YOLOv5导出onnx、TrensorRT部署（LINUX）

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前言

提示：这里可以添加本文要记录的大概内容：

本文在linux下对yolov5导出onnx模型进行修改，导出trtmodel,实现C++部署。
可能涉及到模型压缩剪枝。

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提示：以下是本篇文章正文内容，下面案例可供参考

一、版本声明

YOLOv5版本：YOLOv5-6.0：https://github.com/ultralytics/yolov5/tree/v6.0

二、实现步骤

1.训练一个目标检测模型

此处以YOLOv5s为例在私人数据集上进行训练得到以下模型：

模型压缩：稀疏训练、剪枝、微调 参考：https://blog.csdn.net/qq_46098574/article/details/125174256?spm=1001.2014.3001.5502

2.导出onnx模型

# YOLOv5  by Ultralytics, GPL-3.0 license


import argparse
import json
import os
import subprocess
import sys
import time
from pathlib import Path

import torch
import torch.nn as nn
from torch.utils.mobile_optimizer import optimize_for_mobile

FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

from models.common import Conv
from models.experimental import attempt_load
from models.yolo import Detect
from utils.activations import SiLU
from utils.datasets import LoadImages
from utils.general import (LOGGER, check_dataset, check_img_size, check_requirements, colorstr, file_size, print_args,
                           url2file)
from utils.torch_utils import select_device


def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
    # YOLOv5 TorchScript model export
    try:
        LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
        f = file.with_suffix('.torchscript')

        ts = torch.jit.trace(model, im, strict=False)
        d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
        extra_files = {'config.txt': json.dumps(d)}  # torch._C.ExtraFilesMap()
        (optimize_for_mobile(ts) if optimize else ts).save(str(f), _extra_files=extra_files)

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
    except Exception as e:
        LOGGER.info(f'{prefix} export failure: {e}')


def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorstr('ONNX:')):
    # YOLOv5 ONNX export
    try:
        check_requirements(('onnx',))
        import onnx

        LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
        f = file.with_suffix('.onnx')

        torch.onnx.export(model, im, f, verbose=False, opset_version=opset,
                          training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
                          do_constant_folding=not train,
                          input_names=['images'],
                          output_names=['output'],
                          dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'},  # shape(1,3,640,640)
                                        'output': {0: 'batch', 1: 'anchors'}  # shape(1,25200,85)
                                        } if dynamic else None)

        # Checks
        model_onnx = onnx.load(f)  # load onnx model
        onnx.checker.check_model(model_onnx)  # check onnx model
        # LOGGER.info(onnx.helper.printable_graph(model_onnx.graph))  # print

        # Simplify
        if simplify:
            try:
                check_requirements(('onnx-simplifier',))
                import onnxsim

                LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
                model_onnx, check = onnxsim.simplify(
                    model_onnx,
                    dynamic_input_shape=dynamic,
                    input_shapes={'images': list(im.shape)} if dynamic else None)
                assert check, 'assert check failed'
                onnx.save(model_onnx, f)
            except Exception as e:
                LOGGER.info(f'{prefix} simplifier failure: {e}')
        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
        LOGGER.info(f"{prefix} run --dynamic ONNX model inference with: 'python detect.py --weights {f}'")
    except Exception as e:
        LOGGER.info(f'{prefix} export failure: {e}')


def export_coreml(model, im, file, prefix=colorstr('CoreML:')):
    # YOLOv5 CoreML export
    ct_model = None
    try:
        check_requirements(('coremltools',))
        import coremltools as ct

        LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
        f = file.with_suffix('.mlmodel')

        model.train()  # CoreML exports should be placed in model.train() mode
        ts = torch.jit.trace(model, im, strict=False)  # TorchScript model
        ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
        ct_model.save(f)

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')

    return ct_model


def export_openvino(model, im, file, prefix=colorstr('OpenVINO:')):
    # YOLOv5 OpenVINO export
    try:
        check_requirements(('openvino-dev',))  # requires openvino-dev: https://pypi.org/project/openvino-dev/
        import openvino.inference_engine as ie

        LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
        f = str(file).replace('.pt', '_openvino_model' + os.sep)

        cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f}"
        subprocess.check_output(cmd, shell=True)

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')


def export_saved_model(model, im, file, dynamic,
                       tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45,
                       conf_thres=0.25, prefix=colorstr('TensorFlow saved_model:')):
    # YOLOv5 TensorFlow saved_model export
    keras_model = None
    try:
        import tensorflow as tf
        from tensorflow import keras

        from models.tf import TFDetect, TFModel

        LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
        f = str(file).replace('.pt', '_saved_model')
        batch_size, ch, *imgsz = list(im.shape)  # BCHW

        tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
        im = tf.zeros((batch_size, *imgsz, 3))  # BHWC order for TensorFlow
        y = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
        inputs = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
        outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
        keras_model = keras.Model(inputs=inputs, outputs=outputs)
        keras_model.trainable = False
        keras_model.summary()
        keras_model.save(f, save_format='tf')

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')

    return keras_model


def export_pb(keras_model, im, file, prefix=colorstr('TensorFlow GraphDef:')):
    # YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
    try:
        import tensorflow as tf
        from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2

        LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
        f = file.with_suffix('.pb')

        m = tf.function(lambda x: keras_model(x))  # full model
        m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
        frozen_func = convert_variables_to_constants_v2(m)
        frozen_func.graph.as_graph_def()
        tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')


def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('TensorFlow Lite:')):
    # YOLOv5 TensorFlow Lite export
    try:
        import tensorflow as tf

        from models.tf import representative_dataset_gen

        LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
        batch_size, ch, *imgsz = list(im.shape)  # BCHW
        f = str(file).replace('.pt', '-fp16.tflite')

        converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
        converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
        converter.target_spec.supported_types = [tf.float16]
        converter.optimizations = [tf.lite.Optimize.DEFAULT]
        if int8:
            dataset = LoadImages(check_dataset(data)['train'], img_size=imgsz, auto=False)  # representative data
            converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib)
            converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
            converter.target_spec.supported_types = []
            converter.inference_input_type = tf.uint8  # or tf.int8
            converter.inference_output_type = tf.uint8  # or tf.int8
            converter.experimental_new_quantizer = False
            f = str(file).replace('.pt', '-int8.tflite')

        tflite_model = converter.convert()
        open(f, "wb").write(tflite_model)
        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')

    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')


def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
    # YOLOv5 TensorFlow.js export
    try:
        check_requirements(('tensorflowjs',))
        import re

        import tensorflowjs as tfjs

        LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
        f = str(file).replace('.pt', '_web_model')  # js dir
        f_pb = file.with_suffix('.pb')  # *.pb path
        f_json = f + '/model.json'  # *.json path

        cmd = f"tensorflowjs_converter --input_format=tf_frozen_model " \
              f"--output_node_names='Identity,Identity_1,Identity_2,Identity_3' {f_pb} {f}"
        subprocess.run(cmd, shell=True)

        json = open(f_json).read()
        with open(f_json, 'w') as j:  # sort JSON Identity_* in ascending order
            subst = re.sub(
                r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
                r'"Identity.?.?": {"name": "Identity.?.?"}, '
                r'"Identity.?.?": {"name": "Identity.?.?"}, '
                r'"Identity.?.?": {"name": "Identity.?.?"}}}',
                r'{"outputs": {"Identity": {"name": "Identity"}, '
                r'"Identity_1": {"name": "Identity_1"}, '
                r'"Identity_2": {"name": "Identity_2"}, '
                r'"Identity_3": {"name": "Identity_3"}}}',
                json)
            j.write(subst)

