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   -> 人工智能 -> Yolox 训练自己的数据集 -> 正文阅读

[人工智能]Yolox 训练自己的数据集

目录

Yolo系列因为其灵活性,一直是目标检测热门算法,近期旷视的研究者提出了Yolox高性能目标检测器,将Anchor free引入了Yolo算法,是除YOLOV1之后,第二个将Anchor free研究思路用到Yolo的算法。
Yolox的创新点除了Anchor free思想之外,个人认为最重要的是解耦头(Decoupled head)的使用,在一定程度上解决了分类和回归任务问题;当然,除此之外,标签分配策略(SimOTA)也是其的一大亮点。
在这里插入图片描述

1、文章链接和源码

1. 文章链接 :https://arxiv.org/abs/2107.08430
2.
源码:https://github.com/Megvii-BaseDetection/YOLOX

2、数据准备(VOC格式)

文章作者使用COCO数据集格式训练模型,在这里使用另一种格式VOC数据集来训练自己的模型

2.1 VOC数据集格式
Annotations文件夹放置.xml标签文件;JPEGImages文件夹放置训练原图;ImageSets文件夹下的Main文件夹存放训练、验证、测试数据集的.txt文件。 具体格式如下:

在这里插入图片描述

test.txt,train.txt,trainval.txt,val.txt文件内容如下图所示,存放的是各个图片的名称。

在这里插入图片描述

利用VOC格式训练Yolox模型这些.txt文件是必须的;相应的生成代码如下:

import os
import random 
random.seed(0)

xmlfilepath='VOC数据集Annotations文件夹路径'#xml文件存放地址,在训练自己数据集的时候,改成自己的数据路径
saveBasePath="VOC数据集ImageSets\\Main文件夹路径"#存放test.txt,train.txt,trainval.txt,val.txt文件路径
 
#----------------------------------------------------------------------#
#   根据自己的需求更改trainval_percent和train_percent的比例
#----------------------------------------------------------------------#
trainval_percent=0.9
train_percent=1

temp_xml = os.listdir(xmlfilepath)
total_xml = []
for xml in temp_xml:
    if xml.endswith(".xml"):
        total_xml.append(xml)

num=len(total_xml)  
list=range(num)  
tv=int(num*trainval_percent)  
tr=int(tv*train_percent)  
trainval= random.sample(list,tv)  
train=random.sample(trainval,tr)  
 
print("train and val size",tv)
print("traub suze",tr)
ftrainval = open(os.path.join(saveBasePath,'trainval.txt'), 'w')  
ftest = open(os.path.join(saveBasePath,'test.txt'), 'w')  
ftrain = open(os.path.join(saveBasePath,'train.txt'), 'w')  
fval = open(os.path.join(saveBasePath,'val.txt'), 'w')  
 
for i  in list:  
    name=total_xml[i][:-4]+'\n'  
    if i in trainval:  
        ftrainval.write(name)  
        if i in train:  
            ftrain.write(name)  
        else:  
            fval.write(name)  
    else:  
        ftest.write(name)  
  
ftrainval.close()  
ftrain.close()  
fval.close()  
ftest .close()

3、配置文件修改

3.1 修改检测类别
根据自己的需求修改检测类别名称,我做的项目是人脸检测,检测类别只有face,所以VOC_CLASS内容只写“face”。检测类别名称对应源码文件:D:\YOLOX-main\yolox\data\datasets\voc_classes.py,只修改该文件夹下的检测类别,各个类别间必须用逗号隔开,最后一个类别也必须加逗号,如下图所示:


在这里插入图片描述

3.2 修改训练参数
根据需求修改D:\YOLOX-main\yolox\exp\yolox_base.py文件下的相关参数,在这我只修改了self.num_classes = 1,其他参数可根据自己需求进行修改。

import os
import random

import torch
import torch.distributed as dist
import torch.nn as nn

from .base_exp import BaseExp


class Exp(BaseExp):

    def __init__(self):
        super().__init__()

        # ---------------- model config ---------------- #
        self.num_classes = 1
        self.depth = 1.00
        self.width = 1.00

        # ---------------- dataloader config ---------------- #
        # set worker to 4 for shorter dataloader init time
        self.data_num_workers = 4
        self.input_size = (640, 640)
        self.random_size = (14, 26)
        self.data_dir = None
        self.train_ann = "instances_train2017.json"
        self.val_ann = "instances_val2017.json"

