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,
}
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint)
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 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 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, 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
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,
}
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint)
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
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))
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 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_predict:
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.
while len(eval_examples) % FLAGS.predict_batch_size != 0:
eval_examples.append(classifier_utils.PaddingInputExample())
error_analysis_file = os.path.join(FLAGS.output_dir,
"error_analysis.tf_record")
classifier_utils.file_based_convert_examples_to_features(
eval_examples, label_list, FLAGS.max_seq_length, tokenizer,
error_analysis_file, task_name)
tf.logging.info("***** Running error analysis on dev set*****")
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.predict_batch_size)
error_analysis_drop_remainder = True if FLAGS.use_tpu else False
error_analysis_input_fn = classifier_utils.file_based_input_fn_builder(
input_file=error_analysis_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=error_analysis_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=error_analysis_input_fn,
checkpoint_path=checkpoint_path)
output_error_analysis_predict_file = os.path.join(
FLAGS.output_dir, "error_analysis_test_results.tsv")
output_error_analysis_submit_file = os.path.join(
FLAGS.output_dir, "error_analysis_submit_results.tsv")
with tf.gfile.GFile(output_error_analysis_predict_file, "w") as pred_writer,\
tf.gfile.GFile(output_error_analysis_submit_file, "w") as sub_writer:
sub_writer.write("index" + "\t" + "text_a" + "\t" + "text_b" + "\t" +
"prediction" + "\t" + "label" + "\n")
num_written_lines = 0
tf.logging.info("***** Error analysis results *****")
for (i, (example, prediction)) in\
enumerate(zip(eval_examples, result)):
probabilities = prediction["probabilities"]
if i >= num_actual_eval_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" + str(example.text_a) + "\t" +
str(example.text_b) + "\t" + str(actual_label) +
"\t" + str(example.label) + "\n")
num_written_lines += 1
assert num_written_lines == num_actual_eval_examples
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
processors = {"race": race_utils.RaceProcessor}
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint)
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)
tf.get_logger().propagate = False
albert_config = modeling.AlbertConfig.from_json_file(
FLAGS.albert_config_file)
validate_flags_or_throw(albert_config)
tf.gfile.MakeDirs(FLAGS.output_dir)
print("Output:", FLAGS.output_dir)
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,
keep_checkpoint_max=0,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
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
num_train_steps = None
num_warmup_steps = None
train_examples = squad_utils.read_squad_examples(
input_file=FLAGS.train_file, is_training=True)
num_train_steps = int(
len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
if FLAGS.do_train:
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
# Pre-shuffle the input to avoid having to make a very large shuffle
# buffer in in the `input_fn`.
rng = random.Random(12345)
rng.shuffle(train_examples)
model_fn = squad_utils.v2_model_fn_builder(
albert_config=albert_config,
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,
max_seq_length=FLAGS.max_seq_length,
start_n_top=FLAGS.start_n_top,
end_n_top=FLAGS.end_n_top,
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,
predict_batch_size=FLAGS.predict_batch_size)
if FLAGS.do_train:
# We write to a temporary file to avoid storing very large constant tensors
# in memory.
if not tf.gfile.Exists(FLAGS.train_feature_file):
train_writer = squad_utils.FeatureWriter(filename=os.path.join(
FLAGS.train_feature_file),
is_training=True)
squad_utils.convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=FLAGS.max_seq_length,
doc_stride=FLAGS.doc_stride,
max_query_length=FLAGS.max_query_length,
is_training=True,
output_fn=train_writer.process_feature,
do_lower_case=FLAGS.do_lower_case)
train_writer.close()
tf.logging.info("***** Running training *****")
tf.logging.info(" Num orig examples = %d", len(train_examples))
# tf.logging.info(" Num split examples = %d", train_writer.num_features)
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", num_train_steps)
del train_examples
train_input_fn = squad_utils.input_fn_builder(
input_file=FLAGS.train_feature_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.train_batch_size,
is_v2=True)
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
if FLAGS.do_predict:
with tf.gfile.Open(FLAGS.predict_file) as predict_file:
prediction_json = json.load(predict_file)["data"]
eval_examples = squad_utils.read_squad_examples(
input_file=FLAGS.predict_file, is_training=False)
if (tf.gfile.Exists(FLAGS.predict_feature_file)
and tf.gfile.Exists(FLAGS.predict_feature_left_file)):
tf.logging.info("Loading eval features from {}".format(
FLAGS.predict_feature_left_file))
with tf.gfile.Open(FLAGS.predict_feature_left_file, "rb") as fin:
eval_features = pickle.load(fin)
else:
eval_writer = squad_utils.FeatureWriter(
filename=FLAGS.predict_feature_file, is_training=False)
eval_features = []
def append_feature(feature):
eval_features.append(feature)
eval_writer.process_feature(feature)
squad_utils.convert_examples_to_features(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=FLAGS.max_seq_length,
doc_stride=FLAGS.doc_stride,
max_query_length=FLAGS.max_query_length,
is_training=False,
output_fn=append_feature,
do_lower_case=FLAGS.do_lower_case)
eval_writer.close()
with tf.gfile.Open(FLAGS.predict_feature_left_file, "wb") as fout:
pickle.dump(eval_features, fout)
tf.logging.info("***** Running predictions *****")
tf.logging.info(" Num orig examples = %d", len(eval_examples))
tf.logging.info(" Num split examples = %d", len(eval_features))
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
predict_input_fn = squad_utils.input_fn_builder(
input_file=FLAGS.predict_feature_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=False,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.predict_batch_size,
is_v2=True)
def get_result(checkpoint):
"""Evaluate the checkpoint on SQuAD v2.0."""
# If running eval on the TPU, you will need to specify the number of
# steps.
reader = tf.train.NewCheckpointReader(checkpoint)
global_step = reader.get_tensor(tf.GraphKeys.GLOBAL_STEP)
all_results = []
for result in estimator.predict(predict_input_fn,
yield_single_examples=True,
checkpoint_path=checkpoint):
if len(all_results) % 1000 == 0:
tf.logging.info("Processing example: %d" %
(len(all_results)))
unique_id = int(result["unique_ids"])
start_top_log_probs = ([
float(x) for x in result["start_top_log_probs"].flat
])
start_top_index = [
int(x) for x in result["start_top_index"].flat
]
end_top_log_probs = ([
float(x) for x in result["end_top_log_probs"].flat
])
end_top_index = [int(x) for x in result["end_top_index"].flat]
cls_logits = float(result["cls_logits"].flat[0])
all_results.append(
squad_utils.RawResultV2(
unique_id=unique_id,
start_top_log_probs=start_top_log_probs,
start_top_index=start_top_index,
end_top_log_probs=end_top_log_probs,
end_top_index=end_top_index,
cls_logits=cls_logits))
output_prediction_file = os.path.join(FLAGS.output_dir,
"predictions.json")
output_nbest_file = os.path.join(FLAGS.output_dir,
"nbest_predictions.json")
output_null_log_odds_file = os.path.join(FLAGS.output_dir,
"null_odds.json")
result_dict = {}
cls_dict = {}
squad_utils.accumulate_predictions_v2(
result_dict, cls_dict, eval_examples, eval_features,
all_results, FLAGS.n_best_size, FLAGS.max_answer_length,
FLAGS.start_n_top, FLAGS.end_n_top)
return squad_utils.evaluate_v2(
result_dict, cls_dict, prediction_json, eval_examples,
eval_features, all_results, FLAGS.n_best_size,
FLAGS.max_answer_length, output_prediction_file,
output_nbest_file, output_null_log_odds_file), int(global_step)
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 = "f1"
writer = tf.gfile.GFile(output_eval_file, "w")
if tf.gfile.Exists(checkpoint_path + ".index"):
result = get_result(checkpoint_path)
best_perf = result[0][key_name]
global_step = result[1]
else:
global_step = -1
best_perf = -1
checkpoint_path = None
