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ece1e4cd
编写于
11月 16, 2020
作者:
K
Kaipeng Deng
提交者:
GitHub
11月 16, 2020
浏览文件
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电子邮件补丁
差异文件
Add weighted random sampler (#28545)
* add WeightedRandomSampler. test=develop
上级
2cb71c0c
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
171 addition
and
11 deletion
+171
-11
python/paddle/fluid/dataloader/sampler.py
python/paddle/fluid/dataloader/sampler.py
+86
-1
python/paddle/fluid/tests/unittests/test_batch_sampler.py
python/paddle/fluid/tests/unittests/test_batch_sampler.py
+83
-9
python/paddle/io/__init__.py
python/paddle/io/__init__.py
+2
-1
未找到文件。
python/paddle/fluid/dataloader/sampler.py
浏览文件 @
ece1e4cd
...
@@ -16,8 +16,11 @@ from __future__ import print_function
...
@@ -16,8 +16,11 @@ from __future__ import print_function
from
__future__
import
division
from
__future__
import
division
import
numpy
as
np
import
numpy
as
np
from
..
import
core
__all__
=
[
"Sampler"
,
"SequenceSampler"
,
"RandomSampler"
]
__all__
=
[
"Sampler"
,
"SequenceSampler"
,
"RandomSampler"
,
"WeightedRandomSampler"
]
class
Sampler
(
object
):
class
Sampler
(
object
):
...
@@ -234,3 +237,85 @@ class RandomSampler(Sampler):
...
@@ -234,3 +237,85 @@ class RandomSampler(Sampler):
def
__len__
(
self
):
def
__len__
(
self
):
return
self
.
num_samples
return
self
.
num_samples
def
_weighted_sample
(
weights
,
num_samples
,
replacement
=
True
):
if
isinstance
(
weights
,
core
.
LoDTensor
):
weights
=
weights
.
numpy
()
if
isinstance
(
weights
,
(
list
,
tuple
)):
weights
=
np
.
array
(
weights
)
assert
isinstance
(
weights
,
np
.
ndarray
),
\
"weights should be paddle.Tensor, numpy.ndarray, list or tuple"
assert
len
(
weights
.
shape
)
<=
2
,
\
"weights should be a 1-D or 2-D array"
weights
=
weights
.
reshape
((
-
1
,
weights
.
shape
[
-
1
]))
assert
np
.
all
(
weights
>=
0.
),
\
"weights should be positive value"
assert
not
np
.
any
(
weights
==
np
.
inf
),
\
"weights shoule not be INF"
assert
not
np
.
any
(
weights
==
np
.
nan
),
\
"weights shoule not be NaN"
non_zeros
=
np
.
sum
(
weights
>
0.
,
axis
=
1
)
assert
np
.
all
(
non_zeros
>
0
),
\
"weights should have positive values"
if
not
replacement
:
assert
np
.
all
(
non_zeros
>=
num_samples
),
\
"weights positive value number should not "
\
"less than num_samples when replacement=False"
weights
=
weights
/
weights
.
sum
(
axis
=
1
)
rets
=
[]
for
i
in
range
(
weights
.
shape
[
0
]):
ret
=
np
.
random
.
choice
(
weights
.
shape
[
1
],
num_samples
,
replacement
,
weights
[
i
])
rets
.
append
(
ret
)
return
np
.
array
(
rets
)
class
WeightedRandomSampler
(
Sampler
):
"""
Random sample with given weights (probabilities), sampe index will be in range
[0, len(weights) - 1], if :attr:`replacement` is True, index can be sampled
multiple times.
Args:
weights(numpy.ndarray|paddle.Tensor|list|tuple): sequence of weights,
should be numpy array, paddle.Tensor, list or tuple
num_samples(int): set sample number to draw from sampler.
replacement(bool): Whether to draw sample with replacements, default True
Returns:
Sampler: a Sampler yield sample index randomly by given weights
Examples:
.. code-block:: python
from paddle.io import WeightedRandomSampler
sampler = WeightedRandomSampler(weights=[0.1, 0.3, 0.5, 0.7, 0.2],
num_samples=5,
replacement=True)
for index in sampler:
print(index)
"""
def
__init__
(
self
,
weights
,
num_samples
,
replacement
=
True
):
if
not
isinstance
(
num_samples
,
int
)
or
num_samples
<=
0
:
raise
ValueError
(
"num_samples should be a positive integer"
)
if
not
isinstance
(
replacement
,
bool
):
raise
ValueError
(
"replacement should be a boolean value"
)
self
.
weights
=
weights
self
.
num_samples
=
num_samples
self
.
replacement
=
replacement
def
__iter__
(
self
):
idxs
=
_weighted_sample
(
self
.
weights
,
self
.
num_samples
,
self
.
replacement
)
return
iter
(
idxs
.
reshape
((
-
1
)).
tolist
())
def
__len__
(
self
):
mul
=
np
.
prod
(
self
.
weights
.
shape
)
//
self
.
weights
.
shape
[
-
1
]
return
self
.
num_samples
*
mul
python/paddle/fluid/tests/unittests/test_batch_sampler.py
浏览文件 @
ece1e4cd
...
@@ -16,8 +16,10 @@ from __future__ import division
...
