def load(self):
run_config = t2t_trainer.create_run_config(self.hp)
self.hp.add_hparam("model_dir", run_config.model_dir)
self.estimator = trainer_lib.create_estimator(
self.model,
self.hp,
run_config,
decode_hparams=self.decode_hp,
use_tpu=self.use_tpu)
self.estimator_predictor = tf.contrib.predictor.from_estimator(
self.estimator,
self.input_fn,
config=tf.ConfigProto(log_device_placement=True,
allow_soft_placement=True))
FLAGS.problem = "translate_enfr_wmt32k_rev"
self.problem = "translate_enfr_wmt32k_rev"
self.problem_name = self.problem
FLAGS.checkpoint_path = os.path.join(
os.getcwd(), "checkpoints/fren/model.ckpt-500000")
run_config = t2t_trainer.create_run_config(self.hp)
self.hp.model_dir = run_config.model_dir
self.estimator = trainer_lib.create_estimator(
self.model,
self.hp,
run_config,
decode_hparams=self.decode_hp,
use_tpu=self.use_tpu)
self.estimator_decoder_predictor = tf.contrib.predictor.from_estimator(
self.estimator,
self.input_fn,
config=tf.ConfigProto(log_device_placement=True,
allow_soft_placement=True))
def main(argv):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if FLAGS.cloud_mlengine:
cloud_mlengine.launch()
return
if FLAGS.generate_data:
t2t_trainer.generate_data()
if cloud_mlengine.job_dir():
FLAGS.output_dir = cloud_mlengine.job_dir()
if argv:
t2t_trainer.set_hparams_from_args(argv[1:])
root_output_dir = FLAGS.output_dir
if FLAGS.teacher_dir:
teacher_dir = FLAGS.teacher_dir
else:
teacher_dir = os.path.join(root_output_dir, "teacher")
# Train Teacher ============
if FLAGS.skip_teacher_training:
tf.logging.info("training teacher skipped")
else:
hparams = t2t_trainer.create_hparams()
hparams.distill_phase = "train"
FLAGS.output_dir = teacher_dir
exp_fn = t2t_trainer.create_experiment_fn()
run_config = t2t_trainer.create_run_config(hparams)
exp = exp_fn(run_config, hparams)
if t2t_trainer.is_chief():
t2t_trainer.save_metadata(hparams)
t2t_trainer.execute_schedule(exp)
# ==========================
# Train Student ============
hparams = t2t_trainer.create_hparams()
hparams.add_hparam("teacher_dir", teacher_dir)
hparams.distill_phase = "distill"
if FLAGS.student_dir:
student_dir = FLAGS.student_dir
else:
student_dir = os.path.join(root_output_dir, "student")
FLAGS.output_dir = student_dir
hparams.add_hparam("student_dir", student_dir)
exp_fn = t2t_trainer.create_experiment_fn()
run_config = t2t_trainer.create_run_config(hparams)
exp = exp_fn(run_config, hparams)
if t2t_trainer.is_chief():
t2t_trainer.save_metadata(hparams)
t2t_trainer.execute_schedule(exp)
def main(argv):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if FLAGS.cloud_mlengine:
cloud_mlengine.launch()
return
if FLAGS.generate_data:
t2t_trainer.generate_data()
if cloud_mlengine.job_dir():
FLAGS.output_dir = cloud_mlengine.job_dir()
if argv:
t2t_trainer.set_hparams_from_args(argv[1:])
with t2t_trainer.maybe_cloud_tpu():
root_output_dir = FLAGS.output_dir
# Train Teacher ============
hparams = t2t_trainer.create_hparams()
hparams.distill_phase = "train"
teacher_dir = os.path.join(root_output_dir, "teacher")
FLAGS.output_dir = teacher_dir
exp_fn = t2t_trainer.create_experiment_fn()
run_config = t2t_trainer.create_run_config(hparams)
exp = exp_fn(run_config, hparams)
if t2t_trainer.is_chief():
t2t_trainer.save_metadata(hparams)
t2t_trainer.execute_schedule(exp)
# ==========================
# Train Student ============
hparams = t2t_trainer.create_hparams()
hparams.add_hparam("teacher_dir", teacher_dir)
hparams.distill_phase = "distill"
student_dir = os.path.join(root_output_dir, "student")
FLAGS.output_dir = student_dir
exp_fn = t2t_trainer.create_experiment_fn()
run_config = t2t_trainer.create_run_config(hparams)
exp = exp_fn(run_config, hparams)
if t2t_trainer.is_chief():
t2t_trainer.save_metadata(hparams)
t2t_trainer.execute_schedule(exp)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
ckpt_dir = os.path.expanduser(FLAGS.output_dir)
hparams = create_hparams()
hparams.no_data_parallelism = True # To clear the devices
problem = hparams.problem
if FLAGS.export_as_tfhub:
export_as_tfhub_module(hparams, problem, ckpt_dir)
return
run_config = t2t_trainer.create_run_config(hparams)
estimator = create_estimator(run_config, hparams)
exporter = tf.estimator.FinalExporter(
"exporter", lambda: problem.serving_input_fn(hparams), as_text=True)
export_dir = os.path.join(ckpt_dir, "export")
exporter.export(estimator,
export_dir,
checkpoint_path=tf.train.latest_checkpoint(ckpt_dir),
eval_result=None,
is_the_final_export=True)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
if FLAGS.score_file:
filename = os.path.expanduser(FLAGS.score_file)
if not tf.gfile.Exists(filename):
raise ValueError("The file to score doesn't exist: %s" % filename)
results = score_file(filename)
if not FLAGS.decode_to_file:
raise ValueError("To score a file, specify --decode_to_file for results.")
write_file = tf.gfile.Open(os.path.expanduser(FLAGS.decode_to_file), "w")
for score in results:
write_file.write("%.6f\n" % score)
write_file.close()
return
hp = create_hparams()
decode_hp = create_decode_hparams()
estimator = trainer_lib.create_estimator(
FLAGS.model,
hp,
t2t_trainer.create_run_config(hp),
decode_hparams=decode_hp,
use_tpu=FLAGS.use_tpu)
decode(estimator, hp, decode_hp)
def _init_env(self):
FLAGS.use_tpu = False
tf.logging.set_verbosity(tf.logging.DEBUG)
tf.logging.info("Import usr dir from %s", self._usr_dir)
if self._usr_dir != None:
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
tf.logging.info("Start to create hparams,for %s of %s", self._problem,
self._hparams_set)
self._hparams = create_hparams()
self._hparams_decode = create_decode_hparams(
extra_length=self._extra_length,
batch_size=self._batch_size,
beam_size=self._beam_size,
alpha=self._alpha,
return_beams=self._return_beams,
write_beam_scores=self._write_beam_scores)
self.estimator = trainer_lib.create_estimator(
FLAGS.model,
self._hparams,
t2t_trainer.create_run_config(self._hparams),
decode_hparams=self._hparams_decode,
use_tpu=False)
tf.logging.info("Finish intialize environment")
def t2t_decoder(problem_name, data_dir, decode_from_file, decode_to_file,
checkpoint_path):
trainer_lib.set_random_seed(FLAGS.random_seed)
hp = trainer_lib.create_hparams(FLAGS.hparams_set,
FLAGS.hparams,
data_dir=os.path.expanduser(data_dir),
problem_name=problem_name)
decode_hp = decoding.decode_hparams(FLAGS.decode_hparams)
decode_hp.shards = FLAGS.decode_shards
decode_hp.shard_id = FLAGS.worker_id
decode_in_memory = FLAGS.decode_in_memory or decode_hp.decode_in_memory
decode_hp.decode_in_memory = decode_in_memory
decode_hp.decode_to_file = decode_to_file
decode_hp.decode_reference = None
FLAGS.checkpoint_path = checkpoint_path
estimator = trainer_lib.create_estimator(FLAGS.model,
hp,
t2t_trainer.create_run_config(hp),
decode_hparams=decode_hp,
use_tpu=FLAGS.use_tpu)
decode_from_text_file(estimator,
problem_name,
decode_from_file,
hp,
decode_hp,
decode_to_file,
checkpoint_path=checkpoint_path)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
if FLAGS.score_file:
filename = os.path.expanduser(FLAGS.score_file)
if not tf.gfile.Exists(filename):
raise ValueError("The file to score doesn't exist: %s" % filename)
results = score_file(filename)
# if not FLAGS.decode_to_file:
# raise ValueError("To score a file, specify --decode_to_file for results.")
# write_file = tf.gfile.Open(os.path.expanduser(FLAGS.decode_to_file), "w")
# for sentence, score in results:
# write_file.write(sentence + "\t" + "SCORE:" + "%.6f\n" % score)
# write_file.close()
return
hp = create_hparams()
decode_hp = create_decode_hparams()
run_config = t2t_trainer.create_run_config(hp)
if FLAGS.disable_grappler_optimizations:
run_config.session_config.graph_options.rewrite_options.disable_meta_optimizer = True
# summary-hook in tf.estimator.EstimatorSpec requires
# hparams.model_dir to be set.
hp.add_hparam("model_dir", run_config.model_dir)
estimator = trainer_lib.create_estimator(FLAGS.model,
hp,
run_config,
decode_hparams=decode_hp,
use_tpu=FLAGS.use_tpu)
decode(estimator, hp, decode_hp)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
if FLAGS.score_file:
filename = os.path.expanduser(FLAGS.score_file)
if not tf.gfile.Exists(filename):
raise ValueError("The file to score doesn't exist: %s" % filename)
results = score_file(filename)
if not FLAGS.decode_to_file:
raise ValueError(
"To score a file, specify --decode_to_file for results.")
