refBoxesIoUDiff[j], 1.0f - iou);
if (1.0 - iou < boxes_iou_diff)
{
matched = true;
if (!matchedRefBoxes[i] && refScores[i] > confThreshold)
{
std::cout << cv::format("Unmatched reference: class %d score %f box ",
- refClassIds[i], refScores[i]) << refBoxes[i] << std::endl;
+ refClassIds[i], refScores[i]) << refBoxes[i]
+ << " IoU diff: " << refBoxesIoUDiff[i]
+ << std::endl;
EXPECT_LE(refScores[i], confThreshold) << comment;
}
}
}
+ if (cvIsNaN(iouDiff))
+ {
+ if (b == 0)
+ std::cout << "Skip accuracy checks" << std::endl;
+ continue;
+ }
+
normAssertDetections(refClassIds[b], refConfidences[b], refBoxes[b], nms_classIds,
nms_confidences, nms_boxes, format("batch size %d, sample %d\n", batch_size, b).c_str(), confThreshold, scoreDiff, iouDiff);
}
}
INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets_async, Combine(
- Values("yolo-voc", "tiny-yolo-voc", "yolov3", "yolov4"),
+ Values("yolo-voc", "tiny-yolo-voc", "yolov3", "yolov4", "yolov4-tiny"),
dnnBackendsAndTargets()
));
}
}
+TEST_P(Test_Darknet_nets, YOLOv4_tiny)
+{
+ applyTestTag(
+ target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB
+ );
+
+ const double confThreshold = 0.5;
+ // batchId, classId, confidence, left, top, right, bottom
+ const int N0 = 2;
+ const int N1 = 3;
+ static const float ref_[/* (N0 + N1) * 7 */] = {
+0, 7, 0.85935f, 0.593484f, 0.141211f, 0.920356f, 0.291593f,
+0, 16, 0.795188f, 0.169207f, 0.386886f, 0.423753f, 0.933004f,
+
+1, 2, 0.996832f, 0.653802f, 0.464573f, 0.815193f, 0.653292f,
+1, 2, 0.963325f, 0.451151f, 0.458915f, 0.496255f, 0.52241f,
+1, 0, 0.926244f, 0.194851f, 0.361743f, 0.260277f, 0.632364f,
+ };
+ Mat ref(N0 + N1, 7, CV_32FC1, (void*)ref_);
+
+ double scoreDiff = 0.01f;
+ double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.15 : 0.01f;
+
+ std::string config_file = "yolov4-tiny.cfg";
+ std::string weights_file = "yolov4-tiny.weights";
+
+#if defined(INF_ENGINE_RELEASE)
+ if (target == DNN_TARGET_MYRIAD) // bad accuracy
+ iouDiff = std::numeric_limits<double>::quiet_NaN();
+ if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL)
+ iouDiff = std::numeric_limits<double>::quiet_NaN();
+ if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
+ backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && DNN_TARGET_OPENCL_FP16)
+ iouDiff = std::numeric_limits<double>::quiet_NaN();
+#endif
+
+ {
+ SCOPED_TRACE("batch size 1");
+ testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff, confThreshold);
+ }
+
+ {
+ SCOPED_TRACE("batch size 2");
+ testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff, confThreshold);
+ }
+
+#if defined(INF_ENGINE_RELEASE)
+ if (target == DNN_TARGET_MYRIAD) // bad accuracy
+ applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+ if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL)
+ applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+ if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
+ backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && DNN_TARGET_OPENCL_FP16)
+ applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+#endif
+}
+
INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets, dnnBackendsAndTargets());