def add(self, score):
import melt.utils.logging as logging
if not tf.executing_eagerly():
weight = self.sess.run(self.weight_op)
else:
weight = self.weight_op
#print(weight, score, self.score, self.patience)
if (not self.cmp) and self.score:
if score > self.score:
self.cmp = lambda x, y: x > y
else:
self.cmp = lambda x, y: x < y
logging.info('decay cmp:', self.cmp)
if not self.score or self.cmp(score, self.score):
self.score = score
self.patience = 0
else:
self.patience += 1
# epoch is set during training loop
epoch = melt.epoch()
logging.info('patience:', self.patience)
if epoch < self.decay_start_epoch:
return
if self.patience >= self.max_patience:
self.count += 1
self.patience = 0
self.score = score
decay = self.decay
pre_weight = weight
#weight *= decay
weight = weight * decay
# decay
if self.min_weight and weight < self.min_weight:
weight = self.min_weight
decay = weight / pre_weight
if decay > 1.:
decay = 1.
logging.info('!decay count:', self.count, self.name, 'now:', weight)
if not tf.executing_eagerly():
self.sess.run(tf.assign(self.weight_op, tf.constant(weight, dtype=tf.float32)))
else:
self.weight_op = weight
if 'learning_rate' in self.name:
if not tf.executing_eagerly():
melt.multiply_learning_rate(tf.constant(decay, dtype=tf.float32), self.sess)
else:
# TODO need to test eager mode
#learning_rate = tf.get_collection('learning_rate')[-1]
#if learning_rate * decay > self.min_learning_rate:
#tf.get_collection('learning_rate')[-1] *= decay
tf.get_collection('learning_rate')[-1].assign(tf.get_collection('learning_rate')[-1] * decay)
return weight
def add(self, scores):
import melt.utils.logging as logging
scores = np.array(scores)
#print(scores.shape, self.scores.shape, len(self.names))
logging.info('diff:', list(zip(self.names, scores - self.scores)))
if not tf.executing_eagerly():
weights = self.sess.run(self.weights_op)
weights_ = weights
else:
weights = self.weights_op
weights_ = weights.numpy()
if (not self.cmp) and self.scores:
if scores[0] > self.scores[0]:
self.cmp = lambda x, y: x > y
else:
self.cmp = lambda x, y: x < y
logging.info('decay cmp:', self.cmp)
# epoch is set during training loop
epoch = melt.epoch()
for i, score in enumerate(scores):
if self.scores is None or self.cmp(score, self.scores[i]):
self.scores[i] = score
self.patience[i] = 0
else:
self.patience[i] += 1
logging.info('patience_%s %d' % (self.names[i], self.patience[i]))
if epoch < self.decay_start_epoch:
continue
if self.patience[i] >= self.max_patience:
self.count[i] += 1
self.patience[i] = 0
self.scores[i] = score
decay = self.decay if not isinstance(self.decay, (list, tuple)) else self.decay[i]
weights_[i] *= decay
if not self.min_weight:
if weights_[i] < self.min_weight:
weights_[i] = self.min_weight
#logging.info('!%s decay count:%d decay ratio:%f lr ratios now:%f' % (self.names[i], self.count[i], self.decay, weights[i]))
if not tf.executing_eagerly():
self.sess.run(tf.assign(self.weights_op, tf.constant(weights_, dtype=tf.float32)))
else:
self.weights_op.assign(weights_)
return weights_
def train_flow(ops,
names=None,
gen_feed_dict_fn=None,
deal_results_fn=melt.print_results,
eval_ops=None,
eval_names=None,
gen_eval_feed_dict_fn=None,
deal_eval_results_fn=melt.print_results,
optimizer=None,
learning_rate=0.1,
num_steps_per_epoch=None,
model_dir=None,
metric_eval_fn=None,
debug=False,
summary_excls=None,
init_fn=None,
sess=None):
if sess is None:
sess = melt.get_session()
if debug:
sess = tf_debug.LocalCLIDebugWrapperSession(sess)
logging.info('learning_rate:{}'.format(FLAGS.learning_rate))
#batch size right now not define here, but in app code like input_app.py
melt.set_global('batch_size', FLAGS.batch_size)
melt.set_global('num_gpus', max(FLAGS.num_gpus, 1))
#NOTICE since melt.__init__.py with from melt.flow import * then you can not
#use melt.flow.train.train_flow but you can always use
#from melt.flow.train.train_flow import train_flow
if optimizer is None:
optimizer = FLAGS.optimizer
# Set up the training ops.
#notice '' only works in tf >= 0.11, for 0.10 will always add OptimeizeLoss scope
#the diff is 0.10 use variable_op_scope and 0.11 use variable_scope
optimize_scope = None if FLAGS.optimize_has_scope else ''
#or judge by FLAGS.num_gpus
if not isinstance(ops[0], (list, tuple)):
learning_rate, learning_rate_decay_fn = gen_learning_rate()
train_op = tf.contrib.layers.optimize_loss(
loss=ops[0],
global_step=None,
learning_rate=learning_rate,
optimizer=melt.util.get_optimizer(optimizer),
clip_gradients=FLAGS.clip_gradients,
learning_rate_decay_fn=learning_rate_decay_fn,
name=optimize_scope)
else:
#---as in cifa10 example, put all but tower loss on cpu, wiki say, that will be faster,
#but here I find without setting to cpu will be faster..