write_file = tf.gfile.Open(os.path.expanduser(FLAGS.decode_to_file),
"w")
for score in results:
write_file.write("%.6f\n" % score)
write_file.close()
return
hp = create_hparams()
decode_hp = create_decode_hparams()
estimator = trainer_lib.create_estimator(FLAGS.model,
hp,
t2t_trainer.create_run_config(hp),
decode_hparams=decode_hp,
use_tpu=FLAGS.use_tpu)
decode(estimator, hp, decode_hp)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
if FLAGS.checkpoint_path:
checkpoint_path = FLAGS.checkpoint_path
ckpt_dir = os.path.dirname(checkpoint_path)
else:
ckpt_dir = os.path.expanduser(FLAGS.output_dir)
checkpoint_path = tf.train.latest_checkpoint(ckpt_dir)
hparams = create_hparams()
hparams.no_data_parallelism = True # To clear the devices
problem = hparams.problem
export_dir = FLAGS.export_dir or os.path.join(ckpt_dir, "export")
if FLAGS.export_as_tfhub:
checkpoint_path = tf.train.latest_checkpoint(ckpt_dir)
decode_hparams = decoding.decode_hparams(FLAGS.decode_hparams)
export_as_tfhub_module(FLAGS.model, hparams, decode_hparams, problem,
checkpoint_path, export_dir)
return
run_config = t2t_trainer.create_run_config(hparams)
estimator = create_estimator(run_config, hparams)
estimator.export_savedmodel(export_dir,
lambda: problem.serving_input_fn(hparams),
as_text=False,
checkpoint_path=checkpoint_path)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
hparams = trainer_lib.create_hparams(FLAGS.hparams_set,
FLAGS.hparams,
data_dir=FLAGS.data_dir,
problem_name=FLAGS.problem)
# set appropriate dataset-split, if flags.eval_use_test_set.
dataset_split = "test" if FLAGS.eval_use_test_set else None
dataset_kwargs = {"dataset_split": dataset_split}
eval_input_fn = hparams.problem.make_estimator_input_fn(
tf.estimator.ModeKeys.EVAL, hparams, dataset_kwargs=dataset_kwargs)
config = t2t_trainer.create_run_config(hparams)
# summary-hook in tf.estimator.EstimatorSpec requires
# hparams.model_dir to be set.
hparams.add_hparam("model_dir", config.model_dir)
estimator = trainer_lib.create_estimator(FLAGS.model,
hparams,
config,
use_tpu=FLAGS.use_tpu)
ckpt_iter = trainer_lib.next_checkpoint(hparams.model_dir,
FLAGS.eval_timeout_mins)
for ckpt_path in ckpt_iter:
predictions = estimator.evaluate(eval_input_fn,
steps=FLAGS.eval_steps,
checkpoint_path=ckpt_path)
tf.logging.info(predictions)
def create_new_estimator(hp,decode_hp):
estimator = trainer_lib.create_estimator(
FLAGS.model,
hp,
t2t_trainer.create_run_config(hp),
decode_hparams=decode_hp,
use_tpu=FLAGS.use_tpu)
return estimator
def main(argv):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if FLAGS.generate_data:
t2t_trainer.generate_data()
if argv:
t2t_trainer.set_hparams_from_args(argv[1:])
hparams = t2t_trainer.create_hparams()
trainer_lib.add_problem_hparams(hparams, FLAGS.problem)
pruning_params = create_pruning_params()
pruning_strategy = create_pruning_strategy(pruning_params.strategy)
config = t2t_trainer.create_run_config(hparams)
params = {"batch_size": hparams.batch_size}
# add "_rev" as a hack to avoid image standardization
problem = registry.problem(FLAGS.problem)
input_fn = problem.make_estimator_input_fn(tf.estimator.ModeKeys.EVAL,
hparams)
dataset = input_fn(params, config).repeat()
features, labels = dataset.make_one_shot_iterator().get_next()
sess = tf.Session()
model_fn = t2t_model.T2TModel.make_estimator_model_fn(
FLAGS.model, hparams, use_tpu=FLAGS.use_tpu)
spec = model_fn(
features,
labels,
tf.estimator.ModeKeys.EVAL,
params=hparams,
config=config)
# Restore weights
saver = tf.train.Saver()
checkpoint_path = os.path.expanduser(FLAGS.output_dir or
FLAGS.checkpoint_path)
saver.restore(sess, tf.train.latest_checkpoint(checkpoint_path))
def eval_model():
preds = spec.predictions["predictions"]
preds = tf.argmax(preds, -1, output_type=labels.dtype)
_, acc_update_op = tf.metrics.accuracy(labels=labels, predictions=preds)
sess.run(tf.initialize_local_variables())
for _ in range(FLAGS.eval_steps):
acc = sess.run(acc_update_op)
return acc
pruning_utils.sparsify(sess, eval_model, pruning_strategy, pruning_params)
def main(argv):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if FLAGS.generate_data:
t2t_trainer.generate_data()
if argv:
t2t_trainer.set_hparams_from_args(argv[1:])
hparams = t2t_trainer.create_hparams()
trainer_lib.add_problem_hparams(hparams, FLAGS.problem)
pruning_params = create_pruning_params()
pruning_strategy = create_pruning_strategy(pruning_params.strategy)
config = t2t_trainer.create_run_config(hparams)
params = {"batch_size": hparams.batch_size}
# add "_rev" as a hack to avoid image standardization
problem = registry.problem(FLAGS.problem)
input_fn = problem.make_estimator_input_fn(tf.estimator.ModeKeys.EVAL,
hparams)
dataset = input_fn(params, config).repeat()
features, labels = dataset.make_one_shot_iterator().get_next()
sess = tf.Session()
model_fn = t2t_model.T2TModel.make_estimator_model_fn(
FLAGS.model, hparams, use_tpu=FLAGS.use_tpu)
spec = model_fn(
features,
labels,
tf.estimator.ModeKeys.EVAL,
params=hparams,
config=config)
# Restore weights
saver = tf.train.Saver()
checkpoint_path = os.path.expanduser(FLAGS.output_dir or
FLAGS.checkpoint_path)
saver.restore(sess, tf.train.latest_checkpoint(checkpoint_path))
def eval_model():
preds = spec.predictions["predictions"]
preds = tf.argmax(preds, -1, output_type=labels.dtype)
_, acc_update_op = tf.metrics.accuracy(labels=labels, predictions=preds)
sess.run(tf.initialize_local_variables())
for _ in range(FLAGS.eval_steps):
acc = sess.run(acc_update_op)
return acc
pruning_utils.sparsify(sess, eval_model, pruning_strategy, pruning_params)
def main(_):
FLAGS.decode_interactive = True
hp = create_hparams()
decode_hp = create_decode_hparams()
estimator = trainer_lib.create_estimator(FLAGS.model,
hp,
t2t_trainer.create_run_config(hp),
decode_hparams=decode_hp,
use_tpu=False)
decode(estimator, hp, decode_hp)
def main(_):
import ipdb
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
if FLAGS.score_file:
filename = os.path.expanduser(FLAGS.score_file)
if not tf.gfile.Exists(filename):
raise ValueError("The file to score doesn't exist: %s" % filename)
results = score_file(filename)
if not FLAGS.decode_to_file:
raise ValueError("To score a file, specify --decode_to_file for results.")
write_file = tf.gfile.Open(os.path.expanduser(FLAGS.decode_to_file), "w")
for score in results:
write_file.write("%.6f\n" % score)
write_file.close()
return
hp = create_hparams()
decode_hp = create_decode_hparams()
# eval_input_fn = hp.problem.make_estimator_input_fn(
# tf.estimator.ModeKeys.TRAIN, hp, dataset_kwargs={"dataset_split": "eval"})
# print(eval_input_fn)
# for foo in eval_input_fn(None, None):
# print(type(foo[0]['targets']))
# print(foo[0]['targets'].numpy())
# exit()
run_config = t2t_trainer.create_run_config(hp)
if FLAGS.disable_grappler_optimizations:
run_config.session_config.graph_options.rewrite_options.disable_meta_optimizer = True
# summary-hook in tf.estimator.EstimatorSpec requires
# hparams.model_dir to be set.
hp.add_hparam("model_dir", run_config.model_dir)
estimator = trainer_lib.create_estimator(
FLAGS.model,
hp,
run_config,
decode_hparams=decode_hp,
use_tpu=FLAGS.use_tpu)
decode(estimator, hp, decode_hp)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
if FLAGS.score_file:
filename = os.path.expanduser(FLAGS.score_file)
if not tf.gfile.Exists(filename):
raise ValueError("The file to score doesn't exist: %s" % filename)
results = score_file(filename)
if not FLAGS.decode_to_file:
raise ValueError(
"To score a file, specify --decode_to_file for results.")
write_file = tf.gfile.Open(os.path.expanduser(FLAGS.decode_to_file),
"w")
for score in results:
write_file.write("%.6f\n" % score)
write_file.close()
return
hp = create_hparams()
decode_hp = create_decode_hparams()
estimator = trainer_lib.create_estimator(FLAGS.model,
hp,
t2t_trainer.create_run_config(hp),
decode_hparams=decode_hp,
use_tpu=FLAGS.use_tpu)
decode(estimator, hp, decode_hp)
# Post-process decodings (if necessary).
if FLAGS.decode_to_file and FLAGS.output_line_prefix_tag:
decode_filename_original = FLAGS.decode_to_file
decode_filename_prefixed = "%s-%s" % (decode_filename_original,
FLAGS.output_line_prefix_tag)
tf.logging.info("Writing prefexed decodes into %s" %
decode_filename_prefixed)
# Read original lines.
with tf.gfile.Open(decode_filename_original, "r") as original_fp:
original_lines = original_fp.readlines()
# Write prefixed lines.
prefix = "<%s> " % FLAGS.output_line_prefix_tag
prefixed_fp = tf.gfile.Open(decode_filename_prefixed, "w")
for line in original_lines:
prefixed_fp.write(prefix + line)
prefixed_fp.flush()
prefixed_fp.close()
tf.logging.info("Done.")
