def main(_):
print('learning_rate', FLAGS.learning_rate)
global train_feed_dict, test_feed_dict
train_feed_dict = gen_feed_dict(p.inputs, p.actual_index_dists, (11, 20))
test_feed_dict = gen_feed_dict(p.inputs, p.actual_index_dists, (6, 10))
logging.set_logging_path(gezi.get_dir('/home/gezi/temp/pointer.model'))
melt.apps.train_flow(ops,
eval_ops=eval_ops,
gen_feed_dict_fn=lambda: train_feed_dict,
gen_eval_feed_dict_fn=lambda: test_feed_dict)
def main(_):
print('learning_rate', FLAGS.learning_rate)
logging.set_logging_path(gezi.get_dir(FLAGS.model_dir))
melt.apps.train_flow(ops,
eval_ops=eval_ops,
eval_names=eval_names,
gen_feed_dict_fn=gen_feed_dict,
gen_eval_feed_dict_fn=gen_feed_dict,
deal_eval_results_fn=deal_eval_results,
model_dir=FLAGS.model_dir)
def main(_):
#-----------init global resource
logging.set_logging_path(gezi.get_dir(FLAGS.model_dir))
vocabulary.init()
text2ids.init()
#evaluator.init()
logging.info('algo:{}'.format(FLAGS.algo))
logging.info('monitor_level:{}'.format(FLAGS.monitor_level))
global_scope = ''
if FLAGS.add_global_scope:
global_scope = FLAGS.global_scope if FLAGS.global_scope else FLAGS.algo
global sess
sess = melt.get_session(log_device_placement=FLAGS.log_device_placement)
with tf.variable_scope(global_scope):
train()
def main(_):
#-----------init global resource
logging.set_logging_path(gezi.get_dir(FLAGS.model_dir))
melt.apps.train.init()
has_image_model = FLAGS.image_checkpoint_file and os.path.exists(
FLAGS.image_checkpoint_file)
if has_image_model:
print('image_endpoint_feature_name:',
FLAGS.image_endpoint_feature_name)
melt.apps.image_processing.init(
FLAGS.image_model_name,
feature_name=FLAGS.image_endpoint_feature_name)
FLAGS.pre_calc_image_feature = FLAGS.pre_calc_image_feature or (
not has_image_model)
vocabulary.init()
text2ids.init()
## TODO FIXME if evaluator init before main graph(assistant preidictor with image model) then will wrong for finetune later,
## image scope as not defined, to set reuse = None? though assistant in different scope graph still scope reused??
## so right now just let evaluator lazy init, init when used after main graph build
# try:
# evaluator.init()
# except Exception:
# print(traceback.format_exc(), file=sys.stderr)
# print('evaluator init fail will not do metric eval')
# FLAGS.metric_eval = False
logging.info('algo:{}'.format(FLAGS.algo))
logging.info('monitor_level:{}'.format(FLAGS.monitor_level))
global sess
sess = melt.get_session(log_device_placement=FLAGS.log_device_placement)
global_scope = melt.apps.train.get_global_scope()
with tf.variable_scope(global_scope):
train()
def main(_):
print('learning_rate', FLAGS.learning_rate)
logging.set_logging_path(gezi.get_dir(FLAGS.model_dir))
if FLAGS.mode_ == 'train':
melt.apps.train_flow(ops,
eval_ops=eval_ops,
eval_names=eval_names,
gen_feed_dict_fn=gen_feed_dict,
gen_eval_feed_dict_fn=gen_feed_dict,
deal_eval_results_fn=deal_eval_results,
model_dir=FLAGS.model_dir)
else:
melt.flow.test_flow(
eval_ops,
names=eval_names,
gen_feed_dict_fn=gen_feed_dict,
deal_results_fn=deal_eval_results,
model_dir=FLAGS.model_dir,
interval_steps=FLAGS.interval_steps,
eval_times=FLAGS.eval_times)
def main(_):
#-----------init global resource
logging.set_logging_path(gezi.get_dir(FLAGS.model_dir))
if not FLAGS.pre_calc_image_feature:
melt.apps.image_processing.init()
InputApp.init()
vocabulary.init()
text2ids.init()
evaluator.init()
logging.info('algo:{}'.format(FLAGS.algo))
logging.info('monitor_level:{}'.format(FLAGS.monitor_level))
global sess
sess = melt.get_session(log_device_placement=FLAGS.log_device_placement)
global_scope = ''
if FLAGS.add_global_scope:
global_scope = FLAGS.global_scope if FLAGS.global_scope else FLAGS.algo
with tf.variable_scope(global_scope):
train()
def tf_train_flow(
train_once_fn,
model_dir=None,
log_dir=None,
max_models_keep=1,
save_interval_seconds=600,
save_interval_steps=1000,
num_epochs=None,
num_steps=None,
save_model=True,
save_interval_epochs=None,
freeze_graph=False,
num_steps_per_epoch=0,
restore_from_latest=True,
metric_eval_fn=None,
valid_interval_epochs=0,
inference_fn=None,
inference_interval_epochs=0,
init_fn=None,
restore_fn=None,
restore_include=None,
restore_exclude=None,
save_all_scope=False, #TODO save load from restore scope only but svae all
variables_to_restore=None,
variables_to_save=None, #by default will be the same as variables_to_restore
output_collection_names=None,
output_node_names=None,
learning_rate=None, #not use yet, just use in train_once
learning_rate_patience=None,
learning_rate_decay_factor=None,
write_during_train=True,
model=None,
sess=None):
"""
similary flow as tf_flow, but add model try reload and save
"""
use_horovod = 'OMPI_COMM_WORLD_RANK' in os.environ
model_dir_ = model_dir
if use_horovod and hvd.rank() != 0:
model_dir = None
if sess is None:
#TODO melt.get_session is global session but may cause non close at last
sess = melt.get_session()
if FLAGS.use_tpu:
sess.run(tpu.initialize_system())
#logging.info('tf_train_flow start')
#logging.info('max_models_keep:', max_models_keep)
#logging.info('save_interval_seconds:', save_interval_seconds)
if model_dir:
if model:
checkpoint = tf.train.Checkpoint(model=model)
ckpt_dir = model_dir + '/ckpt'
checkpoint_prefix = os.path.join(ckpt_dir, 'ckpt')
#this is usefull for you use another model with another scope, and just load and restore/save initalize your scope vars!
