def forward_pass_and_gradients(train_dataset):
"""
Create forward loss and grads on each device
"""
train_image = train_dataset['image']
train_filename = train_dataset['filename']
train_bboxes = train_dataset['bboxes']
prediction_dict = model(train_image, train_bboxes, is_training=True)
total_loss = model.loss(prediction_dict)
# TODO: Is this necesarry? Couldn't we just get them from the
# trainable vars collection? We should probably improve our
# usage of collections.
trainable_vars = model.get_trainable_vars()
# Compute, clip and apply gradients
with tf.name_scope('gradients'):
grads_and_vars = optimizer.compute_gradients(
total_loss, trainable_vars)
if config.train.clip_by_norm:
grads_and_vars = clip_gradients_by_norm(grads_and_vars)
return prediction_dict, total_loss, grads_and_vars
def run(
config,
target="",
cluster_spec=None,
is_chief=True,
job_name=None,
task_index=None,
get_model_fn=get_model,
get_dataset_fn=get_dataset,
environment=None,
):
model_class = get_model_fn(config.model.type)
image_vis = config.train.get("image_vis")
var_vis = config.train.get("var_vis")
if config.train.get("seed") is not None:
tf.set_random_seed(config.train.seed)
log_prefix = (
"[{}-{}] - ".format(job_name, task_index)
if job_name is not None and task_index is not None
else ""
)
if config.train.debug or config.train.tf_debug:
tf.logging.set_verbosity(tf.logging.DEBUG)
else:
tf.logging.set_verbosity(tf.logging.INFO)
model = model_class(config)
# Placement of ops on devices using replica device setter
# which automatically places the parameters on the `ps` server
# and the `ops` on the workers
#
# See:
# https://www.tensorflow.org/api_docs/python/tf/train/replica_device_setter
with tf.device(tf.train.replica_device_setter(cluster=cluster_spec)):
try:
config["dataset"]["type"]
except KeyError:
raise KeyError("dataset.type should be set on the custom config.")
try:
dataset_class = get_dataset_fn(config.dataset.type)
dataset = dataset_class(config)
train_dataset = dataset()
except InvalidDataDirectory as exc:
tf.logging.error("Error while reading dataset, {}".format(exc))
sys.exit(1)
train_image = train_dataset["image"]
train_filename = train_dataset["filename"]
train_bboxes = train_dataset["bboxes"]
prediction_dict = model(train_image, train_bboxes, is_training=True)
total_loss = model.loss(prediction_dict)
global_step = tf.train.get_or_create_global_step()
optimizer = get_optimizer(config.train, global_step)
# TODO: Is this necesarry? Couldn't we just get them from the
# trainable vars collection? We should probably improve our
# usage of collections.
trainable_vars = model.get_trainable_vars()
# Compute, clip and apply gradients
with tf.name_scope("gradients"):
grads_and_vars = optimizer.compute_gradients(total_loss, trainable_vars)
if config.train.clip_by_norm:
grads_and_vars = clip_gradients_by_norm(grads_and_vars)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.apply_gradients(
grads_and_vars, global_step=global_step
)
# Create custom init for slots in optimizer, as we don't save them to
# our checkpoints. An example of slots in an optimizer are the Momentum
# variables in MomentumOptimizer. We do this because slot variables can
# effectively duplicate the size of your checkpoint!
slot_variables = [
optimizer.get_slot(var, name)
for name in optimizer.get_slot_names()
for var in trainable_vars
]
slot_init = tf.variables_initializer(
slot_variables, name="optimizer_slots_initializer"
)
# Create saver for saving/restoring model
model_saver = tf.train.Saver(
set(tf.global_variables()) - set(slot_variables),
name="model_saver",
max_to_keep=config.train.get("checkpoints_max_keep", 1),
)
# Create saver for loading pretrained checkpoint into base network
base_checkpoint_vars = model.get_base_network_checkpoint_vars()
checkpoint_file = model.get_checkpoint_file()
if base_checkpoint_vars and checkpoint_file:
base_net_checkpoint_saver = tf.train.Saver(
base_checkpoint_vars, name="base_net_checkpoint_saver"
)
# We'll send this fn to Scaffold init_fn
def load_base_net_checkpoint(_, session):
base_net_checkpoint_saver.restore(session, checkpoint_file)
else:
load_base_net_checkpoint = None
tf.logging.info("{}Starting training for {}".format(log_prefix, model))
