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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.
# ==============================================================================
"""Factory module for different encoder/decoder network models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from nets import ptn_encoder
from nets import ptn_im_decoder
from nets import ptn_rotator
_NAME_TO_NETS = {
'ptn_encoder': ptn_encoder,
'ptn_rotator': ptn_rotator,
'ptn_im_decoder': ptn_im_decoder,
}
def _get_network(name):
"""Gets a single network component."""
if name not in _NAME_TO_NETS:
raise ValueError('Network name [%s] not recognized.' % name)
return _NAME_TO_NETS[name].model
def get(params, is_training=False, reuse=False):
"""Factory function to retrieve a network model.
Args:
params: Different parameters used througout ptn, typically FLAGS (dict)
is_training: Set to True if while training (boolean)
reuse: Set as True if either using a pre-trained model or
in the training loop while the graph has already been built (boolean)
Returns:
Model function for network (inputs to outputs)
"""
def model(inputs):
"""Model function corresponding to a specific network architecture."""
outputs = {}
# First, build the encoder.
encoder_fn = _get_network(params.encoder_name)
with tf.variable_scope('encoder', reuse=reuse):
# Produces id/pose units
features = encoder_fn(inputs['images_0'], params, is_training)
outputs['ids'] = features['ids']
outputs['poses_0'] = features['poses']
# Second, build the rotator and decoder.
rotator_fn = _get_network(params.rotator_name)
with tf.variable_scope('rotator', reuse=reuse):
outputs['poses_1'] = rotator_fn(outputs['poses_0'], inputs['actions'],
params, is_training)
decoder_fn = _get_network(params.decoder_name)
with tf.variable_scope('decoder', reuse=reuse):
dec_output = decoder_fn(outputs['ids'], outputs['poses_1'], params,
is_training)
outputs['images_1'] = dec_output['images']
outputs['masks_1'] = dec_output['masks']
# Third, build the recurrent connection
for k in range(1, params.step_size):
with tf.variable_scope('rotator', reuse=True):
outputs['poses_%d' % (k + 1)] = rotator_fn(
outputs['poses_%d' % k], inputs['actions'], params, is_training)
with tf.variable_scope('decoder', reuse=True):
dec_output = decoder_fn(outputs['ids'], outputs['poses_%d' % (k + 1)],
params, is_training)
outputs['images_%d' % (k + 1)] = dec_output['images']
outputs['masks_%d' % (k + 1)] = dec_output['masks']
return outputs
return model
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