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
FLAGS.use_tpu = False # decoding not supported on TPU
hp = create_hparams()
decode_hp = create_decode_hparams()
estimator = trainer_lib.create_estimator(FLAGS.model,
hp,
t2t_trainer.create_run_config(hp),
decode_hparams=decode_hp,
use_tpu=False)
decode(estimator, hp, decode_hp)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
hp = t2t_decoder.create_hparams()
decode_hp = t2t_decoder.create_decode_hparams()
estimator = trainer_lib.create_estimator(FLAGS.model,
hp,
t2t_trainer.create_run_config(hp),
decode_hparams=decode_hp,
use_tpu=FLAGS.use_tpu)
decode(estimator, hp, decode_hp)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
FLAGS.use_tpu = False # decoding not supported on TPU
hp = create_hparams()
decode_hp = create_decode_hparams()
estimator = trainer_lib.create_estimator(
FLAGS.model,
hp,
t2t_trainer.create_run_config(hp),
decode_hparams=decode_hp,
use_tpu=False)
decode(estimator, hp, decode_hp)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
# Fathom start
checkpoint_path = fathom_t2t_model_setup()
# Fathom end
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
if FLAGS.score_file:
filename = os.path.expanduser(FLAGS.score_file)
if not tf.gfile.Exists(filename):
raise ValueError("The file to score doesn't exist: %s" % filename)
results = score_file(filename)
if not FLAGS.decode_to_file:
raise ValueError(
"To score a file, specify --decode_to_file for results.")
write_file = tf.gfile.Open(os.path.expanduser(FLAGS.decode_to_file),
"w")
for score in results:
write_file.write("%.6f\n" % score)
write_file.close()
return
hp = create_hparams()
decode_hp = create_decode_hparams()
estimator = trainer_lib.create_estimator(FLAGS.model,
hp,
t2t_trainer.create_run_config(hp),
decode_hparams=decode_hp,
use_tpu=FLAGS.use_tpu)
decode(estimator, hp, decode_hp)
# Fathom
# This xcom is here so that tasks after decode know the local path to the
# downloaded model. Train does this same xcom echo.
# Decode, predict, and evaluate code should
# converge to use the same fathom_t2t_model_setup.
# TODO: since the truncation-boundary xcom value should be available in
# the hparams_set, we should probably have consumers access this via a
# SavedModel.hparams property rather than XCOM
echo_yaml_for_xcom_ingest({
'output-dir': os.path.dirname(checkpoint_path),
'output-file': FLAGS.decode_output_file,
'truncation-boundary': hp.max_input_seq_length
})
def __init__(self, processor_configuration):
"""Creates the Transformer estimator.
Args:
processor_configuration: A ProcessorConfiguration protobuffer with the
transformer fields populated.
"""
# Do the pre-setup tensor2tensor requires for flags and configurations.
transformer_config = processor_configuration["transformer"]
FLAGS.output_dir = transformer_config["model_dir"]
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
data_dir = os.path.expanduser(transformer_config["data_dir"])
# Create the basic hyper parameters.
self.hparams = trainer_lib.create_hparams(
transformer_config["hparams_set"],
transformer_config["hparams"],
data_dir=data_dir,
problem_name=transformer_config["problem"])
decode_hp = decoding.decode_hparams()
decode_hp.add_hparam("shards", 1)
decode_hp.add_hparam("shard_id", 0)
# Create the estimator and final hyper parameters.
self.estimator = trainer_lib.create_estimator(
transformer_config["model"],
self.hparams,
t2t_trainer.create_run_config(self.hparams),
decode_hparams=decode_hp,
use_tpu=False)
# Fetch the vocabulary and other helpful variables for decoding.
self.source_vocab = self.hparams.problem_hparams.vocabulary["inputs"]
self.targets_vocab = self.hparams.problem_hparams.vocabulary["targets"]
self.const_array_size = 10000
# Prepare the Transformer's debug data directory.
run_dirs = sorted(
glob.glob(os.path.join("/tmp/t2t_server_dump", "run_*")))
for run_dir in run_dirs:
shutil.rmtree(run_dir)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
ckpt_dir = os.path.expanduser(FLAGS.output_dir)
hparams = create_hparams()
hparams.no_data_parallelism = True # To clear the devices
run_config = t2t_trainer.create_run_config(hparams)
estimator = create_estimator(run_config, hparams)
problem = hparams.problem
strategy = trainer_lib.create_export_strategy(problem, hparams)
export_dir = os.path.join(ckpt_dir, "export", strategy.name)
strategy.export(estimator,
export_dir,
checkpoint_path=tf.train.latest_checkpoint(ckpt_dir))
def __init__(self, processor_configuration):
"""Creates the Transformer estimator.
Args:
processor_configuration: A ProcessorConfiguration protobuffer with the
transformer fields populated.
"""
# Do the pre-setup tensor2tensor requires for flags and configurations.
transformer_config = processor_configuration["transformer"]
FLAGS.output_dir = transformer_config["model_dir"]
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
data_dir = os.path.expanduser(transformer_config["data_dir"])
# Create the basic hyper parameters.
self.hparams = trainer_lib.create_hparams(
transformer_config["hparams_set"],
transformer_config["hparams"],
data_dir=data_dir,
problem_name=transformer_config["problem"])
decode_hp = decoding.decode_hparams()
decode_hp.add_hparam("shards", 1)
decode_hp.add_hparam("shard_id", 0)
# Create the estimator and final hyper parameters.
self.estimator = trainer_lib.create_estimator(
transformer_config["model"],
self.hparams,
t2t_trainer.create_run_config(self.hparams),
decode_hparams=decode_hp, use_tpu=False)
# Fetch the vocabulary and other helpful variables for decoding.
self.source_vocab = self.hparams.problem_hparams.vocabulary["inputs"]
self.targets_vocab = self.hparams.problem_hparams.vocabulary["targets"]
self.const_array_size = 10000
# Prepare the Transformer's debug data directory.
run_dirs = sorted(glob.glob(os.path.join("/tmp/t2t_server_dump", "run_*")))
for run_dir in run_dirs:
shutil.rmtree(run_dir)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
if FLAGS.checkpoint_path:
checkpoint_path = FLAGS.checkpoint_path
ckpt_dir = os.path.dirname(checkpoint_path)
else:
ckpt_dir = os.path.expanduser(FLAGS.output_dir)
checkpoint_path = tf.train.latest_checkpoint(ckpt_dir)
hparams = create_hparams()
hparams.no_data_parallelism = True # To clear the devices
problem = hparams.problem
export_dir = FLAGS.export_dir or os.path.join(ckpt_dir, "export")
if FLAGS.export_as_tfhub:
checkpoint_path = tf.train.latest_checkpoint(ckpt_dir)
decode_hparams = decoding.decode_hparams(FLAGS.decode_hparams)
export_as_tfhub_module(FLAGS.model, hparams, decode_hparams, problem,
checkpoint_path, export_dir)
return
run_config = t2t_trainer.create_run_config(hparams)
estimator = create_estimator(run_config, hparams)
exporter = tf.estimator.FinalExporter(
"exporter", lambda: problem.serving_input_fn(hparams), as_text=True)
exporter.export(
estimator,
export_dir,
checkpoint_path=checkpoint_path,
eval_result=None,
is_the_final_export=True)
def create_hp_and_estimator(problem_name, data_dir, checkpoint_path):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
hp = trainer_lib.create_hparams(FLAGS.hparams_set,
FLAGS.hparams,
data_dir=os.path.expanduser(data_dir),
problem_name=problem_name)
decode_hp = decoding.decode_hparams(FLAGS.decode_hparams)
decode_hp.shards = FLAGS.decode_shards
decode_hp.shard_id = FLAGS.worker_id
decode_in_memory = FLAGS.decode_in_memory or decode_hp.decode_in_memory
decode_hp.decode_in_memory = decode_in_memory
decode_hp.decode_to_file = None
decode_hp.decode_reference = None
FLAGS.checkpoint_path = checkpoint_path
estimator = trainer_lib.create_estimator(FLAGS.model,
hp,
t2t_trainer.create_run_config(hp),
decode_hparams=decode_hp,
use_tpu=FLAGS.use_tpu)
return hp, decode_hp, estimator
def main(argv):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if FLAGS.cloud_mlengine:
cloud_mlengine.launch()
return
if FLAGS.generate_data:
t2t_trainer.generate_data()
if cloud_mlengine.job_dir():
FLAGS.output_dir = cloud_mlengine.job_dir()
if argv:
t2t_trainer.set_hparams_from_args(argv[1:])
if FLAGS.surrogate_attack:
tf.logging.warn("Performing surrogate model attack.")
sur_hparams = create_surrogate_hparams()
trainer_lib.add_problem_hparams(sur_hparams, FLAGS.problem)
hparams = t2t_trainer.create_hparams()
trainer_lib.add_problem_hparams(hparams, FLAGS.problem)
attack_params = create_attack_params()
attack_params.add_hparam(attack_params.epsilon_name, 0.0)
if FLAGS.surrogate_attack:
sur_config = create_surrogate_run_config(sur_hparams)
config = t2t_trainer.create_run_config(hparams)
params = {
"batch_size": hparams.batch_size,
"use_tpu": FLAGS.use_tpu,
}
# add "_rev" as a hack to avoid image standardization
problem = registry.problem(FLAGS.problem + "_rev")
inputs, labels, features = prepare_data(problem, hparams, params, config)
sess = tf.Session()
if FLAGS.surrogate_attack:
sur_model_fn = t2t_model.T2TModel.make_estimator_model_fn(
FLAGS.surrogate_model, sur_hparams, use_tpu=FLAGS.use_tpu)
sur_ch_model = adv_attack_utils.T2TAttackModel(
sur_model_fn, features, params, sur_config, scope="surrogate")
# Dummy call to construct graph
sur_ch_model.get_probs(inputs)
checkpoint_path = os.path.expanduser(FLAGS.surrogate_output_dir)
tf.contrib.framework.init_from_checkpoint(
tf.train.latest_checkpoint(checkpoint_path), {"/": "surrogate/"})
sess.run(tf.global_variables_initializer())
other_vars = set(tf.global_variables())
model_fn = t2t_model.T2TModel.make_estimator_model_fn(
FLAGS.model, hparams)
ch_model = adv_attack_utils.T2TAttackModel(model_fn, features, params, config)
acc_mask = None
probs = ch_model.get_probs(inputs)
if FLAGS.ignore_incorrect:
preds = tf.argmax(probs, -1, output_type=labels.dtype)
preds = tf.reshape(preds, labels.shape)
acc_mask = tf.to_float(tf.equal(labels, preds))
one_hot_labels = tf.one_hot(labels, probs.shape[-1])
if FLAGS.surrogate_attack:
attack = create_attack(attack_params.attack)(sur_ch_model, sess=sess)
else:
attack = create_attack(attack_params.attack)(ch_model, sess=sess)
new_vars = set(tf.global_variables()) - other_vars
# Restore weights
saver = tf.train.Saver(new_vars)
checkpoint_path = os.path.expanduser(FLAGS.output_dir)
saver.restore(sess, tf.train.latest_checkpoint(checkpoint_path))
# reuse variables
tf.get_variable_scope().reuse_variables()
def compute_accuracy(x, l, mask):
"""Compute model accuracy."""