run_options = None
if config.train.full_trace:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
# Create custom Scaffold to make sure we run our own init_op when model
# is not restored from checkpoint.
summary_op = [model.summary]
summaries = tf.summary.merge_all()
if summaries is not None:
summary_op.append(summaries)
summary_op = tf.summary.merge(summary_op)
# `ready_for_local_init_op` is hardcoded to 'ready' as local init doesn't
# depend on global init and `local_init_op` only runs when it is set as
# 'ready' (an empty string tensor sets it as ready).
scaffold = tf.train.Scaffold(
saver=model_saver,
init_op=tf.global_variables_initializer() if is_chief else tf.no_op(),
local_init_op=tf.group(tf.initialize_local_variables(), slot_init),
ready_for_local_init_op=tf.constant([], dtype=tf.string),
summary_op=summary_op,
init_fn=load_base_net_checkpoint,
)
# Custom hooks for our session
hooks = []
chief_only_hooks = []
if config.train.tf_debug:
debug_hook = tf_debug.LocalCLIDebugHook()
debug_hook.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
hooks.extend([debug_hook])
if not config.train.job_dir:
tf.logging.warning(
"`job_dir` is not defined. Checkpoints and logs will not be saved."
)
checkpoint_dir = None
elif config.train.run_name:
# Use run_name when available
checkpoint_dir = os.path.join(config.train.job_dir, config.train.run_name)
else:
checkpoint_dir = config.train.job_dir
should_add_hooks = (
config.train.display_every_steps
or config.train.display_every_secs
and checkpoint_dir is not None
)
if should_add_hooks:
if not config.train.debug and image_vis == "debug":
tf.logging.warning("ImageVisHook will not run without debug mode.")
elif image_vis is not None:
# ImageVis only runs on the chief.
chief_only_hooks.append(
ImageVisHook(
prediction_dict,
image=train_dataset["image"],
gt_bboxes=train_dataset["bboxes"],
config=config.model,
output_dir=checkpoint_dir,
every_n_steps=config.train.display_every_steps,
every_n_secs=config.train.display_every_secs,
image_visualization_mode=image_vis,
)
)
if var_vis is not None:
# VarVis only runs on the chief.
chief_only_hooks.append(
VarVisHook(
every_n_steps=config.train.display_every_steps,
every_n_secs=config.train.display_every_secs,
mode=var_vis,
output_dir=checkpoint_dir,
vars_summary=model.vars_summary,
)
)
step = -1
with tf.train.MonitoredTrainingSession(
master=target,
is_chief=is_chief,
checkpoint_dir=checkpoint_dir,
scaffold=scaffold,
hooks=hooks,
chief_only_hooks=chief_only_hooks,
save_checkpoint_secs=config.train.save_checkpoint_secs,
save_summaries_steps=config.train.save_summaries_steps,
save_summaries_secs=config.train.save_summaries_secs,
) as sess:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
while not coord.should_stop():
before = time.time()
_, train_loss, step, filename = sess.run(
[train_op, total_loss, global_step, train_filename],
options=run_options,
)
# TODO: Add image summary every once in a while.
tf.logging.info(
"{}step: {}, file: {}, train_loss: {}, in {:.2f}s".format(
log_prefix, step, filename, train_loss, time.time() - before
)
)
if is_chief and step == 1:
# We save the run after first batch to make sure everything
# works properly.
save_run(config, environment=environment)
except tf.errors.OutOfRangeError:
tf.logging.info(
"{}finished training after {} epoch limit".format(
log_prefix, config.train.num_epochs
)
)
# TODO: Print summary
finally:
coord.request_stop()
