推荐|基于YOLOV5的路面障碍检测系统（算法设计）

第一：了解什么是YOLOv5算法

官网：Ultralytics YOLOv5 -Ultralytics YOLO 文档综合指南
Yolo系列内容介绍：https://zhuanlan.zhihu.com/p/143747206

这里说很详细，你自己看就可以了。写论文你绝对用得上，但既然称之为Yolov5，也有很多非常不错的地方值得我们学习。不过因为Yolov5的网络结构和Yolov3、Yolov4相比，不好可视化。

1.Yolov5四种网络结构

Yolov5官方代码中，给出的目标检测网络中一共有4个版本，分别是Yolov5s、Yolov5m、Yolov5l、Yolov5x四个模型。学习一个新的算法，最好在脑海中对算法网络的整体架构有一个清晰的理解。但比较尴尬的是，Yolov5代码中给出的网络文件是yaml格式，和原本Yolov3、Yolov4中的cfg不同。因此无法用netron工具直接可视化的查看网络结构，造成有的同学不知道如何去学习这样的网络。

比如下载了Yolov5的四个pt格式的权重模型：

Yolov5网络结构图

2.Yolov5核心基础内容

Yolov5的结构和Yolov4很相似，但也有一些不同，大白还是按照从整体到细节的方式，对每个板块进行讲解。

上图即Yolov5的网络结构图，可以看出，还是分为输入端、Backbone、Neck、Prediction四个部分。

上面是Yolov5作者的算法性能测试图：

1、Yolov5的项目结构

看这篇博客：你会学会很多，写论问也用得上。（博主连接如下）

YOLOv5源码逐行超详细注释与解读（1）——项目目录结构解析-CSDN博客

2、YOLO总体架构图

3、训练自己的自定义数据集YOLOv5。

培训自定义数据 -Ultralytics YOLO 文档


# coding:utf-8
# ----------------------------------------------------------------------------
# Pytorch multi-GPU YOLOV5 based UMT
 
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
 
 
import argparse
import logging
import math
import os
import random
import sys
import time
import warnings
import yaml
import numpy as np
from copy import deepcopy
from pathlib import Path
from threading import Thread
from tqdm import tqdm
 
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torch.utils.data
 
from torch.cuda import amp
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.tensorboard import SummaryWriter
 
FILE = Path(__file__).absolute()
sys.path.append(FILE.parents[0].as_posix())  # add yolov5/ to path
 
import ssda_yolov5_test as test  # for end-of-epoch mAP
from models.experimental import attempt_load
from models.yolo import Model
 
from utils.autoanchor import check_anchors
from utils.datasets import create_dataloader
from utils.datasets_single import create_dataloader_single
from utils.google_utils import attempt_download
from utils.loss import ComputeLoss
 
from utils.torch_utils import ModelEMA, WeightEMA, select_device, intersect_dicts, torch_distributed_zero_first, de_parallel
from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume
from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
from utils.metrics import fitness
 
from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
    strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \
    check_requirements, print_mutation, set_logging, one_cycle, colorstr, \
    non_max_suppression, check_dataset_umt, xyxy2xywhn
 
 
logger = logging.getLogger(__name__)
LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
RANK = int(os.getenv('RANK', -1))
WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
 
# hyp means path/to/hyp.yaml or hyp dictionary
def train(hyp, opt, device):
 
    save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, notest, nosave, workers, = \
        opt.save_dir, opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
        opt.resume, opt.notest, opt.nosave, opt.workers
 
    teacher_alpha, conf_thres, iou_thres, max_gt_boxes, lambda_weight, student_weight, teacher_weight = \
        opt.teacher_alpha, opt.conf_thres, opt.iou_thres, opt.max_gt_boxes, opt.lambda_weight, \
        opt.student_weight, opt.teacher_weight
 
    all_shift = opt.consistency_loss
 
    # Directories
    save_dir = Path(save_dir)
    wdir = save_dir / 'weights'
    wdir.mkdir(parents=True, exist_ok=True)  # make dir
    last_student, last_teacher = wdir / 'last_student.pt', wdir / 'last_teacher.pt'
    best_student, best_teacher = wdir / 'best_student.pt', wdir / 'best_teacher.pt'
    results_file = save_dir / 'results.txt'
 
    # Hyperparameters
    if isinstance(hyp, str):
        with open(hyp) as f:  # default path data/hyps/hyp.scratch.yaml
            hyp = yaml.safe_load(f)  # load hyps dict
    logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
 
    # Save run settings
    with open(save_dir / 'hyp.yaml', 'w') as f:
        yaml.safe_dump(hyp, f, sort_keys=False)
    with open(save_dir / 'opt.yaml', 'w') as f:
        yaml.safe_dump(vars(opt), f, sort_keys=False)
 
    # Configure
    plots = not evolve  # create plots
    cuda = device.type != 'cpu'
    init_seeds(1 + RANK)
    with open(data) as f:
        data_dict = yaml.safe_load(f)  # data dict
 
    # Loggers
    loggers = {'wandb': None, 'tb': None}  # loggers dict
    if RANK in [-1, 0]:
        # TensorBoard
        if not evolve:
            prefix = colorstr('tensorboard: ')
            logger.info(f"{prefix}Start with 'tensorboard --logdir {opt.project}', view at http://localhost:6006/")
            loggers['tb'] = SummaryWriter(str(save_dir))
 
        # W&B
        opt.hyp = hyp  # add hyperparameters
        run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
        run_id = run_id if opt.resume else None  # start fresh run if transfer learning
        wandb_logger = WandbLogger(opt, save_dir.stem, run_id, data_dict)
        loggers['wandb'] = wandb_logger.wandb
        if loggers['wandb']:
            data_dict = wandb_logger.data_dict
            weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp  # may update weights, epochs if resuming
 
    nc = 1 if single_cls else int(data_dict['nc'])  # number of classes
    names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
    assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, data)  # check
    is_coco = data.endswith('coco.yaml') and nc == 80  # COCO dataset
 
