YOLOv5白皮书-第Y5周：yolo.py文件解读

本周任务：将yolov5s网络模型中的C3模块按照下图方式修改形成C2模块，并将C2模块插入第2层与第3层之间，且跑通yolov5。

任务提示：
– 提示1：需要修改./models/common.py、./models/yolo.py、./models/yolov5s.yaml文件
– 提示2：C2模块与C3模块是非常相似的两个模块，我们要插入C2到模型当中，只需要找到哪里有C3模块，然后在其附近加上C2即可。

一、前言

文件位置: ../mode1s/yolo.py
这个文件是YOLOv5网络模型的搭建文件,如果你想改进YOLOv5,那么这么文件是你必须进行修改的文件之
一。文件内容看起来多，实真正有用的代码不多的,重点理解文中提到的一个函数两个类即可。

注:由于YOLOv5版本众多,同一个文件对于细节处你可能会看到不同的版本,不用担心这都是正常的，注意把握好整体架构即可。 

 C2模块结构图

 算法修改示意图

 二、导入需要的包和基本配置

import argparse     # 解析命令行参数模块
import contextlib
import os
import platform
import sys          # sys系统模块，包含了与Python解释器和它的环境有关的函数
from copy import deepcopy  # 数据拷贝模块，深拷贝
from pathlib import Path   # Path将str转换为Path对象，使字符串路径易于操作

FILE = Path(__file__).resolve()
ROOT = FILE.parents[1]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
if platform.system() != 'Windows':
    ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

from models.common import *
from models.experimental import *
from utils.autoanchor import check_anchor_order
from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
from utils.plots import feature_visualization
from utils.torch_utils import (fuse_conv_and_bn, initialize_weights, model_info, profile, scale_img, select_device,
                               time_sync)

# 导入thop包，用于计算FLOPs
try:
    import thop  # for FLOPs computation
except ImportError:
    thop = None

三、parse_ model函数

这个函数用于将模型的模块拼接起来，搭建完成的网络模型。后续如果需要动模型框架的话,你需要对这个函数做相应的改动。

def parse_model(d, ch):  # model_dict, input_channels(3)
    # Parse a YOLOv5 model.yaml dictionary
    ''' 用在上面DetectionModel模块中
    解析模型文件(字典形式)，并搭建网络结构
    这个函数其实主要做的就是：
        更新当前层的args(参数)，计算c2(当前层的输出channel)
        ->使用当前层的参数搭建当前层
        ->生成 layers + save
    :params d: model_dict模型文件，字典形式{dice: 7}(yolov5s.yaml中的6个元素 + ch)
    :params ch: 记录模型每一层的输出channel，初始ch=[3]，后面会删除
    :return nn.Sequential(*layers): 网络的每一层的层结构
    :return sorted(save): 把所有层结构中的from不是-1的值记下，并排序[4,6,10,14,17,20,23]
    '''
    LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
    # 读取字典d中的anchors和parameters(nc,depth_multiple,width_multiple)
    anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
    if act:
        Conv.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
        LOGGER.info(f"{colorstr('activation:')} {act}")  # print
    # na: number of anchors 每一个predict head上的anchor数=3
    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
    # no: number of outputs 每一个predict head层的输出channel=anchors*(classes+5)=75(VOC)
    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)

    ''' 开始搭建网络
    layers: 保存每一层的层结构
    save: 记录下所有层结构中from不是-1的层结构序号
    c2: 保存当前层的输出channel
    '''
    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    # from: 当前层输入来自哪些层
    # number: 当前层数，初定
    # module: 当前层类别
    # args: 当前层类参数，初定
    # 遍历backbone和head的每一层
    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
        # 得到当前层的真实类名，例如：m = Focus -> 
        m = eval(m) if isinstance(m, str) else m  # eval strings
        # 没什么用
        for j, a in enumerate(args):
            with contextlib.suppress(NameError):
                args[j] = eval(a) if isinstance(a, str) else a  # eval strings

