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bob
bob.learn.tensorflow
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2851e388
Commit
2851e388
authored
8 years ago
by
Tiago de Freitas Pereira
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Implemented a simple version of Facenet
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bob/learn/tensorflow/network/FaceNetSimple.py
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#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
# @date: Wed 11 May 2016 09:39:36 CEST
import
tensorflow
as
tf
from
.SequenceNetwork
import
SequenceNetwork
from
..layers
import
Conv2D
,
FullyConnected
,
MaxPooling
from
bob.learn.tensorflow.initialization
import
Xavier
from
bob.learn.tensorflow.initialization
import
Constant
class
FaceNetSimple
(
SequenceNetwork
):
"""
Class that creates the The FaceNet architecture used in
Schroff, Florian, Dmitry Kalenichenko, and James Philbin.
"
Facenet: A unified embedding for face recognition and clustering.
"
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
This CNN DOES NOT HAVE THE 2d convolution of 1x1 described in:
Ahuja, Ravindra K., Thomas L. Magnanti, and James B. Orlin.
"
Network flows: theory, algorithms, and applications.
"
(1993).
"""
def
__init__
(
self
,
### First macro layer
conv1_kernel_size
=
7
,
conv1_output
=
64
,
conv1_stride
=
[
1
,
2
,
2
,
1
],
#[1, 2, 2, 1],
pool1_shape
=
[
1
,
3
,
3
,
1
],
pool1_stride
=
[
1
,
2
,
2
,
1
],
### Second macro layer
conv2_kernel_size
=
3
,
conv2_output
=
192
,
pool2_shape
=
[
1
,
3
,
3
,
1
],
pool2_stride
=
[
1
,
2
,
2
,
1
],
### Third macro layer
conv3_kernel_size
=
3
,
conv3_output
=
192
,
pool3_shape
=
[
1
,
3
,
3
,
1
],
pool3_stride
=
[
1
,
2
,
2
,
1
],
### Forth macro layer
conv4_kernel_size
=
3
,
conv4_output
=
384
,
### Fifth macro layer
conv5_kernel_size
=
3
,
conv5_output
=
256
,
### Sixth macro layer
conv6_kernel_size
=
3
,
conv6_output
=
256
,
pool6_shape
=
[
1
,
3
,
3
,
1
],
pool6_stride
=
[
1
,
2
,
2
,
1
],
fc1_output
=
256
,
fc2_output
=
128
,
fc7128_output
=
128
,
default_feature_layer
=
"
fc7128
"
,
seed
=
10
,
use_gpu
=
False
):
"""
Create all the necessary variables for this CNN
**Parameters**
conv1_kernel_size=5,
conv1_output=32,
conv2_kernel_size=5,
conv2_output=64,
fc1_output=400,
n_classes=10
seed = 10
"""
super
(
FaceNetSimple
,
self
).
__init__
(
default_feature_layer
=
default_feature_layer
,
use_gpu
=
use_gpu
)
self
.
add
(
Conv2D
(
name
=
"
conv1
"
,
kernel_size
=
conv1_kernel_size
,
filters
=
conv1_output
,
activation
=
tf
.
nn
.
relu
,
stride
=
conv1_stride
,
weights_initialization
=
Xavier
(
seed
=
seed
,
use_gpu
=
self
.
use_gpu
),
bias_initialization
=
Constant
(
use_gpu
=
self
.
use_gpu
)
))
self
.
add
(
MaxPooling
(
name
=
"
pooling1
"
,
shape
=
pool1_shape
,
strides
=
pool1_stride
))
##########
self
.
add
(
Conv2D
(
name
=
"
conv2
"
,
kernel_size
=
conv2_kernel_size
,
filters
=
conv2_output
,
activation
=
tf
.
nn
.
relu
,
weights_initialization
=
Xavier
(
seed
=
seed
,
use_gpu
=
self
.
use_gpu
),
bias_initialization
=
Constant
(
use_gpu
=
self
.
use_gpu
)
))
self
.
add
(
MaxPooling
(
name
=
"
pooling2
"
,
shape
=
pool2_shape
,
strides
=
pool2_stride
))
##########
self
.
add
(
Conv2D
(
name
=
"
conv3
"
,
kernel_size
=
conv3_kernel_size
,
filters
=
conv3_output
,
activation
=
tf
.
nn
.
relu
,
weights_initialization
=
Xavier
(
seed
=
seed
,
use_gpu
=
self
.
use_gpu
),
bias_initialization
=
Constant
(
use_gpu
=
self
.
use_gpu
)
))
self
.
add
(
MaxPooling
(
name
=
"
pooling3
"
,
shape
=
pool3_shape
,
strides
=
pool3_stride
))
##########
self
.
add
(
Conv2D
(
name
=
"
conv4
"
,
kernel_size
=
conv4_kernel_size
,
filters
=
conv4_output
,
activation
=
tf
.
nn
.
relu
,
weights_initialization
=
Xavier
(
seed
=
seed
,
use_gpu
=
self
.
use_gpu
),
bias_initialization
=
Constant
(
use_gpu
=
self
.
use_gpu
)
))
##########
self
.
add
(
Conv2D
(
name
=
"
conv5
"
,
kernel_size
=
conv5_kernel_size
,
filters
=
conv5_output
,
activation
=
tf
.
nn
.
relu
,
weights_initialization
=
Xavier
(
seed
=
seed
,
use_gpu
=
self
.
use_gpu
),
bias_initialization
=
Constant
(
use_gpu
=
self
.
use_gpu
)
))
##########
self
.
add
(
Conv2D
(
name
=
"
conv6
"
,
kernel_size
=
conv6_kernel_size
,
filters
=
conv6_output
,
activation
=
tf
.
nn
.
relu
,
weights_initialization
=
Xavier
(
seed
=
seed
,
use_gpu
=
self
.
use_gpu
),
bias_initialization
=
Constant
(
use_gpu
=
self
.
use_gpu
)
))
self
.
add
(
MaxPooling
(
name
=
"
pooling6
"
,
shape
=
pool6_shape
,
strides
=
pool6_stride
))
self
.
add
(
FullyConnected
(
name
=
"
fc1
"
,
output_dim
=
fc1_output
,
activation
=
tf
.
nn
.
relu
,
weights_initialization
=
Xavier
(
seed
=
seed
,
use_gpu
=
self
.
use_gpu
),
bias_initialization
=
Constant
(
use_gpu
=
self
.
use_gpu
)
))
self
.
add
(
FullyConnected
(
name
=
"
fc2
"
,
output_dim
=
fc2_output
,
activation
=
tf
.
nn
.
relu
,
weights_initialization
=
Xavier
(
seed
=
seed
,
use_gpu
=
self
.
use_gpu
),
bias_initialization
=
Constant
(
use_gpu
=
self
.
use_gpu
)
))
self
.
add
(
FullyConnected
(
name
=
"
fc7128
"
,
output_dim
=
fc7128_output
,
activation
=
None
,
weights_initialization
=
Xavier
(
seed
=
seed
,
use_gpu
=
self
.
use_gpu
),
bias_initialization
=
Constant
(
use_gpu
=
self
.
use_gpu
)
))
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