#this is not for finetune but mainly for like using another model as in predict like this introducing graph other model scope and ignore here
# var_list = None if not restore_scope else tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=restore_scope)
# #logging.info('-------------var_list', var_list)
# if not variables_to_restore:
# variables_to_restore = var_list
if not variables_to_restore:
variables_to_restore = slim.get_variables_to_restore(
include=restore_include, exclude=restore_exclude)
if not variables_to_save:
variables_to_save = variables_to_restore
if save_all_scope:
variables_to_save = None
#if variables_to_restore is None:
logging.info('variables_to_restore from %s' % model_dir)
#load all var in checkpoint try to save all var(might more then original checkpoint) if not specifiy variables_to_save
varnames_in_checkpoint = melt.get_checkpoint_varnames(model_dir)
#logging.info('varnames_in_checkpoint: {}'.format(varnames_in_checkpoint))
# TODO has someproblem say tf.Variable 'r_net/text_encoder/cudnn_rnn/cu_dnngru/recurrent_kernel/adam_v:0' even though in checkpoint I have renated it as ignore/rnet
variables_to_restore_from_model = slim.get_variables_to_restore(
include=varnames_in_checkpoint)
#logging.info('variables_to_restore_from_model: {}'.format(variables_to_restore_from_model))
if not variables_to_restore:
variables_to_restore = variables_to_restore_from_model
else:
variables_to_restore = [
v for v in variables_to_restore
if v in variables_to_restore_from_model
]
if restore_exclude:
for excl in restore_exclude:
variables_to_restore = [
v for v in variables_to_restore if not excl in v.name
]
#--tf 1.6 adadelta will have same vars...
variables_to_restore = list(set(variables_to_restore))
#logging.info('variables_to_restore', variables_to_restore[:100])
logging.info('variables_to_restore', [
x for x in variables_to_restore if not 'OptimizeLoss' in x.name
][:100])
##finally remove global_step since melt.apps.train will handle it!
global_step = tf.train.get_or_create_global_step()
#variables_to_restore = [v for v in variables_to_restore if not tf.GraphKeys.GLOBAL_STEP in v.name]
#variables_to_restore = [v for v in variables_to_restore if not 'learning_rate' in v.name]
# TODO fixme if step, step2.. and in checkpoint step then here will be step2...
#print('------------', [v for v in variables_to_restore if 'step' in v.name])
loader = tf.train.Saver(var_list=variables_to_restore)
logging.info('max models to keep {}, keep every {} hours'.format(
max_models_keep, save_interval_seconds / 3600.0))
saver = tf.train.Saver(
max_to_keep=max_models_keep,
keep_checkpoint_every_n_hours=save_interval_seconds / 3600.0,
var_list=variables_to_save)
epoch_saver = tf.train.Saver(var_list=variables_to_save, max_to_keep=1000)
best_epoch_saver = tf.train.Saver(var_list=variables_to_save)
#logging.info('variables_to_save:{}'.format(variables_to_save))
# # #TODO for safe restore all init will be ok ?
# # if variables_to_restore is None:
init_op = tf.group(
tf.global_variables_initializer(
), #variables_initializer(global_variables())
tf.local_variables_initializer()
) #variables_initializer(local_variables())
# # else:
# # init_op = tf.group(tf.variables_initializer(variables_to_restore),
# # tf.local_variables_initializer())
##--mostly this will be fine except for using assistant predictor, initialize again! will make assistant predictor wrong
##so assume to all run init op! if using assistant predictor, make sure it use another session
# https://stackoverflow.com/questions/35164529/in-tensorflow-is-there-any-way-to-just-initialize-uninitialised-variables
# def guarantee_initialized_variables(session, list_of_variables = None):
# if list_of_variables is None:
# list_of_variables = tf.global_variables()
# uninitialized_variables = list(tf.get_variable(name) for name in
# session.run(tf.report_uninitialized_variables(list_of_variables)))
# return unintialized_variables
# unintialized_variables = guarantee_initialized_variables(sess)
# init_op = tf.group(tf.initialize_variables(uninitialized_vars), tf.local_variables_initializer())
timer = gezi.Timer('sess run init_op in melt.tf_train_flow')
#model.save('./weights')
# notice
sess.run(init_op)
timer.print_elapsed()
#melt.init_uninitialized_variables(sess)
#pre_step means the step last saved, train without pretrained,then -1
pre_step = -1
fixed_pre_step = -1 #fixed pre step is for epoch num to be correct if you change batch size
#print(model_dir)
pre_epoch = None
if model_dir:
model_path = _get_model_path(model_dir, save_model)
# if not model_path:
# model_path = _get_model_path(os.path.join(model_dir, 'epoch'))
#print(model_path)
model_dir = gezi.get_dir(
model_dir) #incase you pass ./model/model-ckpt1000 -> ./model
if model_path is not None:
if not restore_from_latest:
logging.info('using recent but not latest model')
model_path = melt.recent_checkpoint(model_dir)
model_name = os.path.basename(model_path)
timer = gezi.Timer(
'Loading and training from existing model [%s]' % model_path)
if restore_fn is not None:
restore_fn(sess)
loader.restore(sess, model_path)
## not supported
#model.save()
#model.save_weights('./weights')
timer.print()
#pre_step = melt.get_model_step(model_path) - 1 if FLAGS.global_step is None else FLAGS.global_step -1
# TODO check ..
pre_step = sess.run(tf.train.get_global_step()) - 1
pre_epoch = melt.get_model_epoch(
model_path
) if FLAGS.global_epoch is None else FLAGS.global_epoch
fixed_pre_step = pre_step
# if pre_epoch is not None:
# #like using batch size 32, then reload train using batch size 64
# if abs(pre_step / num_steps_per_epoch - pre_epoch) > 0.1:
# fixed_pre_step = int(pre_epoch * num_steps_per_epoch)
# logging.info('Warning, epoch is diff with pre_step / num_steps_per_epoch:{}, pre_epoch:{},maybe you change batch size and we will adjust to set pre_step as {}'\
# .format(pre_step / num_steps_per_epoch, pre_epoch, fixed_pre_step))
else:
latest_checkpoint = None
if not use_horovod: #now will hang
try:
latest_checkpoint = tf.train.latest_checkpoint(ckpt_dir)
if latest_checkpoint:
logging.info(
'Try start from eager trained mode, latest checkpoint:',
latest_checkpoint)
checkpoint.restore(latest_checkpoint).run_restore_ops(
session=sess)
pre_epoch = int(latest_checkpoint.split('-')[-1])
#pre_step = pre_epoch * num_steps_per_epoch - 1
# TODO check
pre_step = sess.run(tf.train.get_global_step()) - 1
fixed_pre_step = pre_step
logging.info('Start step is:', pre_step)
except Exception:
logging.info(
'Something wrong with restore from eager trained model'
)
if latest_checkpoint is None:
logging.info('Train all start step 0')
#https://stackoverflow.com/questions/40220201/tensorflow-tf-initialize-all-variables-vs-tf-initialize-local-variables
#tf.initialize_all_variables() is a shortcut to tf.initialize_variables(tf.all_variables()),
#tf.initialize_local_variables() is a shortcut to tf.initialize_variables(tf.local_variables()),
#which initializes variables in GraphKeys.VARIABLES and GraphKeys.LOCAL_VARIABLE collections, respectively.