        # --------------------更新当前层的args(参数)，计算c2(当前层的输出channel)--------------------
        # depth gain 控制深度，如yolov5s: n*0.33，n: 当前模块的次数(间接控制深度)
        n = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gain
        if m in {
                Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
                BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:
            # c1: 当前层的输入channel数; c2: 当前层的输出channel数(初定); ch: 记录着所有层的输出channel数
            c1, c2 = ch[f], args[0]
            # no=75，只有最后一层c2=no，最后一层不用控制宽度，输出channel必须是no
            if c2 != no:  # if not output
                # width gain 控制宽度，如yolov5s: c2*0.5; c2: 当前层的最终输出channel数(间接控制宽度)
                c2 = make_divisible(c2 * gw, 8)

            # 在初始args的基础上更新，加入当前层的输入channel并更新当前层
            # [in_channels, out_channels, *args[1:]]
            args = [c1, c2, *args[1:]]
            # 如果当前层是BottleneckCSP/C3/C3TR/C3Ghost/C3x，则需要在args中加入Bottleneck的个数
            # [in_channels, out_channels, Bottleneck个数, Bool(shortcut有无标记)]
            if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}:
                args.insert(2, n)  # number of repeats 在第二个位置插入Bottleneck的个数n
                n = 1 # 恢复默认值1
        elif m is nn.BatchNorm2d:
            # BN层只需要返回上一层的输出channel
            args = [ch[f]]
        elif m is Concat:
            # Concat层则将f中所有的输出累加得到这层的输出channel
            c2 = sum(ch[x] for x in f)
        # TODO: channel, gw, gd
        elif m in {Detect, Segment}:  # Detect/Segment(YOLO Layer)层
            # 在args中加入三个Detect层的输出channel
            args.append([ch[x] for x in f])
            if isinstance(args[1], int):  # number of anchors 几乎不执行
                args[1] = [list(range(args[1] * 2))] * len(f)
            if m is Segment:
                args[3] = make_divisible(args[3] * gw, 8)
        elif m is Contract:  # 不怎么用
            c2 = ch[f] * args[0] ** 2
        elif m is Expand:  # 不怎么用
            c2 = ch[f] // args[0] ** 2
        else:  # Upsample
            c2 = ch[f]  # args不变
        # -------------------------------------------------------------------------------------------

        # m_: 得到当前层的module，如果n>1就创建多个m(当前层结构)，如果n=1就创建一个m
        m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
        # 打印当前层结构的一些基本信息
        t = str(m)[8:-2].replace('__main__.', '')  # module type  <'modules.common.Focus'>
        np = sum(x.numel() for x in m_.parameters())  # number params 计算这一层的参数量
        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
        LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f}  {t:<40}{str(args):<30}')  # print
        # 把所有层结构中的from不是-1的值记下 [6,4,14,10,17,20,23]
        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
        # 将当前层结构module加入layers中
        layers.append(m_)
        if i == 0:
            ch = []  # 去除输入channel[3]
        # 把当前层的输出channel数加入ch
        ch.append(c2)
    return nn.Sequential(*layers), sorted(save)

 四、Detect类

Detect模块是用来构建Detect层的，将输入的feature map通过一个卷积操作和公式计算到我们想要的shape，为后面的计算损失率或者NMS做准备。

Detect模块代码:

class Detect(nn.Module):
    # YOLOv5 Detect head for detection models
    ''' Detect模块是用来构建Detect层的
    将输入的feature map通过一个卷积操作和公式计算到我们想要的shape，为后面的计算损失率或者NMS做准备
    '''
    stride = None  # strides computed during build
    dynamic = False  # force grid reconstruction
    export = False  # export mode

