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bob
bob.learn.tensorflow
Commits
3287c426
Commit
3287c426
authored
Sep 19, 2017
by
Tiago de Freitas Pereira
Browse files
New networks
parent
be94b635
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2
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bob/learn/tensorflow/network/InceptionResnetV2.py
0 → 100644
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3287c426
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Contains the definition of the Inception Resnet V2 architecture.
As described in http://arxiv.org/abs/1602.07261.
Inception-v4, Inception-ResNet and the Impact of Residual Connections
on Learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
"""
from
__future__
import
absolute_import
from
__future__
import
division
from
__future__
import
print_function
import
tensorflow
as
tf
import
tensorflow.contrib.slim
as
slim
# Inception-Renset-A
def
block35
(
net
,
scale
=
1.0
,
activation_fn
=
tf
.
nn
.
relu
,
scope
=
None
,
reuse
=
None
):
"""Builds the 35x35 resnet block."""
with
tf
.
variable_scope
(
scope
,
'Block35'
,
[
net
],
reuse
=
reuse
):
with
tf
.
variable_scope
(
'Branch_0'
):
tower_conv
=
slim
.
conv2d
(
net
,
32
,
1
,
scope
=
'Conv2d_1x1'
)
with
tf
.
variable_scope
(
'Branch_1'
):
tower_conv1_0
=
slim
.
conv2d
(
net
,
32
,
1
,
scope
=
'Conv2d_0a_1x1'
)
tower_conv1_1
=
slim
.
conv2d
(
tower_conv1_0
,
32
,
3
,
scope
=
'Conv2d_0b_3x3'
)
with
tf
.
variable_scope
(
'Branch_2'
):
tower_conv2_0
=
slim
.
conv2d
(
net
,
32
,
1
,
scope
=
'Conv2d_0a_1x1'
)
tower_conv2_1
=
slim
.
conv2d
(
tower_conv2_0
,
48
,
3
,
scope
=
'Conv2d_0b_3x3'
)
tower_conv2_2
=
slim
.
conv2d
(
tower_conv2_1
,
64
,
3
,
scope
=
'Conv2d_0c_3x3'
)
mixed
=
tf
.
concat
([
tower_conv
,
tower_conv1_1
,
tower_conv2_2
],
3
)
up
=
slim
.
conv2d
(
mixed
,
net
.
get_shape
()[
3
],
1
,
normalizer_fn
=
None
,
activation_fn
=
None
,
scope
=
'Conv2d_1x1'
)
net
+=
scale
*
up
if
activation_fn
:
net
=
activation_fn
(
net
)
return
net
# Inception-Renset-B
def
block17
(
net
,
scale
=
1.0
,
activation_fn
=
tf
.
nn
.
relu
,
scope
=
None
,
reuse
=
None
):
"""Builds the 17x17 resnet block."""
with
tf
.
variable_scope
(
scope
,
'Block17'
,
[
net
],
reuse
=
reuse
):
with
tf
.
variable_scope
(
'Branch_0'
):
tower_conv
=
slim
.
conv2d
(
net
,
192
,
1
,
scope
=
'Conv2d_1x1'
)
with
tf
.
variable_scope
(
'Branch_1'
):
tower_conv1_0
=
slim
.
conv2d
(
net
,
128
,
1
,
scope
=
'Conv2d_0a_1x1'
)
tower_conv1_1
=
slim
.
conv2d
(
tower_conv1_0
,
160
,
[
1
,
7
],
scope
=
'Conv2d_0b_1x7'
)
tower_conv1_2
=
slim
.
conv2d
(
tower_conv1_1
,
192
,
[
7
,
1
],
scope
=
'Conv2d_0c_7x1'
)
mixed
=
tf
.
concat
([
tower_conv
,
tower_conv1_2
],
3
)
up
=
slim
.
conv2d
(
mixed
,
net
.
get_shape
()[
3
],
1
,
normalizer_fn
=
None
,
activation_fn
=
None
,
scope
=
'Conv2d_1x1'
)
net
+=
scale
*
up
if
activation_fn
:
net
=
activation_fn
(
net
)
return
net
# Inception-Resnet-C
def
block8
(
net
,
scale
=
1.0
,
activation_fn
=
tf
.
nn
.
relu
,
scope
=
None
,
reuse
=
None
):
"""Builds the 8x8 resnet block."""
with
tf
.
variable_scope
(
scope
,
'Block8'
,
[
net
],
reuse
=
reuse
):
with
tf
.
