YOLOv5代码解析——yolo.py

       

 yolo.py的主要功能是构建模型。

1、最主要的函数是parse_model，用于解析yaml文件，并根据解析的结果搭建网络。这个函数的注释如下：

def parse_model(d, ch):  # model_dict, input_channels(3)"""解析模型文件，并搭建网络结构主要实现功能：更新当前层args，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:params ch: 记录模型每一层的输出channel，初始 ch=[3],后边会删除:return nn.Sequential(*layers): 网络的每一层的层结构:return sorted(save): 把所有层结构中from不是-1的值记下，并排序[4,6,10,14,17,20,23]"""# Parse a YOLOv5 model.yaml dictionaryLOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")# 可以在yaml文件中指定激活函数，如何使用待定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# 三个检测头的参数：3*(20+5) = 75(VOC)na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors = 3no = 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的结构序号# c2：保存当前层输出的channellayers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out# from(当前输入来自哪些层) number(当前的层数) module(当前层类别) args(当前层参数)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 m = Focus -> <class 'models.common.Focus'>m = eval(m) if isinstance(m, str) else m  # eval stringsfor j, a in enumerate(args):# 处理eval函数中出现变量未定义的情况(NameError)# 如果a是一个字符串类型（str），则将其作为表达式进行求值，得到结果。如果a不是字符串类型，则直接使用a的值。# 把结果赋值给args[j]，以此实现动态地根据字符串表达式来更新args参数列表with contextlib.suppress(NameError):args[j] = eval(a) if isinstance(a, str) else a  # eval strings# n当前层数 gd depth_multiple n = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gainif m in {Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:# c1当前层的输出channel c2当前层的输出channel ch 记录所有层的输出channelc1, c2 = ch[f], args[0]# 确保c2*gw能够被8整除，如果不能返回一个能够被8整除的最接近于c2*gw的数# 如果不最后一层的output，就控制宽度，最后一层的channel必须是noif c2 != no:  # if not outputc2 = make_divisible(c2 * gw, 8)# 在初始args上更新，加入当前层的输入channelargs = [c1, c2, *args[1:]] # [in_channel,out_channel,*args[1:]]if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}:# 在第二个位置上插入bottleneck的个数nargs.insert(2, n)  # number of repeats n = 1 # 恢复默认值1elif m is nn.BatchNorm2d:# 返回上一层的输出channelargs = [ch[f]]elif m is Concat:# 把f中的输出累加到这层的channelc2 = sum(ch[x] for x in f)# TODO: channel, gw, gdelif m in {Detect, Segment}:# 在args中加入三个Detect层的输出channelargs.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: # 几乎不使用c2 = ch[f] * args[0] ** 2elif m is Expand: # 几乎不使用c2 = ch[f] // args[0] ** 2else:# Unsample args不变c2 = ch[f]# 调用m(类)根据参数args创建当前层的module并赋值给m_，创建数量为n，m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module# 打印一些基本信息t = str(m)[8:-2].replace('__main__.', '')  # module typenp = sum(x.numel() for x in m_.parameters())  # number paramsm_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number paramsLOGGER.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# 把当前层结构module加入到layers中layers.append(m_)if i == 0:ch = [] # 去除输入channel# 把当前层输出的channel加入chch.append(c2)return nn.Sequential(*layers), sorted(save)

parse_model在DetectionModel的__init__函数中调用。

# 调用parse_modelself.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelistself.names = [str(i) for i in range(self.yaml['nc'])]  # default namesself.inplace = self.yaml.get('inplace', True)

