YOLOv5-7.0添加解耦头

Decoupled Head

Decoupled Head是由YOLOX提出的用来替代YOLO Head,可以用来提升目标检测的精度。那么为什么解耦头可以提升检测效果呢?
在阅读YOLOX论文时,找到了两篇引用的论文,并加以阅读。
第一篇文献是Song等人在CVPR2020发表的“Revisiting the Sibling Head in Object Detector”。
这篇论文中提出了,在目标检测任务的定位和分类任务中,存在spatial misalignment问题,即两个任务所聚焦和感兴趣的地方不同,分类更加关注所提取的特征与已有的类别哪一类更为相近,定位则更加关注与GT Box的位置坐标从而进行边界修正。因此如果采用一个特征图进行分类和定位,效果会不好,产生所谓的spatial misalignment问题。
第二篇文献是Wu等人(也是旷视的团队)在CVPR2020发表的“Rethinking Classification and Localization for Object Detection”
这篇论文重新对检测任务中的分类和定位两个子任务进行解读,结果发现:fc-head更适合分类任务,conv-head更适合定位任务。
总的来说,解耦头考虑到分类和定位所关注的内容不同,所以采用不同的分支进行计算,有利于提升效果!

YOLOv5-7.0引入解耦头

解耦头的网络结构如下图所示:
YOLOv5-7.0添加解耦头_第1张图片

2.1 修改common.py文件

在common.py文件中加解耦头代码

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

2.2 修改yolo.py文件

修改完common.py文件后,需要修改yolo.py文件。

  1. 在yolo.py添加Decoupled_Detect代码
class Decouple(nn.Module):
    # Decoupled convolution
    def __init__(self, c1, nc=80, na=3):  # ch_in, num_classes, num_anchors
        super().__init__()
        c_ = min(c1, 256)  # min(c1, nc * na)
        self.na = na  # number of anchors
        self.nc = nc  # number of classes
        self.a = Conv(c1, c_, 1)
        c = [int(x + na * 5) for x in (c_ - na * 5) * torch.linspace(1, 0, 4)]  # linear channel descent
 
        self.b1, self.b2, self.b3 = Conv(c_, c[1], 3), Conv(c[1], c[2], 3), nn.Conv2d(c[2], na * 5, 1)  # vc
 
        self.c1, self.c2, self.c3 = Conv(c_, c_, 1), Conv(c_, c_, 1), nn.Conv2d(c_, na * nc, 1)  # cls
 
    def forward(self, x):
        bs, nc, ny, nx = x.shape  # BCHW
        x = self.a(x)
        b = self.b3(self.b2(self.b1(x)))
        c = self.c3(self.c2(self.c1(x)))
        return torch.cat((b.view(bs, self.na, 5, ny, nx), c.view(bs, self.na, self.nc, ny, nx)), 2).view(bs, -1, ny, nx)  
 
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(Decouple(x, self.nc, self.na)  for x in ch)   #yolov5 provide ,  old Decouple too much FLOP
        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, torch_1_10=check_version(torch.__version__, '1.10.0')):
        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)
        yv, xv = torch.meshgrid(y, x, indexing='ij') if torch_1_10 else torch.meshgrid(y, x)  # torch>=0.7 compatibility
        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
  1. 在BaseModel类中中修改代码,加入解耦头检测
    def _apply(self, fn):
        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
        self = super()._apply(fn)
        m = self.model[-1]  # Detect()
        if isinstance(m, (Detect, Segment,Decoupled_Detect)):
            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
  1. 在DetectionModel中修改代码
    def _initialize_dh_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() module
        for mi, s in zip(m.m, m.stride):  # from
            # reg_bias = mi.reg_preds.bias.view(m.na, -1).detach()
            # reg_bias += math.log(8 / (640 / s) ** 2)
            # mi.reg_preds.bias = torch.nn.Parameter(reg_bias.view(-1), requires_grad=True)
 
            # cls_bias = mi.cls_preds.bias.view(m.na, -1).detach()
            # cls_bias += math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # cls
            # mi.cls_preds.bias = torch.nn.Parameter(cls_bias.view(-1), requires_grad=True)
            b = mi.b3.bias.view(m.na, -1)
            b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
            mi.b3.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
            b = mi.c3.bias.data
            b += math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # cls
            mi.c3.bias = torch.nn.Parameter(b, requires_grad=True)
if isinstance(m, (Detect, Segment,ASFF_Detect)):
          s = 256  # 2x min stride
          m.inplace = self.inplace
          forward = 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))])  # forward
          check_anchor_order(m)
          m.anchors /= m.stride.view(-1, 1, 1)
          self.stride = m.stride
          self._initialize_biases()  # only run once
elif 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_dh_biases()  # only run once
  1. 修改parse_model
         elif m in {Detect, Segment,Decoupled_Detect}:
            args.append([ch[x] for x in f])
            if isinstance(args[1], int):  # number of anchors
                args[1] = [list(range(args[1] * 2))] * len(f)
            if m is Segment:
                args[3] = make_divisible(args[3] * gw, 8)

2.3 修改模型的yaml文件

在模型的yaml文件中修改最后一层检测头的结构,把检测头修改为解耦头

# YOLOv5  by Ultralytics, GPL-3.0 license

# Parameters
nc: 8  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32

# YOLOv5 v7.0 backbone
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, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 6, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 3, C3, [1024]],
   [-1, 1, SPPF, [1024, 5]],  # 9
  ]

# YOLOv5 v7.0 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 13

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 14], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)

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

  ]


接着训练YOLOv5s,如下图所示:

python train.py --workers 8\
	--cache \
	--cfg yolov5s.yaml \
	--epochs 300\
	--img 800\
    --batch-size 16\
	--data ' '\
	--weights yolov5s.pt\
	--hyp data/hyps/hyp.scratch-low.yaml\
	--name yolov5s_decoupled_head\

YOLOv5-7.0添加解耦头_第2张图片

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