tm_yolox_opendla.cpp

/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * License); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * AS IS BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 * KIND, either express or implied.  See the License for the
 * specific language governing permissions and limitations
 * under the License.
 */

/*
 * Copyright (c) 2021, OPEN AI LAB
 * Author: xwwang@openailab.com
 * Author: 774074168@qq.com
 * Author: honghao@openailab.com
 * original model: https://github.com/Megvii-BaseDetection/YOLOX
 */

#include <vector>
#include <string>
#include <algorithm>
#include <cmath>
#include <stdlib.h>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#include "common.h"
#include "tengine/c_api.h"
#include "tengine_operations.h"

struct Object
{
    cv::Rect_<float> rect;
    int label;
    float prob;
};

static inline float intersection_area(const Object& a, const Object& b)
{
    cv::Rect_<float> inter = a.rect & b.rect;
    return inter.area();
}

static void qsort_descent_inplace(std::vector<Object>& faceobjects, int left, int right)
{
    int i = left;
    int j = right;
    float p = faceobjects[(left + right) / 2].prob;

    while (i <= j)
    {
        while (faceobjects[i].prob > p)
            i++;

        while (faceobjects[j].prob < p)
            j--;

        if (i <= j)
        {
            // swap
            std::swap(faceobjects[i], faceobjects[j]);

            i++;
            j--;
        }
    }

#pragma omp parallel sections
    {
#pragma omp section
        {
            if (left < j) qsort_descent_inplace(faceobjects, left, j);
        }
#pragma omp section
        {
            if (i < right) qsort_descent_inplace(faceobjects, i, right);
        }
    }
}

static void qsort_descent_inplace(std::vector<Object>& faceobjects)
{
    if (faceobjects.empty())
        return;

    qsort_descent_inplace(faceobjects, 0, faceobjects.size() - 1);
}

static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold)
{
    picked.clear();

    const int n = faceobjects.size();

    std::vector<float> areas(n);
    for (int i = 0; i < n; i++)
    {
        areas[i] = faceobjects[i].rect.area();
    }

    for (int i = 0; i < n; i++)
    {
        const Object& a = faceobjects[i];

        int keep = 1;
        for (int j = 0; j < (int)picked.size(); j++)
        {
            const Object& b = faceobjects[picked[j]];

            // intersection over union
            float inter_area = intersection_area(a, b);
            float union_area = areas[i] + areas[picked[j]] - inter_area;
            // float IoU = inter_area / union_area
            if (inter_area / union_area > nms_threshold)
                keep = 0;
        }

        if (keep)
            picked.push_back(i);
    }
}

static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& objects)
{
    static const char* class_names[] = {
        "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"};

    cv::Mat image = bgr.clone();

    for (size_t i = 0; i < objects.size(); i++)
    {
        const Object& obj = objects[i];

        fprintf(stderr, "%2d: %3.0f%%, [%4.0f, %4.0f, %4.0f, %4.0f], %s\n", obj.label, obj.prob * 100, obj.rect.x,
                obj.rect.y, obj.rect.x + obj.rect.width, obj.rect.y + obj.rect.height, class_names[obj.label]);

        cv::rectangle(image, obj.rect, cv::Scalar(255, 0, 0));

        char text[256];
        sprintf(text, "%s %.1f%%", class_names[obj.label], obj.prob * 100);

        int baseLine = 0;
        cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);

        int x = obj.rect.x;
        int y = obj.rect.y - label_size.height - baseLine;
        if (y < 0)
            y = 0;
        if (x + label_size.width > image.cols)
            x = image.cols - label_size.width;

        cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)),
                      cv::Scalar(255, 255, 255), -1);

        cv::putText(image, text, cv::Point(x, y + label_size.height), cv::FONT_HERSHEY_SIMPLEX, 0.5,
                    cv::Scalar(0, 0, 0));
    }

    cv::imwrite("yolox_timvx_out.jpg", image);
}

struct GridAndStride
{
    int grid0;
    int grid1;
    int stride;
};

static void generate_grids_and_stride(const int target_w, const int target_h, std::vector<int>& strides, std::vector<GridAndStride>& grid_strides)
{
    for (auto stride : strides)
    {
        int num_grid_w = target_w / stride;
        int num_grid_h = target_h / stride;
        for (int g1 = 0; g1 < num_grid_h; g1++)
        {
            for (int g0 = 0; g0 < num_grid_w; g0++)
            {
                GridAndStride gs;
                gs.grid0 = g0;
                gs.grid1 = g1;
                gs.stride = stride;
                grid_strides.push_back(gs);
            }
        }
    }
}

static void generate_yolox_proposals(std::vector<GridAndStride> grid_strides, float* feat_ptr, float prob_threshold, std::vector<Object>& objects)
{
    const int num_grid = 3549;
    const int num_class = 80;
    const int num_anchors = grid_strides.size();