        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')


def export_engine(model, im, file, train, half, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
    try:
        check_requirements(('tensorrt',))
        import tensorrt as trt

        opset = (12, 13)[trt.__version__[0] == '8']  # test on TensorRT 7.x and 8.x
        export_onnx(model, im, file, opset, train, False, simplify)
        onnx = file.with_suffix('.onnx')
        assert onnx.exists(), f'failed to export ONNX file: {onnx}'

        LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
        f = file.with_suffix('.engine')  # TensorRT engine file
        logger = trt.Logger(trt.Logger.INFO)
        if verbose:
            logger.min_severity = trt.Logger.Severity.VERBOSE

        builder = trt.Builder(logger)
        config = builder.create_builder_config()
        config.max_workspace_size = workspace * 1 << 30

        flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
        network = builder.create_network(flag)
        parser = trt.OnnxParser(network, logger)
        if not parser.parse_from_file(str(onnx)):
            raise RuntimeError(f'failed to load ONNX file: {onnx}')

        inputs = [network.get_input(i) for i in range(network.num_inputs)]
        outputs = [network.get_output(i) for i in range(network.num_outputs)]
        LOGGER.info(f'{prefix} Network Description:')
        for inp in inputs:
            LOGGER.info(f'{prefix}\tinput "{inp.name}" with shape {inp.shape} and dtype {inp.dtype}')
        for out in outputs:
            LOGGER.info(f'{prefix}\toutput "{out.name}" with shape {out.shape} and dtype {out.dtype}')

        half &= builder.platform_has_fast_fp16
        LOGGER.info(f'{prefix} building FP{16 if half else 32} engine in {f}')
        if half:
            config.set_flag(trt.BuilderFlag.FP16)
        with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
            t.write(engine.serialize())
        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')

    except Exception as e:
        LOGGER.info(f'\n{prefix} export failure: {e}')


@torch.no_grad()
def run(data=ROOT / 'data/coco128.yaml',  # 'dataset.yaml path'
        weights=ROOT / 'yolov5s.pt',  # weights path
        imgsz=(640, 640),  # image (height, width)
        batch_size=1,  # batch size
        device='cpu',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        include=('torchscript', 'onnx'),  # include formats
        half=False,  # FP16 half-precision export
        inplace=False,  # set YOLOv5 Detect() inplace=True
        train=False,  # model.train() mode
        optimize=False,  # TorchScript: optimize for mobile
        int8=False,  # CoreML/TF INT8 quantization
        dynamic=False,  # ONNX/TF: dynamic axes
        simplify=False,  # ONNX: simplify model
        opset=12,  # ONNX: opset version
        verbose=False,  # TensorRT: verbose log
        workspace=4,  # TensorRT: workspace size (GB)
        nms=False,  # TF: add NMS to model
        agnostic_nms=False,  # TF: add agnostic NMS to model
        topk_per_class=100,  # TF.js NMS: topk per class to keep
        topk_all=100,  # TF.js NMS: topk for all classes to keep
        iou_thres=0.45,  # TF.js NMS: IoU threshold
        conf_thres=0.25  # TF.js NMS: confidence threshold
        ):
    t = time.time()
    include = [x.lower() for x in include]
    tf_exports = list(x in include for x in ('saved_model', 'pb', 'tflite', 'tfjs'))  # TensorFlow exports
    file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)

    # Checks
    imgsz *= 2 if len(imgsz) == 1 else 1  # expand
    opset = 12 if ('openvino' in include) else opset  # OpenVINO requires opset <= 12

    # Load PyTorch model
    device = select_device(device)
    assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0'
    model = attempt_load(weights, map_location=device, inplace=True, fuse=True)  # load FP32 model
    nc, names = model.nc, model.names  # number of classes, class names

    # Input
    gs = int(max(model.stride))  # grid size (max stride)
    imgsz = [check_img_size(x, gs) for x in imgsz]  # verify img_size are gs-multiples
    im = torch.zeros(batch_size, 3, *imgsz).to(device)  # image size(1,3,320,192) BCHW iDetection

