YOLO v5 引入解耦头部

YOLO v5 引入解耦头部

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文章目录

前言

在 YOLO x中,使用了解耦头部的方法，从而加快网络收敛速度和提高精度，因此解耦头被广泛应用于目标检测算法任务中。因此也想在YOLO v5的检测头部引入了解耦头部的方法，从而来提高检测精度和加快网络收敛，但这里与 YOLO x 解耦头部使用的检测方法稍微不同，在YOLO v5中引入的解耦头部依旧还是基于 anchor 检测的方法。

一、解耦头部示意图

在YOLO x中，使用了解耦头部的方法，具体论文请参考：https://arxiv.org/pdf/2107.08430.pdf
于是按照论文中的介绍就可以简单的画出解耦头部，在YOLO v5中引入的解耦头部最终还是基于 anchor 检测的方法。

二、在YOLO v5 中引入解耦头部

1.修改common.py文件

在common.py文件中加入以下代码。

class DecoupledHead(nn.Module):
    def __init__(self, ch=256, nc=80, anchors=()):super().__init__()
        self.nc = nc  # number of classes
        self.nl =len(anchors)  # number of detection layers
        self.na =len(anchors[0])// 2  # number of anchors
        self.merge =Conv(ch,256,1,1)
        self.cls_convs1 =Conv(256,256,3,1,1)
        self.cls_convs2 =Conv(256,256,3,1,1)
        self.reg_convs1 =Conv(256,256,3,1,1)
        self.reg_convs2 =Conv(256,256,3,1,1)
        self.cls_preds = nn.Conv2d(256, self.nc * self.na,1)
        self.reg_preds = nn.Conv2d(256,4* self.na,1)
        self.obj_preds = nn.Conv2d(256,1* 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 out

2.修改yolo.py文件

修改后common.py文件后，需要修改yolo.py文件，主要修改两个部分：
1.在model函数，只需修改一句代码，修改后如下：

ifisinstance(m, Detect) or isinstance(m, Decoupled_Detect):

2.在parse_model函数中，修改后代码如下：

3.在yolo.py增加Decoupled_Detect代码

class Decoupled_Detect(nn.Module):
    stride = None  # strides computed during build
    onnx_dynamic = False  # ONNX export parameter
    export = False  # export mode

    def __init__(self, nc=80, anchors=(), ch=(), inplace=True):  # detection layer
        super().__init__()

        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.zeros(1)]* self.nl  # init grid
        self.anchor_grid =[torch.zeros(1)]* self.nl  # init anchor grid
        self.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 output
        for i in range(self.nl):
            x[i]= self.m[i](x[i])  # conv
            bs, _, 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:  # inference
                if 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]  # xy
                    y[...,2:4]=(y[...,2:4]*2)**2* self.anchor_grid[i]  # wh
                else:  # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
                    xy, wh, conf = y.split((2,2, self.nc +1),4)  # y.tensor_split((2,4,5),4)  # torch 1.8.0
                    xy =(xy *2+ self.grid[i])* self.stride[i]  # xy
                    wh =(wh *2)**2* self.anchor_grid[i]  # wh
                    y = 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, nx=20, ny=20, i=0):
        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)ifcheck_version(torch.__version__,'1.10.0'):  # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility
            yv, xv = torch.meshgrid(y, x, indexing='ij')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 -0.5
        anchor_grid =(self.anchors[i]* self.stride[i]).view((1, self.na,1,1,2)).expand(shape)return grid, anchor_grid

3.在model函数中，修改Build strides, anchors部分代码，修改后代码如下：

#Build strides, anchors
        m = self.model[-1]  # Detect()ifisinstance(m, Detect) or isinstance(m, Decoupled_Detect):
            s =256  # 2x min stride
            m.inplace = self.inplace
            m.stride = torch.tensor([s / x.shape[-2]for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
            check_anchor_order(m)  # must be in pixel-space(not grid-space)
            m.anchors /= m.stride.view(-1,1,1)
            self.stride = m.stride
            #self._initialize_biases()  # only run once
            try :
                self._initialize_biases()  # only run once
                LOGGER.info('initialize_biases done')
            except :
                LOGGER.info('decoupled no biase ')initialize_weights(self)
        self.info()
        LOGGER.info('')

3.修改模型的yaml文件

在模型的yaml文件中，修改最后一层检测的头的结构，我修改yolo v5s模型的最后一层检测结构如下：

[[17,20,23],1, Decoupled_Detect,[nc, anchors]],         # Detect(P3, P4, P5)

总结

至于单独的增加解耦头部，我还没有对自己的数据集进行单独的训练，一般都是解耦头部和其他模型结合在一起进行训练，如果后期在训练的时候map有提升的话，我在把实验结果放在上面，最近也在跑实验结果对比。