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# 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 paddle.nn as nn
def constant_init(param, **kwargs):
"""
Initialize the `param` with constants.
Args:
param (Tensor): Tensor that needs to be initialized.
Examples:
from paddleseg.cvlibs import param_init
import paddle.nn as nn
linear = nn.Linear(2, 4)
param_init.constant_init(linear.weight, value=2.0)
print(linear.weight.numpy())
# result is [[2. 2. 2. 2.], [2. 2. 2. 2.]]
"""
initializer = nn.initializer.Constant(**kwargs)
initializer(param, param.block)
def normal_init(param, **kwargs):
"""
Initialize the `param` with a Normal distribution.
Args:
param (Tensor): Tensor that needs to be initialized.
Examples:
from paddleseg.cvlibs import param_init
import paddle.nn as nn
linear = nn.Linear(2, 4)
param_init.normal_init(linear.weight, loc=0.0, scale=1.0)
"""
initializer = nn.initializer.Normal(**kwargs)
initializer(param, param.block)
def kaiming_normal_init(param, **kwargs):
r"""
Initialize the input tensor with Kaiming Normal initialization.
This function implements the `param` initialization from the paper
`Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification <https://arxiv.org/abs/1502.01852>`
by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. This is a
robust initialization method that particularly considers the rectifier
nonlinearities. In case of Uniform distribution, the range is [-x, x], where
.. math::
x = \sqrt{\\frac{6.0}{fan\_in}}
In case of Normal distribution, the mean is 0 and the standard deviation
is
.. math::
\sqrt{\\frac{2.0}{fan\_in}}
Args:
param (Tensor): Tensor that needs to be initialized.
Examples:
from paddleseg.cvlibs import param_init
import paddle.nn as nn
linear = nn.Linear(2, 4)
# uniform is used to decide whether to use uniform or normal distribution
param_init.kaiming_normal_init(linear.weight)
"""
initializer = nn.initializer.KaimingNormal(**kwargs)
initializer(param, param.block)
def kaiming_uniform(param, **kwargs):
r"""Implements the Kaiming Uniform initializer
This class implements the weight initialization from the paper
`Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification <https://arxiv.org/abs/1502.01852>`_
by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. This is a
robust initialization method that particularly considers the rectifier
nonlinearities.
In case of Uniform distribution, the range is [-x, x], where
.. math::
x = \sqrt{\\frac{6.0}{fan\_in}}
Args:
param (Tensor): Tensor that needs to be initialized.
Examples:
from paddleseg.cvlibs import param_init
import paddle.nn as nn
linear = nn.Linear(2, 4)
param_init.kaiming_uniform(linear.weight)
"""
initializer = nn.initializer.KaimingUniform(**kwargs)
initializer(param, param.block)
def xavier_uniform(param, **kwargs):
r"""
This implements the Xavier weight initializer from the paper
`Understanding the difficulty of training deep feedforward neural
networks <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_
by Xavier Glorot and Yoshua Bengio.
This initializer is designed to keep the scale of the gradients
approximately same in all the layers. In case of Uniform distribution,
the range is [-x, x], where
.. math::
x = \sqrt{\frac{6.0}{fan\_in + fan\_out}}
Args:
param (Tensor): Tensor that needs to be initialized.
Examples:
from paddleseg.cvlibs import param_init
import paddle.nn as nn
linear = nn.Linear(2, 4)
param_init.xavier_uniform(linear.weight)
"""
initializer = nn.initializer.XavierUniform(**kwargs)
initializer(param, param.block) |