    # Update model
    if half:
        im, model = im.half(), model.half()  # to FP16
    model.train() if train else model.eval()  # training mode = no Detect() layer grid construction
    for k, m in model.named_modules():
        if isinstance(m, Conv):  # assign export-friendly activations
            if isinstance(m.act, nn.SiLU):
                m.act = SiLU()
        elif isinstance(m, Detect):
            m.inplace = inplace
            m.onnx_dynamic = dynamic
            # m.forward = m.forward_export  # assign forward (optional)

    for _ in range(2):
        y = model(im)  # dry runs
    LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} ({file_size(file):.1f} MB)")

    # Exports
    if 'torchscript' in include:
        export_torchscript(model, im, file, optimize)
    if ('onnx' in include) or ('openvino' in include):  # OpenVINO requires ONNX
        export_onnx(model, im, file, opset, train, dynamic, simplify)
    if 'engine' in include:
        export_engine(model, im, file, train, half, simplify, workspace, verbose)
    if 'coreml' in include:
        export_coreml(model, im, file)
    if 'openvino' in include:
        export_openvino(model, im, file)

    # TensorFlow Exports
    if any(tf_exports):
        pb, tflite, tfjs = tf_exports[1:]
        assert not (tflite and tfjs), 'TFLite and TF.js models must be exported separately, please pass only one type.'
        model = export_saved_model(model, im, file, dynamic, tf_nms=nms or agnostic_nms or tfjs,
                                   agnostic_nms=agnostic_nms or tfjs, topk_per_class=topk_per_class, topk_all=topk_all,
                                   conf_thres=conf_thres, iou_thres=iou_thres)  # keras model
        if pb or tfjs:  # pb prerequisite to tfjs
            export_pb(model, im, file)
        if tflite:
            export_tflite(model, im, file, int8=int8, data=data, ncalib=100)
        if tfjs:
            export_tfjs(model, im, file)

    # Finish
    LOGGER.info(f'\nExport complete ({time.time() - t:.2f}s)'
                f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
                f'\nVisualize with https://netron.app')


def parse_opt():
    parser = argparse.ArgumentParser()
    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)')
    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640, 640], help='image (h, w)')
    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
    parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
    parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True')
    parser.add_argument('--train', action='store_true', help='model.train() mode')
    parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
    parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
    parser.add_argument('--dynamic', action='store_true', help='ONNX/TF: dynamic axes')
    parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
    parser.add_argument('--opset', type=int, default=12, help='ONNX: opset version')
    parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log')
    parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)')
    parser.add_argument('--nms', action='store_true', help='TF: add NMS to model')
    parser.add_argument('--agnostic-nms', action='store_true', help='TF: add agnostic NMS to model')
    parser.add_argument('--topk-per-class', type=int, default=100, help='TF.js NMS: topk per class to keep')
    parser.add_argument('--topk-all', type=int, default=100, help='TF.js NMS: topk for all classes to keep')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='TF.js NMS: IoU threshold')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold')
    parser.add_argument('--include', nargs='+',
                        default=['torchscript', 'onnx'],
                        help='available formats are (torchscript, onnx, engine, coreml, saved_model, pb, tflite, tfjs)')
    opt = parser.parse_args()
    print_args(FILE.stem, opt)
    return opt


def main(opt):
    for opt.weights in (opt.weights if isinstance(opt.weights, list) else [opt.weights]):
        run(**vars(opt))


if __name__ == "__main__":
    opt = parse_opt()
    main(opt)

3.Netron可视化

netron yolov5s.onnx

存在多输出问题，修改models/yolo.py。

![在这里插入图片描述](https://img-blog.csdnimg.cn/7755710e413141058febe3be39349e3e.jpeg

修改后可视化：

4.编译成trtmodel和部署

// tensorRT include
// 编译用的头文件
#include 

// onnx解析器的头文件
#include 

// 推理用的运行时头文件
#include 

// cuda include
#include 

// system include
#include 
#include 

#include 
#include 
#include 
#include 
#include 
#include 

#include 

using namespace std;

