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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.
# ==============================================================================
"""Script to train the Attention OCR model.
A simple usage example:
python train.py
"""
import collections
import logging
import tensorflow as tf
from tensorflow.contrib import slim
from tensorflow import app
from tensorflow.python.platform import flags
from tensorflow.contrib.tfprof import model_analyzer
import data_provider
import common_flags
FLAGS = flags.FLAGS
common_flags.define()
# yapf: disable
flags.DEFINE_integer('task', 0,
'The Task ID. This value is used when training with '
'multiple workers to identify each worker.')
flags.DEFINE_integer('ps_tasks', 0,
'The number of parameter servers. If the value is 0, then'
' the parameters are handled locally by the worker.')
flags.DEFINE_integer('save_summaries_secs', 60,
'The frequency with which summaries are saved, in '
'seconds.')
flags.DEFINE_integer('save_interval_secs', 600,
'Frequency in seconds of saving the model.')
flags.DEFINE_integer('max_number_of_steps', int(1e10),
'The maximum number of gradient steps.')
flags.DEFINE_string('checkpoint_inception', '',
'Checkpoint to recover inception weights from.')
flags.DEFINE_float('clip_gradient_norm', 2.0,
'If greater than 0 then the gradients would be clipped by '
'it.')
flags.DEFINE_bool('sync_replicas', False,
'If True will synchronize replicas during training.')
flags.DEFINE_integer('replicas_to_aggregate', 1,
'The number of gradients updates before updating params.')
flags.DEFINE_integer('total_num_replicas', 1,
'Total number of worker replicas.')
flags.DEFINE_integer('startup_delay_steps', 15,
'Number of training steps between replicas startup.')
flags.DEFINE_boolean('reset_train_dir', False,
'If true will delete all files in the train_log_dir')
flags.DEFINE_boolean('show_graph_stats', False,
'Output model size stats to stderr.')
# yapf: enable
TrainingHParams = collections.namedtuple('TrainingHParams', [
'learning_rate',
'optimizer',
'momentum',
'use_augment_input',
])
def get_training_hparams():
return TrainingHParams(
learning_rate=FLAGS.learning_rate,
optimizer=FLAGS.optimizer,
momentum=FLAGS.momentum,
use_augment_input=FLAGS.use_augment_input)
def create_optimizer(hparams):
"""Creates optimized based on the specified flags."""
if hparams.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(
hparams.learning_rate, momentum=hparams.momentum)
elif hparams.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(hparams.learning_rate)
elif hparams.optimizer == 'adadelta':
optimizer = tf.train.AdadeltaOptimizer(hparams.learning_rate)
elif hparams.optimizer == 'adagrad':
optimizer = tf.train.AdagradOptimizer(hparams.learning_rate)
elif hparams.optimizer == 'rmsprop':
optimizer = tf.train.RMSPropOptimizer(
hparams.learning_rate, momentum=hparams.momentum)
return optimizer
def train(loss, init_fn, hparams):
"""Wraps slim.learning.train to run a training loop.
Args:
loss: a loss tensor
init_fn: A callable to be executed after all other initialization is done.
hparams: a model hyper parameters
"""
optimizer = create_optimizer(hparams)
if FLAGS.sync_replicas:
replica_id = tf.constant(FLAGS.task, tf.int32, shape=())
optimizer = tf.LegacySyncReplicasOptimizer(
opt=optimizer,
replicas_to_aggregate=FLAGS.replicas_to_aggregate,
replica_id=replica_id,
total_num_replicas=FLAGS.total_num_replicas)
sync_optimizer = optimizer
startup_delay_steps = 0
else:
startup_delay_steps = 0
sync_optimizer = None
train_op = slim.learning.create_train_op(
loss,
optimizer,
summarize_gradients=True,
clip_gradient_norm=FLAGS.clip_gradient_norm)
slim.learning.train(
train_op=train_op,
logdir=FLAGS.train_log_dir,
graph=loss.graph,
master=FLAGS.master,
is_chief=(FLAGS.task == 0),
number_of_steps=FLAGS.max_number_of_steps,
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs,
startup_delay_steps=startup_delay_steps,
sync_optimizer=sync_optimizer,
init_fn=init_fn)
def prepare_training_dir():
if not tf.gfile.Exists(FLAGS.train_log_dir):
logging.info('Create a new training directory %s', FLAGS.train_log_dir)
tf.gfile.MakeDirs(FLAGS.train_log_dir)
else:
if FLAGS.reset_train_dir:
logging.info('Reset the training directory %s', FLAGS.train_log_dir)
tf.gfile.DeleteRecursively(FLAGS.train_log_dir)
tf.gfile.MakeDirs(FLAGS.train_log_dir)
else:
logging.info('Use already existing training directory %s',
FLAGS.train_log_dir)
def calculate_graph_metrics():
param_stats = model_analyzer.print_model_analysis(
tf.get_default_graph(),
tfprof_options=model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
return param_stats.total_parameters
def main(_):
prepare_training_dir()
dataset = common_flags.create_dataset(split_name=FLAGS.split_name)
model = common_flags.create_model(dataset.num_char_classes,
dataset.max_sequence_length,
dataset.num_of_views, dataset.null_code)
hparams = get_training_hparams()
# If ps_tasks is zero, the local device is used. When using multiple
# (non-local) replicas, the ReplicaDeviceSetter distributes the variables
# across the different devices.
device_setter = tf.train.replica_device_setter(
FLAGS.ps_tasks, merge_devices=True)
with tf.device(device_setter):
data = data_provider.get_data(
dataset,
FLAGS.batch_size,
augment=hparams.use_augment_input,
central_crop_size=common_flags.get_crop_size())
endpoints = model.create_base(data.images, data.labels_one_hot)
total_loss = model.create_loss(data, endpoints)
model.create_summaries(data, endpoints, dataset.charset, is_training=True)
init_fn = model.create_init_fn_to_restore(FLAGS.checkpoint,
FLAGS.checkpoint_inception)
if FLAGS.show_graph_stats:
logging.info('Total number of weights in the graph: %s',
calculate_graph_metrics())
train(total_loss, init_fn, hparams)
if __name__ == '__main__':
app.run()
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