Added ptn directory

ptn/nets/ptn_vox_decoder.py

+# 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 decoder 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
+
+
+@tf.contrib.framework.add_arg_scope
+def conv3d_transpose(inputs,
+                     num_outputs,
+                     kernel_size,
+                     stride=1,
+                     padding='SAME',
+                     activation_fn=tf.nn.relu,
+                     weights_initializer=tf.contrib.layers.xavier_initializer(),
+                     biases_initializer=tf.zeros_initializer(),
+                     reuse=None,
+                     trainable=True,
+                     scope=None):
+  """Wrapper for conv3d_transpose layer.
+
+  This function wraps the tf.conv3d_transpose with basic non-linearity.
+  Tt creates a variable called `weights`, representing the kernel,
+  that is convoled with the input. A second varibale called `biases'
+  is added to the result of operation.
+  """
+  with tf.variable_scope(
+      scope, 'Conv3d_transpose', [inputs], reuse=reuse):
+    dtype = inputs.dtype.base_dtype
+    kernel_d, kernel_h, kernel_w = kernel_size[0:3]
+    num_filters_in = inputs.get_shape()[4]
+
+    weights_shape = [kernel_d, kernel_h, kernel_w, num_outputs, num_filters_in]
+    weights = tf.get_variable('weights',
+                              shape=weights_shape,
+                              dtype=dtype,
+                              initializer=weights_initializer,
+                              trainable=trainable)
+    tf.contrib.framework.add_model_variable(weights)
+
+    input_shape = inputs.get_shape().as_list()
+    batch_size = input_shape[0]
+    depth = input_shape[1]
+    height = input_shape[2]
+    width = input_shape[3]
+
+    def get_deconv_dim(dim_size, stride_size):
+      # Only support padding='SAME'.
+      if isinstance(dim_size, tf.Tensor):
+        dim_size = tf.multiply(dim_size, stride_size)
+      elif dim_size is not None:
+        dim_size *= stride_size
+      return dim_size
+
+    out_depth = get_deconv_dim(depth, stride)
+    out_height = get_deconv_dim(height, stride)
+    out_width = get_deconv_dim(width, stride)
+
+    out_shape = [batch_size, out_depth, out_height, out_width, num_outputs]
+    outputs = tf.nn.conv3d_transpose(inputs, weights, out_shape,
+                                     [1, stride, stride, stride, 1],
+                                     padding=padding)
+
+    outputs.set_shape(out_shape)
+
+    if biases_initializer is not None:
+      biases = tf.get_variable('biases',
+                               shape=[num_outputs,],
+                               dtype=dtype,
+                               initializer=biases_initializer,
+                               trainable=trainable)
+      tf.contrib.framework.add_model_variable(biases)
+      outputs = tf.nn.bias_add(outputs, biases)
+
+    if activation_fn:
+      outputs = activation_fn(outputs)
+    return outputs
+
+
+def model(identities, params, is_training):
+  """Model transforming embedding to voxels."""
+  del is_training  # Unused
+  f_dim = params.f_dim
+
+  # Please refer to the original implementation: github.com/xcyan/nips16_PTN
+  # In TF replication, we use a slightly different architecture.
+  with slim.arg_scope(
+      [slim.fully_connected, conv3d_transpose],
+      weights_initializer=tf.truncated_normal_initializer(stddev=0.02, seed=1)):
+    h0 = slim.fully_connected(
+        identities, 4 * 4 * 4 * f_dim * 8, activation_fn=tf.nn.relu)
+    h1 = tf.reshape(h0, [-1, 4, 4, 4, f_dim * 8])
+    h1 = conv3d_transpose(
+        h1, f_dim * 4, [4, 4, 4], stride=2, activation_fn=tf.nn.relu)
+    h2 = conv3d_transpose(
+        h1, int(f_dim * 3 / 2), [5, 5, 5], stride=2, activation_fn=tf.nn.relu)
+    h3 = conv3d_transpose(
+        h2, 1, [6, 6, 6], stride=2, activation_fn=tf.nn.sigmoid)
+  return h3

ptn/pretrain_rotator.py

+# 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.
+# ==============================================================================
+
+"""Contains training plan for the Rotator model (Pretraining in NIPS16)."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+import numpy as np
+import tensorflow as tf
+
+from tensorflow import app
+
+import model_rotator as model
+
+flags = tf.app.flags
+slim = tf.contrib.slim
+
+flags.DEFINE_string('inp_dir', '',
+                    'Directory path containing the input data (tfrecords).')
