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tf_utils.py
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tf_utils.py
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# Copyright 2016 Paul Balanca. 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.
# ==============================================================================
"""Diverse TensorFlow utils, for training, evaluation and so on!
"""
import os
from pprint import pprint
import tensorflow as tf
from tensorflow.contrib.slim.python.slim.data import parallel_reader
slim = tf.contrib.slim
# =========================================================================== #
# General tools.
# =========================================================================== #
def reshape_list(l, shape=None):
"""Reshape list of (list): 1D to 2D or the other way around.
Args:
l: List or List of list.
shape: 1D or 2D shape.
Return
Reshaped list.
"""
r = []
if shape is None:
# Flatten everything.
for a in l:
if isinstance(a, (list, tuple)):
r = r + list(a)
else:
r.append(a)
else:
# Reshape to list of list.
i = 0
for s in shape:
if s == 1:
r.append(l[i])
else:
r.append(l[i:i+s])
i += s
return r
# =========================================================================== #
# Training utils.
# =========================================================================== #
def print_configuration(flags, ssd_params, data_sources, save_dir=None):
"""Print the training configuration.
"""
def print_config(stream=None):
print('\n# =========================================================================== #', file=stream)
print('# Training | Evaluation flags:', file=stream)
print('# =========================================================================== #', file=stream)
pprint(flags, stream=stream)
print('\n# =========================================================================== #', file=stream)
print('# SSD net parameters:', file=stream)
print('# =========================================================================== #', file=stream)
pprint(dict(ssd_params._asdict()), stream=stream)
print('\n# =========================================================================== #', file=stream)
print('# Training | Evaluation dataset files:', file=stream)
print('# =========================================================================== #', file=stream)
data_files = parallel_reader.get_data_files(data_sources)
pprint(data_files, stream=stream)
print('', file=stream)
print_config(None)
# Save to a text file as well.
if save_dir is not None:
if not os.path.exists(save_dir):
os.makedirs(save_dir)
path = os.path.join(save_dir, 'training_config.txt')
with open(path, "w") as out:
print_config(out)
def configure_learning_rate(flags, num_samples_per_epoch, global_step):
"""Configures the learning rate.
Args:
num_samples_per_epoch: The number of samples in each epoch of training.
global_step: The global_step tensor.
Returns:
A `Tensor` representing the learning rate.
"""
decay_steps = int(num_samples_per_epoch / flags.batch_size *
flags.num_epochs_per_decay)
if flags.learning_rate_decay_type == 'exponential':
return tf.train.exponential_decay(flags.learning_rate,
global_step,
decay_steps,
flags.learning_rate_decay_factor,
staircase=True,
name='exponential_decay_learning_rate')
elif flags.learning_rate_decay_type == 'fixed':
return tf.constant(flags.learning_rate, name='fixed_learning_rate')
elif flags.learning_rate_decay_type == 'polynomial':
return tf.train.polynomial_decay(flags.learning_rate,
global_step,
decay_steps,
flags.end_learning_rate,
power=1.0,
cycle=False,
name='polynomial_decay_learning_rate')
else:
raise ValueError('learning_rate_decay_type [%s] was not recognized',
flags.learning_rate_decay_type)
def configure_optimizer(flags, learning_rate):
"""Configures the optimizer used for training.
Args:
learning_rate: A scalar or `Tensor` learning rate.
Returns:
An instance of an optimizer.
"""
if flags.optimizer == 'adadelta':
optimizer = tf.train.AdadeltaOptimizer(
learning_rate,
rho=flags.adadelta_rho,
epsilon=flags.opt_epsilon)
elif flags.optimizer == 'adagrad':
optimizer = tf.train.AdagradOptimizer(
learning_rate,
initial_accumulator_value=flags.adagrad_initial_accumulator_value)
elif flags.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(
learning_rate,
beta1=flags.adam_beta1,
beta2=flags.adam_beta2,
epsilon=flags.opt_epsilon)
elif flags.optimizer == 'ftrl':
optimizer = tf.train.FtrlOptimizer(
learning_rate,
learning_rate_power=flags.ftrl_learning_rate_power,
initial_accumulator_value=flags.ftrl_initial_accumulator_value,
l1_regularization_strength=flags.ftrl_l1,
l2_regularization_strength=flags.ftrl_l2)
elif flags.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(
learning_rate,
momentum=flags.momentum,
name='Momentum')
elif flags.optimizer == 'rmsprop':
optimizer = tf.train.RMSPropOptimizer(
learning_rate,
decay=flags.rmsprop_decay,
momentum=flags.rmsprop_momentum,
epsilon=flags.opt_epsilon)
elif flags.optimizer == 'sgd':
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
else:
raise ValueError('Optimizer [%s] was not recognized', flags.optimizer)
return optimizer
def add_variables_summaries(learning_rate):
summaries = []
for variable in slim.get_model_variables():
summaries.append(tf.summary.histogram(variable.op.name, variable))
summaries.append(tf.summary.scalar('training/Learning Rate', learning_rate))
return summaries
def update_model_scope(var, ckpt_scope, new_scope):
return var.op.name.replace(new_scope,'vgg_16')
def get_init_fn(flags):
"""Returns a function run by the chief worker to warm-start the training.
Note that the init_fn is only run when initializing the model during the very
first global step.
Returns:
An init function run by the supervisor.
"""
if flags.checkpoint_path is None:
return None
# Warn the user if a checkpoint exists in the train_dir. Then ignore.
if tf.train.latest_checkpoint(flags.train_dir):
tf.logging.info(
'Ignoring --checkpoint_path because a checkpoint already exists in %s'
% flags.train_dir)
return None
exclusions = []
if flags.checkpoint_exclude_scopes:
exclusions = [scope.strip()
for scope in flags.checkpoint_exclude_scopes.split(',')]
# TODO(sguada) variables.filter_variables()
variables_to_restore = []
for var in slim.get_model_variables():
excluded = False
for exclusion in exclusions:
if var.op.name.startswith(exclusion):
excluded = True
break
if not excluded:
variables_to_restore.append(var)
# Change model scope if necessary.
if flags.checkpoint_model_scope is not None:
variables_to_restore = \
{var.op.name.replace(flags.model_name,
flags.checkpoint_model_scope): var
for var in variables_to_restore}
if tf.gfile.IsDirectory(flags.checkpoint_path):
checkpoint_path = tf.train.latest_checkpoint(flags.checkpoint_path)
else:
checkpoint_path = flags.checkpoint_path
tf.logging.info('Fine-tuning from %s' % checkpoint_path)
return slim.assign_from_checkpoint_fn(
checkpoint_path,
variables_to_restore,
ignore_missing_vars=flags.ignore_missing_vars)
def get_variables_to_train(flags):
"""Returns a list of variables to train.
Returns:
A list of variables to train by the optimizer.
"""
if flags.trainable_scopes is None:
return tf.trainable_variables()
else:
scopes = [scope.strip() for scope in flags.trainable_scopes.split(',')]
variables_to_train = []
for scope in scopes:
variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)
variables_to_train.extend(variables)
return variables_to_train
# =========================================================================== #
# Evaluation utils.
# =========================================================================== #