def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
processors = {
"cola": classifier_utils.ColaProcessor,
"mnli": classifier_utils.MnliProcessor,
"mismnli": classifier_utils.MisMnliProcessor,
"mrpc": classifier_utils.MrpcProcessor,
"rte": classifier_utils.RteProcessor,
"sst-2": classifier_utils.Sst2Processor,
"sts-b": classifier_utils.StsbProcessor,
"qqp": classifier_utils.QqpProcessor,
"qnli": classifier_utils.QnliProcessor,
"wnli": classifier_utils.WnliProcessor,
}
if not (FLAGS.do_train or FLAGS.do_eval or FLAGS.do_predict or
FLAGS.export_dir):
raise ValueError(
"At least one of `do_train`, `do_eval`, `do_predict' or `export_dir` "
"must be True.")
if not FLAGS.albert_config_file and not FLAGS.albert_hub_module_handle:
raise ValueError("At least one of `--albert_config_file` and "
"`--albert_hub_module_handle` must be set")
if FLAGS.albert_config_file:
albert_config = modeling.AlbertConfig.from_json_file(
FLAGS.albert_config_file)
if FLAGS.max_seq_length > albert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the ALBERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, albert_config.max_position_embeddings))
else:
albert_config = None # Get the config from TF-Hub.
tf.gfile.MakeDirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name](
use_spm=True if FLAGS.spm_model_file else False,
do_lower_case=FLAGS.do_lower_case)
label_list = processor.get_labels()
tokenizer = fine_tuning_utils.create_vocab(
vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case,
spm_model_file=FLAGS.spm_model_file,
hub_module=FLAGS.albert_hub_module_handle)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
if FLAGS.do_train:
iterations_per_loop = int(min(FLAGS.iterations_per_loop,
FLAGS.save_checkpoints_steps))
else:
iterations_per_loop = FLAGS.iterations_per_loop
run_config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=int(FLAGS.save_checkpoints_steps),
keep_checkpoint_max=0,
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
train_examples = None
if FLAGS.do_train:
train_examples = processor.get_train_examples(FLAGS.data_dir)
model_fn = classifier_utils.model_fn_builder(
albert_config=albert_config,
num_labels=len(label_list),
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=FLAGS.train_step,
num_warmup_steps=FLAGS.warmup_step,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu,
task_name=task_name,
hub_module=FLAGS.albert_hub_module_handle,
optimizer=FLAGS.optimizer)
if not math.isnan(FLAGS.threshold_to_export):
model_fn = _add_threshold_to_model_fn(model_fn, FLAGS.threshold_to_export)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = contrib_tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
predict_batch_size=FLAGS.predict_batch_size,
export_to_tpu=False) # http://yaqs/4707241341091840
if FLAGS.do_train:
cached_dir = FLAGS.cached_dir
if not cached_dir:
cached_dir = FLAGS.output_dir
train_file = os.path.join(cached_dir, task_name + "_train.tf_record")
if not tf.gfile.Exists(train_file):
classifier_utils.file_based_convert_examples_to_features(
train_examples, label_list, FLAGS.max_seq_length, tokenizer,
train_file, task_name)
tf.logging.info("***** Running training *****")
tf.logging.info(" Num examples = %d", len(train_examples))
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", FLAGS.train_step)
train_input_fn = classifier_utils.file_based_input_fn_builder(
input_file=train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True,
task_name=task_name,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.train_batch_size)
estimator.train(input_fn=train_input_fn, max_steps=FLAGS.train_step)
if FLAGS.do_eval:
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
num_actual_eval_examples = len(eval_examples)
if FLAGS.use_tpu:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on. These do NOT count towards the metric (all tf.metrics
# support a per-instance weight, and these get a weight of 0.0).
while len(eval_examples) % FLAGS.eval_batch_size != 0:
eval_examples.append(classifier_utils.PaddingInputExample())
cached_dir = FLAGS.cached_dir
if not cached_dir:
cached_dir = FLAGS.output_dir
eval_file = os.path.join(cached_dir, task_name + "_eval.tf_record")
if not tf.gfile.Exists(eval_file):
classifier_utils.file_based_convert_examples_to_features(
eval_examples, label_list, FLAGS.max_seq_length, tokenizer,
eval_file, task_name)
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(eval_examples), num_actual_eval_examples,
len(eval_examples) - num_actual_eval_examples)
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
# This tells the estimator to run through the entire set.
eval_steps = None
# However, if running eval on the TPU, you will need to specify the
# number of steps.
if FLAGS.use_tpu:
assert len(eval_examples) % FLAGS.eval_batch_size == 0
eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)
eval_drop_remainder = True if FLAGS.use_tpu else False
eval_input_fn = classifier_utils.file_based_input_fn_builder(
input_file=eval_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=eval_drop_remainder,
task_name=task_name,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.eval_batch_size)
best_trial_info_file = os.path.join(FLAGS.output_dir, "best_trial.txt")
def _best_trial_info():
"""Returns information about which checkpoints have been evaled so far."""