        # --------------- transform config ----------------- #
        self.degrees = 10.0
        self.translate = 0.1
        self.scale = (0.1, 2)
        self.mscale = (0.8, 1.6)
        self.shear = 2.0
        self.perspective = 0.0
        self.enable_mixup = True

        # --------------  training config --------------------- #
        self.warmup_epochs = 5
        self.max_epoch = 200
        self.warmup_lr = 0
        self.basic_lr_per_img = 0.01 / 64.0
        self.scheduler = "yoloxwarmcos"
        self.no_aug_epochs = 15
        self.min_lr_ratio = 0.05
        self.ema = True

        self.weight_decay = 5e-4
        self.momentum = 0.9
        self.print_interval = 10
        self.eval_interval = 10
        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

        # -----------------  testing config ------------------ #
        self.test_size = (640, 640)
        self.test_conf = 0.01
        self.nmsthre = 0.65

    def get_model(self):
        from yolox.models import YOLOX, YOLOPAFPN, YOLOXHead

        def init_yolo(M):
            for m in M.modules():
                if isinstance(m, nn.BatchNorm2d):
                    m.eps = 1e-3
                    m.momentum = 0.03

        if getattr(self, "model", None) is None:
            in_channels = [256, 512, 1024]
            backbone = YOLOPAFPN(self.depth, self.width, in_channels=in_channels)
            head = YOLOXHead(self.num_classes, self.width, in_channels=in_channels)
            self.model = YOLOX(backbone, head)

        self.model.apply(init_yolo)
        self.model.head.initialize_biases(1e-2)
        return self.model

    def get_data_loader(self, batch_size, is_distributed, no_aug=False):
        from yolox.data import (
            COCODataset,
            TrainTransform,
            YoloBatchSampler,
            DataLoader,
            InfiniteSampler,
            MosaicDetection,
        )

        dataset = COCODataset(
            data_dir=self.data_dir,
            json_file=self.train_ann,
            img_size=self.input_size,
            preproc=TrainTransform(
                rgb_means=(0.485, 0.456, 0.406),
                std=(0.229, 0.224, 0.225),
                max_labels=50,
            ),
        )

        dataset = MosaicDetection(
            dataset,
            mosaic=not no_aug,
            img_size=self.input_size,
            preproc=TrainTransform(
                rgb_means=(0.485, 0.456, 0.406),
                std=(0.229, 0.224, 0.225),
                max_labels=120,
            ),
            degrees=self.degrees,
            translate=self.translate,
            scale=self.scale,
            shear=self.shear,
            perspective=self.perspective,
            enable_mixup=self.enable_mixup,
        )

        self.dataset = dataset

        if is_distributed:
            batch_size = batch_size // dist.get_world_size()

        sampler = InfiniteSampler(
            len(self.dataset), seed=self.seed if self.seed else 0
        )

        batch_sampler = YoloBatchSampler(
            sampler=sampler,
            batch_size=batch_size,
            drop_last=False,
            input_dimension=self.input_size,
            mosaic=not no_aug,
        )

        dataloader_kwargs = {"num_workers": self.data_num_workers, "pin_memory": True}
        dataloader_kwargs["batch_sampler"] = batch_sampler
        train_loader = DataLoader(self.dataset, **dataloader_kwargs)

        return train_loader

    def random_resize(self, data_loader, epoch, rank, is_distributed):
        tensor = torch.LongTensor(2).cuda()

        if rank == 0:
            size_factor = self.input_size[1] * 1. / self.input_size[0]
            size = random.randint(*self.random_size)
            size = (int(32 * size), 32 * int(size * size_factor))
            tensor[0] = size[0]
            tensor[1] = size[1]

        if is_distributed:
            dist.barrier()
            dist.broadcast(tensor, 0)

        input_size = data_loader.change_input_dim(
            multiple=(tensor[0].item(), tensor[1].item()), random_range=None
        )
        return input_size

    def get_optimizer(self, batch_size):
        if "optimizer" not in self.__dict__:
            if self.warmup_epochs > 0:
                lr = self.warmup_lr
            else:
                lr = self.basic_lr_per_img * batch_size

