MobileNetV1作为CenterNet的Backbone尝试

1、CenterNet对于Backbone的要求是：

输入为512*512，输出为：heatmap（128*128*C）、wh（128*128*2）、reg（128*128*2）。

2、原生的MobileNet V1输出：

最后一层通过x.view(x.size(0), -1)和nn.Linear后得到[batch_num * class_num]

3、改造输出使得输出符合业务要求：

将之前的x.view(x.size(0), -1)修改为:

x.view(x.shape[0], -1, 128, 128)，其中第2个维度为16。

然后通过1*1卷积核进行通道数的升、降维。

网络定义阶段：

self.hm = nn.Conv2d(16, class_num, kernel_size=1)
self.wh = nn.Conv2d(16, 2, kernel_size=1)
self.reg = nn.Conv2d(16, 2, kernel_size=1)

forward阶段：
y = x.view(x.shape[0], -1, 128, 128)
z = {}
z['hm'] = self.hm(y)
z['wh'] = self.wh(y)
z['reg'] = self.reg(y)

4、完整MobileNetv1适配centerNet的代码如下：

import torch
import torch.nn as nn


class BasicConv2dBlock(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, downsample=True, **kwargs):
        super(BasicConv2dBlock, self).__init__()
        stride = 2 if downsample else 1
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, **kwargs)
        self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x

class DepthSeperabelConv2dBlock(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
        super(DepthSeperabelConv2dBlock, self).__init__()

        # 深度卷积
        self.depth_wise = nn.Sequential(
            nn.Conv2d(in_channels, in_channels, kernel_size, groups=in_channels, **kwargs),
            nn.BatchNorm2d(in_channels),
            nn.ReLU(inplace=True)
        )

        # 逐点卷积
        self.point_wise = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, 1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        x = self.depth_wise(x)
        x = self.point_wise(x)
        return x

class MobileNet(nn.Module):
    def __init__(self, heads):
        super(MobileNet, self).__init__()
        class_num = heads['hm']

        self.stem = nn.Sequential(
            BasicConv2dBlock(3, 32, kernel_size=3, padding=1, bias=False),
            DepthSeperabelConv2dBlock(32, 64, kernel_size=3, padding=1, bias=False)
        )

        self.conv1 = nn.Sequential(
            DepthSeperabelConv2dBlock(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
            DepthSeperabelConv2dBlock(128, 128, kernel_size=3, padding=1, bias=False)
        )

        self.conv2 = nn.Sequential(
            DepthSeperabelConv2dBlock(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
            DepthSeperabelConv2dBlock(256, 256, kernel_size=3, padding=1, bias=False)
        )

        self.conv3 = nn.Sequential(
            DepthSeperabelConv2dBlock(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
            DepthSeperabelConv2dBlock(512, 512, kernel_size=3, padding=1, bias=False),
            DepthSeperabelConv2dBlock(512, 512, kernel_size=3, padding=1, bias=False),
            DepthSeperabelConv2dBlock(512, 512, kernel_size=3, padding=1, bias=False),
            DepthSeperabelConv2dBlock(512, 512, kernel_size=3, padding=1, bias=False),
            DepthSeperabelConv2dBlock(512, 512, kernel_size=3, padding=1, bias=False)
        )

        self.conv4 = nn.Sequential(
            DepthSeperabelConv2dBlock(512, 1024, kernel_size=3, stride=2, padding=1, bias=False),
            DepthSeperabelConv2dBlock(1024, 1024, kernel_size=3, padding=1, bias=False)
        )


        self.hm = nn.Conv2d(16, class_num, kernel_size=1)
        self.wh = nn.Conv2d(16, 2, kernel_size=1)
        self.reg = nn.Conv2d(16, 2, kernel_size=1)



    def forward(self, x):
        x = self.stem(x)
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)

        y = x.view(x.shape[0], -1, 128, 128)

        z = {}
        z['hm'] = self.hm(y)
        z['wh'] = self.wh(y)
        z['reg'] = self.reg(y)
        return [z]

def main():
    heads = {'hm': 10, 'wh': 2, 'reg': 2}
    model = MobileNet(heads)

    for _,param in enumerate(model.named_parameters()):
        print(param[0], param[1].shape, param[1].shape.numel())
    param_num = sum(x.numel() for x in model.parameters())
    print('model parameters num:', param_num)

    x = torch.rand([1, 3, 512, 512])
    y = model(x)
    print(y[-1]['hm'].shape)
    print(y[-1]['wh'].shape)
    print(y[-1]['reg'].shape)


if __name__ == '__main__':
    main()

5、与DLASeg的对比

（1）效果：

DLASeg的训练集loss可以降到1.0以下，而MobileNet v1的训练姐loss只能降到3.0左右。

在车牌目标检测的试验中，MobileNet v1最好的效果如下，效果不佳。

（2）性能（CPU下）

DLASeg的模型参数有19655846个，大致为2000W个；而MobileNetv1的模型参数有3213030个，大致为320W个。所以参数量上降了一个数量级。

DLASeg的cpu下一次推理平均需要1.2s左右，而mobilenet v1在未剪枝情况下为232ms，这一块到时提升比较明显。