variable_scope
(
'Branch_0'
):
tower_conv
=
slim
.
conv2d
(
net
,
192
,
1
,
scope
=
'Conv2d_1x1'
)
with
tf
.
variable_scope
(
'Branch_1'
):
tower_conv1_0
=
slim
.
conv2d
(
net
,
192
,
1
,
scope
=
'Conv2d_0a_1x1'
)
tower_conv1_1
=
slim
.
conv2d
(
tower_conv1_0
,
224
,
[
1
,
3
],
scope
=
'Conv2d_0b_1x3'
)
tower_conv1_2
=
slim
.
conv2d
(
tower_conv1_1
,
256
,
[
3
,
1
],
scope
=
'Conv2d_0c_3x1'
)
mixed
=
tf
.
concat
([
tower_conv
,
tower_conv1_2
],
3
)
up
=
slim
.
conv2d
(
mixed
,
net
.
get_shape
()[
3
],
1
,
normalizer_fn
=
None
,
activation_fn
=
None
,
scope
=
'Conv2d_1x1'
)
net
+=
scale
*
up
if
activation_fn
:
net
=
activation_fn
(
net
)
return
net
def
inference
(
images
,
keep_probability
,
phase_train
=
True
,
bottleneck_layer_size
=
128
,
weight_decay
=
0.0
,
reuse
=
None
):
batch_norm_params
=
{
# Decay for the moving averages.
'decay'
:
0.995
,
# epsilon to prevent 0s in variance.
'epsilon'
:
0.001
,
# force in-place updates of mean and variance estimates
'updates_collections'
:
None
,
# Moving averages ends up in the trainable variables collection
'variables_collections'
:
[
tf
.
GraphKeys
.
TRAINABLE_VARIABLES
],
}
with
slim
.
arg_scope
([
slim
.
conv2d
,
slim
.
fully_connected
],
weights_initializer
=
tf
.
truncated_normal_initializer
(
stddev
=
0.1
),
weights_regularizer
=
slim
.
l2_regularizer
(
weight_decay
),
normalizer_fn
=
slim
.
batch_norm
,
normalizer_params
=
batch_norm_params
):
return
inception_resnet_v2
(
images
,
is_training
=
phase_train
,
dropout_keep_prob
=
keep_probability
,
bottleneck_layer_size
=
bottleneck_layer_size
,
reuse
=
reuse
)
def
inception_resnet_v2
(
inputs
,
is_training
=
True
,
dropout_keep_prob
=
0.8
,
bottleneck_layer_size
=
128
,
reuse
=
None
,
scope
=
'InceptionResnetV2'
):
"""Creates the Inception Resnet V2 model.
Args:
inputs: a 4-D tensor of size [batch_size, height, width, 3].
num_classes: number of predicted classes.
is_training: whether is training or not.
dropout_keep_prob: float, the fraction to keep before final layer.
reuse: whether or not the network and its variables should be reused. To be
able to reuse 'scope' must be given.
scope: Optional variable_scope.
Returns:
logits: the logits outputs of the model.
end_points: the set of end_points from the inception model.
"""
end_points
=
{}
with
tf
.
variable_scope
(
scope
,
'InceptionResnetV2'
,
[
inputs
],
reuse
=
reuse
):
with
slim
.
arg_scope
([
slim
.
batch_norm
,
slim
.
dropout
],
is_training
=
is_training
):
with
slim
.
arg_scope
([
slim
.
conv2d
,
slim
.
max_pool2d
,
slim
.
avg_pool2d
],
stride
=
1
,
padding
=
'SAME'
):
# 149 x 149 x 32
net
=
slim
.
conv2d
(
inputs
,
32
,
3
,
stride
=
2
,
padding
=
'VALID'
,
scope
=
'Conv2d_1a_3x3'
)
end_points
[
'Conv2d_1a_3x3'
]
=
net
# 147 x 147 x 32
net
=
slim
.
conv2d
(
net
,
32
,
3
,
padding
=
'VALID'
,
scope
=
'Conv2d_2a_3x3'
)
end_points
[
'Conv2d_2a_3x3'
]
=
net
# 147 x 147 x 64
net
=
slim
.
conv2d
(
net
,
64
,
3
,
scope
=
'Conv2d_2b_3x3'
)
end_points
[
'Conv2d_2b_3x3'
]
=
net
# 73 x 73 x 64
net
=
slim
.
