YOLO v5 引入解耦头部

• 最近想开个 深度学习模型搭建, opencv方面的训练营,有兴趣可以私聊我

---

文章目录

• YOLO v5 引入解耦头部
• 前言
• 一、解耦头部示意图
• 二、在YOLO v5 中引入解耦头部
• • 1.修改common.py文件
  • 2.修改yolo.py文件
  • 3.修改模型的yaml文件
• 总结

---

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。

---

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。

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：

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

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)        if check_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

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()        if isinstance(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)

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。

来源地址：https://blog.csdn.net/m0_60890175/article/details/126532372