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南宁网站seo顾问,个人工商户做网站备案,wordpress小图标,上海注册建网站经典目标检测YOLO系列(一)实现YOLOV1网络(1)总体架构 实现原版的YOLOv1并没有多大的意义#xff0c;因此#xff0c;根据《YOLO目标检测》(ISBN:9787115627094)一书#xff0c;在不脱离YOLOv1的大部分核心理念的前提下#xff0c;重构一款较新的YOLOv1检测器#xff0c;来…经典目标检测YOLO系列(一)实现YOLOV1网络(1)总体架构 实现原版的YOLOv1并没有多大的意义因此根据《YOLO目标检测》(ISBN:9787115627094)一书在不脱离YOLOv1的大部分核心理念的前提下重构一款较新的YOLOv1检测器来对YOLOV1有更加深刻的认识。 书中源码连接:GitHub - yjh0410/RT-ODLab: YOLO Tutorial 对比原始YOLOV1网络主要改进点如下 将主干网络替换为ResNet-18添加了后续YOLO中使用的neck即SPPF模块删除全连接层修改为检测头预测层的组合并且使用普遍用在RetinaNet、FCOS、YOLOX等通用目标检测网络中的解耦检测头Decoupled head修改损失函数将YOLOV1原本的MSE loss分类分支替换为BCE loss回归分支替换为GIou loss。 我们按照现在目标检测的整体架构来搭建自己的YOLOV1网络概览图如下 1、替换主干网络 使用ResNet-18作为主干网络(backbone)替换YOLOV1原版的GoogLeNet风格的主干网络。替换后图片的下采样从64倍降低为32倍。我们更改图像大小由原版中的448×448变为416×416。这里不同于分类网络我们去掉网络中的平均池化层以及分类层。一张图片经过主干网络得到13×13×512的特征图相对于原本输出的7×7网格这里输出的网格要更加精细。 # RT-ODLab/models/detectors/yolov1/yolov1_backbone.pyimport torch import torch.nn as nn import torch.utils.model_zoo as model_zoo# 只会导入 all 列出的成员可以其他成员都被排除在外 all [ResNet, resnet18, resnet34, resnet50, resnet101, resnet152]model_urls {resnet18: https://download.pytorch.org/models/resnet18-5c106cde.pth,resnet34: https://download.pytorch.org/models/resnet34-333f7ec4.pth,resnet50: https://download.pytorch.org/models/resnet50-19c8e357.pth,resnet101: https://download.pytorch.org/models/resnet101-5d3b4d8f.pth,resnet152: https://download.pytorch.org/models/resnet152-b121ed2d.pth, }# ——————— Basic Module ———————– def conv3x3(in_planes, out_planes, stride1):3x3 convolution with paddingreturn nn.Conv2d(in_planes, out_planes, kernel_size3, stridestride,padding1, biasFalse)def conv1x1(in_planes, out_planes, stride1):1x1 convolutionreturn nn.Conv2d(in_planes, out_planes, kernel_size1, stridestride, biasFalse)class BasicBlock(nn.Module):expansion 1def init(self, inplanes, planes, stride1, downsampleNone):super(BasicBlock, self).init()self.conv1 conv3x3(inplanes, planes, stride)self.bn1 nn.BatchNorm2d(planes)self.relu nn.ReLU(inplaceTrue)self.conv2 conv3x3(planes, planes)self.bn2 nn.BatchNorm2d(planes)self.downsample downsampleself.stride stridedef forward(self, x):identity xout self.conv1(x)out self.bn1(out)out self.relu(out)out self.conv2(out)out self.bn2(out)if self.downsample is not None:identity self.downsample(x)out identityout self.relu(out)return outclass Bottleneck(nn.Module):expansion 4def init(self, inplanes, planes, stride1, downsampleNone):super(Bottleneck, self).init()self.conv1 conv1x1(inplanes, planes)self.bn1 nn.BatchNorm2d(planes)self.conv2 conv3x3(planes, planes, stride)self.bn2 nn.BatchNorm2d(planes)self.conv3 conv1x1(planes, planes * self.expansion)self.bn3 nn.BatchNorm2d(planes * self.expansion)self.relu nn.ReLU(inplaceTrue)self.downsample downsampleself.stride stridedef forward(self, x):identity xout self.conv1(x)out self.bn1(out)out self.relu(out)out self.conv2(out)out self.bn2(out)out self.relu(out)out