#define checkRuntime(op)  __check_cuda_runtime((op), #op, __FILE__, __LINE__)

bool __check_cuda_runtime(cudaError_t code, const char* op, const char* file, int line){
    if(code != cudaSuccess){    
        const char* err_name = cudaGetErrorName(code);    
        const char* err_message = cudaGetErrorString(code);  
        printf("runtime error %s:%d  %s failed. \n  code = %s, message = %s\n", file, line, op, err_name, err_message);   
        return false;
    }
    return true;
}

inline const char* severity_string(nvinfer1::ILogger::Severity t){
    switch(t){
        case nvinfer1::ILogger::Severity::kINTERNAL_ERROR: return "internal_error";
        case nvinfer1::ILogger::Severity::kERROR:   return "error";
        case nvinfer1::ILogger::Severity::kWARNING: return "warning";
        case nvinfer1::ILogger::Severity::kINFO:    return "info";
        case nvinfer1::ILogger::Severity::kVERBOSE: return "verbose";
        default: return "unknow";
    }
}

// coco数据集的labels，关于coco：https://cocodataset.org/#home
static const char* cocolabels[] = {
    "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"
};

// hsv转bgr
static std::tuple<uint8_t, uint8_t, uint8_t> hsv2bgr(float h, float s, float v){
    const int h_i = static_cast<int>(h * 6);
    const float f = h * 6 - h_i;
    const float p = v * (1 - s);
    const float q = v * (1 - f*s);
    const float t = v * (1 - (1 - f) * s);
    float r, g, b;
    switch (h_i) {
    case 0:r = v; g = t; b = p;break;
    case 1:r = q; g = v; b = p;break;
    case 2:r = p; g = v; b = t;break;
    case 3:r = p; g = q; b = v;break;
    case 4:r = t; g = p; b = v;break;
    case 5:r = v; g = p; b = q;break;
    default:r = 1; g = 1; b = 1;break;}
    return make_tuple(static_cast<uint8_t>(b * 255), static_cast<uint8_t>(g * 255), static_cast<uint8_t>(r * 255));
}

static std::tuple<uint8_t, uint8_t, uint8_t> random_color(int id){
    float h_plane = ((((unsigned int)id << 2) ^ 0x937151) % 100) / 100.0f;;
    float s_plane = ((((unsigned int)id << 3) ^ 0x315793) % 100) / 100.0f;
    return hsv2bgr(h_plane, s_plane, 1);
}

class TRTLogger : public nvinfer1::ILogger{
public:
    virtual void log(Severity severity, nvinfer1::AsciiChar const* msg) noexcept override{
        if(severity <= Severity::kWARNING){
            // 打印带颜色的字符，格式如下：
            // printf("\033[47;33m打印的文本\033[0m");
            // 其中 \033[ 是起始标记
            //      47    是背景颜色
            //      ;     分隔符
            //      33    文字颜色
            //      m     开始标记结束
            //      \033[0m 是终止标记
            // 其中背景颜色或者文字颜色可不写
            // 部分颜色代码 https://blog.csdn.net/ericbar/article/details/79652086
            if(severity == Severity::kWARNING){
                printf("\033[33m%s: %s\033[0m\n", severity_string(severity), msg);
            }
            else if(severity <= Severity::kERROR){
                printf("\033[31m%s: %s\033[0m\n", severity_string(severity), msg);
            }
            else{
                printf("%s: %s\n", severity_string(severity), msg);
            }
        }
    }
} logger;

// 通过智能指针管理nv返回的指针参数
// 内存自动释放，避免泄漏
template<typename _T>
shared_ptr<_T> make_nvshared(_T* ptr){
    return shared_ptr<_T>(ptr, [](_T* p){p->destroy();});
}

bool exists(const string& path){

#ifdef _WIN32
    return ::PathFileExistsA(path.c_str());
#else
    return access(path.c_str(), R_OK) == 0;
#endif
}