preds = ch_model.get_probs(x)
preds = tf.squeeze(preds)
preds = tf.argmax(preds, -1, output_type=l.dtype)
_, acc_update_op = tf.metrics.accuracy(l, preds, weights=mask)
if FLAGS.surrogate_attack:
preds = sur_ch_model.get_probs(x)
preds = tf.squeeze(preds)
preds = tf.argmax(preds, -1, output_type=l.dtype)
acc_update_op = tf.tuple((acc_update_op,
tf.metrics.accuracy(l, preds, weights=mask)[1]))
sess.run(tf.initialize_local_variables())
for i in range(FLAGS.eval_steps):
tf.logging.info(
"\tEvaluating batch [%d / %d]" % (i + 1, FLAGS.eval_steps))
acc = sess.run(acc_update_op)
if FLAGS.surrogate_attack:
tf.logging.info("\tFinal acc: (%.4f, %.4f)" % (acc[0], acc[1]))
else:
tf.logging.info("\tFinal acc: %.4f" % acc)
return acc
epsilon_acc_pairs = []
for epsilon in attack_params.attack_epsilons:
tf.logging.info("Attacking @ eps=%.4f" % epsilon)
attack_params.set_hparam(attack_params.epsilon_name, epsilon)
adv_x = attack.generate(inputs, y=one_hot_labels, **attack_params.values())
acc = compute_accuracy(adv_x, labels, acc_mask)
epsilon_acc_pairs.append((epsilon, acc))
for epsilon, acc in epsilon_acc_pairs:
if FLAGS.surrogate_attack:
tf.logging.info(
"Accuracy @ eps=%.4f: (%.4f, %.4f)" % (epsilon, acc[0], acc[1]))
else:
tf.logging.info("Accuracy @ eps=%.4f: %.4f" % (epsilon, acc))
def main(argv):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if FLAGS.cloud_mlengine:
cloud_mlengine.launch()
return
if FLAGS.generate_data:
t2t_trainer.generate_data()
if cloud_mlengine.job_dir():
FLAGS.output_dir = cloud_mlengine.job_dir()
if argv:
t2t_trainer.set_hparams_from_args(argv[1:])
hparams = t2t_trainer.create_hparams()
trainer_lib.add_problem_hparams(hparams, FLAGS.problem)
attack_params = create_attack_params()
attack_params.add_hparam("eps", 0.0)
config = t2t_trainer.create_run_config(hparams)
params = {"batch_size": hparams.batch_size}
# add "_rev" as a hack to avoid image standardization
problem = registry.problem(FLAGS.problem + "_rev")
input_fn = problem.make_estimator_input_fn(
tf.estimator.ModeKeys.EVAL, hparams)
dataset = input_fn(params, config).repeat()
features, _ = dataset.make_one_shot_iterator().get_next()
inputs, labels = features["targets"], features["inputs"]
inputs = tf.to_float(inputs)
labels = tf.squeeze(labels)
sess = tf.Session()
model_fn = t2t_model.T2TModel.make_estimator_model_fn(
FLAGS.model, hparams, use_tpu=FLAGS.use_tpu)
ch_model = adv_attack_utils.T2TAttackModel(model_fn, params, config)
acc_mask = None
probs = ch_model.get_probs(inputs)
if FLAGS.ignore_incorrect:
preds = tf.argmax(probs, -1)
preds = tf.squeeze(preds)
acc_mask = tf.to_float(tf.equal(labels, preds))
one_hot_labels = tf.one_hot(labels, probs.shape[-1])
attack = create_attack(attack_params.attack)(ch_model, sess=sess)
# Restore weights
saver = tf.train.Saver()
checkpoint_path = os.path.expanduser(FLAGS.output_dir or
FLAGS.checkpoint_path)
saver.restore(sess, tf.train.latest_checkpoint(checkpoint_path))
# reuse variables
tf.get_variable_scope().reuse_variables()
def compute_accuracy(x, labels, mask):
preds = ch_model.get_probs(x)
preds = tf.squeeze(preds)
preds = tf.argmax(preds, -1, output_type=labels.dtype)
_, acc_update_op = tf.metrics.accuracy(
labels=labels, predictions=preds, weights=mask)
sess.run(tf.initialize_local_variables())
for _ in range(FLAGS.eval_steps):
acc = sess.run(acc_update_op)
return acc
acc = compute_accuracy(inputs, labels, acc_mask)
epsilon_acc_pairs = [(0.0, acc)]
for epsilon in attack_params.attack_epsilons:
attack_params.eps = epsilon
adv_x = attack.generate(inputs, y=one_hot_labels, **attack_params.values())
acc = compute_accuracy(adv_x, labels, acc_mask)
epsilon_acc_pairs.append((epsilon, acc))
for epsilon, acc in epsilon_acc_pairs:
tf.logging.info("Accuracy @ eps=%f: %f" % (epsilon, acc))
def _init_env(self):
FLAGS.use_tpu = False
tf.logging.set_verbosity(tf.logging.DEBUG)
tf.logging.info("Import usr dir from %s", self._usr_dir)
if self._usr_dir != None:
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
tf.logging.info("Start to create hparams,for %s of %s", self._problem,
self._hparams_set)
self._hparams = create_hparams()
self._hparams_decode = create_decode_hparams(
extra_length=self._extra_length,
batch_size=self._batch_size,
beam_size=self._beam_size,
alpha=self._alpha,
return_beams=self._return_beams,
write_beam_scores=self._write_beam_scores,
force_decode_length=self._force_decode_length)
self.estimator = trainer_lib.create_estimator(
FLAGS.model,
self._hparams,
t2t_trainer.create_run_config(self._hparams),
decode_hparams=self._hparams_decode,
use_tpu=False)
tf.logging.info("Finish intialize environment")
#######
### make input placeholder
#self._inputs_ph = tf.placeholder(dtype=tf.int32) # shape not specified,any shape
# x=tf.placeholder(dtype=tf.int32)
# x.set_shape([None, None]) # ? -> (?,?)
# x = tf.expand_dims(x, axis=[2])# -> (?,?,1)
# x = tf.to_int32(x)
# self._inputs_ph=x
#batch_inputs = tf.reshape(self._inputs_ph, [self._batch_size, -1, 1, 1])
#batch_inputs=x
###
# batch_inputs = tf.reshape(self._inputs_ph, [-1, -1, 1, 1])
#targets_ph = tf.placeholder(dtype=tf.int32)
#batch_targets = tf.reshape(targets_ph, [1, -1, 1, 1])
self.inputs_ph = tf.placeholder(tf.int32,
shape=(None, None, 1, 1),
name='inputs')
self.targets_ph = tf.placeholder(tf.int32,
shape=(None, None, None, None),
name='targets')
self.input_extra_length_ph = tf.placeholder(dtype=tf.int32, shape=[])
self._features = {
"inputs": self.inputs_ph,
"problem_choice": 0, # We run on the first problem here.
"input_space_id": self._hparams.problem_hparams.input_space_id,
"target_space_id": self._hparams.problem_hparams.target_space_id
}
### 加入 decode length 变长的
self._features['decode_length'] = self.input_extra_length_ph
## target
self._features['targets'] = self.targets_ph
## 去掉 整数的
del self._features["problem_choice"]
del self._features["input_space_id"]
del self._features["target_space_id"]
#del self._features['decode_length']
####
mode = tf.estimator.ModeKeys.EVAL
translate_model = registry.model(self._model_name)(
hparams=self._hparams,
decode_hparams=self._hparams_decode,
mode=mode)
self.predict_dict = {}
### get logit ,attention mats
self.logits, _ = translate_model(self._features) #[? ? ? 1 vocabsz]
#translate_model(features)
from visualization import get_att_mats
self.att_mats = get_att_mats(translate_model,
self._model_name) # enc, dec, encdec
### get infer
translate_model.set_mode(tf.estimator.ModeKeys.PREDICT)
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
self.outputs_scores = translate_model.infer(
features=self._features,
decode_length=self._extra_length,
beam_size=self._beam_size,
top_beams=self._beam_size,
alpha=self._alpha) #outputs 4,4,63
######
tf.logging.info("Start to init tf session")
if self._isGpu:
print('Using GPU in Decoder')
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=self._fraction)
self._sess = tf.Session(
config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False,
gpu_options=gpu_options))
else:
print('Using CPU in Decoder')
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0)
config = tf.ConfigProto(gpu_options=gpu_options)
config.allow_soft_placement = True
config.log_device_placement = False
self._sess = tf.Session(config=config)
with self._sess.as_default():
ckpt = saver_mod.get_checkpoint_state(self._model_dir)
saver = tf.train.Saver()
tf.logging.info("Start to restore the parameters from %s",
ckpt.model_checkpoint_path)
saver.restore(self._sess, ckpt.model_checkpoint_path)
tf.logging.info("Finish intialize environment")
def main(argv):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if FLAGS.cloud_mlengine:
cloud_mlengine.launch()
return
if FLAGS.generate_data:
t2t_trainer.generate_data()
if cloud_mlengine.job_dir():
