run_classifier.py

# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""ALBERT classification finetuning runner in tf2.x."""

import json
import os
# Import libraries
from absl import app
from absl import flags
from absl import logging
import tensorflow as tf
from official.common import distribute_utils
from official.nlp.albert import configs as albert_configs
from official.nlp.bert import bert_models
from official.nlp.bert import run_classifier as run_classifier_bert


FLAGS = flags.FLAGS


def predict(strategy, albert_config, input_meta_data, predict_input_fn):
  """Function outputs both the ground truth predictions as .tsv files."""
  with strategy.scope():
    classifier_model = bert_models.classifier_model(
        albert_config, input_meta_data['num_labels'])[0]
    checkpoint = tf.train.Checkpoint(model=classifier_model)
    latest_checkpoint_file = (
        FLAGS.predict_checkpoint_path or
        tf.train.latest_checkpoint(FLAGS.model_dir))
    assert latest_checkpoint_file
    logging.info('Checkpoint file %s found and restoring from '
                 'checkpoint', latest_checkpoint_file)
    checkpoint.restore(
        latest_checkpoint_file).assert_existing_objects_matched()
    preds, ground_truth = run_classifier_bert.get_predictions_and_labels(
        strategy, classifier_model, predict_input_fn, return_probs=True)
    output_predict_file = os.path.join(FLAGS.model_dir, 'test_results.tsv')
    with tf.io.gfile.GFile(output_predict_file, 'w') as writer:
      logging.info('***** Predict results *****')
      for probabilities in preds:
        output_line = '\t'.join(
            str(class_probability)
            for class_probability in probabilities) + '\n'
        writer.write(output_line)
    ground_truth_labels_file = os.path.join(FLAGS.model_dir,
                                            'output_labels.tsv')
    with tf.io.gfile.GFile(ground_truth_labels_file, 'w') as writer:
      logging.info('***** Ground truth results *****')
      for label in ground_truth:
        output_line = '\t'.join(str(label)) + '\n'
        writer.write(output_line)
  return


def main(_):
  with tf.io.gfile.GFile(FLAGS.input_meta_data_path, 'rb') as reader:
    input_meta_data = json.loads(reader.read().decode('utf-8'))

  if not FLAGS.model_dir:
    FLAGS.model_dir = '/tmp/bert20/'

  strategy = distribute_utils.get_distribution_strategy(
      distribution_strategy=FLAGS.distribution_strategy,
      num_gpus=FLAGS.num_gpus,
      tpu_address=FLAGS.tpu)
  max_seq_length = input_meta_data['max_seq_length']
  train_input_fn = run_classifier_bert.get_dataset_fn(
      FLAGS.train_data_path,
      max_seq_length,
      FLAGS.train_batch_size,
      is_training=True)
  eval_input_fn = run_classifier_bert.get_dataset_fn(
      FLAGS.eval_data_path,
      max_seq_length,
      FLAGS.eval_batch_size,
      is_training=False)

  albert_config = albert_configs.AlbertConfig.from_json_file(
      FLAGS.bert_config_file)
  if FLAGS.mode == 'train_and_eval':
    run_classifier_bert.run_bert(strategy, input_meta_data, albert_config,
                                 train_input_fn, eval_input_fn)
  elif FLAGS.mode == 'predict':
    predict(strategy, albert_config, input_meta_data, eval_input_fn)
  else:
    raise ValueError('Unsupported mode is specified: %s' % FLAGS.mode)
  return

if __name__ == '__main__':
  flags.mark_flag_as_required('bert_config_file')
  flags.mark_flag_as_required('input_meta_data_path')
  flags.mark_flag_as_required('model_dir')
  app.run(main)