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# Copyright 2017 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.
# ==============================================================================
"""Common blocks which work as operators on other blocks."""
import tensorflow as tf
import block_base
# pylint: disable=not-callable
class CompositionOperator(block_base.BlockBase):
"""Composition of several blocks."""
def __init__(self, block_list, name=None):
"""Initialization of the composition operator.
Args:
block_list: List of blocks.BlockBase that are chained to create
a new blocks.BlockBase.
name: Name of this block.
"""
super(CompositionOperator, self).__init__(name)
self._blocks = block_list
def _Apply(self, x):
"""Apply successively all the blocks on the given input tensor."""
h = x
for layer in self._blocks:
h = layer(h)
return h
class LineOperator(block_base.BlockBase):
"""Repeat the same block over all the lines of an input tensor."""
def __init__(self, block, name=None):
super(LineOperator, self).__init__(name)
self._block = block
def _Apply(self, x):
height = x.get_shape()[1].value
if height is None:
raise ValueError('Unknown tensor height')
all_line_x = tf.split(value=x, num_or_size_splits=height, axis=1)
y = []
for line_x in all_line_x:
y.append(self._block(line_x))
y = tf.concat(values=y, axis=1)
return y
class TowerOperator(block_base.BlockBase):
"""Parallel execution with concatenation of several blocks."""
def __init__(self, block_list, dim=3, name=None):
"""Initialization of the parallel exec + concat (Tower).
Args:
block_list: List of blocks.BlockBase that are chained to create
a new blocks.BlockBase.
dim: the dimension on which to concat.
name: Name of this block.
"""
super(TowerOperator, self).__init__(name)
self._blocks = block_list
self._concat_dim = dim
def _Apply(self, x):
"""Apply successively all the blocks on the given input tensor."""
outputs = [layer(x) for layer in self._blocks]
return tf.concat(outputs, self._concat_dim)
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