459 lines
18 KiB
Python
459 lines
18 KiB
Python
# 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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"""Contains the pooling layer classes and their functional aliases.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.keras import layers as keras_layers
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from tensorflow.python.layers import base
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from tensorflow.python.util.tf_export import tf_export
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@tf_export('layers.AveragePooling1D')
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class AveragePooling1D(keras_layers.AveragePooling1D, base.Layer):
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"""Average Pooling layer for 1D inputs.
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Arguments:
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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, 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, length, channels)` while `channels_first` corresponds to
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inputs with shape `(batch, channels, length)`.
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name: A string, the name of the layer.
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"""
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def __init__(self, pool_size, strides,
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padding='valid', data_format='channels_last',
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name=None, **kwargs):
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if strides is None:
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raise ValueError('Argument `strides` must not be None.')
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super(AveragePooling1D, self).__init__(
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pool_size=pool_size,
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strides=strides,
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padding=padding,
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data_format=data_format,
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name=name,
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**kwargs)
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@tf_export('layers.average_pooling1d')
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def average_pooling1d(inputs, pool_size, strides,
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padding='valid', data_format='channels_last',
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name=None):
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"""Average Pooling layer for 1D inputs.
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Arguments:
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inputs: The tensor over which to pool. Must have rank 3.
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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, 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, length, channels)` while `channels_first` corresponds to
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inputs with shape `(batch, channels, length)`.
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name: A string, the name of the layer.
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Returns:
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The output tensor, of rank 3.
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Raises:
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ValueError: if eager execution is enabled.
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"""
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layer = AveragePooling1D(pool_size=pool_size,
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strides=strides,
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padding=padding,
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data_format=data_format,
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name=name)
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return layer.apply(inputs)
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@tf_export('layers.MaxPooling1D')
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class MaxPooling1D(keras_layers.MaxPooling1D, base.Layer):
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"""Max Pooling layer for 1D inputs.
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Arguments:
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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, 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, length, channels)` while `channels_first` corresponds to
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inputs with shape `(batch, channels, length)`.
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name: A string, the name of the layer.
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"""
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def __init__(self, pool_size, strides,
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padding='valid', data_format='channels_last',
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name=None, **kwargs):
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if strides is None:
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raise ValueError('Argument `strides` must not be None.')
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super(MaxPooling1D, self).__init__(
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pool_size=pool_size,
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strides=strides,
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padding=padding,
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data_format=data_format,
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name=name,
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**kwargs)
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@tf_export('layers.max_pooling1d')
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def max_pooling1d(inputs, pool_size, strides,
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padding='valid', data_format='channels_last',
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name=None):
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"""Max Pooling layer for 1D inputs.
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Arguments:
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inputs: The tensor over which to pool. Must have rank 3.
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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, 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, length, channels)` while `channels_first` corresponds to
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inputs with shape `(batch, channels, length)`.
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name: A string, the name of the layer.
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Returns:
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The output tensor, of rank 3.
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Raises:
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ValueError: if eager execution is enabled.
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"""
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layer = MaxPooling1D(pool_size=pool_size,
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strides=strides,
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padding=padding,
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data_format=data_format,
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name=name)
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return layer.apply(inputs)
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@tf_export('layers.AveragePooling2D')
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class AveragePooling2D(keras_layers.AveragePooling2D, base.Layer):
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"""Average pooling layer for 2D inputs (e.g. images).
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Arguments:
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pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
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specifying the size of the pooling window.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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strides: An integer or tuple/list of 2 integers,
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specifying the strides of the pooling operation.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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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. The ordering of the dimensions in the inputs.
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`channels_last` (default) and `channels_first` are supported.
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`channels_last` corresponds to inputs with shape
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`(batch, height, width, channels)` while `channels_first` corresponds to
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inputs with shape `(batch, channels, height, width)`.
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name: A string, the name of the layer.
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"""
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def __init__(self, pool_size, strides,
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padding='valid', data_format='channels_last',
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name=None, **kwargs):
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if strides is None:
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raise ValueError('Argument `strides` must not be None.')