if tf.gfile.Exists(best_trial_info_file):
with tf.gfile.GFile(best_trial_info_file, "r") as best_info:
global_step, best_metric_global_step, metric_value = (
best_info.read().split(":"))
global_step = int(global_step)
best_metric_global_step = int(best_metric_global_step)
metric_value = float(metric_value)
else:
metric_value = -1
best_metric_global_step = -1
global_step = -1
tf.logging.info(
"Best trial info: Step: %s, Best Value Step: %s, "
"Best Value: %s", global_step, best_metric_global_step, metric_value)
return global_step, best_metric_global_step, metric_value
def _remove_checkpoint(checkpoint_path):
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = checkpoint_path + ".{}".format(ext)
tf.logging.info("removing {}".format(src_ckpt))
tf.gfile.Remove(src_ckpt)
def _find_valid_cands(curr_step):
filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
candidates = []
for filename in filenames:
if filename.endswith(".index"):
ckpt_name = filename[:-6]
idx = ckpt_name.split("-")[-1]
if int(idx) > curr_step:
candidates.append(filename)
return candidates
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
if task_name == "sts-b":
key_name = "pearson"
elif task_name == "cola":
key_name = "matthew_corr"
else:
key_name = "eval_accuracy"
global_step, best_perf_global_step, best_perf = _best_trial_info()
writer = tf.gfile.GFile(output_eval_file, "w")
while global_step < FLAGS.train_step:
steps_and_files = {}
filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
for filename in filenames:
if filename.endswith(".index"):
ckpt_name = filename[:-6]
cur_filename = os.path.join(FLAGS.output_dir, ckpt_name)
if cur_filename.split("-")[-1] == "best":
continue
gstep = int(cur_filename.split("-")[-1])
if gstep not in steps_and_files:
tf.logging.info("Add {} to eval list.".format(cur_filename))
steps_and_files[gstep] = cur_filename
tf.logging.info("found {} files.".format(len(steps_and_files)))
if not steps_and_files:
tf.logging.info("found 0 file, global step: {}. Sleeping."
.format(global_step))
time.sleep(60)
else:
for checkpoint in sorted(steps_and_files.items()):
step, checkpoint_path = checkpoint
if global_step >= step:
if (best_perf_global_step != step and
len(_find_valid_cands(step)) > 1):
_remove_checkpoint(checkpoint_path)
continue
result = estimator.evaluate(
input_fn=eval_input_fn,
steps=eval_steps,
checkpoint_path=checkpoint_path)
global_step = result["global_step"]
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
writer.write("best = {}\n".format(best_perf))
if result[key_name] > best_perf:
best_perf = result[key_name]
best_perf_global_step = global_step
elif len(_find_valid_cands(global_step)) > 1:
_remove_checkpoint(checkpoint_path)
writer.write("=" * 50 + "\n")
writer.flush()
with tf.gfile.GFile(best_trial_info_file, "w") as best_info:
best_info.write("{}:{}:{}".format(
global_step, best_perf_global_step, best_perf))
writer.close()
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = "model.ckpt-{}.{}".format(best_perf_global_step, ext)
tgt_ckpt = "model.ckpt-best.{}".format(ext)
tf.logging.info("saving {} to {}".format(src_ckpt, tgt_ckpt))
tf.io.gfile.rename(
os.path.join(FLAGS.output_dir, src_ckpt),
os.path.join(FLAGS.output_dir, tgt_ckpt),
overwrite=True)
if FLAGS.do_predict:
predict_examples = processor.get_test_examples(FLAGS.data_dir)
num_actual_predict_examples = len(predict_examples)
if FLAGS.use_tpu:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on.
while len(predict_examples) % FLAGS.predict_batch_size != 0:
predict_examples.append(classifier_utils.PaddingInputExample())
predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
classifier_utils.file_based_convert_examples_to_features(
predict_examples, label_list,
FLAGS.max_seq_length, tokenizer,
predict_file, task_name)
tf.logging.info("***** Running prediction*****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(predict_examples), num_actual_predict_examples,
len(predict_examples) - num_actual_predict_examples)
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
predict_drop_remainder = True if FLAGS.use_tpu else False
predict_input_fn = classifier_utils.file_based_input_fn_builder(
input_file=predict_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=predict_drop_remainder,
task_name=task_name,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.predict_batch_size)
checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best")
result = estimator.predict(
input_fn=predict_input_fn,
checkpoint_path=checkpoint_path)
output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv")
output_submit_file = os.path.join(FLAGS.output_dir, "submit_results.tsv")
with tf.gfile.GFile(output_predict_file, "w") as pred_writer,\
tf.gfile.GFile(output_submit_file, "w") as sub_writer:
sub_writer.write("index" + "\t" + "prediction\n")
num_written_lines = 0
tf.logging.info("***** Predict results *****")
for (i, (example, prediction)) in\
enumerate(zip(predict_examples, result)):
probabilities = prediction["probabilities"]
if i >= num_actual_predict_examples:
break
output_line = "\t".join(
str(class_probability)
for class_probability in probabilities) + "\n"
pred_writer.write(output_line)
if task_name != "sts-b":
actual_label = label_list[int(prediction["predictions"])]
else:
actual_label = str(prediction["predictions"])
sub_writer.write(example.guid + "\t" + actual_label + "\n")
num_written_lines += 1
assert num_written_lines == num_actual_predict_examples
if FLAGS.export_dir:
tf.gfile.MakeDirs(FLAGS.export_dir)
checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best")
tf.logging.info("Starting to export model.")
