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bob.learn.tensorflow
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bob
bob.learn.tensorflow
Commits
14147b9a
Commit
14147b9a
authored
Oct 07, 2016
by
Tiago de Freitas Pereira
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CASIA Training
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bob/learn/tensorflow/script/train_siamese_casia_webface.py
bob/learn/tensorflow/script/train_siamese_casia_webface.py
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14147b9a
#!/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
"""
Simple script that trains CASIA WEBFACE
Usage:
train_siamese_casia_webface.py [--batch-size=<arg> --validation-batch-size=<arg> --iterations=<arg> --validation-interval=<arg> --use-gpu]
train_siamese_casia_webface.py -h | --help
Options:
-h --help Show this screen.
--batch-size=<arg> [default: 1]
--validation-batch-size=<arg> [default:128]
--iterations=<arg> [default: 30000]
--validation-interval=<arg> [default: 100]
"""
from
docopt
import
docopt
import
tensorflow
as
tf
from
..
import
util
SEED
=
10
from
bob.learn.tensorflow.data
import
MemoryDataShuffler
,
TextDataShuffler
from
bob.learn.tensorflow.network
import
Lenet
,
MLP
,
LenetDropout
,
VGG
,
Chopra
,
Dummy
from
bob.learn.tensorflow.trainers
import
SiameseTrainer
from
bob.learn.tensorflow.loss
import
ContrastiveLoss
import
numpy
def
main
():
args
=
docopt
(
__doc__
,
version
=
'Mnist training with TensorFlow'
)
BATCH_SIZE
=
int
(
args
[
'--batch-size'
])
VALIDATION_BATCH_SIZE
=
int
(
args
[
'--validation-batch-size'
])
ITERATIONS
=
int
(
args
[
'--iterations'
])
VALIDATION_TEST
=
int
(
args
[
'--validation-interval'
])
USE_GPU
=
args
[
'--use-gpu'
]
perc_train
=
0.9
import
bob.db.mobio
db_mobio
=
bob
.
db
.
mobio
.
Database
()
import
bob.db.casia_webface
db_casia
=
bob
.
db
.
casia_webface
.
Database
()
# Preparing train set
train_objects
=
db_casia
.
objects
(
groups
=
"world"
)
#train_objects = db.objects(groups="world")
train_labels
=
[
int
(
o
.
client_id
)
for
o
in
train_objects
]
directory
=
"/idiap/resource/database/CASIA-WebFace/CASIA-WebFace"
train_file_names
=
[
o
.
make_path
(
directory
=
directory
,
extension
=
""
)
for
o
in
train_objects
]
train_data_shuffler
=
TextDataShuffler
(
train_file_names
,
train_labels
,
input_shape
=
[
125
,
125
,
3
],
batch_size
=
BATCH_SIZE
)
# Preparing train set
directory
=
"/idiap/temp/tpereira/DEEP_FACE/CASIA/preprocessed"
validation_objects
=
db_mobio
.
objects
(
protocol
=
"male"
,
groups
=
"dev"
)
validation_labels
=
[
o
.
client_id
for
o
in
validation_objects
]
validation_file_names
=
[
o
.
make_path
(
directory
=
directory
,
extension
=
".hdf5"
)
for
o
in
validation_objects
]
validation_data_shuffler
=
TextDataShuffler
(
validation_file_names
,
validation_labels
,
input_shape
=
[
125
,
125
,
3
],
batch_size
=
VALIDATION_BATCH_SIZE
)
# Preparing the architecture
# LENET PAPER CHOPRA
architecture
=
Chopra
(
seed
=
SEED
)
loss
=
ContrastiveLoss
(
contrastive_margin
=
50.
)
optimizer
=
tf
.
train
.
GradientDescentOptimizer
(
0.00001
)
trainer
=
SiameseTrainer
(
architecture
=
architecture
,
loss
=
loss
,
iterations
=
ITERATIONS
,
snapshot
=
VALIDATION_TEST
,
optimizer
=
optimizer
)
trainer
.
train
(
train_data_shuffler
,
validation_data_shuffler
)
#trainer.train(train_data_shuffler)
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