#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorlayer.layers.core import Layer
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
__all__ = [
'PoolLayer',
'MaxPool1d',
'MeanPool1d',
'MaxPool2d',
'MeanPool2d',
'MaxPool3d',
'MeanPool3d',
'GlobalMaxPool1d',
'GlobalMeanPool1d',
'GlobalMaxPool2d',
'GlobalMeanPool2d',
'GlobalMaxPool3d',
'GlobalMeanPool3d',
]
[docs]class PoolLayer(Layer):
"""
The :class:`PoolLayer` class is a Pooling layer.
You can choose ``tf.nn.max_pool`` and ``tf.nn.avg_pool`` for 2D input or
``tf.nn.max_pool3d`` and ``tf.nn.avg_pool3d`` for 3D input.
Parameters
----------
prev_layer : :class:`Layer`
The previous layer.
ksize : tuple of int
The size of the window for each dimension of the input tensor.
Note that: len(ksize) >= 4.
strides : tuple of int
The stride of the sliding window for each dimension of the input tensor.
Note that: len(strides) >= 4.
padding : str
The padding algorithm type: "SAME" or "VALID".
pool : pooling function
One of ``tf.nn.max_pool``, ``tf.nn.avg_pool``, ``tf.nn.max_pool3d`` and ``f.nn.avg_pool3d``.
See `TensorFlow pooling APIs <https://www.tensorflow.org/versions/master/api_docs/python/nn.html#pooling>`__
name : str
A unique layer name.
Examples
--------
- see :class:`Conv2dLayer`.
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self,
prev_layer,
ksize=(1, 2, 2, 1),
strides=(1, 2, 2, 1),
padding='SAME',
pool=tf.nn.max_pool,
name='pool_layer',
):
super(PoolLayer, self).__init__(prev_layer=prev_layer, name=name)
logging.info(
"PoolLayer %s: ksize: %s strides: %s padding: %s pool: %s" %
(self.name, str(ksize), str(strides), padding, pool.__name__)
)
self.outputs = pool(self.inputs, ksize=ksize, strides=strides, padding=padding, name=name)
self._add_layers(self.outputs)
[docs]class MaxPool1d(Layer):
"""Max pooling for 1D signal.
Parameters
----------
prev_layer : :class:`Layer`
The previous layer with a output rank as 3 [batch, length, channel].
filter_size : tuple of int
Pooling window size.
strides : tuple of int
Strides of the pooling operation.
padding : str
The padding method: 'valid' or 'same'.
data_format : str
One of channels_last (default, [batch, length, channel]) or channels_first. The ordering of the dimensions in the inputs.
name : str
A unique layer name.
"""
@deprecated_alias(net='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self, prev_layer, filter_size=3, strides=2, padding='valid', data_format='channels_last', name='maxpool1d'
):
super(MaxPool1d, self).__init__(prev_layer=prev_layer, name=name)
logging.info(
"MaxPool1d %s: filter_size: %s strides: %s padding: %s" %
(self.name, str(filter_size), str(strides), str(padding))
)
self.outputs = tf.layers.max_pooling1d(
self.inputs, filter_size, strides, padding=padding, data_format=data_format, name=name
)
self._add_layers(self.outputs)
[docs]class MeanPool1d(Layer):
"""Mean pooling for 1D signal.
Parameters
------------
prev_layer : :class:`Layer`
The previous layer with a output rank as 3.
filter_size : tuple of int
Pooling window size.
strides : tuple of int
Strides of the pooling operation.
padding : str
The padding method: 'valid' or 'same'.
data_format : str
One of channels_last (default, [batch, length, channel]) or channels_first. The ordering of the dimensions in the inputs.
name : str
A unique layer name.
