（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。
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
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。
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。