FLAGS.output_dir = cloud_mlengine.job_dir()
if argv:
t2t_trainer.set_hparams_from_args(argv[1:])
hparams = t2t_trainer.create_hparams()
trainer_lib.add_problem_hparams(hparams, FLAGS.problem)
attack_params = create_attack_params()
attack_params.add_hparam("eps", 0.0)
config = t2t_trainer.create_run_config(hparams)
params = {"batch_size": hparams.batch_size}
# add "_rev" as a hack to avoid image standardization
problem = registry.problem(FLAGS.problem + "_rev")
input_fn = problem.make_estimator_input_fn(tf.estimator.ModeKeys.EVAL,
hparams)
dataset = input_fn(params, config).repeat()
features, _ = dataset.make_one_shot_iterator().get_next()
inputs, labels = features["targets"], features["inputs"]
inputs = tf.to_float(inputs)
labels = tf.squeeze(labels)
sess = tf.Session()
model_fn = t2t_model.T2TModel.make_estimator_model_fn(
FLAGS.model, hparams, use_tpu=FLAGS.use_tpu)
ch_model = adv_attack_utils.T2TAttackModel(model_fn, params, config)
acc_mask = None
probs = ch_model.get_probs(inputs)
if FLAGS.ignore_incorrect:
preds = tf.argmax(probs, -1)
preds = tf.squeeze(preds)
acc_mask = tf.to_float(tf.equal(labels, preds))
one_hot_labels = tf.one_hot(labels, probs.shape[-1])
attack = create_attack(attack_params.attack)(ch_model, sess=sess)
# Restore weights
saver = tf.train.Saver()
checkpoint_path = os.path.expanduser(FLAGS.output_dir
or FLAGS.checkpoint_path)
saver.restore(sess, tf.train.latest_checkpoint(checkpoint_path))
# reuse variables
tf.get_variable_scope().reuse_variables()
def compute_accuracy(x, labels, mask):
preds = ch_model.get_probs(x)
preds = tf.squeeze(preds)
preds = tf.argmax(preds, -1, output_type=labels.dtype)
_, acc_update_op = tf.metrics.accuracy(labels=labels,
predictions=preds,
weights=mask)
sess.run(tf.initialize_local_variables())
for _ in range(FLAGS.eval_steps):
acc = sess.run(acc_update_op)
return acc
acc = compute_accuracy(inputs, labels, acc_mask)
epsilon_acc_pairs = [(0.0, acc)]
for epsilon in attack_params.attack_epsilons:
attack_params.eps = epsilon
adv_x = attack.generate(inputs,
y=one_hot_labels,
**attack_params.values())
acc = compute_accuracy(adv_x, labels, acc_mask)
epsilon_acc_pairs.append((epsilon, acc))
for epsilon, acc in epsilon_acc_pairs:
tf.logging.info("Accuracy @ eps=%f: %f" % (epsilon, acc))
def _init_env(self):
FLAGS.use_tpu = False
tf.logging.set_verbosity(tf.logging.DEBUG)
tf.logging.info("Import usr dir from %s", self._usr_dir)
if self._usr_dir != None:
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
tf.logging.info("Start to create hparams,for %s of %s", self._problem,
self._hparams_set)
self._hparams = create_hparams()
self._hparams_decode = create_decode_hparams(
extra_length=self._extra_length,
batch_size=self._batch_size,
beam_size=self._beam_size,
alpha=self._alpha,
return_beams=self._return_beams,
write_beam_scores=self._write_beam_scores)
self.estimator = trainer_lib.create_estimator(
FLAGS.model,
self._hparams,
t2t_trainer.create_run_config(self._hparams),
decode_hparams=self._hparams_decode,
use_tpu=False)
tf.logging.info("Finish intialize environment")
####### problem type :输出分类 还是序列 还是语言模型
self.problem_type = self._hparams.problems[0].target_modality[
0] # class? symble
self._whether_has_inputs = self._hparams.problem_instances[
0].has_inputs
self._beam_size = 1 if self.problem_type == 'class_label' else self._beam_size
#######
### make input placeholder
self._inputs_ph = tf.placeholder(
dtype=tf.int32) # shape not specified,any shape
x = tf.placeholder(dtype=tf.int32)
x.set_shape([None, None]) # ? -> (?,?)
x = tf.expand_dims(x, axis=[2]) # -> (?,?,1)
# EVAL MODEL
x = tf.expand_dims(x, axis=[3]) # -> (?,?,1,1)
x = tf.to_int32(x)
self._inputs_ph = x
#batch_inputs = tf.reshape(self._inputs_ph, [self._batch_size, -1, 1, 1])
batch_inputs = x #[?,?,1,1]
# batch_inputs = tf.reshape(self._inputs_ph, [-1, -1, 1, 1])
#targets_ph = tf.placeholder(dtype=tf.int32)
#batch_targets = tf.reshape(targets_ph, [1, -1, 1, 1])
self._features = {
"inputs": batch_inputs,
"problem_choice": 0, # We run on the first problem here.
"input_space_id": self._hparams.problems[0].input_space_id,
"target_space_id": self._hparams.problems[0].target_space_id
}
### 加入 decode length 变长的
#self.input_extra_length_ph = tf.placeholder(dtype=tf.int32)
#self._features['decode_length'] = self.input_extra_length_ph
#### EVAL MODE target
self._targets_ph = tf.placeholder(tf.int32,
shape=(1, None, 1, 1),
name='targets')
self._features['targets'] = self._targets_ph #batch targets
del self._features['problem_choice']
del self._features['input_space_id']
del self._features['target_space_id']
####
mode = tf.estimator.ModeKeys.EVAL
predictions_dict = self.estimator._call_model_fn(
self._features, None, mode,
t2t_trainer.create_run_config(self._hparams))
self._predictions_dict = predictions_dict.predictions
#self._predictions = self._predictions_dict["outputs"]
# self._scores=predictions_dict['scores'] not return when greedy search
tf.logging.info("Start to init tf session")
if self._isGpu:
print('Using GPU in Decoder')
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=self._fraction)
self._sess = tf.Session(
config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False,
gpu_options=gpu_options))
else:
print('Using CPU in Decoder')
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0)
config = tf.ConfigProto(gpu_options=gpu_options)
config.allow_soft_placement = True
config.log_device_placement = False
self._sess = tf.Session(config=config)
with self._sess.as_default():
ckpt = saver_mod.get_checkpoint_state(self._model_dir)
saver = tf.train.Saver()
tf.logging.info("Start to restore the parameters from %s",
ckpt.model_checkpoint_path)
saver.restore(self._sess, ckpt.model_checkpoint_path)
tf.logging.info("Finish intialize environment")
def _init_env(self):
FLAGS.use_tpu = False
#tf.logging.set_verbosity(tf.logging.DEBUG)
tf.logging.info("Import usr dir from %s", self._usr_dir)
if self._usr_dir != None:
#usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
usr_dir.import_usr_dir(self._usr_dir)
tf.logging.info("Start to create hparams,for %s of %s", self._problem, self._hparams_set)
self._hparams = create_hparams()
self._hparams_decode = create_decode_hparams(extra_length=self._extra_length,
batch_size=self._batch_size,
beam_size=self._beam_size,
alpha=self._alpha,
return_beams=self._return_beams,
write_beam_scores=self._write_beam_scores,
force_decode_length=self._force_decode_length)
self.estimator = trainer_lib.create_estimator(
FLAGS.model,
self._hparams,
t2t_trainer.create_run_config(self._hparams),
decode_hparams=self._hparams_decode,
use_tpu=False)
tf.logging.info("Finish intialize environment")
####### problem type :输出分类 还是序列 还是语言模型
#self.problem_type = self._hparams.problem_hparams[0].target_modality[0] #class? symble
self.problem_type = self._hparams.problem_hparams.target_modality[0]
#self._whether_has_inputs = self._hparams.problem[0].has_inputs
self._whether_has_inputs = self._hparams.problem.has_inputs
self._beam_size=1 if self._customer_problem_type=='classification' else self._beam_size
### make input placeholder
#self._inputs_ph = tf.placeholder(dtype=tf.int32) # shape not specified,any shape
# x=tf.placeholder(dtype=tf.int32)
# x.set_shape([None, None]) # ? -> (?,?)
# x = tf.expand_dims(x, axis=[2])# -> (?,?,1)
# x = tf.to_int32(x)
#self._inputs_ph=x
#batch_inputs = tf.reshape(self._inputs_ph, [self._batch_size, -1, 1, 1])
#batch_inputs=x
# batch_inputs = tf.reshape(self._inputs_ph, [-1, -1, 1, 1])
batch_inputs,self._targets_ph,self.input_extra_length_ph=get_ph(x_dim_3=True)
#targets_ph = tf.placeholder(dtype=tf.int32)
#batch_targets = tf.reshape(targets_ph, [1, -1, 1, 1])
self._features = {"inputs": batch_inputs,
"problem_choice": 0, # We run on the first problem here.
