xref: /aosp_15_r20/external/armnn/tests/TfLiteBenchmark-Armnn/TfLiteBenchmark-Armnn.cpp (revision 89c4ff92f2867872bb9e2354d150bf0c8c502810)

//
// Copyright © 2020 STMicroelectronics and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//

#include <algorithm>
#include <getopt.h>
#include <numeric>
#include <signal.h>
#include <string>
#include <sys/time.h>
#include <vector>

#include <armnn/BackendId.hpp>
#include <armnn/BackendRegistry.hpp>
#include <armnn/IRuntime.hpp>
#include <armnn/utility/NumericCast.hpp>
#include <armnnTfLiteParser/ITfLiteParser.hpp>

// Application parameters
std::vector<armnn::BackendId> default_preferred_backends_order = {armnn::Compute::CpuAcc, armnn::Compute::CpuRef};
std::vector<armnn::BackendId> preferred_backends_order;
std::string model_file_str;
std::string preferred_backend_str;
int nb_loops = 1;

double get_us(struct timeval t)
{
    return (armnn::numeric_cast<double>(t.tv_sec) *
            armnn::numeric_cast<double>(1000000) +
            armnn::numeric_cast<double>(t.tv_usec));
}

double get_ms(struct timeval t)
{
    return (armnn::numeric_cast<double>(t.tv_sec) *
            armnn::numeric_cast<double>(1000) +
            armnn::numeric_cast<double>(t.tv_usec) / 1000);
}

static void print_help(char** argv)
{
    std::cout <<
        "Usage: " << argv[0] << " -m <model .tflite>\n"
        "\n"
        "-m --model_file <.tflite file path>:  .tflite model to be executed\n"
        "-b --backend <device>:                preferred backend device to run layers on by default. Possible choices: "
                                               << armnn::BackendRegistryInstance().GetBackendIdsAsString() << "\n"
        "                                      (by default CpuAcc, CpuRef)\n"
        "-l --loops <int>:                     provide the number of times the inference will be executed\n"
        "                                      (by default nb_loops=1)\n"
        "--help:                               show this help\n";
    exit(1);
}

void process_args(int argc, char** argv)
{
    const char* const short_opts = "m:b:l:h";
    const option long_opts[] = {
        {"model_file",   required_argument, nullptr, 'm'},
        {"backend",      required_argument, nullptr, 'b'},
        {"loops",        required_argument, nullptr, 'l'},
        {"help",         no_argument,       nullptr, 'h'},
        {nullptr,        no_argument,       nullptr, 0}
    };

    while (true)
    {
        const auto opt = getopt_long(argc, argv, short_opts, long_opts, nullptr);

        if (-1 == opt)
        {
            break;
        }

        switch (opt)
        {
        case 'm':
            model_file_str = std::string(optarg);
            std::cout << "model file set to: " << model_file_str << std::endl;
            break;
        case 'b':
            preferred_backend_str = std::string(optarg);
            // Overwrite the backend
            preferred_backends_order.push_back(preferred_backend_str);

            std::cout << "backend device set to:" << preferred_backend_str << std::endl;;
            break;
        case 'l':
            nb_loops = std::stoi(optarg);
            std::cout << "benchmark will execute " << nb_loops << " inference(s)" << std::endl;
            break;
        case 'h': // -h or --help
        case '?': // Unrecognized option
        default:
            print_help(argv);
            break;
        }
    }

    if (model_file_str.empty())
    {
        print_help(argv);
    }
}

int main(int argc, char* argv[])
{
    std::vector<double> inferenceTimes;

    // Get options
    process_args(argc, argv);

    // Create the runtime
    armnn::IRuntime::CreationOptions options;
    armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));

    // Create Parser
    armnnTfLiteParser::ITfLiteParserPtr armnnparser(armnnTfLiteParser::ITfLiteParser::Create());

    // Create a network
    armnn::INetworkPtr network = armnnparser->CreateNetworkFromBinaryFile(model_file_str.c_str());
    if (!network)
    {
        throw armnn::Exception("Failed to create an ArmNN network");
    }

    // Optimize the network
    if (preferred_backends_order.size() == 0)
    {
        preferred_backends_order = default_preferred_backends_order;
    }
    armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(*network,
                                                               preferred_backends_order,
                                                               runtime->GetDeviceSpec());
    armnn::NetworkId networkId;

    // Load the network in to the runtime
    runtime->LoadNetwork(networkId, std::move(optimizedNet));