@@ -16,8 +16,10 @@ from __future__ import division
import
unittest
import
unittest
import
numpy
as
np
import
paddle.fluid
as
fluid
import
paddle.fluid
as
fluid
from
paddle.io
import
BatchSampler
,
Dataset
,
Sampler
,
SequenceSampler
,
RandomSampler
from
paddle.io
import
BatchSampler
,
Dataset
,
Sampler
,
SequenceSampler
,
\
RandomSampler
,
WeightedRandomSampler
from
paddle.io
import
DistributedBatchSampler
from
paddle.io
import
DistributedBatchSampler
...
@@ -195,14 +197,86 @@ class TestBatchSamplerWithSamplerShuffle(unittest.TestCase):
...
@@ -195,14 +197,86 @@ class TestBatchSamplerWithSamplerShuffle(unittest.TestCase):
pass
pass
class
TestDistributedBatchSamplerWithSampler
(
TestBatchSampler
):
class
TestWeightedRandomSampler
(
unittest
.
TestCase
):
def
init_batch_sampler
(
self
):
def
init_probs
(
self
,
total
,
pos
):
dataset
=
RandomDataset
(
1000
,
10
)
pos_probs
=
np
.
random
.
random
((
pos
,
)).
astype
(
'float32'
)
bs
=
DistributedBatchSampler
(
probs
=
np
.
zeros
((
total
,
)).
astype
(
'float32'
)
dataset
=
dataset
,
probs
[:
pos
]
=
pos_probs
batch_size
=
self
.
batch_size
,
np
.
random
.
shuffle
(
probs
)
drop_last
=
self
.
drop_last
)
return
probs
return
bs
def
test_replacement
(
self
):
probs
=
self
.
init_probs
(
20
,
10
)
sampler
=
WeightedRandomSampler
(
probs
,
30
,
True
)
assert
len
(
sampler
)
==
30
for
idx
in
iter
(
sampler
):
assert
probs
[
idx
]
>
0.
def
test_no_replacement
(
self
):
probs
=
self
.
init_probs
(
20
,
10
)
sampler
=
WeightedRandomSampler
(
probs
,
10
,
False
)
assert
len
(
sampler
)
==
10
idxs
=
[]
for
idx
in
iter
(
sampler
):
assert
probs
[
idx
]
>
0.
idxs
.
append
(
idx
)
assert
len
(
set
(
idxs
))
==
len
(
idxs
)
def
test_assert
(
self
):
# all zeros
probs
=
np
.
zeros
((
10
,
)).
astype
(
'float32'
)
sampler
=
WeightedRandomSampler
(
probs
,
10
,
True
)
try
:
for
idx
in
iter
(
sampler
):
pass
self
.
assertTrue
(
False
)
except
AssertionError
:
self
.
assertTrue
(
True
)
# not enough pos
probs
=
self
.
init_probs
(
10
,
5
)
sampler
=
WeightedRandomSampler
(
probs
,
10
,
False
)
try
:
for
idx
in
iter
(
sampler
):
pass
self
.
assertTrue
(
False
)
except
AssertionError
:
self
.
assertTrue
(
True
)
# neg probs
probs
=
-
1.0
*
np
.
ones
((
10
,
)).
astype
(
'float32'
)
sampler
=
WeightedRandomSampler
(
probs
,
10
,
True
)
try
:
for
idx
in
iter
(
sampler
):
pass
self
.
assertTrue
(
False
)
except
AssertionError
:
self
.
assertTrue
(
True
)
def
test_raise
(
self
):
# float num_samples
probs
=
self
.
init_probs
(
10
,
5
)
try
:
sampler
=
WeightedRandomSampler
(
probs
,
2.3
,
True
)
self
.
assertTrue
(
False
)
except
ValueError
:
self
.
assertTrue
(
True
)
# neg num_samples
probs
=
self
.
init_probs
(
10
,
5
)
try
:
sampler
=
WeightedRandomSampler
(
probs
,
-
1
,
True
)
self
.
assertTrue
(
False
)
except
ValueError
:
self
.
assertTrue
(
True
)
# no-bool replacement
probs
=
self
.
init_probs
(
10
,
5
)
try
:
sampler
=
WeightedRandomSampler
(
probs
,
5
,
5
)
self
.
assertTrue
(
False
)
except
ValueError
:
self
.
assertTrue
(
True
)
if
__name__
==
'__main__'
:
if
__name__
==
'__main__'
:
...
...
python/paddle/io/__init__.py
浏览文件 @
ece1e4cd
...
@@ -27,9 +27,10 @@ __all__ = [
...
@@ -27,9 +27,10 @@ __all__ = [
'Sampler'
,
'Sampler'
,
'SequenceSampler'
,
'SequenceSampler'
,
'RandomSampler'
,
'RandomSampler'
,
'WeightedRandomSampler'
,
]
]
from
..fluid.io
import
DataLoader
from
..fluid.io
import
DataLoader
from
..fluid.dataloader
import
Dataset
,
IterableDataset
,
BatchSampler
,
get_worker_info
,
\
from
..fluid.dataloader
import
Dataset
,
IterableDataset
,
BatchSampler
,
get_worker_info
,
\
TensorDataset
,
Sampler
,
SequenceSampler
,
RandomSampler
,
DistributedBatchSampler
,
\
TensorDataset
,
Sampler
,
SequenceSampler
,
RandomSampler
,
DistributedBatchSampler
,
\
ComposeDataset
,
ChainDataset
ComposeDataset
,
ChainDataset
,
WeightedRandomSampler
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