#init_op = tf.group(tf.global_variables_initializer(),
# tf.local_variables_initializer())
#[var for var in tf.all_variables() if var.op.name.startswith(restore_scope)] will be the same as tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=restore_scope)
#sess.run(init_op)
#like use image model, build image graph, reload first train, and then will go to same checkpoint all varaible just restore will ok
#for finetune from loading other model init
if init_fn is not None:
init_fn(sess)
if gezi.env_has('METRIC'):
l = metric_eval_fn(model_path)
print(list(zip(l[1], l[0])))
exit(0)
#sess.run(tf.assign(global_step, tf.constant(global_step_val, dtype=tf.int64)))
try:
learning_rate = tf.get_collection('learning_rate')[-1]
learning_rate_weight = tf.get_collection('learning_rate_weight')[-1]
sess.run(tf.assign(learning_rate,
learning_rate * learning_rate_weight))
except Exception:
# if not using weight_decay but using optimizer decay then will go here as learning rate is a tensor can not assign
pass
try:
logging.info('Actual start global step:',
sess.run(global_step), 'learning rate:',
sess.run(learning_rate), 'learning_rate_weight:',
sess.run(learning_rate_weight))
except Exception:
pass
if model_dir_:
#if save_interval_epochs and num_steps_per_epoch and num_steps >= 0:
epoch_dir = os.path.join(model_dir_, 'epoch')
gezi.try_mkdir(epoch_dir)
checkpoint_path = os.path.join(model_dir_, 'model.ckpt')
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
if use_horovod:
bcast = hvd.broadcast_global_variables(0)
sess.run(bcast)
#tf.train.write_graph(sess.graph_def, model_dir, 'train.pbtxt')
only_one_step = False
try:
if use_horovod:
## TODO FIXME why bcast here not work ? simple test work see tests/bcast.py
#comm.bcast(pre_step, root=0)
temp = np.array([pre_step, fixed_pre_step])
comm.Bcast(temp, root=0)
pre_step = temp[0]
fixed_pre_step = temp[1]
step = start = pre_step + 1
fixed_step = fixed_pre_step + 1
#first = True
#hack just for save one model after load
if num_steps < 0 or (num_steps and num_steps < step):
logging.info('just load and resave then exit')
model_path_ = _get_checkpoint_path(checkpoint_path,
step,
num_steps_per_epoch,
epoch=pre_epoch)
saver.save(sess, model_path_, global_step=step + 1)
if freeze_graph:
melt.freeze_graph(sess, model_path_, step + 1,
output_collection_names, output_node_names)
sess.close()
exit(0)
if num_epochs < 0:
only_one_step = True
logging.info('just run one step')
if FLAGS.work_mode != 'train':
assert not os.path.isdir(FLAGS.model_dir), FLAGS.model_dir
if 'valid' in FLAGS.work_mode:
vals, names = metric_eval_fn(FLAGS.model_dir)
logging.info(list(zip(names, vals)))
if 'test' in FLAGS.work_mode:
inference_fn(FLAGS.model_dir)
exit(0)
#early_stop = True #TODO allow config
num_bad_epochs = 0
pre_epoch_eval_loss = 1e20
best_epoch_eval_loss = 1e20
num_allowed_bad_epochs = 4 #allow 5 non decrease eval loss epochs before stop
epoch_saved_step = 0
while not coord.should_stop():
model_step_path = None
if model_dir_:
model_path_ = os.path.join(
epoch_dir, 'model.ckpt-%.2f' %
(fixed_step / float(num_steps_per_epoch)))
model_step_path_ = model_path_ + '-' + str(step)
if (write_during_train and metric_eval_fn is not None
and valid_interval_epochs and fixed_step %
int(num_steps_per_epoch * valid_interval_epochs) == 0):
model_step_path = model_step_path_
else:
model_step_path = None
if step == 0:
model_step_path = None
#print('--------------------step', step)
stop = train_once_fn(
sess,
step,
is_start=(step == start),
fixed_step=fixed_step,
num_epochs=num_epochs,
model_path=model_step_path,
use_horovod=use_horovod,
## TODO FIXME this line will cause tensorflow.python.framework.errors_impl.NotFoundError: Resource localhost/save_counter/N10tensorflow3VarE does not exist.
)
#first = False
if only_one_step:
stop = True
step += 1
fixed_step += 1
if save_model and step and model_dir:
#step 0 is also saved! actually train one step and save
if step % save_interval_steps == 0:
timer = gezi.Timer(
'save model step %d to %s' % (step, checkpoint_path),
False)
model_path_ = _get_checkpoint_path(checkpoint_path,
fixed_step,
num_steps_per_epoch)
saver.save(sess, model_path_, global_step=step)
if freeze_graph:
melt.freeze_graph(sess, model_path_, step,
output_collection_names,
output_node_names)
#if log_dir != model_dir:
# assert log_dir
# command = 'rsync -l -r -t %s/* %s' % (log_dir, model_dir)
# print(command, file=sys.stderr)
# os.system(command)
timer.print_elapsed()
if save_interval_steps and num_steps_per_epoch and fixed_step % int(
num_steps_per_epoch * save_interval_epochs) == 0:
# TODO only epoch in name not sep ?
epoch_saved_step = step
model_path_ = os.path.join(
epoch_dir, 'model.ckpt-%.2f' %
(fixed_step / float(num_steps_per_epoch)))
model_step_path = model_path_ + '-' + str(step)
epoch_saver.save(sess, model_path_, global_step=step)
#epoch_saver.save(sess, model_path_)
