(五)Yolov5--yolo.py文件解读

参考网址:https://blog.csdn.net/qq_27889941/article/details/128443507

                   https://blog.csdn.net/Z960515/article/details/122330311

上次对common.py文件进行了解读,这次将对yolo.py文件进行代码解读。

1 头文件解读

        如下所示为部分库的解读和说明。

(五)Yolov5--yolo.py文件解读_第1张图片

(五)Yolov5--yolo.py文件解读_第2张图片

2 parse_model函数

        该函数用于搭建网络模型,若要修改网络模型框架,则需要对此进行相应的改动。

def parse_model(d, ch):  # model_dict, input_channels(3)
    # Parse a YOLOv5 model.yaml dictionary
    '''
    用在DetectionModel模块中
    解析模型文件(字典形式),并搭建网络结构
    该函数主要完成:
            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
    参数 -- d: model_dict模型文件,字典形式{dice: 7}(yolov5s.yaml中的6个元素 + ch)
          ch: 记录模型每一层的输出channel,初始ch=[3],后面会删除
    返回 -- nn.Sequential(*layers): 网络的每一层的层结构
           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}")
    # 读取模型文件(字典类型)的相关参数
    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
    # 每一个predict head上的anchor数
    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
    # 每一个predict head层的输出个数
    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)

    ''' 
    正式搭建网络部分
    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
    # f(from): 当前层输入来自哪些层
    # n(number): 当前层数,
    # m(module): 当前层类别
    # args: 当前层类参数列表,包括channel、kernel_size、stride、padding和bias等
    # 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
        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)--------------------
        # gd: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
                # gw: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, 原args[1:]]
            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
                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:
            c2 = ch[f] * args[0] ** 2
        elif m is Expand:
            c2 = ch[f] // args[0] ** 2
        else:
            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
        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)

3 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
'''
class Detect(nn.Module):
    # YOLOv5 Detect head for detection models
    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
        参数 -- nc: number of classes
             -- anchors: 传入3个feature map上的所有anchor的大小(P3/P4/P5)
             -- ch: [128,256,512] 3个输出feature map的channel
        '''
        super().__init__()
        '''        
        nc:类别数
        no:每个anchor的输出数,为(x,y,w,h,conf) + nc = 5 + nc 的总数
        nl:预测层数,此次为3
        na:anchors的数量,此次为3
        grid:格子坐标系,左上角为(1,1),右下角为(input.w/stride,input.h/stride)
        '''
        self.nc = nc  # number of classes
        self.no = nc + 5  # number of outputs per anchor
        self.nl = len(anchors)  # number of detection layers
        self.na = len(anchors[0]) // 2  # number of anchors
        self.grid = [torch.empty(0) for _ in range(self.nl)]  # init grid
        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)  将anchors(shape=[3,6])转为shape=[3,3,2]

        # output conv 对每个输出的feature map都要调用一次conv1 x 1
        # 将输出通过卷积到 self.no * self.na 的通道,达到全连接的作用
        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)

    '''
    返回: 一个tensor list,存放三个元素
    [bs, anchor_num, grid_w, grid_h, xywh+c+classes] bs-batch size即多少张图片
    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]
    '''
    def forward(self, x):
        z = []  # inference output
        # 对3个feature map分别进行处理
        for i in range(self.nl):
            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()

            if not self.training:  # inference
                '''
                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
                即向前传播时需要将相对坐标转换到grid绝对坐标系中
                生成的grid坐标系
                grid[i].shape = [1,1,ny,nx,2]
                [[[[1,1],[1,2],...[1,nx]],
                 [[2,1],[2,2],...[2,nx]],
                     ...,
                 [[ny,1],[ny,2],...[ny,nx]]]]
                '''
                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 中心点
                    wh = (wh * 2) ** 2 * self.anchor_grid[i]  # wh 宽高
                    y = torch.cat((xy, wh, conf), 4) #conf 可信度
                # 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

4 Model类

        这个模块是整个模型的搭建模块。在yolov5中,该模块不仅包含模型的搭建,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))。yolov5的6.0版本包括BaseModel类和DetectionModel类,如下所示。

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

    '''
    参数 -- x: 输入图像
         -- profile: True 可以做一些性能评估
         -- visualize: True 可以做一些特征可视化
    返回 -- 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]
    '''
    def _forward_once(self, x, profile=False, visualize=False):

        # 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 如果输入不是来自于上一层
                # 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,因为这个过程只用于推理阶段)
        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fuse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
        '''
        参数 -- cfg: 模型配置文件
             -- ch: input img channels 一般是3(RGB文件)
             -- nc: number of classes 数据集的类别个数
             -- 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,因为我们输入参数是yaml文件
            import yaml  # for torch hub
            self.yaml_file = Path(cfg).name
            # 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:
                self.yaml = yaml.safe_load(f)  # model dict

        # Define model
        ch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channels
        # 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 调用torch_utils.py下initialize_weights初始化模型权重
        initialize_weights(self)
        self.info() # 打印模型信息
        LOGGER.info('')

    def forward(self, x, augment=False, profile=False, visualize=False):
        # 若需要进行数据增强则进行上下flip/左右flip
        if augment:
            return self._forward_augment(x)  # augmented inference, None
        # 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 缩放程度
        f = [None, 3, None]  # flips (2-ud up-down上下, 3-lr left-right左右)
        y = []  # outputs
        for si, fi in zip(s, f):
            # 缩放图片尺寸(根据f先进行相应的翻转)
            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
            # 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_forward_augment函数上
        将推理结果恢复到原图图片尺寸上 TTA中用到
        参数 -- p: 推理结果
             -- flips: 翻转标记(2-ud上下, 3-lr左右)
             -- scale: 图片缩放比例
             -- 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)

5 修改模型

(五)Yolov5--yolo.py文件解读_第3张图片

        现将C3模块按照如上所示进行修改,并将C2模块插入第2层和第3层之间,修改内容包括:

(1)在common.py中增加C2的实现

class C2(nn.Module):
    # CSP Bottleneck with 2 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):
        return torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)

(2)在yolov5s.yaml中,在原来的第2层和第3层之间插入C2模块

# YOLOv5 v6.0 backbone
backbone:
  # [from, number, module, args] args:channel、kernel_size、stride、padding、bias
  [[-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]], # 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
  ]

(3)在yolo.py中的parse_model函数增加对C2层的处理。

(五)Yolov5--yolo.py文件解读_第4张图片

         经过上述修改后,将数据集进行重新训练,以下是输出的部分信息。黄色部分的输出信息可知,添加的C2模块已成功插入到指定位置处。

(五)Yolov5--yolo.py文件解读_第5张图片

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