// 上一节的代码
bool build_model(){

    if(exists("yolov5s.trtmodel")){
        printf("yolov5s.trtmodel has exists.\n");
        return true;
    }

    TRTLogger logger;

    // 这是基本需要的组件
    auto builder = make_nvshared(nvinfer1::createInferBuilder(logger));
    auto config = make_nvshared(builder->createBuilderConfig());
    auto network = make_nvshared(builder->createNetworkV2(1));

    // 通过onnxparser解析器解析的结果会填充到network中，类似addConv的方式添加进去
    auto parser = make_nvshared(nvonnxparser::createParser(*network, logger));
    if(!parser->parseFromFile("newp05best.onnx", 1)){
        printf("Failed to parse yolov5s.onnx\n");

        // 注意这里的几个指针还没有释放，是有内存泄漏的，后面考虑更优雅的解决
        return false;
    }
    
    int maxBatchSize = 10;
    printf("Workspace Size = %.2f MB\n", (1 << 28) / 1024.0f / 1024.0f);
    config->setMaxWorkspaceSize(1 << 28);

    // 如果模型有多个输入，则必须多个profile
    auto profile = builder->createOptimizationProfile();
    auto input_tensor = network->getInput(0);
    auto input_dims = input_tensor->getDimensions();
    
    // 配置最小、最优、最大范围
    input_dims.d[0] = 1;
    profile->setDimensions(input_tensor->getName(), nvinfer1::OptProfileSelector::kMIN, input_dims);
    profile->setDimensions(input_tensor->getName(), nvinfer1::OptProfileSelector::kOPT, input_dims);
    input_dims.d[0] = maxBatchSize;
    profile->setDimensions(input_tensor->getName(), nvinfer1::OptProfileSelector::kMAX, input_dims);
    config->addOptimizationProfile(profile);

    auto engine = make_nvshared(builder->buildEngineWithConfig(*network, *config));
    if(engine == nullptr){
        printf("Build engine failed.\n");
        return false;
    }

    // 将模型序列化，并储存为文件
    auto model_data = make_nvshared(engine->serialize());
    FILE* f = fopen("yolov5s.trtmodel", "wb");
    fwrite(model_data->data(), 1, model_data->size(), f);
    fclose(f);

    // 卸载顺序按照构建顺序倒序
    printf("Build Done.\n");
    return true;
}

///

vector<unsigned char> load_file(const string& file){
    ifstream in(file, ios::in | ios::binary);
    if (!in.is_open())
        return {};

    in.seekg(0, ios::end);
    size_t length = in.tellg();

    std::vector<uint8_t> data;
    if (length > 0){
        in.seekg(0, ios::beg);
        data.resize(length);

        in.read((char*)&data[0], length);
    }
    in.close();
    return data;
}

void inference(){

    TRTLogger logger;
    auto engine_data = load_file("yolov5s.trtmodel");
    auto runtime   = make_nvshared(nvinfer1::createInferRuntime(logger));
    auto engine = make_nvshared(runtime->deserializeCudaEngine(engine_data.data(), engine_data.size()));
    if(engine == nullptr){
        printf("Deserialize cuda engine failed.\n");
        runtime->destroy();
        return;
    }

    if(engine->getNbBindings() != 2){
        printf("你的onnx导出有问题，必须是1个输入和1个输出，你这明显有：%d个输出.\n", engine->getNbBindings() - 1);
        return;
    }

    cudaStream_t stream = nullptr;
    checkRuntime(cudaStreamCreate(&stream));
    auto execution_context = make_nvshared(engine->createExecutionContext());

    int input_batch = 1;
    int input_channel = 3;
    int input_height = 640;
    int input_width = 640;
    int input_numel = input_batch * input_channel * input_height * input_width;
    float* input_data_host = nullptr;
    float* input_data_device = nullptr;
    checkRuntime(cudaMallocHost(&input_data_host, input_numel * sizeof(float)));
    checkRuntime(cudaMalloc(&input_data_device, input_numel * sizeof(float)));

