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from torch.nn import (
Linear,
Conv2d,
BatchNorm1d,
BatchNorm2d,
PReLU,
ReLU,
Sigmoid,
Dropout,
MaxPool2d,
AdaptiveAvgPool2d,
Sequential,
Module,
Parameter,
)
import torch
from collections import namedtuple
import math
################################## Original Arcface Model #############################################################
class Flatten(Module):
def forward(self, input):
return input.view(input.size(0), -1)
def l2_norm(input, axis=1):
norm = torch.norm(input, 2, axis, True)
output = torch.div(input, norm)
return output
class SEModule(Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.avg_pool = AdaptiveAvgPool2d(1)
self.fc1 = Conv2d(
channels, channels // reduction, kernel_size=1, padding=0, bias=False
)
self.relu = ReLU(inplace=True)
self.fc2 = Conv2d(
channels // reduction, channels, kernel_size=1, padding=0, bias=False
)
self.sigmoid = Sigmoid()
def forward(self, x):
module_input = x
x = self.avg_pool(x)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.sigmoid(x)
return module_input * x
class bottleneck_IR(Module):
def __init__(self, in_channel, depth, stride):
super(bottleneck_IR, self).__init__()
if in_channel == depth:
self.shortcut_layer = MaxPool2d(1, stride)
else:
self.shortcut_layer = Sequential(
Conv2d(in_channel, depth, (1, 1), stride, bias=False),
BatchNorm2d(depth),
)
self.res_layer = Sequential(
BatchNorm2d(in_channel),
Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
PReLU(depth),
Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
BatchNorm2d(depth),
)
def forward(self, x):
shortcut = self.shortcut_layer(x)
res = self.res_layer(x)
return res + shortcut
class bottleneck_IR_SE(Module):
def __init__(self, in_channel, depth, stride):
super(bottleneck_IR_SE, self).__init__()
if in_channel == depth:
self.shortcut_layer = MaxPool2d(1, stride)
else:
self.shortcut_layer = Sequential(
Conv2d(in_channel, depth, (1, 1), stride, bias=False),
BatchNorm2d(depth),
)
self.res_layer = Sequential(
BatchNorm2d(in_channel),
Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False),
PReLU(depth),
Conv2d(depth, depth, (3, 3), stride, 1, bias=False),
BatchNorm2d(depth),
SEModule(depth, 16),
)
def forward(self, x):
shortcut = self.shortcut_layer(x)
res = self.res_layer(x)
return res + shortcut
class Bottleneck(namedtuple("Block", ["in_channel", "depth", "stride"])):
"""A named tuple describing a ResNet block."""
def get_block(in_channel, depth, num_units, stride=2):
return [Bottleneck(in_channel, depth, stride)] + [
Bottleneck(depth, depth, 1) for i in range(num_units - 1)
]
def get_blocks(num_layers):
if num_layers == 50:
blocks = [
get_block(in_channel=64, depth=64, num_units=3),
get_block(in_channel=64, depth=128, num_units=4),
get_block(in_channel=128, depth=256, num_units=14),
get_block(in_channel=256, depth=512, num_units=3),
]
elif num_layers == 100:
blocks = [
get_block(in_channel=64, depth=64, num_units=3),
get_block(in_channel=64, depth=128, num_units=13),
get_block(in_channel=128, depth=256, num_units=30),
get_block(in_channel=256, depth=512, num_units=3),
]
elif num_layers == 152:
blocks = [
get_block(in_channel=64, depth=64, num_units=3),
get_block(in_channel=64, depth=128, num_units=8),
get_block(in_channel=128, depth=256, num_units=36),
get_block(in_channel=256, depth=512, num_units=3),
]
return blocks
class Backbone(Module):
def __init__(self, num_layers, drop_ratio, mode="ir"):
super(Backbone, self).__init__()
assert num_layers in [50, 100, 152], "num_layers should be 50,100, or 152"
assert mode in ["ir", "ir_se"], "mode should be ir or ir_se"
blocks = get_blocks(num_layers)
if mode == "ir":
unit_module = bottleneck_IR
elif mode == "ir_se":
unit_module = bottleneck_IR_SE
self.input_layer = Sequential(
Conv2d(3, 64, (3, 3), 1, 1, bias=False), BatchNorm2d(64), PReLU(64)
)
self.output_layer = Sequential(
BatchNorm2d(512),
Dropout(drop_ratio),
Flatten(),
Linear(512 * 7 * 7, 512),
BatchNorm1d(512),
)
modules = []
for block in blocks:
for bottleneck in block:
modules.append(
unit_module(
bottleneck.in_channel, bottleneck.depth, bottleneck.stride
)
)
self.body = Sequential(*modules)
def forward(self, x):
x = self.input_layer(x)
x = self.body(x)
x = self.output_layer(x)
return l2_norm(x)
################################## MobileFaceNet #############################################################
class Conv_block(Module):
def __init__(
self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1
):
super(Conv_block, self).__init__()
self.conv = Conv2d(
in_c,
out_channels=out_c,
kernel_size=kernel,
groups=groups,
stride=stride,
padding=padding,
bias=False,
)
self.bn = BatchNorm2d(out_c)
self.prelu = PReLU(out_c)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.prelu(x)
return x
class Linear_block(Module):
def __init__(
self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1
):
super(Linear_block, self).__init__()
self.conv = Conv2d(
in_c,
out_channels=out_c,
kernel_size=kernel,
groups=groups,
stride=stride,
padding=padding,
bias=False,
)
self.bn = BatchNorm2d(out_c)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return x
class Depth_Wise(Module):
def __init__(
self,
in_c,
out_c,
residual=False,
kernel=(3, 3),
stride=(2, 2),