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super(AveragePooling2D, self).__init__(
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pool_size=pool_size, strides=strides,
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padding=padding, data_format=data_format, name=name, **kwargs)
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@tf_export('layers.average_pooling2d')
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def average_pooling2d(inputs,
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pool_size, strides,
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padding='valid', data_format='channels_last',
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name=None):
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"""Average pooling layer for 2D inputs (e.g. images).
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Arguments:
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inputs: The tensor over which to pool. Must have rank 4.
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pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
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specifying the size of the pooling window.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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strides: An integer or tuple/list of 2 integers,
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specifying the strides of the pooling operation.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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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. The ordering of the dimensions in the inputs.
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`channels_last` (default) and `channels_first` are supported.
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`channels_last` corresponds to inputs with shape
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`(batch, height, width, channels)` while `channels_first` corresponds to
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inputs with shape `(batch, channels, height, width)`.
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name: A string, the name of the layer.
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Returns:
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Output tensor.
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Raises:
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ValueError: if eager execution is enabled.
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"""
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layer = AveragePooling2D(pool_size=pool_size, strides=strides,
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padding=padding, data_format=data_format,
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name=name)
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return layer.apply(inputs)
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@tf_export('layers.MaxPooling2D')
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class MaxPooling2D(keras_layers.MaxPooling2D, base.Layer):
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"""Max pooling layer for 2D inputs (e.g. images).
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Arguments:
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pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
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specifying the size of the pooling window.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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strides: An integer or tuple/list of 2 integers,
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specifying the strides of the pooling operation.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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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. The ordering of the dimensions in the inputs.
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`channels_last` (default) and `channels_first` are supported.
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`channels_last` corresponds to inputs with shape
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`(batch, height, width, channels)` while `channels_first` corresponds to
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inputs with shape `(batch, channels, height, width)`.
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name: A string, the name of the layer.
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"""
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def __init__(self, pool_size, strides,
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padding='valid', data_format='channels_last',
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name=None, **kwargs):
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if strides is None:
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raise ValueError('Argument `strides` must not be None.')
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super(MaxPooling2D, self).__init__(
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pool_size=pool_size, strides=strides,
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padding=padding, data_format=data_format, name=name, **kwargs)
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@tf_export('layers.max_pooling2d')
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def max_pooling2d(inputs,
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pool_size, strides,
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padding='valid', data_format='channels_last',
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name=None):
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"""Max pooling layer for 2D inputs (e.g. images).
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Arguments:
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inputs: The tensor over which to pool. Must have rank 4.
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pool_size: An integer or tuple/list of 2 integers: (pool_height, pool_width)
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specifying the size of the pooling window.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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strides: An integer or tuple/list of 2 integers,
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specifying the strides of the pooling operation.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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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. The ordering of the dimensions in the inputs.
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`channels_last` (default) and `channels_first` are supported.
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`channels_last` corresponds to inputs with shape
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`(batch, height, width, channels)` while `channels_first` corresponds to
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inputs with shape `(batch, channels, height, width)`.
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name: A string, the name of the layer.
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Returns:
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Output tensor.
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Raises:
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ValueError: if eager execution is enabled.
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"""
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layer = MaxPooling2D(pool_size=pool_size, strides=strides,
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padding=padding, data_format=data_format,
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name=name)
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return layer.apply(inputs)
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@tf_export('layers.AveragePooling3D')
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class AveragePooling3D(keras_layers.AveragePooling3D, base.Layer):
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"""Average pooling layer for 3D inputs (e.g. volumes).
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Arguments:
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pool_size: An integer or tuple/list of 3 integers:
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(pool_depth, pool_height, pool_width)
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specifying the size of the pooling window.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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strides: An integer or tuple/list of 3 integers,
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specifying the strides of the pooling operation.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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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. The ordering of the dimensions in the inputs.
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`channels_last` (default) and `channels_first` are supported.
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`channels_last` corresponds to inputs with shape
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`(batch, depth, height, width, channels)` while `channels_first`
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corresponds to inputs with shape
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`(batch, channels, depth, height, width)`.