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 ele in sorted(steps_and_files.items()):
step, checkpoint_path = ele
print("GS: ", global_step, step)
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, global_step = get_result(checkpoint_path)
print("EVAL RESULTS")
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])))
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))
writer.write("best {} = {}\n".format(key_name, best_perf))
tf.logging.info(" best {} = {}\n".format(
key_name, best_perf))
if len(_find_valid_cands(global_step)) > 2:
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")
print("Sleeping")
time.sleep(10)
checkpoint_path = os.path.join(FLAGS.output_dir, "model.ckpt-best")
result, global_step = get_result(checkpoint_path)
tf.logging.info("***** Final 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 perf happened at step: {}".format(global_step))
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
albert_config = modeling.AlbertConfig.from_json_file(
FLAGS.albert_config_file)
validate_flags_or_throw(albert_config)
tf.gfile.MakeDirs(FLAGS.output_dir)
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,
keep_checkpoint_max=0,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
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
num_train_steps = None
num_warmup_steps = None
# if FLAGS.do_train:
# train_examples = squad_utils.read_squad_examples(
# input_file=FLAGS.train_file, is_training=True)
# 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)
#
# # Pre-shuffle the input to avoid having to make a very large shuffle
# # buffer in in the `input_fn`.
# rng = random.Random(12345)
# rng.shuffle(train_examples)
model_fn = squad_utils.v2_model_fn_builder(
albert_config=albert_config,
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,
max_seq_length=FLAGS.max_seq_length,
start_n_top=FLAGS.start_n_top,
end_n_top=FLAGS.end_n_top,
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,
predict_batch_size=FLAGS.predict_batch_size)
if FLAGS.do_train:
# We write to a temporary file to avoid storing very large constant tensors
# in memory.
if not tf.gfile.Exists(FLAGS.train_feature_file):
train_writer = squad_utils.FeatureWriter(filename=os.path.join(
FLAGS.train_feature_file),
is_training=True)
squad_utils.convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=FLAGS.max_seq_length,
doc_stride=FLAGS.doc_stride,
max_query_length=FLAGS.max_query_length,
is_training=True,
output_fn=train_writer.process_feature,
do_lower_case=FLAGS.do_lower_case)
train_writer.close()
tf.logging.info("***** Running training *****")
tf.logging.info(" Num orig examples = %d", len(train_examples))
# tf.logging.info(" Num split examples = %d", train_writer.num_features)
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", num_train_steps)
del train_examples
train_input_fn = squad_utils.input_fn_builder(
input_file=FLAGS.train_feature_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.train_batch_size,
is_v2=True)
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
if FLAGS.do_predict:
with tf.gfile.Open(FLAGS.predict_file) as predict_file:
prediction_json = json.load(predict_file)["data"]
eval_examples = squad_utils.read_squad_examples(
input_file=FLAGS.predict_file, is_training=False)
if (tf.gfile.Exists(FLAGS.predict_feature_file)
and tf.gfile.Exists(FLAGS.predict_feature_left_file)):
tf.logging.info("Loading eval features from {}".format(
FLAGS.predict_feature_left_file))
with tf.gfile.Open(FLAGS.predict_feature_left_file, "rb") as fin:
eval_features = pickle.load(fin)
else:
eval_writer = squad_utils.FeatureWriter(
filename=FLAGS.predict_feature_file, is_training=False)
eval_features = []
def append_feature(feature):
eval_features.append(feature)
eval_writer.process_feature(feature)
squad_utils.convert_examples_to_features(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=FLAGS.max_seq_length,
doc_stride=FLAGS.doc_stride,
max_query_length=FLAGS.max_query_length,
is_training=False,
output_fn=append_feature,
do_lower_case=FLAGS.do_lower_case)
eval_writer.close()
with tf.gfile.Open(FLAGS.predict_feature_left_file, "wb") as fout:
pickle.dump(eval_features, fout)
tf.logging.info("***** Running predictions *****")
tf.logging.info(" Num orig examples = %d", len(eval_examples))
tf.logging.info(" Num split examples = %d", len(eval_features))
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
predict_input_fn = squad_utils.input_fn_builder(
input_file=FLAGS.predict_feature_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=False,
use_tpu=FLAGS.use_tpu,
bsz=FLAGS.predict_batch_size,
is_v2=True)
def get_result(checkpoint):
"""Evaluate the checkpoint on SQuAD v2.0."""