subfolder = estimator.export_saved_model(
export_dir_base=FLAGS.export_dir,
serving_input_receiver_fn=_serving_input_receiver_fn,
checkpoint_path=checkpoint_path)
tf.logging.info("Model exported to %s.", subfolder)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
processors = {"race": race_utils.RaceProcessor}
if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
raise ValueError(
"At least one of `do_train`, `do_eval` or `do_predict' must be True."
)
albert_config = modeling.AlbertConfig.from_json_file(
FLAGS.albert_config_file)
if FLAGS.max_seq_length > albert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the ALBERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, albert_config.max_position_embeddings))
tf.gfile.MakeDirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name](
use_spm=True if FLAGS.spm_model_file else False,
do_lower_case=FLAGS.do_lower_case,
high_only=FLAGS.high_only,
middle_only=FLAGS.middle_only)
label_list = processor.get_labels()
tokenizer = fine_tuning_utils.create_vocab(
vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case,
spm_model_file=FLAGS.spm_model_file,
hub_module=FLAGS.albert_hub_module_handle)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
if FLAGS.do_train:
iterations_per_loop = int(
min(FLAGS.iterations_per_loop, FLAGS.save_checkpoints_steps))
else:
iterations_per_loop = FLAGS.iterations_per_loop
run_config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=int(FLAGS.save_checkpoints_steps),
keep_checkpoint_max=0,
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
train_examples = None
if FLAGS.do_train:
train_examples = processor.get_train_examples(FLAGS.data_dir)
model_fn = race_utils.model_fn_builder(
albert_config=albert_config,
num_labels=len(label_list),
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=FLAGS.train_step,
num_warmup_steps=FLAGS.warmup_step,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu,
max_seq_length=FLAGS.max_seq_length,
dropout_prob=FLAGS.dropout_prob,
hub_module=FLAGS.albert_hub_module_handle)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = contrib_tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
if FLAGS.do_train:
if not tf.gfile.Exists(FLAGS.train_file):
race_utils.file_based_convert_examples_to_features(
train_examples, label_list, FLAGS.max_seq_length, tokenizer,
FLAGS.train_file, FLAGS.max_qa_length)
tf.logging.info("***** Running training *****")
tf.logging.info(" Num examples = %d", len(train_examples))
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", FLAGS.train_step)
train_input_fn = classifier_utils.file_based_input_fn_builder(
input_file=FLAGS.train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True,
task_name=task_name,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.train_batch_size,
multiple=len(label_list))
estimator.train(input_fn=train_input_fn, max_steps=FLAGS.train_step)
if FLAGS.do_eval:
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
num_actual_eval_examples = len(eval_examples)
if FLAGS.use_tpu:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on. These do NOT count towards the metric (all tf.metrics
# support a per-instance weight, and these get a weight of 0.0).
while len(eval_examples) % FLAGS.eval_batch_size != 0:
eval_examples.append(classifier_utils.PaddingInputExample())
if not tf.gfile.Exists(FLAGS.eval_file):
race_utils.file_based_convert_examples_to_features(
eval_examples, label_list, FLAGS.max_seq_length, tokenizer,
FLAGS.eval_file, FLAGS.max_qa_length)
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(eval_examples), num_actual_eval_examples,
len(eval_examples) - num_actual_eval_examples)
