DAMO-YOLO的Neck（ Efficient RepGFPN）详解_efficient-repgfpn

 这个图是有点问题的，在GiraffeNeckV2代码中只有了5个Fusion Block（图中有6个）

https://github.com/tinyvision/DAMO-YOLO/blob/master/damo/base_models/necks/giraffe_fpn_btn.py

代码中只有5个CSPStage

所以我自己画了一个总体图，在github上提了个issue，得到了原作者的肯定

I think the pictures in your paper are not rigorous in several places · Issue #91 · tinyvision/DAMO-YOLO · GitHub

 

想要看懂Neck部分，只需要看懂Fusion Block在做什么就行了，其他部分和PAN差不太多

class CSPStage(nn.Module):   
    def __init__(self,
                 block_fn,
                 ch_in,
                 ch_hidden_ratio,
                 ch_out,
                 n,
                 act='swish',
                 spp=False):
        super(CSPStage, self).__init__()
 
        split_ratio = 2
        ch_first = int(ch_out // split_ratio)
        ch_mid = int(ch_out - ch_first)
        self.conv1 = ConvBNAct(ch_in, ch_first, 1, act=act)
        self.conv2 = ConvBNAct(ch_in, ch_mid, 1, act=act)
        self.convs = nn.Sequential()
 
        next_ch_in = ch_mid
        for i in range(n):
            if block_fn == 'BasicBlock_3x3_Reverse':
                self.convs.add_module(
                    str(i),
                    BasicBlock_3x3_Reverse(next_ch_in,
                                           ch_hidden_ratio,
                                           ch_mid,
                                           act=act,
                                           shortcut=True))
            else:
                raise NotImplementedError
            if i == (n - 1) // 2 and spp:
                self.convs.add_module(
                    'spp', SPP(ch_mid * 4, ch_mid, 1, [5, 9, 13], act=act))
            next_ch_in = ch_mid
        self.conv3 = ConvBNAct(ch_mid * n + ch_first, ch_out, 1, act=act)
 
    def forward(self, x):
        y1 = self.conv1(x)
        y2 = self.conv2(x)
 
        mid_out = [y1]
        for conv in self.convs:
            y2 = conv(y2)
            mid_out.append(y2)
        y = torch.cat(mid_out, axis=1)
        y = self.conv3(y)
        return y

以上是CSPStage的代码，要想看懂，我们得先看懂ConvBNAct、BasicBlock_3x3_Reverse这两个类

class ConvBNAct(nn.Module):
    """A Conv2d -> Batchnorm -> silu/leaky relu block"""
    def __init__(
        self,
        in_channels,
        out_channels,
        ksize,
        stride=1,
        groups=1,
        bias=False,
        act='silu',
        norm='bn',
        reparam=False,
    ):
        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,
        )
        if norm is not None:
            self.bn = get_norm(norm, out_channels, inplace=True)
        if act is not None:
            self.act = get_activation(act, inplace=True)
        self.with_norm = norm is not None
        self.with_act = act is not None
 
    def forward(self, x):
        x = self.conv(x)
        if self.with_norm:
            x = self.bn(x)
        if self.with_act:
            x = self.act(x)
        return x
 
    def fuseforward(self, x):
        return self.act(self.conv(x))

ConvBNAct还是很好看懂的，Conv +BN + SiLU就完事了（也可用别的激活函数，文章用SiLU）

 如果设置了groups参数就变成了组卷积了

class BasicBlock_3x3_Reverse(nn.Module):
    def __init__(self,
                 ch_in,
                 ch_hidden_ratio,
                 ch_out,
                 act='relu',
                 shortcut=True):
        super(BasicBlock_3x3_Reverse, self).__init__()
        assert ch_in == ch_out
        ch_hidden = int(ch_in * ch_hidden_ratio)
        self.conv1 = ConvBNAct(ch_hidden, ch_out, 3, stride=1, act=act)
        self.conv2 = RepConv(ch_in, ch_hidden, 3, stride=1, act=act)
        self.shortcut = shortcut
 
    def forward(self, x):
        y = self.conv2(x)
        y = self.conv1(y)
        if self.shortcut:
            return x + y
        else:
            return y