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name: A string, the name of the layer.
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"""
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def __init__(self, pool_size, strides,
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padding='valid', data_format='channels_last',
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name=None, **kwargs):
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if strides is None:
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raise ValueError('Argument `strides` must not be None.')
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super(AveragePooling3D, self).__init__(
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pool_size=pool_size, strides=strides,
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padding=padding, data_format=data_format, name=name, **kwargs)
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@tf_export('layers.average_pooling3d')
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def average_pooling3d(inputs,
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pool_size, strides,
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padding='valid', data_format='channels_last',
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name=None):
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"""Average pooling layer for 3D inputs (e.g. volumes).
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Arguments:
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inputs: The tensor over which to pool. Must have rank 5.
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pool_size: An integer or tuple/list of 3 integers:
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(pool_depth, pool_height, pool_width)
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specifying the size of the pooling window.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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strides: An integer or tuple/list of 3 integers,
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specifying the strides of the pooling operation.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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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. The ordering of the dimensions in the inputs.
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`channels_last` (default) and `channels_first` are supported.
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`channels_last` corresponds to inputs with shape
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`(batch, depth, height, width, channels)` while `channels_first`
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corresponds to inputs with shape
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`(batch, channels, depth, height, width)`.
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name: A string, the name of the layer.
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Returns:
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Output tensor.
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Raises:
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ValueError: if eager execution is enabled.
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"""
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layer = AveragePooling3D(pool_size=pool_size, strides=strides,
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padding=padding, data_format=data_format,
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name=name)
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return layer.apply(inputs)
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@tf_export('layers.MaxPooling3D')
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class MaxPooling3D(keras_layers.MaxPooling3D, base.Layer):
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"""Max pooling layer for 3D inputs (e.g. volumes).
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Arguments:
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pool_size: An integer or tuple/list of 3 integers:
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(pool_depth, pool_height, pool_width)
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specifying the size of the pooling window.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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strides: An integer or tuple/list of 3 integers,
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specifying the strides of the pooling operation.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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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. The ordering of the dimensions in the inputs.
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`channels_last` (default) and `channels_first` are supported.
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`channels_last` corresponds to inputs with shape
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`(batch, depth, height, width, channels)` while `channels_first`
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corresponds to inputs with shape
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`(batch, channels, depth, height, width)`.
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name: A string, the name of the layer.
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"""
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def __init__(self, pool_size, strides,
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padding='valid', data_format='channels_last',
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name=None, **kwargs):
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if strides is None:
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raise ValueError('Argument `strides` must not be None.')
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super(MaxPooling3D, self).__init__(
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pool_size=pool_size, strides=strides,
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padding=padding, data_format=data_format, name=name, **kwargs)
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@tf_export('layers.max_pooling3d')
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def max_pooling3d(inputs,
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pool_size, strides,
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padding='valid', data_format='channels_last',
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name=None):
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"""Max pooling layer for 3D inputs (e.g. volumes).
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Arguments:
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inputs: The tensor over which to pool. Must have rank 5.
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pool_size: An integer or tuple/list of 3 integers:
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(pool_depth, pool_height, pool_width)
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specifying the size of the pooling window.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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strides: An integer or tuple/list of 3 integers,
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specifying the strides of the pooling operation.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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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. The ordering of the dimensions in the inputs.
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`channels_last` (default) and `channels_first` are supported.
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`channels_last` corresponds to inputs with shape
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`(batch, depth, height, width, channels)` while `channels_first`
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corresponds to inputs with shape
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`(batch, channels, depth, height, width)`.
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name: A string, the name of the layer.
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Returns:
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Output tensor.
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Raises:
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ValueError: if eager execution is enabled.
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"""
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layer = MaxPooling3D(pool_size=pool_size, strides=strides,
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padding=padding, data_format=data_format,
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name=name)
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return layer.apply(inputs)
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# Aliases
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AvgPool2D = AveragePooling2D
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MaxPool2D = MaxPooling2D
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max_pool2d = max_pooling2d
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avg_pool2d = average_pooling2d
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