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
# This tells the estimator to run through the entire set.
eval_steps = None
# However, if running eval on the TPU, you will need to specify the
# number of steps.
if FLAGS.use_tpu:
assert len(eval_examples) % FLAGS.eval_batch_size == 0
eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)
eval_drop_remainder = True if FLAGS.use_tpu else False
eval_input_fn = classifier_utils.file_based_input_fn_builder(
input_file=FLAGS.eval_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=eval_drop_remainder,
task_name=task_name,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.eval_batch_size,
multiple=len(label_list))
def _find_valid_cands(curr_step):
filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
candidates = []
for filename in filenames:
if filename.endswith(".index"):
ckpt_name = filename[:-6]
idx = ckpt_name.split("-")[-1]
if idx != "best" and int(idx) > curr_step:
candidates.append(filename)
return candidates
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best")
key_name = "eval_accuracy"
if tf.gfile.Exists(checkpoint_path + ".index"):
result = estimator.evaluate(input_fn=eval_input_fn,
steps=eval_steps,
checkpoint_path=checkpoint_path)
best_perf = result[key_name]
global_step = result["global_step"]
else:
global_step = -1
best_perf = -1
checkpoint_path = None
writer = tf.gfile.GFile(output_eval_file, "w")
while global_step < FLAGS.train_step:
steps_and_files = {}
filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
for filename in filenames:
if filename.endswith(".index"):
ckpt_name = filename[:-6]
cur_filename = os.path.join(FLAGS.output_dir, ckpt_name)
if cur_filename.split("-")[-1] == "best":
continue
gstep = int(cur_filename.split("-")[-1])
if gstep not in steps_and_files:
tf.logging.info(
"Add {} to eval list.".format(cur_filename))
steps_and_files[gstep] = cur_filename
tf.logging.info("found {} files.".format(len(steps_and_files)))
# steps_and_files = sorted(steps_and_files, key=lambda x: x[0])
if not steps_and_files:
tf.logging.info(
"found 0 file, global step: {}. Sleeping.".format(
global_step))
time.sleep(1)
else:
for ele in sorted(steps_and_files.items()):
step, checkpoint_path = ele
if global_step >= step:
if len(_find_valid_cands(step)) > 1:
for ext in [
"meta", "data-00000-of-00001", "index"
]:
src_ckpt = checkpoint_path + ".{}".format(ext)
tf.logging.info("removing {}".format(src_ckpt))
tf.gfile.Remove(src_ckpt)
continue
result = estimator.evaluate(
input_fn=eval_input_fn,
steps=eval_steps,
checkpoint_path=checkpoint_path)
global_step = result["global_step"]
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
writer.write("best = {}\n".format(best_perf))
if result[key_name] > best_perf:
best_perf = result[key_name]
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = checkpoint_path + ".{}".format(ext)
tgt_ckpt = checkpoint_path.rsplit(
"-", 1)[0] + "-best.{}".format(ext)
tf.logging.info("saving {} to {}".format(
src_ckpt, tgt_ckpt))
tf.gfile.Copy(src_ckpt, tgt_ckpt, overwrite=True)
writer.write("saved {} to {}\n".format(
src_ckpt, tgt_ckpt))
if len(_find_valid_cands(global_step)) > 1:
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = checkpoint_path + ".{}".format(ext)
tf.logging.info("removing {}".format(src_ckpt))
tf.gfile.Remove(src_ckpt)
writer.write("=" * 50 + "\n")
writer.close()
if FLAGS.do_predict:
predict_examples = processor.get_test_examples(FLAGS.data_dir)
num_actual_predict_examples = len(predict_examples)
if FLAGS.use_tpu:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on.
while len(predict_examples) % FLAGS.predict_batch_size != 0:
predict_examples.append(classifier_utils.PaddingInputExample())
assert len(predict_examples) % FLAGS.predict_batch_size == 0
predict_steps = int(
len(predict_examples) // FLAGS.predict_batch_size)
predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
race_utils.file_based_convert_examples_to_features(
predict_examples, label_list, FLAGS.max_seq_length, tokenizer,
predict_file, FLAGS.max_qa_length)
tf.logging.info("***** Running prediction*****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(predict_examples), num_actual_predict_examples,
len(predict_examples) - num_actual_predict_examples)
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
predict_drop_remainder = True if FLAGS.use_tpu else False
predict_input_fn = classifier_utils.file_based_input_fn_builder(
input_file=predict_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=predict_drop_remainder,
task_name=task_name,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.predict_batch_size,
multiple=len(label_list))
checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best")
result = estimator.evaluate(input_fn=predict_input_fn,
steps=predict_steps,
checkpoint_path=checkpoint_path)
output_predict_file = os.path.join(FLAGS.output_dir,
"predict_results.txt")
with tf.gfile.GFile(output_predict_file, "w") as pred_writer:
# num_written_lines = 0
tf.logging.info("***** Predict results *****")
pred_writer.write("***** Predict results *****\n")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
pred_writer.write("%s = %s\n" % (key, str(result[key])))
pred_writer.write("best = {}\n".format(best_perf))
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
processors = CommonsenseQAProcessor
if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
raise ValueError(
"At least one of `do_train`, `do_eval` or `do_predict' must be True.")
if not FLAGS.albert_config_file:
raise ValueError("At least one of `--albert_config_file`must be set")
if FLAGS.albert_config_file:
albert_config = modeling.AlbertConfig.from_json_file(FLAGS.albert_config_file)
if FLAGS.max_seq_length > albert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the ALBERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, albert_config.max_position_embeddings))
else:
albert_config = None # Get the config from TF-Hub.
