110 lines
4.0 KiB
Python
110 lines
4.0 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Private base class for pooling 1D layers."""
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import tensorflow.compat.v2 as tf
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from keras import backend
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from keras.engine.base_layer import Layer
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from keras.engine.input_spec import InputSpec
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from keras.utils import conv_utils
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class Pooling1D(Layer):
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"""Pooling layer for arbitrary pooling functions, for 1D inputs.
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This class only exists for code reuse. It will never be an exposed API.
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Args:
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pool_function: The pooling function to apply, e.g. `tf.nn.max_pool2d`.
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pool_size: An integer or tuple/list of a single integer,
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representing the size of the pooling window.
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strides: An integer or tuple/list of a single integer, specifying the
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strides of the pooling operation.
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padding: A string. The padding method, either 'valid' or 'same'.
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Case-insensitive.
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data_format: A string,
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one of `channels_last` (default) or `channels_first`.
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The ordering of the dimensions in the inputs.
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`channels_last` corresponds to inputs with shape
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`(batch, steps, features)` while `channels_first`
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corresponds to inputs with shape
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`(batch, features, steps)`.
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name: A string, the name of the layer.
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"""
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def __init__(
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self,
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pool_function,
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pool_size,
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strides,
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padding="valid",
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data_format="channels_last",
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name=None,
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**kwargs
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):
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super().__init__(name=name, **kwargs)
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if data_format is None:
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data_format = backend.image_data_format()
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if strides is None:
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strides = pool_size
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self.pool_function = pool_function
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self.pool_size = conv_utils.normalize_tuple(pool_size, 1, "pool_size")
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self.strides = conv_utils.normalize_tuple(
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strides, 1, "strides", allow_zero=True
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)
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self.padding = conv_utils.normalize_padding(padding)
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self.data_format = conv_utils.normalize_data_format(data_format)
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self.input_spec = InputSpec(ndim=3)
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def call(self, inputs):
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pad_axis = 2 if self.data_format == "channels_last" else 3
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inputs = tf.expand_dims(inputs, pad_axis)
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outputs = self.pool_function(
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inputs,
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self.pool_size + (1,),
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strides=self.strides + (1,),
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padding=self.padding,
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data_format=self.data_format,
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)
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return tf.squeeze(outputs, pad_axis)
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def compute_output_shape(self, input_shape):
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input_shape = tf.TensorShape(input_shape).as_list()
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if self.data_format == "channels_first":
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steps = input_shape[2]
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features = input_shape[1]
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else:
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steps = input_shape[1]
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features = input_shape[2]
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length = conv_utils.conv_output_length(
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steps, self.pool_size[0], self.padding, self.strides[0]
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)
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if self.data_format == "channels_first":
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return tf.TensorShape([input_shape[0], features, length])
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else:
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return tf.TensorShape([input_shape[0], length, features])
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def get_config(self):
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config = {
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"strides": self.strides,
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"pool_size": self.pool_size,
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"padding": self.padding,
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"data_format": self.data_format,
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}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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