#https://github.com/tensorflow/tensorflow/issues/4881
#I've noticed same thing on cirrascale GPU machines - putting parameters on gpu:0 and using gpu->gpu transfer was a bit faster. I suppose this depends on particular details of hardware -- if you don't have p2p connectivity between your video cards then keeping parameters on CPU:0 gives faster training.
#err but for my pc no p2p, with PHB connection nvidia-smi topo -m, still hurt by set cpu.. may be should not put cpu here
#with tf.device('/cpu:0'):
learning_rate, learning_rate_decay_fn = gen_learning_rate()
train_op = melt.layers.optimize_loss(
losses=ops[0],
num_gpus=FLAGS.num_gpus,
global_step=None,
learning_rate=learning_rate,
optimizer=melt.util.get_optimizer(optimizer),
clip_gradients=FLAGS.clip_gradients,
learning_rate_decay_fn=learning_rate_decay_fn,
name=optimize_scope)
#set the last tower loss as loss in ops
ops[0] = ops[0][-1]
ops.insert(0, train_op)
#-----------post deal
save_interval_seconds = FLAGS.save_interval_seconds if FLAGS.save_interval_seconds > 0 \
else FLAGS.save_interval_hours * 3600
interval_steps = FLAGS.interval_steps
eval_interval_steps = FLAGS.eval_interval_steps
metric_eval_interval_steps = FLAGS.metric_eval_interval_steps
save_model = FLAGS.save_model
save_interval_steps = FLAGS.save_interval_steps
if not save_interval_steps:
save_interval_steps = 1000000000000
if FLAGS.work_mode == 'train':
eval_ops = None
metric_eval_fn = None
logging.info('running train only mode')
elif FLAGS.work_mode == 'train_metric':
eval_ops = None
assert metric_eval_fn is not None, 'set metric_eval to 1'
logging.info('running train+metric mode')
elif FLAGS.work_mode == 'train_valid':
metric_eval_fn = None
logging.info('running train+valid mode')
elif FLAGS.work_mode == 'test':
ops = None
logging.info('running test only mode')
interval_steps = 0
eval_interval_steps = 1
metric_eval_interval_steps /= FLAGS.eval_interval_steps
save_model = False
return melt.flow.train_flow(
ops,
names=names,
gen_feed_dict_fn=gen_feed_dict_fn,
deal_results_fn=deal_results_fn,
eval_ops=eval_ops,
eval_names=eval_names,
gen_eval_feed_dict_fn=gen_eval_feed_dict_fn,
deal_eval_results_fn=deal_eval_results_fn,
interval_steps=interval_steps,
eval_interval_steps=eval_interval_steps,
num_epochs=FLAGS.num_epochs,
num_steps=FLAGS.num_steps,
save_interval_seconds=save_interval_seconds,
save_interval_steps=save_interval_steps,
save_model=save_model,
save_interval_epochs=FLAGS.save_interval_epochs,
#optimizer=optimizer,
optimizer=
None, #must set None since here we have done choosing optimizer
learning_rate=learning_rate,
num_steps_per_epoch=num_steps_per_epoch,
max_models_keep=FLAGS.max_models_keep,
model_dir=model_dir,
restore_from_latest=FLAGS.restore_from_latest,
metric_eval_fn=metric_eval_fn,
metric_eval_interval_steps=metric_eval_interval_steps,
no_log=FLAGS.no_log,
summary_excls=summary_excls,
init_fn=init_fn,
sess=sess)
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 logging_results(results, names, tag=''): \
logging.info('\t'.join(
[tag] + ['%s:[%.4f]'%(name, result) for name, result in zip(names, results)]))
def parse_results(results, names=None):
def train_once(
sess,
step,
ops,
names=None,
gen_feed_dict_fn=None,
deal_results_fn=None,
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,
print_time=True,
print_avg_loss=True,
model_dir=None,
log_dir=None,
is_start=False,
num_steps_per_epoch=None,
metric_eval_fn=None,
metric_eval_interval_steps=0,
summary_excls=None,
fixed_step=None, # for epoch only, incase you change batch size
eval_loops=1,
learning_rate=None,
learning_rate_patience=None,
learning_rate_decay_factor=None,
num_epochs=None,
model_path=None,
use_horovod=False,
):
use_horovod = 'OMPI_COMM_WORLD_RANK' in os.environ
#is_start = False # force not to evaluate at first step
#print('-----------------global_step', sess.run(tf.train.get_or_create_global_step()))
timer = gezi.Timer()
if print_time:
if not hasattr(train_once, 'timer'):
train_once.timer = Timer()
train_once.eval_timer = Timer()
train_once.metric_eval_timer = Timer()
melt.set_global('step', step)
epoch = (fixed_step
or step) / num_steps_per_epoch if num_steps_per_epoch else -1
if not num_epochs:
epoch_str = 'epoch:%.3f' % (epoch) if num_steps_per_epoch else ''
else:
epoch_str = 'epoch:%.3f/%d' % (
epoch, num_epochs) if num_steps_per_epoch else ''
melt.set_global('epoch', '%.2f' % (epoch))
info = IO()
stop = False
if eval_names is None:
if names:
eval_names = ['eval/' + x for x in names]
if names:
names = ['train/' + x for x in names]
if eval_names:
eval_names = ['eval/' + x for x in eval_names]
is_eval_step = is_start or valid_interval_steps and step % valid_interval_steps == 0
summary_str = []
eval_str = ''
if is_eval_step:
# deal with summary
if log_dir:
if not hasattr(train_once, 'summary_op'):
#melt.print_summary_ops()
if summary_excls is None:
train_once.summary_op = tf.summary.merge_all()
else:
summary_ops = []
for op in tf.get_collection(tf.GraphKeys.SUMMARIES):
for summary_excl in summary_excls:
if not summary_excl in op.name:
summary_ops.append(op)
print('filtered summary_ops:')
for op in summary_ops:
print(op)
train_once.summary_op = tf.summary.merge(summary_ops)
#train_once.summary_train_op = tf.summary.merge_all(key=melt.MonitorKeys.TRAIN)
train_once.summary_writer = tf.summary.FileWriter(
log_dir, sess.graph)
tf.contrib.tensorboard.plugins.projector.visualize_embeddings(
train_once.summary_writer, projector_config)
# if eval ops then should have bee rank 0
if eval_ops:
#if deal_eval_results_fn is None and eval_names is not None:
# deal_eval_results_fn = lambda x: melt.print_results(x, eval_names)
for i in range(eval_loops):
eval_feed_dict = {} if gen_eval_feed_dict_fn is None else gen_eval_feed_dict_fn(
)
#eval_feed_dict.update(feed_dict)
