research/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.compat.v1 as tf
from object_detection.utils import learning_schedules
from object_detection.utils import tf_version
# pylint: disable=g-import-not-at-top
if tf_version.is_tf2():
from official.modeling.optimization import ema_optimizer
# pylint: enable=g-import-not-at-top
try:
from tensorflow.contrib import opt as tf_opt # pylint: disable=g-import-not-at-top
except: # pylint: disable=bare-except
pass
def build_optimizers_tf_v1(optimizer_config, global_step=None):
"""Create a TF v1 compatible optimizer based on config.
Args:
optimizer_config: A Optimizer proto message.
global_step: A variable representing the current step.
If None, defaults to tf.train.get_or_create_global_step()
Returns:
An optimizer and a list of variables for summary.
Raises:
ValueError: when using an unsupported input data type.
"""
optimizer_type = optimizer_config.WhichOneof('optimizer')
optimizer = None
summary_vars = []
if optimizer_type == 'rms_prop_optimizer':
config = optimizer_config.rms_prop_optimizer
learning_rate = _create_learning_rate(config.learning_rate,
global_step=global_step)
summary_vars.append(learning_rate)
optimizer = tf.train.RMSPropOptimizer(
learning_rate,
decay=config.decay,
momentum=config.momentum_optimizer_value,
epsilon=config.epsilon)
if optimizer_type == 'momentum_optimizer':
config = optimizer_config.momentum_optimizer
learning_rate = _create_learning_rate(config.learning_rate,
global_step=global_step)
summary_vars.append(learning_rate)
optimizer = tf.train.MomentumOptimizer(
learning_rate,
momentum=config.momentum_optimizer_value)
if optimizer_type == 'adam_optimizer':
config = optimizer_config.adam_optimizer
learning_rate = _create_learning_rate(config.learning_rate,
global_step=global_step)
summary_vars.append(learning_rate)
optimizer = tf.train.AdamOptimizer(learning_rate, epsilon=config.epsilon)
if optimizer is None:
raise ValueError('Optimizer %s not supported.' % optimizer_type)
if optimizer_config.use_moving_average:
optimizer = tf_opt.MovingAverageOptimizer(
optimizer, average_decay=optimizer_config.moving_average_decay)
return optimizer, summary_vars
def build_optimizers_tf_v2(optimizer_config, global_step=None):
"""Create a TF v2 compatible optimizer based on config.
Args:
optimizer_config: A Optimizer proto message.
global_step: A variable representing the current step.
If None, defaults to tf.train.get_or_create_global_step()
Returns:
An optimizer and a list of variables for summary.
Raises:
ValueError: when using an unsupported input data type.
"""
optimizer_type = optimizer_config.WhichOneof('optimizer')
optimizer = None
summary_vars = []
if optimizer_type == 'rms_prop_optimizer':
config = optimizer_config.rms_prop_optimizer
learning_rate = _create_learning_rate(config.learning_rate,
global_step=global_step)
summary_vars.append(learning_rate)
optimizer = tf.keras.optimizers.RMSprop(
learning_rate,
decay=config.decay,
momentum=config.momentum_optimizer_value,
epsilon=config.epsilon)
if optimizer_type == 'momentum_optimizer':
config = optimizer_config.momentum_optimizer
learning_rate = _create_learning_rate(config.learning_rate,
global_step=global_step)
summary_vars.append(learning_rate)
optimizer = tf.keras.optimizers.SGD(
learning_rate,
momentum=config.momentum_optimizer_value)
if optimizer_type == 'adam_optimizer':
config = optimizer_config.adam_optimizer
learning_rate = _create_learning_rate(config.learning_rate,
global_step=global_step)
summary_vars.append(learning_rate)
optimizer = tf.keras.optimizers.Adam(learning_rate, epsilon=config.epsilon)
if optimizer is None:
raise ValueError('Optimizer %s not supported.' % optimizer_type)
if optimizer_config.use_moving_average:
optimizer = ema_optimizer.ExponentialMovingAverage(
optimizer=optimizer,
average_decay=optimizer_config.moving_average_decay)
return optimizer, summary_vars
def build(config, global_step=None):
if tf.executing_eagerly():
return build_optimizers_tf_v2(config, global_step)
else:
return build_optimizers_tf_v1(config, global_step)
def _create_learning_rate(learning_rate_config, global_step=None):
"""Create optimizer learning rate based on config.
Args:
learning_rate_config: A LearningRate proto message.
global_step: A variable representing the current step.
If None, defaults to tf.train.get_or_create_global_step()
Returns:
A learning rate.
Raises:
ValueError: when using an unsupported input data type.
"""
if global_step is None:
global_step = tf.train.get_or_create_global_step()
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 = tf.constant(config.learning_rate, dtype=tf.float32,
name='learning_rate')
if learning_rate_type == 'exponential_decay_learning_rate':
config = learning_rate_config.exponential_decay_learning_rate
learning_rate = learning_schedules.exponential_decay_with_burnin(
global_step,
config.initial_learning_rate,
config.decay_steps,
config.decay_factor,
burnin_learning_rate=config.burnin_learning_rate,
burnin_steps=config.burnin_steps,
min_learning_rate=config.min_learning_rate,
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(
global_step, learning_rate_step_boundaries,
learning_rate_sequence, config.warmup)
if learning_rate_type == 'cosine_decay_learning_rate':
config = learning_rate_config.cosine_decay_learning_rate
learning_rate = learning_schedules.cosine_decay_with_warmup(
global_step,
config.learning_rate_base,
config.total_steps,
config.warmup_learning_rate,
config.warmup_steps,
config.hold_base_rate_steps)
if learning_rate is None:
raise ValueError('Learning_rate %s not supported.' % learning_rate_type)
return learning_rate