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bob
bob.learn.tensorflow
Commits
2851e388
Commit
2851e388
authored
Oct 27, 2016
by
Tiago de Freitas Pereira
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Implemented a simple version of Facenet
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ac089c6c
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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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