+flags.DEFINE_string(
+    'dataset_name', 'shapenet_chair',
+    'Dataset name that is to be used for training and evaluation.')
+flags.DEFINE_integer('z_dim', 512, '')
+flags.DEFINE_integer('a_dim', 3, '')
+flags.DEFINE_integer('f_dim', 64, '')
+flags.DEFINE_integer('fc_dim', 1024, '')
+flags.DEFINE_integer('num_views', 24, 'Num of viewpoints in the input data.')
+flags.DEFINE_integer('image_size', 64,
+                     'Input images dimension (pixels) - width & height.')
+flags.DEFINE_integer('step_size', 1, 'Steps to take for rotation in pretraining.')
+flags.DEFINE_integer('batch_size', 32, 'Batch size for training.')
+flags.DEFINE_string('encoder_name', 'ptn_encoder',
+                    'Name of the encoder network being used.')
+flags.DEFINE_string('decoder_name', 'ptn_im_decoder',
+                    'Name of the decoder network being used.')
+flags.DEFINE_string('rotator_name', 'ptn_rotator',
+                    'Name of the rotator network being used.')
+# Save options
+flags.DEFINE_string('checkpoint_dir', '/tmp/ptn_train/',
+                    'Directory path for saving trained models and other data.')
+flags.DEFINE_string('model_name', 'deeprotator_pretrain',
+                    'Name of the model used in naming the TF job. Must be different for each run.')
+flags.DEFINE_string('init_model', None,
+                    'Checkpoint path of the model to initialize with.')
+flags.DEFINE_integer('save_every', 1000,
+                     'Average period of steps after which we save a model.')
+# Optimization
+flags.DEFINE_float('image_weight', 10, 'Weighting factor for image loss.')
+flags.DEFINE_float('mask_weight', 1, 'Weighting factor for mask loss.')
+flags.DEFINE_float('learning_rate', 0.0001, 'Learning rate.')
+flags.DEFINE_float('weight_decay', 0.001, 'Weight decay parameter while training.')
+flags.DEFINE_float('clip_gradient_norm', 0, 'Gradient clim norm, leave 0 if no gradient clipping.')
+flags.DEFINE_integer('max_number_of_steps', 320000, 'Maximum number of steps for training.')
+# Summary
+flags.DEFINE_integer('save_summaries_secs', 15, 'Seconds interval for dumping TF summaries.')
+flags.DEFINE_integer('save_interval_secs', 60 * 5, 'Seconds interval to save models.')
+# Distribution
+flags.DEFINE_string('master', '', 'The address of the tensorflow master if running distributed.')
+flags.DEFINE_bool('sync_replicas', False, 'Whether to sync gradients between replicas for optimizer.')
+flags.DEFINE_integer('worker_replicas', 1, 'Number of worker replicas (train tasks).')
+flags.DEFINE_integer('backup_workers', 0, 'Number of backup workers.')
+flags.DEFINE_integer('ps_tasks', 0, 'Number of ps tasks.')
+flags.DEFINE_integer('task', 0,
+                     'Task identifier flag to be set for each task running in distributed manner. Task number 0 '
+                     'will be chosen as the chief.')
