def p_net():
return Sequential(Conv2d((3, 3),
10,
strides=1,
auto_pad=False,
use_bias=True,
name='conv1'),
PRelu(num_parameters=1),
MaxPool2d((2, 2), strides=2, auto_pad=False),
Conv2d((3, 3),
16,
strides=1,
auto_pad=False,
use_bias=True,
name='conv2'),
PRelu(num_parameters=1),
Conv2d((3, 3),
32,
strides=1,
auto_pad=False,
use_bias=True,
name='conv3'),
PRelu(num_parameters=1),
ModuleDict(
{
'conv4_1':
Conv2d((1, 1),
1,
strides=1,
auto_pad=False,
use_bias=True,
activation='sigmoid',
name='conv4_1'),
'conv4_2':
Conv2d((1, 1),
4,
strides=1,
auto_pad=False,
use_bias=True,
name='conv4_2'),
'conv4_3':
Conv2d((1, 1),
10,
strides=1,
auto_pad=False,
use_bias=True,
name='conv4_3')
},
is_multicasting=True),
name='pnet')
def o_net():
return Sequential(Conv2d((3, 3),
32,
strides=1,
auto_pad=False,
use_bias=True,
name='conv1'),
PRelu(num_parameters=1),
MaxPool2d((3, 3), strides=2, auto_pad=False),
Conv2d((3, 3),
64,
strides=1,
auto_pad=False,
use_bias=True,
name='conv2'),
PRelu(num_parameters=1),
MaxPool2d((3, 3), strides=2, auto_pad=False),
Conv2d((3, 3),
64,
strides=1,
auto_pad=False,
use_bias=True,
name='conv3'),
PRelu(num_parameters=1),
MaxPool2d((2, 2), strides=2, auto_pad=False),
Conv2d((2, 2),
128,
strides=1,
auto_pad=False,
use_bias=True,
name='conv4'),
PRelu(num_parameters=1),
Flatten(),
Dense(256, activation=None, use_bias=True, name='conv5'),
PRelu(num_parameters=1),
Combine(
Dense(1,
activation='sigmoid',
use_bias=True,
name='conv6_1'),
Dense(4,
activation=None,
use_bias=True,
name='conv6_2'),
Dense(10,
activation=None,
use_bias=True,
name='conv6_3')),
name='onet')
def BottleNeck_IR_SE(num_filters, strides, keep_filter=True):
blocks = OrderedDict()
blocks['res_layer'] = Sequential(BatchNorm2d(),
Conv2d_Block(
(3, 3),
num_filters=num_filters,
strides=1,
auto_pad=True,
use_bias=False,
activation=PRelu(num_filters)),
Conv2d_Block((3, 3),
num_filters,
strides=strides,
use_bias=False,
activation=None,
normalization='batch'),
SqueezeExcite(num_filters // 16,
num_filters),
name='res_layer')
if keep_filter:
blocks['shortcut_layer'] = MaxPool2d(1,
strides=strides,
name='shortcut_layer')
else:
blocks['shortcut_layer'] = Conv2d_Block((1, 1),
num_filters,
strides=strides,
use_bias=False,
activation=None,
normalization='batch',
name='shortcut_layer')
return ShortCut2d(blocks, mode='add')
def p_net():
return Sequential(Conv2d((3, 3),
10,
strides=1,
auto_pad=False,
use_bias=True,
name='conv1'),
PRelu(num_parameters=1),
MaxPool2d((2, 2), strides=2, auto_pad=False),
Conv2d((3, 3),
16,
strides=1,
auto_pad=False,
use_bias=True,
name='conv2'),
PRelu(num_parameters=1),
Conv2d((3, 3),
32,
strides=1,
auto_pad=False,
use_bias=True,
name='conv3'),
PRelu(num_parameters=1),
Combine(
Conv2d((1, 1),
1,
strides=1,
auto_pad=False,
use_bias=True,
activation='sigmoid',
name='conv4_1'),
Conv2d((1, 1),
4,
strides=1,
auto_pad=False,
use_bias=True,
name='conv4_2'),
Conv2d((1, 1),
10,
strides=1,
auto_pad=False,
use_bias=True,
name='conv4_3')),
name='pnet')
def r_net():
return Sequential(
Conv2d((3, 3),
28,
strides=1,
auto_pad=False,
use_bias=True,
name='conv1'),
PRelu(num_parameters=1),
MaxPool2d((3, 3), strides=2, auto_pad=False),
Conv2d((3, 3),
48,
strides=1,
auto_pad=False,
use_bias=True,
name='conv2'),
PRelu(num_parameters=1),
MaxPool2d((3, 3), strides=2, auto_pad=False),
Conv2d((2, 2),
64,
strides=1,
auto_pad=False,
use_bias=True,
name='conv3'),
PRelu(num_parameters=1),
Flatten(),
Dense(128, activation=None, use_bias=True, name='conv4'),
PRelu(num_parameters=1),
ModuleDict(
{
'conv5_1':
Dense(1, activation='sigmoid', use_bias=True, name='conv5_1'),
'conv5_2':
Dense(4, activation=None, use_bias=True, name='conv5_2'),
'conv5_3':
Dense(10, activation=None, use_bias=True, name='conv5_3')
},
is_multicasting=True),
name='rnet')
def SEResNet_IR(include_top=True,
num_layers=50,
Bottleneck=BottleNeck_IR_SE,
drop_ratio=0.4,
feature_dim=128,
input_shape=(3, 112, 112)):
blocks = OrderedDict()
blocks['input_layer'] = Conv2d_Block((3, 3),
64,
strides=1,
auto_pad=True,
use_bias=False,
activation=PRelu(64),
normalization='batch',
name='input_layer')
blocks['body'] = Sequential(
get_block(Bottleneck, out_channel=64, num_units=3, keep_filter=True) +
get_block(Bottleneck, out_channel=128, num_units=4, keep_filter=False)
+ get_block(
Bottleneck, out_channel=256, num_units=14, keep_filter=False) +
get_block(Bottleneck, out_channel=512, num_units=3, keep_filter=False))
blocks['output_layer'] = Sequential(BatchNorm2d(),
Dropout(drop_ratio),
Flatten(),
Dense(feature_dim),
BatchNorm(),
name='output_layer')
facenet = Sequential(blocks).to(_device)
facenet.name = camel2snake('SEResNet_IR')
model = FaceRecognitionModel(input_shape=input_shape, output=facenet)
model.preprocess_flow = [
Resize((input_shape[1], input_shape[2]), keep_aspect=True),
Normalize(0, 255),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
#model.summary()
return model