tf.gfile.MakeDirs(FLAGS.output_dir)
processor = processors(
use_spm=True if FLAGS.spm_model_file else False,
do_lower_case=FLAGS.do_lower_case)
label_list = processor.get_labels()
tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case,
spm_model_file=FLAGS.spm_model_file)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
if FLAGS.do_train:
iterations_per_loop = int(min(FLAGS.iterations_per_loop, FLAGS.save_checkpoints_steps))
else:
iterations_per_loop = FLAGS.iterations_per_loop
run_config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=int(FLAGS.save_checkpoints_steps),
keep_checkpoint_max=0,
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
train_examples = processor.get_train_examples(FLAGS.data_dir)
num_train_steps = int(len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
model_fn = model_fn_builder(
albert_config=albert_config,
num_labels=len(label_list),
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps= num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu,
optimizer=FLAGS.optimizer)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = contrib_tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
if FLAGS.do_train:
cached_dir = FLAGS.cached_dir
if not cached_dir:
cached_dir = FLAGS.output_dir
train_file = os.path.join(cached_dir, "train.tf_record")
if not tf.gfile.Exists(train_file):
file_based_convert_examples_to_features(
train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file)
tf.logging.info("***** Running training *****")
tf.logging.info(" Num examples = %d", len(train_examples))
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", num_train_steps)
train_input_fn = file_based_input_fn_builder(
input_file=train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True)
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
if FLAGS.do_eval:
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
num_actual_eval_examples = len(eval_examples)
if FLAGS.use_tpu:
while len(eval_examples) % FLAGS.eval_batch_size != 0:
eval_examples.append(classifier_utils.PaddingInputExample())
cached_dir = FLAGS.cached_dir
if not cached_dir:
cached_dir = FLAGS.output_dir
eval_file = os.path.join(cached_dir, "eval.tf_record")
if not tf.gfile.Exists(eval_file):
file_based_convert_examples_to_features(
eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file)
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(eval_examples), num_actual_eval_examples,
len(eval_examples) - num_actual_eval_examples)
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
# This tells the estimator to run through the entire set.
eval_steps = None
# However, if running eval on the TPU, you will need to specify the
# number of steps.
if FLAGS.use_tpu:
assert len(eval_examples) % FLAGS.eval_batch_size == 0
eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)
eval_drop_remainder = True if FLAGS.use_tpu else False
eval_input_fn = file_based_input_fn_builder(
input_file=eval_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=eval_drop_remainder)
best_trial_info_file = os.path.join(FLAGS.output_dir, "best_trial.txt")
def _best_trial_info():
"""Returns information about which checkpoints have been evaled so far."""
if tf.gfile.Exists(best_trial_info_file):
with tf.gfile.GFile(best_trial_info_file, "r") as best_info:
global_step, best_metric_global_step, metric_value = (
best_info.read().split(":"))
global_step = int(global_step)
best_metric_global_step = int(best_metric_global_step)
metric_value = float(metric_value)
else:
metric_value = -1
best_metric_global_step = -1
global_step = -1
tf.logging.info(
"Best trial info: Step: %s, Best Value Step: %s, "
"Best Value: %s", global_step, best_metric_global_step, metric_value)
return global_step, best_metric_global_step, metric_value
def _remove_checkpoint(checkpoint_path):
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = checkpoint_path + ".{}".format(ext)
tf.logging.info("removing {}".format(src_ckpt))
tf.gfile.Remove(src_ckpt)
def _find_valid_cands(curr_step):
filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
candidates = []
for filename in filenames:
if filename.endswith(".index"):
ckpt_name = filename[:-6]
idx = ckpt_name.split("-")[-1]
if int(idx) > curr_step:
candidates.append(filename)
return candidates
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
global_step, best_perf_global_step, best_perf = _best_trial_info()
writer = tf.gfile.GFile(output_eval_file, "w")
while global_step < num_train_steps:
steps_and_files = {}
filenames = tf.gfile.ListDirectory(FLAGS.output_dir)
for filename in filenames:
if filename.endswith(".index"):
ckpt_name = filename[:-6]
cur_filename = os.path.join(FLAGS.output_dir, ckpt_name)
if cur_filename.split("-")[-1] == "best":
continue
gstep = int(cur_filename.split("-")[-1])
if gstep not in steps_and_files:
tf.logging.info("Add {} to eval list.".format(cur_filename))
steps_and_files[gstep] = cur_filename
tf.logging.info("found {} files.".format(len(steps_and_files)))
if not steps_and_files:
tf.logging.info("found 0 file, global step: {}. Sleeping."