# if use horovod let each rant use same sess.run!
if not log_dir or train_once.summary_op is None or gezi.env_has(
'EVAL_NO_SUMMARY') or use_horovod:
#if not log_dir or train_once.summary_op is None:
eval_results = sess.run(eval_ops, feed_dict=eval_feed_dict)
else:
eval_results = sess.run(eval_ops + [train_once.summary_op],
feed_dict=eval_feed_dict)
summary_str = eval_results[-1]
eval_results = eval_results[:-1]
eval_loss = gezi.get_singles(eval_results)
#timer_.print()
eval_stop = False
if use_horovod:
sess.run(hvd.allreduce(tf.constant(0)))
#if not use_horovod or hvd.local_rank() == 0:
# @TODO user print should also use logging as a must ?
#print(gezi.now_time(), epoch_str, 'eval_step: %d'%step, 'eval_metrics:', end='')
eval_names_ = melt.adjust_names(eval_loss, eval_names)
#if not use_horovod or hvd.rank() == 0:
# logging.info2('{} eval_step:{} eval_metrics:{}'.format(epoch_str, step, melt.parse_results(eval_loss, eval_names_)))
eval_str = 'valid:{}'.format(
melt.parse_results(eval_loss, eval_names_))
# if deal_eval_results_fn is not None:
# eval_stop = deal_eval_results_fn(eval_results)
assert len(eval_loss) > 0
if eval_stop is True:
stop = True
eval_names_ = melt.adjust_names(eval_loss, eval_names)
if not use_horovod or hvd.rank() == 0:
melt.set_global('eval_loss',
melt.parse_results(eval_loss, eval_names_))
elif interval_steps != valid_interval_steps:
#print()
pass
metric_evaluate = False
# if metric_eval_fn is not None \
# and (is_start \
# or (num_steps_per_epoch and step % num_steps_per_epoch == 0) \
# or (metric_eval_interval_steps \
# and step % metric_eval_interval_steps == 0)):
# metric_evaluate = True
if metric_eval_fn is not None \
and ((is_start or metric_eval_interval_steps \
and step % metric_eval_interval_steps == 0) or model_path):
metric_evaluate = True
if 'EVFIRST' in os.environ:
if os.environ['EVFIRST'] == '0':
if is_start:
metric_evaluate = False
else:
if is_start:
metric_evaluate = True
if step == 0 or 'QUICK' in os.environ:
metric_evaluate = False
#print('------------1step', step, 'pre metric_evaluate', metric_evaluate, hvd.rank())
if metric_evaluate:
if use_horovod:
print('------------metric evaluate step', step, model_path,
hvd.rank())
if not model_path or 'model_path' not in inspect.getargspec(
metric_eval_fn).args:
metric_eval_fn_ = metric_eval_fn
else:
metric_eval_fn_ = lambda: metric_eval_fn(model_path=model_path)
try:
l = metric_eval_fn_()
if isinstance(l, tuple):
num_returns = len(l)
if num_returns == 2:
evaluate_results, evaluate_names = l
evaluate_summaries = None
else:
assert num_returns == 3, 'retrun 1,2,3 ok 4.. not ok'
evaluate_results, evaluate_names, evaluate_summaries = l
else: #return dict
evaluate_results, evaluate_names = tuple(zip(*dict.items()))
evaluate_summaries = None
except Exception:
logging.info('Do nothing for metric eval fn with exception:\n',
traceback.format_exc())
if not use_horovod or hvd.rank() == 0:
#logging.info2('{} valid_step:{} {}:{}'.format(epoch_str, step, 'valid_metrics' if model_path is None else 'epoch_valid_metrics', melt.parse_results(evaluate_results, evaluate_names)))
logging.info2('{} valid_step:{} {}:{}'.format(
epoch_str, step, 'valid_metrics',
melt.parse_results(evaluate_results, evaluate_names)))
if learning_rate is not None and (learning_rate_patience
and learning_rate_patience > 0):
assert learning_rate_decay_factor > 0 and learning_rate_decay_factor < 1
valid_loss = evaluate_results[0]
if not hasattr(train_once, 'min_valid_loss'):
train_once.min_valid_loss = valid_loss
train_once.deacy_steps = []
train_once.patience = 0
else:
if valid_loss < train_once.min_valid_loss:
train_once.min_valid_loss = valid_loss
train_once.patience = 0
else:
train_once.patience += 1
logging.info2('{} valid_step:{} patience:{}'.format(
epoch_str, step, train_once.patience))
if learning_rate_patience and train_once.patience >= learning_rate_patience:
lr_op = ops[1]
lr = sess.run(lr_op) * learning_rate_decay_factor
train_once.deacy_steps.append(step)
logging.info2(
'{} valid_step:{} learning_rate_decay by *{}, learning_rate_decay_steps={}'
.format(epoch_str, step, learning_rate_decay_factor,
','.join(map(str, train_once.deacy_steps))))
sess.run(tf.assign(lr_op, tf.constant(lr, dtype=tf.float32)))
train_once.patience = 0
train_once.min_valid_loss = valid_loss
if ops is not None:
#if deal_results_fn is None and names is not None:
# deal_results_fn = lambda x: melt.print_results(x, names)
feed_dict = {} if gen_feed_dict_fn is None else gen_feed_dict_fn()
# NOTICE ops[2] should be scalar otherwise wrong!! loss should be scalar
#print('---------------ops', ops)
if eval_ops is not None or not log_dir or not hasattr(
train_once,
'summary_op') or train_once.summary_op is None or use_horovod:
feed_dict[K.learning_phase()] = 1
results = sess.run(ops, feed_dict=feed_dict)
else:
## TODO why below ?
#try:
feed_dict[K.learning_phase()] = 1
results = sess.run(ops + [train_once.summary_op],
feed_dict=feed_dict)
summary_str = results[-1]
results = results[:-1]
# except Exception:
# logging.info('sess.run(ops + [train_once.summary_op], feed_dict=feed_dict) fail')
# results = sess.run(ops, feed_dict=feed_dict)
#print('------------results', results)
# #--------trace debug
# if step == 210:
# run_metadata = tf.RunMetadata()
# results = sess.run(
# ops,
# feed_dict=feed_dict,
# options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),
# run_metadata=run_metadata)
# from tensorflow.python.client import timeline
# trace = timeline.Timeline(step_stats=run_metadata.step_stats)
# trace_file = open('timeline.ctf.json', 'w')
# trace_file.write(trace.generate_chrome_trace_format())
#reults[0] assume to be train_op, results[1] to be learning_rate
learning_rate = results[1]
results = results[2:]
#@TODO should support aver loss and other avg evaluations like test..
if print_avg_loss:
if not hasattr(train_once, 'avg_loss'):
train_once.avg_loss = AvgScore()
#assume results[0] as train_op return, results[1] as loss
loss = gezi.get_singles(results)
train_once.avg_loss.add(loss)
steps_per_second = None
instances_per_second = None
hours_per_epoch = None
#step += 1
#if is_start or interval_steps and step % interval_steps == 0:
interval_ok = not use_horovod or hvd.local_rank() == 0
if interval_steps and step % interval_steps == 0 and interval_ok:
train_average_loss = train_once.avg_loss.avg_score()
if print_time:
duration = timer.elapsed()
duration_str = 'duration:{:.2f} '.format(duration)
melt.set_global('duration', '%.2f' % duration)
#info.write(duration_str)
elapsed = train_once.timer.elapsed()
steps_per_second = interval_steps / elapsed
batch_size = melt.batch_size()
num_gpus = melt.num_gpus()
instances_per_second = interval_steps * batch_size / elapsed
gpu_info = '' if num_gpus <= 1 else ' gpus:[{}]'.format(
num_gpus)
if num_steps_per_epoch is None:
epoch_time_info = ''
else:
hours_per_epoch = num_steps_per_epoch / interval_steps * elapsed / 3600
epoch_time_info = '1epoch:[{:.2f}h]'.format(
hours_per_epoch)
info.write(
'elapsed:[{:.2f}] batch_size:[{}]{} batches/s:[{:.2f}] insts/s:[{:.2f}] {} lr:[{:.6f}]'
.format(elapsed, batch_size, gpu_info, steps_per_second,
instances_per_second, epoch_time_info,
learning_rate))
if print_avg_loss:
#info.write('train_avg_metrics:{} '.format(melt.value_name_list_str(train_average_loss, names)))
names_ = melt.adjust_names(train_average_loss, names)
#info.write('train_avg_metric:{} '.format(melt.parse_results(train_average_loss, names_)))
info.write(' train:{} '.format(
melt.parse_results(train_average_loss, names_)))
#info.write('train_avg_loss: {} '.format(train_average_loss))
info.write(eval_str)
#print(gezi.now_time(), epoch_str, 'train_step:%d'%step, info.getvalue(), end=' ')
logging.info2('{} {} {}'.format(epoch_str, 'step:%d' % step,
info.getvalue()))
if deal_results_fn is not None:
stop = deal_results_fn(results)
summary_strs = gezi.to_list(summary_str)
if metric_evaluate:
if evaluate_summaries is not None:
summary_strs += evaluate_summaries
if step > 1:
if is_eval_step:
# deal with summary
if log_dir:
summary = tf.Summary()
if eval_ops is None:
if train_once.summary_op is not None:
for summary_str in summary_strs:
train_once.summary_writer.add_summary(
summary_str, step)
else:
for summary_str in summary_strs:
train_once.summary_writer.add_summary(
summary_str, step)
suffix = 'valid' if not eval_names else ''
# loss/valid
melt.add_summarys(summary,
eval_results,
eval_names_,
suffix=suffix)
if ops is not None:
try:
# loss/train_avg
melt.add_summarys(summary,
train_average_loss,
names_,
suffix='train_avg')
except Exception:
pass
##optimizer has done this also
melt.add_summary(summary, learning_rate, 'learning_rate')
melt.add_summary(summary,
melt.batch_size(),
'batch_size',
prefix='other')
melt.add_summary(summary,
melt.epoch(),
'epoch',
prefix='other')
if steps_per_second:
melt.add_summary(summary,
steps_per_second,
'steps_per_second',
prefix='perf')
if instances_per_second:
melt.add_summary(summary,
instances_per_second,
'instances_per_second',
prefix='perf')
if hours_per_epoch:
melt.add_summary(summary,
hours_per_epoch,
'hours_per_epoch',
prefix='perf')
if metric_evaluate:
#melt.add_summarys(summary, evaluate_results, evaluate_names, prefix='eval')
prefix = 'step_eval'
if model_path:
prefix = 'eval'
if not hasattr(train_once, 'epoch_step'):
train_once.epoch_step = 1
else:
train_once.epoch_step += 1
step = train_once.epoch_step
# eval/loss eval/auc ..
melt.add_summarys(summary,
evaluate_results,
evaluate_names,
prefix=prefix)
train_once.summary_writer.add_summary(summary, step)
train_once.summary_writer.flush()
return stop
elif metric_evaluate and log_dir:
summary = tf.Summary()
for summary_str in summary_strs:
train_once.summary_writer.add_summary(summary_str, step)
#summary.ParseFromString(evaluate_summaries)
summary_writer = train_once.summary_writer
prefix = 'step_eval'
if model_path:
prefix = 'eval'
if not hasattr(train_once, 'epoch_step'):
## TODO.. restart will get 1 again..
#epoch_step = tf.Variable(0, trainable=False, name='epoch_step')
#epoch_step += 1
#train_once.epoch_step = sess.run(epoch_step)
valid_interval_epochs = 1.
try:
valid_interval_epochs = FLAGS.valid_interval_epochs
except Exception:
pass
train_once.epoch_step = 1 if melt.epoch() <= 1 else int(
int(melt.epoch() * 10) /
int(valid_interval_epochs * 10))
logging.info('train_once epoch start step is',
train_once.epoch_step)
else:
#epoch_step += 1
train_once.epoch_step += 1
step = train_once.epoch_step
#melt.add_summarys(summary, evaluate_results, evaluate_names, prefix='eval')
melt.add_summarys(summary,
evaluate_results,
evaluate_names,
prefix=prefix)
summary_writer.add_summary(summary, step)
summary_writer.flush()
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 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 get_num_gpus():
if 'CUDA_VISIBLE_DEVICES' in os.environ:
num_gpus = len(os.environ['CUDA_VISIBLE_DEVICES'].split(','))
logging.info('CUDA_VISIBLE_DEVICES is %s' %
(os.environ['CUDA_VISIBLE_DEVICES']))
return num_gpus
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
def train_once(
sess,
step,
ops,
names=None,
gen_feed_dict_fn=None,
deal_results_fn=None,
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=None,
log_dir=None,
is_start=False,
num_steps_per_epoch=None,
metric_eval_fn=None,
metric_eval_interval_steps=0,
summary_excls=None,
fixed_step=None, # for epoch only, incase you change batch size
eval_loops=1,
learning_rate=None,
learning_rate_patience=None,
learning_rate_decay_factor=None,
num_epochs=None,
model_path=None,
):
#is_start = False # force not to evaluate at first step
#print('-----------------global_step', sess.run(tf.train.get_or_create_global_step()))
timer = gezi.Timer()
if print_time:
if not hasattr(train_once, 'timer'):
train_once.timer = Timer()
train_once.eval_timer = Timer()
train_once.metric_eval_timer = Timer()
melt.set_global('step', step)
epoch = (fixed_step
or step) / num_steps_per_epoch if num_steps_per_epoch else -1
if not num_epochs:
epoch_str = 'epoch:%.3f' % (epoch) if num_steps_per_epoch else ''
else:
epoch_str = 'epoch:%.3f/%d' % (
epoch, num_epochs) if num_steps_per_epoch else ''
melt.set_global('epoch', '%.2f' % (epoch))
info = IO()
stop = False
if eval_names is None:
if names:
eval_names = ['eval/' + x for x in names]
if names:
names = ['train/' + x for x in names]
if eval_names:
eval_names = ['eval/' + x for x in eval_names]
is_eval_step = is_start or eval_interval_steps and step % eval_interval_steps == 0
summary_str = []
if is_eval_step:
# deal with summary
if log_dir:
if not hasattr(train_once, 'summary_op'):
#melt.print_summary_ops()
if summary_excls is None:
train_once.summary_op = tf.summary.merge_all()
else:
summary_ops = []
for op in tf.get_collection(tf.GraphKeys.SUMMARIES):
for summary_excl in summary_excls:
if not summary_excl in op.name:
summary_ops.append(op)
print('filtered summary_ops:')
for op in summary_ops:
print(op)
train_once.summary_op = tf.summary.merge(summary_ops)
#train_once.summary_train_op = tf.summary.merge_all(key=melt.MonitorKeys.TRAIN)
train_once.summary_writer = tf.summary.FileWriter(
log_dir, sess.graph)
tf.contrib.tensorboard.plugins.projector.visualize_embeddings(
train_once.summary_writer, projector_config)
if eval_ops is not None:
#if deal_eval_results_fn is None and eval_names is not None:
# deal_eval_results_fn = lambda x: melt.print_results(x, eval_names)
for i in range(eval_loops):
eval_feed_dict = {} if gen_eval_feed_dict_fn is None else gen_eval_feed_dict_fn(
)
#eval_feed_dict.update(feed_dict)
# might use EVAL_NO_SUMMARY if some old code has problem TODO CHECK
if not log_dir or train_once.summary_op is None or gezi.env_has(
'EVAL_NO_SUMMARY'):
#if not log_dir or train_once.summary_op is None:
eval_results = sess.run(eval_ops, feed_dict=eval_feed_dict)
else:
eval_results = sess.run(eval_ops + [train_once.summary_op],
feed_dict=eval_feed_dict)
summary_str = eval_results[-1]
eval_results = eval_results[:-1]
eval_loss = gezi.get_singles(eval_results)
#timer_.print()