    def __init__(self, nc=80, anchors=(), ch=(), inplace=True):  # detection layer
        ''' detection layer 相当于yolov3中的YOLO Layer层
        :params nc: number of classes
        :params anchors: 传入3个feature map上的所有anchor的大小(P3/P4/P5)
        :params ch: [128,256,512] 3个输出feature map的channel
        '''
        super().__init__()
        self.nc = nc  # number of classes  VOC: 20
        self.no = nc + 5  # number of outputs per anchor  VOC: 5(xywhc)+20(classes)=25
        self.nl = len(anchors)  # number of detection layers  Detect的个数=3
        self.na = len(anchors[0]) // 2  # number of anchors  每个feature map的anchor个数=3
        self.grid = [torch.empty(0) for _ in range(self.nl)]  # init grid  {list: 3} tensor([0.])X3
        self.anchor_grid = [torch.empty(0) for _ in range(self.nl)]  # init anchor grid
        '''  模型中需要保存的参数一般有两种：
        一种是反向传播需要被optimizer更新的，称为parameter;另一种不需要被更新，称为buffer
        buffer的参数更新是在forward中，而optim.step只能更新nn.parameter参数
        '''
        self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2))  # shape(nl,na,2)
        # output conv 对每个输出的feature map都要调用一次conv1 x 1
        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
        # 一般都是True，默认不使用AWS，Inferentia加速
        self.inplace = inplace  # use inplace ops (e.g. slice assignment)

    def forward(self, x):
        '''
        :return train: 一个tensor list，存放三个元素
        [bs, anchor_num, grid_w, grid_h, xywh+c+classes]
        分别是[1,3,80,80,25] [1,3,40,40,25] [1,3,20,20,25]
        inference: 0 [1,19200+4800+1200,25]=[bs,anchor_num*grid_w*grid_h,xywh+c+classes]
        '''
        z = []  # inference output
        for i in range(self.nl):  # 对3个feature map分别进行处理
            x[i] = self.m[i](x[i])  # conv  xi[bs,128/256/512,80,80] to [bs,75,80,80]
            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
            # [bs,75,80,80] to [1,3,25,80,80] to [1,3,80,80,25]
            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

            ''' 构造网格
            因为推理返回的不是归一化后的网络偏移量，需要加上网格的位置，得到最终的推理坐标，再送入NMS
            所以这里构建网络就是为了记录每个grid的网格坐标，方便后面使用
            '''
            if not self.training:  # inference
                if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
                    self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)

                if isinstance(self, Segment):  # (boxes + masks)
                    xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4)
                    xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i]  # xy
                    wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i]  # wh
                    y = torch.cat((xy, wh, conf.sigmoid(), mask), 4)
                else:  # Detect (boxes only)
                    xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4)
                    xy = (xy * 2 + self.grid[i]) * self.stride[i]  # xy
                    wh = (wh * 2) ** 2 * self.anchor_grid[i]  # wh
                    y = torch.cat((xy, wh, conf), 4)
                # z是一个tensor list，有三个元素，分别是[1,19200,25] [1,4800,25] [1,1200,25]
                z.append(y.view(bs, self.na * nx * ny, self.no))

        return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)

    def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, '1.10.0')):
        ''' 构造网格 '''
        d = self.anchors[i].device
        t = self.anchors[i].dtype
        shape = 1, self.na, ny, nx, 2  # grid shape
        y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t)
        yv, xv = torch.meshgrid(y, x, indexing='ij') if torch_1_10 else torch.meshgrid(y, x)  # torch>=0.7 compatibility
        grid = torch.stack((xv, yv), 2).expand(shape) - 0.5  # add grid offset, i.e. y = 2.0 * x - 0.5
        anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape)
        return grid, anchor_grid