2、Detect类用于构建最后的detect层，在parse_model函数中调用

# 构建Detect层，把feature map通过一个卷积操作和公式计算到需要的shape，为后边计算loss和NMS做准备。
class Detect(nn.Module):# YOLOv5 Detect head for detection modelsstride = None  # strides computed during builddynamic = False  # force grid reconstructionexport = False  # export modedef __init__(self, nc=80, anchors=(), ch=(), inplace=True):  # detection layer"""detection layer相当于yolov3的YOLOLayer层:params nc: number of class: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feanture map的chaannel"""super().__init__()self.nc = nc  # number of classesself.no = nc + 5  # number of outputs per anchorself.nl = len(anchors)  # number of detection layersself.na = len(anchors[0]) // 2  # number of anchors 每个feature map的anchor个数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有两种：一种是需要使用optimizer更新的，一种是不需要被更新的称为buffer# buffer的参数更新在forward，而optim.step只能更新nn.parameter类型的参数# anchor.shape = shape(nl,na,2)self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2))  # shape(nl,na,2)# 对每个输出feature map都调用一次conv1*1self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv# 默认为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+classes]以VOC为例,[1, 3, 80, 80, 25] [1, 3, 40, 40, 25] [1, 3, 20, 20, 25]inference:0 preds [1, 19200+4800+1200, 25] = [bs, anchor_num*grid_w*grid_h, xywh+classes]1 train_out :一个tensor list存放三个元素[bs, anchor_num, grid_w, grid_h, xywh+classes][1, 3, 80, 80, 25] [1, 3, 40, 40, 25] [1, 3, 20, 20, 25]"""z = []  # inference outputfor i in range(self.nl): # 对3个feature map分别进行处理x[i] = self.m[i](x[i])  # convbs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)# [bs, 75, 80, 80] to [bs, 3, 25, 80, 80] to [bs, 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# inference 返回的不是归一化后的网格的偏移量，需要加上网格的位置，得到最终的预测坐标，再送入NMS# 构建网络就是为了记录每个grid的网格坐标，方便后边使用# dynamic默认为falseif 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]  # xywh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i]  # why = 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]  # xywh = (wh * 2) ** 2 * self.anchor_grid[i]  # why = torch.cat((xy, wh, conf), 4)# z是一个teensor list，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')):"""生成网格和锚框的张量，网格形状由ny和nx居多，锚框形状由self.anchors[i]和self.stride[i]决定:params nx: 网格宽度:params ny: 网格高度 :params i: 锚框索引:params torch_1_10:判断torch版本是否大于1.10.0:return grid::return anchor_grid:"""d = self.anchors[i].device # 锚框的devicet = self.anchors[i].dtype # 锚框的数据类型# self.na 每个feature map的anchor的个数shape = 1, self.na, ny, nx, 2  # grid shape# y是长度为ny的张量，x是长度为nx的张量，y和x分别表示网格的纵坐标和横坐标y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t)# 如果torch_1_10为True，表示torch版本大于等于1.10.0，代码使用torch.meshgrid函数生成网格坐标，采用'ij'索引方式。# 否则，代码使用torch.meshgrid函数生成网格坐标，采用默认的索引方式yv, xv = torch.meshgrid(y, x, indexing='ij') if torch_1_10 else torch.meshgrid(y, x)  # torch>=0.7 compatibility# [ny, nx, 2] to [1, self.na, ny, nx, 2] - 0.5 # 以此得到网格的偏移量grid = torch.stack((xv, yv), 2).expand(shape) - 0.5  # add grid offset, i.e. y = 2.0 * x - 0.5anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape)return grid, anchor_grid

