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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. | |
| # ============================================================================== | |
| """Creates rotator network model. | |
| This model performs the out-of-plane rotations given input image and action. | |
| The action is either no-op, rotate clockwise or rotate counter-clockwise. | |
| """ | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import tensorflow as tf | |
| def bilinear(input_x, input_y, output_size): | |
| """Define the bilinear transformation layer.""" | |
| shape_x = input_x.get_shape().as_list() | |
| shape_y = input_y.get_shape().as_list() | |
| weights_initializer = tf.truncated_normal_initializer(stddev=0.02, | |
| seed=1) | |
| biases_initializer = tf.constant_initializer(0.0) | |
| matrix = tf.get_variable("Matrix", [shape_x[1], shape_y[1], output_size], | |
| tf.float32, initializer=weights_initializer) | |
| bias = tf.get_variable("Bias", [output_size], | |
| initializer=biases_initializer) | |
| # Add to GraphKeys.MODEL_VARIABLES | |
| tf.contrib.framework.add_model_variable(matrix) | |
| tf.contrib.framework.add_model_variable(bias) | |
| # Define the transformation | |
| h0 = tf.matmul(input_x, tf.reshape(matrix, | |
| [shape_x[1], shape_y[1]*output_size])) | |
| h0 = tf.reshape(h0, [-1, shape_y[1], output_size]) | |
| h1 = tf.tile(tf.reshape(input_y, [-1, shape_y[1], 1]), | |
| [1, 1, output_size]) | |
| h1 = tf.multiply(h0, h1) | |
| return tf.reduce_sum(h1, 1) + bias | |
| def model(poses, actions, params, is_training): | |
| """Model for performing rotation.""" | |
| del is_training # Unused | |
| return bilinear(poses, actions, params.z_dim) | |