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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
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