# Wait for all threads to stop.
coord.join(threads)
return step
def run(config,
target='',
cluster_spec=None,
is_chief=True,
job_name=None,
task_index=None,
get_model_fn=get_model,
get_dataset_fn=get_dataset,
environment=None):
model_class = get_model_fn(config.model.type)
image_vis = config.train.get('image_vis')
var_vis = config.train.get('var_vis')
if config.train.get('seed') is not None:
tf.set_random_seed(config.train.seed)
log_prefix = '[{}-{}] - '.format(job_name, task_index) \
if job_name is not None and task_index is not None else ''
if config.train.debug or config.train.tf_debug:
tf.logging.set_verbosity(tf.logging.DEBUG)
else:
tf.logging.set_verbosity(tf.logging.INFO)
model = model_class(config)
# Placement of ops on devices using replica device setter
# which automatically places the parameters on the `ps` server
# and the `ops` on the workers
#
# See:
# https://www.tensorflow.org/api_docs/python/tf/train/replica_device_setter
with tf.device(tf.train.replica_device_setter(cluster=cluster_spec)):
try:
config['dataset']['type']
except KeyError:
raise KeyError('dataset.type should be set on the custom config.')
try:
dataset_class = get_dataset_fn(config.dataset.type)
dataset = dataset_class(config)
train_dataset = dataset()
except InvalidDataDirectory as exc:
tf.logging.error("Error while reading dataset, {}".format(exc))
sys.exit(1)
train_image = train_dataset['image']
train_filename = train_dataset['filename']
train_bboxes = train_dataset['bboxes']
prediction_dict = model(train_image, train_bboxes, is_training=True)
total_loss = model.loss(prediction_dict)
global_step = tf.train.get_or_create_global_step()
optimizer = get_optimizer(config.train, global_step)
trainable_vars = model.get_trainable_vars()
with tf.name_scope('gradients'):
# Compute, clip and apply gradients
grads_and_vars = optimizer.compute_gradients(
total_loss, trainable_vars)
# Clip by norm.
if config.train.clip_by_norm:
grads_and_vars = clip_gradients_by_norm(grads_and_vars)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
tf.logging.info('{}Starting training for {}'.format(log_prefix, model))
run_options = None
if config.train.full_trace:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
if is_chief:
# Load pretrained weights needs to be called before defining the train
# op. After it, variables for the optimizer are created.
with tf.control_dependencies([tf.global_variables_initializer()]):
with tf.control_dependencies([model.load_pretrained_weights()]):
init_op = tf.no_op(name='global_init_load_pretrained')
else:
init_op = tf.no_op()
# Create custom Scaffold to make sure we run our own init_op when model
# is not restored from checkpoint.
summary_op = [model.summary]
summaries = tf.summary.merge_all()
if summaries is not None:
summary_op.append(summaries)
summary_op = tf.summary.merge(summary_op)
scaffold = tf.train.Scaffold(
# Initialize local and global variables.
init_op=init_op,
# Queue-related variables need a special initializer.
local_init_op=tf.local_variables_initializer(),
summary_op=summary_op,
)
# Custom hooks for our session
hooks = []
chief_only_hooks = []
if config.train.tf_debug:
debug_hook = tf_debug.LocalCLIDebugHook()
debug_hook.add_tensor_filter('has_inf_or_nan', tf_debug.has_inf_or_nan)
hooks.extend([debug_hook])
if not config.train.job_dir:
tf.logging.warning(
'`job_dir` is not defined. Checkpoints and logs will not be saved.'
)
checkpoint_dir = None
elif config.train.run_name:
# Use run_name when available
checkpoint_dir = os.path.join(config.train.job_dir,
config.train.run_name)
else:
checkpoint_dir = config.train.job_dir
should_add_hooks = (config.train.display_every_steps
or config.train.display_every_secs
and checkpoint_dir is not None)
if should_add_hooks:
if not config.train.debug and image_vis == 'debug':
tf.logging.warning('ImageVisHook will not run without debug mode.')
