YOLOv5-Y5周：yolo.py文件解读

本文为🔗365天深度学习训练营中的学习记录博客
原作者：K同学啊|接辅导、项目定制

我的环境：

1.语言：python3.7

2.编译器：pycharm

3.深度学习框架Tensorflow/Pytorch 1.8.0+cu111

一、代码解读

import argparse
import contextlib
import os
import platform
import sys
from copy import deepcopy
from pathlib import PathFILE = 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()))  # relativefrom models.common import *  # noqa
from models.experimental import *  # noqa
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)try:import thop  # for FLOPs computation
except ImportError:thop = None

argparse 用于命令行参数解析
contextlib 用于上下文管理
os 和 platform 用于操作系统和平台相关的功能
deepcopy 用于深拷贝对象
Path 用于处理文件路径
尝试导入 thop 库，用于计算模型的浮点运算量

FILE 是当前文件的绝对路径

Detect类

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 layersuper().__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 anchorsself.grid = [torch.empty(0) for _ in range(self.nl)]  # init gridself.anchor_grid = [torch.empty(0) for _ in range(self.nl)]  # init anchor gridself.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2))  # shape(nl,na,2)self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output convself.inplace = inplace  # use inplace ops (e.g. slice assignment)def forward(self, x):z = []  # inference outputfor i in range(self.nl):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)x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()if not self.training:  # inferenceif 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.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')):d = self.anchors[i].devicet = self.anchors[i].dtypeshape = 1, self.na, ny, nx, 2  # grid shapey, 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 compatibilitygrid = 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

stride：用于存储在构建期间计算的步幅（strides），在前向传播中使用。
dynamic 和 export：这两个属性都是布尔值，分别用于指示是否强制进行网格重构和导出模式。
__init__ 方法：初始化函数，接受一些参数，包括 nc（类别数）、anchors（锚框）、ch（通道数）、inplace（是否使用原地操作）。
- nc：类别数
- no：每个锚框的输出数（类别数加上5）
- nl：检测层的数量（锚框的数量）
- na：每个检测层的锚框数量
- grid 和 anchor_grid：用于存储网格和锚框网格的空列表
- anchors：将锚框转换为张量并注册为缓冲区
- m：输出卷积的模块列表
forward 方法：前向传播函数，接受输入张量 x，并返回输出张量。
- 循环遍历每个检测层
- 对输入进行卷积操作，并调整形状以适应后续处理
- 如果不是训练模式，则进行推理操作
- 根据是否是分割模式，对不同的输出进行不同的处理
- 将处理后的输出添加到列表 z 中
- 返回输出张量 x（如果是训练模式）、合并后的检测结果张量（如果是导出模式）或者分别返回这两者（如果不是训练模式且不是导出模式）
_make_grid 方法：用于生成网格和锚框网格。
- 创建网格和锚框网格
- 根据输入的尺寸和索引调整形状
- 返回网格和锚框网格

parse_model函数

def parse_model(d, ch):  # model_dict, input_channels(3)# Parse a YOLOv5 model.yaml dictionaryLOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")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}")  # printna = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchorsno = na * (nc + 5)  # number of outputs = anchors * (classes + 5)layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch outfor i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, argsm = eval(m) if isinstance(m, str) else m  # eval stringsfor j, a in enumerate(args):with contextlib.suppress(NameError):args[j] = eval(a) if isinstance(a, str) else a  # eval stringsn = 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, c2 = ch[f], args[0]if c2 != no:  # if not outputc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}:args.insert(2, n)  # number of repeatsn = 1elif m is nn.BatchNorm2d:args = [ch[f]]elif m is Concat:c2 = sum(ch[x] for x in f)# TODO: channel, gw, gdelif m in {Detect, Segment}:args.append([ch[x] for x in f])if isinstance(args[1], int):  # number of anchorsargs[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] ** 2elif m is Expand:c2 = ch[f] // args[0] ** 2else:c2 = ch[f]m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # modulet = 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}')  # printsave.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelistlayers.append(m_)if i == 0:ch = []ch.append(c2)return nn.Sequential(*layers), sorted(save)

该函数将模型的模块拼接起来，搭建完成网络模型。如果要改动模型框架，需要修改此函数。

从配置信息中提取 anchors、nc（类别数）、gd（深度倍数）、gw（宽度倍数）和激活函数类型。
遍历配置中的 backbone 和 head，这两个部分描述了模型的骨干网络和检测头。
对于每个模块，根据其类型进行相应的处理：
- 如果是卷积层（如 Conv、Bottleneck 等），根据深度倍数和宽度倍数调整输出通道数，并创建相应的模块。
- 如果是 BatchNorm2d，则根据输入通道数创建模块。
- 如果是 Concat，则根据输入通道数的总和创建模块。
- 如果是 Detect 或 Segment，则根据输入通道数列表创建模块，并根据宽度倍数调整参数。
- 如果是 Contract 或 Expand，则根据输入通道数和倍数调整输出通道数。
创建模块实例，并记录相关信息，如模块类型、参数数量等。
将构建好的模块添加到网络层序列中，并将需要保存输出的层索引记录下来。
最后返回构建好的模型和需要保存输出的层索引。