    ///
    // letter box
    auto image = cv::imread("zhishanlouxi_wanshang0003109.jpg");
    // 通过双线性插值对图像进行resize
    float scale_x = input_width / (float)image.cols;
    float scale_y = input_height / (float)image.rows;
    float scale = std::min(scale_x, scale_y);
    float i2d[6], d2i[6];
    // resize图像，源图像和目标图像几何中心的对齐
    i2d[0] = scale;  i2d[1] = 0;  i2d[2] = (-scale * image.cols + input_width + scale  - 1) * 0.5;
    i2d[3] = 0;  i2d[4] = scale;  i2d[5] = (-scale * image.rows + input_height + scale - 1) * 0.5;

    cv::Mat m2x3_i2d(2, 3, CV_32F, i2d);  // image to dst(network), 2x3 matrix
    cv::Mat m2x3_d2i(2, 3, CV_32F, d2i);  // dst to image, 2x3 matrix
    cv::invertAffineTransform(m2x3_i2d, m2x3_d2i);  // 计算一个反仿射变换

    cv::Mat input_image(input_height, input_width, CV_8UC3);
    cv::warpAffine(image, input_image, m2x3_i2d, input_image.size(), cv::INTER_LINEAR, cv::BORDER_CONSTANT, cv::Scalar::all(114));  // 对图像做平移缩放旋转变换,可逆
    cv::imwrite("input-image.jpg", input_image);

    int image_area = input_image.cols * input_image.rows;
    unsigned char* pimage = input_image.data;
    float* phost_b = input_data_host + image_area * 0;
    float* phost_g = input_data_host + image_area * 1;
    float* phost_r = input_data_host + image_area * 2;
    for(int i = 0; i < image_area; ++i, pimage += 3){
        // 注意这里的顺序rgb调换了
        *phost_r++ = pimage[0] / 255.0f;
        *phost_g++ = pimage[1] / 255.0f;
        *phost_b++ = pimage[2] / 255.0f;
    }
    ///
    checkRuntime(cudaMemcpyAsync(input_data_device, input_data_host, input_numel * sizeof(float), cudaMemcpyHostToDevice, stream));

    // 3x3输入，对应3x3输出
    auto output_dims = engine->getBindingDimensions(1);
    int output_numbox = output_dims.d[1];
    int output_numprob = output_dims.d[2];
    int num_classes = output_numprob - 5;
    int output_numel = input_batch * output_numbox * output_numprob;
    float* output_data_host = nullptr;
    float* output_data_device = nullptr;
    checkRuntime(cudaMallocHost(&output_data_host, sizeof(float) * output_numel));
    checkRuntime(cudaMalloc(&output_data_device, sizeof(float) * output_numel));

    // 明确当前推理时，使用的数据输入大小
    auto input_dims = engine->getBindingDimensions(0);
    input_dims.d[0] = input_batch;

    execution_context->setBindingDimensions(0, input_dims);
    float* bindings[] = {input_data_device, output_data_device};
    bool success      = execution_context->enqueueV2((void**)bindings, stream, nullptr);
    checkRuntime(cudaMemcpyAsync(output_data_host, output_data_device, sizeof(float) * output_numel, cudaMemcpyDeviceToHost, stream));
    checkRuntime(cudaStreamSynchronize(stream));

    // decode box：从不同尺度下的预测狂还原到原输入图上(包括:预测框，类被概率，置信度）
    vector<vector<float>> bboxes;
    float confidence_threshold = 0.25;
    float nms_threshold = 0.5;
    for(int i = 0; i < output_numbox; ++i){
        float* ptr = output_data_host + i * output_numprob;
        float objness = ptr[4];
        if(objness < confidence_threshold)
            continue;

        float* pclass = ptr + 5;
        int label     = std::max_element(pclass, pclass + num_classes) - pclass;
        float prob    = pclass[label];
        float confidence = prob * objness;
        if(confidence < confidence_threshold)
            continue;