    //const float* feat_ptr = feat_blob;
    for (int anchor_idx = 0; anchor_idx < num_anchors; anchor_idx++)
    {
        // printf("%d,%d\n",num_anchors,anchor_idx);
        const int grid0 = grid_strides[anchor_idx].grid0;
        const int grid1 = grid_strides[anchor_idx].grid1;
        const int stride = grid_strides[anchor_idx].stride;

        // yolox/models/yolo_head.py decode logic
        //  outputs[..., :2] = (outputs[..., :2] + grids) * strides
        //  outputs[..., 2:4] = torch.exp(outputs[..., 2:4]) * strides
        float x_center = (feat_ptr[0] + grid0) * stride;
        float y_center = (feat_ptr[1] + grid1) * stride;
        float w = exp(feat_ptr[2]) * stride;
        float h = exp(feat_ptr[3]) * stride;
        float x0 = x_center - w * 0.5f;
        float y0 = y_center - h * 0.5f;

        float box_objectness = feat_ptr[4];

        for (int class_idx = 0; class_idx < num_class; class_idx++)
        {
            float box_cls_score = feat_ptr[5 + class_idx];
            float box_prob = box_objectness * box_cls_score;
            if (box_prob > prob_threshold)
            {
                Object obj;
                obj.rect.x = x0;
                obj.rect.y = y0;
                obj.rect.width = w;
                obj.rect.height = h;
                obj.label = class_idx;
                obj.prob = box_prob;

                objects.push_back(obj);
            }

        } // class loop
        feat_ptr += 85;

    } // point anchor loop
}

void show_usage()
{
    fprintf(
        stderr,
        "[Usage]:  [-h]\n    [-m model_file] [-i image_file] [-r repeat_count] [-t thread_count]\n");
}

void get_input_data_focus_int8(const char* image_file, int8_t* input_data, int letterbox_rows, int letterbox_cols, const float* mean,
                               const float* scale, float input_scale, int zero_point)
{
    cv::Mat sample = cv::imread(image_file, 1);
    cv::Mat img;

    if (sample.channels() == 1)
        cv::cvtColor(sample, img, cv::COLOR_GRAY2RGB);
    else
        cv::cvtColor(sample, img, cv::COLOR_BGR2RGB);

    /* letterbox process to support different letterbox size */
    float scale_letterbox;
    int resize_rows;
    int resize_cols;
    if ((letterbox_rows * 1.0 / img.rows) < (letterbox_cols * 1.0 / img.cols))
    {
        scale_letterbox = letterbox_rows * 1.0 / img.rows;
    }
    else
    {
        scale_letterbox = letterbox_cols * 1.0 / img.cols;
    }
    resize_cols = int(scale_letterbox * img.cols);
    resize_rows = int(scale_letterbox * img.rows);

    cv::resize(img, img, cv::Size(resize_cols, resize_rows));

    img.convertTo(img, CV_32FC3);
    // Generate a gray image for letterbox using opencv
    cv::Mat img_new(letterbox_rows, letterbox_cols, CV_32FC3, cv::Scalar(0, 0, 0) /*cv::Scalar(0.5/scale[0] + mean[0], 0.5/scale[1] + mean[1], 0.5/ scale[2] + mean[2])*/);
    int top = 0;
    int bot = letterbox_rows - resize_rows;
    int left = 0;
    int right = letterbox_cols - resize_cols;
    // Letterbox filling
    cv::copyMakeBorder(img, img_new, top, bot, left, right, cv::BORDER_CONSTANT, cv::Scalar(114.f, 114.f, 114.f));

    img_new.convertTo(img_new, CV_32FC3);
    float* img_data = (float*)img_new.data;
    std::vector<float> input_temp(3 * letterbox_cols * letterbox_rows);

    /* nhwc to nchw */
    for (int h = 0; h < letterbox_rows; h++)
    {
        for (int w = 0; w < letterbox_cols; w++)
        {
            for (int c = 0; c < 3; c++)
            {
                int in_index = h * letterbox_cols * 3 + w * 3 + c;
                int out_index = c * letterbox_rows * letterbox_cols + h * letterbox_cols + w;
                //new release of yolox has deleted the preprocess,
                //if you use the new version of yolox please use this code ==> input_temp[out_index] = img_data[in_index]
                input_temp[out_index] = (img_data[in_index] - mean[c]) * scale[c];
            }
        }
    }