## TODO FIXME do not support tf.keras save currently with horovod
# if model:
# #model.save_weights(epoch_dir + '/ckpt-%.2f' % (fixed_step / float(num_steps_per_epoch)))
# # TODO FIXME if restart will save from 1... again..
# checkpoint.save(checkpoint_prefix, session=sess)
# #print(sess.run(checkpoint.save_counter))
if freeze_graph:
melt.freeze_graph(sess, model_path_, step,
output_collection_names,
output_node_names)
if write_during_train:
if inference_fn is not None and inference_interval_epochs and fixed_step % int(
num_steps_per_epoch *
inference_interval_epochs) == 0:
model_step_path = model_path_ + '-' + str(step)
try:
#print('--------------inference fn')
inference_fn(model_path=model_step_path)
except Exception:
logging.info(traceback.format_exc())
# if metric_eval_fn is not None and valid_interval_epochs and fixed_step % int(num_steps_per_epoch * valid_interval_epochs) == 0:
# model_step_path = model_path_ + '-' + str(step)
# try:
# metric_eval_fn(model_path=model_step_path)
# except Exception:
# logging.info(traceback.format_exc())
if stop is True:
print('Early stop running %d stpes' % (step), file=sys.stderr)
raise tf.errors.OutOfRangeError(
None, None, 'Early stop running %d stpes' % (step))
if num_steps and (step + 1) == start + num_steps:
raise tf.errors.OutOfRangeError(None, None,
'Reached max num steps')
#max_num_epochs = 1000
max_num_epochs = num_epochs
#if max_num_epochs and num_steps_per_epoch and fixed_step // num_steps_per_epoch >= max_num_epochs:
if max_num_epochs and num_steps_per_epoch and fixed_step / num_steps_per_epoch > max_num_epochs:
raise tf.errors.OutOfRangeError(
None, None,
'Reached max num epochs of %d' % max_num_epochs)
#except tf.errors.OutOfRangeError, e:
except tf.errors.OutOfRangeError:
# if run 2 epoch and we have just epoch saved, do not need to save only 1 step more model
if (step - epoch_saved_step > 1) and not (
step == start
) and save_model and step % save_interval_steps != 0 and model_dir:
model_path_ = _get_checkpoint_path(checkpoint_path, step,
num_steps_per_epoch)
saver.save(sess, model_path_, global_step=step)
if freeze_graph:
melt.freeze_graph(sess, model_path_, step,
output_collection_names, output_node_names)
if log_dir != model_dir:
assert log_dir
command = 'rsync -l -r -t %s/* %s' % (log_dir, model_dir)
print(command, file=sys.stderr)
os.system(command)
if only_one_step:
logging.info('Done one step')
exit(0)
# if (step - epoch_saved_step > 1) and metric_eval_fn is not None:
# metric_eval_fn(model_path=model_step_path)
if (num_epochs and fixed_step / num_steps_per_epoch >= num_epochs) or (
num_steps and step == start + num_steps):
logging.info('Done training for %.3f epochs, %d steps.' %
(fixed_step / num_steps_per_epoch, step))
#FIXME becase coord.join seems not work, RuntimeError: Coordinator stopped with threads still running: Thread-9
exit(0)
else:
logging.info('Should not stop, but stopped at epoch: %.3f' %
(fixed_step / num_steps_per_epoch))
logging.info(traceback.format_exc())
#raise e
finally:
coord.request_stop()
coord.join(threads, stop_grace_period_secs=5)
#FIMXE due to use melt.get_session(global not handle del well)
#Done training for 3090020 steps.
#Exception TypeError: "'NoneType' object is not callable" in <bound method Session.__del__ of <tensorflow.python.client.session.Session object at 0x7f6cf33cd450>> ignored
if FLAGS.use_tpu:
sess.run(tpu.shutdown_system())
sess.close()
def main(_):
#-----------init global resource
print('batch_size:', FLAGS.batch_size)
logging.set_logging_path(gezi.get_dir(FLAGS.model_dir))
train()
def tf_train_flow(
train_once_fn,
model_dir='./model',
max_models_keep=1,
save_interval_seconds=600,
save_interval_steps=1000,
num_epochs=None,
num_steps=None,
save_model=True,
save_interval_epochs=1,
num_steps_per_epoch=0,
restore_from_latest=True,
metric_eval_fn=None,
init_fn=None,
restore_fn=None,
restore_scope=None,
save_all_scope=False, #TODO save load from restore scope only but svae all
variables_to_restore=None,
variables_to_save=None, #by default will be the same as variables_to_restore
sess=None):
"""
similary flow as tf_flow, but add model try reload and save
"""
if sess is None:
#TODO melt.get_session is global session but may cause non close at last
sess = melt.get_session()
logging.info('tf_train_flow start')
print('max_models_keep:', max_models_keep, file=sys.stderr)
print('save_interval_seconds:', save_interval_seconds, file=sys.stderr)
#this is usefull for you use another model with another scope, and just load and restore/save initalize your scope vars!
#this is not for finetune but mainly for like using another model as in predict like this introducing graph other model scope and ignore here
var_list = None if not restore_scope else tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES, scope=restore_scope)
if not variables_to_restore:
variables_to_restore = var_list
if not variables_to_save:
variables_to_save = variables_to_restore
if save_all_scope:
variables_to_save = None
if variables_to_restore is None:
#load all var in checkpoint try to save all var(might more then original checkpoint) if not specifiy variables_to_save
varnames_in_checkpoint = melt.get_checkpoint_varnames(model_dir)
#print(varnames_in_checkpoint)
variables_to_restore = slim.get_variables_to_restore(
include=varnames_in_checkpoint)
#logging.info('variables_to_restore:{}'.format(variables_to_restore))
loader = tf.train.Saver(var_list=variables_to_restore)
saver = tf.train.Saver(
max_to_keep=max_models_keep,
keep_checkpoint_every_n_hours=save_interval_seconds / 3600.0,
var_list=variables_to_save)
epoch_saver = tf.train.Saver(var_list=variables_to_save, max_to_keep=1000)
best_epoch_saver = tf.train.Saver(var_list=variables_to_save)
##TODO for safe restore all init will be ok ?
#if variables_to_restore is None:
init_op = tf.group(
tf.global_variables_initializer(
), #variables_initializer(global_variables())
tf.local_variables_initializer()
) #variables_initializer(local_variables())
# else:
# init_op = tf.group(tf.variables_initializer(variables_to_restore),
# tf.local_variables_initializer())
##--mostly this will be fine except for using assistant predictor, initialize again! will make assistant predictor wrong
##so assume to all run init op! if using assistant predictor, make sure it use another session
sess.run(init_op)
#melt.init_uninitialized_variables(sess)
#pre_step means the step last saved, train without pretrained,then -1
pre_step = -1
fixed_pre_step = -1 #fixed pre step is for epoch num to be correct if yu change batch size
model_path = _get_model_path(model_dir, save_model)
model_dir = gezi.get_dir(
model_dir) #incase you pass ./model/model-ckpt1000 -> ./model
if model_path is not None:
if not restore_from_latest:
print('using recent but not latest model', file=sys.stderr)
model_path = melt.recent_checkpoint(model_dir)
model_name = os.path.basename(model_path)
timer = gezi.Timer('Loading and training from existing model [%s]' %
model_path)
if restore_fn is not None:
restore_fn(sess)
loader.restore(sess, model_path)
timer.print()
pre_step = melt.get_model_step(model_path)
pre_epoch = melt.get_model_epoch(model_path)
fixed_pre_step = pre_step
if pre_epoch is not None:
#like using batch size 32, then reload train using batch size 64
if abs(pre_step / num_steps_per_epoch - pre_epoch) > 0.1:
fixed_pre_step = int(pre_epoch * num_steps_per_epoch)
logging.info('Warning, epoch is diff with pre_step / num_steps_per_epoch:{}, pre_epoch:{},maybe you change batch size and we will adjust to set pre_step as {}'\
.format(pre_step / num_steps_per_epoch, pre_epoch, fixed_pre_step))
else:
print('Train all start step 0', file=sys.stderr)
#https://stackoverflow.com/questions/40220201/tensorflow-tf-initialize-all-variables-vs-tf-initialize-local-variables
#tf.initialize_all_variables() is a shortcut to tf.initialize_variables(tf.all_variables()),
#tf.initialize_local_variables() is a shortcut to tf.initialize_variables(tf.local_variables()),
#which initializes variables in GraphKeys.VARIABLES and GraphKeys.LOCAL_VARIABLE collections, respectively.