"""
# logging.info("MeanPool1d %s: filter_size: %s strides: %s padding: %s" % (self.name, str(filter_size), str(strides), str(padding)))
# outputs = tf.layers.average_pooling1d(prev_layer.outputs, filter_size, strides, padding=padding, data_format=data_format, name=name)
#
# net_new = copy.copy(prev_layer)
# net_new.outputs = outputs
# net_new.all_layers.extend([outputs])
# return net_new
@deprecated_alias(net='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self, prev_layer, filter_size=3, strides=2, padding='valid', data_format='channels_last', name='meanpool1d'
):
super(MeanPool1d, self).__init__(prev_layer=prev_layer, name=name)
logging.info(
"MeanPool1d %s: filter_size: %s strides: %s padding: %s" %
(self.name, str(filter_size), str(strides), str(padding))
)
self.outputs = tf.layers.average_pooling1d(
prev_layer.outputs, filter_size, strides, padding=padding, data_format=data_format, name=name
)
self._add_layers(self.outputs)
[docs]class MaxPool2d(Layer):
"""Max pooling for 2D image.
Parameters
-----------
prev_layer : :class:`Layer`
The previous layer with a output rank as 4.
filter_size : tuple of int
(height, width) for filter size.
strides : tuple of int
(height, width) for strides.
padding : str
The padding method: 'valid' or 'same'.
data_format : str
One of channels_last (default, [batch, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
name : str
A unique layer name.
"""
@deprecated_alias(net='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self, prev_layer, filter_size=(3, 3), strides=(2, 2), padding='SAME', data_format='channels_last',
name='maxpool2d'
):
if strides is None:
strides = filter_size
super(MaxPool2d, self).__init__(prev_layer=prev_layer, name=name)
logging.info(
"MaxPool2d %s: filter_size: %s strides: %s padding: %s" %
(self.name, str(filter_size), str(strides), str(padding))
)
self.outputs = tf.layers.max_pooling2d(
self.inputs, filter_size, strides, padding=padding, data_format=data_format, name=name
)
self._add_layers(self.outputs)
[docs]class MeanPool2d(Layer):
"""Mean pooling for 2D image [batch, height, width, channel].
Parameters
-----------
prev_layer : :class:`Layer`
The previous layer with a output rank as 4 [batch, height, width, channel].
filter_size : tuple of int
(height, width) for filter size.
strides : tuple of int
(height, width) for strides.
padding : str
The padding method: 'valid' or 'same'.
data_format : str
One of channels_last (default, [batch, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
name : str
A unique layer name.
"""
@deprecated_alias(net='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self, prev_layer, filter_size=(3, 3), strides=(2, 2), padding='SAME', data_format='channels_last',
name='meanpool2d'
):
if strides is None:
strides = filter_size
super(MeanPool2d, self).__init__(prev_layer=prev_layer, name=name)
logging.info(
"MeanPool2d %s: filter_size: %s strides: %s padding: %s" %
(self.name, str(filter_size), str(strides), str(padding))
)
self.outputs = tf.layers.average_pooling2d(
self.inputs, filter_size, strides, padding=padding, data_format=data_format, name=name
)
self._add_layers(self.outputs)
[docs]class MaxPool3d(Layer):
"""Max pooling for 3D volume.
Parameters
------------
prev_layer : :class:`Layer`
The previous layer with a output rank as 5.
filter_size : tuple of int
Pooling window size.
strides : tuple of int
Strides of the pooling operation.
padding : str
The padding method: 'valid' or 'same'.
data_format : str
One of channels_last (default, [batch, depth, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
name : str
A unique layer name.
Returns
-------
:class:`Layer`
A max pooling 3-D layer with a output rank as 5.
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self, prev_layer, filter_size=(3, 3, 3), strides=(2, 2, 2), padding='valid', data_format='channels_last',
name='maxpool3d'
):
super(MaxPool3d, self).__init__(prev_layer=prev_layer, name=name)
logging.info(
"MaxPool3d %s: filter_size: %s strides: %s padding: %s" %
(self.name, str(filter_size), str(strides), str(padding))
)
self.outputs = tf.layers.max_pooling3d(
self.inputs, filter_size, strides, padding=padding, data_format=data_format, name=name
)
self._add_layers(self.outputs)
[docs]class MeanPool3d(Layer):
"""Mean pooling for 3D volume.
Parameters
------------
prev_layer : :class:`Layer`
The previous layer with a output rank as 5.
filter_size : tuple of int
Pooling window size.
strides : tuple of int
Strides of the pooling operation.
padding : str
The padding method: 'valid' or 'same'.
data_format : str
One of channels_last (default, [batch, depth, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
name : str
A unique layer name.
Returns
-------
:class:`Layer`
A mean pooling 3-D layer with a output rank as 5.
