#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
from tensorlayer.layers.core import Layer
from tensorlayer.layers.utils import flatten_reshape
__all__ = [
'Flatten',
'Reshape',
'Transpose',
'Shuffle',
]
[docs]class Flatten(Layer):
"""A layer that reshapes high-dimension input into a vector.
Then we often apply Dense, RNN, Concat and etc on the top of a flatten layer.
[batch_size, mask_row, mask_col, n_mask] ---> [batch_size, mask_row * mask_col * n_mask]
Parameters
----------
name : None or str
A unique layer name.
Examples
--------
>>> x = tl.layers.Input([8, 4, 3], name='input')
>>> y = tl.layers.Flatten(name='flatten')(x)
[8, 12]
"""
def __init__(self, name=None): #'flatten'):
super(Flatten, self).__init__(name)
self.build()
self._built = True
logging.info("Flatten %s:" % (self.name))
def __repr__(self):
s = '{classname}('
s += 'name=\'{name}\''
s += ')'
return s.format(classname=self.__class__.__name__, **self.__dict__)
def build(self, inputs_shape=None):
pass
# @tf.function
def forward(self, inputs):
outputs = flatten_reshape(inputs, name=self.name)
return outputs
[docs]class Reshape(Layer):
"""A layer that reshapes a given tensor.
Parameters
----------
shape : tuple of int
The output shape, see ``tf.reshape``.
name : str
A unique layer name.
Examples
--------
>>> x = tl.layers.Input([8, 4, 3], name='input')
>>> y = tl.layers.Reshape(shape=[-1, 12], name='reshape')(x)
(8, 12)
"""
def __init__(self, shape, name=None): #'reshape'):
super(Reshape, self).__init__(name)
self.shape = shape
logging.info("Reshape %s" % (self.name))
self.build()
self._built = True
def __repr__(self):
s = '{classname}('
s += 'shape={shape},'
s += 'name=\'{name}\''
s += ')'
return s.format(classname=self.__class__.__name__, **self.__dict__)
def build(self, inputs_shape=None):
pass
# @tf.function
def forward(self, inputs):
outputs = tf.reshape(inputs, shape=self.shape, name=self.name)
return outputs
[docs]class Transpose(Layer):
"""A layer that transposes the dimension of a tensor.
See `tf.transpose() <https://www.tensorflow.org/api_docs/python/tf/transpose>`__ .
Parameters
----------
perm: list of int
The permutation of the dimensions, similar with ``numpy.transpose``.
If None, it is set to (n-1...0), where n is the rank of the input tensor.
conjugate: bool
By default False. If True, returns the complex conjugate of complex numbers (and transposed)
For example [[1+1j, 2+2j]] --> [[1-1j], [2-2j]]
name : str
A unique layer name.
Examples
----------
>>> x = tl.layers.Input([8, 4, 3], name='input')
>>> y = tl.layers.Transpose(perm=[0, 2, 1], conjugate=False, name='trans')(x)
(8, 3, 4)
"""
def __init__(self, perm=None, conjugate=False, name=None): #'transpose'):
super(Transpose, self).__init__(name)
self.perm = perm
self.conjugate = conjugate
logging.info("Transpose %s: perm: %s, conjugate: %s" % (self.name, self.perm, self.conjugate))
self.build()
self._built = True
def __repr__(self):
s = '{classname}('
s += 'perm={perm},'
s += 'conjugate={conjugate},'
s += 'name=\'{name}\''
s += ')'
return s.format(classname=self.__class__.__name__, **self.__dict__)
def build(self, inputs_shape=None):
pass
# @tf.function
def forward(self, inputs):
outputs = tf.transpose(a=inputs, perm=self.perm, conjugate=self.conjugate, name=self.name)
return outputs
[docs]class Shuffle(Layer):
"""A layer that shuffle a 2D image [batch, height, width, channel], see `here <https://arxiv.org/abs/1707.01083>`__.
Parameters
----------
group: int
The number of groups.
name : str
A unique layer name.
Examples
--------
>>> x = tl.layers.Input([1, 16, 16, 8], name='input')
>>> y = tl.layers.Shuffle(group=2, name='shuffle')(x)
(1, 16, 16, 8)
"""
def __init__(self, group, name=None): #'reshape'):
super(Shuffle, self).__init__(name)
self.group = group
logging.info("Shuffle %s" % (self.name))
self.build()
self._built = True
def __repr__(self):
s = '{classname}('
s += 'group={group},'
s += 'name=\'{name}\''
s += ')'
return s.format(classname=self.__class__.__name__, **self.__dict__)
def build(self, inputs_shape=None):
pass
# @tf.function
def forward(self, inputs):
in_shape = inputs.get_shape().as_list()
h, w, in_channel = in_shape[1:]
if in_channel % self.group != 0:
raise ValueError(
"The in_channel must be a multiple of the number of groups. The in_channel got %d and the number of groups is %d."
% (in_channel, self.group)
)
temp = tf.reshape(inputs, [-1, h, w, in_channel // self.group, self.group])
temp = tf.transpose(temp, [0, 1, 2, 4, 3])
outputs = tf.reshape(temp, [-1, h, w, in_channel], name=self.name)
return outputs