#init_op = tf.group(tf.global_variables_initializer(),
# tf.local_variables_initializer())
#[var for var in tf.all_variables() if var.op.name.startswith(restore_scope)] will be the same as tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=restore_scope)
#sess.run(init_op)
#like use image model, build image graph, reload first train, and then will go to same checkpoint all varaible just restore will ok
#for finetune from loading other model init
if init_fn is not None:
init_fn(sess)
if save_interval_epochs and num_steps_per_epoch:
epoch_dir = os.path.join(model_dir, 'epoch')
gezi.try_mkdir(epoch_dir)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
checkpoint_path = os.path.join(model_dir, 'model.ckpt')
tf.train.write_graph(sess.graph_def, model_dir, 'train.pbtxt')
only_one_step = False
try:
step = start = pre_step + 1
fixed_step = fixed_pre_step + 1
#hack just for save one model after load
if num_steps < 0 or (num_steps and num_steps < step):
print('just load and resave then exit', file=sys.stderr)
saver.save(sess,
_get_checkpoint_path(checkpoint_path, step,
num_steps_per_epoch),
global_step=step)
sess.close()
exit(0)
if num_epochs < 0:
only_one_step = True
print('just run one step', file=sys.stderr)
early_stop = True #TODO allow config
num_bad_epochs = 0
pre_epoch_eval_loss = 1e20
best_epoch_eval_loss = 1e20
num_allowed_bad_epochs = 4 #allow 5 non decrease eval loss epochs before stop
while not coord.should_stop():
stop = train_once_fn(sess,
step,
is_start=(step == start),
fixed_step=fixed_step)
if only_one_step:
stop = True
if save_model and step:
#step 0 is also saved! actually train one step and save
if step % save_interval_steps == 0:
timer = gezi.Timer('save model step %d to %s' %
(step, checkpoint_path))
saver.save(sess,
_get_checkpoint_path(checkpoint_path,
fixed_step,
num_steps_per_epoch),
global_step=step)
timer.print()
#if save_interval_epochs and num_steps_per_epoch and step % (num_steps_per_epoch * save_interval_epochs) == 0:
#if save_interval_epochs and num_steps_per_epoch and step % num_steps_per_epoch == 0:
if save_interval_epochs and num_steps_per_epoch and fixed_step % num_steps_per_epoch == 0:
#epoch = step // num_steps_per_epoch
epoch = fixed_step // num_steps_per_epoch
eval_loss = melt.eval_loss()
if eval_loss:
#['eval_loss:3.2','eal_accuracy:4.3']
eval_loss = float(
eval_loss.strip('[]').split(',')[0].strip(
"'").split(':')[-1])
if os.path.exists(
os.path.join(epoch_dir, 'best_eval_loss.txt')):
with open(
os.path.join(epoch_dir,
'best_eval_loss.txt')) as f:
best_epoch_eval_loss = float(
f.readline().split()[-1].strip())
if eval_loss < best_epoch_eval_loss:
best_epoch_eval_loss = eval_loss
logging.info(
'Now best eval loss is epoch %d eval_loss:%f' %
(epoch, eval_loss))
with open(
os.path.join(epoch_dir,
'best_eval_loss.txt'),
'w') as f:
f.write('%d %d %f\n' %
(epoch, step, best_epoch_eval_loss))
best_epoch_saver.save(
sess, os.path.join(epoch_dir,
'model.ckpt-best'))
with open(os.path.join(epoch_dir, 'eval_loss.txt'),
'a') as f:
f.write('%d %d %f\n' % (epoch, step, eval_loss))
if eval_loss >= pre_epoch_eval_loss:
num_bad_epochs += 1
if num_bad_epochs > num_allowed_bad_epochs:
logging.warning(
'Evaluate loss not decrease for last %d epochs'
% (num_allowed_bad_epochs + 1))
if not os.path.exists(
os.path.join(epoch_dir,
'model.ckpt-noimprove')):
best_epoch_saver.save(
sess,
os.path.join(epoch_dir,
'model.ckpt-noimprove'))
##-------well remove it since
#if early_stop:
# stop = True
else:
num_bad_epochs = 0
pre_epoch_eval_loss = eval_loss
if step % (num_steps_per_epoch *
save_interval_epochs) == 0:
epoch_saver.save(sess,
os.path.join(epoch_dir,
'model.ckpt-%d' % epoch),
global_step=step)
#--------do not add step
# epoch_saver.save(sess,
# os.path.join(epoch_dir,'model.ckpt-%d'%epoch))
if stop is True:
print('Early stop running %d stpes' % (step), file=sys.stderr)
raise tf.errors.OutOfRangeError(
None, None, 'Early stop running %d stpes' % (step))
if num_steps and (step + 1) == start + num_steps:
raise tf.errors.OutOfRangeError(None, None,
'Reached max num steps')
#max_num_epochs = 1000
max_num_epochs = num_epochs
if max_num_epochs and num_steps_per_epoch and step // num_steps_per_epoch >= max_num_epochs:
raise tf.errors.OutOfRangeError(
None, None,
'Reached max num epochs of %d' % max_num_epochs)
step += 1
fixed_step += 1
except tf.errors.OutOfRangeError, e:
if not (step
== start) and save_model and step % save_interval_steps != 0:
saver.save(sess,
_get_checkpoint_path(checkpoint_path, step,
num_steps_per_epoch),
global_step=step)
if only_one_step:
print('Done one step', file=sys.stderr)
exit(0)
if metric_eval_fn is not None:
metric_eval_fn()
if (num_epochs and step / num_steps_per_epoch >= num_epochs) or (
num_steps and (step + 1) == start + num_steps):
print('Done training for %.3f epochs, %d steps.' %
(step / num_steps_per_epoch, step + 1),
file=sys.stderr)
#FIXME becase coord.join seems not work, RuntimeError: Coordinator stopped with threads still running: Thread-9
exit(0)
else:
print('Should not stop, but stopped at epoch: %.3f' %
(step / num_steps_per_epoch),
file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
raise e
def main(_):
logging.set_logging_path(gezi.get_dir(FLAGS.model_dir))
train()
def train_flow(ops,
names=None,
gen_feed_dict_fn=None,
deal_results_fn=melt.print_results,
interval_steps=100,
eval_ops=None,
eval_names=None,
gen_eval_feed_dict_fn=None,
deal_eval_results_fn=melt.print_results,
valid_interval_steps=100,
eval_loops=1,
print_time=True,
print_avg_loss=True,
model_dir=None,
max_models_keep=5,
save_interval_seconds=600,
save_interval_steps=1000,
freeze_graph=False,
log_dir=None,
no_log=False,
num_epochs=None,
num_steps=None,
num_steps_per_epoch=None,
optimizer=None,
learning_rate=None,
learning_rate_patience=None,
learning_rate_decay_factor=None,
save_model=True,
save_interval_epochs=None,
add_train_var_histogram=False,
restore_from_latest=True,
metric_eval_fn=None,
metric_eval_interval_steps=0,
valid_interval_epochs=0,
inference_fn=None,
inference_interval_epochs=0,
summary_excls=None,
init_fn=None,
restore_fn=None,
restore_include=None,
restore_exclude=None,
save_all_scope=False,
variables_to_restore=None,
variables_to_save=None,
output_collection_names=None,
output_node_names=None,
write_during_train=True,
use_horovod=False,
model=None,
sess=None):
"""
train flow for tr records, with model saving/reload and summary considered
summary logs will also write to model_dir
see examples/sparse-tensor-classification/train-melt-savemodel.py
NOTICE: first ops must be train_op(optimizer related) which will later ignored
#@TODO allow adding momentum for optimzer
allow mutliple gpu
@TODO can we show epoch num info ?