    /* focus process */
    for (int i = 0; i < 2; i++) // corresponding to rows
    {
        for (int g = 0; g < 2; g++) // corresponding to cols
        {
            for (int c = 0; c < 3; c++)
            {
                for (int h = 0; h < letterbox_rows / 2; h++)
                {
                    for (int w = 0; w < letterbox_cols / 2; w++)
                    {
                        int in_index = i + g * letterbox_cols + c * letterbox_cols * letterbox_rows + h * 2 * letterbox_cols + w * 2;
                        int out_index = i * 2 * 3 * (letterbox_cols / 2) * (letterbox_rows / 2) + g * 3 * (letterbox_cols / 2) * (letterbox_rows / 2) + c * (letterbox_cols / 2) * (letterbox_rows / 2) + h * (letterbox_cols / 2) + w;

                        /* quant to int8 */
                        int idata = (round)(input_temp[in_index] / input_scale + (float)zero_point);
                        if (idata > 127)
                            idata = 127;
                        else if (idata < -127)
                            idata = -127;

                        input_data[out_index] = idata;
                    }
                }
            }
        }
    }
}

int main(int argc, char* argv[])
{
    const char* model_file = nullptr;
    const char* image_file = nullptr;

    int img_c = 3;
    const float mean[3] = {255.f * 0.485f, 255.f * 0.456, 255.f * 0.406f};
    const float scale[3] = {1 / (255.f * 0.229f), 1 / (255.f * 0.224f), 1 / (255.f * 0.225f)};

    // allow none square letterbox, set default letterbox size
    int letterbox_rows = 416;
    int letterbox_cols = 416;

    int repeat_count = 1;
    int num_thread = 1;

    int res;
    while ((res = getopt(argc, argv, "m:i:r:t:h:")) != -1)
    {
        switch (res)
        {
        case 'm':
            model_file = optarg;
            break;
        case 'i':
            image_file = optarg;
            break;
        case 'r':
            repeat_count = std::strtoul(optarg, nullptr, 10);
            break;
        case 't':
            num_thread = std::strtoul(optarg, nullptr, 10);
            break;
        case 'h':
            show_usage();
            return 0;
        default:
            break;
        }
    }

    /* check files */
    if (nullptr == model_file)
    {
        fprintf(stderr, "Error: Tengine model file not specified!\n");
        show_usage();
        return -1;
    }

    if (nullptr == image_file)
    {
        fprintf(stderr, "Error: Image file not specified!\n");
        show_usage();
        return -1;
    }

    if (!check_file_exist(model_file) || !check_file_exist(image_file))
        return -1;

    cv::Mat img = cv::imread(image_file, 1);
    if (img.empty())
    {
        fprintf(stderr, "cv::imread %s failed\n", image_file);
        return -1;
    }

    /* set runtime options */
    struct options opt;
    opt.num_thread = num_thread;
    opt.cluster = TENGINE_CLUSTER_ALL;
    opt.precision = TENGINE_MODE_INT8;
    opt.affinity = 0;

    /* inital tengine */
    if (init_tengine() != 0)
    {
        fprintf(stderr, "Initial tengine failed.\n");
        return -1;
    }
    fprintf(stderr, "tengine-lite library version: %s\n", get_tengine_version());
    context_t odla_context = create_context("odla", 1);
    int rtt = set_context_device(odla_context, "OPENDLA", NULL, 0);
    if (0 > rtt)
    {
        fprintf(stderr, " add_context_device VSI DEVICE failed.\n");
        return -1;
    }

    /* create graph, load tengine model xxx.tmfile */
    graph_t graph = create_graph(odla_context, "tengine", model_file);
    if (graph == nullptr)
    {
        fprintf(stderr, "Create graph failed.\n");
        return -1;
    }

    int img_size = letterbox_rows * letterbox_cols * img_c;
    int dims[] = {1, 12, int(letterbox_rows / 2), int(letterbox_cols / 2)};
    std::vector<int8_t> input_data(img_size);

    tensor_t input_tensor = get_graph_input_tensor(graph, 0, 0);

    if (input_tensor == nullptr)
    {
        fprintf(stderr, "Get input tensor failed\n");
        return -1;
    }

    if (set_tensor_shape(input_tensor, dims, 4) < 0)
    {
        fprintf(stderr, "Set input tensor shape failed\n");
        return -1;
    }

    if (set_tensor_buffer(input_tensor, input_data.data(), img_size) < 0)
    {
        fprintf(stderr, "Set input tensor buffer failed\n");
        return -1;
    }

