广州网站建设技术,深圳市制作网站,redis wordpress 设置密码,深圳宝安房价【YOLOX】用YOLOv5框架YOLOX一、新建common_x.py二、修改yolo.py三、新建yolox.yaml四、训练最近在跑YOLO主流框架的对比实验#xff0c;发现了一个很奇怪的问题#xff0c;就是同一个数据集#xff0c;在不同YOLO框架下训练出的结果差距竟然大的离谱。我使用ultralytics公司…
【YOLOX】用YOLOv5框架YOLOX一、新建common_x.py二、修改yolo.py三、新建yolox.yaml四、训练最近在跑YOLO主流框架的对比实验发现了一个很奇怪的问题就是同一个数据集在不同YOLO框架下训练出的结果差距竟然大的离谱。我使用ultralytics公司出品的v5、v3框架跑出的结果精度差距是合理的然而用该Up主写的Yolov4代码竟与ultralytics公司出品的v5、v3框架跑出的结果精度能低20-30%帧率低的离谱。并且YOLOX也是一样结果。虽然不知道为什么但确实无法进行对比实验于是只能将Yolov4结构与YoloX结构在Yolov5框架中实现。Yolov4在Yolov5框架中的实现我参考了这个博主的博客大家有需求可以参考yolov4_u5版复现。是一系列文章。 下面我来实现将YoloX结构移植到Yolov5框架中以下是结合网络结构以及YoloX源码进行实现 一、新建common_x.py
该python文件存放的是YOLOX中用到的模块主要包括BaseConv、CSPLayer、Dark代码如下
import torch
import torch.nn as nnclass SiLU(nn.Module):export-friendly version of nn.SiLU()staticmethoddef forward(x):return x * torch.sigmoid(x)def get_activation(namesilu, inplaceTrue):if name silu:module nn.SiLU(inplaceinplace)elif name relu:module nn.ReLU(inplaceinplace)elif name lrelu:module nn.LeakyReLU(0.1, inplaceinplace)else:raise AttributeError(Unsupported act type: {}.format(name))return moduleclass BaseConv(nn.Module):A Conv2d - Batchnorm - silu/leaky relu blockdef __init__(self, in_channels, out_channels, ksize, stride, groups1, biasFalse, actsilu):super().__init__()# same paddingpad (ksize - 1) // 2self.conv nn.Conv2d(in_channels,out_channels,kernel_sizeksize,stridestride,paddingpad,groupsgroups,biasbias,)self.bn nn.BatchNorm2d(out_channels)self.act get_activation(act, inplaceTrue)def forward(self, x):return self.act(self.bn(self.conv(x)))def fuseforward(self, x):return self.act(self.conv(x))class DWConv(nn.Module):Depthwise Conv Convdef __init__(self, in_channels, out_channels, ksize, stride1, actsilu):super().__init__()self.dconv BaseConv(in_channels,in_channels,ksizeksize,stridestride,groupsin_channels,actact,)self.pconv BaseConv(in_channels, out_channels, ksize1, stride1, groups1, actact)def forward(self, x):x self.dconv(x)return self.pconv(x)class Bottleneck(nn.Module):# Standard bottleneckdef __init__(self,in_channels,out_channels,shortcutTrue,expansion0.5,depthwiseFalse,actsilu,):super().__init__()hidden_channels int(out_channels * expansion)Conv DWConv if depthwise else BaseConvself.conv1 BaseConv(in_channels, hidden_channels, 1, stride1, actact)self.conv2 Conv(hidden_channels, out_channels, 3, stride1, actact)self.use_add shortcut and in_channels out_channelsdef forward(self, x):y self.conv2(self.conv1(x))if self.use_add:y y xreturn yclass ResLayer(nn.Module):Residual layer with in_channels inputs.def __init__(self, in_channels: int):super().__init__()mid_channels in_channels // 2self.layer1 BaseConv(in_channels, mid_channels, ksize1, stride1, actlrelu)self.layer2 BaseConv(mid_channels, in_channels, ksize3, stride1, actlrelu)def forward(self, x):out self.layer2(self.layer1(x))return x outclass SPPBottleneck(nn.Module):Spatial pyramid pooling layer used in YOLOv3-SPPdef __init__(self, in_channels, out_channels, kernel_sizes(5, 9, 13), activationsilu):super().__init__()hidden_channels in_channels // 2self.conv1 BaseConv(in_channels, hidden_channels, 1, stride1, actactivation)self.m nn.ModuleList([nn.MaxPool2d(kernel_sizeks, stride1, paddingks // 2)for ks in kernel_sizes])conv2_channels hidden_channels * (len(kernel_sizes) 1)self.conv2 BaseConv(conv2_channels, out_channels, 1, stride1, actactivation)def forward(self, x):x self.conv1(x)x torch.cat([x] [m(x) for m in self.m], dim1)x self.conv2(x)return xclass CSPLayer(nn.Module):C3 in yolov5, CSP Bottleneck with 3 convolutionsdef __init__(self,in_channels,out_channels,n1,shortcutTrue,expansion0.5,depthwiseFalse,actsilu,):Args:in_channels (int): input channels.out_channels (int): output channels.n (int): number of Bottlenecks. Default value: 1.# ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__()hidden_channels int(out_channels * expansion) # hidden channelsself.conv1 BaseConv(in_channels, hidden_channels, 1, stride1, actact)self.conv2 BaseConv(in_channels, hidden_channels, 1, stride1, actact)self.conv3 BaseConv(2 * hidden_channels, out_channels, 1, stride1, actact)module_list [Bottleneck(hidden_channels, hidden_channels, shortcut, 1.0, depthwise, actact)for _ in range(n)]self.m nn.Sequential(*module_list)def forward(self, x):x_1 self.conv1(x)x_2 self.conv2(x)x_1 self.m(x_1)x torch.cat((x_1, x_2), dim1)return self.conv3(x)class Dark(nn.Module):def __init__(self, c1, c2, n1, actsilu):super().__init__()self.cv1 BaseConv(c1, c2, 3, 2, actact)self.cv2 CSPLayer(c2, c2, nn, depthwiseFalse, actact)def forward(self, x):return self.cv2(self.cv1(x))
