BlazeFace.py

import torch
import torch.nn as nn

class BlazeBlock(nn.Module):
    def __init__(self, in_channels,out_channels,mid_channels=None,stride=1):
        super(BlazeBlock, self).__init__()
        mid_channels = mid_channels or in_channels
        assert stride in [1, 2]
        if stride>1:
            self.use_pool = True
        else:
            self.use_pool = False

        self.branch1 = nn.Sequential(
            nn.Conv2d(in_channels=in_channels,out_channels=mid_channels,kernel_size=5,stride=stride,padding=2,groups=in_channels),
            nn.BatchNorm2d(mid_channels),
            nn.Conv2d(in_channels=mid_channels,out_channels=out_channels,kernel_size=1,stride=1),
            nn.BatchNorm2d(out_channels),
        )

        if self.use_pool:
            self.shortcut = nn.Sequential(
                nn.MaxPool2d(kernel_size=stride, stride=stride),
                nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),
                nn.BatchNorm2d(out_channels),
            )

        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        branch1 = self.branch1(x)
        out = (branch1+self.shortcut(x)) if self.use_pool else (branch1+x)
        return self.relu(out)

class DoubleBlazeBlock(nn.Module):
    def __init__(self,in_channels,out_channels,mid_channels=None,stride=1):
        super(DoubleBlazeBlock, self).__init__()
        mid_channels = mid_channels or in_channels
        assert stride in [1, 2]
        if stride > 1:
            self.use_pool = True
        else:
            self.use_pool = False

        self.branch1 = nn.Sequential(
            nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=5, stride=stride,padding=2,groups=in_channels),
            nn.BatchNorm2d(in_channels),
            nn.Conv2d(in_channels=in_channels, out_channels=mid_channels, kernel_size=1, stride=1),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(in_channels=mid_channels, out_channels=mid_channels, kernel_size=5, stride=1,padding=2),
            nn.BatchNorm2d(mid_channels),
            nn.Conv2d(in_channels=mid_channels, out_channels=out_channels, kernel_size=1, stride=1),
            nn.BatchNorm2d(out_channels),
        )

        if self.use_pool:
            self.shortcut = nn.Sequential(
                nn.MaxPool2d(kernel_size=stride, stride=stride),
                nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),
                nn.BatchNorm2d(out_channels),
            )

        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        branch1 = self.branch1(x)
        out = (branch1 + self.shortcut(x)) if self.use_pool else (branch1 + x)
        return self.relu(out)


class BlazeFace(nn.Module):
    def __init__(self):
        super(BlazeFace, self).__init__()

        self.firstconv = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=24, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(24),
            nn.ReLU(inplace=True),
        )

        self.blazeBlock = nn.Sequential(
            BlazeBlock(in_channels=24, out_channels=24),
            BlazeBlock(in_channels=24, out_channels=24),
            BlazeBlock(in_channels=24, out_channels=48, stride=2),
            BlazeBlock(in_channels=48, out_channels=48),
            BlazeBlock(in_channels=48, out_channels=48),
        )

        self.doubleBlazeBlock = nn.Sequential(
            DoubleBlazeBlock(in_channels=48, out_channels=96, mid_channels=24, stride=2),
            DoubleBlazeBlock(in_channels=96, out_channels=96, mid_channels=24),
            DoubleBlazeBlock(in_channels=96, out_channels=96, mid_channels=24),
            DoubleBlazeBlock(in_channels=96, out_channels=96, mid_channels=24, stride=2),
            DoubleBlazeBlock(in_channels=96, out_channels=96, mid_channels=24),
            DoubleBlazeBlock(in_channels=96, out_channels=96, mid_channels=24),
        )
        self.initialize()

    def initialize(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def forward(self, x):
        x = self.firstconv(x)
        x = self.blazeBlock(x)
        x = self.doubleBlazeBlock(x)
        return x

if __name__=='__main__':
    model = BlazeFace()
    print(model)

    input = torch.randn(1, 3, 128, 128)
    out = model(input)
    print(out.shape)