Spaces:
Runtime error
Runtime error
File size: 3,425 Bytes
0924f30 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
# coding=utf-8
# Copyright 2021 The Deeplab2 Authors.
#
# 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.
"""Defines a set of useful activation functions."""
import functools
import tensorflow as tf
def gelu(input_tensor, approximate=False):
"""Gaussian Error Linear Unit.
Reference:
Gaussian Error Linear Units (GELUs), Dan Hendrycks, Kevin Gimpel, arXiv 2016.
Args:
input_tensor: A tensor with an arbitrary shape.
approximate: A boolean, whether to enable approximation.
Returns:
The activated input tensor.
"""
return tf.keras.activations.gelu(input_tensor, approximate=approximate)
def hard_sigmoid(input_tensor):
"""Hard sigmoid activation function.
Args:
input_tensor: A tensor with an arbitrary shape.
Returns:
The activated input tensor.
"""
input_tensor = tf.convert_to_tensor(input_tensor)
return tf.nn.relu6(input_tensor + tf.constant(3.)) * 0.16667
def relu6(input_tensor):
"""Relu6 activation function.
Args:
input_tensor: A tensor with an arbitrary shape.
Returns:
The activated input tensor.
"""
input_tensor = tf.convert_to_tensor(input_tensor)
return tf.nn.relu6(input_tensor)
def swish(input_tensor):
"""Swish or SiLU activation function.
Args:
input_tensor: A tensor with an arbitrary shape.
Returns:
The activated input tensor.
"""
input_tensor = tf.convert_to_tensor(input_tensor)
return tf.nn.silu(input_tensor)
def hard_swish(input_tensor):
"""Hard Swish function.
Args:
input_tensor: A tensor with an arbitrary shape.
Returns:
The activated input tensor.
"""
input_tensor = tf.convert_to_tensor(input_tensor)
return input_tensor * tf.nn.relu6(
input_tensor + tf.constant(3.)) * (1. / 6.)
def identity(input_tensor):
"""Identity function.
Useful for helping in quantization.
Args:
input_tensor: A tensor with an arbitrary shape.
Returns:
The activated input tensor.
"""
input_tensor = tf.convert_to_tensor(input_tensor)
return tf.identity(input_tensor)
def get_activation(identifier):
"""Gets activation function via input identifier.
This function returns the specified customized activation function, if there
is any. Otherwise, tf.keras.activations.get is called.
Args:
identifier: A string, name of the activation function.
Returns:
The specified activation function.
"""
if isinstance(identifier, str):
name_to_fn = {
'gelu': functools.partial(gelu, approximate=False),
'approximated_gelu': functools.partial(gelu, approximate=True),
'silu': swish,
'swish': swish,
'hard_swish': hard_swish,
'relu6': relu6,
'hard_sigmoid': hard_sigmoid,
'identity': identity,
'none': identity,
}
identifier = str(identifier).lower()
if identifier in name_to_fn:
return name_to_fn[identifier]
return tf.keras.activations.get(identifier)
|