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ca289201
编写于
9月 28, 2020
作者:
W
whs
提交者:
GitHub
9月 28, 2020
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Add label smooth to functional package (#27642)
上级
0ecf441a
变更
3
隐藏空白更改
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Showing
3 changed file
with
208 addition
and
2 deletion
+208
-2
python/paddle/fluid/tests/unittests/test_label_smooth_functional.py
...dle/fluid/tests/unittests/test_label_smooth_functional.py
+127
-0
python/paddle/nn/functional/__init__.py
python/paddle/nn/functional/__init__.py
+1
-1
python/paddle/nn/functional/common.py
python/paddle/nn/functional/common.py
+80
-1
未找到文件。
python/paddle/fluid/tests/unittests/test_label_smooth_functional.py
0 → 100644
浏览文件 @
ca289201
# Copyright (c) 2020 PaddlePaddle 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.
import
numpy
as
np
import
paddle
from
paddle
import
fluid
,
nn
import
paddle.fluid.dygraph
as
dg
import
paddle.nn.functional
as
F
import
paddle.fluid.initializer
as
I
import
unittest
class
LabelSmoothTestCase
(
unittest
.
TestCase
):
def
__init__
(
self
,
methodName
=
'runTest'
,
label_shape
=
(
20
,
1
),
prior_dist
=
None
,
epsilon
=
0.1
,
dtype
=
"float32"
):
super
(
LabelSmoothTestCase
,
self
).
__init__
(
methodName
)
self
.
label_shape
=
label_shape
self
.
prior_dist
=
prior_dist
self
.
dtype
=
dtype
self
.
epsilon
=
epsilon
def
setUp
(
self
):
self
.
label
=
np
.
random
.
randn
(
*
(
self
.
label_shape
)).
astype
(
self
.
dtype
)
def
fluid_layer
(
self
,
place
):
paddle
.
enable_static
()
main
=
fluid
.
Program
()
start
=
fluid
.
Program
()
with
fluid
.
unique_name
.
guard
():
with
fluid
.
program_guard
(
main
,
start
):
label_var
=
fluid
.
data
(
"input"
,
self
.
label_shape
,
dtype
=
self
.
dtype
)
y_var
=
fluid
.
layers
.
label_smooth
(
label_var
,
prior_dist
=
self
.
prior_dist
,
epsilon
=
self
.
epsilon
,
dtype
=
self
.
dtype
)
feed_dict
=
{
"input"
:
self
.
label
}
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
start
)
y_np
,
=
exe
.
run
(
main
,
feed
=
feed_dict
,
fetch_list
=
[
y_var
])
return
y_np
def
functional
(
self
,
place
):
paddle
.
enable_static
()
main
=
fluid
.
Program
()
start
=
fluid
.
Program
()
with
fluid
.
unique_name
.
guard
():
with
fluid
.
program_guard
(
main
,
start
):
label_var
=
fluid
.
data
(
"input"
,
self
.
label_shape
,
dtype
=
self
.
dtype
)
y_var
=
F
.
label_smooth
(
label_var
,
prior_dist
=
self
.
prior_dist
,
epsilon
=
self
.
epsilon
)
feed_dict
=
{
"input"
:
self
.
label
}
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
start
)
y_np
,
=
exe
.
run
(
main
,
feed
=
feed_dict
,
fetch_list
=
[
y_var
])
return
y_np
def
paddle_dygraph_layer
(
self
):
paddle
.
disable_static
()
label_var
=
dg
.
to_variable
(
self
.
label
)
y_var
=
F
.
label_smooth
(
label_var
,
prior_dist
=
self
.
prior_dist
,
epsilon
=
self
.
epsilon
)
y_np
=
y_var
.
numpy
()
return
y_np
def
_test_equivalence
(
self
,
place
):
place
=
fluid
.
CPUPlace
()
result1
=
self
.
fluid_layer
(
place
)
result2
=
self
.
functional
(
place
)
result3
=
self
.
paddle_dygraph_layer
()
np
.
testing
.
assert_array_almost_equal
(
result1
,
result2
)
np
.
testing
.
assert_array_almost_equal
(
result2
,
result3
)
def
runTest
(
self
):
place
=
fluid
.
CPUPlace
()
self
.
_test_equivalence
(
place
)
if
fluid
.
core
.
is_compiled_with_cuda
():
place
=
fluid
.
CUDAPlace
(
0
)
self
.
_test_equivalence
(
place
)
class
LabelSmoothErrorTestCase
(
LabelSmoothTestCase
):
def
runTest
(
self
):
place
=
fluid
.
CPUPlace
()
with
dg
.
guard
(
place
):
with
self
.
assertRaises
(
ValueError
):
self
.
paddle_dygraph_layer
()
def
add_cases
(
suite
):
suite
.
addTest
(
LabelSmoothTestCase
(
methodName
=
'runTest'
))
suite
.
addTest
(
LabelSmoothTestCase
(
methodName
=
'runTest'
,
label_shape
=
[
2
,
3
,
1
]))
def
add_error_cases
(
suite
):
suite
.
addTest
(
LabelSmoothErrorTestCase
(
methodName
=
'runTest'
,
epsilon
=
2
))
def
load_tests
(
loader
,
standard_tests
,
pattern
):
suite
=
unittest
.