elif image_vis is not None:
# ImageVis only runs on the chief.
chief_only_hooks.append(
ImageVisHook(prediction_dict,
image=train_dataset['image'],
gt_bboxes=train_dataset['bboxes'],
with_rcnn=config.model.network.with_rcnn,
output_dir=checkpoint_dir,
every_n_steps=config.train.display_every_steps,
every_n_secs=config.train.display_every_secs,
image_visualization_mode=image_vis))
if var_vis is not None:
# VarVis only runs on the chief.
chief_only_hooks.append(
VarVisHook(
every_n_steps=config.train.display_every_steps,
every_n_secs=config.train.display_every_secs,
mode=var_vis,
output_dir=checkpoint_dir,
vars_summary=model.vars_summary,
))
step = -1
with tf.train.MonitoredTrainingSession(
master=target,
is_chief=is_chief,
checkpoint_dir=checkpoint_dir,
scaffold=scaffold,
hooks=hooks,
chief_only_hooks=chief_only_hooks,
save_checkpoint_secs=config.train.save_checkpoint_secs,
save_summaries_steps=config.train.save_summaries_steps,
save_summaries_secs=config.train.save_summaries_secs,
) as sess:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
while not coord.should_stop():
before = time.time()
_, train_loss, step, filename = sess.run(
[train_op, total_loss, global_step, train_filename],
options=run_options)
# TODO: Add image summary every once in a while.
tf.logging.info(
'{}step: {}, file: {}, train_loss: {}, in {:.2f}s'.format(
log_prefix, step, filename, train_loss,
time.time() - before))
if is_chief and step == 1:
# We save the run after first batch to make sure everything
# works properly.
save_run(config, environment=environment)
except tf.errors.OutOfRangeError:
tf.logging.info('{}finished training after {} epoch limit'.format(
log_prefix, config.train.num_epochs))
# TODO: Print summary
finally:
coord.request_stop()
# Wait for all threads to stop.
coord.join(threads)
return step
def run(custom_config,
model_type,
override_params,
target='',
cluster_spec=None,
is_chief=True,
job_name=None,
task_index=None,
get_model_fn=get_model,
get_dataset_fn=get_dataset):
model_class = get_model_fn(model_type)
config = get_model_config(
model_class.base_config,
custom_config,
override_params,
)
if config.train.get('seed') is not None:
tf.set_random_seed(config.train.seed)
log_prefix = '[{}-{}] - '.format(job_name, task_index) \
if job_name is not None and task_index is not None else ''
if config.train.debug or config.train.tf_debug:
tf.logging.set_verbosity(tf.logging.DEBUG)
else:
tf.logging.set_verbosity(tf.logging.INFO)
model = model_class(config)
# Placement of ops on devices using replica device setter
# which automatically places the parameters on the `ps` server
# and the `ops` on the workers
#
# See:
# https://www.tensorflow.org/api_docs/python/tf/train/replica_device_setter
with tf.device(tf.train.replica_device_setter(cluster=cluster_spec)):
try:
config['dataset']['type']
except KeyError:
raise KeyError('dataset.type should be set on the custom config.')
dataset_class = get_dataset_fn(config.dataset.type)
dataset = dataset_class(config)
train_dataset = dataset()
train_image = train_dataset['image']
train_filename = train_dataset['filename']
train_bboxes = train_dataset['bboxes']
# TODO: This is not the best place to configure rank? Why is rank not
# transmitted through the queue
train_image.set_shape((None, None, 3))
# We add fake batch dimension to train data.
# TODO: DEFINITELY NOT THE BEST PLACE
train_image = tf.expand_dims(train_image, 0)
prediction_dict = model(train_image, train_bboxes, is_training=True)
total_loss = model.loss(prediction_dict)
global_step = tf.contrib.framework.get_or_create_global_step()
optimizer = get_optimizer(config.train, global_step)
trainable_vars = model.get_trainable_vars()
with tf.name_scope('gradients'):
# Compute, clip and apply gradients
grads_and_vars = optimizer.compute_gradients(
total_loss, trainable_vars)
# Clip by norm. TODO: Configurable
grads_and_vars = clip_gradients_by_norm(grads_and_vars)
train_op = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
tf.logging.info('{}Starting training for {}'.format(log_prefix, model))
run_options = None
if config.train.full_trace:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
if is_chief:
# Load pretrained weights needs to be called before defining the train
# op. After it, variables for the optimizer are created.
with tf.control_dependencies([tf.global_variables_initializer()]):
with tf.control_dependencies([model.load_pretrained_weights()]):
init_op = tf.no_op(name='global_init_load_pretrained')
else:
init_op = tf.no_op()