五、Model类

这个模块是整个模型的搭建模块。且yolov5的作者将这个模块的功能写的很全，不光包含模型的搭建，还扩展了很多功能，如：特征可视化、打印模型信息、TTA推理增强、融合Conv + BN加速推理、模型搭载NMS功能、Autoshape函数（模型包含前处理、推理、后处理的模块(预处理 + 推理 + NMS)。感兴趣的可以仔细看看，不感兴趣的可以直接看__init__、forward两个函数即可。

class BaseModel(nn.Module):
    # YOLOv5 base model
    def forward(self, x, profile=False, visualize=False):
        return self._forward_once(x, profile, visualize)  # single-scale inference, train

    def _forward_once(self, x, profile=False, visualize=False):
        '''
        :params x: 输入图像
        :params profile: True 可以做一些性能评估
        :params visualize: True 可以做一些特征可视化
        :return train: 一个tensor，存放三个元素 [bs, anchor_num, grid_w, grid_h, xywh+c+classes]
                    inference: 0 [1,19200+4800+1200,25]=[bs,anchor_num*grid_w*grid_h,xywh+c+classes]
        '''
        # y: 存放着self.save=True的每一层的输出，因为后面的层结构Concat等操作要用到
        # dt: 在profile中做性能评估时使用
        y, dt = [], []  # outputs
        for m in self.model:
            # 前向推理每一层结构 m.i=index; m.f=from; m.type=类名; m.np=number of parameters
            if m.f != -1:  # if not from previous layer  m.f=当前层的输入来自哪一层的输出，-1表示上一层
                # 这里需要做4个Concat操作和一个Detect操作
                # Concat: 如m.f=[-1,6] x就有两个元素，一个是上一层的输出，一个是index=6的层的输出，再送到x=m(x)做Concat操作
                # Detect: 如m.f=[17, 20, 23] x就有三个元素，分别存放第17层第20层第23层的输出，再送到x=m(x)做Detect的forward
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
            # 打印日志信息  FLOPs time等
            if profile:
                self._profile_one_layer(m, x, dt)
            x = m(x)  # run  正向推理
            # 存放着self.save的每一层的输出，因为后面需要用来做Concat等操作，不在self.save层的输出就为None
            y.append(x if m.i in self.save else None)  # save output
            # 特征可视化，可以自己改动想要那层的特征进行可视化
            if visualize:
                feature_visualization(x, m.type, m.i, save_dir=visualize)
        return x

    def _profile_one_layer(self, m, x, dt):
        c = m == self.model[-1]  # is final layer, copy input as inplace fix
        o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
        t = time_sync()
        for _ in range(10):
            m(x.copy() if c else x)
        dt.append((time_sync() - t) * 100)
        if m == self.model[0]:
            LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  module")
        LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}')
        if c:
            LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")

    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
        ''' 用在detect.py、val.py中
        fuse model Conv2d() + BatchNorm2d() layers
        调用torch_utils.py中的fuse_conv_and_bn函数和common.py中的forward_fuse函数
        '''
        LOGGER.info('Fusing layers... ')  # 日志
        for m in self.model.modules():  # 遍历每一层结构
            # 如果当前层是卷积层Conv且有BN结构，那么就调用fuse_conv_and_bn函数将Conv和BN进行融合，加速推理
            if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv  融合
                delattr(m, 'bn')  # remove batchnorm  移除BN
                m.forward = m.forward_fuse  # update forward  更新前向传播(反向传播不用管，因为这个过程只用再推理阶段)
        self.info()  # 打印Conv+BN融合后的模型信息
        return self

    def info(self, verbose=False, img_size=640):  # print model information
        ''' 用在上面的__init__函数上
        调用torch_utils.py下model_info函数打印模型信息
        '''
        model_info(self, verbose, img_size)

    def _apply(self, fn):
        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
        self = super()._apply(fn)
        m = self.model[-1]  # Detect()
        if isinstance(m, (Detect, Segment)):
            m.stride = fn(m.stride)
            m.grid = list(map(fn, m.grid))
            if isinstance(m.anchor_grid, list):
                m.anchor_grid = list(map(fn, m.anchor_grid))
        return self


class DetectionModel(BaseModel):
    # YOLOv5 detection model
    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):  # model, input channels, number of classes
        '''
        :params cfg: 模型配置文件
        :params ch: input img channels 一般是3(RGB文件)
        :params nc: number of classes 数据集的类别个数
        :params anchors: 一般是None
        '''
        super().__init__()
        if isinstance(cfg, dict):
            self.yaml = cfg  # model dict
        else:  # is *.yaml  一般执行这里
            import yaml  # for torch hub
            self.yaml_file = Path(cfg).name  # cfg file name = 'yolov5s.yaml'
            # 如果配置文件中有中文，打开时要加encoding参数
            with open(cfg, encoding='ascii', errors='ignore') as f:  # encoding='utf-8'
                self.yaml = yaml.safe_load(f)  # model dict