            pg0, pg1, pg2 = [], [], []  # optimizer parameter groups

            for k, v in self.model.named_modules():
                if hasattr(v, "bias") and isinstance(v.bias, nn.Parameter):
                    pg2.append(v.bias)  # biases
                if isinstance(v, nn.BatchNorm2d) or "bn" in k:
                    pg0.append(v.weight)  # no decay
                elif hasattr(v, "weight") and isinstance(v.weight, nn.Parameter):
                    pg1.append(v.weight)  # apply decay

            optimizer = torch.optim.SGD(
                pg0, lr=lr, momentum=self.momentum, nesterov=True
            )
            optimizer.add_param_group(
                {"params": pg1, "weight_decay": self.weight_decay}
            )  # add pg1 with weight_decay
            optimizer.add_param_group({"params": pg2})
            self.optimizer = optimizer

        return self.optimizer

    def get_lr_scheduler(self, lr, iters_per_epoch):
        from yolox.utils import LRScheduler
        scheduler = LRScheduler(
            self.scheduler,
            lr,
            iters_per_epoch,
            self.max_epoch,
            warmup_epochs=self.warmup_epochs,
            warmup_lr_start=self.warmup_lr,
            no_aug_epochs=self.no_aug_epochs,
            min_lr_ratio=self.min_lr_ratio,
        )
        return scheduler

    def get_eval_loader(self, batch_size, is_distributed, testdev=False):
        from yolox.data import COCODataset, ValTransform

        valdataset = COCODataset(
            data_dir=self.data_dir,
            json_file=self.val_ann if not testdev else "image_info_test-dev2017.json",
            name="val2017" if not testdev else "test2017",
            img_size=self.test_size,
            preproc=ValTransform(
                rgb_means=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)
            ),
        )

        if is_distributed:
            batch_size = batch_size // dist.get_world_size()
            sampler = torch.utils.data.distributed.DistributedSampler(
                valdataset, shuffle=False
            )
        else:
            sampler = torch.utils.data.SequentialSampler(valdataset)

        dataloader_kwargs = {
            "num_workers": self.data_num_workers,
            "pin_memory": True,
            "sampler": sampler,
        }
        dataloader_kwargs["batch_size"] = batch_size
        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)

        return val_loader

    def get_evaluator(self, batch_size, is_distributed, testdev=False):
        from yolox.evaluators import COCOEvaluator

        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev)
        evaluator = COCOEvaluator(
            dataloader=val_loader,
            img_size=self.test_size,
            confthre=self.test_conf,
            nmsthre=self.nmsthre,
            num_classes=self.num_classes,
            testdev=testdev,
        )
        return evaluator

    def eval(self, model, evaluator, is_distributed, half=False):
        return evaluator.evaluate(model, is_distributed, half)

3.3 修改训练数据集路径
Yolox作者准备了VOC数据集文件,D:\YOLOX-main\exps\example\yolox_voc\yolox_voc_s.py,但是需要对其进行修改,这个文件需要修改的有:
(1)self.num_classes = 1
(2)get_data_loder 下的 VOCDetection 下的 image_sets=[(‘2007’, ‘trainval’), (‘2012’, ‘trainval’)],将其修改为image_sets=[(‘2007’, ‘train’)],;
*第一点修改根据个人需求决定,第二点我是根据自己使用习惯进行修改的,**如果准备的VOC数据集里面有trainval.txt文件,*那么可以不对源码进行修改。
==有的人在训练的过程中会出现AP=0的现象,这是因为函数get_eval_loader下的image_sets=[(‘2007’, ‘test’)]下test文件对应的test.txt里面内容为空,在第二步数据准备的时候一定要使得test.txt文件内容存在。==修改代码如下:

# encoding: utf-8
import os

import torch
import torch.distributed as dist

from yolox.data import get_yolox_datadir
from yolox.exp import Exp as MyExp


class Exp(MyExp):
    def __init__(self):
        super(Exp, self).__init__()
        self.num_classes = 1
        self.depth = 0.33
        self.width = 0.50
        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

    def get_data_loader(self, batch_size, is_distributed, no_aug=False):
        from yolox.data import (
            VOCDetection,
            TrainTransform,
            YoloBatchSampler,
            DataLoader,
            InfiniteSampler,
            MosaicDetection,
        )

        dataset = VOCDetection(
            data_dir=os.path.join(get_yolox_datadir(), "VOCdevkit"),
            image_sets=[('2007', 'train')],
            img_size=self.input_size,
            preproc=TrainTransform(
                rgb_means=(0.485, 0.456, 0.406),
                std=(0.229, 0.224, 0.225),
                max_labels=50,
            ),
        )

        dataset = MosaicDetection(
            dataset,
            mosaic=not no_aug,
            img_size=self.input_size,
            preproc=TrainTransform(
                rgb_means=(0.485, 0.456, 0.406),
                std=(0.229, 0.224, 0.225),
                max_labels=120,
            ),
            degrees=self.degrees,
            translate=self.translate,
            scale=self.scale,
            shear=self.shear,
            perspective=self.perspective,
            enable_mixup=self.enable_mixup,
        )

        self.dataset = dataset

        if is_distributed:
            batch_size = batch_size // dist.get_world_size()

        sampler = InfiniteSampler(
            len(self.dataset), seed=self.seed if self.seed else 0
        )

        batch_sampler = YoloBatchSampler(
            sampler=sampler,
            batch_size=batch_size,
            drop_last=False,
            input_dimension=self.input_size,
            mosaic=not no_aug,
        )

        dataloader_kwargs = {"num_workers": self.data_num_workers, "pin_memory": True}
        dataloader_kwargs["batch_sampler"] = batch_sampler
        train_loader = DataLoader(self.dataset, **dataloader_kwargs)

        return train_loader

    def get_eval_loader(self, batch_size, is_distributed, testdev=False):
        from yolox.data import VOCDetection, ValTransform

        valdataset = VOCDetection(
            data_dir=os.path.join(get_yolox_datadir(), "VOCdevkit"),
            image_sets=[('2007', 'test')],
            img_size=self.test_size,
            preproc=ValTransform(
                rgb_means=(0.485, 0.456, 0.406),
                std=(0.229, 0.224, 0.225),
            ),
        )

        if is_distributed:
            batch_size = batch_size // dist.get_world_size()
            sampler = torch.utils.data.distributed.DistributedSampler(
                valdataset, shuffle=False
            )
        else:
            sampler = torch.utils.data.SequentialSampler(valdataset)

        dataloader_kwargs = {
            "num_workers": self.data_num_workers,
            "pin_memory": True,
            "sampler": sampler,
        }
        dataloader_kwargs["batch_size"] = batch_size
        val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)

        return val_loader

    def get_evaluator(self, batch_size, is_distributed, testdev=False):
        from yolox.evaluators import VOCEvaluator

        val_loader = self.get_eval_loader(batch_size, is_distributed, testdev=testdev)
        evaluator = VOCEvaluator(
            dataloader=val_loader,
            img_size=self.test_size,
            confthre=self.test_conf,
            nmsthre=self.nmsthre,
            num_classes=self.num_classes,
        )
        return evaluator

3.4 修改Voc.py文件中Annotations文件的读取格式
修改源码 D:\YOLOX-main\yolox\data\datasets\voc.py文件下的_do_python_eval函数,将annopath = os.path.join(rootpath, "Annotations", "{:s}.xml")修改为:annopath = os.path.join(rootpath, "Annotations", "{}.xml")

4、训练

有两种训练方式:
(1)直接在train.py文件下修改超参数;
(2)在终端训练,源码中的ReadMe有说明;
选用第一种方式训练,下面代码是我作的修改,在要修改的地方做了注释。

import argparse
import random
import warnings
from loguru import logger

import torch
import torch.backends.cudnn as cudnn

from yolox.core import Trainer, launch
from yolox.exp import get_exp
from yolox.utils import configure_nccl, configure_omp


def make_parser():
    parser = argparse.ArgumentParser("YOLOX train parser")
    parser.add_argument("-expn", "--experiment-name", type=str, default=None)
    parser.add_argument("-n", "--name", type=str, default='yolox-s', help="model name")#要使用那个模型,写那个模型的名称,yolox-s对应的yolox-s.model,还可以写成yolox-l等模型