self.conv3(out)out self.bn3(out)if self.downsample is not None:identity self.downsample(x)out identityout self.relu(out)return out# ——————— ResNet ———————– class ResNet(nn.Module):def init(self, block, layers, zero_init_residualFalse):super(ResNet, self).init()self.inplanes 64self.conv1 nn.Conv2d(3, 64, kernel_size7, stride2, padding3,biasFalse)self.bn1 nn.BatchNorm2d(64)self.relu nn.ReLU(inplaceTrue)self.maxpool nn.MaxPool2d(kernel_size3, stride2, padding1)self.layer1 self._make_layer(block, 64, layers[0])self.layer2 self._make_layer(block, 128, layers[1], stride2)self.layer3 self._make_layer(block, 256, layers[2], stride2)self.layer4 self._make_layer(block, 512, layers[3], stride2)for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaimingnormal(m.weight, modefanout, nonlinearityrelu)elif isinstance(m, nn.BatchNorm2d):nn.init.constant(m.weight, 1)nn.init.constant_(m.bias, 0)# Zero-initialize the last BN in each residual branch,# so that the residual branch starts with zeros, and each residual block behaves like an identity.# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677if zero_initresidual:for m in self.modules():if isinstance(m, Bottleneck):nn.init.constant(m.bn3.weight, 0)elif isinstance(m, BasicBlock):nn.init.constant_(m.bn2.weight, 0)def _make_layer(self, block, planes, blocks, stride1):downsample Noneif stride ! 1 or self.inplanes ! planes * block.expansion:downsample nn.Sequential(conv1x1(self.inplanes, planes * block.expansion, stride),nn.BatchNorm2d(planes * block.expansion),)layers []layers.append(block(self.inplanes, planes, stride, downsample))self.inplanes planes * block.expansionfor _ in range(1, blocks):layers.append(block(self.inplanes, planes))return nn.Sequential(*layers)def forward(self, x):Input:x: (Tensor) - [B, C, H, W]Output:c5: (Tensor) - [B, C, H/32, W/32]c1 self.conv1(x) # [B, C, H/2, W/2]c1 self.bn1(c1) # [B, C, H/2, W/2]c1 self.relu(c1) # [B, C, H/2, W/2]c2 self.maxpool(c1) # [B, C, H/4, W/4]c2 self.layer1(c2) # [B, C, H/4, W/4]c3 self.layer2(c2) # [B, C, H/8, W/8]c4 self.layer3(c3) # [B, C, H/16, W/16]c5 self.layer4(c4) # [B, C, H/32, W/32]return c5# ——————— Fsnctions ———————– def resnet18(pretrainedFalse, **kwargs):Constructs a ResNet-18 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNetmodel ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)if pretrained:# strict False as we dont need fc layer params.model.load_state_dict(model_zoo.load_url(model_urls[resnet18]), strictFalse)return modeldef resnet34(pretrainedFalse, **kwargs):Constructs a ResNet-34 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNetmodel ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)if pretrained:model.load_state_dict(model_zoo.load_url(model_urls[resnet34]), strictFalse)return modeldef resnet50(pretrainedFalse, **kwargs):Constructs a ResNet-50 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNetmodel ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)if