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bob
bob.learn.tensorflow
Commits
70e0b851
Commit
70e0b851
authored
5 years ago
by
Amir MOHAMMADI
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Allow shuffle on epoch end in generator
parent
6aabd230
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1 merge request
!79
Add keras-based models, add pixel-wise loss, other improvements
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bob/learn/tensorflow/dataset/generator.py
+23
-16
23 additions, 16 deletions
bob/learn/tensorflow/dataset/generator.py
with
23 additions
and
16 deletions
bob/learn/tensorflow/dataset/generator.py
+
23
−
16
View file @
70e0b851
import
six
import
six
import
tensorflow
as
tf
import
tensorflow
as
tf
import
random
import
logging
import
logging
logger
=
logging
.
getLogger
(
__name__
)
logger
=
logging
.
getLogger
(
__name__
)
...
@@ -22,18 +23,21 @@ class Generator:
...
@@ -22,18 +23,21 @@ class Generator:
which takes a sample and loads it.
which takes a sample and loads it.
samples : [:obj:`object`]
samples : [:obj:`object`]
A list of samples to be given to ``reader`` to load the data.
A list of samples to be given to ``reader`` to load the data.
shuffle_on_epoch_end : :obj:`bool`, optional
If True, it shuffle the samples at the end of each epoch.
output_types : (object, object, object)
output_types : (object, object, object)
The types of the returned samples.
The types of the returned samples.
output_shapes : ``(tf.TensorShape, tf.TensorShape, tf.TensorShape)``
output_shapes : ``(tf.TensorShape, tf.TensorShape, tf.TensorShape)``
The shapes of the returned samples.
The shapes of the returned samples.
"""
"""
def
__init__
(
self
,
samples
,
reader
,
multiple_samples
=
False
,
**
kwargs
):
def
__init__
(
self
,
samples
,
reader
,
multiple_samples
=
False
,
shuffle_on_epoch_end
=
False
,
**
kwargs
):
super
().
__init__
(
**
kwargs
)
super
().
__init__
(
**
kwargs
)
self
.
reader
=
reader
self
.
reader
=
reader
self
.
samples
=
list
(
samples
)
self
.
samples
=
list
(
samples
)
self
.
multiple_samples
=
multiple_samples
self
.
multiple_samples
=
multiple_samples
self
.
epoch
=
0
self
.
epoch
=
0
self
.
shuffle_on_epoch_end
=
shuffle_on_epoch_end
# load one data to get its type and shape
# load one data to get its type and shape
dlk
=
self
.
reader
(
self
.
samples
[
0
])
dlk
=
self
.
reader
(
self
.
samples
[
0
])
...
@@ -81,31 +85,34 @@ class Generator:
...
@@ -81,31 +85,34 @@ class Generator:
yield
dlk
yield
dlk
self
.
epoch
+=
1
self
.
epoch
+=
1
logger
.
info
(
"
Elapsed %d epoch(s)
"
,
self
.
epoch
)
logger
.
info
(
"
Elapsed %d epoch(s)
"
,
self
.
epoch
)
if
self
.
shuffle_on_epoch_end
:
logger
.
info
(
"
Shuffling samples
"
)
random
.
shuffle
(
self
.
samples
)
def
dataset_using_generator
(
*
args
,
**
kwargs
):
def
dataset_using_generator
(
samples
,
reader
,
**
kwargs
):
"""
"""
A generator class which wraps samples so that they can
A generator class which wraps samples so that they can
be used with tf.data.Dataset.from_generator
be used with tf.data.Dataset.from_generator
Attribu
tes
Parame
te
r
s
----------
----------
samples : [:obj:`object`]
A list of samples to be given to ``reader`` to load the data.
samples : [:obj:`object`]
reader : :obj:`object`, optional
A list of samples to be given to ``reader`` to load the data.
A callable with the signature of ``data, label, key = reader(sample)``
which takes a sample and loads it.
reader : :obj:`object`, optional
**kwargs
A callable with the signature of ``data, label, key = reader(sample)``
Extra keyword arguments are passed to Generator
which takes a sample and loads it.
Returns
multiple_samples : :obj:`bool`, optional
-------
If true, it assumes that the bio database
'
s samples actually contain
object
multiple samples. This is useful for when you want to for example treat
A tf.data.Dataset
video databases as image databases.
"""
"""
generator
=
Generator
(
*
args
,
**
kwargs
)
generator
=
Generator
(
samples
,
reader
,
**
kwargs
)
dataset
=
tf
.
data
.
Dataset
.
from_generator
(
dataset
=
tf
.
data
.
Dataset
.
from_generator
(
generator
,
generator
.
output_types
,
generator
.
output_shapes
generator
,
generator
.
output_types
,
generator
.
output_shapes
)
)
...
...
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