    # Model
    pretrained = weights.endswith('.pt')
    # torch.cuda.empty_cache()
    # strip_optimizer(weights)  # strip optimizers, this will apparently reduce the model size
    if pretrained:
        with torch_distributed_zero_first(RANK):
            weights = attempt_download(weights)  # download if not found locally
        ckpt = torch.load(weights, map_location=device)  # load checkpoint
        # model_student
        model_student = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
        # model_teacher
        model_teacher = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
        exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else []  # exclude keys
        state_dict = ckpt['model'].float().state_dict()  # to FP32
        state_dict = intersect_dicts(state_dict, model_student.state_dict(), exclude=exclude)  # intersect
        model_student.load_state_dict(state_dict, strict=False)  # load
        # model_teacher.load_state_dict(state_dict, strict=False)  # load
        model_teacher.load_state_dict(state_dict.copy(), strict=False)  # load
        logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model_student.state_dict()), weights))  # report
    else:
        model_student = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
        model_teacher = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device)  # create
    
    
    # Update models weights [only by this way, we can resume the old training normally...][ref models.experimental.attempt_load()]
    if student_weight != "None" and teacher_weight != "None":  # update model_student and model_teacher
        torch.cuda.empty_cache()
        ckpt_student = torch.load(student_weight, map_location=device)  # load checkpoint
        state_dict_student = ckpt_student['ema' if ckpt_student.get('ema') else 'model'].float().half().state_dict()  # to FP32
        model_student.load_state_dict(state_dict_student, strict=False)  # load
        del ckpt_student, state_dict_student
        
        ckpt_teacher = torch.load(teacher_weight, map_location=device)  # load checkpoint
        state_dict_teacher = ckpt_teacher['ema' if ckpt_teacher.get('ema') else 'model'].float().half().state_dict()  # to FP32
        model_teacher.load_state_dict(state_dict_teacher, strict=False)  # load
        del ckpt_teacher, state_dict_teacher
        
        
 
    
    # Dataset
    with torch_distributed_zero_first(RANK):
        # check_dataset(data_dict)  # check, need to be re-write or command out
        check_dataset_umt(data_dict)  # check, need to be re-write or command out
    train_path_source_real = data_dict['train_source_real']  # training source dataset w labels
    train_path_source_fake = data_dict['train_source_fake']  # training target-like dataset w labels
    train_path_target_real = data_dict['train_target_real']  # training target dataset w/o labels
    train_path_target_fake = data_dict['train_target_fake']  # training source-like dataset w/o labels
    test_path_target_real = data_dict['test_target_real']  # test on target dataset w labels, should not use testset to train
    # test_path_target_real = data_dict['train_target_real']  # test on target dataset w labels, remember val in 'test_target_real'
    
    
    # Freeze 
    freeze_student = []  # parameter names to freeze (full or partial)
    for k, v in model_student.named_parameters():
        v.requires_grad = True  # train all layers
        if any(x in k for x in freeze_student):
            print('freezing %s' % k)
            v.requires_grad = False
    freeze_teacher = []  # parameter names to freeze (full or partial)
    for k, v in model_teacher.named_parameters():
        v.requires_grad = True  # train all layers
        if any(x in k for x in freeze_teacher):
            print('freezing %s' % k)
            v.requires_grad = False
 
    # Optimizer
    nbs = 64  # nominal batch size
    accumulate = max(round(nbs / batch_size), 1)  # accumulate loss before optimizing
    hyp['weight_decay'] *= batch_size * accumulate / nbs  # scale weight_decay
    logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
 
    pg0, pg1, pg2 = [], [], []  # optimizer parameter groups
    for k, v in model_student.named_modules():
        if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
            pg2.append(v.bias)  # biases
        if isinstance(v, nn.BatchNorm2d):
            pg0.append(v.weight)  # no decay
        elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
            pg1.append(v.weight)  # apply decay
 
    if opt.adam:
        student_optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentum
    else:
        student_optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
 
    student_optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']})  # add pg1 with weight_decay
    student_optimizer.add_param_group({'params': pg2})  # add pg2 (biases)
    logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
    del pg0, pg1, pg2
 
    # UMT algorithm
    student_detection_params = []
    for key, value in model_student.named_parameters():
        if value.requires_grad:
            student_detection_params += [value]
    teacher_detection_params = []
    for key, value in model_teacher.named_parameters():
        if value.requires_grad:
            teacher_detection_params += [value]
            value.requires_grad = False
    teacher_optimizer = WeightEMA(teacher_detection_params, student_detection_params, alpha=teacher_alpha)
 
 
    # For debugging
    # for k, v in model_student.named_parameters():
        # print(k, v.requires_grad)
    # for k, v in model_teacher.named_parameters():
        # print(k, v.requires_grad)
        
 
    # Scheduler https://arxiv.org/pdf/1812.01187.pdf
    # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
    if opt.linear_lr:
        lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf']  # linear
    else:
        lf = one_cycle(1, hyp['lrf'], epochs)  # cosine 1->hyp['lrf']
    scheduler = lr_scheduler.LambdaLR(student_optimizer, lr_lambda=lf)
    # plot_lr_scheduler(

第一：了解什么是YOLOv5算法

1.Yolov5四种网络结构

2.Yolov5核心基础内容

1、Yolov5的项目结构

2、YOLO总体架构图

3、训练自己的自定义数据集YOLOv5。

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