+
+FLAGS = flags.FLAGS
+
+
+def main(_):
+  train_dir = os.path.join(FLAGS.checkpoint_dir, FLAGS.model_name, 'train')
+  save_image_dir = os.path.join(train_dir, 'images')
+  if not os.path.exists(train_dir):
+    os.makedirs(train_dir)
+  if not os.path.exists(save_image_dir):
+    os.makedirs(save_image_dir)
+
+  g = tf.Graph()
+  with g.as_default():
+    with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
+      global_step = slim.get_or_create_global_step()
+      ##########
+      ## data ##
+      ##########
+      train_data = model.get_inputs(
+          FLAGS.inp_dir,
+          FLAGS.dataset_name,
+          'train',
+          FLAGS.batch_size,
+          FLAGS.image_size,
+          is_training=True)
+      inputs = model.preprocess(train_data, FLAGS.step_size)
+      ###########
+      ## model ##
+      ###########
+      model_fn = model.get_model_fn(FLAGS, is_training=True)
+      outputs = model_fn(inputs)
+      ##########
+      ## loss ##
+      ##########
+      task_loss = model.get_loss(inputs, outputs, FLAGS)
+      regularization_loss = model.get_regularization_loss(
+          ['encoder', 'rotator', 'decoder'], FLAGS)
+      loss = task_loss + regularization_loss
+      ###############
+      ## optimizer ##
+      ###############
+      optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
+      if FLAGS.sync_replicas:
+        optimizer = tf.train.SyncReplicasOptimizer(
+            optimizer,
+            replicas_to_aggregate=FLAGS.workers_replicas - FLAGS.backup_workers,
+            total_num_replicas=FLAGS.worker_replicas)
+
+      ##############
+      ## train_op ##
+      ##############
+      train_op = model.get_train_op_for_scope(
+          loss, optimizer, ['encoder', 'rotator', 'decoder'], FLAGS)
+      ###########
+      ## saver ##
+      ###########
+      saver = tf.train.Saver(max_to_keep=np.minimum(5,
+                                                    FLAGS.worker_replicas + 1))
+
+      if FLAGS.task == 0:
+        val_data = model.get_inputs(
+            FLAGS.inp_dir,
+            FLAGS.dataset_name,
+            'val',
+            FLAGS.batch_size,
+            FLAGS.image_size,
+            is_training=False)
+        val_inputs = model.preprocess(val_data, FLAGS.step_size)
+        # Note: don't compute loss here
+        reused_model_fn = model.get_model_fn(
+            FLAGS, is_training=False, reuse=True)
+        val_outputs = reused_model_fn(val_inputs)
+        with tf.device(tf.DeviceSpec(device_type='CPU')):
+          if FLAGS.step_size == 1:
+            vis_input_images = val_inputs['images_0'] * 255.0
+            vis_output_images = val_inputs['images_1'] * 255.0
+            vis_pred_images = val_outputs['images_1'] * 255.0
+            vis_pred_masks = (val_outputs['masks_1'] * (-1) + 1) * 255.0
+          else:
+            rep_times = int(np.ceil(32.0 / float(FLAGS.step_size)))
+            vis_list_1 = []
+            vis_list_2 = []
+            vis_list_3 = []
+            vis_list_4 = []
+            for j in xrange(rep_times):
+              for k in xrange(FLAGS.step_size):
+                vis_input_image = val_inputs['images_0'][j],
+                vis_output_image = val_inputs['images_%d' % (k + 1)][j]
+                vis_pred_image = val_outputs['images_%d' % (k + 1)][j]
+                vis_pred_mask = val_outputs['masks_%d' % (k + 1)][j]
+                vis_list_1.append(tf.expand_dims(vis_input_image, 0))
+                vis_list_2.append(tf.expand_dims(vis_output_image, 0))
+                vis_list_3.append(tf.expand_dims(vis_pred_image, 0))
+                vis_list_4.append(tf.expand_dims(vis_pred_mask, 0))
+
+            vis_list_1 = tf.reshape(
+                tf.stack(vis_list_1), [
+                    rep_times * FLAGS.step_size, FLAGS.image_size,
+                    FLAGS.image_size, 3
+                ])
+            vis_list_2 = tf.reshape(
+                tf.stack(vis_list_2), [
+                    rep_times * FLAGS.step_size, FLAGS.image_size,
+                    FLAGS.image_size, 3
+                ])
+            vis_list_3 = tf.reshape(
+                tf.stack(vis_list_3), [
+                    rep_times * FLAGS.step_size, FLAGS.image_size,
+                    FLAGS.image_size, 3
+                ])
+            vis_list_4 = tf.reshape(
+                tf.stack(vis_list_4), [
+                    rep_times * FLAGS.step_size, FLAGS.image_size,
+                    FLAGS.image_size, 1
+                ])
+
+            vis_input_images = vis_list_1 * 255.0
+            vis_output_images = vis_list_2 * 255.0
+            vis_pred_images = vis_list_3 * 255.0
+            vis_pred_masks = (vis_list_4 * (-1) + 1) * 255.0
+
+          write_disk_op = model.write_disk_grid(
+              global_step=global_step,
+              summary_freq=FLAGS.save_every,
+              log_dir=save_image_dir,
+              input_images=vis_input_images,
+              output_images=vis_output_images,
+              pred_images=vis_pred_images,
+              pred_masks=vis_pred_masks)
+        with tf.control_dependencies([write_disk_op]):
+          train_op = tf.identity(train_op)
+
+      #############
+      ## init_fn ##
+      #############
+      init_fn = model.get_init_fn(['encoder, ' 'rotator', 'decoder'], FLAGS)
+
+      ##############
+      ## training ##
+      ##############
+      slim.learning.train(
+          train_op=train_op,
+          logdir=train_dir,
+          init_fn=init_fn,
+          master=FLAGS.master,
+          is_chief=(FLAGS.task == 0),
+          number_of_steps=FLAGS.max_number_of_steps,
+          saver=saver,
+          save_summaries_secs=FLAGS.save_summaries_secs,
+          save_interval_secs=FLAGS.save_interval_secs)
+
+
+if __name__ == '__main__':
+  app.run()

ptn/train_ptn.py

+# 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.