# Create custom Scaffold to make sure we run our own init_op when model
# is not restored from checkpoint.
scaffold = tf.train.Scaffold(
# Initialize local and global variables.
init_op=init_op,
# Queue-related variables need a special initializer.
local_init_op=tf.local_variables_initializer(),
summary_op=tf.summary.merge([
tf.summary.merge_all(),
model.summary,
]))
# Custom hooks for our session
hooks = []
chief_only_hooks = []
if config.train.tf_debug:
debug_hook = tf_debug.LocalCLIDebugHook()
debug_hook.add_tensor_filter('has_inf_or_nan', tf_debug.has_inf_or_nan)
hooks.extend([debug_hook])
if not config.train.job_dir:
tf.logging.warning(
'`job_dir` is not defined. Checkpoints and logs will not be saved.'
)
elif config.train.run_name:
# Use run_name when available
checkpoint_dir = os.path.join(config.train.job_dir,
config.train.run_name)
else:
checkpoint_dir = config.train.job_dir
if config.train.display_every_steps or config.train.display_every_secs:
if not config.train.debug:
tf.logging.warning('ImageVisHook will not run without debug mode.')
else:
# ImageVis only runs on the chief.
chief_only_hooks.append(
ImageVisHook(prediction_dict,
with_rcnn=config.network.with_rcnn,
output_dir=checkpoint_dir,
every_n_steps=config.train.display_every_steps,
every_n_secs=config.train.display_every_secs))
with tf.train.MonitoredTrainingSession(
master=target,
is_chief=is_chief,
checkpoint_dir=checkpoint_dir,
scaffold=scaffold,
hooks=hooks,
chief_only_hooks=chief_only_hooks,
save_checkpoint_secs=config.train.save_checkpoint_secs,
save_summaries_steps=config.train.save_summaries_steps,
save_summaries_secs=config.train.save_summaries_secs,
) as sess:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
while not coord.should_stop():
before = time.time()
_, train_loss, step, filename = sess.run(
[train_op, total_loss, global_step, train_filename],
options=run_options)
# TODO: Add image summary every once in a while.
tf.logging.info(
'{}step: {}, file: {}, train_loss: {}, in {:.2f}s'.format(
log_prefix, step, filename, train_loss,
time.time() - before))
except tf.errors.OutOfRangeError:
tf.logging.info('{}finished training after {} epoch limit'.format(
log_prefix, config.train.num_epochs))
# TODO: Print summary
finally:
coord.request_stop()
# Wait for all threads to stop.
coord.join(threads)
def run(config, target='', cluster_spec=None, is_chief=True, job_name=None,
task_index=None, get_model_fn=get_model, get_dataset_fn=get_dataset,
environment=None):
model_class = get_model_fn(config.model.type)
image_vis = config.train.get('image_vis')
var_vis = config.train.get('var_vis')
if config.train.get('seed') is not None:
tf.set_random_seed(config.train.seed)
log_prefix = '[{}-{}] - '.format(job_name, task_index) \
if job_name is not None and task_index is not None else ''
if config.train.debug or config.train.tf_debug:
tf.logging.set_verbosity(tf.logging.DEBUG)
else:
tf.logging.set_verbosity(tf.logging.INFO)
model = model_class(config)
# Placement of ops on devices using replica device setter
# which automatically places the parameters on the `ps` server
# and the `ops` on the workers
#
# See:
# https://www.tensorflow.org/api_docs/python/tf/train/replica_device_setter
with tf.device(tf.train.replica_device_setter(cluster=cluster_spec)):
try:
config['dataset']['type']
except KeyError:
raise KeyError('dataset.type should be set on the custom config.')
try:
dataset_class = get_dataset_fn(config.dataset.type)
dataset = dataset_class(config)
train_dataset = dataset()
except InvalidDataDirectory as exc:
tf.logging.error(
"Error while reading dataset, {}".format(exc)
)
sys.exit(1)
train_image = train_dataset['image']
train_filename = train_dataset['filename']
train_bboxes = train_dataset['bboxes']
prediction_dict = model(train_image, train_bboxes, is_training=True)
total_loss = model.loss(prediction_dict)
global_step = tf.train.get_or_create_global_step()
optimizer = get_optimizer(config.train, global_step)
trainable_vars = model.get_trainable_vars()
with tf.name_scope('gradients'):
# Compute, clip and apply gradients
grads_and_vars = optimizer.compute_gradients(
total_loss, trainable_vars
)