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self, prev_layer, filter_size=(3, 3, 3), strides=(2, 2, 2), padding='valid', data_format='channels_last',
name='meanpool3d'
):
super(MeanPool3d, self).__init__(prev_layer=prev_layer, name=name)
logging.info(
"MeanPool3d %s: filter_size: %s strides: %s padding: %s" %
(self.name, str(filter_size), str(strides), str(padding))
)
self.outputs = tf.layers.average_pooling3d(
prev_layer.outputs, filter_size, strides, padding=padding, data_format=data_format, name=name
)
self._add_layers(self.outputs)
[docs]class GlobalMaxPool1d(Layer):
"""The :class:`GlobalMaxPool1d` class is a 1D Global Max Pooling layer.
Parameters
------------
prev_layer : :class:`Layer`
The previous layer with a output rank as 3 [batch, length, channel].
data_format : str
One of channels_last (default, [batch, length, channel]) or channels_first. The ordering of the dimensions in the inputs.
name : str
A unique layer name.
Examples
---------
>>> x = tf.placeholder("float32", [None, 100, 30])
>>> n = InputLayer(x, name='in')
>>> n = GlobalMaxPool1d(n)
[None, 30]
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(self, prev_layer, data_format="channels_last", name='globalmaxpool1d'):
super(GlobalMaxPool1d, self).__init__(prev_layer=prev_layer, name=name)
logging.info("GlobalMaxPool1d %s" % self.name)
if data_format == 'channels_last':
self.outputs = tf.reduce_max(self.inputs, axis=1, name=name)
elif data_format == 'channels_first':
self.outputs = tf.reduce_max(self.inputs, axis=2, name=name)
else:
raise ValueError(
"`data_format` should have one of the following values: [`channels_last`, `channels_first`]"
)
self._add_layers(self.outputs)
[docs]class GlobalMeanPool1d(Layer):
"""The :class:`GlobalMeanPool1d` class is a 1D Global Mean Pooling layer.
Parameters
------------
prev_layer : :class:`Layer`
The previous layer with a output rank as 3 [batch, length, channel].
data_format : str
One of channels_last (default, [batch, length, channel]) or channels_first. The ordering of the dimensions in the inputs.
name : str
A unique layer name.
Examples
---------
>>> import tensorflow as tf
>>> import tensorlayer as tl
>>> x = tf.placeholder("float32", [None, 100, 30])
>>> n = tl.layers.InputLayer(x, name='in')
>>> n = tl.layers.GlobalMeanPool1d(n)
[None, 30]
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(self, prev_layer, data_format='channels_last', name='globalmeanpool1d'):
super(GlobalMeanPool1d, self).__init__(prev_layer=prev_layer, name=name)
logging.info("GlobalMeanPool1d %s" % self.name)
if data_format == 'channels_last':
self.outputs = tf.reduce_mean(self.inputs, axis=1, name=name)
elif data_format == 'channels_first':
self.outputs = tf.reduce_mean(self.inputs, axis=2, name=name)
else:
raise ValueError(
"`data_format` should have one of the following values: [`channels_last`, `channels_first`]"
)
self._add_layers(self.outputs)
[docs]class GlobalMaxPool2d(Layer):
"""The :class:`GlobalMaxPool2d` class is a 2D Global Max Pooling layer.
Parameters
------------
prev_layer : :class:`Layer`
The previous layer with a output rank as 4 [batch, height, width, channel].
data_format : str
One of channels_last (default, [batch, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
name : str
A unique layer name.
Examples
---------
>>> import tensorflow as tf
>>> import tensorlayer as tl
>>> x = tf.placeholder("float32", [None, 100, 100, 30])
>>> n = tl.layers.InputLayer(x, name='in2')
>>> n = tl.layers.GlobalMaxPool2d(n)
[None, 30]
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(self, prev_layer, data_format='channels_last', name='globalmaxpool2d'):
super(GlobalMaxPool2d, self).__init__(prev_layer=prev_layer, name=name)
logging.info("GlobalMaxPool2d %s" % self.name)
if data_format == 'channels_last':
self.outputs = tf.reduce_max(self.inputs, axis=[1, 2], name=name)
elif data_format == 'channels_first':
self.outputs = tf.reduce_max(self.inputs, axis=[2, 3], name=name)
else:
raise ValueError(
"`data_format` should have one of the following values: [`channels_last`, `channels_first`]"
)
self._add_layers(self.outputs)
[docs]class GlobalMeanPool2d(Layer):
"""The :class:`GlobalMeanPool2d` class is a 2D Global Mean Pooling layer.