    # distributed
    parser.add_argument(
        "--dist-backend", default="nccl", type=str, help="distributed backend"
    )
    parser.add_argument(
        "--dist-url",
        default=None,
        type=str,
        help="url used to set up distributed training",
    )
    parser.add_argument("-b", "--batch-size", type=int, default=16, help="batch size")#根据电脑显卡的显存大小设置对应的batch-size
    parser.add_argument(
        "-d", "--devices", default=0, type=int, help="device for training"#只有一张显卡,所以设置为0
    )
    parser.add_argument(
        "-f",
        "--exp_file",
        default='D:\\YOLOX-main\\exps\\example\\yolox_voc\\yolox_voc_s.py',#训练数据集绝对路径
        type=str,
        help="plz input your expriment description file",
    )
    parser.add_argument(
        "--resume", default=False, action="store_true", help="resume training"
    )
    parser.add_argument("-c", "--ckpt", default='D:\\YOLOX-main\\yolox_s.pth', type=str, help="checkpoint file")#预训练权重
    parser.add_argument(
        "-e",
        "--start_epoch",
        default=0,
        type=int,
        help="resume training start epoch",
    )
    parser.add_argument(
        "--num_machines", default=1, type=int, help="num of node for training"
    )
    parser.add_argument(
        "--machine_rank", default=0, type=int, help="node rank for multi-node training"
    )
    parser.add_argument(
        "--fp16",
        dest="fp16",
        default=True,
        action="store_true",
        help="Adopting mix precision training.",
    )
    parser.add_argument(
        "-o",
        "--occupy",
        dest="occupy",
        default=False,
        action="store_true",
        help="occupy GPU memory first for training.",
    )
    parser.add_argument(
        "opts",
        help="Modify config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )
    return parser


@logger.catch
def main(exp, args):
    if exp.seed is not None:
        random.seed(exp.seed)
        torch.manual_seed(exp.seed)
        cudnn.deterministic = True
        warnings.warn(
            "You have chosen to seed training. This will turn on the CUDNN deterministic setting, "
            "which can slow down your training considerably! You may see unexpected behavior "
            "when restarting from checkpoints."
        )

    # set environment variables for distributed training
    configure_nccl()
    configure_omp()
    cudnn.benchmark = True

    trainer = Trainer(exp, args)
    trainer.train()


if __name__ == "__main__":
    args = make_parser().parse_args()
    exp = get_exp(args.exp_file, args.name)
    exp.merge(args.opts)

    if not args.experiment_name:
        args.experiment_name = exp.exp_name

    num_gpu = torch.cuda.device_count() if args.devices is None else args.devices
    assert num_gpu <= torch.cuda.device_count()

    dist_url = "auto" if args.dist_url is None else args.dist_url
    launch(
        main,
        num_gpu,
        args.num_machines,
        args.machine_rank,
        backend=args.dist_backend,
        dist_url=dist_url,
        args=(exp, args),
    )

yolox-s预训练权重我放在百度网盘:链接:https://pan.baidu.com/s/1kmNzX0FE555P1t9fzBqIJw 提取码:0r71
在这里插入图片描述

5、使用自己的模型完成检测任务

使用demo.py文件来检测,其中要注意引用VOC数据集的类别。
修改1

from yolox.data.datasets import voc_classes#在训练的时候使用voc数据集的类别,所以在检测的时候要引入voc文件对应的类

修改2

 predictor = Predictor(model, exp, voc_classes.VOC_CLASSES,, trt_file, decoder, args.device)

修改3

cls_names=voc_classes.VOC_CLASSES,

修改后代码

import argparse
import os
import time
from loguru import logger

import cv2

import torch

from yolox.data.data_augment import preproc
from yolox.data.datasets import voc_classes
from yolox.exp import get_exp
from yolox.utils import fuse_model, get_model_info, postprocess, vis

IMAGE_EXT = [".jpg", ".jpeg", ".webp", ".bmp", ".png"]


def make_parser():
    parser = argparse.ArgumentParser("YOLOX Demo!")
    parser.add_argument(
        "--demo", default="image", help="demo type, eg. image, video and webcam"
    )
    parser.add_argument("-expn", "--experiment-name", type=str, default=None)
    parser.add_argument("-n", "--name", type=str, default='yolox-s', help="model name")

    parser.add_argument(
        "--path", default="要检测图像的路径", help="path to images or video"
    )
    parser.add_argument("--camid", type=int, default=0, help="webcam demo camera id")
    parser.add_argument(
        "--save_result",
        action="store_true",
        default=True,#如果要保存检测结果,需要设置默认值为True
        help="whether to save the inference result of image/video",
    )