+# ==============================================================================
+
+"""Contains training plan for the Im2vox model."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+import numpy as np
+import tensorflow as tf
+
+from tensorflow import app
+
+import model_ptn
+
+flags = tf.app.flags
+slim = tf.contrib.slim
+
+flags.DEFINE_string('inp_dir',
+                    '',
+                    'Directory path containing the input data (tfrecords).')
+flags.DEFINE_string(
+    'dataset_name', 'shapenet_chair',
+    'Dataset name that is to be used for training and evaluation.')
+flags.DEFINE_integer('z_dim', 512, '')
+flags.DEFINE_integer('f_dim', 64, '')
+flags.DEFINE_integer('fc_dim', 1024, '')
+flags.DEFINE_integer('num_views', 24, 'Num of viewpoints in the input data.')
+flags.DEFINE_integer('image_size', 64,
+                     'Input images dimension (pixels) - width & height.')
+flags.DEFINE_integer('vox_size', 32, 'Voxel prediction dimension.')
+flags.DEFINE_integer('step_size', 24, 'Steps to take in rotation to fetch viewpoints.')
+flags.DEFINE_integer('batch_size', 1, 'Batch size while training.')
+flags.DEFINE_float('focal_length', 0.866, 'Focal length parameter used in perspective projection.')
+flags.DEFINE_float('focal_range', 1.732, 'Focal length parameter used in perspective projection.')
+flags.DEFINE_string('encoder_name', 'ptn_encoder',
+                    'Name of the encoder network being used.')
+flags.DEFINE_string('decoder_name', 'ptn_vox_decoder',
+                    'Name of the decoder network being used.')
+flags.DEFINE_string('projector_name', 'perspective_projector',
+                    'Name of the projector network being used.')
+# Save options
+flags.DEFINE_string('checkpoint_dir', '/tmp/ptn_train/',
+                    'Directory path for saving trained models and other data.')
+flags.DEFINE_string('model_name', 'ptn_finetune',
+                    'Name of the model used in naming the TF job. Must be different for each run.')
+flags.DEFINE_string('init_model', None,
+                    'Checkpoint path of the model to initialize with.')
+flags.DEFINE_integer('save_every', 1000,
+                     'Average period of steps after which we save a model.')
+# Optimization
+flags.DEFINE_float('proj_weight', 10, 'Weighting factor for projection loss.')
+flags.DEFINE_float('volume_weight', 0, 'Weighting factor for volume loss.')
+flags.DEFINE_float('viewpoint_weight', 1, 'Weighting factor for viewpoint loss.')
+flags.DEFINE_float('learning_rate', 0.0001, 'Learning rate.')
+flags.DEFINE_float('weight_decay', 0.001, 'Weight decay parameter while training.')
+flags.DEFINE_float('clip_gradient_norm', 0, 'Gradient clim norm, leave 0 if no gradient clipping.')
+flags.DEFINE_integer('max_number_of_steps', 10000, 'Maximum number of steps for training.')
+# Summary
+flags.DEFINE_integer('save_summaries_secs', 15, 'Seconds interval for dumping TF summaries.')
+flags.DEFINE_integer('save_interval_secs', 60 * 5, 'Seconds interval to save models.')
+
+# Scheduling
+flags.DEFINE_string('master', '', 'The address of the tensorflow master')
+flags.DEFINE_bool('sync_replicas', False, 'Whether to sync gradients between replicas for optimizer.')
+flags.DEFINE_integer('worker_replicas', 1, 'Number of worker replicas (train tasks).')
+flags.DEFINE_integer('backup_workers', 0, 'Number of backup workers.')
+flags.DEFINE_integer('ps_tasks', 0, 'Number of ps tasks.')
+flags.DEFINE_integer('task', 0,
+                     'Task identifier flag to be set for each task running in distributed manner. Task number 0 '
+                     'will be chosen as the chief.')