Parameters
------------
prev_layer : :class:`Layer`
The previous layer with a output rank as 4 [batch, height, width, channel].
data_format : str
One of channels_last (default, [batch, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
name : str
A unique layer name.
Examples
---------
>>> import tensorflow as tf
>>> import tensorlayer as tl
>>> x = tf.placeholder("float32", [None, 100, 100, 30])
>>> n = tl.layers.InputLayer(x, name='in2')
>>> n = tl.layers.GlobalMeanPool2d(n)
[None, 30]
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(self, prev_layer, data_format='channels_last', name='globalmeanpool2d'):
super(GlobalMeanPool2d, self).__init__(prev_layer=prev_layer, name=name)
logging.info("GlobalMeanPool2d %s" % self.name)
if data_format == 'channels_last':
self.outputs = tf.reduce_mean(self.inputs, axis=[1, 2], name=name)
elif data_format == 'channels_first':
self.outputs = tf.reduce_mean(self.inputs, axis=[2, 3], name=name)
else:
raise ValueError(
"`data_format` should have one of the following values: [`channels_last`, `channels_first`]"
)
self._add_layers(self.outputs)
[docs]class GlobalMaxPool3d(Layer):
"""The :class:`GlobalMaxPool3d` class is a 3D Global Max Pooling layer.
Parameters
------------
prev_layer : :class:`Layer`
The previous layer with a output rank as 5 [batch, depth, height, width, channel].
data_format : str
One of channels_last (default, [batch, depth, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
name : str
A unique layer name.
Examples
---------
>>> import tensorflow as tf
>>> import tensorlayer as tl
>>> x = tf.placeholder("float32", [None, 100, 100, 100, 30])
>>> n = tl.layers.InputLayer(x, name='in')
>>> n = tl.layers.GlobalMaxPool3d(n)
[None, 30]
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(self, prev_layer, data_format='channels_last', name='globalmaxpool3d'):
super(GlobalMaxPool3d, self).__init__(prev_layer=prev_layer, name=name)
logging.info("GlobalMaxPool3d %s" % self.name)
if data_format == 'channels_last':
self.outputs = tf.reduce_max(self.inputs, axis=[1, 2, 3], name=name)
elif data_format == 'channels_first':
self.outputs = tf.reduce_max(self.inputs, axis=[2, 3, 4], name=name)
else:
raise ValueError(
"`data_format` should have one of the following values: [`channels_last`, `channels_first`]"
)
self._add_layers(self.outputs)
[docs]class GlobalMeanPool3d(Layer):
"""The :class:`GlobalMeanPool3d` class is a 3D Global Mean Pooling layer.
Parameters
------------
prev_layer : :class:`Layer`
The previous layer with a output rank as 5 [batch, depth, height, width, channel].
data_format : str
One of channels_last (default, [batch, depth, height, width, channel]) or channels_first. The ordering of the dimensions in the inputs.
name : str
A unique layer name.
Examples
---------
>>> import tensorflow as tf
>>> import tensorlayer as tl
>>> x = tf.placeholder("float32", [None, 100, 100, 100, 30])
>>> n = tl.layers.InputLayer(x, name='in')
>>> n = tl.layers.GlobalMeanPool2d(n)
[None, 30]
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(self, prev_layer, data_format='channels_last', name='globalmeanpool3d'):
super(GlobalMeanPool3d, self).__init__(prev_layer=prev_layer, name=name)
logging.info("GlobalMeanPool3d %s" % self.name)
if data_format == 'channels_last':
self.outputs = tf.reduce_mean(self.inputs, axis=[1, 2, 3], name=name)
elif data_format == 'channels_first':
self.outputs = tf.reduce_mean(self.inputs, axis=[2, 3, 4], name=name)
else:
raise ValueError(
"`data_format` should have one of the following values: [`channels_last`, `channels_first`]"
)
self._add_layers(self.outputs)