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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) | |