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'''
Adapted from https://github.com/cavalleria/cavaface.pytorch/blob/master/backbone/mobilefacenet.py
Original author cavalleria
'''
import torch.nn as nn
from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Sequential, Module
import torch
class Flatten(Module):
def forward(self, x):
return x.view(x.size(0), -1)
class ConvBlock(Module):
def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
super(ConvBlock, self).__init__()
self.layers = nn.Sequential(
Conv2d(in_c, out_c, kernel, groups=groups, stride=stride, padding=padding, bias=False),
BatchNorm2d(num_features=out_c),
PReLU(num_parameters=out_c)
)
def forward(self, x):
return self.layers(x)
class LinearBlock(Module):
def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
super(LinearBlock, self).__init__()
self.layers = nn.Sequential(
Conv2d(in_c, out_c, kernel, stride, padding, groups=groups, bias=False),
BatchNorm2d(num_features=out_c)
)
def forward(self, x):
return self.layers(x)
class DepthWise(Module):
def __init__(self, in_c, out_c, residual=False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1):
super(DepthWise, self).__init__()
self.residual = residual
self.layers = nn.Sequential(
ConvBlock(in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1)),
ConvBlock(groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride),
LinearBlock(groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1))
)
def forward(self, x):
short_cut = None
if self.residual:
short_cut = x
x = self.layers(x)
if self.residual:
output = short_cut + x
else:
output = x
return output
class Residual(Module):
def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1), padding=(1, 1)):
super(Residual, self).__init__()
modules = []
for _ in range(num_block):
modules.append(DepthWise(c, c, True, kernel, stride, padding, groups))
self.layers = Sequential(*modules)
def forward(self, x):
return self.layers(x)
class GDC(Module):
def __init__(self, embedding_size):
super(GDC, self).__init__()
self.layers = nn.Sequential(
LinearBlock(512, 512, groups=512, kernel=(7, 7), stride=(1, 1), padding=(0, 0)),
Flatten(),
Linear(512, embedding_size, bias=False),
BatchNorm1d(embedding_size))
def forward(self, x):
return self.layers(x)
class MobileFaceNet(Module):
def __init__(self, fp16=False, num_features=512):
super(MobileFaceNet, self).__init__()
scale = 2
self.fp16 = fp16
self.layers = nn.Sequential(
ConvBlock(3, 64 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1)),
ConvBlock(64 * scale, 64 * scale, kernel=(3, 3), stride=(1, 1), padding=(1, 1), groups=64),
DepthWise(64 * scale, 64 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=128),
Residual(64 * scale, num_block=4, groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1)),
DepthWise(64 * scale, 128 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=256),
Residual(128 * scale, num_block=6, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)),
DepthWise(128 * scale, 128 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=512),
Residual(128 * scale, num_block=2, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)),
)
self.conv_sep = ConvBlock(128 * scale, 512, kernel=(1, 1), stride=(1, 1), padding=(0, 0))
self.features = GDC(num_features)
self._initialize_weights()
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
with torch.cuda.amp.autocast(self.fp16):
x = self.layers(x)
x = self.conv_sep(x.float() if self.fp16 else x)
x = self.features(x)
return x
def get_mbf(fp16, num_features):
return MobileFaceNet(fp16, num_features) |