Add Tensorflow Object Detection API. (#1561)

For details see our paper:
"Speed/accuracy trade-offs for modern convolutional object detectors."
Huang J, Rathod V, Sun C, Zhu M, Korattikara A, Fathi A, Fischer I,
Wojna Z, Song Y, Guadarrama S, Murphy K, CVPR 2017
https://arxiv.org/abs/1611.10012

object_detection/builders/optimizer_builder.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.
+# ==============================================================================
+
+"""Functions to build DetectionModel training optimizers."""
+
+import tensorflow as tf
+from object_detection.utils import learning_schedules
+
+slim = tf.contrib.slim
+
+
+def build(optimizer_config, global_summaries):
+  """Create optimizer based on config.
+
+  Args:
+    optimizer_config: A Optimizer proto message.
+    global_summaries: A set to attach learning rate summary to.
+
+  Returns:
+    An optimizer.
+
+  Raises:
+    ValueError: when using an unsupported input data type.
+  """
+  optimizer_type = optimizer_config.WhichOneof('optimizer')
+  optimizer = None
+
+  if optimizer_type == 'rms_prop_optimizer':
+    config = optimizer_config.rms_prop_optimizer
+    optimizer = tf.train.RMSPropOptimizer(
+        _create_learning_rate(config.learning_rate, global_summaries),
+        decay=config.decay,
+        momentum=config.momentum_optimizer_value,
+        epsilon=config.epsilon)
+
+  if optimizer_type == 'momentum_optimizer':
+    config = optimizer_config.momentum_optimizer
+    optimizer = tf.train.MomentumOptimizer(
+        _create_learning_rate(config.learning_rate, global_summaries),
+        momentum=config.momentum_optimizer_value)
+
+  if optimizer_type == 'adam_optimizer':
+    config = optimizer_config.adam_optimizer
+    optimizer = tf.train.AdamOptimizer(
+        _create_learning_rate(config.learning_rate, global_summaries))
+
+  if optimizer is None:
+    raise ValueError('Optimizer %s not supported.' % optimizer_type)
+
+  if optimizer_config.use_moving_average:
+    optimizer = tf.contrib.opt.MovingAverageOptimizer(
+        optimizer, average_decay=optimizer_config.moving_average_decay)
+
+  return optimizer
+
+
+def _create_learning_rate(learning_rate_config, global_summaries):
+  """Create optimizer learning rate based on config.
+
+  Args:
+    learning_rate_config: A LearningRate proto message.
+    global_summaries: A set to attach learning rate summary to.
+
+  Returns:
+    A learning rate.
+
+  Raises:
+    ValueError: when using an unsupported input data type.
+  """
+  learning_rate = None
+  learning_rate_type = learning_rate_config.WhichOneof('learning_rate')
+  if learning_rate_type == 'constant_learning_rate':
+    config = learning_rate_config.constant_learning_rate
+    learning_rate = config.learning_rate
+
+  if learning_rate_type == 'exponential_decay_learning_rate':
+    config = learning_rate_config.exponential_decay_learning_rate
+    learning_rate = tf.train.exponential_decay(
+        config.initial_learning_rate,
+        slim.get_or_create_global_step(),
+        config.decay_steps,
+        config.decay_factor,
+        staircase=config.staircase)
+
+  if learning_rate_type == 'manual_step_learning_rate':
+    config = learning_rate_config.manual_step_learning_rate
+    if not config.schedule:
+      raise ValueError('Empty learning rate schedule.')
+    learning_rate_step_boundaries = [x.step for x in config.schedule]
+    learning_rate_sequence = [config.initial_learning_rate]
+    learning_rate_sequence += [x.learning_rate for x in config.schedule]
+    learning_rate = learning_schedules.manual_stepping(
+        slim.get_or_create_global_step(), learning_rate_step_boundaries,
+        learning_rate_sequence)
+
+  if learning_rate is None:
+    raise ValueError('Learning_rate %s not supported.' % learning_rate_type)
+
+  global_summaries.add(tf.summary.scalar('Learning Rate', learning_rate))
+  return learning_rate