"input_space_id": self._hparams.problem_hparams.input_space_id,
"target_space_id": self._hparams.problem_hparams.target_space_id}
### 加入 decode length 变长的
#self.input_extra_length_ph = tf.placeholder(dtype=tf.int32,shape=[])
self._features['decode_length'] = self.input_extra_length_ph # total_decode=input_len+extra_len| extra of chunkProblem =0
# real_decode_length=len(input)+extra_length
##
#self._features['decode_length_decide_end'] = True
#### 如果是relative 参数
if self._hparams_set=="transformer_relative":
del self._features['problem_choice']
del self._features['input_space_id']
del self._features['target_space_id']
if self._customer_problem_type=='languageModel_pp':
del self._features['problem_choice']
del self._features['input_space_id']
del self._features['target_space_id']
if self._model_name in ['slice_net','transformer_encoder']:
del self._features['problem_choice']
del self._features['input_space_id']
del self._features['target_space_id']
if self._model_name=='transformer' and self._customer_problem_type=='classification':
del self._features['problem_choice']
del self._features['input_space_id']
del self._features['target_space_id']
###### target if transformer_scorer
if self._customer_problem_type=='classification':
self._targets_ph = tf.placeholder(tf.int32, shape=(None, None, None, None), name='targets')
self._features['targets'] = self._targets_ph # batch targets
if self._customer_problem_type=='languageModel_pp':
self._targets_ph = tf.placeholder(tf.int32, shape=(None, None, None, None), name='targets')
self._features['targets']= self._targets_ph
#### mode
mode = tf.estimator.ModeKeys.PREDICT
if self._customer_problem_type == 'languageModel_pp':
mode = tf.estimator.ModeKeys.EVAL
elif self._customer_problem_type=='classification' and 'score' not in self._model_name:
mode = tf.estimator.ModeKeys.EVAL
# estimator_spec = model_builder.model_fn(self._model_name, features, mode, self._hparams,
# problem_names=[self._problem], decode_hparams=self._hparams_dc)
predictions_dict = self.estimator._call_model_fn(self._features,None,mode,t2t_trainer.create_run_config(self._hparams))
self._predictions_dict=predictions_dict.predictions
# score -> score_yr
if self._customer_problem_type=='classification' and 'score' in self._model_name:
self._score=predictions_dict.predictions.get('scores')
if self._score!=None: #[batch,beam] [batch,]
self._predictions_dict['scores_class']=tf.exp(common_layers.log_prob_from_logits(self._score))
elif self._customer_problem_type=='classification' and 'score' not in self._model_name:
self._score = predictions_dict.predictions.get('predictions')
if self._score!=None: #[batch,beam] [batch,]
self._predictions_dict['scores_class']=tf.exp(common_layers.log_prob_from_logits(self._score))
#self._predictions = self._predictions_dict["outputs"]
# self._scores=predictions_dict['scores'] not return when greedy search
tf.logging.info("Start to init tf session")
if self._isGpu:
print('Using GPU in Decoder')
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=self._fraction)
self._sess = tf.Session(
config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False, gpu_options=gpu_options))
else:
print('Using CPU in Decoder')
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0)
config = tf.ConfigProto(gpu_options=gpu_options)
config.allow_soft_placement = True
config.log_device_placement = False
self._sess = tf.Session(config=config)
with self._sess.as_default():
ckpt = saver_mod.get_checkpoint_state(self._model_dir)
saver = tf.train.Saver(allow_empty=True)
tf.logging.info("Start to restore the parameters from %s", ckpt.model_checkpoint_path)
saver.restore(self._sess, ckpt.model_checkpoint_path)
tf.logging.info("Finish intialize environment")
def main(argv):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if FLAGS.generate_data:
t2t_trainer.generate_data()
if argv:
t2t_trainer.set_hparams_from_args(argv[1:])
# hparams = t2t_trainer.create_hparams()
# hparams.add_hparam("data_dir", FLAGS.data_dir)
# trainer_lib.add_problem_hparams(hparams, FLAGS.problem)
hparams_path = os.path.join(FLAGS.output_dir, "hparams.json")
hparams = trainer_lib.create_hparams(
FLAGS.hparams_set, FLAGS.hparams, data_dir=FLAGS.data_dir,
problem_name=FLAGS.problem, hparams_path=hparams_path)
hparams.add_hparam("model_dir", FLAGS.output_dir)
config = t2t_trainer.create_run_config(hparams)
params = {"batch_size": hparams.batch_size}
# add "_rev" as a hack to avoid image standardization
problem = registry.problem(FLAGS.problem)
input_fn = problem.make_estimator_input_fn(tf.estimator.ModeKeys.EVAL,
hparams)
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False))
model_fn = t2t_model.T2TModel.make_estimator_model_fn(
FLAGS.model, hparams, use_tpu=False)
dataset = input_fn(params, config).repeat()
dataset_iteraor = dataset.make_one_shot_iterator()
features, labels = dataset_iteraor.get_next()
# tf.logging.info("### t2t_wei_feat_distrib.py features %s", features)
spec = model_fn(
features,
labels,
tf.estimator.ModeKeys.EVAL,
params=hparams,
config=config)
# get the summary model structure graph
summary_writer = tf.summary.FileWriter(FLAGS.log_dir, sess.graph)
# Restore weights
saver = tf.train.Saver()
checkpoint_path = os.path.expanduser(FLAGS.output_dir or
FLAGS.checkpoint_path)
tf.logging.info("### t2t_wei_feat_distrib.py checkpoint_path %s", checkpoint_path)
# saver.restore(sess, tf.train.latest_checkpoint(checkpoint_path))
# Load weights from checkpoint.
ckpts = tf.train.get_checkpoint_state(checkpoint_path)
ckpt = ckpts.model_checkpoint_path
saver.restore(sess, ckpt)
# saver.restore(sess, checkpoint_path+'/model.ckpt-1421000')
# initialize_from_ckpt(checkpoint_path)
# get parameter
pruning_params = create_pruning_params()
pruning_strategy = create_pruning_strategy(pruning_params.strategy)
# get evalutaion graph
if 'image' in FLAGS.problem:
acc, acc_update_op = get_eval_graph_image(spec, labels)
tf.summary.scalar('accuracy', acc)
# define evaluation function
def eval_model():
sess.run(tf.initialize_local_variables())
for _ in range(FLAGS.eval_steps):
acc = sess.run(acc_update_op)
return acc
elif 'translate' in FLAGS.problem:
bleu_op = get_eval_graph_trans(spec, labels)
# tf.summary.scalar('bleu', bleu_op)
# define evaluation function
def eval_model():
bleu_value = 0
# sess.run(tf.initialize_local_variables())
# sess.run()
# local_vars = tf.local_variables()
# tf.logging.info("###!!!!!!! t2t_wei_feat_distrib.py local_vars %s", local_vars)
# for _ in range(FLAGS.eval_steps):
for _ in range(FLAGS.eval_steps):
# outputs_tensor, labels_tensor, preds_tensor = sess.run([outputs, labels, preds])
bleu = sess.run(bleu_op)
# tf.logging.info("### t2t_wei_feat_distrib.py outputs_tensor %s", outputs_tensor[0].shape)
# tf.logging.info("### t2t_wei_feat_distrib.py labels_tensor %s", labels_tensor[0].shape)
# tf.logging.info("### t2t_wei_feat_distrib.py preds %s", preds_tensor[0].shape)
bleu_value += bleu
bleu_value /= FLAGS.eval_steps
return bleu_value
# get weight distribution graph
wei_feat_distrib.get_weight_distrib_graph(pruning_params)
# do accuracy sparsity tradeoff for model weights
wei_feat_distrib.wei_sparsity_acc_tradeoff(sess, eval_model, pruning_strategy, pruning_params, summary_writer)
# do accuracy sparsity tradeoff for model weights
# save the summary
summary_writer.close()
sess.run(tf.initialize_local_variables())
preds = spec.predictions["predictions"]
# features_shape=tf.shape(features)
pred_shape=tf.shape(preds)
labels_shape=tf.shape(labels)
#tf.app.run()
import os
# rootpath=os.environ['ROOT_PATH']
rootpath = '../'
FLAGS.data_dir = rootpath + '/data'
FLAGS.problem = 'reason_problem'
FLAGS.model = 'transformer_ae'
FLAGS.hparams_set = 'transformer_ae_small'
FLAGS.t2t_usr_dir = rootpath + '/src'
FLAGS.output_dir = rootpath + '/model'
FLAGS.decode_from_file = './input_string.txt'
FLAGS.decode_to_file = './output_string.txt'
tf.logging.set_verbosity(tf.logging.INFO)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
FLAGS.use_tpu = False # decoding not supported on TPU
hp = create_hparams()
decode_hp = create_decode_hparams()
estimator = trainer_lib.create_estimator(FLAGS.model,
hp,
t2t_trainer.create_run_config(hp),
decode_hparams=decode_hp,
use_tpu=False)
decode(estimator, hp, decode_hp)
def _init_env(self):
FLAGS.use_tpu = False
tf.logging.set_verbosity(tf.logging.DEBUG)
tf.logging.info("Import usr dir from %s", self._usr_dir)
if self._usr_dir != None:
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
tf.logging.info("Start to create hparams,for %s of %s", self._problem,
self._hparams_set)
self._hparams = create_hparams()
self._hparams_decode = create_decode_hparams(
extra_length=self._extra_length,
batch_size=self._batch_size,
beam_size=self._beam_size,
alpha=self._alpha,
return_beams=self._return_beams,
write_beam_scores=self._write_beam_scores)
# self.estimator_spec = t2t_model.T2TModel.make_estimator_model_fn(
# self._model_name, self._hparams, decode_hparams=self._hparams_decode, use_tpu=False)
self.estimator = trainer_lib.create_estimator(
FLAGS.model,
self._hparams,
t2t_trainer.create_run_config(self._hparams),
decode_hparams=self._hparams_decode,
use_tpu=False)
tf.logging.info("Finish intialize environment")
#######
### make input placeholder
self._inputs_ph = tf.placeholder(
dtype=tf.int32) # shape not specified,any shape
x = tf.placeholder(dtype=tf.int32)
x.set_shape([None, None]) # ? -> (?,?)
x = tf.expand_dims(x, axis=[2]) # -> (?,?,1)
x = tf.to_int32(x)
self._inputs_ph = x
#batch_inputs = tf.reshape(self._inputs_ph, [self._batch_size, -1, 1, 1])
batch_inputs = x
# batch_inputs = tf.reshape(self._inputs_ph, [-1, -1, 1, 1])
#targets_ph = tf.placeholder(dtype=tf.int32)
#batch_targets = tf.reshape(targets_ph, [1, -1, 1, 1])
self._features = {
"inputs": batch_inputs,
"problem_choice": 0, # We run on the first problem here.
