基于C++的TNN分割代码

以下是一个基于C++的TNN分割代码的示例：

#include <tnn/core/macro.h>
#include <tnn/utils/blob_converter.h>
#include <tnn/utils/dims_utils.h>
#include <tnn/utils/omp_utils.h>
#include <tnn/utils/cpu_utils.h>
#include <tnn/device/cpu/cpu_device.h>
#include <tnn/interpreter/default_model_interpreter.h>
#include <tnn/interpreter/layer_resource_generator.h>
#include <tnn/network/tensorrt/tensorrt_network.h>
#include <tnn/utils/string_utils_inner.h>
#include <tnn/utils/string_utils.h>
#include <tnn/utils/mat_utils.h>
#include <string>
#include <iostream>
#include <opencv2/opencv.hpp>

using namespace TNN_NS;
using namespace cv;

int main(int argc, char** argv) {
    if (argc != 3) {
        std::cerr << "Usage: " << argv[0] << " proto_file model_file" << std::endl;
        return 1;
    }

    // create network
    auto proto_content = fdLoadFile(argv[1]);
    auto model_content = fdLoadFile(argv[2]);
    auto interpreter = std::make_shared<DefaultModelInterpreter>();
    auto network = interpreter->CreateNetwork(proto_content, model_content);
    CHECK_TNN_OK(network->SetCpuNumThreads(4));
    CHECK_TNN_OK(network->SetDeviceType(DeviceType::DEVICE_ARM));

    // get input info
    auto input_info_map = network->GetInputInfo();
    auto input_name = input_info_map.begin()->first;
    auto input_info = input_info_map.begin()->second;
    auto input_dims = input_info->shape;

    // prepare input blob
    auto input_data_type = input_info->data_type;
    auto input_blob = std::make_shared<Blob>(input_data_type, input_dims);
    auto input_mat = cv::imread("input.jpg");
    auto input_mat_resize = Mat(input_dims[2], input_dims[3], CV_32FC3);
    cv::resize(input_mat, input_mat_resize, input_mat_resize.size(), 0, 0, cv::INTER_LINEAR);
    auto input_data = input_blob->GetHandle().base;
    ConvertFromMat(input_mat_resize, input_data, input_dims);

    // forward
    std::vector<std::shared_ptr<Blob>> input_blobs = {input_blob};
    auto status = network->Forward(input_blobs);
    if (status != TNN_OK) {
        std::cerr << "Forward Failed: " << TNN_NS::StatusUtils::StatusDescription(status) << std::endl;
        return 2;
    }

    // get output info
    auto output_info_map = network->GetOutputInfo();
    auto output_name = output_info_map.begin()->first;
    auto output_info = output_info_map.begin()->second;
    auto output_dims = output_info->shape;

    // prepare output blob
    auto output_data_type = output_info->data_type;
    auto output_blob = std::make_shared<Blob>(output_data_type, output_dims);
    auto output_data = output_blob->GetHandle().base;

    // get output blob data
    auto output_size = DimsVectorUtils::Count(output_dims);
    auto output_data_size = DataTypeUtils::GetBytesSize(output_data_type) * output_size;
    std::memcpy(output_data, output_info->resource->data, output_data_size);

    // postprocess
    std::vector<float> output_data_vec(output_size);
    ConvertFromRawData(output_data, output_data_vec.data(), output_data_type, output_size);
    auto output_mat = Mat(output_dims[2], output_dims[3], CV_32FC1, output_data_vec.data());
    cv::threshold(output_mat, output_mat, 0.5, 255, cv::THRESH_BINARY);

    // save output
    imwrite("output.jpg", output_mat);

    return 0;
}

该示例中，使用一个给定的TNN模型进行图像分割。需要提供模型的 `.tnnproto` 和 `.tnnmodel` 文件。然后使用 OpenCV 加载输入图像，将其转换为输入 Blob，并在网络上执行正向传递。最后，将输出 Blob 转换为 OpenCV Mat，进行二值化并保存为输出图像。