        # Define model
        ch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channels  ch=3
        # 设置类别数，一般不执行，因为nc=self.yaml['nc']恒成立
        if nc and nc != self.yaml['nc']:
            LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
            self.yaml['nc'] = nc  # override yaml value
        # 重写anchors，一般不执行，因为传进来的anchors一般都是None
        if anchors:
            LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')
            self.yaml['anchors'] = round(anchors)  # override yaml value
        # 创建网络模型
        # self.model: 初始化的整个网络模型(包括Detect层结构)
        # self.save: 所有层结构中from不等于-1的序号，并排好序  [4,6,10,14,17,20,23]
        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist
        # default class names ['0','1','2',...,'19']
        self.names = [str(i) for i in range(self.yaml['nc'])]  # default names
        # self.inplace=True  默认True，不使用加速推理
        # AWS Inferentia Inplace compatiability
        # https://github.com/ultralytics/yolov5/pull/2953
        self.inplace = self.yaml.get('inplace', True)

        # Build strides, anchors
        # 获取Detect模块的stride(相对输入图像的下采样率)和anchors在当前Detect输出的feature map的尺寸
        m = self.model[-1]  # Detect()
        if isinstance(m, (Detect, Segment)):
            s = 256  # 2x min stride
            m.inplace = self.inplace
            forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)
            # 计算三个feature map的anchor大小，如[10,13]/8 -> [1.25,1.625]
            m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))])  # forward
            # 检查anchor顺序与stride顺序是否一致
            check_anchor_order(m)
            m.anchors /= m.stride.view(-1, 1, 1)
            self.stride = m.stride
            self._initialize_biases()  # only run once  初始化偏置

        # Init weights, biases
        initialize_weights(self)  # 调用torch_utils.py下initialize_weights初始化模型权重
        self.info()  # 打印模型信息
        LOGGER.info('')

    def forward(self, x, augment=False, profile=False, visualize=False):
        # 是否在测试时也使用数据增强 Test Time Augmentation(TTA)
        if augment:
            return self._forward_augment(x)  # augmented inference, None  上下flip/左右flip
        # 默认执行，正常前向推理
        return self._forward_once(x, profile, visualize)  # single-scale inference, train

    def _forward_augment(self, x):
        ''' TTA Test Time Augmentation '''
        img_size = x.shape[-2:]  # height, width
        s = [1, 0.83, 0.67]  # scales
        f = [None, 3, None]  # flips (2-ud上下, 3-lr左右)
        y = []  # outputs
        for si, fi in zip(s, f):
            # scale_img缩放图片尺寸
            xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
            yi = self._forward_once(xi)[0]  # forward
            # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
            # _descale_pred将推理结果恢复到相对原图图片尺寸
            yi = self._descale_pred(yi, fi, si, img_size)
            y.append(yi)
        y = self._clip_augmented(y)  # clip augmented tails
        return torch.cat(y, 1), None  # augmented inference, train

    def _descale_pred(self, p, flips, scale, img_size):
        # de-scale predictions following augmented inference (inverse operation)
        ''' 用在上面的__init__函数上
        将推理结果恢复到原图图片尺寸上 TTA中用到
        :params p: 推理结果
        :params flips: 翻转标记(2-ud上下, 3-lr左右)
        :params scale: 图片缩放比例
        :params img_size: 原图图片尺寸
        '''
        # 不同的方式前向推理使用公式不同，具体可看Detect函数
        if self.inplace:  # 默认执行True，不使用AWS Inferentia
            p[..., :4] /= scale  # de-scale
            if flips == 2:
                p[..., 1] = img_size[0] - p[..., 1]  # de-flip ud
            elif flips == 3:
                p[..., 0] = img_size[1] - p[..., 0]  # de-flip lr
        else:
            x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale  # de-scale
            if flips == 2:
                y = img_size[0] - y  # de-flip ud
            elif flips == 3:
                x = img_size[1] - x  # de-flip lr
            p = torch.cat((x, y, wh, p[..., 4:]), -1)
        return p

