Method ParseDetectionPostProcess

src/armnnTfLiteParser/TfLiteParser.cpp:3968–4047

}

void TfLiteParserImpl::ParseDetectionPostProcess(size_t subgraphIndex, size_t operatorIndex)
{
    CHECK_MODEL(m_Model, subgraphIndex, operatorIndex);

    const auto& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];

    auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex);
    auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex);
    CHECK_VALID_SIZE(outputs.size(), 4);

    // Obtain custom options from flexbuffers
    auto custom_options = operatorPtr->custom_options;
    const flexbuffers::Map& m = flexbuffers::GetRoot(custom_options.data(), custom_options.size()).AsMap();

    // Obtain descriptor information from tf lite
    DetectionPostProcessDescriptor desc;
    desc.m_MaxDetections           = m["max_detections"].AsUInt32();
    desc.m_MaxClassesPerDetection  = m["max_classes_per_detection"].AsUInt32();
    desc.m_NmsScoreThreshold       = m["nms_score_threshold"].AsFloat();
    desc.m_NmsIouThreshold         = m["nms_iou_threshold"].AsFloat();
    desc.m_NumClasses              = m["num_classes"].AsUInt32();
    desc.m_ScaleH                  = m["h_scale"].AsFloat();
    desc.m_ScaleW                  = m["w_scale"].AsFloat();
    desc.m_ScaleX                  = m["x_scale"].AsFloat();
    desc.m_ScaleY                  = m["y_scale"].AsFloat();

    if (!(m["use_regular_nms"].IsNull()))
    {
        desc.m_UseRegularNms       = m["use_regular_nms"].AsBool();
    }
    if (!(m["detections_per_class"].IsNull()))
    {
        desc.m_DetectionsPerClass  = m["detections_per_class"].AsUInt32();
    }

    if (desc.m_NmsIouThreshold <= 0.0f || desc.m_NmsIouThreshold > 1.0f)
    {
        throw InvalidArgumentException("DetectionPostProcessTFLiteParser: Intersection over union threshold "
                                       "must be positive and less than or equal to 1.");
    }

    armnn::TensorInfo anchorTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 2);
    auto anchorTensorAndData = CreateConstTensorNonPermuted(inputs[2], anchorTensorInfo);

    auto layerName = fmt::format("DetectionPostProcess:{}:{}", subgraphIndex, operatorIndex);
    IConnectableLayer* layer = m_Network->AddDetectionPostProcessLayer(desc, anchorTensorAndData,
                                                                       layerName.c_str());

    if (!layer)
    {
        throw NullPointerException(fmt::format("Layer {} pointer is null {}",
                                   operatorIndex, CHECK_LOCATION().AsString()));
    }

    // The model does not specify the output shapes.
    // The output shapes are calculated from the max_detection and max_classes_per_detection.
    unsigned int numDetectedBox = desc.m_MaxDetections * desc.m_MaxClassesPerDetection;
    m_OverriddenOutputShapes.push_back({ 1, numDetectedBox, 4 });