max_pool2d
(
net
,
3
,
stride
=
2
,
padding
=
'VALID'
,
scope
=
'MaxPool_3a_3x3'
)
end_points
[
'MaxPool_3a_3x3'
]
=
net
# 73 x 73 x 80
net
=
slim
.
conv2d
(
net
,
80
,
1
,
padding
=
'VALID'
,
scope
=
'Conv2d_3b_1x1'
)
end_points
[
'Conv2d_3b_1x1'
]
=
net
# 71 x 71 x 192
net
=
slim
.
conv2d
(
net
,
192
,
3
,
padding
=
'VALID'
,
scope
=
'Conv2d_4a_3x3'
)
end_points
[
'Conv2d_4a_3x3'
]
=
net
# 35 x 35 x 192
net
=
slim
.
max_pool2d
(
net
,
3
,
stride
=
2
,
padding
=
'VALID'
,
scope
=
'MaxPool_5a_3x3'
)
end_points
[
'MaxPool_5a_3x3'
]
=
net
# 35 x 35 x 320
with
tf
.
variable_scope
(
'Mixed_5b'
):
with
tf
.
variable_scope
(
'Branch_0'
):
tower_conv
=
slim
.
conv2d
(
net
,
96
,
1
,
scope
=
'Conv2d_1x1'
)
with
tf
.
variable_scope
(
'Branch_1'
):
tower_conv1_0
=
slim
.
conv2d
(
net
,
48
,
1
,
scope
=
'Conv2d_0a_1x1'
)
tower_conv1_1
=
slim
.
conv2d
(
tower_conv1_0
,
64
,
5
,
scope
=
'Conv2d_0b_5x5'
)
with
tf
.
variable_scope
(
'Branch_2'
):
tower_conv2_0
=
slim
.
conv2d
(
net
,
64
,
1
,
scope
=
'Conv2d_0a_1x1'
)
tower_conv2_1
=
slim
.
conv2d
(
tower_conv2_0
,
96
,
3
,
scope
=
'Conv2d_0b_3x3'
)
tower_conv2_2
=
slim
.
conv2d
(
tower_conv2_1
,
96
,
3
,
scope
=
'Conv2d_0c_3x3'
)
with
tf
.
variable_scope
(
'Branch_3'
):
tower_pool
=
slim
.
avg_pool2d
(
net
,
3
,
stride
=
1
,
padding
=
'SAME'
,
scope
=
'AvgPool_0a_3x3'
)
tower_pool_1
=
slim
.
conv2d
(
tower_pool
,
64
,
1
,
scope
=
'Conv2d_0b_1x1'
)
net
=
tf
.
concat
([
tower_conv
,
tower_conv1_1
,
tower_conv2_2
,
tower_pool_1
],
3
)
end_points
[
'Mixed_5b'
]
=
net
net
=
slim
.
repeat
(
net
,
10
,
block35
,
scale
=
0.17
)
# 17 x 17 x 1024
with
tf
.
variable_scope
(
'Mixed_6a'
):
with
tf
.
variable_scope
(
'Branch_0'
):
tower_conv
=
slim
.
conv2d
(
net
,
384
,
3
,
stride
=
2
,
padding
=
'VALID'
,
scope
=
'Conv2d_1a_3x3'
)
with
tf
.
variable_scope
(
'Branch_1'
):
tower_conv1_0
=
slim
.
conv2d
(
net
,
256
,
1
,
scope
=
'Conv2d_0a_1x1'
)
tower_conv1_1
=
slim
.
conv2d
(
tower_conv1_0
,
256
,
3
,
scope
=
'Conv2d_0b_3x3'
)
tower_conv1_2
=
slim
.
conv2d
(
tower_conv1_1
,
384
,
3
,
stride
=
2
,
padding
=
'VALID'
,
scope
=
'Conv2d_1a_3x3'
)
with
tf
.
variable_scope
(
'Branch_2'
):
tower_pool
=
slim
.
max_pool2d
(
net
,
3
,
stride
=
2
,
padding
=
'VALID'
,
scope
=
'MaxPool_1a_3x3'
)
net
=
tf
.
concat
([
tower_conv
,
tower_conv1_2
,
tower_pool
],
3
)
end_points
[
'Mixed_6a'
]
=
net
net
=
slim
.
repeat
(
net
,
20
,
block17
,
scale
=
0.10
)
with
tf
.
variable_scope
(
'Mixed_7a'
):
with
tf
.
variable_scope
(
'Branch_0'
):
tower_conv
=
slim
.