"input_space_id": self._hparams.problem_hparams.input_space_id,
"target_space_id": self._hparams.problem_hparams.target_space_id
}
### 加入 decode length 变长的
self.input_extra_length_ph = tf.placeholder(dtype=tf.int32)
#self._features['decode_length'] = [self.input_extra_length_ph]
#### 采样 c(s)
###
self.cache_ph = tf.placeholder(dtype=tf.int32)
#self._features['cache_raw']=tf.reshape(self.cache_ph,[1,2,1])
## 去掉 整数的
del self._features["problem_choice"]
del self._features["input_space_id"]
del self._features["target_space_id"]
####
mode = tf.estimator.ModeKeys.PREDICT
# estimator_spec = model_builder.model_fn(self._model_name, features, mode, self._hparams,
# problem_names=[self._problem], decode_hparams=self._hparams_dc)
######
from tensor2tensor.models import transformer_vae
model_i = transformer_vae.TransformerAE(
hparams=self._hparams,
mode=mode,
decode_hparams=self._hparams_decode)
# Transformer_(hparams=self._hparams,
# mode=mode, decode_hparams=self._hparams_decode)
# #problem_hparams=p_hparams,
# self._beam_result = model_i._fast_decode(self._features, decode_length=5, beam_size=10, top_beams=10,
# alpha=0.6) #fail
# self._beam_result = model_i._beam_decode(self._features,
# decode_length=5,
# beam_size=self._beam_size,
# top_beams=self._beam_size,
# alpha=0.6)
self.result_dict = model_i.infer(self._features)
print ''
#### add target,丢了一些KEY 不能单独拿出来MODEL_FN
# from tensor2tensor.layers import common_layers
# features=self._features
# batch_size = common_layers.shape_list(features["inputs"])[0]
# length = common_layers.shape_list(features["inputs"])[1]
# target_length = tf.to_int32(2.0 * tf.to_float(length))
# initial_output = tf.zeros((batch_size, target_length, 1, 1),
# dtype=tf.int64)
# features["targets"] = initial_output
# ### input
# if "inputs" in features and len(features["inputs"].shape) < 4:
# inputs_old = features["inputs"]
# features["inputs"] = tf.expand_dims(features["inputs"], 2)
# #### model_fn
# self.result_dict=model_i.model_fn(features)
print ''
"""
######
predictions_dict = self.estimator._call_model_fn(self._features,None,mode,t2t_trainer.create_run_config(self._hparams))
self._predictions_dict=predictions_dict.predictions
"""
#self._predictions = self._predictions_dict["outputs"]
# self._scores=predictions_dict['scores'] not return when greedy search
tf.logging.info("Start to init tf session")
if self._isGpu:
print('Using GPU in Decoder')
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=self._fraction)
self._sess = tf.Session(
config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False,
gpu_options=gpu_options))
else:
print('Using CPU in Decoder')
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0)
config = tf.ConfigProto(gpu_options=gpu_options)
config.allow_soft_placement = True
config.log_device_placement = False
self._sess = tf.Session(config=config)
with self._sess.as_default():
ckpt = saver_mod.get_checkpoint_state(self._model_dir)
saver = tf.train.Saver()
tf.logging.info("Start to restore the parameters from %s",
ckpt.model_checkpoint_path)
saver.restore(self._sess, ckpt.model_checkpoint_path)
tf.logging.info("Finish intialize environment")
def _init_env(self):
FLAGS.use_tpu = False
tf.logging.set_verbosity(tf.logging.DEBUG)
tf.logging.info("Import usr dir from %s", self._usr_dir)
if self._usr_dir != None:
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
tf.logging.info("Start to create hparams,for %s of %s", self._problem,
self._hparams_set)
self._hparams = create_hparams()
self._hparams_decode = create_decode_hparams(
extra_length=self._extra_length,
batch_size=self._batch_size,
beam_size=self._beam_size,
alpha=self._alpha,
return_beams=self._return_beams,
write_beam_scores=self._write_beam_scores,
force_decode_length=self._force_decode_length)
# self.estimator_spec = t2t_model.T2TModel.make_estimator_model_fn(
# self._model_name, self._hparams, decode_hparams=self._hparams_decode, use_tpu=False)
self.estimator = trainer_lib.create_estimator(
FLAGS.model,
self._hparams,
t2t_trainer.create_run_config(self._hparams),
decode_hparams=self._hparams_decode,
use_tpu=False)
tf.logging.info("Finish intialize environment")
#######
### make input placeholder
self._inputs_ph = tf.placeholder(
dtype=tf.int32) # shape not specified,any shape
x = tf.placeholder(dtype=tf.int32)
x.set_shape([None, None]) # ? -> (?,?)
x = tf.expand_dims(x, axis=[2]) # -> (?,?,1)
x = tf.to_int32(x)
self._inputs_ph = x
#batch_inputs = tf.reshape(self._inputs_ph, [self._batch_size, -1, 1, 1])
batch_inputs = x
###
# batch_inputs = tf.reshape(self._inputs_ph, [-1, -1, 1, 1])
#targets_ph = tf.placeholder(dtype=tf.int32)
#batch_targets = tf.reshape(targets_ph, [1, -1, 1, 1])
#self.inputs_ph = tf.placeholder(tf.int32, shape=(None, None, 1, 1), name='inputs')
#self.targets_ph = tf.placeholder(tf.int32, shape=(None, None, None, None), name='targets')
self.inputs_ph = tf.placeholder(tf.int32,
shape=(None, None, 1, 1),
name='inputs')
self.targets_ph = tf.placeholder(tf.int32,
shape=(None, None, 1, 1),
name='targets')
self._features = {
"inputs": self.inputs_ph,
"problem_choice": 0, # We run on the first problem here.
"input_space_id": self._hparams.problem_hparams.input_space_id,
"target_space_id": self._hparams.problem_hparams.target_space_id
}
### 加入 decode length 变长的
self.input_extra_length_ph = tf.placeholder(dtype=tf.int32)
self._features['decode_length'] = self.input_extra_length_ph
## target
#self._targets_ph= tf.placeholder(tf.int32, shape=(None, None, None, None), name='targets')
self._features['targets'] = self.targets_ph
target_pretend = np.zeros((1, 1, 1, 1))
## 去掉 整数的
del self._features["problem_choice"]
del self._features["input_space_id"]
del self._features["target_space_id"]
del self._features['decode_length']
####
#mode = tf.estimator.ModeKeys.PREDICT # affect last_only t2t_model._top_single ,[1,?,1,512]->[1,1,1,1,64]
# if self.predict_or_eval=='EVAL':
# mode = tf.estimator.ModeKeys.EVAL # affect last_only t2t_model._top_single ,[1,?,1,512]->[1,?,1,1,64]
# # estimator_spec = model_builder.model_fn(self._model_name, features, mode, self._hparams,
# # problem_names=[self._problem], decode_hparams=self._hparams_dc)
# if self.predict_or_eval=='PREDICT':
# mode = tf.estimator.ModeKeys.PREDICT
if self.predict_or_eval == 'and':
mode = tf.estimator.ModeKeys.TRAIN
###########
# registry.model
############
translate_model = registry.model(self._model_name)(
hparams=self._hparams,
decode_hparams=self._hparams_decode,
mode=mode)
self.predict_dict = {}
# if self.predict_or_eval == 'EVAL':
# self.logits,_=translate_model(self._features)
# self.predict_dict['scores']=self.logits
#
# if self.predict_or_eval == 'PREDICT':
#
# self.predict_dict=translate_model.infer(features=self._features,
# decode_length=50,
# beam_size=1,
# top_beams=1)
# print ''
if self.predict_or_eval == 'and':
### get logit EVAL mode
#self._features['targets'] = [[self._targets_ph]] # function body()
self.logits, self.ret2 = translate_model(self._features)
##################
## model_fn fetch logits FAIL : key not found
#############
# logits,_=translate_model.model_fn(self._features)
# self._beam_result = model_i._fast_decode(self._features, decode_length=5, beam_size=10, top_beams=10,
# alpha=0.6) #fail
# self._beam_result = model_i._beam_decode(self._features,
# decode_length=5,
# beam_size=self._beam_size,
# top_beams=self._beam_size,
# alpha=0.6)
##########
# logits,_=model_i.model_fn(self._features)
# assert len(logits.shape) == 5
# logits = tf.squeeze(logits, [2, 3])
# # Compute the log probabilities
# from tensor2tensor.layers import common_layers
# self.log_probs = common_layers.log_prob_from_logits(logits)
######
#self._predictions = self._predictions_dict["outputs"]
# self._scores=predictions_dict['scores'] not return when greedy search
tf.logging.info("Start to init tf session")
if self._isGpu:
print('Using GPU in Decoder')
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=self._fraction)
self._sess = tf.Session(
config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False,
gpu_options=gpu_options))
else:
print('Using CPU in Decoder')
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0)
config = tf.ConfigProto(gpu_options=gpu_options)
config.allow_soft_placement = True
config.log_device_placement = False
self._sess = tf.Session(config=config)
with self._sess.as_default():
ckpt = saver_mod.get_checkpoint_state(self._model_dir)
saver = tf.train.Saver()
#tf.logging.info("Start to restore the parameters from %s", ckpt.model_checkpoint_path)
#saver.restore(self._sess, ckpt.model_checkpoint_path)
########## 重新初始化参数
self._sess.run(tf.global_variables_initializer())
tf.logging.info("Finish intialize environment")
def _init_env(self):
FLAGS.use_tpu = False
tf.logging.set_verbosity(tf.logging.DEBUG)
tf.logging.info("Import usr dir from %s", self._usr_dir)
if self._usr_dir != None:
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
tf.logging.info("Start to create hparams,for %s of %s", self._problem,
self._hparams_set)
self._hparams = create_hparams()
self._hparams_decode = create_decode_hparams(
extra_length=self._extra_length,
batch_size=self._batch_size,
beam_size=self._beam_size,
alpha=self._alpha,
return_beams=self._return_beams,
write_beam_scores=self._write_beam_scores,
force_decode_length=self._force_decode_length)
# self.estimator_spec = t2t_model.T2TModel.make_estimator_model_fn(
# self._model_name, self._hparams, decode_hparams=self._hparams_decode, use_tpu=False)
self.estimator = trainer_lib.create_estimator(
FLAGS.model,
self._hparams,
t2t_trainer.create_run_config(self._hparams),
decode_hparams=self._hparams_decode,
use_tpu=False)
tf.logging.info("Finish intialize environment")
#######
### make input placeholder
self._inputs_ph = tf.placeholder(
dtype=tf.int32) # shape not specified,any shape
x = tf.placeholder(dtype=tf.int32)
x.set_shape([None, None]) # ? -> (?,?)
x = tf.expand_dims(x, axis=[2]) # -> (?,?,1)
x = tf.to_int32(x)
self._inputs_ph = x
#batch_inputs = tf.reshape(self._inputs_ph, [self._batch_size, -1, 1, 1])
batch_inputs = x
###
# batch_inputs = tf.reshape(self._inputs_ph, [-1, -1, 1, 1])
#targets_ph = tf.placeholder(dtype=tf.int32)
#batch_targets = tf.reshape(targets_ph, [1, -1, 1, 1])
#self.inputs_ph = tf.placeholder(tf.int32, shape=(None, None, 1, 1), name='inputs')
#self.targets_ph = tf.placeholder(tf.int32, shape=(None, None, None, None), name='targets')
self.inputs_ph = tf.placeholder(tf.int32,
shape=(None, None, 1, 1),
name='inputs')
self.targets_ph = tf.placeholder(tf.int32,
shape=(None, None, 1, 1),
name='targets')
self.targets_ph_2 = tf.placeholder(tf.int32,
shape=(None, None, 1, 1),
name='targets')
self._features = {
"inputs": self.inputs_ph,
"problem_choice": 0, # We run on the first problem here.
