YOLOv8改进：华为诺亚2023极简的神经网络模型 VanillaNet---VanillaBlock助力检测，实现涨点

  来自华为诺亚、悉尼大学的研究者们提出了一种极简的神经网络模型 VanillaNet，以极简主义的设计为理念，网络中仅仅包含最简单的卷积计算，去掉了残差和注意力模块，在计算机视觉中的各种任务上都取得了不俗的效果。 

    VanillaNet，这是一种设计优雅的神经网络架构。 通过避免高深度、shortcuts和自注意力等复杂操作，VanillaNet 简洁明了但功能强大。 

class VanillaBlock(nn.Module):
    def __init__(self, dim, dim_out, act_num=3, stride=2, deploy=False, ada_pool=None):
        super().__init__()
        self.act_learn = 1
        self.deploy = deploy
        if self.deploy:
            self.conv = nn.Conv2d(dim, dim_out, kernel_size=1)
        else:
            self.conv1 = nn.Sequential(
                nn.Conv2d(dim, dim, kernel_size=1),
                nn.BatchNorm2d(dim, eps=1e-6),
            )
            self.conv2 = nn.Sequential(
                nn.Conv2d(dim, dim_out, kernel_size=1),
                nn.BatchNorm2d(dim_out, eps=1e-6)
            )

        if not ada_pool:
            self.pool = nn.Identity() if stride == 1 else nn.MaxPool2d(stride)
        else:
            self.pool = nn.Identity() if stride == 1 else nn.AdaptiveMaxPool2d((ada_pool, ada_pool))

        self.act = activation(dim_out, act_num)
 
    def forward(self, x):
        if self.deploy:
            x = self.conv(x)
        else:
            x = self.conv1(x)
            x = torch.nn.functional.leaky_relu(x,self.act_learn)
            x = self.conv2(x)

        x = self.pool(x)
        x = self.act(x)
        return x

    def _fuse_bn_tensor(self, conv, bn):
        kernel = conv.weight
        bias = conv.bias
        running_mean = bn.running_mean
        running_var = bn.running_var
        gamma = bn.weight
        beta = bn.bias
        eps = bn.eps
        std = (running_var + eps).sqrt()
        t = (gamma / std).reshape(-1, 1, 1, 1)
        return kernel * t, beta + (bias - running_mean) * gamma / std
    
    def switch_to_deploy(self):
        kernel, bias = self._fuse_bn_tensor(self.conv1[0], self.conv1[1])
        self.conv1[0].weight.data = kernel
        self.conv1[0].bias.data = bias
        # kernel, bias = self.conv2[0].weight.data, self.conv2[0].bias.data
        kernel, bias = self._fuse_bn_tensor(self.conv2[0], self.conv2[1])
        self.conv = self.conv2[0]
        self.conv.weight.data = torch.matmul(kernel.transpose(1,3), self.conv1[0].weight.data.squeeze(3).squeeze(2)).transpose(1,3)
        self.conv.bias.data = bias + (self.conv1[0].bias.data.view(1,-1,1,1)*kernel).sum(3).sum(2).sum(1)
        self.__delattr__('conv1')
        self.__delattr__('conv2')
        self.act.switch_to_deploy()
        self.deploy = True