    /* prerun graph, set work options(num_thread, cluster, precision) */
    if (prerun_graph_multithread(graph, opt) < 0)
    {
        fprintf(stderr, "Prerun multithread graph failed.\n");
        return -1;
    }

    /* prepare process input data, set the data mem to input tensor */
    float input_scale = 0.f;
    int input_zero_point = 0;
    get_tensor_quant_param(input_tensor, &input_scale, &input_zero_point, 1);
    get_input_data_focus_int8(image_file, input_data.data(), letterbox_rows, letterbox_cols, mean, scale, input_scale, input_zero_point);

    /* run graph */
    double min_time = DBL_MAX;
    double max_time = DBL_MIN;
    double total_time = 0.;
    for (int i = 0; i < repeat_count; i++)
    {
        double start = get_current_time();
        if (run_graph(graph, 1) < 0)
        {
            fprintf(stderr, "Run graph failed\n");
            return -1;
        }
        double end = get_current_time();
        double cur = end - start;
        total_time += cur;
        min_time = std::min(min_time, cur);
        max_time = std::max(max_time, cur);
    }
    fprintf(stderr, "Repeat %d times, thread %d, avg time %.2f ms, max_time %.2f ms, min_time %.2f ms\n", repeat_count, num_thread,
            total_time / repeat_count, max_time, min_time);
    fprintf(stderr, "--------------------------------------\n");

    /* yolox postprocess */
    tensor_t p8_output = get_graph_output_tensor(graph, 0, 0);
    int8_t* output_i8 = (int8_t*)get_tensor_buffer(p8_output);
    int output_size = get_tensor_buffer_size(p8_output);

    /* dequant */
    float output_scale = 0.f;
    int output_zero_point = 0;
    get_tensor_quant_param(p8_output, &output_scale, &output_zero_point, 1);
    std::vector<float> p8_data(output_size);
    for (int i = 0; i < output_size; i++)
        p8_data[i] = ((float)output_i8[i] - (float)output_zero_point) * output_scale;

    /* postprocess */
    const float prob_threshold = 0.3f;
    const float nms_threshold = 0.65f;

    std::vector<Object> proposals;
    std::vector<Object> objects;

    std::vector<int> strides = {8, 16, 32}; // might have stride=64
    std::vector<GridAndStride> grid_strides;
    generate_grids_and_stride(letterbox_cols, letterbox_rows, strides, grid_strides);
    generate_yolox_proposals(grid_strides, p8_data.data(), prob_threshold, proposals);
    qsort_descent_inplace(proposals);
    std::vector<int> picked;
    nms_sorted_bboxes(proposals, picked, nms_threshold);

    /* yolox draw the result */
    float scale_letterbox;
    int resize_rows;
    int resize_cols;
    if ((letterbox_rows * 1.0 / img.rows) < (letterbox_cols * 1.0 / img.cols))
    {
        scale_letterbox = letterbox_rows * 1.0 / img.rows;
    }
    else
    {
        scale_letterbox = letterbox_cols * 1.0 / img.cols;
    }

    int count = picked.size();
    fprintf(stderr, "detection num: %d\n", count);

    objects.resize(count);
    for (int i = 0; i < count; i++)
    {
        objects[i] = proposals[picked[i]];
        float x0 = (objects[i].rect.x) / scale_letterbox;
        float y0 = (objects[i].rect.y) / scale_letterbox;
        float x1 = (objects[i].rect.x + objects[i].rect.width) / scale_letterbox;
        float y1 = (objects[i].rect.y + objects[i].rect.height) / scale_letterbox;
        x0 = std::max(std::min(x0, (float)(img.cols - 1)), 0.f);
        y0 = std::max(std::min(y0, (float)(img.rows - 1)), 0.f);
        x1 = std::max(std::min(x1, (float)(img.cols - 1)), 0.f);
        y1 = std::max(std::min(y1, (float)(img.rows - 1)), 0.f);

        objects[i].rect.x = x0;
        objects[i].rect.y = y0;
        objects[i].rect.width = x1 - x0;
        objects[i].rect.height = y1 - y0;
    }

    draw_objects(img, objects);

    /* release tengine */
    postrun_graph(graph);
    destroy_graph(graph);
    release_tengine();
}