    # exp file
    parser.add_argument(
        "-f",
        "--exp_file",
        default='D:\\YOLOX-main\\exps\\default\\yolox_s.py',
        type=str,
        help="pls input your expriment description file",
    )
    parser.add_argument("-c", "--ckpt", default='训练好模型的路径', type=str, help="ckpt for eval")
    parser.add_argument(
        "--device",
        default="gpu",
        type=str,
        help="device to run our model, can either be cpu or gpu",
    )
    #------------------------------------------------------------------#
    #根据自己的项目需求进行修改
    parser.add_argument("--conf", default=0.35, type=float, help="test conf")
    parser.add_argument("--nms", default=0.5, type=float, help="test nms threshold")
    parser.add_argument("--tsize", default=640, type=int, help="test img size")
    parser.add_argument(
        "--fp16",
        dest="fp16",
        default=True,
        action="store_true",
        help="Adopting mix precision evaluating.",
    )
    parser.add_argument(
        "--fuse",
        dest="fuse",
        default=False,
        action="store_true",
        help="Fuse conv and bn for testing.",
    )
    parser.add_argument(
        "--trt",
        dest="trt",
        default=False,
        action="store_true",
        help="Using TensorRT model for testing.",
    )
    return parser


def get_image_list(path):
    image_names = []
    for maindir, subdir, file_name_list in os.walk(path):
        for filename in file_name_list:
            apath = os.path.join(maindir, filename)
            ext = os.path.splitext(apath)[1]
            if ext in IMAGE_EXT:
                image_names.append(apath)
    return image_names


class Predictor(object):
    def __init__(
        self,
        model,
        exp,
        cls_names=voc_classes.VOC_CLASSES,
        trt_file=None,
        decoder=None,
        device="gpu",
    ):
        self.model = model
        self.cls_names = cls_names
        self.decoder = decoder
        self.num_classes = exp.num_classes
        self.confthre = exp.test_conf
        self.nmsthre = exp.nmsthre
        self.test_size = exp.test_size
        self.device = device
        if trt_file is not None:
            from torch2trt import TRTModule

            model_trt = TRTModule()
            model_trt.load_state_dict(torch.load(trt_file))

            x = torch.ones(1, 3, exp.test_size[0], exp.test_size[1]).cuda()
            self.model(x)
            self.model = model_trt
        self.rgb_means = (0.485, 0.456, 0.406)
        self.std = (0.229, 0.224, 0.225)

    def inference(self, img):
        img_info = {"id": 0}
        if isinstance(img, str):
            img_info["file_name"] = os.path.basename(img)
            img = cv2.imread(img)
        else:
            img_info["file_name"] = None

        height, width = img.shape[:2]
        img_info["height"] = height
        img_info["width"] = width
        img_info["raw_img"] = img

        img, ratio = preproc(img, self.test_size, self.rgb_means, self.std)
        img_info["ratio"] = ratio
        img = torch.from_numpy(img).unsqueeze(0)
        if self.device == "gpu":
            img = img.cuda()

        with torch.no_grad():
            t0 = time.time()
            outputs = self.model(img)
            if self.decoder is not None:
                outputs = self.decoder(outputs, dtype=outputs.type())
            outputs = postprocess(
                outputs, self.num_classes, self.confthre, self.nmsthre
            )
            logger.info("Infer time: {:.4f}s".format(time.time() - t0))
        return outputs, img_info

    def visual(self, output, img_info, cls_conf=0.35):
        ratio = img_info["ratio"]
        img = img_info["raw_img"]
        if output is None:
            return img
        output = output.cpu()

        bboxes = output[:, 0:4]