    def _clip_augmented(self, y):
        # Clip YOLOv5 augmented inference tails
        nl = self.model[-1].nl  # number of detection layers (P3-P5)
        g = sum(4 ** x for x in range(nl))  # grid points
        e = 1  # exclude layer count
        i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e))  # indices
        y[0] = y[0][:, :-i]  # large
        i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indices
        y[-1] = y[-1][:, i:]  # small
        return y

    def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
        ''' 用在上面的__init__函数上 '''
        # https://arxiv.org/abs/1708.02002 section 3.3
        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
        m = self.model[-1]  # Detect() module
        for mi, s in zip(m.m, m.stride):  # from
            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
            b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
            b.data[:, 5:5 + m.nc] += math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum())  # cls
            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)


Model = DetectionModel  # retain YOLOv5 'Model' class for backwards compatibility

六、调整模型

./models/common.py 参考C3模块结构增加C2模块

class C2(nn.Module):
    # CSP Bottleneck with 3 convolutions
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
        super().__init__()
        c_ = int(c2 * 0.5)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c1, c_, 1, 1)
        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))

    def forward(self, x):
        # 移除cv3卷积层后，若要保持最终输出的channel仍为c2，则中间层的channel需为c2/2
        # 设置e=0.5即可，取默认值不变
        return torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)


class C3(nn.Module):
    # CSP Bottleneck with 3 convolutions
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
        ''' 在C3RT模块和yolo.py的parse_model函数中被调用
        :params c1: 整个C3的输入channel
        :params c2: 整个C3的输出channel
        :params n: 有n个子模块[Bottleneck/CrossConv]
        :params shortcut: bool值，子模块[Bottlenec/CrossConv]中是否有shortcut，默认True
        :params g: 子模块[Bottlenec/CrossConv]中的3x3卷积类型，=1普通卷积，>1深度可分离卷积
        :params e: expansion ratio，e*c2=中间其它所有层的卷积核个数=中间所有层的的输入输出channel
        '''
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c1, c_, 1, 1)
        self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)
        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
        # 实验性 CrossConv
        #self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])

    def forward(self, x):
        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))

./models/yolo.py 在parse_model中增加对C2的解析

def parse_model(d, ch):  # model_dict, input_channels(3)
    # Parse a YOLOv5 model.yaml dictionary
    ''' 用在上面DetectionModel模块中
    解析模型文件(字典形式)，并搭建网络结构
    这个函数其实主要做的就是：
        更新当前层的args(参数)，计算c2(当前层的输出channel)
        ->使用当前层的参数搭建当前层
        ->生成 layers + save
    :params d: model_dict模型文件，字典形式{dice: 7}(yolov5s.yaml中的6个元素 + ch)
    :params ch: 记录模型每一层的输出channel，初始ch=[3]，后面会删除
    :return nn.Sequential(*layers): 网络的每一层的层结构
    :return sorted(save): 把所有层结构中的from不是-1的值记下，并排序[4,6,10,14,17,20,23]
    '''
    LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
    # 读取字典d中的anchors和parameters(nc,depth_multiple,width_multiple)
    anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
    if act:
        Conv.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
        LOGGER.info(f"{colorstr('activation:')} {act}")  # print
    # na: number of anchors 每一个predict head上的anchor数=3
    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
    # no: number of outputs 每一个predict head层的输出channel=anchors*(classes+5)=75(VOC)
    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)