"""
if optimizer is not None:
loss = ops[0]
if isinstance(optimizer, str):
train_op = melt.gen_train_op_byname(loss, learning_rate, optimizer)
else:
train_op = optimizer(learning_rate).minimize(loss)
ops = list(ops)
ops.insert(0, train_op)
if not model_dir:
if log_dir and no_log:
log_dir = None
if not use_horovod:
return simple_train_flow(
ops,
names,
gen_feed_dict_fn,
deal_results_fn,
interval_steps,
eval_ops,
eval_names,
gen_eval_feed_dict_fn,
deal_eval_results_fn,
valid_interval_steps,
print_time,
print_avg_loss,
log_dir,
num_steps,
num_steps_per_epoch=num_steps_per_epoch,
metric_eval_fn=metric_eval_fn,
metric_eval_interval_steps=metric_eval_interval_steps,
sess=sess)
#if not set log dir try to use model dir to store log
#so defaut is write log, if only want save model but disable log, set no_log=True
if save_model:
print('Will save model to %s' % model_dir)
else:
no_log = True
print('Will not save model, only read model from %s if exists' %
model_dir)
if not log_dir and not no_log:
log_dir = gezi.get_dir(model_dir)
if log_dir:
print('Will save log to %s' % log_dir)
if add_train_var_histogram:
# Add histograms for trainable variables.
#this is also great for you to see all the trainable variables on tensorboard
#NOTICE for big model this is too slow!
melt.monitor_train_vars()
else:
print('Will not save log')
def train_once_(sess,
step,
is_start=False,
fixed_step=None,
num_epochs=None,
model_path=None,
use_horovod=False):
train_once(sess,
step,
ops,
names,
gen_feed_dict_fn,
deal_results_fn,
interval_steps,
eval_ops,
eval_names,
gen_eval_feed_dict_fn,
deal_eval_results_fn,
valid_interval_steps,
print_time,
print_avg_loss,
model_dir,
log_dir,
is_start,
num_steps_per_epoch,
metric_eval_fn=metric_eval_fn,
metric_eval_interval_steps=metric_eval_interval_steps,
summary_excls=summary_excls,
fixed_step=fixed_step,
eval_loops=eval_loops,
learning_rate=learning_rate,
learning_rate_patience=learning_rate_patience,
learning_rate_decay_factor=learning_rate_decay_factor,
num_epochs=num_epochs,
model_path=model_path,
use_horovod=use_horovod)
#print('1.2--------------------OMPI_COMM_WORLD_RANK in', 'OMPI_COMM_WORLD_RANK' in os.environ, hvd.rank())
tf_train_flow(train_once_,
model_dir,
log_dir,
max_models_keep,
save_interval_seconds,
save_interval_steps,
num_epochs,
num_steps,
save_model=save_model,
save_interval_epochs=save_interval_epochs,
freeze_graph=freeze_graph,
num_steps_per_epoch=num_steps_per_epoch,
restore_from_latest=restore_from_latest,
metric_eval_fn=metric_eval_fn,
valid_interval_epochs=valid_interval_epochs,
inference_fn=inference_fn,
inference_interval_epochs=inference_interval_epochs,
init_fn=init_fn,
restore_fn=restore_fn,
restore_include=restore_include,
restore_exclude=restore_exclude,
save_all_scope=save_all_scope,
variables_to_restore=variables_to_restore,
variables_to_save=variables_to_save,
output_collection_names=output_collection_names,
output_node_names=output_node_names,
learning_rate=learning_rate,
learning_rate_patience=learning_rate_patience,
learning_rate_decay_factor=learning_rate_decay_factor,
write_during_train=write_during_train,
model=model,
sess=sess)
def tf_train_flow(train_once_fn,
model_dir='./model',
max_models_keep=1,
save_interval_seconds=600,
save_interval_steps=1000,
num_epochs=None,
num_steps=None,
save_model=True,
save_interval_epochs=1,
num_steps_per_epoch=0,
restore_from_latest=True,
metric_eval_fn=None,
init_fn=None,
sess=None):
"""
similary flow as tf_flow, but add model try reload and save
"""
if sess is None:
#TODO melt.get_session is global session but may cause
sess = melt.get_session()
logging.info('tf_train_flow start')
print('max_models_keep:', max_models_keep)
print('save_interval_seconds:', save_interval_seconds)
saver = tf.train.Saver(
max_to_keep=max_models_keep,
keep_checkpoint_every_n_hours=save_interval_seconds / 3600.0)
epoch_saver = tf.train.Saver()
best_epoch_saver = tf.train.Saver()
#pre_step means the step last saved, train without pretrained,then -1
pre_step = -1;
model_path = _get_model_path(model_dir, save_model)
model_dir = gezi.get_dir(model_dir) #incase you pass ./model/model-ckpt1000 -> ./model
if model_path is not None:
if not restore_from_latest:
print('using recent but not latest model', file=sys.stderr)
model_path = melt.recent_checkpoint(model_dir)
model_name = os.path.basename(model_path)
timer = gezi.Timer('Loading and training from existing model [%s]'%model_path)
saver.restore(sess, model_path)
timer.print()
pre_step = melt.get_model_step(model_path)
if 'epoch' in model_name:
pre_step *= num_steps_per_epoch
#for non 0 eopochs without this will be
#Attempting to use uninitialized value input/input_producer/limit_epochs/epochs
sess.run(tf.local_variables_initializer())
else:
print('Train all start step 0', file=sys.stderr)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess.run(init_op)
if init_fn is not None:
init_fn(sess)
if save_interval_epochs and num_steps_per_epoch:
epoch_dir = os.path.join(model_dir, 'epoch')
gezi.try_mkdir(epoch_dir)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
checkpoint_path = os.path.join(model_dir, 'model.ckpt')
tf.train.write_graph(sess.graph_def, model_dir, 'train.pbtxt')
try:
step = start = pre_step + 1
#hack just for save one model after load
if num_steps and num_steps < step:
print('just load and resave then exit', file=sys.stderr)
saver.save(sess,
_get_checkpoint_path(checkpoint_path, step, num_steps_per_epoch),
global_step=step)
sess.close()
exit(0)
early_stop = True #TODO allow config
num_bad_epochs = 0
pre_epoch_eval_loss = 1e20
best_epoch_eval_loss = 1e20
num_allowed_bad_epochs = 4 #allow 5 non decrease eval loss epochs before stop
while not coord.should_stop():
stop = train_once_fn(sess, step, is_start=(step==start))
if save_model and step:
#step 0 is also saved! actually train one step and save
if step % save_interval_steps == 0:
timer = gezi.Timer('save model step %d to %s'%(step, checkpoint_path))
saver.save(sess,
_get_checkpoint_path(checkpoint_path, step, num_steps_per_epoch),