# If running eval on the TPU, you will need to specify the number of
# steps.
reader = tf.train.NewCheckpointReader(checkpoint)
global_step = reader.get_tensor(tf.GraphKeys.GLOBAL_STEP)
all_results = []
for result in estimator.predict(predict_input_fn,
yield_single_examples=True,
checkpoint_path=checkpoint):
if len(all_results) % 1000 == 0:
tf.logging.info("Processing example: %d" %
(len(all_results)))
unique_id = int(result["unique_ids"])
cls_logits = float(result["cls_logits"].flat[0])
all_results.append(
squad_utils.RawResultV2(unique_id=unique_id,
cls_logits=cls_logits))
output_prediction_file = os.path.join(FLAGS.output_dir,
"predictions.json")
output_nbest_file = os.path.join(FLAGS.output_dir,
"nbest_predictions.json")
output_null_log_odds_file = os.path.join(FLAGS.output_dir,
"null_odds.json")
result_dict = {}
cls_dict = {}
squad_utils.accumulate_predictions_v2(
result_dict, cls_dict, eval_examples, eval_features,
all_results, FLAGS.n_best_size, FLAGS.max_answer_length,
FLAGS.start_n_top, FLAGS.end_n_top)
from squad_utils import make_qid_to_has_ans
import numpy as np
qid_to_has_ans = make_qid_to_has_ans(
prediction_json) # maps qid to True/False
has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
print("has_ans", len(has_ans_qids))
print("no_ans", len(no_ans_qids))
def compute_metrics_with_threshold(threshold):
nonlocal result_dict
result_dict = {}
tp = 0
tn = 0
fp = 0
fn = 0
for example_index, example in enumerate(eval_examples):
m = np.min(cls_dict[example_index])
predict_is_impossible = 1 / (1 + np.exp(-m)) > threshold
# predict_is_impossible = m > threshold
result_dict[example.qas_id] = m
if example.is_impossible:
if predict_is_impossible:
tp += 1
else:
fn += 1
else:
if predict_is_impossible:
fp += 1
else:
tn += 1
precision = tp / (tp + fp)
recall = tp / (fn + tp)
f1 = 2 * tp / (2 * tp + fp + fn)
tf.logging.info(f"precision: {precision}"
f"recall: {recall}"
f"f1: {f1}")
return precision, recall, f1
# precision, recall, f1 = compute_metrics_with_threshold(0.4)
precision, recall, f1 = compute_metrics_with_threshold(0.5)
# precision, recall, f1 = compute_metrics_with_threshold(0.6)
with tf.gfile.GFile(output_prediction_file, "w") as writer:
writer.write(json.dumps(result_dict, indent=4) + "\n")
return {
"precision": precision,
"recall": recall,
"f1": f1,
"total": len(eval_examples)
}, int(global_step)
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 = "f1"
# writer = tf.gfile.GFile(output_eval_file, "w")
# if tf.gfile.Exists(checkpoint_path + ".index"):
# result = get_result(checkpoint_path)
# best_perf = result[0][key_name]
# global_step = result[1]
# else:
# global_step = -1
# best_perf = -1
# checkpoint_path = None
# 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 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, global_step = get_result(checkpoint_path)
# 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])))
# 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))
# writer.write("best {} = {}\n".format(key_name, best_perf))
# tf.logging.info(" best {} = {}\n".format(key_name, best_perf))
#
# if len(_find_valid_cands(global_step)) > 2:
# 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")
result, global_step = get_result(FLAGS.init_checkpoint)
