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facenet.py
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import torch
import torch.nn as nn
from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, ReLU, Sigmoid, Dropout, MaxPool2d, \
AdaptiveAvgPool2d, Sequential, Module
from collections import namedtuple
# Support: ['IR_50', 'IR_101', 'IR_152', 'IR_SE_50', 'IR_SE_101', 'IR_SE_152']
class FaceNet(nn.Module):
def __init__(self, num_classes = 1000):
super(FaceNet, self).__init__()
self.feature = IR_50_112((112, 112))
self.feat_dim = 512
self.num_classes = num_classes
self.fc_layer = nn.Sequential(
nn.Linear(self.feat_dim, self.num_classes),
nn.Softmax(dim = 1))
def forward(self, x):
feat = self.feature(x)
feat = feat.view(feat.size(0), -1)
out = self.fc_layer(feat)
__, iden = torch.max(out, dim = 1)
iden = iden.view(-1, 1)
return feat, out, iden
class FaceNet64(nn.Module):
def __init__(self, num_classes = 1000):
super(FaceNet64, self).__init__()
self.feature = IR_50_64((64, 64))
self.feat_dim = 512
self.num_classes = num_classes
self.output_layer = nn.Sequential(nn.BatchNorm2d(512),
nn.Dropout(),
Flatten(),
nn.Linear(512 * 4 * 4, 512),
nn.BatchNorm1d(512))
self.fc_layer = nn.Sequential(
nn.Linear(self.feat_dim, self.num_classes),
nn.Softmax(dim = 1))
def forward(self, x):
feat = self.feature(x)
feat = self.output_layer(feat)
feat = feat.view(feat.size(0), -1)
out = self.fc_layer(feat)
__, iden = torch.max(out, dim = 1)
iden = iden.view(-1, 1)
return feat, out, iden
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)
nn.init.xavier_uniform_(self.fc1.weight.data)
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 Backbone64(Module):
def __init__(self, input_size, num_layers, mode='ir'):
super(Backbone64, self).__init__()
assert input_size[0] in [64], "input_size should be [112, 112] or [224, 224]"
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(),
Flatten(),
Linear(512 * 14 * 14, 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)
self._initialize_weights()
def forward(self, x):
x = self.input_layer(x)
x = self.body(x)
return x
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight.data)
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.BatchNorm1d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight.data)
if m.bias is not None:
m.bias.data.zero_()
class Backbone112(Module):
def __init__(self, input_size, num_layers, mode='ir'):
super(Backbone112, self).__init__()
assert input_size[0] in [112], "input_size should be [112, 112] or [224, 224]"
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))
if input_size[0] == 112:
self.output_layer = Sequential(BatchNorm2d(512),
Dropout(),
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)
self._initialize_weights()
def forward(self, x):
x = self.input_layer(x)
x = self.body(x)
x = self.output_layer(x)
return x
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight.data)
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.BatchNorm1d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight.data)
if m.bias is not None:
m.bias.data.zero_()
def IR_50_64(input_size):
"""Constructs a ir-50 model.
"""
model = Backbone64(input_size, 50, 'ir')
return model
def IR_50_112(input_size):
"""Constructs a ir-50 model.
"""
model = Backbone112(input_size, 50, 'ir')
return model
def IR_101(input_size):
"""Constructs a ir-101 model.
"""
model = Backbone(input_size, 100, 'ir')
return model
def IR_152_64(input_size):
"""Constructs a ir-152 model.
"""
model = Backbone64(input_size, 152, 'ir')
return model
def IR_152_112(input_size):
"""Constructs a ir-152 model.
"""
model = Backbone112(input_size, 152, 'ir')
return model
def IR_SE_50(input_size):
"""Constructs a ir_se-50 model.
"""
model = Backbone(input_size, 50, 'ir_se')
return model
def IR_SE_101(input_size):
"""Constructs a ir_se-101 model.
"""
model = Backbone(input_size, 100, 'ir_se')
return model
def IR_SE_152(input_size):
"""Constructs a ir_se-152 model.
"""
model = Backbone(input_size, 152, 'ir_se')
return model