# Clip by norm.
if config.train.clip_by_norm:
grads_and_vars = clip_gradients_by_norm(grads_and_vars)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.apply_gradients(
grads_and_vars, global_step=global_step
)
tf.logging.info('{}Starting training for {}'.format(log_prefix, model))
run_options = None
if config.train.full_trace:
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE
)
if is_chief:
# Load pretrained weights needs to be called before defining the train
# op. After it, variables for the optimizer are created.
with tf.control_dependencies([tf.global_variables_initializer()]):
with tf.control_dependencies([model.load_pretrained_weights()]):
init_op = tf.no_op(name='global_init_load_pretrained')
else:
init_op = tf.no_op()
# Create custom Scaffold to make sure we run our own init_op when model
# is not restored from checkpoint.
summary_op = [model.summary]
summaries = tf.summary.merge_all()
if summaries is not None:
summary_op.append(summaries)
summary_op = tf.summary.merge(summary_op)
scaffold = tf.train.Scaffold(
# Initialize local and global variables.
init_op=init_op,
# Queue-related variables need a special initializer.
local_init_op=tf.local_variables_initializer(),
summary_op=summary_op,
)
# Custom hooks for our session
hooks = []
chief_only_hooks = []
if config.train.tf_debug:
debug_hook = tf_debug.LocalCLIDebugHook()
debug_hook.add_tensor_filter(
'has_inf_or_nan', tf_debug.has_inf_or_nan
)
hooks.extend([debug_hook])
if not config.train.job_dir:
tf.logging.warning(
'`job_dir` is not defined. Checkpoints and logs will not be saved.'
)
checkpoint_dir = None
elif config.train.run_name:
# Use run_name when available
checkpoint_dir = os.path.join(
config.train.job_dir, config.train.run_name
)
else:
checkpoint_dir = config.train.job_dir
should_add_hooks = (
config.train.display_every_steps
or config.train.display_every_secs
and checkpoint_dir is not None
)
if should_add_hooks:
if not config.train.debug and image_vis == 'debug':
tf.logging.warning('ImageVisHook will not run without debug mode.')
elif image_vis is not None:
# ImageVis only runs on the chief.
chief_only_hooks.append(
ImageVisHook(
prediction_dict,
image=train_dataset['image'],
gt_bboxes=train_dataset['bboxes'],
with_rcnn=config.model.network.with_rcnn,
output_dir=checkpoint_dir,
every_n_steps=config.train.display_every_steps,
every_n_secs=config.train.display_every_secs,
image_visualization_mode=image_vis
)
)
if var_vis is not None:
# VarVis only runs on the chief.
chief_only_hooks.append(
VarVisHook(
every_n_steps=config.train.display_every_steps,
every_n_secs=config.train.display_every_secs,
mode=var_vis,
output_dir=checkpoint_dir,
vars_summary=model.vars_summary,
)
)
step = -1
with tf.train.MonitoredTrainingSession(
master=target,
is_chief=is_chief,
checkpoint_dir=checkpoint_dir,
scaffold=scaffold,
hooks=hooks,
chief_only_hooks=chief_only_hooks,
save_checkpoint_secs=config.train.save_checkpoint_secs,
save_summaries_steps=config.train.save_summaries_steps,
save_summaries_secs=config.train.save_summaries_secs,
) as sess:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
while not coord.should_stop():
before = time.time()
_, train_loss, step, filename = sess.run([
train_op, total_loss, global_step, train_filename
], options=run_options)
# TODO: Add image summary every once in a while.
tf.logging.info(
'{}step: {}, file: {}, train_loss: {}, in {:.2f}s'.format(
log_prefix, step, filename, train_loss,
time.time() - before
))
if is_chief and step == 1:
# We save the run after first batch to make sure everything
# works properly.
save_run(config, environment=environment)
except tf.errors.OutOfRangeError:
tf.logging.info(
'{}finished training after {} epoch limit'.format(
log_prefix, config.train.num_epochs
)
)
# TODO: Print summary
finally:
coord.request_stop()
# Wait for all threads to stop.
coord.join(threads)
return step
