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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. | |
| # ============================================================================== | |
| """Image/Mask decoder used while pretraining the network.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import tensorflow as tf | |
| slim = tf.contrib.slim | |
| _FEATURE_MAP_SIZE = 8 | |
| def _postprocess_im(images): | |
| """Performs post-processing for the images returned from conv net. | |
| Transforms the value from [-1, 1] to [0, 1]. | |
| """ | |
| return (images + 1) * 0.5 | |
| def model(identities, poses, params, is_training): | |
| """Decoder model to get image and mask from latent embedding.""" | |
| del is_training | |
| f_dim = params.f_dim | |
| fc_dim = params.fc_dim | |
| outputs = dict() | |
| with slim.arg_scope( | |
| [slim.fully_connected, slim.conv2d_transpose], | |
| weights_initializer=tf.truncated_normal_initializer(stddev=0.02, seed=1)): | |
| # Concatenate the identity and pose units | |
| h0 = tf.concat([identities, poses], 1) | |
| h0 = slim.fully_connected(h0, fc_dim, activation_fn=tf.nn.relu) | |
| h1 = slim.fully_connected(h0, fc_dim, activation_fn=tf.nn.relu) | |
| # Mask decoder | |
| dec_m0 = slim.fully_connected( | |
| h1, (_FEATURE_MAP_SIZE**2) * f_dim * 2, activation_fn=tf.nn.relu) | |
| dec_m0 = tf.reshape( | |
| dec_m0, [-1, _FEATURE_MAP_SIZE, _FEATURE_MAP_SIZE, f_dim * 2]) | |
| dec_m1 = slim.conv2d_transpose( | |
| dec_m0, f_dim, [5, 5], stride=2, activation_fn=tf.nn.relu) | |
| dec_m2 = slim.conv2d_transpose( | |
| dec_m1, int(f_dim / 2), [5, 5], stride=2, activation_fn=tf.nn.relu) | |
| dec_m3 = slim.conv2d_transpose( | |
| dec_m2, 1, [5, 5], stride=2, activation_fn=tf.nn.sigmoid) | |
| # Image decoder | |
| dec_i0 = slim.fully_connected( | |
| h1, (_FEATURE_MAP_SIZE**2) * f_dim * 4, activation_fn=tf.nn.relu) | |
| dec_i0 = tf.reshape( | |
| dec_i0, [-1, _FEATURE_MAP_SIZE, _FEATURE_MAP_SIZE, f_dim * 4]) | |
| dec_i1 = slim.conv2d_transpose( | |
| dec_i0, f_dim * 2, [5, 5], stride=2, activation_fn=tf.nn.relu) | |
| dec_i2 = slim.conv2d_transpose( | |
| dec_i1, f_dim * 2, [5, 5], stride=2, activation_fn=tf.nn.relu) | |
| dec_i3 = slim.conv2d_transpose( | |
| dec_i2, 3, [5, 5], stride=2, activation_fn=tf.nn.tanh) | |
| outputs = dict() | |
| outputs['images'] = _postprocess_im(dec_i3) | |
| outputs['masks'] = dec_m3 | |
| return outputs | |