二、修改yolo.py
由于YOLOX里面使用的是Decoupled Head解藕头所以需要重新设计Detect部分这里参考了这位博主的博客YOLO v5 引入解耦头部。
这里有一个需要注意的点是在 class DecoupledHead 当中的 self.gd0.5 这个参数对应中间隐藏层的输出通道数原博主这里默认的256yolox源代码里面这里是乘了 width_multiple 用来控制宽度因此为了同样实现 -s 模型我把他乘了对应的系数 self.gd 其对应于 yaml 文件中的 width_multiple 参数控制实现yolox的不同版本。因此如果想要实现 yolox 的 -m -x -l版本修改yaml的 width_multiple 参数的时候不要忘记改这里的 self.gd。 这里因为还得修改函数接口等就暂时以这种形式实现后续有时间把这修改一下。
将以下代码放入yolo.py
class DecoupledHead(nn.Module):def __init__(self, ch256, nc80, anchors()):super().__init__()self.nc nc # number of classes# 中间隐藏层的输出通道数比例self.gd 0.5self.nl len(anchors) # number of detection layersself.na len(anchors[0]) // 2 # number of anchorsc_ int(ch * self.gd)self.merge Conv(ch, c_, 1, 1)self.cls_convs1 Conv(c_, c_, 3, 1, 1)self.cls_convs2 Conv(c_, c_, 3, 1, 1)self.reg_convs1 Conv(c_, c_, 3, 1, 1)self.reg_convs2 Conv(c_, c_, 3, 1, 1)self.cls_preds nn.Conv2d(c_, self.nc * self.na, 1)self.reg_preds nn.Conv2d(c_, 4 * self.na, 1)self.obj_preds nn.Conv2d(c_, 1 * self.na, 1)def forward(self, x):x self.merge(x)x1 self.cls_convs1(x)x1 self.cls_convs2(x1)x1 self.cls_preds(x1)x2 self.reg_convs1(x)x2 self.reg_convs2(x2)x21 self.reg_preds(x2)x22 self.obj_preds(x2)out torch.cat([x21, x22, x1], 1)return outclass Decoupled_Detect(nn.Module):stride None # strides computed during buildonnx_dynamic False # ONNX export parameterexport False # export modedef __init__(self, nc80, anchors(), ch(), inplaceTrue): # 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.zeros(1)] * self.nl # init gridself.anchor_grid [torch.zeros(1)] * 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(DecoupledHead(x, nc, anchors) for x in ch)self.inplace inplace # use in-place 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.onnx_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)y x[i].sigmoid()if self.inplace:y[..., 0:2] (y[..., 0:2] * 2 self.grid[i]) * self.stride[i] # xyy[..., 2:4] (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # whelse: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953xy, wh, conf y.split((2, 2, self.nc 1), 4) # y.tensor_split((2, 4, 5), 4) # torch 1.8.0xy (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, -1, 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, nx20, ny20, i0):d self.anchors[i].devicet self.anchors[i].dtypeshape 1, self.na, ny, nx, 2 # grid shapey, x torch.arange(ny, deviced, dtypet), torch.arange(nx, deviced, dtypet)if check_version(torch.__version__, 1.10.0): # torch1.10.0 meshgrid workaround for torch0.7 compatibilityyv, xv torch.meshgrid(y, x, indexingij)else:yv, xv torch.meshgrid(y, x)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修改yolo.py中的parse_model函数 在下面添加BaseConv、CSPLayer、Dark模块 添加Decoupled_Detect模块
三、新建yolox.yaml
新建yolox.yaml
# parameters
nc: 80 # number of classes
depth_multiple: 0.33 # expand model depth
width_multiple: 0.5 # expand layer channels# anchors
anchors:- [12,16, 19,36, 40,28] # P3/8- [36,75, 76,55, 72,146] # P4/16- [142,110, 192,243, 459,401] # P5/32# yolov4l backbone
backbone:# [from, number, module, args][[-1, 1, Focus, [64, 3, 1]], # 0[-1, 3, Dark, [128]], # 1-P1/2[-1, 9, Dark, [256]],[-1, 9, Dark, [512]], # 3-P2/4[-1, 3, Dark, [1024]],]# yolov4l head
# na len(anchors[0])
head:[[-1, 1, BaseConv, [512, 1, 1]], # 11[-1, 1, nn.Upsample, [None, 2, nearest]],[[-1, 3], 1, Concat, [1]],[-1, 3, CSPLayer, [512]], # 16[-1, 1, BaseConv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, nearest]],[[-1, 2], 1, Concat, [1]],[-1, 3, CSPLayer, [256]], # 21[-1, 1, BaseConv, [256, 3, 2]],[[-1, 9], 1, Concat, [1]], # cat[-1, 3, CSPLayer, [512]], # 25[-1, 1, BaseConv, [512, 3, 2]], # route backbone P3[[-1, 5], 1, Concat, [1]], # cat[-1, 3, CSPLayer, [1024]], # 29[[12,15,18], 1, Decoupled_Detect, [nc, anchors]], # Detect(P3, P4, P5)]四、训练
以上配置完之后其他操作与训练Yolov5步骤一致最终训练出来的效果要比原YoloX训练结果好不少看起来更加合理与Yolov5训练结果差距也是在合理范围内。