global_step=step)
timer.print()
#if save_interval_epochs and num_steps_per_epoch and step % (num_steps_per_epoch * save_interval_epochs) == 0:
if save_interval_epochs and num_steps_per_epoch and step % num_steps_per_epoch == 0:
epoch = step // num_steps_per_epoch
eval_loss = melt.eval_loss()
if eval_loss:
#['eval_loss:3.2','eal_accuracy:4.3']
eval_loss = float(eval_loss.strip('[]').split(',')[0].strip("'").split(':')[-1])
if os.path.exists(os.path.join(epoch_dir, 'best_eval_loss.txt')):
with open(os.path.join(epoch_dir, 'best_eval_loss.txt')) as f:
best_epoch_eval_loss = float(f.readline().split()[-1].strip())
if eval_loss < best_epoch_eval_loss:
best_epoch_eval_loss = eval_loss
logging.info('Now best eval loss is epoch %d eval_loss:%f' % (epoch, eval_loss))
with open(os.path.join(epoch_dir, 'best_eval_loss.txt'), 'w') as f:
f.write('%d %d %f\n'%(epoch, step, best_epoch_eval_loss))
best_epoch_saver.save(sess,
os.path.join(epoch_dir,'model.cpkt-best'))
with open(os.path.join(epoch_dir, 'eval_loss.txt'), 'a') as f:
f.write('%d %d %f\n'%(epoch, step, eval_loss))
if eval_loss >= pre_epoch_eval_loss:
num_bad_epochs += 1
if num_bad_epochs > num_allowed_bad_epochs:
logging.warning('Evaluate loss not decrease for last %d epochs'% (num_allowed_bad_epochs + 1))
if not os.path.exists(os.path.join(epoch_dir,'model.cpkt-noimprove')):
best_epoch_saver.save(sess, os.path.join(epoch_dir,'model.cpkt-noimprove'))
##-------well remove it since
#if early_stop:
# stop = True
else:
num_bad_epochs = 0
pre_epoch_eval_loss = eval_loss
if step % (num_steps_per_epoch * save_interval_epochs) == 0:
epoch_saver.save(sess,
os.path.join(epoch_dir,'model.cpkt-%d'%epoch),
global_step=step)
#--------do not add step
# epoch_saver.save(sess,
# os.path.join(epoch_dir,'model.cpkt-%d'%epoch))
if stop is True:
print('Early stop running %d stpes'%(step), file=sys.stderr)
raise tf.errors.OutOfRangeError(None, None,'Early stop running %d stpes'%(step))
if num_steps and (step + 1) == start + num_steps:
raise tf.errors.OutOfRangeError(None, None,'Reached max num steps')
#max_num_epochs = 1000
max_num_epochs = num_epochs
if num_steps_per_epoch and step // num_steps_per_epoch >= max_num_epochs:
raise tf.errors.OutOfRangeError(None, None,'Reached max num epochs of %d'%max_num_epochs)
step += 1
except tf.errors.OutOfRangeError, e:
if not (step==start) and save_model and step % save_interval_steps != 0:
saver.save(sess,
_get_checkpoint_path(checkpoint_path, step, num_steps_per_epoch),
global_step=step)
if metric_eval_fn is not None:
metric_eval_fn()
if (num_epochs and step / num_steps_per_epoch >= num_epochs) or (num_steps and (step + 1) == start + num_steps) :
print('Done training for %.3f epochs, %d steps.' % (step / num_steps_per_epoch, step + 1), file=sys.stderr)
#FIXME becase coord.join seems not work, RuntimeError: Coordinator stopped with threads still running: Thread-9
exit(0)
else:
print('Should not stop, but stopped at epoch: %.3f'%(step / num_steps_per_epoch), file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
raise e
def main(_):
print('eval_rank:', FLAGS.eval_rank, 'eval_translation:',
FLAGS.eval_translation)
epoch_dir = os.path.join(FLAGS.model_dir, 'epoch')
logging.set_logging_path(gezi.get_dir(epoch_dir))
log_dir = epoch_dir
sess = tf.Session()
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
Predictor = TextPredictor
image_model = None
if FLAGS.image_checkpoint_file:
#feature_name = None, since in show and tell predictor will use gen_features not gen_feature
image_model = melt.image.ImageModel(FLAGS.image_checkpoint_file,
FLAGS.image_model_name,
feature_name=None)
evaluator.init(image_model)
visited_path = os.path.join(epoch_dir, 'visited.pkl')
if not os.path.exists(visited_path):
visited_checkpoints = set()
else:
visited_checkpoints = pickle.load(open(visited_path, 'rb'))
visited_checkpoints = set([x.split('/')[-1] for x in visited_checkpoints])
while True:
suffix = '.data-00000-of-00001'
files = glob.glob(
os.path.join(epoch_dir, 'model.ckpt*.data-00000-of-00001'))
#from epoch 1, 2, ..
files.sort(key=os.path.getmtime)
files = [file.replace(suffix, '') for file in files]
for i, file in enumerate(files):
if 'best' in file:
continue
if FLAGS.start_epoch and i + 1 < FLAGS.start_epoch:
continue
file_ = file.split('/')[-1]
if file_ not in visited_checkpoints:
visited_checkpoints.add(file_)
epoch = int(file_.split('-')[-2])
logging.info('mointor_epoch:%d from %d model files' %
(epoch, len(visited_checkpoints)))
#will use predict_text in eval_translation , predict in eval_rank
predictor = Predictor(file,
image_model=image_model,
feature_name=melt.get_features_name(
FLAGS.image_model_name))
summary = tf.Summary()
scores, metrics = evaluator.evaluate(
predictor,
eval_rank=FLAGS.eval_rank,
eval_translation=FLAGS.eval_translation)
melt.add_summarys(summary, scores, metrics)
summary_writer.add_summary(summary, epoch)
summary_writer.flush()
pickle.dump(visited_checkpoints, open(visited_path, 'wb'))
time.sleep(5)
def train_flow(ops,
names=None,
gen_feed_dict_fn=None,
deal_results_fn=melt.print_results,
interval_steps=100,
eval_ops=None,
eval_names=None,
gen_eval_feed_dict_fn=None,
deal_eval_results_fn=melt.print_results,
eval_interval_steps=100,
print_time=True,
print_avg_loss=True,
model_dir='./model',
max_models_keep=5,
save_interval_seconds=600,
save_interval_steps=1000,
log_dir=None,
no_log=False,
num_epochs=None,
num_steps=None,
num_steps_per_epoch=None,
optimizer=None,
learning_rate=0.1,
save_model=True,
save_interval_epochs=True,
add_train_var_histogram=False,
restore_from_latest=True,
metric_eval_fn=None,
metric_eval_interval_steps=0,
summary_excls=None,
init_fn=None,
sess=None):
"""
train flow for tr records, with model saving/reload and summary considered
summary logs will also write to model_dir
see examples/sparse-tensor-classification/train-melt-savemodel.py
NOTICE: first ops must be train_op(optimizer related) which will later ignored
#@TODO allow adding momentum for optimzer
allow mutliple gpu
@TODO can we show epoch num info ?
