You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
239 lines
8.7 KiB
Python
239 lines
8.7 KiB
Python
import torch
|
|
from collections import namedtuple
|
|
from torch.nn import (AdaptiveAvgPool2d, BatchNorm1d, BatchNorm2d, Conv2d, Dropout, Linear, MaxPool2d, Module, PReLU,
|
|
ReLU, Sequential, Sigmoid)
|
|
|
|
# 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)
|