eval_stop = False
# @TODO user print should also use logging as a must ?
#print(gezi.now_time(), epoch_str, 'eval_step: %d'%step, 'eval_metrics:', end='')
eval_names_ = melt.adjust_names(eval_loss, eval_names)
logging.info2('{} eval_step:{} eval_metrics:{}'.format(
epoch_str, step,
melt.parse_results(eval_loss, eval_names_)))
# if deal_eval_results_fn is not None:
# eval_stop = deal_eval_results_fn(eval_results)
assert len(eval_loss) > 0
if eval_stop is True:
stop = True
eval_names_ = melt.adjust_names(eval_loss, eval_names)
melt.set_global('eval_loss',
melt.parse_results(eval_loss, eval_names_))
elif interval_steps != eval_interval_steps:
#print()
pass
metric_evaluate = False
# if metric_eval_fn is not None \
# and (is_start \
# or (num_steps_per_epoch and step % num_steps_per_epoch == 0) \
# or (metric_eval_interval_steps \
# and step % metric_eval_interval_steps == 0)):
# metric_evaluate = True
if metric_eval_fn is not None \
and ((is_start or metric_eval_interval_steps \
and step % metric_eval_interval_steps == 0) or model_path):
metric_evaluate = True
#if (is_start or step == 0) and (not 'EVFIRST' in os.environ):
if ((step == 0) and
(not 'EVFIRST' in os.environ)) or ('QUICK' in os.environ) or (
'EVFIRST' in os.environ and os.environ['EVFIRST'] == '0'):
metric_evaluate = False
if metric_evaluate:
# TODO better
if not model_path or 'model_path' not in inspect.getargspec(
metric_eval_fn).args:
l = metric_eval_fn()
if len(l) == 2:
evaluate_results, evaluate_names = l
evaluate_summaries = None
else:
evaluate_results, evaluate_names, evaluate_summaries = l
else:
try:
l = metric_eval_fn(model_path=model_path)
if len(l) == 2:
evaluate_results, evaluate_names = l
evaluate_summaries = None
else:
evaluate_results, evaluate_names, evaluate_summaries = l
except Exception:
logging.info('Do nothing for metric eval fn with exception:\n',
traceback.format_exc())
logging.info2('{} valid_step:{} {}:{}'.format(
epoch_str, step,
'valid_metrics' if model_path is None else 'epoch_valid_metrics',
melt.parse_results(evaluate_results, evaluate_names)))
if learning_rate is not None and (learning_rate_patience
and learning_rate_patience > 0):
assert learning_rate_decay_factor > 0 and learning_rate_decay_factor < 1
valid_loss = evaluate_results[0]
if not hasattr(train_once, 'min_valid_loss'):
train_once.min_valid_loss = valid_loss
train_once.deacy_steps = []
train_once.patience = 0
else:
if valid_loss < train_once.min_valid_loss:
train_once.min_valid_loss = valid_loss
train_once.patience = 0
else:
train_once.patience += 1
logging.info2('{} valid_step:{} patience:{}'.format(
epoch_str, step, train_once.patience))
if learning_rate_patience and train_once.patience >= learning_rate_patience:
lr_op = ops[1]
lr = sess.run(lr_op) * learning_rate_decay_factor
train_once.deacy_steps.append(step)
logging.info2(
'{} valid_step:{} learning_rate_decay by *{}, learning_rate_decay_steps={}'
.format(epoch_str, step, learning_rate_decay_factor,
','.join(map(str, train_once.deacy_steps))))
sess.run(tf.assign(lr_op, tf.constant(lr, dtype=tf.float32)))
train_once.patience = 0
train_once.min_valid_loss = valid_loss
if ops is not None:
#if deal_results_fn is None and names is not None:
# deal_results_fn = lambda x: melt.print_results(x, names)
feed_dict = {} if gen_feed_dict_fn is None else gen_feed_dict_fn()
# NOTICE ops[2] should be scalar otherwise wrong!! loss should be scalar
#print('---------------ops', ops)
if eval_ops is not None or not log_dir or not hasattr(
train_once, 'summary_op') or train_once.summary_op is None:
results = sess.run(ops, feed_dict=feed_dict)
else:
#try:
results = sess.run(ops + [train_once.summary_op],
feed_dict=feed_dict)
summary_str = results[-1]
results = results[:-1]
# except Exception:
# logging.info('sess.run(ops + [train_once.summary_op], feed_dict=feed_dict) fail')
# results = sess.run(ops, feed_dict=feed_dict)
#print('------------results', results)
# #--------trace debug
# if step == 210:
# run_metadata = tf.RunMetadata()
# results = sess.run(
# ops,
# feed_dict=feed_dict,
# options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),
# run_metadata=run_metadata)
# from tensorflow.python.client import timeline
# trace = timeline.Timeline(step_stats=run_metadata.step_stats)
# trace_file = open('timeline.ctf.json', 'w')
# trace_file.write(trace.generate_chrome_trace_format())
#reults[0] assume to be train_op, results[1] to be learning_rate
learning_rate = results[1]
results = results[2:]
#@TODO should support aver loss and other avg evaluations like test..