    ''' 开始搭建网络
    layers: 保存每一层的层结构
    save: 记录下所有层结构中from不是-1的层结构序号
    c2: 保存当前层的输出channel
    '''
    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    # from: 当前层输入来自哪些层
    # number: 当前层数，初定
    # module: 当前层类别
    # args: 当前层类参数，初定
    # 遍历backbone和head的每一层
    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
        # 得到当前层的真实类名，例如：m = Focus -> 
        m = eval(m) if isinstance(m, str) else m  # eval strings
        # 没什么用
        for j, a in enumerate(args):
            with contextlib.suppress(NameError):
                args[j] = eval(a) if isinstance(a, str) else a  # eval strings

        # --------------------更新当前层的args(参数)，计算c2(当前层的输出channel)--------------------
        # depth gain 控制深度，如yolov5s: n*0.33，n: 当前模块的次数(间接控制深度)
        n = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gain
        if m in {
                Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
                BottleneckCSP, C2, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:
            # c1: 当前层的输入channel数; c2: 当前层的输出channel数(初定); ch: 记录着所有层的输出channel数
            c1, c2 = ch[f], args[0]
            # no=75，只有最后一层c2=no，最后一层不用控制宽度，输出channel必须是no
            if c2 != no:  # if not output
                # width gain 控制宽度，如yolov5s: c2*0.5; c2: 当前层的最终输出channel数(间接控制宽度)
                c2 = make_divisible(c2 * gw, 8)

            # 在初始args的基础上更新，加入当前层的输入channel并更新当前层
            # [in_channels, out_channels, *args[1:]]
            args = [c1, c2, *args[1:]]
            # 如果当前层是BottleneckCSP/C2/C3/C3TR/C3Ghost/C3x，则需要在args中加入Bottleneck的个数
            # [in_channels, out_channels, Bottleneck个数, Bool(shortcut有无标记)]
            if m in {BottleneckCSP, C2, C3, C3TR, C3Ghost, C3x}:
                args.insert(2, n)  # number of repeats 在第二个位置插入Bottleneck的个数n
                n = 1 # 恢复默认值1
        elif m is nn.BatchNorm2d:
            # BN层只需要返回上一层的输出channel
            args = [ch[f]]
        elif m is Concat:
            # Concat层则将f中所有的输出累加得到这层的输出channel
            c2 = sum(ch[x] for x in f)
        # TODO: channel, gw, gd
        elif m in {Detect, Segment}:  # Detect/Segment(YOLO Layer)层
            # 在args中加入三个Detect层的输出channel
            args.append([ch[x] for x in f])
            if isinstance(args[1], int):  # number of anchors 几乎不执行
                args[1] = [list(range(args[1] * 2))] * len(f)
            if m is Segment:
                args[3] = make_divisible(args[3] * gw, 8)
        elif m is Contract:  # 不怎么用
            c2 = ch[f] * args[0] ** 2
        elif m is Expand:  # 不怎么用
            c2 = ch[f] // args[0] ** 2
        else:  # Upsample
            c2 = ch[f]  # args不变
        # -------------------------------------------------------------------------------------------

        # m_: 得到当前层的module，如果n>1就创建多个m(当前层结构)，如果n=1就创建一个m
        m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
        # 打印当前层结构的一些基本信息
        t = str(m)[8:-2].replace('__main__.', '')  # module type  <'modules.common.Focus'>
        np = sum(x.numel() for x in m_.parameters())  # number params 计算这一层的参数量
        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
        LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f}  {t:<40}{str(args):<30}')  # print
        # 把所有层结构中的from不是-1的值记下 [6,4,14,10,17,20,23]
        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
        # 将当前层结构module加入layers中
        layers.append(m_)
        if i == 0:
            ch = []  # 去除输入channel[3]
        # 把当前层的输出channel数加入ch
        ch.append(c2)
    return nn.Sequential(*layers), sorted(save)