.format(global_step))
time.sleep(60)
else:
for checkpoint in sorted(steps_and_files.items()):
step, checkpoint_path = checkpoint
if global_step >= step:
if (best_perf_global_step != step and
len(_find_valid_cands(step)) > 1):
_remove_checkpoint(checkpoint_path)
continue
result = estimator.evaluate(
input_fn=eval_input_fn,
steps=eval_steps,
checkpoint_path=checkpoint_path)
global_step = result["global_step"]
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
writer.write("best = {}\n".format(best_perf))
if result["eval_accuracy"] > best_perf:
best_perf = result["eval_accuracy"]
best_perf_global_step = global_step
elif len(_find_valid_cands(global_step)) > 1:
_remove_checkpoint(checkpoint_path)
writer.write("=" * 50 + "\n")
writer.flush()
with tf.gfile.GFile(best_trial_info_file, "w") as best_info:
best_info.write("{}:{}:{}".format(
global_step, best_perf_global_step, best_perf))
writer.close()
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = "model.ckpt-{}.{}".format(best_perf_global_step, ext)
tgt_ckpt = "model.ckpt-best.{}".format(ext)
tf.logging.info("saving {} to {}".format(src_ckpt, tgt_ckpt))
tf.io.gfile.rename(
os.path.join(FLAGS.output_dir, src_ckpt),
os.path.join(FLAGS.output_dir, tgt_ckpt),
overwrite=True)
if FLAGS.do_predict:
predict_examples = processor.get_test_examples(FLAGS.data_dir)
num_actual_predict_examples = len(predict_examples)
if FLAGS.use_tpu:
while len(predict_examples) % FLAGS.predict_batch_size != 0:
predict_examples.append(classifier_utils.PaddingInputExample())
predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
file_based_convert_examples_to_features(
predict_examples, label_list,
FLAGS.max_seq_length, tokenizer,
predict_file)
tf.logging.info("***** Running prediction*****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(predict_examples), num_actual_predict_examples,
len(predict_examples) - num_actual_predict_examples)
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
predict_drop_remainder = True if FLAGS.use_tpu else False
predict_input_fn = file_based_input_fn_builder(
input_file=predict_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=predict_drop_remainder)
checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best")
result = estimator.predict(
input_fn=predict_input_fn,
checkpoint_path=checkpoint_path)
output_predict_file = os.path.join(FLAGS.output_dir, "test_results.csv")
output_submit_file = os.path.join(FLAGS.output_dir, "submit_results.csv")
with tf.gfile.GFile(output_predict_file, "w") as pred_writer,\
tf.gfile.GFile(output_submit_file, "w") as sub_writer:
sub_writer.write("index" + "\t" + "prediction\n")
num_written_lines = 0
tf.logging.info("***** Predict results *****")
for (i, (example, prediction)) in\
enumerate(zip(predict_examples, result)):
probabilities = prediction["probabilities"]
if i >= num_actual_predict_examples:
break
output_line = "\t".join(
str(class_probability)
for class_probability in probabilities) + "\n"
pred_writer.write(output_line)
actual_label = label_list[int(prediction["predictions"])]
sub_writer.write(example.guid + "\t" + actual_label + "\n")
num_written_lines += 1
assert num_written_lines == num_actual_predict_examples
def fine_tune_albert(config):
tf.logging.set_verbosity(tf.logging.INFO)
if (not config.get("do_train", False) and not config.get("do_eval", False)
and not config.get("do_predict", False)):
raise ValueError(
"At least one of `do_train`, `do_eval` or `do_predict' must be True."
)
if not config.get("albert_config_file", None) and not config.get(
"albert_hub_module_handle", None):
raise ValueError("At least one of `--albert_config_file` and "
"`--albert_hub_module_handle` must be set")
if config.get("albert_config_file", None):
albert_config = modeling.AlbertConfig.from_json_file(
config.get("albert_config_file", None))
if (config.get("max_seq_length", 512) >
albert_config.max_position_embeddings):
raise ValueError(
"Cannot use sequence length %d because the ALBERT model "
"was only trained up to sequence length %d" % (
config.get("max_seq_length", 512),
albert_config.max_position_embeddings,
))
else:
albert_config = None # Get the config from TF-Hub.
tf.gfile.MakeDirs(config.get("output_dir", None))
task_name = config.get("task_name", None).lower()
processor = SemEval(config)
label_list = processor.get_labels()
tokenizer = fine_tuning_utils.create_vocab(
vocab_file=config.get("vocab_file", None),
do_lower_case=config.get("do_lower_case", True),
spm_model_file=config.get("spm_model_file", None),
hub_module=config.get("albert_hub_module_handle", None),
)
tpu_cluster_resolver = None
if config.get("use_tpu", False) and config.get("tpu_name", None):
tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
config.get("tpu_name", None),