if print_avg_loss:
if not hasattr(train_once, 'avg_loss'):
train_once.avg_loss = AvgScore()
if interval_steps != eval_interval_steps:
train_once.avg_loss2 = AvgScore()
#assume results[0] as train_op return, results[1] as loss
loss = gezi.get_singles(results)
train_once.avg_loss.add(loss)
if interval_steps != eval_interval_steps:
train_once.avg_loss2.add(loss)
steps_per_second = None
instances_per_second = None
hours_per_epoch = None
#step += 1
if is_start or interval_steps and step % interval_steps == 0:
train_average_loss = train_once.avg_loss.avg_score()
if print_time:
duration = timer.elapsed()
duration_str = 'duration:{:.3f} '.format(duration)
melt.set_global('duration', '%.3f' % duration)
info.write(duration_str)
elapsed = train_once.timer.elapsed()
steps_per_second = interval_steps / elapsed
batch_size = melt.batch_size()
num_gpus = melt.num_gpus()
instances_per_second = interval_steps * batch_size / elapsed
gpu_info = '' if num_gpus <= 1 else ' gpus:[{}]'.format(
num_gpus)
if num_steps_per_epoch is None:
epoch_time_info = ''
else:
hours_per_epoch = num_steps_per_epoch / interval_steps * elapsed / 3600
epoch_time_info = ' 1epoch:[{:.2f}h]'.format(
hours_per_epoch)
info.write(
'elapsed:[{:.3f}] batch_size:[{}]{} batches/s:[{:.2f}] insts/s:[{:.2f}] {} lr:[{:.8f}]'
.format(elapsed, batch_size, gpu_info, steps_per_second,
instances_per_second, epoch_time_info,
learning_rate))
if print_avg_loss:
#info.write('train_avg_metrics:{} '.format(melt.value_name_list_str(train_average_loss, names)))
names_ = melt.adjust_names(train_average_loss, names)
#info.write('train_avg_metric:{} '.format(melt.parse_results(train_average_loss, names_)))
info.write(' train:{} '.format(
melt.parse_results(train_average_loss, names_)))
#info.write('train_avg_loss: {} '.format(train_average_loss))
#print(gezi.now_time(), epoch_str, 'train_step:%d'%step, info.getvalue(), end=' ')
logging.info2('{} {} {}'.format(epoch_str, 'step:%d' % step,
info.getvalue()))
if deal_results_fn is not None:
stop = deal_results_fn(results)
summary_strs = gezi.to_list(summary_str)
if metric_evaluate:
if evaluate_summaries is not None:
summary_strs += evaluate_summaries
if step > 1:
if is_eval_step:
# deal with summary
if log_dir:
# if not hasattr(train_once, 'summary_op'):
# melt.print_summary_ops()
# if summary_excls is None:
# train_once.summary_op = tf.summary.merge_all()
# else:
# summary_ops = []
# for op in tf.get_collection(tf.GraphKeys.SUMMARIES):
# for summary_excl in summary_excls:
# if not summary_excl in op.name:
# summary_ops.append(op)
# print('filtered summary_ops:')
# for op in summary_ops:
# print(op)
# train_once.summary_op = tf.summary.merge(summary_ops)
# print('-------------summary_op', train_once.summary_op)
# #train_once.summary_train_op = tf.summary.merge_all(key=melt.MonitorKeys.TRAIN)
# train_once.summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
# tf.contrib.tensorboard.plugins.projector.visualize_embeddings(train_once.summary_writer, projector_config)
summary = tf.Summary()
# #so the strategy is on eval_interval_steps, if has eval dataset, then tensorboard evluate on eval dataset
# #if not have eval dataset, will evaluate on trainset, but if has eval dataset we will also monitor train loss
# assert train_once.summary_train_op is None
# if train_once.summary_train_op is not None:
# summary_str = sess.run(train_once.summary_train_op, feed_dict=feed_dict)
# train_once.summary_writer.add_summary(summary_str, step)
if eval_ops is None:
# #get train loss, for every batch train
# if train_once.summary_op is not None:
# #timer2 = gezi.Timer('sess run')
# try:
# # TODO FIXME so this means one more train batch step without adding to global step counter ?! so should move it earlier
# summary_str = sess.run(train_once.summary_op, feed_dict=feed_dict)
# except Exception:
# if not hasattr(train_once, 'num_summary_errors'):
# logging.warning('summary_str = sess.run(train_once.summary_op, feed_dict=feed_dict) fail')
# train_once.num_summary_errors = 1
# logging.warning(traceback.format_exc())
# summary_str = ''
# # #timer2.print()
if train_once.summary_op is not None:
for summary_str in summary_strs:
train_once.summary_writer.add_summary(
summary_str, step)
else:
# #get eval loss for every batch eval, then add train loss for eval step average loss
# try:
# summary_str = sess.run(train_once.summary_op, feed_dict=eval_feed_dict) if train_once.summary_op is not None else ''
# except Exception:
# if not hasattr(train_once, 'num_summary_errors'):
# logging.warning('summary_str = sess.run(train_once.summary_op, feed_dict=eval_feed_dict) fail')
# train_once.num_summary_errors = 1
# logging.warning(traceback.format_exc())
# summary_str = ''
#all single value results will be add to summary here not using tf.scalar_summary..
