110 lines
4.0 KiB
Python
110 lines
4.0 KiB
Python
# Copyright 2015 The TensorFlow 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.
|
|
# ==============================================================================
|
|
"""Private base class for pooling 1D layers."""
|
|
|
|
|
|
import tensorflow.compat.v2 as tf
|
|
|
|
from keras import backend
|
|
from keras.engine.base_layer import Layer
|
|
from keras.engine.input_spec import InputSpec
|
|
from keras.utils import conv_utils
|
|
|
|
|
|
class Pooling1D(Layer):
|
|
"""Pooling layer for arbitrary pooling functions, for 1D inputs.
|
|
|
|
This class only exists for code reuse. It will never be an exposed API.
|
|
|
|
Args:
|
|
pool_function: The pooling function to apply, e.g. `tf.nn.max_pool2d`.
|
|
pool_size: An integer or tuple/list of a single integer,
|
|
representing the size of the pooling window.
|
|
strides: An integer or tuple/list of a single integer, specifying the
|
|
strides of the pooling operation.
|
|
padding: A string. The padding method, either 'valid' or 'same'.
|
|
Case-insensitive.
|
|
data_format: A string,
|
|
one of `channels_last` (default) or `channels_first`.
|
|
The ordering of the dimensions in the inputs.
|
|
`channels_last` corresponds to inputs with shape
|
|
`(batch, steps, features)` while `channels_first`
|
|
corresponds to inputs with shape
|
|
`(batch, features, steps)`.
|
|
name: A string, the name of the layer.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
pool_function,
|
|
pool_size,
|
|
strides,
|
|
padding="valid",
|
|
data_format="channels_last",
|
|
name=None,
|
|
**kwargs
|
|
):
|
|
super().__init__(name=name, **kwargs)
|
|
if data_format is None:
|
|
data_format = backend.image_data_format()
|
|
if strides is None:
|
|
strides = pool_size
|
|
self.pool_function = pool_function
|
|
self.pool_size = conv_utils.normalize_tuple(pool_size, 1, "pool_size")
|
|
self.strides = conv_utils.normalize_tuple(
|
|
strides, 1, "strides", allow_zero=True
|
|
)
|
|
self.padding = conv_utils.normalize_padding(padding)
|
|
self.data_format = conv_utils.normalize_data_format(data_format)
|
|
self.input_spec = InputSpec(ndim=3)
|
|
|
|
def call(self, inputs):
|
|
pad_axis = 2 if self.data_format == "channels_last" else 3
|
|
inputs = tf.expand_dims(inputs, pad_axis)
|
|
outputs = self.pool_function(
|
|
inputs,
|
|
self.pool_size + (1,),
|
|
strides=self.strides + (1,),
|
|
padding=self.padding,
|
|
data_format=self.data_format,
|
|
)
|
|
return tf.squeeze(outputs, pad_axis)
|
|
|
|
def compute_output_shape(self, input_shape):
|
|
input_shape = tf.TensorShape(input_shape).as_list()
|
|
if self.data_format == "channels_first":
|
|
steps = input_shape[2]
|
|
features = input_shape[1]
|
|
else:
|
|
steps = input_shape[1]
|
|
features = input_shape[2]
|
|
length = conv_utils.conv_output_length(
|
|
steps, self.pool_size[0], self.padding, self.strides[0]
|
|
)
|
|
if self.data_format == "channels_first":
|
|
return tf.TensorShape([input_shape[0], features, length])
|
|
else:
|
|
return tf.TensorShape([input_shape[0], length, features])
|
|
|
|
def get_config(self):
|
|
config = {
|
|
"strides": self.strides,
|
|
"pool_size": self.pool_size,
|
|
"padding": self.padding,
|
|
"data_format": self.data_format,
|
|
}
|
|
base_config = super().get_config()
|
|
return dict(list(base_config.items()) + list(config.items()))
|