提交 45183e48 编写于 作者: W wanghaoshuang

Add label smooth to functional package

test=develop
无相关合并请求
# 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()
......@@ -1482,3 +1482,83 @@ 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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