#summary.ParseFromString(summary_str)
for summary_str in summary_strs:
train_once.summary_writer.add_summary(
summary_str, step)
suffix = 'eval' if not eval_names else ''
melt.add_summarys(summary,
eval_results,
eval_names_,
suffix=suffix)
if ops is not None:
melt.add_summarys(summary,
train_average_loss,
names_,
suffix='train_avg')
##optimizer has done this also
melt.add_summary(summary, learning_rate, 'learning_rate')
melt.add_summary(summary, melt.batch_size(), 'batch_size')
melt.add_summary(summary, melt.epoch(), 'epoch')
if steps_per_second:
melt.add_summary(summary, steps_per_second,
'steps_per_second')
if instances_per_second:
melt.add_summary(summary, instances_per_second,
'instances_per_second')
if hours_per_epoch:
melt.add_summary(summary, hours_per_epoch,
'hours_per_epoch')
if metric_evaluate:
#melt.add_summarys(summary, evaluate_results, evaluate_names, prefix='eval')
prefix = 'step/valid'
if model_path:
prefix = 'epoch/valid'
if not hasattr(train_once, 'epoch_step'):
train_once.epoch_step = 1
else:
train_once.epoch_step += 1
step = train_once.epoch_step
melt.add_summarys(summary,
evaluate_results,
evaluate_names,
prefix=prefix)
train_once.summary_writer.add_summary(summary, step)
train_once.summary_writer.flush()
#timer_.print()
# if print_time:
# full_duration = train_once.eval_timer.elapsed()
# if metric_evaluate:
# metric_full_duration = train_once.metric_eval_timer.elapsed()
# full_duration_str = 'elapsed:{:.3f} '.format(full_duration)
# #info.write('duration:{:.3f} '.format(timer.elapsed()))
# duration = timer.elapsed()
# info.write('duration:{:.3f} '.format(duration))
# info.write(full_duration_str)
# info.write('eval_time_ratio:{:.3f} '.format(duration/full_duration))
# if metric_evaluate:
# info.write('metric_time_ratio:{:.3f} '.format(duration/metric_full_duration))
# #print(gezi.now_time(), epoch_str, 'eval_step: %d'%step, info.getvalue())
# logging.info2('{} {} {}'.format(epoch_str, 'eval_step: %d'%step, info.getvalue()))
return stop
elif metric_evaluate:
summary = tf.Summary()
for summary_str in summary_strs:
train_once.summary_writer.add_summary(summary_str, step)
#summary.ParseFromString(evaluate_summaries)
summary_writer = train_once.summary_writer
prefix = 'step/valid'
if model_path:
prefix = 'epoch/valid'
if not hasattr(train_once, 'epoch_step'):
## TODO.. restart will get 1 again..
#epoch_step = tf.Variable(0, trainable=False, name='epoch_step')
#epoch_step += 1
#train_once.epoch_step = sess.run(epoch_step)
valid_interval_epochs = 1.
try:
valid_interval_epochs = FLAGS.valid_interval_epochs
except Exception:
pass
train_once.epoch_step = 1 if melt.epoch() <= 1 else int(
int(melt.epoch() * 10) /
int(valid_interval_epochs * 10))
logging.info('train_once epoch start step is',
train_once.epoch_step)
else:
#epoch_step += 1
train_once.epoch_step += 1
step = train_once.epoch_step
#melt.add_summarys(summary, evaluate_results, evaluate_names, prefix='eval')
melt.add_summarys(summary,
evaluate_results,
evaluate_names,
prefix=prefix)
summary_writer.add_summary(summary, step)
summary_writer.flush()
def __init__(self,
weights_op='learning_rate_weights',
patience=3,
decay=0.8,
cmp=None,
names=None,
num_weights=None,
min_weight=None,
min_learning_rate=None,
initial_learning_rate=None,
initial_score=None,
decay_start_epoch=0,
sess=None):
import melt.utils.logging as logging
if not tf.executing_eagerly():
self.sess = sess or melt.get_session()
if num_weights is None:
assert names
num_weights = len(names)
logging.info('decay:', decay, 'cmp:', cmp)
assert cmp == 'less' or cmp == 'greater'
if cmp == 'less':
self.cmp = lambda x, y: x < y
self.scores = np.ones([num_weights]) * 1e10
elif cmp == 'greater':
self.cmp = lambda x, y: x > y
self.scores = np.ones([num_weights]) * -1e10
else:
# TODO...
self.cmp = cmp
assert initial_score
self.scores = [initial_score] * num_weights
#self.scores = None
self.max_patience = patience
self.decay = decay
# TODO patience also varaible so can save and restore ?
self.patience = [0] * num_weights
self.count = [0] * num_weights
self.names = names or list(map(str, range(num_weights)))
self.min_weight = min_weight
self.decay_start_epoch = decay_start_epoch
if not self.min_weight:
self.min_weight = min_learning_rate / (initial_learning_rate or FLAGS.learning_rate)
if isinstance(weights_op, str):
try:
self.weights_op = tf.get_collection(weights_op)[-1]
except Exception:
#self.weights_op = tf.get_variable('lr_ratios', initializer=tf.ones([num_classes], dtype=tf.float32))
#tf.add_to_collection('lr_ratios', lr_ratios)
raise 'TODO..'
else:
self.weights_op = weights_op