conv2d
(
net
,
256
,
1
,
scope
=
'Conv2d_0a_1x1'
)
tower_conv_1
=
slim
.
conv2d
(
tower_conv
,
384
,
3
,
stride
=
2
,
padding
=
'VALID'
,
scope
=
'Conv2d_1a_3x3'
)
with
tf
.
variable_scope
(
'Branch_1'
):
tower_conv1
=
slim
.
conv2d
(
net
,
256
,
1
,
scope
=
'Conv2d_0a_1x1'
)
tower_conv1_1
=
slim
.
conv2d
(
tower_conv1
,
288
,
3
,
stride
=
2
,
padding
=
'VALID'
,
scope
=
'Conv2d_1a_3x3'
)
with
tf
.
variable_scope
(
'Branch_2'
):
tower_conv2
=
slim
.
conv2d
(
net
,
256
,
1
,
scope
=
'Conv2d_0a_1x1'
)
tower_conv2_1
=
slim
.
conv2d
(
tower_conv2
,
288
,
3
,
scope
=
'Conv2d_0b_3x3'
)
tower_conv2_2
=
slim
.
conv2d
(
tower_conv2_1
,
320
,
3
,
stride
=
2
,
padding
=
'VALID'
,
scope
=
'Conv2d_1a_3x3'
)
with
tf
.
variable_scope
(
'Branch_3'
):
tower_pool
=
slim
.
max_pool2d
(
net
,
3
,
stride
=
2
,
padding
=
'VALID'
,
scope
=
'MaxPool_1a_3x3'
)
net
=
tf
.
concat
([
tower_conv_1
,
tower_conv1_1
,
tower_conv2_2
,
tower_pool
],
3
)
end_points
[
'Mixed_7a'
]
=
net
net
=
slim
.
repeat
(
net
,
9
,
block8
,
scale
=
0.20
)
net
=
block8
(
net
,
activation_fn
=
None
)
net
=
slim
.
conv2d
(
net
,
1536
,
1
,
scope
=
'Conv2d_7b_1x1'
)
end_points
[
'Conv2d_7b_1x1'
]
=
net
with
tf
.
variable_scope
(
'Logits'
):
end_points
[
'PrePool'
]
=
net
#pylint: disable=no-member
net
=
slim
.
avg_pool2d
(
net
,
net
.
get_shape
()[
1
:
3
],
padding
=
'VALID'
,
scope
=
'AvgPool_1a_8x8'
)
net
=
slim
.
flatten
(
net
)
net
=
slim
.
dropout
(
net
,
dropout_keep_prob
,
is_training
=
is_training
,
scope
=
'Dropout'
)
end_points
[
'PreLogitsFlatten'
]
=
net
net
=
slim
.
fully_connected
(
net
,
bottleneck_layer_size
,
activation_fn
=
None
,
scope
=
'Bottleneck'
,
reuse
=
False
)
return
net
,
end_points
bob/learn/tensorflow/network/LightCNN29.py
0 → 100644
View file @
3287c426
#!/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
bob.learn.tensorflow.layers
import
maxout
class
LightCNN29
(
object
):
"""Creates the graph for the Light CNN-9 in
Wu, Xiang, et al. "A light CNN for deep face representation with noisy labels." arXiv preprint arXiv:1511.02683 (2015).
"""
def
__init__
(
self
,
seed
=
10
,
n_classes
=
10
,
device
=
"/cpu:0"
,
batch_norm
=
False
):
self
.
seed
=
seed
self
.
device
=
device
self
.
batch_norm
=
batch_norm
self
.
n_classes
=
n_classes
def
__call__
(
self
,
inputs
,
reuse
=
False
):
slim
=
tf
.
contrib
.
slim
#with tf.device(self.device):
initializer
=
tf
.
contrib
.
layers
.
xavier_initializer
(
uniform
=
False
,
dtype
=
tf
.
float32
,
seed
=
self
.
seed
)
graph
=
slim
.
conv2d
(
inputs
,
96
,
[
5
,
5
],
activation_fn
=
tf
.
nn
.
relu
,
stride
=
1
,
weights_initializer
=
initializer
,
scope
=
'Conv1'
,
reuse
=
reuse
)
graph
=
maxout
(
graph
,
num_units
=
48
,
name
=
'Maxout1'
)
graph
=
slim
.
max_pool2d
(
graph
,
[
2
,
2
],
stride
=
2
,
padding
=
"SAME"
,
scope
=
'Pool1'
)
####
graph
=
slim
.