"""
if optimizer is not None:
loss = ops[0]
if isinstance(optimizer, str):
train_op = melt.gen_train_op_byname(loss, learning_rate, optimizer)
else:
train_op = optimizer(learning_rate).minimize(loss)
ops = list(ops)
ops.insert(0, train_op)
if not model_dir:
if log_dir and no_log:
log_dir = None
return simple_train_flow(
ops,
names,
gen_feed_dict_fn,
deal_results_fn,
interval_steps,
eval_ops,
eval_names,
gen_eval_feed_dict_fn,
deal_eval_results_fn,
eval_interval_steps,
print_time,
print_avg_loss,
log_dir,
num_steps,
num_steps_per_epoch=num_steps_per_epoch,
metric_eval_fn=metric_eval_fn,
metric_eval_interval_steps=metric_eval_interval_steps,
sess=sess)
#if not set log dir try to use model dir to store log
#so defaut is write log, if only want save model but disable log, set no_log=True
if save_model:
print('Will save model to %s' % model_dir)
else:
no_log = True
print('Will not save model, only read model from %s if exists' %
model_dir)
if not log_dir and not no_log:
log_dir = gezi.get_dir(model_dir)
if log_dir:
print('Will save log to %s' % log_dir)
if add_train_var_histogram:
# Add histograms for trainable variables.
#this is also great for you to see all the trainable variables on tensorboard
#NOTICE for big model this is too slow!
melt.monitor_train_vars()
else:
print('Will not save log')
def train_once_(sess, step, is_start=False):
train_once(sess,
step,
ops,
names,
gen_feed_dict_fn,
deal_results_fn,
interval_steps,
eval_ops,
eval_names,
gen_eval_feed_dict_fn,
deal_eval_results_fn,
eval_interval_steps,
print_time,
print_avg_loss,
model_dir,
log_dir,
is_start,
num_steps_per_epoch,
metric_eval_fn=metric_eval_fn,
metric_eval_interval_steps=metric_eval_interval_steps,
summary_excls=summary_excls)
tf_train_flow(train_once_,
model_dir,
max_models_keep,
save_interval_seconds,
save_interval_steps,
num_epochs,
num_steps,
save_model=save_model,
save_interval_epochs=save_interval_epochs,
num_steps_per_epoch=num_steps_per_epoch,
restore_from_latest=restore_from_latest,
metric_eval_fn=metric_eval_fn,
init_fn=init_fn,
sess=sess)
def tf_train_flow(train_once,
model_dir='./model',
max_models_keep=1,
save_interval_seconds=600,
save_interval_steps=1000,
num_epochs=None,
num_steps=None,
save_model=True,
save_interval_epochs=1,
num_steps_per_epoch=0,
restore_from_latest=True,
metric_eval_function=None,
sess=None):
"""
similary flow as tf_flow, but add model try reload and save
"""
if sess is None:
#@TODO may have mutliple session ?
sess = melt.get_session()
logging.info('tf_train_flow start')
print('max_models_keep:', max_models_keep)
print('save_interval_seconds:', save_interval_seconds)
saver = tf.train.Saver(
max_to_keep=max_models_keep,
keep_checkpoint_every_n_hours=save_interval_seconds / 3600.0)
epoch_saver = tf.train.Saver(max_to_keep=max_models_keep,
keep_checkpoint_every_n_hours=24) # TODO
#pre_step means the step last saved, train without pretrained,then -1
pre_step = -1
model_path = _get_model_path(model_dir, save_model)
model_dir = gezi.get_dir(
model_dir) #incase you pass ./model/model-ckpt1000 -> ./model
if model_path is not None:
if not restore_from_latest:
print('using recent but not latest model', file=sys.stderr)
model_path = melt.recent_checkpoint(model_dir)
model_name = os.path.basename(model_path)
timer = gezi.Timer('Loading and training from existing model [%s]' %
model_path)
saver.restore(sess, model_path)
timer.print()
pre_step = melt.get_model_step(model_path)
if 'epoch' in model_name:
pre_step *= num_steps_per_epoch
#for non 0 eopochs without this will be
#Attempting to use uninitialized value input/input_producer/limit_epochs/epochs
try:
sess.run(tf.local_variables_initializer())
except Exception:
sess.run(tf.initialize_local_variables())
else:
print('Train all start step 0', file=sys.stderr)
try:
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
except Exception:
init_op = tf.group(tf.initialize_all_variables(),
tf.initialize_local_variables())
sess.run(init_op)
if save_interval_epochs and num_steps_per_epoch:
epoch_dir = os.path.join(model_dir, 'epoch')
gezi.try_mkdir(epoch_dir)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
checkpoint_path = os.path.join(model_dir, 'model.ckpt')
try:
step = start = pre_step + 1
#hack just for save one model after load
if num_steps and num_steps < step:
print('just load and resave then exit', file=sys.stderr)
saver.save(sess, checkpoint_path, global_step=step)
sess.close()
exit(0)
while not coord.should_stop():
stop = train_once(sess, step, is_start=(step == start))
if save_model and step:
#step 0 is also saved! actually train one step and save
if step % save_interval_steps == 0:
timer = gezi.Timer('save model step %d to %s' %
(step, checkpoint_path))
saver.save(sess, checkpoint_path, global_step=step)
timer.print()
if save_interval_epochs and num_steps_per_epoch and step % (
num_steps_per_epoch * save_interval_epochs) == 0:
epoch_saver.save(sess,
os.path.join(epoch_dir, 'model.epoch'),
global_step=step)
if stop is True:
print('Early stop running %d stpes' % (step), file=sys.stderr)
raise tf.errors.OutOfRangeError(
None, None, 'Early stop running %d stpes' % (step))
if num_steps and (step + 1) == start + num_steps:
raise tf.errors.OutOfRangeError(None, None,
'Reached max num steps')
max_num_epochs = 1000
if num_steps_per_epoch and step // num_steps_per_epoch == max_num_epochs:
raise tf.errors.OutOfRangeError(
None, None,
'Reached max num epochs of %d' % max_num_epochs)
step += 1
except tf.errors.OutOfRangeError, e:
if not (step
== start) and save_model and step % save_interval_steps != 0:
saver.save(sess, checkpoint_path, global_step=step)
if metric_eval_function is not None:
metric_eval_function()
if (num_epochs and step / num_steps_per_epoch >= num_epochs) or (
num_steps and (step + 1) == start + num_steps):
print('Done training for %d steps.' % (step), file=sys.stderr)
#FIXME becase coord.join seems not work, RuntimeError: Coordinator stopped with threads still running: Thread-9
exit(0)
else:
print('Should not stop, but stopped at epoch: %.3f' %
(step / num_steps_per_epoch),
file=sys.stderr)
raise e
