VarGFaceNet
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import torch
import torch.nn as nn
import torchvision
class SqueezeAndExcite(nn.Module):
def __init__(self, in_channels, out_channels, divide=4):
super(SqueezeAndExcite, self).__init__()
mid_channels = in_channels // divide
self.pool = nn.AdaptiveAvgPool2d(1)
self.SEblock = nn.Sequential(
nn.Linear(in_features=in_channels, out_features=mid_channels),
nn.ReLU6(inplace=True),
nn.Linear(in_features=mid_channels, out_features=out_channels),
nn.ReLU6(inplace=True),
)
def forward(self, x):
b, c, h, w = x.size()
out = self.pool(x)
out = out.view(b, -1)
out = self.SEblock(out)
out = out.view(b, c, 1, 1)
return out * x
def Conv1x1BNReLU(in_channels,out_channels):
return nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU6(inplace=True)
)
def Conv3x3BNReLU(in_channels,out_channels,stride):
return nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=stride, padding=1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU6(inplace=True)
)
def VarGConv(in_channels,out_channels,kernel_size,stride,S):
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding=kernel_size // 2, groups=in_channels // S,
bias=False),
nn.BatchNorm2d(out_channels),
nn.PReLU(),
)
def VarGPointConv(in_channels, out_channels,stride,S,isRelu):
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, 1, stride, padding=0, groups=in_channels // S,
bias=False),
nn.BatchNorm2d(out_channels),
nn.PReLU() if isRelu else nn.Sequential(),
)
class VarGBlock_S1(nn.Module):
def __init__(self, in_plances,kernel_size, stride=1, S=8):
super(VarGBlock_S1, self).__init__()
plances = 2 * in_plances
self.varGConv1 = VarGConv(in_plances, plances, kernel_size, stride, S)
self.varGPointConv1 = VarGPointConv(plances, in_plances, stride, S, isRelu=True)
self.varGConv2 = VarGConv(in_plances, plances, kernel_size, stride, S)
self.varGPointConv2 = VarGPointConv(plances, in_plances, stride, S, isRelu=False)
self.se = SqueezeAndExcite(in_plances,in_plances)
self.prelu = nn.PReLU()
def forward(self, x):
out = x
x = self.varGPointConv1(self.varGConv1(x))
x = self.varGPointConv2(self.varGConv2(x))
x = self.se(x)
out += x
return self.prelu(out)
class VarGBlock_S2(nn.Module):
def __init__(self, in_plances,kernel_size, stride=2, S=8):
super(VarGBlock_S2, self).__init__()
plances = 2 * in_plances
self.varGConvBlock_branch1 = nn.Sequential(
VarGConv(in_plances, plances, kernel_size, stride, S),
VarGPointConv(plances, plances, 1, S, isRelu=True),
)
self.varGConvBlock_branch2 = nn.Sequential(
VarGConv(in_plances, plances, kernel_size, stride, S),
VarGPointConv(plances, plances, 1, S, isRelu=True),
)
self.varGConvBlock_3 = nn.Sequential(
VarGConv(plances, plances*2, kernel_size, 1, S),
VarGPointConv(plances*2, plances, 1, S, isRelu=False),
)
self.shortcut = nn.Sequential(
VarGConv(in_plances, plances, kernel_size, stride, S),
VarGPointConv(plances, plances, 1, S, isRelu=False),
)
self.prelu = nn.PReLU()
def forward(self, x):
out = self.shortcut(x)
x1 = x2 = x
x1= self.varGConvBlock_branch1(x1)
x2 = self.varGConvBlock_branch2(x2)
x_new = x1 + x2
x_new = self.varGConvBlock_3(x_new)
out += x_new
return self.prelu(out)
class HeadBlock(nn.Module):
def __init__(self, in_plances, kernel_size, S=8):
super(HeadBlock, self).__init__()
self.varGConvBlock = nn.Sequential(
VarGConv(in_plances, in_plances, kernel_size, 2, S),
VarGPointConv(in_plances, in_plances, 1, S, isRelu=True),
VarGConv(in_plances, in_plances, kernel_size, 1, S),
VarGPointConv(in_plances, in_plances, 1, S, isRelu=False),
)
self.shortcut = nn.Sequential(
VarGConv(in_plances, in_plances, kernel_size, 2, S),
VarGPointConv(in_plances, in_plances, 1, S, isRelu=False),
)
def forward(self, x):
out = self.shortcut(x)
x = self.varGConvBlock(x)
out += x
return out
class TailEmbedding(nn.Module):
def __init__(self, in_plances, plances=512, S=8):
super(TailEmbedding, self).__init__()
self.embedding = nn.Sequential(
Conv1x1BNReLU(in_plances, 1024),
nn.Conv2d(1024, 1024, 7, 1, padding=0, groups=1024 // S,
bias=False),
nn.Conv2d(1024, 512, 1, 1, padding=0, groups=512, bias=False),
)
self.fc = nn.Linear(in_features=512,out_features=plances)
def forward(self, x):
x = self.embedding(x)
x = x.view(x.size(0),-1)
out = self.fc(x)
return out
class VarGFaceNet(nn.Module):
def __init__(self, num_classes=512):
super(VarGFaceNet, self).__init__()
S = 8
self.conv1 = Conv3x3BNReLU(3, 40, 1)
self.head = HeadBlock(40,3)
self.stage2 = nn.Sequential(
VarGBlock_S2(40,3,2),
VarGBlock_S1(80, 3, 1),
VarGBlock_S1(80, 3, 1),
)
self.stage3 = nn.Sequential(
VarGBlock_S2(80, 3, 2),
VarGBlock_S1(160, 3, 1),
VarGBlock_S1(160, 3, 1),
VarGBlock_S1(160, 3, 1),
VarGBlock_S1(160, 3, 1),
VarGBlock_S1(160, 3, 1),
VarGBlock_S1(160, 3, 1),
)
self.stage4 = nn.Sequential(
VarGBlock_S2(160, 3, 2),
VarGBlock_S1(320, 3, 1),
VarGBlock_S1(320, 3, 1),
VarGBlock_S1(320, 3, 1),
)
self.tail = TailEmbedding(320,num_classes)
def init_params(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.Linear) or isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, x):
x = self.conv1(x)
x = self.head(x)
x = self.stage2(x)
x = self.stage3(x)
x = self.stage4(x)
out= self.tail(x)
return out
if __name__ == '__main__':
model = VarGFaceNet()
print(model)
input = torch.randn(1, 3, 112, 112)
out = model(input)
print(out.shape)
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