telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame^] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // See LICENSE file in the project root for full license information. |
| 4 | // |
| 5 | #pragma once |
| 6 | |
| 7 | #include "armnn/ArmNN.hpp" |
| 8 | |
| 9 | #include <boost/log/trivial.hpp> |
| 10 | #include <boost/format.hpp> |
| 11 | #include <boost/program_options.hpp> |
| 12 | |
| 13 | #include <map> |
| 14 | #include <string> |
| 15 | |
| 16 | template<typename TContainer> |
| 17 | inline armnn::InputTensors MakeInputTensors(const std::pair<armnn::LayerBindingId, armnn::TensorInfo>& input, |
| 18 | const TContainer& inputTensorData) |
| 19 | { |
| 20 | if (inputTensorData.size() != input.second.GetNumElements()) |
| 21 | { |
| 22 | throw armnn::Exception(boost::str(boost::format("Input tensor has incorrect size. Expected %1% elements " |
| 23 | "but got %2%.") % input.second.GetNumElements() % inputTensorData.size())); |
| 24 | } |
| 25 | return { { input.first, armnn::ConstTensor(input.second, inputTensorData.data()) } }; |
| 26 | } |
| 27 | |
| 28 | template<typename TContainer> |
| 29 | inline armnn::OutputTensors MakeOutputTensors(const std::pair<armnn::LayerBindingId, armnn::TensorInfo>& output, |
| 30 | TContainer& outputTensorData) |
| 31 | { |
| 32 | if (outputTensorData.size() != output.second.GetNumElements()) |
| 33 | { |
| 34 | throw armnn::Exception("Output tensor has incorrect size"); |
| 35 | } |
| 36 | return { { output.first, armnn::Tensor(output.second, outputTensorData.data()) } }; |
| 37 | } |
| 38 | |
| 39 | template <typename IParser, typename TDataType> |
| 40 | class InferenceModel |
| 41 | { |
| 42 | public: |
| 43 | using DataType = TDataType; |
| 44 | |
| 45 | struct CommandLineOptions |
| 46 | { |
| 47 | std::string m_ModelDir; |
| 48 | armnn::Compute m_ComputeDevice; |
| 49 | }; |
| 50 | |
| 51 | static void AddCommandLineOptions(boost::program_options::options_description& desc, CommandLineOptions& options) |
| 52 | { |
| 53 | namespace po = boost::program_options; |
| 54 | |
| 55 | desc.add_options() |
| 56 | ("model-dir,m", po::value<std::string>(&options.m_ModelDir)->required(), |
| 57 | "Path to directory containing model files (.caffemodel/.prototxt)") |
| 58 | ("compute,c", po::value<armnn::Compute>(&options.m_ComputeDevice)->default_value(armnn::Compute::CpuAcc), |
| 59 | "Which device to run layers on by default. Possible choices: CpuAcc, CpuRef, GpuAcc"); |
| 60 | } |
| 61 | |
| 62 | struct Params |
| 63 | { |
| 64 | std::string m_ModelPath; |
| 65 | std::string m_InputBinding; |
| 66 | std::string m_OutputBinding; |
| 67 | const armnn::TensorShape* m_InputTensorShape; |
| 68 | armnn::Compute m_ComputeDevice; |
| 69 | bool m_IsModelBinary; |
| 70 | |
| 71 | Params() |
| 72 | : m_InputTensorShape(nullptr) |
| 73 | , m_ComputeDevice(armnn::Compute::CpuRef) |
| 74 | , m_IsModelBinary(true) |
| 75 | { |
| 76 | } |
| 77 | }; |
| 78 | |
| 79 | |
| 80 | InferenceModel(const Params& params) |
| 81 | : m_Runtime(armnn::IRuntime::Create(params.m_ComputeDevice)) |
| 82 | { |
| 83 | const std::string& modelPath = params.m_ModelPath; |
| 84 | |
| 85 | // Create a network from a file on disk |
| 86 | auto parser(IParser::Create()); |
| 87 | |
| 88 | std::map<std::string, armnn::TensorShape> inputShapes; |
| 89 | if (params.m_InputTensorShape) |
| 90 | { |
| 91 | inputShapes[params.m_InputBinding] = *params.m_InputTensorShape; |
| 92 | } |
| 93 | std::vector<std::string> requestedOutputs{ params.m_OutputBinding }; |
| 94 | |
| 95 | // Handle text and binary input differently by calling the corresponding parser function |
| 96 | armnn::INetworkPtr network = (params.m_IsModelBinary ? |
| 97 | parser->CreateNetworkFromBinaryFile(modelPath.c_str(), inputShapes, requestedOutputs) : |
| 98 | parser->CreateNetworkFromTextFile(modelPath.c_str(), inputShapes, requestedOutputs)); |
| 99 | |
| 100 | m_InputBindingInfo = parser->GetNetworkInputBindingInfo(params.m_InputBinding); |
| 101 | m_OutputBindingInfo = parser->GetNetworkOutputBindingInfo(params.m_OutputBinding); |
| 102 | |
| 103 | armnn::IOptimizedNetworkPtr optNet = |
| 104 | armnn::Optimize(*network, m_Runtime->GetDeviceSpec()); |
| 105 | |
| 106 | // Load the network into the runtime. |
| 107 | armnn::Status ret = m_Runtime->LoadNetwork(m_NetworkIdentifier, std::move(optNet)); |
| 108 | if (ret == armnn::Status::Failure) |
| 109 | { |
| 110 | throw armnn::Exception("IRuntime::LoadNetwork failed"); |
| 111 | } |
| 112 | } |
| 113 | |
| 114 | unsigned int GetOutputSize() const |
| 115 | { |
| 116 | return m_OutputBindingInfo.second.GetNumElements(); |
| 117 | } |
| 118 | |
| 119 | void Run(const std::vector<TDataType>& input, std::vector<TDataType>& output) |
| 120 | { |
| 121 | BOOST_ASSERT(output.size() == GetOutputSize()); |
| 122 | armnn::Status ret = m_Runtime->EnqueueWorkload(m_NetworkIdentifier, |
| 123 | MakeInputTensors(input), |
| 124 | MakeOutputTensors(output)); |
| 125 | if (ret == armnn::Status::Failure) |
| 126 | { |
| 127 | throw armnn::Exception("IRuntime::EnqueueWorkload failed"); |
| 128 | } |
| 129 | } |
| 130 | |
| 131 | private: |
| 132 | template<typename TContainer> |
| 133 | armnn::InputTensors MakeInputTensors(const TContainer& inputTensorData) |
| 134 | { |
| 135 | return ::MakeInputTensors(m_InputBindingInfo, inputTensorData); |
| 136 | } |
| 137 | |
| 138 | template<typename TContainer> |
| 139 | armnn::OutputTensors MakeOutputTensors(TContainer& outputTensorData) |
| 140 | { |
| 141 | return ::MakeOutputTensors(m_OutputBindingInfo, outputTensorData); |
| 142 | } |
| 143 | |
| 144 | armnn::NetworkId m_NetworkIdentifier; |
| 145 | armnn::IRuntimePtr m_Runtime; |
| 146 | |
| 147 | std::pair<armnn::LayerBindingId, armnn::TensorInfo> m_InputBindingInfo; |
| 148 | std::pair<armnn::LayerBindingId, armnn::TensorInfo> m_OutputBindingInfo; |
| 149 | }; |