Algorithm-Repo/SourceCode/MachineLearningUtils/VINNOFramework/YOLO/examples/YOLOv8-LibTorch-CPP-Inference/main.cc

#include <iostream>

#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/imgcodecs.hpp>
#include <torch/torch.h>
#include <torch/script.h>

using torch::indexing::Slice;
using torch::indexing::None;


float generate_scale(cv::Mat& image, const std::vector<int>& target_size) {
    int origin_w = image.cols;
    int origin_h = image.rows;

    int target_h = target_size[0];
    int target_w = target_size[1];

    float ratio_h = static_cast<float>(target_h) / static_cast<float>(origin_h);
    float ratio_w = static_cast<float>(target_w) / static_cast<float>(origin_w);
    float resize_scale = std::min(ratio_h, ratio_w);
    return resize_scale;
}


float letterbox(cv::Mat &input_image, cv::Mat &output_image, const std::vector<int> &target_size) {
    if (input_image.cols == target_size[1] && input_image.rows == target_size[0]) {
        if (input_image.data == output_image.data) {
            return 1.;
        } else {
            output_image = input_image.clone();
            return 1.;
        }
    }

    float resize_scale = generate_scale(input_image, target_size);
    int new_shape_w = std::round(input_image.cols * resize_scale);
    int new_shape_h = std::round(input_image.rows * resize_scale);
    float padw = (target_size[1] - new_shape_w) / 2.;
    float padh = (target_size[0] - new_shape_h) / 2.;

    int top = std::round(padh - 0.1);
    int bottom = std::round(padh + 0.1);
    int left = std::round(padw - 0.1);
    int right = std::round(padw + 0.1);

    cv::resize(input_image, output_image,
               cv::Size(new_shape_w, new_shape_h),
               0, 0, cv::INTER_AREA);

    cv::copyMakeBorder(output_image, output_image, top, bottom, left, right,
                       cv::BORDER_CONSTANT, cv::Scalar(114.));
    return resize_scale;
}


torch::Tensor xyxy2xywh(const torch::Tensor& x) {
    auto y = torch::empty_like(x);
    y.index_put_({"...", 0}, (x.index({"...", 0}) + x.index({"...", 2})).div(2));
    y.index_put_({"...", 1}, (x.index({"...", 1}) + x.index({"...", 3})).div(2));
    y.index_put_({"...", 2}, x.index({"...", 2}) - x.index({"...", 0}));
    y.index_put_({"...", 3}, x.index({"...", 3}) - x.index({"...", 1}));
    return y;
}


torch::Tensor xywh2xyxy(const torch::Tensor& x) {
    auto y = torch::empty_like(x);
    auto dw = x.index({"...", 2}).div(2);
    auto dh = x.index({"...", 3}).div(2);
    y.index_put_({"...", 0}, x.index({"...", 0}) - dw);
    y.index_put_({"...", 1}, x.index({"...", 1}) - dh);
    y.index_put_({"...", 2}, x.index({"...", 0}) + dw);
    y.index_put_({"...", 3}, x.index({"...", 1}) + dh);
    return y;
}


// Reference: https://github.com/pytorch/vision/blob/main/torchvision/csrc/ops/cpu/nms_kernel.cpp
torch::Tensor nms(const torch::Tensor& bboxes, const torch::Tensor& scores, float iou_threshold) {
    if (bboxes.numel() == 0)
        return torch::empty({0}, bboxes.options().dtype(torch::kLong));

    auto x1_t = bboxes.select(1, 0).contiguous();
    auto y1_t = bboxes.select(1, 1).contiguous();
    auto x2_t = bboxes.select(1, 2).contiguous();
    auto y2_t = bboxes.select(1, 3).contiguous();

    torch::Tensor areas_t = (x2_t - x1_t) * (y2_t - y1_t);

    auto order_t = std::get<1>(
        scores.sort(/*stable=*/true, /*dim=*/0, /* descending=*/true));

    auto ndets = bboxes.size(0);
    torch::Tensor suppressed_t = torch::zeros({ndets}, bboxes.options().dtype(torch::kByte));
    torch::Tensor keep_t = torch::zeros({ndets}, bboxes.options().dtype(torch::kLong));

    auto suppressed = suppressed_t.data_ptr<uint8_t>();
    auto keep = keep_t.data_ptr<int64_t>();
    auto order = order_t.data_ptr<int64_t>();
    auto x1 = x1_t.data_ptr<float>();
    auto y1 = y1_t.data_ptr<float>();
    auto x2 = x2_t.data_ptr<float>();
    auto y2 = y2_t.data_ptr<float>();
    auto areas = areas_t.data_ptr<float>();

    int64_t num_to_keep = 0;

    for (int64_t _i = 0; _i < ndets; _i++) {
        auto i = order[_i];
        if (suppressed[i] == 1)
            continue;
        keep[num_to_keep++] = i;
        auto ix1 = x1[i];
        auto iy1 = y1[i];
        auto ix2 = x2[i];
        auto iy2 = y2[i];
        auto iarea = areas[i];

        for (int64_t _j = _i + 1; _j < ndets; _j++) {
        auto j = order[_j];
        if (suppressed[j] == 1)
            continue;
        auto xx1 = std::max(ix1, x1[j]);
        auto yy1 = std::max(iy1, y1[j]);
        auto xx2 = std::min(ix2, x2[j]);
        auto yy2 = std::min(iy2, y2[j]);

        auto w = std::max(static_cast<float>(0), xx2 - xx1);
        auto h = std::max(static_cast<float>(0), yy2 - yy1);
        auto inter = w * h;
        auto ovr = inter / (iarea + areas[j] - inter);
        if (ovr > iou_threshold)
            suppressed[j] = 1;
        }
    }
    return keep_t.narrow(0, 0, num_to_keep);
}