        // 中心点、宽、高
        float cx     = ptr[0];
        float cy     = ptr[1];
        float width  = ptr[2];
        float height = ptr[3];

        // 预测框
        float left   = cx - width * 0.5;
        float top    = cy - height * 0.5;
        float right  = cx + width * 0.5;
        float bottom = cy + height * 0.5;

        // 对应图上的位置
        float image_base_left   = d2i[0] * left   + d2i[2];
        float image_base_right  = d2i[0] * right  + d2i[2];
        float image_base_top    = d2i[0] * top    + d2i[5];
        float image_base_bottom = d2i[0] * bottom + d2i[5];
        bboxes.push_back({image_base_left, image_base_top, image_base_right, image_base_bottom, (float)label, confidence});
    }
    printf("decoded bboxes.size = %d\n", bboxes.size());

    // nms非极大抑制
    std::sort(bboxes.begin(), bboxes.end(), [](vector<float>& a, vector<float>& b){return a[5] > b[5];});
    std::vector<bool> remove_flags(bboxes.size());
    std::vector<vector<float>> box_result;
    box_result.reserve(bboxes.size()); //预分配空间

    auto iou = [](const vector<float>& a, const vector<float>& b){
        float cross_left   = std::max(a[0], b[0]);
        float cross_top    = std::max(a[1], b[1]);
        float cross_right  = std::min(a[2], b[2]);
        float cross_bottom = std::min(a[3], b[3]);

        float cross_area = std::max(0.0f, cross_right - cross_left) * std::max(0.0f, cross_bottom - cross_top);
        float union_area = std::max(0.0f, a[2] - a[0]) * std::max(0.0f, a[3] - a[1]) 
                         + std::max(0.0f, b[2] - b[0]) * std::max(0.0f, b[3] - b[1]) - cross_area;
        if(cross_area == 0 || union_area == 0) return 0.0f;
        return cross_area / union_area;
    };

    for(int i = 0; i < bboxes.size(); ++i){
        if(remove_flags[i]) continue;

        auto& ibox = bboxes[i];
        box_result.emplace_back(ibox);
        for(int j = i + 1; j < bboxes.size(); ++j){
            if(remove_flags[j]) continue;

            auto& jbox = bboxes[j];
            if(ibox[4] == jbox[4]){
                // class matched
                if(iou(ibox, jbox) >= nms_threshold)
                    remove_flags[j] = true;
            }
        }
    }
    printf("box_result.size = %d\n", box_result.size());

    for(int i = 0; i < box_result.size(); ++i){
        auto& ibox = box_result[i];
        float left = ibox[0];
        float top = ibox[1];
        float right = ibox[2];
        float bottom = ibox[3];
        int class_label = ibox[4];
        float confidence = ibox[5];
        cv::Scalar color;
        tie(color[0], color[1], color[2]) = random_color(class_label);
        cv::rectangle(image, cv::Point(left, top), cv::Point(right, bottom), color, 3);

        auto name      = cocolabels[class_label];
        auto caption   = cv::format("%s %.2f", name, confidence);
        int text_width = cv::getTextSize(caption, 0, 1, 2, nullptr).width + 10;
        cv::rectangle(image, cv::Point(left-3, top-33), cv::Point(left + text_width, top), color, -1);
        cv::putText(image, caption, cv::Point(left, top-5), 0, 1, cv::Scalar::all(0), 2, 16);
    }
    cv::imwrite("image-draw.jpg", image);

    checkRuntime(cudaStreamDestroy(stream));
    checkRuntime(cudaFreeHost(input_data_host));
    checkRuntime(cudaFreeHost(output_data_host));
    checkRuntime(cudaFree(input_data_device));
    checkRuntime(cudaFree(output_data_device));
}

int main(){
    if(!build_model()){
        return -1;
    }
    inference();
    return 0;
}

模型和测试结果

总结

在个人数据集上的结果。
测试集进行性能测试：

提供了关键的部分代码，仅供本人和大家学习和参考。