pretrained:model.load_state_dict(model_zoo.load_url(model_urls[resnet50]), strictFalse)return modeldef resnet101(pretrainedFalse, **kwargs):Constructs a ResNet-101 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNetmodel ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)if pretrained:model.load_state_dict(model_zoo.load_url(model_urls[resnet101]), strictFalse)return modeldef resnet152(pretrainedFalse, **kwargs):Constructs a ResNet-152 model.Args:pretrained (bool): If True, returns a model pre-trained on ImageNetmodel ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)if pretrained:model.load_state_dict(model_zoo.load_url(model_urls[resnet152]))return model## build resnet def build_backbone(model_nameresnet18, pretrainedFalse):if model_name resnet18:model resnet18(pretrained)feat_dim 512elif model_name resnet34:model resnet34(pretrained)feat_dim 512elif model_name resnet50:model resnet34(pretrained)feat_dim 2048elif model_name resnet101:model resnet34(pretrained)feat_dim 2048return model, feat_dimif namemain:model, feat_dim build_backbone(model_nameresnet18, pretrainedTrue)print(model)input torch.randn(1, 3, 416, 416)print(model(input).shape)2、添加neck 在原版的YOLOv1中是没有Neck网络的概念的但随着目标检测领域的发展相关框架的成熟一个通用目标检测网络结构可以被划分为Backbone、Neck、Head三大部分。当前的YOLO工作也符合这一设计。 因此我们遵循当前主流的设计理念为我们的YOLOv1添加一个Neck网络。这里我们选择YOLOV5版本中所用的SPPF模块。 SPPF主要的代码如下:

RT-ODLab/models/detectors/yolov1/yolov1_neck.pyimport torch

import torch.nn as nn from .yolov1_basic import Conv# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher class SPPF(nn.Module):This code referenced to https://github.com/ultralytics/yolov5def init(self, in_dim, out_dim, expand_ratio0.5, pooling_size5, act_typelrelu, norm_typeBN):super().init()inter_dim int(in_dim * expand_ratio)self.out_dim out_dim# 1×1卷积通道降维self.cv1 Conv(in_dim, inter_dim, k1, act_typeact_type, norm_typenorm_type)# 1×1卷积通道降维self.cv2 Conv(inter_dim * 4, out_dim, k1, act_typeact_type, norm_typenorm_type)# 5×5最大池化self.m nn.MaxPool2d(kernel_sizepooling_size, stride1, paddingpooling_size // 2)def forward(self, x):# 1、经过Conv-BN-LeakyReLUx self.cv1(x)# 2、串行经过第1个5×5的最大池化y1 self.m(x)# 3、串行经过第2个5×5的最大池化y2 self.m(y1)# 4、串行经过第3个5×5的最大池化y3 self.m(y2)# 5、将上面4个得到的结果concatconcat_y torch.cat((x, y1, y2, y3), 1)# 6、再经过一个Conv-BN-LeakyReLUreturn self.cv2(concat_y)def build_neck(cfg, in_dim, out_dim):model cfg[neck]print()print(Neck: {}.format(model))# build neckif model sppf:neck SPPF(in_dimin_dim,out_dimout_dim,expand_ratiocfg[expand_ratio], pooling_sizecfg[pooling_size],act_typecfg[neck_act],norm_typecfg[neck_norm])return neck 3、Detection Head网络 原版的YOLOv1采用了全连接层来完成最终的处理即将此前卷积输出的二维 H×W 特征图拉平flatten操作成一维 HW 向量然后接全连接层得到4096维的一维向量。flatten操作会破坏特征的空间结构信息因此我们抛掉这里的flatten操作改用当下主流的基于卷积的检测头。具体来说我们使用普遍用在RetinaNet、FCOS、YOLOX等通用目标检测网络中的解耦检测头Decoupled head即使用两条并行的分支分别用于提取类别特征和位置特征两条分支都由卷积层构成。下图展示了我们所采用的Decoupled head以及后面的预测层结构。 # RT-ODLab/models/detectors/yolov1/yolov1_head.pyimport torch import torch.nn as nn try:from .yolov1_basic import Conv except:from yolov1_basic import Convclass DecoupledHead(nn.Module):def init(self, cfg, in_dim, out_dim, num_classes80):super().init()print()print(Head: Decoupled Head)self.in_dim in_dimself.num_cls_headcfg[num_cls_head]self.num_reg_headcfg[num_reg_head]self.act_typecfg[head_act]self.norm_typecfg[head_norm]# cls headcls_feats []self.cls_out_dim max(out_dim, num_classes)for i in range(cfg[num_cls_head]):if i 0:cls_feats.append(Conv(in_dim, self.cls_out_dim, k3, p1, s1, act_typeself.act_type,norm_typeself.norm_type,depthwisecfg[head_depthwise]))else:cls_feats.append(Conv(self.cls_out_dim, self.cls_out_dim, k3, p1, s1, act_typeself.act_type,norm_typeself.norm_type,depthwisecfg[head_depthwise]))# reg headreg_feats []self.reg_out_dim max(out_dim, 64)for i in range(cfg[num_reg_head]):if i 0:reg_feats.append(Conv(in_dim, self.reg_out_dim, k3, p1, s1, act_typeself.act_type,norm_typeself.norm_type,depthwisecfg[head_depthwise]))else:reg_feats.append(Conv(self.reg_out_dim, self.reg_out_dim, k3, p1, s1, act_typeself.act_type,norm_typeself.norm_type,depthwisecfg[head_depthwise]))self.cls_feats nn.Sequential(*cls_feats)self.reg_feats nn.Sequential(*reg_feats)def forward(self, x):in_feats: (Tensor) [B, C, H, W]cls_feats self.cls_feats(x)reg_feats self.reg_feats(x)return cls_feats, reg_feats# build detection head def build_head(cfg, in_dim, out_dim, num_classes80):head DecoupledHead(cfg, in_dim, out_dim, num_classes) return head4、预测层 检测头最后部分的预测层Prediction layer也要做相应的修改。 我们只要求YOLOv1在每一个网格只需要预测一个bbox而非两个甚至更多的bbox。尽管原版的YOLOv1在每个网格预测两个bbox但在推理阶段每个网格最终只输出一个bbox从结果上来看这和每个网格只预测一个bbox是一样的。objectness的预测。我们在Decoupled head的类别分支的输出后面接一层1x1卷积去做objectness的预测并在最后使用Sigmoid函数来输出objectness预测值。classificaton预测。遵循当下YOLO系列常用的方法我们同样采用Sigmoid函数来输出classification预测值即每个类别的置信度都是01。classificaton预测和objectness预测都采用Decoupled的同一分支。bbox regression预测。另一分支的位置特征就被用于预测边界框的偏移量即我们使用另一层1x1卷积去处理检测头的位置特征得到每个网格的边界框的偏移量预测。

预测层

self.obj_pred nn.Conv2d(head_dim, 1, kernel_size1) self.cls_pred nn.Conv2d(head_dim, num_classes, kernel_size1) self.reg_pred nn.Conv2d(head_dim, 4, kernel_size1)5、改进YOLO的详细网络图 不同于原版的YOLOv1我们在不偏离原版的大部分核心理念的前提下做了更加符合当下的设计理念的修改包括使用更好的Backbone、添加Neck模块、修改检测头等。改进YOLO的详细网络图如下 # RT-ODLab/models/detectors/yolov1/yolov1.pyimport torch import torch.nn as nn import numpy as npfrom utils.misc import multiclass_nmsfrom .yolov1_backbone import build_backbone from .yolov1_neck import build_neck from .yolov1_head import build_head# YOLOv1 class YOLOv1(nn.Module):def init(self,cfg,device,img_sizeNone,num_classes20,conf_thresh0.01,nms_thresh0.5,trainableFalse,deployFalse,nms_class_agnostic :bool False):super(YOLOv1, self).init()# ————————- 基础参数 —————————self.cfg cfg # 模型配置文件self.img_size img_size # 输入图像大小self.device device # cuda或者是cpuself.num_classes num_classes # 类别的数量self.trainable trainable # 训练的标记self.conf_thresh conf_thresh # 得分阈值self.nms_thresh nms_thresh # NMS阈值self.stride 32 # 网络的最大步长self.deploy deployself.nms_class_agnostic nms_class_agnostic# ———————– 模型网络结构 ————————-## 主干网络self.backbone, feat_dim build_backbone(cfg[backbone],trainable cfg[pretrained])## 颈部网络self.neck build_neck(cfg, feat_dim, out_dim512)head_dim self.neck.out_dim## 检测头self.head build_head(cfg, head_dim, head_dim, num_classes)## 预测层self.obj_pred nn.Conv2d(head_dim, 1, kernel_size1)self.cls_pred nn.Conv2d(head_dim, num_classes, kernel_size1)self.reg_pred nn.Conv2d(head_dim, 4, kernel_size1)def create_grid(self, fmp_size): 用于生成G矩阵其中每个元素都是特征图上的像素坐标。passdef decode_boxes(self, pred, fmp_size):将txtytwth转换为常用的x1y1x2y2形式。pred回归预测参数fmp_size特征图宽和高passdef postprocess(self, bboxes, scores):后处理代码包括阈值筛选和非极大值抑制1、滤掉低得分边界框的score低于给定的阈值的预测边界框2、滤掉那些针对同一目标的冗余检测。Input:bboxes: [HxW, 4]scores: [HxW, num_classes]Output:bboxes: [N, 4]score: [N,]labels: [N,]passtorch.no_grad()def inference(self, x):# 测试阶段的前向推理代码# 主干网络feat self.backbone(x)# 颈部网络feat self.neck(feat)# 检测头cls_feat, reg_feat self.head(feat)# 预测层obj_pred self.obj_pred(cls_feat)cls_pred self.cls_pred(cls_feat)reg_pred self.reg_pred(reg_feat)fmp_size obj_pred.shape[-2:]# 对 pred 的size做一些view调整便于后续的处理# [B, C, H, W] - [B, H, W, C] - [B, H*W, C]obj_pred obj_pred.permute(0, 2, 3, 1).contiguous().flatten(1, 2)cls_pred cls_pred.permute(0, 2, 3, 1).contiguous().flatten(1, 2)reg_pred reg_pred.permute(0, 2, 3, 1).contiguous().flatten(1, 2)# 测试时笔者默认batch是1# 因此我们不需要用batch这个维度用[0]将其取走。obj_pred obj_pred[0] # [H*W, 1]cls_pred cls_pred[0] # [H*W, NC]reg_pred reg_pred[0] # [H*W, 4]# 每个边界框的得分scores torch.sqrt(obj_pred.sigmoid() * cls_pred.sigmoid())# 解算边界框, 并归一化边界框: [H*W, 4]bboxes self.decode_boxes(reg_pred, fmp_size)if self.deploy:# [n_anchors_all, 4 C]outputs torch.cat([bboxes, scores], dim-1)return outputselse:# 将预测放在cpu处理上以便进行后处理scores scores.cpu().numpy()bboxes bboxes.cpu().numpy()# 后处理bboxes, scores, labels self.postprocess(bboxes, scores)return bboxes, scores, labelsdef forward(self, x):# 训练阶段的前向推理代码if not self.trainable:return self.inference(x)else:# 主干网络feat self.backbone(x)# 颈部网络feat self.neck(feat)# 检测头cls_feat, reg_feat self.head(feat)# 预测层obj_pred self.obj_pred(cls_feat)cls_pred self.cls_pred(cls_feat)reg_pred self.reg_pred(reg_feat)fmp_size obj_pred.shape[-2:]# 对 pred 的size做一些view调整便于后续的处理# [B, C, H, W] - [B, H, W, C] - [B, H*W, C]obj_pred obj_pred.permute(0, 2, 3, 1).contiguous().flatten(1, 2)cls_pred cls_pred.permute(0, 2, 3, 1).contiguous().flatten(1, 2)reg_pred reg_pred.permute(0, 2, 3, 1).contiguous().flatten(1, 2)# decode bboxbox_pred self.decode_boxes(reg_pred, fmp_size)# 网络输出outputs {pred_obj: obj_pred, # (Tensor) [B, M, 1]pred_cls: cls_pred, # (Tensor) [B, M, C]pred_box: box_pred, # (Tensor) [B, M, 4]stride: self.stride, # (Int)fmp_size: fmp_size # (List) [fmp_h, fmp_w]} return outputs到此我们完成了YOLOv1网络的搭建并且实现了前向推理。但是在推理的代码中还遗留了几个重要的问题尚待处理 如何从边界框偏移量reg_pred解耦出边界框坐标box_pred如何实现后处理操作如何计算训练阶段的损失 当然还有数据集的加载数据集如何增强如何选择正样本进行训练等内容。