yolov5中head修改为decouple head详解

目录

• yolox的decoupled head结构
• 对于decouple head的改进
• 特点
• 疑问
• 总结

yolov5的head修改为decouple head

yolox的decoupled head结构

本来想将yolov5的head修改为decoupled head，与yolox的decouple head对齐，但是没注意，该成了如下结构：

感谢少年肩上杨柳依依的指出，如还有问题欢迎指出

1.修改models下的yolo.py文件中的Detect

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

    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(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
        self.m_box = nn.ModuleList(nn.Conv2d(256, 4 * self.na, 1) for x in ch)  # output conv
        self.m_conf = nn.ModuleList(nn.Conv2d(256, 1 * self.na, 1) for x in ch)  # output conv
        self.m_labels = nn.ModuleList(nn.Conv2d(256, self.nc * self.na, 1) for x in ch)  # output conv
        self.base_conv = nn.ModuleList(BaseConv(in_channels = x, out_channels = 256, ksize = 1, stride = 1) for x in ch)
        self.cls_convs = nn.ModuleList(BaseConv(in_channels = 256, out_channels = 256, ksize = 3, stride = 1) for x in ch)
        self.reg_convs = nn.ModuleList(BaseConv(in_channels = 256, out_channels = 256, ksize = 3, stride = 1) for x in ch)

        # self.m = nn.ModuleList(nn.Conv2d(x, 4 * self.na, 1) for x in ch, nn.Conv2d(x, 1 * self.na, 1) for x in ch,nn.Conv2d(x, self.nc * self.na, 1) for x in ch)
        self.inplace = inplace  # use in-place ops (e.g. slice assignment)self.ch = ch

    def forward(self, x):
        z = []  # inference output
        for i in range(self.nl):
            # # x[i] = self.m[i](x[i])  # convs
            # print("&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&", i)
            # print(x[i].shape)
            # print(self.base_conv[i])
            # print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

            x_feature = self.base_conv[i](x[i])
            # x_feature = x[i]

            cls_feature = self.cls_convs[i](x_feature)
            reg_feature = self.reg_convs[i](x_feature)
            # reg_feature = x_feature

            m_box = self.m_box[i](reg_feature)
            m_conf = self.m_conf[i](reg_feature)
            m_labels = self.m_labels[i](cls_feature)
            x[i] = torch.cat((m_box,m_conf, m_labels),1)
            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 - 0.5 + 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 = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]  # xy
                    wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                    y = torch.cat((xy, wh, y[..., 4:]), -1)
                z.append(y.view(bs, -1, self.no))

        return x if self.training else (torch.cat(z, 1), x)

2.在yolo.py中添加

def get_activation(name="silu", inplace=True):
    if name == "silu":
        module = nn.SiLU(inplace=inplace)
    elif name == "relu":
        module = nn.ReLU(inplace=inplace)
    elif name == "lrelu":
        module = nn.LeakyReLU(0.1, inplace=inplace)
    else:
        raise AttributeError("Unsupported act type: {}".format(name))
    return module

class BaseConv(nn.Module):
    """A Conv2d -> Batchnorm -> silu/leaky relu block"""

    def __init__(
        self, in_channels, out_channels, ksize, stride, groups=1, bias=False, act="silu"
    ):
        super().__init__()
        # same padding
        pad = (ksize - 1) // 2
        self.conv = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size=ksize,
            stride=stride,
            padding=pad,
            groups=groups,
            bias=bias,
        )
        self.bn = nn.BatchNorm2d(out_channels)
        self.act = get_activation(act, inplace=True)

    def forward(self, x):
        # print(self.bn(self.conv(x)).shape)
        return self.act(self.bn(self.conv(x)))
        # return self.bn(self.conv(x))

    def fuseforward(self, x):
        return self.act(self.conv(x))

decouple head的特点：

由于训练模型时，应该是channels = 256的地方改成了channels = x(失误)，所以在decoupled head的部分参数量比yolox要大一些，以下的结果是在channels= x的情况下得出

比yolov5s参数多，计算量大，在我自己的2.5万的数据量下map提升了3%多

1.模型给出的目标cls较高，需要将conf的阈值设置较大（0.5），不然准确率较低

parser.add_argument('--conf-thres', type=float, default=0.5, help='confidence threshold')

2.对于少样本的检测效果较好，召回率的提升比准确率多

3.在conf设置为0.25时，召回率比yolov5s高，但是准确率低；在conf设置为0.5时，召回率与准确率比yolov5s高

4.比yolov5s参数多，计算量大，在2.5万的数据量下map提升了3%多

对于decouple head的改进

改进：

1.将红色框中的conv去掉，缩小参数量和计算量；

2.channels =256 ，512 ，1024是考虑不增加参数，不进行featuremap的信息压缩

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

    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(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
        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 - 0.5 + 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 = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]  # xy
                    wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                    y = torch.cat((xy, wh, y[..., 4:]), -1)
                z.append(y.view(bs, -1, self.no))

        return x if self.training else (torch.cat(z, 1), x)

特点

1.模型给出的目标cls较高，需要将conf的阈值设置较大（0.4），不然准确率较低

2.对于少样本的检测效果较好，准确率的提升比召回率多

3. 准确率的提升比召回率多，

该改进不如上面的模型提升多，但是参数量小，计算量小少9Gflop，占用显存少

decoupled head指标提升的原因：由于yolov5s原本的head不能完全的提取featuremap中的信息，decoupled head能够较为充分的提取featuremap的信息；

疑问

为什么decoupled head目标的cls会比较高，没想明白

为什么去掉base_conv，召回率要比准确率提升少

总结

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