padding=(1, 1),
groups=1,
):
super(Depth_Wise, self).__init__()
self.conv = Conv_block(
in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1)
)
self.conv_dw = Conv_block(
groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride
)
self.project = Linear_block(
groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1)
)
self.residual = residual
def forward(self, x):
if self.residual:
short_cut = x
x = self.conv(x)
x = self.conv_dw(x)
x = self.project(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(
Depth_Wise(
c,
c,
residual=True,
kernel=kernel,
padding=padding,
stride=stride,
groups=groups,
)
)
self.model = Sequential(*modules)
def forward(self, x):
return self.model(x)
class MobileFaceNet(Module):
def __init__(self, embedding_size):
super(MobileFaceNet, self).__init__()
self.conv1 = Conv_block(3, 64, kernel=(3, 3), stride=(2, 2), padding=(1, 1))
self.conv2_dw = Conv_block(
64, 64, kernel=(3, 3), stride=(1, 1), padding=(1, 1), groups=64
)
self.conv_23 = Depth_Wise(
64, 64, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=128
)
self.conv_3 = Residual(
64, num_block=4, groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1)
)
self.conv_34 = Depth_Wise(
64, 128, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=256
)
self.conv_4 = Residual(
128, num_block=6, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)
)
self.conv_45 = Depth_Wise(
128, 128, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=512
)
self.conv_5 = Residual(
128, num_block=2, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)
)
self.conv_6_sep = Conv_block(
128, 512, kernel=(1, 1), stride=(1, 1), padding=(0, 0)
)
self.conv_6_dw = Linear_block(
512, 512, groups=512, kernel=(7, 7), stride=(1, 1), padding=(0, 0)
)
self.conv_6_flatten = Flatten()
self.linear = Linear(512, embedding_size, bias=False)
self.bn = BatchNorm1d(embedding_size)
def forward(self, x):
out = self.conv1(x)
out = self.conv2_dw(out)
out = self.conv_23(out)
out = self.conv_3(out)
out = self.conv_34(out)
out = self.conv_4(out)
out = self.conv_45(out)
out = self.conv_5(out)
out = self.conv_6_sep(out)
out = self.conv_6_dw(out)
out = self.conv_6_flatten(out)
out = self.linear(out)
out = self.bn(out)
return l2_norm(out)
################################## Arcface head #############################################################
class Arcface(Module):
# implementation of additive margin softmax loss in https://arxiv.org/abs/1801.05599
def __init__(self, embedding_size=512, classnum=51332, s=64.0, m=0.5):
super(Arcface, self).__init__()
self.classnum = classnum
self.kernel = Parameter(torch.Tensor(embedding_size, classnum))
# initial kernel
self.kernel.data.uniform_(-1, 1).renorm_(2, 1, 1e-5).mul_(1e5)
self.m = m # the margin value, default is 0.5
self.s = s # scalar value default is 64, see normface https://arxiv.org/abs/1704.06369
self.cos_m = math.cos(m)
self.sin_m = math.sin(m)
self.mm = self.sin_m * m # issue 1
self.threshold = math.cos(math.pi - m)
def forward(self, embbedings, label):
# weights norm
nB = len(embbedings)
kernel_norm = l2_norm(self.kernel, axis=0)
# cos(theta+m)
cos_theta = torch.mm(embbedings, kernel_norm)
# output = torch.mm(embbedings,kernel_norm)
cos_theta = cos_theta.clamp(-1, 1) # for numerical stability
cos_theta_2 = torch.pow(cos_theta, 2)
sin_theta_2 = 1 - cos_theta_2
sin_theta = torch.sqrt(sin_theta_2)
cos_theta_m = cos_theta * self.cos_m - sin_theta * self.sin_m
# this condition controls the theta+m should in range [0, pi]
# 0<=theta+m<=pi
# -m<=theta<=pi-m
cond_v = cos_theta - self.threshold
cond_mask = cond_v <= 0
keep_val = cos_theta - self.mm # when theta not in [0,pi], use cosface instead
cos_theta_m[cond_mask] = keep_val[cond_mask]
output = (
cos_theta * 1.0
) # a little bit hacky way to prevent in_place operation on cos_theta
idx_ = torch.arange(0, nB, dtype=torch.long)
output[idx_, label] = cos_theta_m[idx_, label]
output *= (
self.s
) # scale up in order to make softmax work, first introduced in normface
return output
################################## Cosface head #############################################################
class Am_softmax(Module):
# implementation of additive margin softmax loss in https://arxiv.org/abs/1801.05599
def __init__(self, embedding_size=512, classnum=51332):
super(Am_softmax, self).__init__()
self.classnum = classnum
self.kernel = Parameter(torch.Tensor(embedding_size, classnum))
# initial kernel
self.kernel.data.uniform_(-1, 1).renorm_(2, 1, 1e-5).mul_(1e5)
self.m = 0.35 # additive margin recommended by the paper
self.s = 30.0 # see normface https://arxiv.org/abs/1704.06369
def forward(self, embbedings, label):
kernel_norm = l2_norm(self.kernel, axis=0)
cos_theta = torch.mm(embbedings, kernel_norm)
cos_theta = cos_theta.clamp(-1, 1) # for numerical stability
phi = cos_theta - self.m
label = label.view(-1, 1) # size=(B,1)
index = cos_theta.data * 0.0 # size=(B,Classnum)
index.scatter_(1, label.data.view(-1, 1), 1)
index = index.byte()
output = cos_theta * 1.0
output[index] = phi[index] # only change the correct predicted output
output *= (
self.s
) # scale up in order to make softmax work, first introduced in normface
return output
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