"input_space_id": self._hparams.problem_hparams.input_space_id,
"target_space_id": self._hparams.problem_hparams.target_space_id
}
### 加入 decode length 变长的
self.input_extra_length_ph = tf.placeholder(dtype=tf.int32)
self._features['decode_length'] = self.input_extra_length_ph
## target
#self._targets_ph= tf.placeholder(tf.int32, shape=(None, None, None, None), name='targets')
self._features['targets'] = self.targets_ph
self._features['targets2'] = self.targets_ph_2
target_pretend = np.zeros((1, 1, 1, 1))
## 去掉 整数的
del self._features["problem_choice"]
del self._features["input_space_id"]
del self._features["target_space_id"]
del self._features['decode_length']
####
#mode = tf.estimator.ModeKeys.PREDICT # affect last_only t2t_model._top_single ,[1,?,1,512]->[1,1,1,1,64]
# if self.predict_or_eval=='EVAL':
# mode = tf.estimator.ModeKeys.EVAL # affect last_only t2t_model._top_single ,[1,?,1,512]->[1,?,1,1,64]
# # estimator_spec = model_builder.model_fn(self._model_name, features, mode, self._hparams,
# # problem_names=[self._problem], decode_hparams=self._hparams_dc)
# if self.predict_or_eval=='PREDICT':
# mode = tf.estimator.ModeKeys.PREDICT
if self.predict_or_eval == 'and':
mode = tf.estimator.ModeKeys.TRAIN
###########
# registry.model
############
translate_model = registry.model(self._model_name)(
hparams=self._hparams,
decode_hparams=self._hparams_decode,
mode=mode)
self.predict_dict = {}
# if self.predict_or_eval == 'EVAL':
# self.logits,_=translate_model(self._features)
# self.predict_dict['scores']=self.logits
#
# if self.predict_or_eval == 'PREDICT':
#
# self.predict_dict=translate_model.infer(features=self._features,
# decode_length=50,
# beam_size=1,
# top_beams=1)
# print ''
if self.predict_or_eval == 'and':
### get logit EVAL mode
#self._features['targets'] = [[self._targets_ph]] # function body()
self.logits, self.ret2 = translate_model(self._features)
##################
## model_fn fetch logits FAIL : key not found
#############
# logits,_=translate_model.model_fn(self._features)
# self._beam_result = model_i._fast_decode(self._features, decode_length=5, beam_size=10, top_beams=10,
# alpha=0.6) #fail
# self._beam_result = model_i._beam_decode(self._features,
# decode_length=5,
# beam_size=self._beam_size,
# top_beams=self._beam_size,
# alpha=0.6)
##########
# logits,_=model_i.model_fn(self._features)
# assert len(logits.shape) == 5
# logits = tf.squeeze(logits, [2, 3])
# # Compute the log probabilities
# from tensor2tensor.layers import common_layers
# self.log_probs = common_layers.log_prob_from_logits(logits)
######
#self._predictions = self._predictions_dict["outputs"]
# self._scores=predictions_dict['scores'] not return when greedy search
tf.logging.info("Start to init tf session")
if self._isGpu:
print('Using GPU in Decoder')
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=self._fraction)
self._sess = tf.Session(
config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False,
gpu_options=gpu_options))
else:
print('Using CPU in Decoder')
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0)
config = tf.ConfigProto(gpu_options=gpu_options)
config.allow_soft_placement = True
config.log_device_placement = False
self._sess = tf.Session(config=config)
with self._sess.as_default():
#ckpt = saver_mod.get_checkpoint_state(self._model_dir)
self._sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
#train_handle = sess.run(train_iterator.string_handle())
#dev_handle = sess.run(dev_iterator.string_handle())
if os.path.exists(os.path.join(self._model_dir, "checkpoint")):
saver.restore(self._sess,
tf.train.latest_checkpoint(self._model_dir))
global_step = max(sess.run(translate_model.global_step), 1)
for _ in tqdm(range(global_step, 1000 + 1)):
global_step = sess.run(translate_model.global_step) + 1
loss, train_op = sess.run([self.ret2, model.train_op],
feed_dict={
handle: train_handle,
model.dropout: config.dropout
})
if global_step % config.period == 0:
loss_sum = tf.Summary(value=[
tf.Summary.Value(tag="model/loss", simple_value=loss),
])
writer.add_summary(loss_sum, global_step)
if global_step % config.checkpoint == 0:
_, summ = evaluate_batch(model, config.val_num_batches,
train_eval_file, sess, "train",
handle, train_handle)
for s in summ:
writer.add_summary(s, global_step)
metrics, summ = evaluate_batch(
model, dev_total // config.batch_size + 1,
dev_eval_file, sess, "dev", handle, dev_handle)
dev_f1 = metrics["f1"]
dev_em = metrics["exact_match"]
if dev_f1 < best_f1 and dev_em < best_em:
patience += 1
if patience > config.early_stop:
break
else:
patience = 0
best_em = max(best_em, dev_em)
best_f1 = max(best_f1, dev_f1)
for s in summ:
writer.add_summary(s, global_step)
writer.flush()
filename = os.path.join(
config.save_dir, "model_{}.ckpt".format(global_step))
saver.save(sess, filename)
def main(argv):
tf.logging.set_verbosity(tf.logging.INFO)
trainer_lib.set_random_seed(FLAGS.random_seed)
usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)
t2t_trainer.maybe_log_registry_and_exit()
if FLAGS.cloud_mlengine:
cloud_mlengine.launch()
return
if FLAGS.generate_data:
t2t_trainer.generate_data()
if cloud_mlengine.job_dir():
FLAGS.output_dir = cloud_mlengine.job_dir()
if argv:
t2t_trainer.set_hparams_from_args(argv[1:])
if FLAGS.surrogate_attack:
tf.logging.warn("Performing surrogate model attack.")
sur_hparams = create_surrogate_hparams()
trainer_lib.add_problem_hparams(sur_hparams, FLAGS.problem)
hparams = t2t_trainer.create_hparams()
trainer_lib.add_problem_hparams(hparams, FLAGS.problem)
attack_params = create_attack_params()
attack_params.add_hparam(attack_params.epsilon_name, 0.0)
if FLAGS.surrogate_attack:
sur_config = create_surrogate_run_config(sur_hparams)
config = t2t_trainer.create_run_config(hparams)
params = {
"batch_size": hparams.batch_size,
"use_tpu": FLAGS.use_tpu,
}
# add "_rev" as a hack to avoid image standardization
problem = registry.problem(FLAGS.problem + "_rev")
inputs, labels, features = prepare_data(problem, hparams, params, config)
sess = tf.Session()
if FLAGS.surrogate_attack:
sur_model_fn = t2t_model.T2TModel.make_estimator_model_fn(
FLAGS.surrogate_model, sur_hparams, use_tpu=FLAGS.use_tpu)
sur_ch_model = adv_attack_utils.T2TAttackModel(
sur_model_fn, features, params, sur_config, scope="surrogate")
# Dummy call to construct graph
sur_ch_model.get_probs(inputs)
checkpoint_path = os.path.expanduser(FLAGS.surrogate_output_dir)
tf.train.init_from_checkpoint(
tf.train.latest_checkpoint(checkpoint_path), {"/": "surrogate/"})
sess.run(tf.global_variables_initializer())
other_vars = set(tf.global_variables())
model_fn = t2t_model.T2TModel.make_estimator_model_fn(
FLAGS.model, hparams)
ch_model = adv_attack_utils.T2TAttackModel(model_fn, features, params, config)
acc_mask = None
probs = ch_model.get_probs(inputs)
if FLAGS.ignore_incorrect:
preds = tf.argmax(probs, -1, output_type=labels.dtype)
preds = tf.reshape(preds, labels.shape)
acc_mask = tf.to_float(tf.equal(labels, preds))
one_hot_labels = tf.one_hot(labels, probs.shape[-1])
if FLAGS.surrogate_attack:
attack = create_attack(attack_params.attack)(sur_ch_model, sess=sess)
else:
attack = create_attack(attack_params.attack)(ch_model, sess=sess)
new_vars = set(tf.global_variables()) - other_vars
# Restore weights
saver = tf.train.Saver(new_vars)
checkpoint_path = os.path.expanduser(FLAGS.output_dir)
saver.restore(sess, tf.train.latest_checkpoint(checkpoint_path))
# reuse variables
tf.get_variable_scope().reuse_variables()
def compute_accuracy(x, l, mask):
"""Compute model accuracy."""
preds = ch_model.get_probs(x)
preds = tf.squeeze(preds)
preds = tf.argmax(preds, -1, output_type=l.dtype)
_, acc_update_op = tf.metrics.accuracy(l, preds, weights=mask)
if FLAGS.surrogate_attack:
preds = sur_ch_model.get_probs(x)
preds = tf.squeeze(preds)
preds = tf.argmax(preds, -1, output_type=l.dtype)
acc_update_op = tf.tuple((acc_update_op,
tf.metrics.accuracy(l, preds, weights=mask)[1]))
sess.run(tf.initialize_local_variables())
for i in range(FLAGS.eval_steps):
tf.logging.info(
"\tEvaluating batch [%d / %d]" % (i + 1, FLAGS.eval_steps))
acc = sess.run(acc_update_op)
if FLAGS.surrogate_attack:
tf.logging.info("\tFinal acc: (%.4f, %.4f)" % (acc[0], acc[1]))
else:
tf.logging.info("\tFinal acc: %.4f" % acc)
return acc
epsilon_acc_pairs = []
for epsilon in attack_params.attack_epsilons:
tf.logging.info("Attacking @ eps=%.4f" % epsilon)
attack_params.set_hparam(attack_params.epsilon_name, epsilon)
adv_x = attack.generate(inputs, y=one_hot_labels, **attack_params.values())
acc = compute_accuracy(adv_x, labels, acc_mask)
epsilon_acc_pairs.append((epsilon, acc))
for epsilon, acc in epsilon_acc_pairs:
if FLAGS.surrogate_attack:
tf.logging.info(
"Accuracy @ eps=%.4f: (%.4f, %.4f)" % (epsilon, acc[0], acc[1]))
else:
tf.logging.info("Accuracy @ eps=%.4f: %.4f" % (epsilon, acc))