1 files changed, 0 insertions, 140 deletions
diff --git a/samples/ObjectDetection/src/ArmnnNetworkExecutor.cpp b/samples/ObjectDetection/src/ArmnnNetworkExecutor.cpp
deleted file mode 100644
index cb4c0c9f84..0000000000
--- a/samples/ObjectDetection/src/ArmnnNetworkExecutor.cpp
+++ /dev/null
@@ -1,140 +0,0 @@
-//
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "ArmnnNetworkExecutor.hpp"
-#include "Types.hpp"
-
-#include <random>
-#include <string>
-
-namespace od
-{
-
-armnn::DataType ArmnnNetworkExecutor::GetInputDataType() const
-{
-    return m_inputBindingInfo.second.GetDataType();
-}
-
-ArmnnNetworkExecutor::ArmnnNetworkExecutor(std::string& modelPath,
-                                           std::vector<armnn::BackendId>& preferredBackends)
-: m_Runtime(armnn::IRuntime::Create(armnn::IRuntime::CreationOptions()))
-{
-    // Import the TensorFlow lite model.
-    armnnTfLiteParser::ITfLiteParserPtr parser = armnnTfLiteParser::ITfLiteParser::Create();
-    armnn::INetworkPtr network = parser->CreateNetworkFromBinaryFile(modelPath.c_str());
-
-    std::vector<std::string> inputNames = parser->GetSubgraphInputTensorNames(0);
-
-    m_inputBindingInfo = parser->GetNetworkInputBindingInfo(0, inputNames[0]);
-
-    m_outputLayerNamesList = parser->GetSubgraphOutputTensorNames(0);
-
-    std::vector<armnn::BindingPointInfo> outputBindings;
-    for(const std::string& name : m_outputLayerNamesList)
-    {
-        m_outputBindingInfo.push_back(std::move(parser->GetNetworkOutputBindingInfo(0, name)));
-    }
-
-    std::vector<std::string> errorMessages;
-    // optimize the network.
-    armnn::IOptimizedNetworkPtr optNet = Optimize(*network,
-                                                  preferredBackends,
-                                                  m_Runtime->GetDeviceSpec(),
-                                                  armnn::OptimizerOptions(),
-                                                  armnn::Optional<std::vector<std::string>&>(errorMessages));
-
-    if (!optNet)
-    {
-        const std::string errorMessage{"ArmnnNetworkExecutor: Failed to optimize network"};
-        ARMNN_LOG(error) << errorMessage;
-        throw armnn::Exception(errorMessage);
-    }
-
-    // Load the optimized network onto the m_Runtime device
-    std::string errorMessage;
-    if (armnn::Status::Success != m_Runtime->LoadNetwork(m_NetId, std::move(optNet), errorMessage))
-    {
-        ARMNN_LOG(error) << errorMessage;
-    }
-
-    //pre-allocate memory for output (the size of it never changes)
-    for (int it = 0; it < m_outputLayerNamesList.size(); ++it)
-    {
-        const armnn::DataType dataType = m_outputBindingInfo[it].second.GetDataType();
-        const armnn::TensorShape& tensorShape = m_outputBindingInfo[it].second.GetShape();
-
-        InferenceResult oneLayerOutResult;
-        switch (dataType)
-        {
-            case armnn::DataType::Float32:
-            {
-                oneLayerOutResult.resize(tensorShape.GetNumElements(), 0);
-                break;
-            }
-            default:
-            {
-                errorMessage = "ArmnnNetworkExecutor: unsupported output tensor data type";
-                ARMNN_LOG(error) << errorMessage << " " << log_as_int(dataType);
-                throw armnn::Exception(errorMessage);
-            }
-        }
-
-        m_OutputBuffer.emplace_back(oneLayerOutResult);
-
-        // Make ArmNN output tensors
-        m_OutputTensors.reserve(m_OutputBuffer.size());
-        for (size_t it = 0; it < m_OutputBuffer.size(); ++it)
-        {
-            m_OutputTensors.emplace_back(std::make_pair(
-                    m_outputBindingInfo[it].first,
-                    armnn::Tensor(m_outputBindingInfo[it].second,
-                                  m_OutputBuffer.at(it).data())
-            ));
-        }
-    }
-
-}
-
-void ArmnnNetworkExecutor::PrepareTensors(const void* inputData, const size_t dataBytes)
-{
-    assert(m_inputBindingInfo.second.GetNumBytes() >= dataBytes);
-    m_InputTensors.clear();
-    m_InputTensors = {{ m_inputBindingInfo.first, armnn::ConstTensor(m_inputBindingInfo.second, inputData)}};
-}
-
-bool ArmnnNetworkExecutor::Run(const void* inputData, const size_t dataBytes, InferenceResults& outResults)
-{
-    /* Prepare tensors if they are not ready */
-    ARMNN_LOG(debug) << "Preparing tensors...";
-    this->PrepareTensors(inputData, dataBytes);
-    ARMNN_LOG(trace) << "Running inference...";
-
-    armnn::Status ret = m_Runtime->EnqueueWorkload(m_NetId, m_InputTensors, m_OutputTensors);
-
-    std::stringstream inferenceFinished;
-    inferenceFinished << "Inference finished with code {" << log_as_int(ret) << "}\n";
-
-    ARMNN_LOG(trace) << inferenceFinished.str();
-
-    if (ret == armnn::Status::Failure)
-    {
-        ARMNN_LOG(error) << "Failed to perform inference.";
-    }
-
-    outResults.reserve(m_outputLayerNamesList.size());
-    outResults = m_OutputBuffer;
-
-    return (armnn::Status::Success == ret);
-}
-
-Size ArmnnNetworkExecutor::GetImageAspectRatio()
-{
-    const auto shape = m_inputBindingInfo.second.GetShape();
-    assert(shape.GetNumDimensions() == 4);
-    armnnUtils::DataLayoutIndexed nhwc(armnn::DataLayout::NHWC);
-    return Size(shape[nhwc.GetWidthIndex()],
-                shape[nhwc.GetHeightIndex()]);
-}
-}// namespace od
-\ No newline at end of file