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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. | |
| # ============================================================================== | |
| """Training/Pretraining encoder as used in PTN (NIPS16).""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import tensorflow as tf | |
| slim = tf.contrib.slim | |
| def _preprocess(images): | |
| return images * 2 - 1 | |
| def model(images, params, is_training): | |
| """Model encoding the images into view-invariant embedding.""" | |
| del is_training # Unused | |
| image_size = images.get_shape().as_list()[1] | |
| f_dim = params.f_dim | |
| fc_dim = params.fc_dim | |
| z_dim = params.z_dim | |
| outputs = dict() | |
| images = _preprocess(images) | |
| with slim.arg_scope( | |
| [slim.conv2d, slim.fully_connected], | |
| weights_initializer=tf.truncated_normal_initializer(stddev=0.02, seed=1)): | |
| h0 = slim.conv2d(images, f_dim, [5, 5], stride=2, activation_fn=tf.nn.relu) | |
| h1 = slim.conv2d(h0, f_dim * 2, [5, 5], stride=2, activation_fn=tf.nn.relu) | |
| h2 = slim.conv2d(h1, f_dim * 4, [5, 5], stride=2, activation_fn=tf.nn.relu) | |
| # Reshape layer | |
| s8 = image_size // 8 | |
| h2 = tf.reshape(h2, [-1, s8 * s8 * f_dim * 4]) | |
| h3 = slim.fully_connected(h2, fc_dim, activation_fn=tf.nn.relu) | |
| h4 = slim.fully_connected(h3, fc_dim, activation_fn=tf.nn.relu) | |
| outputs['ids'] = slim.fully_connected(h4, z_dim, activation_fn=tf.nn.relu) | |
| outputs['poses'] = slim.fully_connected(h4, z_dim, activation_fn=tf.nn.relu) | |
| return outputs | |