TestSuite
()
add_cases
(
suite
)
add_error_cases
(
suite
)
return
suite
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/nn/functional/__init__.py
浏览文件 @
ca289201
...
...
@@ -62,7 +62,7 @@ from .common import dropout3d #DEFINE_ALIAS
from
.common
import
alpha_dropout
#DEFINE_ALIAS
# from .common import embedding #DEFINE_ALIAS
# from .common import fc #DEFINE_ALIAS
from
.common
import
label_smooth
#DEFINE_ALIAS
from
.common
import
label_smooth
from
.common
import
one_hot
#DEFINE_ALIAS
from
.common
import
pad
#DEFINE_ALIAS
from
.common
import
pad_constant_like
#DEFINE_ALIAS
...
...
python/paddle/nn/functional/common.py
浏览文件 @
ca289201
...
...
@@ -20,7 +20,6 @@ from paddle.fluid.layers.tensor import Variable, fill_constant, zeros, concat
from
...fluid.layers
import
core
from
...fluid
import
dygraph_utils
# TODO: define the common functions to build a neural network
from
...fluid.layers
import
label_smooth
#DEFINE_ALIAS
from
...fluid
import
one_hot
#DEFINE_ALIAS
from
...fluid.layers
import
pad2d
#DEFINE_ALIAS
from
...fluid.layers
import
unfold
#DEFINE_ALIAS
...
...
@@ -1482,3 +1481,83 @@ def linear(x, weight, bias=None, name=None):
else
:
res
=
tmp
return
res
def
label_smooth
(
label
,
prior_dist
=
None
,
epsilon
=
0.1
,
name
=
None
):
"""
Label smoothing is a mechanism to regularize the classifier layer and is called
label-smoothing regularization (LSR).
Label smoothing is proposed to encourage the model to be less confident,
since optimizing the log-likelihood of the correct label directly may
cause overfitting and reduce the ability of the model to adapt. Label
smoothing replaces the ground-truth label :math:`y` with the weighted sum
of itself and some fixed distribution :math:`\mu`. For class :math:`k`,
i.e.
.. math::
\\
tilde{y_k} = (1 - \epsilon) * y_k + \epsilon * \mu_k,
where :math:`1 - \epsilon` and :math:`\epsilon` are the weights
respectively, and :math:`
\\
tilde{y}_k` is the smoothed label. Usually
uniform distribution is used for :math:`\mu`.
See more details about label smoothing in https://arxiv.org/abs/1512.00567.
Parameters:
label(Tensor): The input variable containing the label data. The
label data should use one-hot representation. It's
a multidimensional tensor with a shape of
:math:`[N_1, ..., Depth]`, where Depth is class number. The dtype can be "float32" and "float64".
prior_dist(Tensor, optional): The prior distribution to be used to smooth
labels. If not provided, an uniform distribution
is used. It's a multidimensional tensor with a shape of
:math:`[1, class\_num]` . The default value is None.
epsilon(float, optional): The weight used to mix up the original ground-truth
distribution and the fixed distribution. The default value is
0.1.
name(str, optional): The default value is None. Normally there is no need for user
to set this property. For more information, please refer to
:ref:`api_guide_Name`.
Returns:
Tensor: The tensor containing the smoothed labels.
Examples:
.. code-block:: python
import paddle
import numpy as np
x_data = np.array([[[0, 1, 0],
[ 1, 0, 1]]]).astype("float32")
print(x_data.shape)
paddle.disable_static()
x = paddle.to_tensor(x_data, stop_gradient=False)
output = paddle.nn.functional.label_smooth(x)
print(output.numpy())
#[[[0.03333334 0.93333334 0.03333334]
# [0.93333334 0.03333334 0.93333334]]]
"""
if
epsilon
>
1.
or
epsilon
<
0.
:
raise
ValueError
(
"The value of epsilon must be between 0 and 1."
)
if
in_dygraph_mode
():
return
core
.
ops
.
label_smooth
(
label
,
prior_dist
,
'epsilon'
,
float
(
epsilon
))
check_variable_and_dtype
(
label
,
'label'
,
[
'float32'
,
'float64'
],
'label_smooth'
)
helper
=
LayerHelper
(
"label_smooth"
,
**
locals
())
label
.
stop_gradient
=
True
smooth_label
=
helper
.
create_variable_for_type_inference
(
label
.
dtype
)
helper
.
append_op
(
type
=
"label_smooth"
,
inputs
=
{
"X"
:
label
,
"PriorDist"
:
prior_dist
}
if
prior_dist
else
{
"X"
:
label
},
outputs
=
{
"Out"
:
smooth_label
},
attrs
=
{
"epsilon"
:
float
(
epsilon
)})
return
smooth_label
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