 ./models/yolov5s.yaml 在原第2层和原第3层之间插入C2模块

# YOLOv5 v6.0 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, C3, [128]],
   [-1, 3, C2, [128]],  # 在原第2层和原第3层之间插入C2模块
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 6, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 3, C3, [1024]],
   [-1, 1, SPPF, [1024, 5]],  # 9
  ]

 七、运行&打印模型查看

python train.py --img 640 --batch 8 --epoch 1 --data data/ab.yaml  --cfg models/yolov5s.yaml

---

TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/
Overriding model.yaml nc=80 with nc=4

                 from  n    params  module                                  arguments
  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]
  2                -1  1     18816  models.common.C3                        [64, 64, 1]
  3                -1  1     14592  models.common.C2                        [64, 64, 1]
  4                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]
  5                -1  2    115712  models.common.C3                        [128, 128, 2]
  6                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]
  7                -1  3    625152  models.common.C3                        [256, 256, 3]
  8                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]
  9                -1  1   1182720  models.common.C3                        [512, 512, 1]
 10                -1  1    656896  models.common.SPPF                      [512, 512, 5]
 11                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]
 12                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']
 13           [-1, 6]  1         0  models.common.Concat                    [1]
 14                -1  1    361984  models.common.C3                        [512, 256, 1, False]
 15                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]
 16                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']
 17           [-1, 4]  1         0  models.common.Concat                    [1]
 18                -1  1     90880  models.common.C3                        [256, 128, 1, False]
 19                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]
 20          [-1, 14]  1         0  models.common.Concat                    [1]
 21                -1  1    329216  models.common.C3                        [384, 256, 1, False]
 22                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]
 23          [-1, 10]  1         0  models.common.Concat                    [1]
 24                -1  1   1313792  models.common.C3                        [768, 512, 1, False]
 25      [17, 20, 23]  1     38097  models.yolo.Detect                      [4, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [256, 384, 768]]
YOLOv5s summary: 229 layers, 7222673 parameters, 7222673 gradients, 17.0 GFLOPs

Transferred 49/373 items from yolov5s.pt
optimizer: SGD(lr=0.01) with parameter groups 61 weight(decay=0.0), 64 weight(decay=0.0005), 64 bias
train: Scanning D:\Out\yolov5-master\paper_data\train.cache... 160 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1
val: Scanning D:\Out\yolov5-master\paper_data\val.cache... 20 images, 0 backgrounds, 0 corrupt: 100%|██████████| 20/20

AutoAnchor: 5.35 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset
Plotting labels to runs\train\exp2\labels.jpg...
Image sizes 640 train, 640 val
Using 4 dataloader workers
Logging results to runs\train\exp2
Starting training for 1 epochs...

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        0/0         0G     0.1101    0.04563     0.0454         49        640: 100%|██████████| 20/20 [13:59<00:00, 41.
                 Class     Images  Instances          P          R      mAP50   mAP50-95:   0%|          | 0/2 [00:00                  Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 2/2 [00:13<0
                   all         20         60     0.0005     0.0577   0.000474   0.000307

1 epochs completed in 0.242 hours.
Optimizer stripped from runs\train\exp2\weights\last.pt, 14.8MB
Optimizer stripped from runs\train\exp2\weights\best.pt, 14.8MB

Validating runs\train\exp2\weights\best.pt...
Fusing layers...
YOLOv5s summary: 168 layers, 7213041 parameters, 0 gradients, 16.8 GFLOPs
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 2/2 [00:10<0
                   all         20         60   0.000542       0.25   0.000685   0.000268
                banana         20         12          0          0          0          0
           snake fruit         20         20          0          0          0          0
          dragon fruit         20         13    0.00217          1    0.00274    0.00107
             pineapple         20         15          0          0          0          0
Results saved to runs\train\exp2

---

参考博文：https://blog.csdn.net/qq_27889941/article/details/128443507

https://blog.csdn.net/XiaoGShou/article/details/117351971

https://blog.csdn.net/weixin_45483906/article/details/115129644

https://blog.csdn.net/qq_38253797/article/details/119684388