torch::Tensor non_max_suppression(torch::Tensor& prediction, float conf_thres = 0.25, float iou_thres = 0.45, int max_det = 300) {
    auto bs = prediction.size(0);
    auto nc = prediction.size(1) - 4;
    auto nm = prediction.size(1) - nc - 4;
    auto mi = 4 + nc;
    auto xc = prediction.index({Slice(), Slice(4, mi)}).amax(1) > conf_thres;

    prediction = prediction.transpose(-1, -2);
    prediction.index_put_({"...", Slice({None, 4})}, xywh2xyxy(prediction.index({"...", Slice(None, 4)})));

    std::vector<torch::Tensor> output;
    for (int i = 0; i < bs; i++) {
        output.push_back(torch::zeros({0, 6 + nm}, prediction.device()));
    }

    for (int xi = 0; xi < prediction.size(0); xi++) {
        auto x = prediction[xi];
        x = x.index({xc[xi]});
        auto x_split = x.split({4, nc, nm}, 1);
        auto box = x_split[0], cls = x_split[1], mask = x_split[2];
        auto [conf, j] = cls.max(1, true);
        x = torch::cat({box, conf, j.toType(torch::kFloat), mask}, 1);
        x = x.index({conf.view(-1) > conf_thres});
        int n = x.size(0);
        if (!n) { continue; }

        // NMS
        auto c = x.index({Slice(), Slice{5, 6}}) * 7680;
        auto boxes = x.index({Slice(), Slice(None, 4)}) + c;
        auto scores = x.index({Slice(), 4});
        auto i = nms(boxes, scores, iou_thres);
        i = i.index({Slice(None, max_det)});
        output[xi] = x.index({i});
    }

    return torch::stack(output);
}


torch::Tensor clip_boxes(torch::Tensor& boxes, const std::vector<int>& shape) {
    boxes.index_put_({"...", 0}, boxes.index({"...", 0}).clamp(0, shape[1]));
    boxes.index_put_({"...", 1}, boxes.index({"...", 1}).clamp(0, shape[0]));
    boxes.index_put_({"...", 2}, boxes.index({"...", 2}).clamp(0, shape[1]));
    boxes.index_put_({"...", 3}, boxes.index({"...", 3}).clamp(0, shape[0]));
    return boxes;
}


torch::Tensor scale_boxes(const std::vector<int>& img1_shape, torch::Tensor& boxes, const std::vector<int>& img0_shape) {
    auto gain = (std::min)((float)img1_shape[0] / img0_shape[0], (float)img1_shape[1] / img0_shape[1]);
    auto pad0 = std::round((float)(img1_shape[1] - img0_shape[1] * gain) / 2. - 0.1);
    auto pad1 = std::round((float)(img1_shape[0] - img0_shape[0] * gain) / 2. - 0.1);

    boxes.index_put_({"...", 0}, boxes.index({"...", 0}) - pad0);
    boxes.index_put_({"...", 2}, boxes.index({"...", 2}) - pad0);
    boxes.index_put_({"...", 1}, boxes.index({"...", 1}) - pad1);
    boxes.index_put_({"...", 3}, boxes.index({"...", 3}) - pad1);
    boxes.index_put_({"...", Slice(None, 4)}, boxes.index({"...", Slice(None, 4)}).div(gain));
    return boxes;
}


int main() {
    // Device
    torch::Device device(torch::cuda::is_available() ? torch::kCUDA :torch::kCPU);

    // Note that in this example the classes are hard-coded
    std::vector<std::string> classes {"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant",
                                      "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra",
                                      "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite",
                                      "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife",
                                      "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair",
                                      "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
                                      "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"};

    try {
        // Load the model (e.g. yolov8s.torchscript)
        std::string model_path = "/path/to/yolov8s.torchscript";
        torch::jit::script::Module yolo_model;
        yolo_model = torch::jit::load(model_path);
        yolo_model.eval();
        yolo_model.to(device, torch::kFloat32);

        // Load image and preprocess
        cv::Mat image = cv::imread("/path/to/bus.jpg");
        cv::Mat input_image;
        letterbox(image, input_image, {640, 640});

        torch::Tensor image_tensor = torch::from_blob(input_image.data, {input_image.rows, input_image.cols, 3}, torch::kByte).to(device);
        image_tensor = image_tensor.toType(torch::kFloat32).div(255);
        image_tensor = image_tensor.permute({2, 0, 1});
        image_tensor = image_tensor.unsqueeze(0);
        std::vector<torch::jit::IValue> inputs {image_tensor};

        // Inference
        torch::Tensor output = yolo_model.forward(inputs).toTensor().cpu();

        // NMS
        auto keep = non_max_suppression(output)[0];
        auto boxes = keep.index({Slice(), Slice(None, 4)});
        keep.index_put_({Slice(), Slice(None, 4)}, scale_boxes({input_image.rows, input_image.cols}, boxes, {image.rows, image.cols}));

        // Show the results
        for (int i = 0; i < keep.size(0); i++) {
            int x1 = keep[i][0].item().toFloat();
            int y1 = keep[i][1].item().toFloat();
            int x2 = keep[i][2].item().toFloat();
            int y2 = keep[i][3].item().toFloat();
            float conf = keep[i][4].item().toFloat();
            int cls = keep[i][5].item().toInt();
            std::cout << "Rect: [" << x1 << "," << y1 << "," << x2 << "," << y2 << "]  Conf: " << conf << "  Class: " << classes[cls] << std::endl;
        }
    } catch (const c10::Error& e) {
        std::cout << e.msg() << std::endl;
    }

    return 0;
}