zone=config.get("tpu_zone", None),
project=config.get("gcp_project", None),
)
is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
if config.get("do_train", False):
iterations_per_loop = int(
min(
config.get("iterations_per_loop", 1000),
config.get("save_checkpoints_steps", 1000),
))
else:
iterations_per_loop = config.get("iterations_per_loop", 1000)
run_config = contrib_tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=config.get("master", None),
model_dir=config.get("output_dir", None),
save_checkpoints_steps=int(config.get("save_checkpoints_steps", 1000)),
keep_checkpoint_max=0,
tpu_config=contrib_tpu.TPUConfig(
iterations_per_loop=iterations_per_loop,
num_shards=config.get("num_tpu_cores", 8),
per_host_input_for_training=is_per_host,
),
)
train_examples = None
if config.get("do_train", False):
train_examples = processor.get_train_examples()
num_train_steps = int(
len(train_examples) / config.get("train_batch_size", 32) *
config.get("num_train_epochs", 100))
model_fn = classifier_utils.model_fn_builder(
albert_config=albert_config,
num_labels=len(label_list),
init_checkpoint=config.get("init_checkpoint", None),
learning_rate=config.get("learning_rate", 5e-5),
num_train_steps=num_train_steps,
num_warmup_steps=config.get("warmup_step", 0),
use_tpu=config.get("use_tpu", False),
use_one_hot_embeddings=config.get("use_tpu", False),
task_name=task_name,
hub_module=config.get("albert_hub_module_handle", None),
optimizer=config.get("optimizer", None),
)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = contrib_tpu.TPUEstimator(
use_tpu=config.get("use_tpu", False),
model_fn=model_fn,
config=run_config,
train_batch_size=config.get("train_batch_size", 32),
eval_batch_size=config.get("eval_batch_size", 8),
predict_batch_size=config.get("predict_batch_size", 8),
)
if config.get("do_train", False):
cached_dir = config.get("cached_dir", None)
if not cached_dir:
cached_dir = config.get("output_dir", None)
train_file = os.path.join(cached_dir, task_name + "_train.tf_record")
if not tf.gfile.Exists(train_file):
classifier_utils.file_based_convert_examples_to_features(
train_examples,
label_list,
config.get("max_seq_length", 512),
tokenizer,
train_file,
task_name,
)
tf.logging.info("***** Running training *****")
tf.logging.info(" Num examples = %d", len(train_examples))
tf.logging.info(" Batch size = %d",
config.get("train_batch_size", 32))
tf.logging.info(" Num steps = %d", num_train_steps)
train_input_fn = classifier_utils.file_based_input_fn_builder(
input_file=train_file,
seq_length=config.get("max_seq_length", 512),
is_training=True,
drop_remainder=True,
task_name=task_name,
use_tpu=config.get("use_tpu", False),
bsz=config.get("train_batch_size", 32),
)
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
if config.get("do_eval", False):
eval_examples = processor.get_dev_examples()
num_actual_eval_examples = len(eval_examples)
if config.get("use_tpu", False):
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on. These do NOT count towards the metric (all tf.metrics
# support a per-instance weight, and these get a weight of 0.0).
while len(eval_examples) % config.get("eval_batch_size", 8) != 0:
eval_examples.append(classifier_utils.PaddingInputExample())
cached_dir = config.get("cached_dir", None)
if not cached_dir:
cached_dir = config.get("output_dir", None)
eval_file = os.path.join(cached_dir, task_name + "_eval.tf_record")
if not tf.gfile.Exists(eval_file):
classifier_utils.file_based_convert_examples_to_features(
eval_examples,
label_list,
config.get("max_seq_length", 512),
tokenizer,
eval_file,
task_name,
)
tf.logging.info("***** Running evaluation *****")
tf.logging.info(
" Num examples = %d (%d actual, %d padding)",
len(eval_examples),
num_actual_eval_examples,
len(eval_examples) - num_actual_eval_examples,
)
tf.logging.info(" Batch size = %d", config.get("eval_batch_size", 8))
# This tells the estimator to run through the entire set.
eval_steps = None
# However, if running eval on the TPU, you will need to specify the
# number of steps.
if config.get("use_tpu", False):
assert len(eval_examples) % config.get("eval_batch_size", 8) == 0
eval_steps = int(
len(eval_examples) // config.get("eval_batch_size", 8))
eval_drop_remainder = True if config.get("use_tpu", False) else False
eval_input_fn = classifier_utils.file_based_input_fn_builder(
input_file=eval_file,
seq_length=config.get("max_seq_length", 512),
is_training=False,
drop_remainder=eval_drop_remainder,
task_name=task_name,
use_tpu=config.get("use_tpu", False),
bsz=config.get("eval_batch_size", 8),
)
best_trial_info_file = os.path.join(config.get("output_dir", None),
"best_trial.txt")
def _best_trial_info():
"""Returns information about which checkpoints have been evaled so far."""