        # preprocessing: resize
        bboxes /= ratio

        cls = output[:, 6]
        scores = output[:, 4] * output[:, 5]

        vis_res = vis(img, bboxes, scores, cls, cls_conf, self.cls_names)
        return vis_res


def image_demo(predictor, vis_folder, path, current_time, save_result):
    if os.path.isdir(path):
        files = get_image_list(path)
    else:
        files = [path]
    files.sort()
    for image_name in files:
        outputs, img_info = predictor.inference(image_name)
        result_image = predictor.visual(outputs[0], img_info, predictor.confthre)
        if save_result:
            save_folder = os.path.join(
                vis_folder, time.strftime("%Y_%m_%d_%H_%M_%S", current_time)
            )
            os.makedirs(save_folder, exist_ok=True)
            save_file_name = os.path.join(save_folder, os.path.basename(image_name))
            logger.info("Saving detection result in {}".format(save_file_name))
            cv2.imwrite(save_file_name, result_image)
        ch = cv2.waitKey(0)
        if ch == 27 or ch == ord("q") or ch == ord("Q"):
            break


def imageflow_demo(predictor, vis_folder, current_time, args):
    cap = cv2.VideoCapture(args.path if args.demo == "video" else args.camid)
    width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # float
    height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # float
    fps = cap.get(cv2.CAP_PROP_FPS)
    save_folder = os.path.join(
        vis_folder, time.strftime("%Y_%m_%d_%H_%M_%S", current_time)
    )
    os.makedirs(save_folder, exist_ok=True)
    if args.demo == "video":
        save_path = os.path.join(save_folder, args.path.split("/")[-1])
    else:
        save_path = os.path.join(save_folder, "camera.mp4")
    logger.info(f"video save_path is {save_path}")
    vid_writer = cv2.VideoWriter(
        save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (int(width), int(height))
    )
    while True:
        ret_val, frame = cap.read()
        if ret_val:
            outputs, img_info = predictor.inference(frame)
            result_frame = predictor.visual(outputs[0], img_info, predictor.confthre)
            if args.save_result:
                vid_writer.write(result_frame)
            ch = cv2.waitKey(1)
            if ch == 27 or ch == ord("q") or ch == ord("Q"):
                break
        else:
            break


def main(exp, args):
    if not args.experiment_name:
        args.experiment_name = exp.exp_name

    file_name = os.path.join(exp.output_dir, args.experiment_name)
    os.makedirs(file_name, exist_ok=True)

    if args.save_result:
        vis_folder = os.path.join(file_name, "vis_res")
        os.makedirs(vis_folder, exist_ok=True)

    if args.trt:
        args.device = "gpu"

    logger.info("Args: {}".format(args))

    if args.conf is not None:
        exp.test_conf = args.conf
    if args.nms is not None:
        exp.nmsthre = args.nms
    if args.tsize is not None:
        exp.test_size = (args.tsize, args.tsize)

    model = exp.get_model()
    logger.info("Model Summary: {}".format(get_model_info(model, exp.test_size)))

    if args.device == "gpu":
        model.cuda()
    model.eval()

    if not args.trt:
        if args.ckpt is None:
            ckpt_file = os.path.join(file_name, "best_ckpt.pth")
        else:
            ckpt_file = args.ckpt
        logger.info("loading checkpoint")
        ckpt = torch.load(ckpt_file, map_location="cpu")
        # load the model state dict
        model.load_state_dict(ckpt["model"])
        logger.info("loaded checkpoint done.")

    if args.fuse:
        logger.info("\tFusing model...")
        model = fuse_model(model)

    if args.trt:
        assert not args.fuse, "TensorRT model is not support model fusing!"
        trt_file = os.path.join(file_name, "model_trt.pth")
        assert os.path.exists(
            trt_file
        ), "TensorRT model is not found!\n Run python3 tools/trt.py first!"
        model.head.decode_in_inference = False
        decoder = model.head.decode_outputs
        logger.info("Using TensorRT to inference")
    else:
        trt_file = None
        decoder = None

    predictor = Predictor(model, exp, voc_classes.VOC_CLASSES, trt_file, decoder, args.device)
    current_time = time.localtime()
    if args.demo == "image":
        image_demo(predictor, vis_folder, args.path, current_time, args.save_result)
    elif args.demo == "video" or args.demo == "webcam":
        imageflow_demo(predictor, vis_folder, current_time, args)


if __name__ == "__main__":
    args = make_parser().parse_args()
    exp = get_exp(args.exp_file, args.name)

    main(exp, args)

检测结果

在这里插入图片描述如有错误,大家私聊我;如有不理解的地方,可以在评论区提问,修改后的代码之后会上传到Github。

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