3、DetectionModel类继承自BaseModel类，用于构建模型，使用时定义了一个全局变量model指向这个类，在其他文件中使用时直接调用model。

class BaseModel(nn.Module):# YOLOv5 base modeldef forward(self, x, profile=False, visualize=False):return self._forward_once(x, profile, visualize)  # single-scale inference, traindef _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"""# y:  存放着self.save = True 的每一层的输出，因为后边层结构concat要用到# dt：在profile中做性能评估时使用y, dt = [], []  # outputs# 前向推理每一层结构 # m.i = index m.f = from m.type = 类名 m.mp = number of parms for m in self.model:# 4个concat操作和1个detect操作if m.f != -1:  # if not from previous layer# concat: m.f=[-1,6] x就有两个元素，一个是上一层的输出，另一个是index层的输出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、timeif profile:self._profile_one_layer(m, x, dt)x = m(x)  # run# 存放着self.save的每一层的输出y.append(x if m.i in self.save else None)  # save outputif visualize:feature_visualization(x, m.type, m.i, save_dir=visualize)return xdef _profile_one_layer(self, m, x, dt):c = m == self.model[-1]  # is final layer, copy input as inplace fixo = thop.profile(m, inputs=(x.copy() if c else x, ), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPst = 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和val.py中fuse model conv2d() + batch norm"""LOGGER.info('Fusing layers... ')for m in self.model.modules():if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update convdelattr(m, 'bn')  # remove batchnormm.forward = m.forward_fuse  # update forwardself.info()return selfdef info(self, verbose=False, img_size=640):  # print model informationmodel_info(self, verbose, img_size)def _apply(self, fn):# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffersself = 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 selfclass DetectionModel(BaseModel):# YOLOv5 detection modeldef __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):  # model, input channels, number of classes"""params cfg: 配置文件params ch: input channelparam nc: number of classesparams anchor: 一般是none"""super().__init__()if isinstance(cfg, dict):self.yaml = cfg  # model dictelse:  # is *.yamlimport yaml  # for torch hubself.yaml_file = Path(cfg).namewith open(cfg, encoding='ascii', errors='ignore') as f:self.yaml = yaml.safe_load(f)  # model dict# Define modelch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channelsif 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 valueif anchors:LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')self.yaml['anchors'] = round(anchors)  # override yaml value# 调用parse_model 创建网络模型# self.model 初始化的整个网络结构# self.save 所有层结构中from不等于-1的序号self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist# default class names ['0', '1', '2', '3', '4'.......]self.names = [str(i) for i in range(self.yaml['nc'])]  # default namesself.inplace = self.yaml.get('inplace', True)# Build strides, anchors# 获取Detect 模块的stride(相对于输入图像的下采样率)和anchors在当前Detect输出的feature map的尺度m = self.model[-1]  # Detect()if isinstance(m, (Detect, Segment)):s = 256  # 2x min stridem.inplace = self.inplaceforward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)# 计算三个feature map的下采样率m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))])  # forward# 检查anchor顺序是否与stride顺序是否一致check_anchor_order(m)# 求出相对于当前feature map的anchor大小     [10,13]/8=[1.25,1.625]m.anchors /= m.stride.view(-1, 1, 1)self.stride = m.strideself._initialize_biases()  # only run once# Init weights, biasesinitialize_weights(self) # 初始化模型权重self.info() LOGGER.info('')def forward(self, x, augment=False, profile=False, visualize=False):# 是否在测试时使用Test Time Augmentation(TTA)if augment:return self._forward_augment(x)  # augmented inference, None# 默认执行前向推理# single-scale inference, train # _forward_once在BaseModel中实现return self._forward_once(x, profile, visualize)  # single-scale inference, traindef _forward_augment(self, x):"""Test Time Augmentation(TTA) """img_size = x.shape[-2:]  # height, widths = [1, 0.83, 0.67]  # scalesf = [None, 3, None]  # flips (2-ud上下, 3-lr左右)y = []  # outputsfor 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# descaleyi = self._descale_pred(yi, fi, si, img_size)y.append(yi)y = self._clip_augmented(y)  # clip augmented tailsreturn torch.cat(y, 1), None  # augmented inference, traindef _descale_pred(self, p, flips, scale, img_size):# de-scale predictions following augmented inference (inverse operation)if self.inplace:p[..., :4] /= scale  # de-scaleif flips == 2:p[..., 1] = img_size[0] - p[..., 1]  # de-flip udelif flips == 3:p[..., 0] = img_size[1] - p[..., 0]  # de-flip lrelse:x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale  # de-scaleif flips == 2:y = img_size[0] - y  # de-flip udelif flips == 3:x = img_size[1] - x  # de-flip lrp = torch.cat((x, y, wh, p[..., 4:]), -1)return pdef _clip_augmented(self, y):# Clip YOLOv5 augmented inference tailsnl = self.model[-1].nl  # number of detection layers (P3-P5)g = sum(4 ** x for x in range(nl))  # grid pointse = 1  # exclude layer counti = (y[0].shape[1] // g) * sum(4 ** x for x in range(e))  # indicesy[0] = y[0][:, :-i]  # largei = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indicesy[-1] = y[-1][:, i:]  # smallreturn ydef _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency# 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() modulefor mi, s in zip(m.m, m.stride):  # fromb = 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())  # clsmi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)

使用语句

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