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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
class Activation(nn.Layer):
"""
The wrapper of activations.
Args:
act (str, optional): The activation name in lowercase. It must be one of ['elu', 'gelu',
'hardshrink', 'tanh', 'hardtanh', 'prelu', 'relu', 'relu6', 'selu', 'leakyrelu', 'sigmoid',
'softmax', 'softplus', 'softshrink', 'softsign', 'tanhshrink', 'logsigmoid', 'logsoftmax',
'hsigmoid']. Default: None, means identical transformation.
Returns:
A callable object of Activation.
Raises:
KeyError: When parameter `act` is not in the optional range.
Examples:
from paddleseg.models.common.activation import Activation
relu = Activation("relu")
print(relu)
# <class 'paddle.nn.layer.activation.ReLU'>
sigmoid = Activation("sigmoid")
print(sigmoid)
# <class 'paddle.nn.layer.activation.Sigmoid'>
not_exit_one = Activation("not_exit_one")
# KeyError: "not_exit_one does not exist in the current dict_keys(['elu', 'gelu', 'hardshrink',
# 'tanh', 'hardtanh', 'prelu', 'relu', 'relu6', 'selu', 'leakyrelu', 'sigmoid', 'softmax',
# 'softplus', 'softshrink', 'softsign', 'tanhshrink', 'logsigmoid', 'logsoftmax', 'hsigmoid'])"
"""
def __init__(self, act=None):
super(Activation, self).__init__()
self._act = act
upper_act_names = nn.layer.activation.__dict__.keys()
lower_act_names = [act.lower() for act in upper_act_names]
act_dict = dict(zip(lower_act_names, upper_act_names))
if act is not None:
if act in act_dict.keys():
act_name = act_dict[act]
self.act_func = eval("nn.layer.activation.{}()".format(
act_name))
else:
raise KeyError("{} does not exist in the current {}".format(
act, act_dict.keys()))
def forward(self, x):
if self._act is not None:
return self.act_func(x)
else:
return x
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