+
+FLAGS = flags.FLAGS
+
+
+def main(_):
+  train_dir = os.path.join(FLAGS.checkpoint_dir, FLAGS.model_name, 'train')
+  save_image_dir = os.path.join(train_dir, 'images')
+  if not os.path.exists(train_dir):
+    os.makedirs(train_dir)
+  if not os.path.exists(save_image_dir):
+    os.makedirs(save_image_dir)
+
+  g = tf.Graph()
+  with g.as_default():
+    with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
+      global_step = slim.get_or_create_global_step()
+      ###########
+      ## model ##
+      ###########
+      model = model_ptn.model_PTN(FLAGS)
+      ##########
+      ## data ##
+      ##########
+      train_data = model.get_inputs(
+          FLAGS.inp_dir,
+          FLAGS.dataset_name,
+          'train',
+          FLAGS.batch_size,
+          FLAGS.image_size,
+          FLAGS.vox_size,
+          is_training=True)
+      inputs = model.preprocess(train_data, FLAGS.step_size)
+      ##############
+      ## model_fn ##
+      ##############
+      model_fn = model.get_model_fn(
+          is_training=True, reuse=False, run_projection=True)
+      outputs = model_fn(inputs)
+      ##################
+      ## train_scopes ##
+      ##################
+      if FLAGS.init_model:
+        train_scopes = ['decoder']
+        init_scopes = ['encoder']
+      else:
+        train_scopes = ['encoder', 'decoder']
+
+      ##########
+      ## loss ##
+      ##########
+      task_loss = model.get_loss(inputs, outputs)
+
+      regularization_loss = model.get_regularization_loss(train_scopes)
+      loss = task_loss + regularization_loss
+      ###############
+      ## optimizer ##
+      ###############
+      optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
+      if FLAGS.sync_replicas:
+        optimizer = tf.train.SyncReplicasOptimizer(
+            optimizer,
+            replicas_to_aggregate=FLAGS.workers_replicas - FLAGS.backup_workers,
+            total_num_replicas=FLAGS.worker_replicas)
+
+      ##############
+      ## train_op ##
+      ##############
+      train_op = model.get_train_op_for_scope(loss, optimizer, train_scopes)
+      ###########
+      ## saver ##
+      ###########
+      saver = tf.train.Saver(max_to_keep=np.minimum(5,
+                                                    FLAGS.worker_replicas + 1))
+
+      if FLAGS.task == 0:
+        params = FLAGS
+        params.batch_size = params.num_views
+        params.step_size = 1
+        model.set_params(params)
+        val_data = model.get_inputs(
+            params.inp_dir,
+            params.dataset_name,
+            'val',
+            params.batch_size,
+            params.image_size,
+            params.vox_size,
+            is_training=False)
+        val_inputs = model.preprocess(val_data, params.step_size)
+        # Note: don't compute loss here
+        reused_model_fn = model.get_model_fn(is_training=False, reuse=True)
+        val_outputs = reused_model_fn(val_inputs)
+
+        with tf.device(tf.DeviceSpec(device_type='CPU')):
+          vis_input_images = val_inputs['images_1'] * 255.0
+          vis_gt_projs = (val_outputs['masks_1'] * (-1) + 1) * 255.0
+          vis_pred_projs = (val_outputs['projs_1'] * (-1) + 1) * 255.0
+
+          vis_gt_projs = tf.concat([vis_gt_projs] * 3, axis=3)
+          vis_pred_projs = tf.concat([vis_pred_projs] * 3, axis=3)
+          # rescale
+          new_size = [FLAGS.image_size] * 2
+          vis_gt_projs = tf.image.resize_nearest_neighbor(
+              vis_gt_projs, new_size)
+          vis_pred_projs = tf.image.resize_nearest_neighbor(
+              vis_pred_projs, new_size)
+          # flip
+          # vis_gt_projs = utils.image_flipud(vis_gt_projs)
+          # vis_pred_projs = utils.image_flipud(vis_pred_projs)
+          # vis_gt_projs is of shape [batch, height, width, channels]
+          write_disk_op = model.write_disk_grid(
+              global_step=global_step,
+              log_dir=save_image_dir,
+              input_images=vis_input_images,
+              gt_projs=vis_gt_projs,
+              pred_projs=vis_pred_projs,
+              input_voxels=val_inputs['voxels'],
+              output_voxels=val_outputs['voxels_1'])
+        with tf.control_dependencies([write_disk_op]):
+          train_op = tf.identity(train_op)
+
+      #############
+      ## init_fn ##
+      #############
+      if FLAGS.init_model:
+        init_fn = model.get_init_fn(init_scopes)
+      else:
+        init_fn = None
+
+      ##############
+      ## training ##
+      ##############
+      slim.learning.train(
+          train_op=train_op,
+          logdir=train_dir,
+          init_fn=init_fn,
+          master=FLAGS.master,
+          is_chief=(FLAGS.task == 0),
+          number_of_steps=FLAGS.max_number_of_steps,
+          saver=saver,
+          save_summaries_secs=FLAGS.save_summaries_secs,
+          save_interval_secs=FLAGS.save_interval_secs)
+
+
+if __name__ == '__main__':
+  app.run()

ptn/utils.py

+# 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.