conv2d
(
graph
,
96
,
[
1
,
1
],
activation_fn
=
tf
.
nn
.
relu
,
stride
=
1
,
weights_initializer
=
initializer
,
scope
=
'Conv2a'
,
reuse
=
reuse
)
graph
=
maxout
(
graph
,
num_units
=
48
,
name
=
'Maxout2a'
)
graph
=
slim
.
conv2d
(
graph
,
192
,
[
3
,
3
],
activation_fn
=
tf
.
nn
.
relu
,
stride
=
1
,
weights_initializer
=
initializer
,
scope
=
'Conv2'
,
reuse
=
reuse
)
graph
=
maxout
(
graph
,
num_units
=
96
,
name
=
'Maxout2'
)
graph
=
slim
.
max_pool2d
(
graph
,
[
2
,
2
],
stride
=
2
,
padding
=
"SAME"
,
scope
=
'Pool2'
)
#####
graph
=
slim
.
conv2d
(
graph
,
192
,
[
1
,
1
],
activation_fn
=
tf
.
nn
.
relu
,
stride
=
1
,
weights_initializer
=
initializer
,
scope
=
'Conv3a'
,
reuse
=
reuse
)
graph
=
maxout
(
graph
,
num_units
=
96
,
name
=
'Maxout3a'
)
graph
=
slim
.
conv2d
(
graph
,
384
,
[
3
,
3
],
activation_fn
=
tf
.
nn
.
relu
,
stride
=
1
,
weights_initializer
=
initializer
,
scope
=
'Conv3'
,
reuse
=
reuse
)
graph
=
maxout
(
graph
,
num_units
=
192
,
name
=
'Maxout3'
)
graph
=
slim
.
max_pool2d
(
graph
,
[
2
,
2
],
stride
=
2
,
padding
=
"SAME"
,
scope
=
'Pool3'
)
#####
graph
=
slim
.
conv2d
(
graph
,
384
,
[
1
,
1
],
activation_fn
=
tf
.
nn
.
relu
,
stride
=
1
,
weights_initializer
=
initializer
,
scope
=
'Conv4a'
,
reuse
=
reuse
)
graph
=
maxout
(
graph
,
num_units
=
192
,
name
=
'Maxout4a'
)
graph
=
slim
.
conv2d
(
graph
,
256
,
[
3
,
3
],
activation_fn
=
tf
.
nn
.
relu
,
stride
=
1
,
weights_initializer
=
initializer
,
scope
=
'Conv4'
,
reuse
=
reuse
)
graph
=
maxout
(
graph
,
num_units
=
128
,
name
=
'Maxout4'
)
#####
graph
=
slim
.
conv2d
(
graph
,
256
,
[
1
,
1
],
activation_fn
=
tf
.
nn
.
relu
,
stride
=
1
,
weights_initializer
=
initializer
,
scope
=
'Conv5a'
,
reuse
=
reuse
)
graph
=
maxout
(
graph
,
num_units
=
128
,
name
=
'Maxout5a'
)
graph
=
slim
.
conv2d
(
graph
,
256
,
[
3
,
3
],
activation_fn
=
tf
.
nn
.
relu
,
stride
=
1
,
weights_initializer
=
initializer
,
scope
=
'Conv5'
,
reuse
=
reuse
)
graph
=
maxout
(
graph
,
num_units
=
128
,
name
=
'Maxout5'
)
graph
=
slim
.
max_pool2d
(
graph
,
[
2
,
2
],
stride
=
2
,
padding
=
"SAME"
,
scope
=
'Pool4'
)
graph
=
slim
.
flatten
(
graph
,
scope
=
'flatten1'
)
#graph = slim.dropout(graph, keep_prob=0.3, scope='dropout1')
graph
=
slim
.
fully_connected
(
graph
,
512
,
weights_initializer
=
initializer
,
activation_fn
=
tf
.
nn
.
relu
,
scope
=
'fc1'
,
reuse
=
reuse
)
graph
=
maxout
(
graph
,
num_units
=
256
,
name
=
'Maxoutfc1'
)
graph
=
slim
.
dropout
(
graph
,
keep_prob
=
0.3
,
scope
=
'dropout1'
)
graph
=
slim
.
fully_connected
(
graph
,
self
.
n_classes
,
weights_initializer
=
initializer
,
activation_fn
=
None
,
scope
=
'fc2'
,
reuse
=
reuse
)
return
graph
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