要看懂BasicBlock_3x3_Reverse这个类，就得了解RepConv类，这个类就是根据RepVGG网络的RepVGGBlock改的

class RepConv(nn.Module):
    '''RepConv is a basic rep-style block, including training and deploy status
    Code is based on https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py
    '''
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size=3,
                 stride=1,
                 padding=1,
                 dilation=1,
                 groups=1,
                 padding_mode='zeros',
                 deploy=False,
                 act='relu',
                 norm=None):
        super(RepConv, self).__init__()
        self.deploy = deploy
        self.groups = groups
        self.in_channels = in_channels
        self.out_channels = out_channels
 
        assert kernel_size == 3
        assert padding == 1
 
        padding_11 = padding - kernel_size // 2
 
        if isinstance(act, str):
            self.nonlinearity = get_activation(act)
        else:
            self.nonlinearity = act
 
        if deploy:
            self.rbr_reparam = nn.Conv2d(in_channels=in_channels,
                                         out_channels=out_channels,
                                         kernel_size=kernel_size,
                                         stride=stride,
                                         padding=padding,
                                         dilation=dilation,
                                         groups=groups,
                                         bias=True,
                                         padding_mode=padding_mode)
 
        else:
            self.rbr_identity = None
            self.rbr_dense = conv_bn(in_channels=in_channels,
                                     out_channels=out_channels,
                                     kernel_size=kernel_size,
                                     stride=stride,
                                     padding=padding,
                                     groups=groups)
            self.rbr_1x1 = conv_bn(in_channels=in_channels,
                                   out_channels=out_channels,
                                   kernel_size=1,
                                   stride=stride,
                                   padding=padding_11,
                                   groups=groups)
 
    def forward(self, inputs):
        '''Forward process'''
        if hasattr(self, 'rbr_reparam'):
            return self.nonlinearity(self.rbr_reparam(inputs))
 
        if self.rbr_identity is None:
            id_out = 0
        else:
            id_out = self.rbr_identity(inputs)
 
        return self.nonlinearity(
            self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out)
 
    def get_equivalent_kernel_bias(self):
        kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)
        kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)
        kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)
        return kernel3x3 + self._pad_1x1_to_3x3_tensor(
            kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid
 
    def _pad_1x1_to_3x3_tensor(self, kernel1x1):
        if kernel1x1 is None:
            return 0
        else:
            return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])
 
    def _fuse_bn_tensor(self, branch):
        if branch is None:
            return 0, 0
        if isinstance(branch, nn.Sequential):
            kernel = branch.conv.weight
            running_mean = branch.bn.running_mean
            running_var = branch.bn.running_var
            gamma = branch.bn.weight
            beta = branch.bn.bias
            eps = branch.bn.eps
        else:
            assert isinstance(branch, nn.BatchNorm2d)
            if not hasattr(self, 'id_tensor'):
                input_dim = self.in_channels // self.groups
                kernel_value = np.zeros((self.in_channels, input_dim, 3, 3),
                                        dtype=np.float32)
                for i in range(self.in_channels):
                    kernel_value[i, i % input_dim, 1, 1] = 1
                self.id_tensor = torch.from_numpy(kernel_value).to(
                    branch.weight.device)
            kernel = self.id_tensor
            running_mean = branch.running_mean
            running_var = branch.running_var
            gamma = branch.weight
            beta = branch.bias
            eps = branch.eps
        std = (running_var + eps).sqrt()
        t = (gamma / std).reshape(-1, 1, 1, 1)
        return kernel * t, beta - running_mean * gamma / std
 
    def switch_to_deploy(self):
        if hasattr(self, 'rbr_reparam'):
            return
        kernel, bias = self.get_equivalent_kernel_bias()
        self.rbr_reparam = nn.Conv2d(
            in_channels=self.rbr_dense.conv.in_channels,
            out_channels=self.rbr_dense.conv.out_channels,
            kernel_size=self.rbr_dense.conv.kernel_size,
            stride=self.rbr_dense.conv.stride,
            padding=self.rbr_dense.conv.padding,
            dilation=self.rbr_dense.conv.dilation,
            groups=self.rbr_dense.conv.groups,
            bias=True)
        self.rbr_reparam.weight.data = kernel
        self.rbr_reparam.bias.data = bias
        for para in self.parameters():
            para.detach_()
        self.__delattr__('rbr_dense')
        self.__delattr__('rbr_1x1')
        if hasattr(self, 'rbr_identity'):
            self.__delattr__('rbr_identity')
        if hasattr(self, 'id_tensor'):
            self.__delattr__('id_tensor')
        self.deploy = True

 RepConv的特点是结构重参数化，训练时采用三条分支，推理时将三个分支融合在一起，大大减少了推理时间（建议看看RepVGG的讲解视频），我图画得太丑了

  RepConv采用的两分支的结构（a）

 其他细节有缘再更，代码不难，慢慢看完全能懂。有写的不对的地方请见谅