if tf.gfile.Exists(best_trial_info_file):
with tf.gfile.GFile(best_trial_info_file, "r") as best_info:
(
global_step,
best_metric_global_step,
metric_value,
) = best_info.read().split(":")
global_step = int(global_step)
best_metric_global_step = int(best_metric_global_step)
metric_value = float(metric_value)
else:
metric_value = -1
best_metric_global_step = -1
global_step = -1
tf.logging.info(
"Best trial info: Step: %s, Best Value Step: %s, "
"Best Value: %s",
global_step,
best_metric_global_step,
metric_value,
)
return global_step, best_metric_global_step, metric_value
def _remove_checkpoint(checkpoint_path):
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = checkpoint_path + ".{}".format(ext)
tf.logging.info("removing {}".format(src_ckpt))
tf.gfile.Remove(src_ckpt)
def _find_valid_cands(curr_step):
filenames = tf.gfile.ListDirectory(config.get("output_dir", None))
candidates = []
for filename in filenames:
if filename.endswith(".index"):
ckpt_name = filename[:-6]
idx = ckpt_name.split("-")[-1]
if int(idx) > curr_step:
candidates.append(filename)
return candidates
output_eval_file = os.path.join(config.get("output_dir", None),
"eval_results.txt")
if task_name == "sts-b":
key_name = "pearson"
elif task_name == "cola":
key_name = "matthew_corr"
else:
key_name = "eval_accuracy"
global_step, best_perf_global_step, best_perf = _best_trial_info()
writer = tf.gfile.GFile(output_eval_file, "w")
while global_step < num_train_steps:
steps_and_files = {}
filenames = tf.gfile.ListDirectory(config.get("output_dir", None))
for filename in filenames:
if filename.endswith(".index"):
ckpt_name = filename[:-6]
cur_filename = os.path.join(config.get("output_dir", None),
ckpt_name)
if cur_filename.split("-")[-1] == "best":
continue
gstep = int(cur_filename.split("-")[-1])
if gstep not in steps_and_files:
tf.logging.info(
"Add {} to eval list.".format(cur_filename))
steps_and_files[gstep] = cur_filename
tf.logging.info("found {} files.".format(len(steps_and_files)))
if not steps_and_files:
tf.logging.info(
"found 0 file, global step: {}. Sleeping.".format(
global_step))
time.sleep(60)
else:
for checkpoint in sorted(steps_and_files.items()):
step, checkpoint_path = checkpoint
if global_step >= step:
if (best_perf_global_step != step
and len(_find_valid_cands(step)) > 1):
_remove_checkpoint(checkpoint_path)
continue
result = estimator.evaluate(
input_fn=eval_input_fn,
steps=eval_steps,
checkpoint_path=checkpoint_path,
)
global_step = result["global_step"]
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
writer.write("best = {}\n".format(best_perf))
if result[key_name] > best_perf:
best_perf = result[key_name]
best_perf_global_step = global_step
elif len(_find_valid_cands(global_step)) > 1:
_remove_checkpoint(checkpoint_path)
writer.write("=" * 50 + "\n")
writer.flush()
with tf.gfile.GFile(best_trial_info_file,
"w") as best_info:
best_info.write("{}:{}:{}".format(
global_step, best_perf_global_step, best_perf))
writer.close()
for ext in ["meta", "data-00000-of-00001", "index"]:
src_ckpt = "model.ckpt-{}.{}".format(best_perf_global_step, ext)
tgt_ckpt = "model.ckpt-best.{}".format(ext)
tf.logging.info("saving {} to {}".format(src_ckpt, tgt_ckpt))
tf.io.gfile.rename(
os.path.join(config.get("output_dir", None), src_ckpt),
os.path.join(config.get("output_dir", None), tgt_ckpt),
overwrite=True,
)
if config.get("do_predict", False):
predict_examples = processor.get_test_examples()
num_actual_predict_examples = len(predict_examples)
if config.get("use_tpu", False):
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on.
while (len(predict_examples) % config.get("predict_batch_size", 8)
!= 0):
predict_examples.append(classifier_utils.PaddingInputExample())
predict_file = os.path.join(config.get("output_dir", None),
"predict.tf_record")
classifier_utils.file_based_convert_examples_to_features(
predict_examples,
label_list,
config.get("max_seq_length", 512),
tokenizer,
predict_file,
task_name,
)
tf.logging.info("***** Running prediction*****")
tf.logging.info(
" Num examples = %d (%d actual, %d padding)",
len(predict_examples),
num_actual_predict_examples,
len(predict_examples) - num_actual_predict_examples,
)
tf.logging.info(" Batch size = %d",
config.get("predict_batch_size", 8))
predict_drop_remainder = (True
if config.get("use_tpu", False) else False)
predict_input_fn = classifier_utils.file_based_input_fn_builder(
input_file=predict_file,
seq_length=config.get("max_seq_length", 512),
is_training=False,
drop_remainder=predict_drop_remainder,
task_name=task_name,
use_tpu=config.get("use_tpu", False),
bsz=config.get("predict_batch_size", 8),
)
checkpoint_path = os.path.join(config.get("output_dir", None),
"model.ckpt-best")
result = estimator.predict(input_fn=predict_input_fn,
checkpoint_path=checkpoint_path)
output_predict_file = os.path.join(config.get("output_dir", None),
"test_results.tsv")
output_submit_file = os.path.join(config.get("output_dir", None),
"submit_results.tsv")
with tf.gfile.GFile(output_predict_file,
"w") as pred_writer, tf.gfile.GFile(
output_submit_file, "w") as sub_writer:
sub_writer.write("index" + "\t" + "prediction\n")
num_written_lines = 0
tf.logging.info("***** Predict results *****")
for (i, (example,
prediction)) in enumerate(zip(predict_examples, result)):
probabilities = prediction["probabilities"]
if i >= num_actual_predict_examples:
break
output_line = ("\t".join(
str(class_probability)
for class_probability in probabilities) + "\n")
pred_writer.write(output_line)
if task_name != "sts-b":
actual_label = label_list[int(prediction["predictions"])]
else:
actual_label = str(prediction["predictions"])
sub_writer.write(example.guid + "\t" + actual_label + "\n")
num_written_lines += 1
assert num_written_lines == num_actual_predict_examples