    // Check the number of subgraph
    if (armnnparser->GetSubgraphCount() != 1)
    {
        std::cout << "Model with more than 1 subgraph is not supported by this benchmark application.\n";
        exit(0);
    }
    size_t subgraphId = 0;

    // Set up the input network
    std::cout << "\nModel information:" << std::endl;
    std::vector<armnnTfLiteParser::BindingPointInfo> inputBindings;
    std::vector<armnn::TensorInfo>                   inputTensorInfos;
    std::vector<std::string> inputTensorNames = armnnparser->GetSubgraphInputTensorNames(subgraphId);
    for (unsigned int i = 0; i < inputTensorNames.size() ; i++)
    {
        std::cout << "inputTensorNames[" << i << "] = " << inputTensorNames[i] << std::endl;
        armnnTfLiteParser::BindingPointInfo inputBinding = armnnparser->GetNetworkInputBindingInfo(
                                                                           subgraphId,
                                                                           inputTensorNames[i]);
        armnn::TensorInfo inputTensorInfo = runtime->GetInputTensorInfo(networkId, inputBinding.first);
        inputBindings.push_back(inputBinding);
        inputTensorInfos.push_back(inputTensorInfo);
    }

    // Set up the output network
    std::vector<armnnTfLiteParser::BindingPointInfo> outputBindings;
    std::vector<armnn::TensorInfo>                   outputTensorInfos;
    std::vector<std::string> outputTensorNames = armnnparser->GetSubgraphOutputTensorNames(subgraphId);
    for (unsigned int i = 0; i < outputTensorNames.size() ; i++)
    {
        std::cout << "outputTensorNames[" << i << "] = " << outputTensorNames[i] << std::endl;
        armnnTfLiteParser::BindingPointInfo outputBinding = armnnparser->GetNetworkOutputBindingInfo(
                                                                             subgraphId,
                                                                             outputTensorNames[i]);
        armnn::TensorInfo outputTensorInfo = runtime->GetOutputTensorInfo(networkId, outputBinding.first);
        outputBindings.push_back(outputBinding);
        outputTensorInfos.push_back(outputTensorInfo);
    }

    // Allocate input tensors
    unsigned int nb_inputs = armnn::numeric_cast<unsigned int>(inputTensorInfos.size());
    armnn::InputTensors inputTensors;
    std::vector<std::vector<float>> in;
    for (unsigned int i = 0 ; i < nb_inputs ; i++)
    {
        std::vector<float> in_data(inputTensorInfos.at(i).GetNumElements());
        in.push_back(in_data);
        inputTensors.push_back({ inputBindings[i].first, armnn::ConstTensor(inputBindings[i].second, in[i].data()) });
    }

    // Allocate output tensors
    unsigned int nb_ouputs = armnn::numeric_cast<unsigned int>(outputTensorInfos.size());
    armnn::OutputTensors outputTensors;
    std::vector<std::vector<float>> out;
    for (unsigned int i = 0; i < nb_ouputs ; i++)
    {
        std::vector<float> out_data(outputTensorInfos.at(i).GetNumElements());
        out.push_back(out_data);
        outputTensors.push_back({ outputBindings[i].first, armnn::Tensor(outputBindings[i].second, out[i].data()) });
    }

    // Run the inferences
    std::cout << "\ninferences are running: " << std::flush;
    for (int i = 0 ; i < nb_loops ; i++)
    {
        struct timeval start_time, stop_time;
        gettimeofday(&start_time, nullptr);

        runtime->EnqueueWorkload(networkId, inputTensors, outputTensors);

        gettimeofday(&stop_time, nullptr);
        inferenceTimes.push_back((get_us(stop_time) - get_us(start_time)));
        std::cout << "# " << std::flush;
    }

    auto maxInfTime = *std::max_element(inferenceTimes.begin(), inferenceTimes.end());
    auto minInfTime = *std::min_element(inferenceTimes.begin(), inferenceTimes.end());
    auto avgInfTime = accumulate(inferenceTimes.begin(), inferenceTimes.end(), 0.0) /
            armnn::numeric_cast<double>(inferenceTimes.size());
    std::cout << "\n\ninference time: ";
    std::cout << "min=" << minInfTime << "us  ";
    std::cout << "max=" << maxInfTime << "us  ";
    std::cout << "avg=" << avgInfTime << "us" << std::endl;

    return 0;
}