BaseModel类

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):y, dt = [], []  # outputsfor m in self.model:if m.f != -1:  # if not from previous layerx = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layersif profile:self._profile_one_layer(m, x, dt)x = m(x)  # runy.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() layersLOGGER.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 self

BaseModel 类是 YOLOv5 模型的基类，包含了一些用于模型前向推断、性能评估和模型信息打印等方法。

forward(self, x, profile=False, visualize=False): 定义了模型的前向传播过程。根据参数 profile 和 visualize 的设置，选择是否进行性能分析和特征可视化。调用了 _forward_once 方法来执行单次前向传播。
_forward_once(self, x, profile=False, visualize=False): 单次前向传播过程。遍历模型中的每一层，根据保存输出的层索引记录下需要的特征。如果设置了 profile 参数，则调用 _profile_one_layer 方法进行性能分析。如果设置了 visualize 参数，则调用 feature_visualization 方法进行特征可视化。
_profile_one_layer(self, m, x, dt): 对单个模块进行性能分析。计算模块的 FLOPs（浮点运算量）和运行时间，并输出日志信息。
fuse(self): 将模型中的 Conv2d() 和 BatchNorm2d() 层融合为单个层。通过遍历模型中的每个模块，对满足条件的模块进行融合操作，并更新模型结构。
info(self, verbose=False, img_size=640): 打印模型的相关信息。调用了 model_info 方法来输出模型的结构、参数数量等信息。
_apply(self, fn): 应用给定的函数到模型的张量上，例如 to(), cpu(), cuda(), half()。在这个方法中，除了将函数应用到模型的张量参数上之外，还更新了 Detect 或 Segment 类型模块中的一些属性，如 stride、grid 和 anchor_grid。

DetectionModel类

class DetectionModel(BaseModel):# YOLOv5 detection modeldef __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):  # model, input channels, number of classessuper().__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 valueself.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)# Build strides, anchorsm = 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)m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))])  # forwardcheck_anchor_order(m)m.anchors /= m.stride.view(-1, 1, 1)self.stride = m.strideself._initialize_biases()  # only run once# Init weights, biasesinitialize_weights(self)self.info()LOGGER.info('')def forward(self, x, augment=False, profile=False, visualize=False):if augment:return self._forward_augment(x)  # augmented inference, Nonereturn self._forward_once(x, profile, visualize)  # single-scale inference, traindef _forward_augment(self, x):img_size = x.shape[-2:]  # height, widths = [1, 0.83, 0.67]  # scalesf = [None, 3, None]  # flips (2-ud, 3-lr)y = []  # outputsfor si, fi in zip(s, f):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])  # saveyi = 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)Model = DetectionModel  # retain YOLOv5 'Model' class for backwards compatibility

DetectionModel 类是基于 BaseModel 类构建的，用于实现 YOLOv5 目标检测模型。它继承了 BaseModel 类的一些方法，并根据 YOLOv5 模型的配置文件初始化模型。

__init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): 初始化方法，接收模型的配置文件路径 cfg、输入通道数 ch、类别数 nc 和 anchors。首先根据配置文件初始化模型，然后根据传入的参数进行相应的修改，如修改输入通道数、类别数或 anchors。接着构建模型，解析配置文件并初始化模型的权重。最后打印模型的信息。
forward(self, x, augment=False, profile=False, visualize=False): 模型的前向传播方法。如果设置了 augment 参数，则执行增强推断，即对输入图像进行尺度变换和翻转操作，然后进行单次前向传播。如果未设置 augment 参数，则执行单次前向传播。根据参数 profile 和 visualize 的设置，选择是否进行性能分析和特征可视化。
_forward_augment(self, x): 执行增强推断的方法。根据预设的尺度因子和翻转方式，对输入图像进行处理，然后进行单次前向传播。最后对预测结果进行逆操作，将结果还原到原始图像尺寸。
_descale_pred(self, p, flips, scale, img_size): 对增强推断得到的预测结果进行逆操作，将预测框的坐标还原到原始图像尺寸。
_clip_augmented(self, y): 对增强推断得到的预测结果进行裁剪，去除多余的预测框。
_initialize_biases(self, cf=None): 初始化模型中的偏置项。根据目标检测中的一些规则，调整偏置项的值以适应目标检测任务。

二、修改部分

1、common.py

在C3类下增加C2类：

class C2(nn.Module):# CSP Bottleneck with 3 convolutionsdef __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__()c_ = int(c2 * e)  # hidden channelsself.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 self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))return torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)

2、yolov5s.yaml文件修改

注意将Y3周时修改的两个C3改回。

增加C2

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]][-1, 1, Conv, [256, 3, 2]],  # 3-P3/8[-1, 6, C3, [256]],          # 4[-1, 1, Conv, [512, 3, 2]],  # 5-P4/16[-1, 9, C3, [512]],          #6[-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32[-1, 3, C3, [1024]],[-1, 1, SPPF, [1024, 5]],  # 9]

3、yolo.py修改

parse_model函数部分修改（添加C2）

        if m in {Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,BottleneckCSP, C3, C2, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:c1, c2 = ch[f], args[0]if c2 != no:  # if not outputc2 = make_divisible(c2 * gw, 8)args = [c1, c2, *args[1:]]if m in {BottleneckCSP, C3, C2, C3TR, C3Ghost, C3x}:args.insert(2, n)  # number of repeatsn = 1