+# ==============================================================================
+
+"""Utility functions."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import StringIO
+from matplotlib import pylab as p
+# axes3d is being used implictly for visualization.
+from mpl_toolkits.mplot3d import axes3d as p3  # pylint:disable=unused-import
+import numpy as np
+from PIL import Image
+from skimage import measure
+
+import tensorflow as tf
+
+
+def save_image(inp_array, image_file):
+  """Function that dumps the image to disk."""
+  inp_array = np.clip(inp_array, 0, 255).astype(np.uint8)
+  image = Image.fromarray(inp_array)
+  buf = StringIO.StringIO()
+  image.save(buf, format='JPEG')
+  with open(image_file, 'w') as f:
+    f.write(buf.getvalue())
+  return None
+
+
+def image_flipud(images):
+  """Function that flip (up-down) the np image."""
+  quantity = images.get_shape().as_list()[0]
+  image_list = []
+  for k in xrange(quantity):
+    image_list.append(tf.image.flip_up_down(images[k, :, :, :]))
+  outputs = tf.stack(image_list)
+  return outputs
+
+
+def resize_image(inp_array, new_height, new_width):
+  """Function that resize the np image."""
+  inp_array = np.clip(inp_array, 0, 255).astype(np.uint8)
+  image = Image.fromarray(inp_array)
+  # Reverse order
+  image = image.resize((new_width, new_height))
+  return np.array(image)
+
+
+def display_voxel(points, vis_size=128):
+  """Function to display 3D voxel."""
+  try:
+    data = visualize_voxel_spectral(points, vis_size)
+  except ValueError:
+    data = visualize_voxel_scatter(points, vis_size)
+  return data
+
+
+def visualize_voxel_spectral(points, vis_size=128):
+  """Function to visualize voxel (spectral)."""
+  points = np.rint(points)
+  points = np.swapaxes(points, 0, 2)
+  fig = p.figure(figsize=(1, 1), dpi=vis_size)
+  verts, faces = measure.marching_cubes(points, 0, spacing=(0.1, 0.1, 0.1))
+  ax = fig.add_subplot(111, projection='3d')
+  ax.plot_trisurf(
+      verts[:, 0], verts[:, 1], faces, verts[:, 2], cmap='Spectral_r', lw=0.1)
+  ax.set_axis_off()
+  fig.tight_layout(pad=0)
+  fig.canvas.draw()
+  data = np.fromstring(
+      fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape(
+          vis_size, vis_size, 3)
+  p.close('all')
+  return data
+
+
+def visualize_voxel_scatter(points, vis_size=128):
+  """Function to visualize voxel (scatter)."""
+  points = np.rint(points)
+  points = np.swapaxes(points, 0, 2)
+  fig = p.figure(figsize=(1, 1), dpi=vis_size)
+  ax = fig.add_subplot(111, projection='3d')
+  x = []
+  y = []
+  z = []
+  (x_dimension, y_dimension, z_dimension) = points.shape
+  for i in range(x_dimension):
+    for j in range(y_dimension):
+      for k in range(z_dimension):
+        if points[i, j, k]:
+          x.append(i)
+          y.append(j)
+          z.append(k)
+  ax.scatter3D(x, y, z)
+  ax.set_axis_off()
+  fig.tight_layout(pad=0)
+  fig.canvas.draw()
+  data = np.fromstring(
+      fig.canvas.tostring_rgb(), dtype=np.uint8, sep='').reshape(
+          vis_size, vis_size, 3)
+  p.close('all')
+  return data
+